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Home My WebLink AboutStaff Report 2697 City of Palo Alto (ID # 2697) Housing Needs Mandate Committee Staff Report Report Type: Meeting Date: 4/19/2012 April 19, 2012 Page 1 of 4 (ID # 2697) Summary Title: Review of SCS Preferred Scenario Title: Review and Direction Regarding Draft Preferred Scenario for Sustainable Communities Strategy (SCS) and Regional Housing Needs Assessment (RHNA) Allocation From: City Manager Lead Department: Planning and Community Environment Recommendation Staff recommends that the Committee provide input and direction regarding the next steps in evaluating and responding to the Draft Preferred Scenario of the Sustainable Communities Strategy, including providing comments regarding: 1) the breakdown of allocations by Priority Development Area (PDA) and transportation analysis zones (TAZs), 2) the allocation between Stanford/Santa Clara County, and 3) the economic and demographic assumptions for the Plan. Background The Association of Bay Area Governments (ABAG) and the Metropolitan Transportation Commission (MTC) released a Draft Preferred Scenario for the Sustainable Communities Strategy (SCS) for the Bay Area on March 9, 2012. Attachment A outlines the Draft Preferred Scenario (Plan Bay Area Jobs-Housing Connection Scenario) and Attachment B provides a presentation format overview. The Preferred Scenario is ABAG’s response to the evaluation of three primary Alternative Land Use Scenarios and is designed to accommodate approximately 1.1 million new jobs and approximately 660,000 new housing units in the Bay Area through the year 2040. This planning effort is intended to implement Senate Bill 375, which is expected to reduce greenhouse gas (GHG) emissions by supporting higher intensity development near transit, along with substantial increases in transportation investments. The Alternative Scenarios anticipated as many as 25,000 new jobs and 12,500 new housing units in the City of Palo Alto over that time period. The Council, upon recommendation from the Regional Housing Mandate Committee, responded to the Alternative Scenarios by letter of March 6, 2012 (Attachment F), objecting to a) the projections of future jobs and housing for the region, b) the inconsistency of estimates for Palo Alto as compared to historical trends and constraints, and c) the nearly negligible benefits of the scenarios for reduction of greenhouse gas (GHG) emissions. The Draft Preferred Scenario outlines projected growth for Palo Alto of April 19, 2012 Page 2 of 4 (ID # 2697) approximately 29,270 jobs and 7,130 new housing units through 2040. Most of the housing unit growth, however, is “back-loaded” in the projections, as the most recent Regional Housing Needs Assessment (RHNA) allocation for the City is estimated at 2,003 units for the planning period 2014-2022. On February 21, 2012, the Council also 1) directed that the City should not request Priority Development Area (PDA) designation for the El Camino Real corridor or the Downtown area, and 2) approved a second letter from the Council to MTC regarding the City’s concerns about the One Bay Area Grant (OBAG) proposal, particularly the requirement for a “certified” housing element as a prerequisite for transportation funding. The One Bay Area Grant process is addressed as a separate agenda item. Discussion Staff has preliminarily reviewed the Draft Preferred Scenario but has not completed its analysis of the document. Presentations of the Draft were made to the Regional Advisory Working Group on April 3 and the Santa Clara County Association of Planning Officials on April 4. The Joint Policy Committee of ABAG and MTC is scheduled to review this draft on May 11, and then will adopt it as a review draft on May 17. Comments and discussion about the scenario will occur over the remainder of 2012, while environmental impacts are evaluated and a final Sustainable Communities Strategy (SCS) Scenario is tentatively scheduled for adoption by early spring of 2013. Economic and Demographic Projections The City has strongly objected to the regional employment and housing projections assumed by ABAG and MTC in developing the SCS scenarios. This objection has been directed to 1) seemingly optimistic employment growth projections well in excess of those seen in the region over the past 2-3 decades, and 2) assumptions of relatively significant immigration despite trends to the contrary. The basis of those ABAG assumptions is summarized in a report from the Center for the Continuing Study of the California Economy (CCSCE), prepared by Stephen Levy, a copy of which is provided as Attachment E to this report. While the housing projections have been reduced considerably for several reasons, the employment assumptions have actually increased slightly (to 1.1 million jobs) over the prior Alternative Scenarios. ABAG notes, however, that many of these jobs (perhaps 300,000) will take the form of re-occupying vacant building space, so there is not an equivalency of growth of new commercial space. ABAG also recently indicated that they will commission a “peer review” of the study, but staff notes that the review is under the auspices of the Bay Area Council Economic Institute, of which Dr. Levy and others with close ABAG ties are participants. Staff has retained Economic Planning Systems, Inc. (Walter Kieser) to continue to review the projections, and he will be available to speak to them at the Committee’s meeting. If information is available to provide in advance, staff will do so. Staff expects that the City will continue to object to some degree to the assumptions, but perhaps it is not as critical if the housing numbers are not increased, as the SCS plan is to be re-evaluated every four years, so further adjustments could be addressed at that time. April 19, 2012 Page 3 of 4 (ID # 2697) Breakdown of Projections/Allocations by Planned Development Area and TAZs Attachments C and D outline in great detail the allocations of employment and housing to each city, county, and Priority Development Area (PDA) in the region. Staff and the economic consultant are reviewing the details of the breakdowns to ascertain how realistic the allocations are and to determine whether some of the allocations are misplaced and should more appropriately be distributed to Santa Clara County (particularly for Stanford campus lands) or Mountain View or other jurisdictions. The Committee should note that the spreadsheets identify the California Avenue PDA, but also should look at the VTA (Valley Transportation Authority) designated PDAs, since those cover El Camino Real and most of downtown. They are not officially PDAs, since the City hasn’t consented to their inclusion, but they are treated as such in the allocation of jobs and housing. Staff will provide the Committee with any information/analysis available prior to the Committee meeting. Regional Housing Needs Assessment (RHNA) The RHNA Housing Methodology Committee is scheduled to meet on April 26th, at which time the committee is expected to recommend its allocations for the 2014-2022 planning period. At the Council Housing Committee’s last meeting, Vice Mayor Scharff and the Planning Director reported that at the last RHNA meeting, the most recent draft methodology reduced the City of Palo Alto’s allocation to approximately 2,003 units for that planning period, nearly half of the likely outcome under the Alternative SCS scenarios previously under consideration. (For comparison, the 2007-2014 RHNA allocation was 2,860 units and the 1999-2006 allocation was 1,397 units.) The basis for this reduction appeared to be a combination of: 1) a lesser regional allocation by the State Housing and Community Development (HCD) Department due to the recession and the high number of foreclosures on the market, b) a revision in methodology to emphasize more of a city’s actual production of affordable housing, c) elimination of reliance on a “quality of life” factor (i.e., schools) in the methodology, and d) a more “corridor-based” approach to distribution of housing units. On the other hand, the City of Palo Alto’s employment allocation through 2040 increased over its already substantial rate. Staff is still evaluating whether ABAG has sufficiently excluded housing attributed to the Stanford campus housing from the City’s total. For the 2007-2014 planning period, this request was made after numbers were already near final, and only a concerted effort by the City in cooperation with the County and Stanford resulted in a reduction of over 600 units from the City’s allocation. The County also made a similar request that such an adjustment be made up front in this process prior to draft numbers being distributed to cities and counties, but it appears that only about 90 units were allocated back to the County subsequent to the requests. Staff has contacted ABAG and provided them with information about Stanford’s General Use Permit and Community Plan, showing that approximately 1 million square feet of non- residential square footage and 1,500 housing units are anticipated on County lands by Stanford’s plans through that timeframe. Staff believes the Stanford/County allocation issue is a major remaining concern for the City and expects that to be a major topic of review and comment to ABAG. April 19, 2012 Page 4 of 4 (ID # 2697) Next Steps Staff expects to comment immediately to ABAG and MTC regarding the issue of Stanford/County allocations, at the direction of the Committee. If the Committee requests that other comments be forwarded to the agencies prior to their action on May 17, staff will prepare such a letter. Otherwise, staff will continue with further analysis and prepare materials for the Planning and Transportation Commission and full Council to consider. An update for the Commission is tentatively scheduled for April 25th. Staff will also prepare a letter to MTC regarding the One Bay Area Transportation Grant Program, pursuant to another agenda item at the Regional Housing Committee’s meeting. cc: Planning and Transportation Commission Charles Carter, Stanford University Attachments: Attachment A: SCS Preferred Scenario - Jobs Housing Connection March 2012 (PDF) Attachment B: Draft Preferred Land Use Scenario Presentation to MTC/ABAG (PDF) Attachment C: Jobs-Housing Connection Employment Distribution Details (PDF) Attachment D: Jobs-Housing Connection Housing Distribution Details (PDF) Attachment E: CCSCE Bay Area Job Growth to 2040 (PDF) Attachment F: March 5, 2012 Palo Alto Response to Alternative Scenarios (PDF) Prepared By: Curtis Williams, Director Department Head: Curtis Williams, Director City Manager Approval: ____________________________________ James Keene, City Manager JOBS-HOUSING CONNECTION SCENARIO DRAFT March 2012 Association of Bay Area Governments TABLE OF CONTENTS INTRODUCTION Plan Bay Area 2 Building Upon Local Plans and Strategies 4 REGIONAL TRENDS Current and Past Trends 6 Regional Growth by 2040 8 Employment Trends by 2040 11 Housing Trends by 2040 16 JOBS AND HOUSING BY PLACE, 2010 ‐ 2040 The Process to Date 21 The Approach 23 Growth Distribution 25 POLICIES AND STRATEGIES ‐ BUILDING COMPLETE COMMUNITIES Jobs-Housing Connection Scenario Benefits 33 Regional Land Use Programs 34 Proposed Policy Framework 38 Next Steps 39 APPENDICES 1 Housing Distribution by Jurisdiction 2 Employment Distribution by Jurisdiction 3 Maps of Priority Development Areas by County 4 Summary of Regional Projection Economic and Demographic Assumptions 5 Housing Distribution Methodology Summary 6 Employment Distribution Methodology Summary 7 Resources Introduction Residents of the Bay Area, one of the most impressive and productive estuaries in the world, have access to a rich mix of ecological, ethnic, and cultural diversity. The seven million of us who currently call this nine- county region home have a strong interest in retaining and enhancing this wealth of features for our children and grandchildren. We must plan to ensure that these precious resources will be preserved for future generations and that our economy will continue to be uniquely intertwined with our natural environment. The Bay Area has supported a successful concentration of global business leaders in high technology and knowledge-based industries. These industries, combined with our role as a center of trade and investments from across the Pacific Rim, have drawn major venture capital and a highly skilled labor force into the region. While the Bay Area has retained many of its major economic assets that will allow recovery from the current recession, the economic downturn is still being felt by many residents. Our most pressing issues today are the sluggish national and regional economy, sharp declines in industrial activity, the needs of a growing senior population, and the widening affordability gap. To prepare the Bay Area to overcome these issues and promote future growth, the region requires an effective strategy based on thoughtful analysis of both on-going and new challenges facing the region. Understanding our development challenges begins with an assessment of how our infrastructure systems, such as transportation, water, housing, and neighborhoods, will be able to support adequate levels of economic growth. Prior generations in the Bay Area built the infrastructure to accommodate our current economy. Several counties passed General Obligation bonds to raise property taxes and build BART. Bridge tolls were increased to maintain and seismically upgrade our lifeline bridges, along with investing in additional regional transit capacity along bridge corridors. Local jurisdictions passed bond and property tax measures to maintain local roads and special districts raised fees to provide services to new development. These actions were essential for the Bay Area to grow into the global economic center it is today. However, almost all of our existing public investment resources today are needed to simply operate and maintain the current system, especially the transportation system. Preparing for future job growth will require ever greater efficiency and creativity in the allocation of our public resources. The task today is to grow the economy by maximizing the urban infrastructure investments that have already been made to date, and by - 1 - recognizing where and when new investments are needed to make our infrastructure as efficient as possible. Local, state, and federal policies over this time period will need to bring together the resources necessary to accommodate this growth in support of a region so vital to the national economy. The challenge ahead for all of us is complex and we cannot assume that our economic growth and our quality of life will continue for current and future generations without strategic planning and investment to protect our future. Plan Bay Area To address this complex challenge, regional agencies, local governments, transportation agencies, community- based organizations, with input from members of the public, have partnered to develop Plan Bay Area—one of our region’s most comprehensive planning efforts to date. Plan Bay Area 2040 is the umbrella for the Sustainable Communities Strategy, a new process identified in California’s regional planning law (Senate Bill 375, Steinberg) to link the development of a land use plan to the transportation investments outlined in the Regional Transportation Plan (RTP). This coordinated planning process encourages participants to come together to develop a vision for the Bay Area’s future and outline a strategy for allocating scarce resources. The goal of the Plan Bay Area effort is to ensure that this growth not only strengthens our economy, but also enhances our quality of life, including the ability to move freely around the region, retain the natural beauty of the area, and provide quality schools, services, and security to Bay Area residents. Plan Bay Area proposes a long-term growth strategy that articulates how the region can capture its economic potential by providing more housing and transportation choices to Bay Area residents and workers. This focus on appropriately located and financed land development would curb major increases in highway congestion, which has the potential to significantly constrain economic growth and trigger many other negative impacts on our quality of life, public health, and time spent with our families. Through investments in local communities, Plan Bay Area addresses the needs of current generations while also preparing for the needs of future generations. The plan recognizes the Bay Area’s many diverse communities and emphasizes investing in existing neighborhoods according to the needs and visions of each community. The plan seeks to provide an array of housing types and transportation choices and envisions a pattern of growth and investment tailored to each of these communities where transit, jobs, schools, services and recreation are conveniently located near people’s homes. It also seeks to identify the strategies and policies beyond transportation and land use changes that will help foster complete communities—including support - 2 - for improved public schools, expanded parks and recreation facilities, and efforts to make neighborhoods safer and reduce crime. Increasing transportation choices makes it easier for people to get around, whether commuting, going to school, shopping, recreating, or visiting friends and family. Neighborhoods that are designed to reduce dependency on the automobile promote healthier communities through reduced pollution and cleaner air. Improving bicycle circulation and enhancing the walking environment with expanded sidewalks, street trees, and pedestrian-scaled lighting increases opportunities for people to be outdoors and physically active as they go about everyday tasks. In addition to addressing the mobility of people, Plan Bay Area also recognizes goods movement corridors and key industrial lands, and highlights strategies to ensure that these essential resources continue to support the regional economic diversity and vitality. Today the region provides neighborhoods with a wide variety of housing types, but affordability remains a challenge for low and moderate income households. In addition, young professionals and young families along with the growing senior population are driving changes in housing preferences and demanding more options closer to services. These trends are addressed in Plan Bay Area by focusing on strategic investments for the production of affordable housing and the preservation of homes that are affordable to low- and moderate-income households. In a shift from recent decades, Plan Bay Area encourages housing development—particularly affordable housing—in locations near transit and services to lower the combined housing and transportation costs for households in these neighborhoods. This allows households to spend money on other essential needs such as food, health care, or education. By concentrating new development in existing neighborhoods, Plan Bay Area helps protect the region’s natural resources, water supply, and open spaces by reducing development pressure on these areas. This allows the region to consume less energy, reducing household costs and the emission of greenhouse gases. The region’s greenbelt of agricultural, natural resource, and open space lands is a treasured asset that both contributes to the region’s quality of life and supports regional economic development. In contrast to previous trends that saw these lands consumed for development, Plan Bay Area encourages the retention of these lands by directing nearly all non-agricultural development within the urban footprint and by supporting the continuation of agricultural activities in rural communities. - 3 - Thus, the Bay Area’s Sustainable Communities Strategy (SCS) provides a range of potential benefits—for the whole region as well as local communities. By focusing on land use and transportation strategies, Plan Bay Area improves health benefits for residents and employees, supports economic competitiveness, promotes greater housing affordability, and protects the region’s natural resources. Building Upon Local Plans and Strategies The SCS builds upon a rich legacy of integrative planning in the Bay Area. For over a decade, the region and its local governments have been working together to encourage growth of jobs and production of housing in areas supported by amenities and infrastructure. In 2008, ABAG and MTC created a regional initiative to support these local efforts called FOCUS. Through FOCUS, local governments have identified Priority Development Areas (PDAs) and Priority Conservation Areas (PCAs). PDAs are areas where new development will support the day-to-day needs of residents and workers in a pedestrian-friendly environment served by transit. While PDAs were originally established to address housing needs in urban settings, they were later broadened to address employment centers and rural settings. Local jurisdictions have defined the character of their PDAs according to existing conditions and future expectations as regional centers, city centers, suburban centers, transit centers or rural centers, among other place types. PCAs are regionally significant open spaces for which there exists a broad consensus for long-term protection. PDAs and PCAs complement one another because promoting compact development within PDAs takes development pressure off the region’s open space and agricultural lands. In a departure from previous regional growth scenarios, Plan Bay Area is designed around places for growth identified by local jurisdictions. Many Bay Area jurisdictions have worked in partnership with MTC and ABAG to plan and advance the implementation of Priority Development Areas as complete communities in recent years. The planning processes for these key infill, transit-oriented neighborhoods are community-based and involve hard work to address a complex range of local goals and issues. Plan Bay Area is designed to advance dialogue around a sustainable regional growth pattern that recognizes local aspirations and the unique characteristics of our region’s neighborhoods and communities. This is not a simple compilation of local proposals; rather it is the result of an ongoing dialogue on enhancing community and regional qualities for future generations. - 4 - - 5 - "' .......... ...,""~..,.-.. &. ••• 1&<.,,» • ........... c_",,0_ • _ .... 0.""', • _-.0_, ""'olr-C ..... • "' .... X ... __ _ .-~ ........ - • .. ......." .. 0 .. , .... ... ".,~ · __ .. _0_ o r.r.d" Co •• on.Il.D J., •• _ .... """U,b.n_ .... l.' Whlo"'l:r.~n c ....... LIml" • P, ........ Opo •• "'C, -_ .... _. __ ........ ---... -... -~ ... -...... """' ... ..-,~--.'" ....,~ ..... Sustainable Communities Strategy , Future Place Type (or Priority Development Areas "' ......... ..., ""~Iop-" a. ••• ,...""' • • _ ..... c.CffC_ · -.... ......... • __ c ..... ","",-.,c_ • ", ... K""""_ .-~ ....... - • ......w .. ""', .... ... ~,~ · --"--o Pd.d" CGG •• n.U.G .b •• ~ ... '''' .. n.b." ......... ' •• ""1lo .. 1hbn c •• """ u...!" • P,., .. , •• .,.. •• poc. -_ ......... __ ..... ---,."." ... _ .... _"..." ... _ ..... EB Sustainable Communities Strategy ,- , Future Place Type for Priority Development Areas Regional Trends The San Francisco Bay Area has one of the strongest economies in the world given the region’s leadership in high technology and innovation, international networks, educational and research institutions, and highly skilled labor force. This economic strength added to its natural resources, vibrant communities, and cultural diversity establishes a good platform to support a healthy region and communities into the future. This is a central focus of Plan Bay Area. Current and Past Trends Today, it is challenging to envision solid and sustained economic growth given the impact of the Great Recession on jobs, housing, mortgages, education and health care among other essential dimensions of life. By the end of 2007 the region was caught in a major national recession triggered by the financial and housing crisis. This recession has been deep and long, particularly in the State of California. Almost 300,000 jobs were lost between 2007 and 2010 in the Bay Area. During the same time frame, venture capital declined by about 20 percent. (Levy 2012) In 2008 the region had about 37,000 homes that experienced foreclosure and 154,000 more foreclosures are expected during the rest of this decade. (Chapple 2012) The national financial and housing crisis had a sharp impact in the Bay Area where access to affordable housing was already a major challenge. Between 2008 and 2009, California and the nation’s median home sale prices saw dramatic declines. Historically, annual average housing production in the Bay Area has resulted in shortfalls of about 30 percent, according to the California Department of Housing and Community Development (State of California 2000). This trend was temporarily reversed during the decade of the 2000s, when the market overpriced housing, spurring construction in areas of the region distant from job centers. The legacy of the 2000s boom remains with us today, not just in the form of high housing vacancy rates (6.4% in 2010) due in part to foreclosed housing stock, but also the specter of foreclosures yet to come. (Chapple 2012) This recession resulting from the financial crisis has impacted people and places much more than what is reflected in the measures of economic output or market values. However, given the Bay Area’s diverse economic assets, economists assert that a steady recovery of the Bay Area is already underway. (Levy 2012; Bay Area Council Economic Institute 2011) By the end of 2011 the San Jose metropolitan statistical area and - 6 - Silicon Valley were experiencing job growth that, while modest relative to recovery periods historically (3 percent), was much higher than the national average. (Levy 2012) Most of this job growth is driven by high technology companies such as Google, Apple, Facebook, and Zynga among others. During 2011 unemployment in the San Jose Metropolitan Area had already declined from 10.5 to 8.6 percent. Many other parts of the Bay Area, particularly inland communities furthest from Silicon Valley have not yet displayed signs of economic recovery, but are expected to experience some growth in 2012 and 2013. (Bay Area Council Economic Institute 2011) As the Bay Area recovers from this severe recession, two converging longer term trends are shaping the future of the region. The first trend is healthy but slower employment growth. Different from prior decades of major employment growth when Silicon Valley was established as a major knowledge center and producer of technology supported by a strong financial center in San Francisco, the upcoming decades will see more moderate growth, reflecting a more mature state and regional economy. This is a shift from outward expansion to redeveloping underutilized land in existing urbanized areas, and reflects an aging population and the pending retirement of the baby boom generation. As a result, the region is forecasted to only slightly exceed national employment growth rates. The second trend is the shift from a dispersed employment and housing growth pattern toward more focused growth. Over the last 40 years the region experienced a pattern of major suburban employment growth and housing production. On housing production, major new subdivisions housed our population in new and small cities within and outside the region. By 2010, cities like Oakley, San Ramon, Brentwood, Windsor, Clayton, and Rohnert Park had grown 8 to 26 times their sizes in 1970. At that time the development of subdivisions was supported by the expansion of the highway transportation network. This suburban population provided a labor force for employment growth at suburban locations. Starting in the 1980s, office jobs moved from the San Francisco Financial District to new office parks in the East Bay or South Bay. At the same time, the growth of Silicon Valley perfected the office park model that was pursued in many other parts of the world, which in turn created more demand for suburban housing. The extension of the transportation system into the Tri-Valley and its proximity to low cost housing areas in the Central Valley further fueled the eastward movement of job growth. By 2010 only 16 percent of total regional employment was in San Francisco, a decline from 33 percent in 1975. - 7 - While this decentralization of jobs created new opportunities for many areas in the region, it also led to high levels of traffic congestion, increases in the cost of and time spent commuting, and the loss of agricultural lands and natural resources. This decentralized development pattern has been addressed in part through the development of policies and regulations to protect open space, including the creation of urban growth boundaries by local jurisdictions, and through investments in existing communities with transit access and proximity to a wide range of services, amenities, and employment opportunities. These initiatives are recognizing the value and scarcity of remaining resource lands and working to maintain the economic vitality of the region through investments that strengthen existing communities. Regional Growth by 2040 Accounting for the strengths and assets of the region, a slower pace of growth than in previous decades, a lower share of the national economy, and a recovery from the recent recession, regional agencies forecast an increase of 1.1 million jobs, or 4.5 million jobs total, by 2040.1 According to Steve Levy, from the Center for Continuing Study of the California Economy, the region could capture another 110,000 jobs of the total national growth. However, the total job growth is constrained by our ability to produce housing, which is ambitiously estimated at 660,000 new units by 2040. This is a higher level of housing production than that estimated by Karen Chapple, UC Berkeley, based on an assessment of previous housing production in the region, which estimates future housing production as low as 80% of existing levels, or less than 600,000.2 (Chapple 2012) 1 Compared to 2007, this is only an increase of 850,000 jobs. 2 The affordable housing production challenge is particularly critical relative to infill development where established neighborhoods are revitalized with new development in the midst of existing communities, land values are high and planning and entitlement processes are often complex and costly. In addition, construction costs of multi-unit structures continue to escalate, particularly due to the cost of steel and other materials. Financing for infill development remains difficult and the “cost of money” remains relatively high due to the perceived riskiness of multi-family construction and the need for large chunks of capital up front. Urban infill development is also challenging due to the need to assemble sites and the extra costs for site preparation, as well as the extra regulatory hurdles in core areas, such as extensive design reviews. And most recently, there is a new threat of lack of institutional capacity to process housing applications, due to the dissolution of redevelopment agencies and the ongoing fiscal stress in local governments. (Chapple 2012) - 8 - This level of housing production of 660,000 will allow the region to accommodate 700,000 new households and 2.1 million people forecasted in the SCS through 2040. This also assumes that the rate of net in- commuting will remain at 2010 levels, and absorption of about 40,000 existing vacant units. Table 1. Regional Totals, 2010 and 2040 2010 2040 Growth 2010 ‐ 2040 Population 7,151,000 9,299, 000 2,148,000 Households 2,608,000 3,308,000 700,000 Housing Units 2,786,000 3,446,000 660,000 Jobs 3,385,000 4,505,000 1,120,000 The forecasted population growth of 9.3 million people by 2040 is based on forecasted regional employment growth shaped by national economic and demographic forecasts. (Levy 2012) The relationship of jobs to population was calculated by Steve Levy based on population characteristics.3 The population characteristics used in the scenario incorporate information from the 2010 Census and a statewide forecast produced by the California Department of Finance.4 Two major demographic changes shape the forecast of household and job growth: the increase in the senior population (more than half of the population growth is people over 55) and the increase in the Latino and Asian populations.5 These demographic changes lead to three major trends in the regional growth by 2040: 1. Increase in group housing: The increase in the senior population results in an increase in the amount of residential care facilities, which is a major component of group housing. More than 66,000 additional group housing residents are forecasted by 2040. This is a conservative estimate based on current conditions. 3 The Jobs-Housing Connection Scenario includes an adjustment of 0.7 percent higher employed residents than the numbers forecast by Levy. This adjustment is the result of retaining the 2010 in-commute ratio out to 2040. 4 The California Department of Finance assumes a statewide net migration averaging 177,000 per year, which represents 35% of the statewide total population growth. (State of California, Department of Finance, Population Projections for California and Its Counties 2000-2050, by Age, Gender and Race/Ethnicity, Sacramento, California, July 2007). 5 Latino and Asian populations projected to increase 11.9 and 2.4 percent respectively. This change is already reflected in the existing population, where the non-hispanic white population makes up 56% of 55-64 year-olds, while only making up 33% of 15-24 year-olds. - 9 - 2. Decline in labor force participation: The overall labor force participation rate declines given the increase in the senior population, even with assumptions of an increased number of people working after 65. This means that, by 2040, 49.9 people out of 100 will be employed or looking for work, compared to 51.6 in 2010. 3. Increase in household size: The number of people per household is expected to increase from 2.69 in 2010 to 2.75 in 2040 as a result of the increase in the Latino and Asian population as well as the number and percentage of multigenerational households. A summary of demographic assumptions is included in Appendix 4, Figure 1. Population by Age, 2010 and 2040 Figure 2. Share of Population by Race and Ethnicity, 2010 and 2040 0% 10% 20% 30% 40% 50% White Latino Asian African American Multirace Pacific Islander and American Indian 2010 2040 2010 US Census, California Department of Finance - 10 - Employment Trends by 2040 The region is forecast to grow slightly faster than the nation. Over half of the 1.1 million job growth is expected to occur between 2010 and 2020, which includes the recovery of close to 300,000 jobs lost since 2007. Many of these jobs will be filled by currently unemployed or underemployed individuals. From 2020 to 2040, the rate of job growth is forecast to slow down as retiring Baby Boomers exit the labor force. (Levy 2012) The growth of 1.1 million jobs does not translate directly into new office, commercial or industrial space. About one third of these jobs could potentially be accommodated within existing offices and facilities given current vacancy rates relative to higher job levels in 2000. Figure 3. Regional Total Employment by Decade, Past and Future 0 1 2 3 4 5 1960 1970 1980 1990 2000 2010 2020 2030 2040 Mi l l i o n s Sources: US Census (1960-1980), DoF (1990-2000), ABAG (2010-2040) - 11 - Growth by economic sectors The leading sectors of the regional economy are defined by those directly involved in knowledge production. This includes Professional Services, Information, Finance, and portions of the Health and Education sectors. They all show high growth rates. (See Table 2). They have become more specialized on the design and development of new products and information, outsourcing the manufacturing and general professional services components. These knowledge-based sectors are supported by a highly educated labor pool and provide high wage jobs. The Bay Area’s labor force has the highest share of college graduates (44 percent) when compared to any other region in the country. (Levy 2012) These leading sectors have represented and will continue to represent a high share of the total regional growth of over one third of total jobs. Although the knowledge-based sectors define the overall pace of growth for the region, their success is supported by and advanced by a very diverse regional economy. Health and Education and Leisure and Hospitality sectors have not experienced the very high job growth of Professional and Business Services, but have displayed steady growth even through periods of overall economic decline. Construction is expected to experience significant employment gains, particularly through the recovery period. Manufacturing and finance jobs have contracted over the last 30 years. Much of the region’s traditional manufacturing employment has relocated to low cost labor regions in Asia and Latin America. More recently high tech manufacturing has also relocated out of Silicon Valley to lower cost locations. Increases in productivity through information technology and automation have impacted all sectors, but manufacturing and finance in particular. While the region continues to be an important financial center, finance-related jobs have been eliminated or relocated out of the Bay Area. Manufacturing and Finance are not expected to contribute much to job growth but will remain stable sectors in the regional economy. The decline of manufacturing and finance has resulted in a loss of some middle-income jobs for the region. This is compounded by the polarized incomes between the highly specialized knowledge-based jobs and service jobs. Similarly, the agricultural sector, where food production is combined with high value tourism, organic markets, and farmers markets, has incorporated a wide range of services and exchange networks with a resulting higher productivity for many businesses. However, the number of jobs is expected to remain the same or decline. - 12 - Table 2. Total Employment and Growth by Sector, 2007, 2010 and 2040 Total Growth Sector 2007 2010 2040 2010-2040 2007-2040 Professional 633,023 596,719 973,617 376,897 340,594 Health and Education 420,055 447,730 698,641 250,910 278,586 Leisure and Hospitality 484,326 472,925 660,562 187,636 176,235 Government 529,426 498,993 565,419 66,426 35,993 Information 123,533 121,067 157,327 36,260 33,795 Transportation and Utilities 111,332 98,708 127,355 28,647 16,023 Financial 219,396 186,073 233,805 47,732 14,409 Construction 211,226 142,336 225,272 82,936 14,046 Retail 373,757 335,934 384,412 48,478 10,655 Agriculture and Natural Resources 27,887 24,650 22,719 -1,931 -5,168 Manufacturing and Wholesale 519,839 460,164 456,090 -4,075 -63,750 All Jobs 3,653,800 3,385,300 4,505,218 1,119,918 851,418 Figure 4. Employment by Sector, Past and Future 0 200 400 600 800 1000 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 Th o u s a n d s Professional Health and Education Leisure and Hospitality Government Manufacturing and Wholesale Retail Financial Construction Information Transportation and Utilities Agriculture and Natural Resources Sources: EDD (1990-2010), ABAG (2010-2040) Note: Higher ABAG numbers in 2010 reflects inclusion of self-employed and domestic workers. - 13 - Growth trends by places Economic sectors organize jobs by activities and products such as sales, computer services, food preparation, or health care, among many others. Each of these sectors includes many different subsectors and each subsector can occupy a wide range of buildings and places. For example, the professional and business services sector include accounting, graphic design, testing laboratories, telephone services, janitorial services, waste collection, among many others. These businesses can occupy an office, an industrial laboratory, a treatment facility, among many other type of buildings. Even within a particular type of business we can find many building types. A graphic designer’s office can be a home office in Orinda or a major portion of a high rise building in San Francisco’s South of Market District. In addition, economic activities are constantly changing their space requirements. A printing company that retains the design component and outsources the actual production would only require a small office. Thus, in order to forecast the regional employment distribution, the sections below summarize key land use trends that capture the ongoing spatial changes as well as changes in the labor force composition and workers’ preferences. Overall trends suggest a transition toward a more focused employment growth pattern for the region. This focused growth takes various forms across the various employment centers through the region. Knowledge-based, culture, and entertainment at regional centers Contrary to previous trends of job decline in major regional centers,6 the recent growth of professional services in close proximity to urban amenities is expected to lead to an increase of job growth in Downtown San Francisco, Downtown Oakland, and Downtown San Jose, assuming an appropriate provision of infrastructure, transit, and access to affordable housing. At these regional centers, leisure and cultural activities have also been fueled by the Bay Area’s confluence of international business and leisure travelers as well as artists and entertainers. Similar to the growth of San Francisco’s financial district in the 1970s, the Bay Area is attracting new businesses and workers seeking to locate in close proximity to related firms, services and amenities. The new wave of businesses and professionals’ demand for building space prioritizes flexibility to adjust spaces to multiple functions and requires less office space per worker relative to the early growth of traditional downtown office space. 6 Regional centers have reduced their office jobs as a share of the region from 49 percent in 1990 to 41 percent in 2010. Downtown San Francisco and Downtown Oakland also reduced their absolute employment levels. Downtown San Jose had a small increase. - 14 - Multiple activities and transit at office parks Office parks have and are expected to continue to accommodate a growing number of employees. However, given the limited land available for new office parks, existing vacant office space, and the preference for walkable, transit-served neighborhoods by a growing number of employers, office parks are expected to grow at a slower pace than in recent decades. Existing office parks are also using less space per worker, providing transit access, and in a few cases adding housing, services and amenities. The emerging private shuttle services run by businesses, particularly in San Mateo and Santa Clara Counties, are expected to grow and improve transit access while lessening, but not fully mitigating increased freeway traffic congestion related to employment growth. Downtown areas and transit corridors serving residents Over the last decade, downtown areas in medium and small cities throughout the region have been expanding their services and jobs. The number of shops and festivals around the historic train station has expanded in Downtown Santa Rosa. Downtown Mountain View has a very active main street with an increasing number of restaurants and bars. New entertainment venues and amenities have located in the core of downtown Livermore. The increase in the senior population, combined with the region’s changing ethnic demographic profile, is expected to increase the need and demand for local services in downtown areas in close proximity to residential locations with greater transportation choices. In the last decade, Priority Development Areas have shown an increased concentration of knowledge-based, arts, recreation, health, and education jobs. New vitality of industrial and agricultural land Manufacturing and wholesale distribution have experienced declining employment in many of the region’s key industrial areas. However, in recent years a different and very diverse mix of businesses has relocated to these areas. In addition to basic services such as shuttle services, refuse collection or concrete plants, industrial lands are now occupied by a wide range of businesses from food processing to high tech product development, car repair, graphic design, and recycling among others. Because of their building and space needs, these economic sectors are coalescing into traditional industrial lands. They provide essential support to the leading knowledge-based sectors of the economy as well as to residents. - 15 - The trends in agricultural land have paralleled those of industrial land in its increasing diversity of activities. But, in the case of agricultural land, growth is related to the addition of services and tourism. The Bay Area’s wealth of agricultural land is unparalleled among our nation’s largest metropolitan regions and provides high quality products including a world-renowned wine industry. Beyond tourism, agricultural land and activity in the region is also a strong quality of life attractor for residents of the Bay Area. For the most part, the region’s remaining farmland has some policy protections from urban expansion. All of the counties outside of San Francisco have a growth management framework in place (such as urban growth boundaries or agricultural zoning). The region needs to maintain these important policy supports to ensure the viability of the agriculture industry. Industrial lands will also require some level of protection given the pressures of infill residential and office development. A number of cities have already taken steps to preserve important industrial lands within their communities. Housing Trends by 2040 Based on the employment forecast and the assessment of previous housing production, the Jobs Housing Connection Scenario assumes the production of 660,000 housing units. This level of production will allow the region to house all its population by 2040. This production of housing will be supported by current and new strategies in order to secure housing for residents at all income levels. A regional concerted effort to support housing affordability will ensure that the Bay Area is able to retain the vitality and competitiveness of its economy and a high quality of life for all residents. Housing and jobs The forecasted employment growth by industry is translated into occupation and wages to assess the income levels by 2040. All income groups show an increase by 2040 with small changes in the distribution: higher shares for the very low and low income households and lower shares for the moderate and above moderate income households. [Add footnote: description of each group] Table 3. Number of Households by Income Group, 2010 and 2040 Very Low Low Moderate Above Moderate Total 2010 25% 15% 18% 42% 100% 2040 26% 17% 17% 39% 100% - 16 - As is the case today, high-income households are likely to have a wide range of housing options. However, in order to ensure a healthy economy the regional efforts will focus on strategies and investment that provide housing for much of the region’s workforce – sales clerks and secretaries, firefighters and police, teachers and health service workers – whose incomes would severely limit their housing choices. This has been an increasing challenge in the region, particularly in employment-rich locations given that market-rate housing development has been increasingly unable to deliver housing for the middle class. Even more challenging is the housing situation encountered by low and very low income households wage workers who struggle to find housing that costs less than 60 percent of their income. Housing choices The demographic changes described above are changing the housing choices among Bay Area residents. The growth of the senior, the Latino and Asian, and “echo boom” populations presents a different set of housing needs and choices. People aged 55 and over are more likely to prioritize public transportation, walking, and access to amenities, and are more receptive to townhouses and condos with smaller yards and smaller units than other types of households.7 Similarly, young singles have been found to be particularly attracted to places that offer walking access to shops, restaurants, cultural events, and clubs. They also prioritize short commutes.8 This so- called “Echo Boom” generation has a particular affinity for neighborhoods where they can walk and bike as an option.9 Analysis indicates that Latino and Asian households have also shown a preference for more housing choices that provide access to services and amenities and that accommodate multigenerational families. Cultural preferences of new immigrants also suggest they may be more willing to utilize public transportation and live in multifamily housing than native-born residents.10 The large number of relatively affluent aging baby boomers, the minimal projected growth of the 35-54 age cohort, and the preference for urban living among echo boomers, suggests future growth in the market for multi-family housing in infill locations.11 7 Myers and Gearin, 2001; Belden Russonello & Steward, 2011. 8 Belden Russonello & Steward, 2011. 9 Department of Transportation statistics show that average daily vehicle miles travel (VMT) for people under 35 has declined steadily since 1995. U.S. Department of Transportation, “Table 33. Vehicle Miles of Travel (VMT) per day for Younger Population Groups by Urban and Rural Household Location 2009 NHTS,” Summary of Travel Trends: 2009 National Household Travel Survey, June 2011, http://nhts.ornl.gov/2009/pub/stt.pdf. 10 Mendez, Michael, "Latino New Urbanism: Building on Cultural Preferences." Opolis: An International Journal of Suburban and Metropolitan Studies, 1.1 (2005) 11 Arthur C. Nelson, The New California Dream: How Demographic and Economic Trends May Shape the Housing Market, A Land Use Scenario for 2020 and 2035, Urban Land Institute, 2011. - 17 - While single family neighborhoods are very desirable for a significant segment of our population, the current stock in relation to changes in our population is likely provide a large supply of this housing type relative to demand in the coming decades. This is in part because single family homes have been the predominant form of housing produced in the region for decades. In contrast, townhouses, apartment buildings, condos, and other multifamily housing options are currently comparatively limited. The Center for Transit Oriented Development’s analysis finds that while about 23 percent of Bay Area households (about 600,000) live near transit today, there is a market demand for up to 38 percent of Bay Area households to live in transit- accessible areas in future decades. (Metropolitan Transportation Commission 2005) Housing production The Jobs Housing Connection Scenario addresses the needs and trends of growing populations by accommodating more than two thirds of the housing production in Priority Development Areas, as places with transit access and a wide range of services and amenities. Those places will diversify housing options by increasing the stock of townhouses, apartments and condos in response to the changes in demographics and preferences in housing types. It is envisioned that the region will continue to produce new subdivisions but those are expected to be located within urban growth boundaries and represent a relatively small percentage of overall housing production through 2040. In spite of multiple challenges, emerging trends indicate some support for housing production in PDAs. Since 2004 the region has increased its production of multifamily housing and multi-family housing has represented the majority of housing-related building permits since 2007. Also, the construction and banking industries increasing familiarity with the multi-unit building type are recent positive factors. Finally, the Governor recently signed several bills that ease regulations for new housing constructed in developed areas where streets, schools, jobs, and services already exist.12 This should help housing construction in Priority Development Areas (PDAs). 12 The Governor recently signed three CEQA reform bills, including SB 226 which exempts some infill construction from CEQA, and more CEQA reform is planned. - 18 - Figure 5. Regional Total Households by Decade, Past and Future 0 1 2 3 4 1960 1970 1980 1990 2000 2010 2020 2030 2040 Mil l i o n s Sources: US Census (1960-2010), ABAG (2020-2040) - 19 - Addressing the Affordable Housing Challenge The production of affordable housing by 2040 relies on several current strategies. These strategies will need to be expanded and new strategies developed. i) Affordable rental housing production. The production, whether through new construction, acquisition/rehabilitation, or adaptation, and whether by nonprofit or profit-motivated developers, of income-restricted rental housing for the Very Low and Low Income households. ii) Inclusionary Housing. Land use regulations and sometimes incentives, enacted at the local governmental level, that mandate the inclusion of affordable housing, whether on-site, off- site, or through the payment of in-lieu fees, in the development of market rate residential developments. This includes Moderate Income households. iii) Habitat for Humanity affordable homeownership. An international charitable organization that includes local chapters that cover all nine counties in the Bay Area. Its mission is to provide affordable homeownership housing opportunities to households that generally fall in the Very Low Income and Low Income categories. Its unique production model relies on sweat equity from the homeowner-to-be as well as labor from volunteers to keep TDCs to minimal levels. iv) Resale of foreclosed residential properties. The foreclosure crisis offers the opportunity for the resale or leasing of foreclosed properties at drastically lowered sales prices or rents, thus bringing a substantial quantity of market rate housing stock within reach of Very Low, Low, and Moderate Income households. v) Housing Choice or Section 8 Vouchers. Arguably the centerpiece of the system of subsidies to low-income renters in the United States.13 While the entities that govern the disbursement of HCVs, public housing authorities (PHAs), can elect to convert up to 20% of the HCVs under their ambit to project-based subsidies, that leaves at least of 80% of HCVs as tenant- based, or portable, subsidies that recipient households use to select, and help pay for, a housing unit of their own choosing in the private rental market. vi) Secondary units. Rental housing units carved out of residential properties or accessory buildings, whether in compliance with local building and zoning codes or not. These units appear to house a substantial number of people in at least certain subregions of the Bay Area. vii) Filtering. The process of market-rate housing stock becoming affordable to lower income groups via depreciation over time. viii) Group quarters. A portion of the population increase in the Bay Area between 2010 and 2040 will be housed in group facilities that have not yet been expanded or built. These include nursing homes, hospitals, homeless shelters, college dormitories, jails, and other facilities. 13 See, for example, Quigley, John. 2008. “Just Suppose: Housing Subsidies for Low-Income Renters” in Revisiting Rental Housing: Policies, Programs, and Priorities, edited by Nicolas P. Retsinas and Eric S. Belsky. Brookings Institution Press, Washington, D.C. - 20 - Jobs and Housing by Place, 2010 - 2040 By 2040, the region is forecast to have 4.5 million jobs and 3.4 million housing units, or an additional 1.1 million jobs and 660,000 housing units. This section explains the distribution of this growth throughout the region. The growth distribution addresses emerging demographic and economic trends, and policy considerations for a healthy economy, equitable access to jobs and housing, and preservation of our natural resources and agricultural land. The process, approach, and growth distribution of this land use pattern are explained below. The Process to Date The growth pattern represented by the Jobs-Housing Connection Scenario has been developed through an extensive dialogue with local jurisdictions and input from stakeholders and the general public. This process has been supported by a team of consultants that provided expertise for the economic, demographic, and housing analysis developed to inform the land use pattern developed.14 Staff relied on this input and analysis to prepare the Initial Vision Scenario as well as the preceding alternative scenarios. These scenarios have been discussed with Congestion Management Agencies, Planning Directors and elected officials at the local and county levels to address the specific needs and challenges of each community.15 The scenarios have been presented at the Joint MTC Planning /ABAG Administrative Committee, the ABAG Executive Board, and the Metropolitan Transportation Commission. The development of the Jobs-Housing Connection Scenario takes into account the input received on previous scenarios and the most recent economic and housing analysis of past and future regional trends. 14 Employment and population dynamics, Center for Continuing Study of the California Economy; employment distribution, Strategic Economics; housing production, household income, and affordability, Karen Chapple, UC Berkeley; housing policy, Amit Ghosh; and regional economic output, Cambridge Systematics. 15 Multiple discussions and county-specific processes have also been created in counties to address local needs and challenges. (www.onebayarea.org) - 21 - Input from Local Jurisdictions The intent of regional agencies has been to develop a regional growth pattern that recognizes local aspirations and the unique characteristics of our region’s neighborhoods and communities. Key input from local jurisdictions reflecting local character and aspirations form the basis of the proposed regional growth pattern. These efforts began before the development of the SCS and Plan Bay Area were underway, and have continued through meetings, workshops, letters, surveys and website postings. Over 70% of the region’s 109 local jurisdictions have nominated Priority Development Areas as complete communities since 2007. Starting in November of 2010, through the development of the Initial Vision Scenario, and subsequently the Alternative Scenarios, local jurisdictions have provided significant input regarding the overall approach to the SCS and specific information on their local plans.16 Some communities described the level of housing growth depicted in the previous scenarios as too high, while other jurisdictions responded that growth levels would be appropriate if additional funding for redevelopment, public schools, transit, and other community infrastructure were available. Much input was received on economic challenges and the need to align current and new investments, including transit services, to support employment growth and housing production. Another area of concern has been the alignment of strategies, investments and regulations across regional and state agencies. The elimination of redevelopment agencies and reductions in transit service in some areas was also highlighted as a major challenge to growth. Input from stakeholders Regional agency staff has worked with stakeholders concerned with the economy, the environment, and equity. Business and economic organizations have raised concerns about supporting a healthy economy by highlighting the need for more affordable workforce housing, increasing options for housing production, removing regulatory barriers for infill development, and addressing infrastructure needs at major and rapidly growing employment centers. Environmental organizations have emphasized the need to address all housing needs to reduce the number of commuters from adjacent regions, improve transit access, retain open space, 16 Local planning staff in the majority of the region’s local jurisdictions provided presentations on the Initial Vision Scenario and/or Alternative Scenarios to their city councils or boards of supervisors. Subsequently local communities provided the regional agencies with direct and specific feedback on the scenarios. - 22 - and direct discretionary transportation funding to communities developing housing in Priority Development Areas. Independent equity groups, as well as the Regional Equity Working Group and MTC’s Policy Advisory Council, have provided input on increasing access to housing and employment and an improved quality of life for residents from all income categories throughout the region. These groups have suggested specific indicators to address housing needs in communities where strong employment growth is expected to attract large numbers of low-income workers. Additional input has been gathered from the public at large through community workshops, county workshops, telephone polls, and website surveys. Details on this public input can be reviewed at (http://www.onebayarea.org/workshops/winter_2012_results.htm). The Approach The Jobs-Housing Connection Scenario links local aspirations for community development with regional objectives, particularly a strong regional economy, and identifies places to accommodate new population and job growth in a way that maximizes the use of existing infrastructure and transit, improves access to services and amenities, and reduces the cost and time of work-related commutes and other day-to-day trip needs. Future job and housing growth is defined by Priority Development Areas and regional growth factors. Priority Development Areas Priority Development Areas are selected and nominated by local jurisdictions as appropriate places for growth according to the qualities of each place. Local jurisdictions choose a Place Type for each PDA (such as regional center, transit neighborhood, or rural town), which provides a general set of guidelines for the character, scale, and density of future growth. Places can range from a major regional center such as Downtown San Jose or San Francisco, to a city center such as Downtown Fremont or Berkeley, to suburban centers such as Downtown Walnut Creek or Hacienda in Pleasanton, to rural towns such as Cloverdale in Sonoma County. There are more than 200 Priority Development Areas, representing a wide range of places; some plan to accommodate a few stores and services while others consider high rise office buildings. Development will be - 23 - very modest in rural towns, whereas regional centers envision new mid-rise and high-rise buildings. Each community plans for new development that compliments what is already in their PDAs. For example, in most small suburban communities the scale of new buildings is a mix of townhouses and 2-3 story commercial structures. Mid-sized communities however, are generally planning for residential and commercial buildings in the 3-5 story range. All communities with Priority Development Areas are seeking to move away from a “project-by-project” development approach, toward the creation of appropriately scaled, attractive complete communities that meet the daily needs of residents and workers. Priority Development Areas are all existing communities that encompass only 4 percent of the region’s land area. As such, focusing growth in PDAs simultaneously supports the retention of Priority Conservation Areas (PCAs), locally identified regionally significant areas for agriculture, natural habitat, and open space.17 Regional growth factors In general, the regional growth factors address the employment trends by economic sector and the changing demographics and housing needs of the region. They are based in part on: the growth potential of areas supported by transit and existing infrastructure; where housing is needed to support access to jobs; and where economic clusters support job growth. Employment growth is organized under three major groups: knowledge-sector jobs, population-serving jobs, and all other jobs. Knowledge-sector jobs, such as information technology companies, legal or engineering offices, or biotechnology firms, are expected to grow based on current concentration, specialization, and past growth as well as transit service and access. Population-serving jobs, such as retail stores, or restaurants, are expected to grow based on the number of residents per place. All other jobs, including government, agriculture and manufacturing, are expected to grow according to the existing distribution of jobs in each of these sectors. (See Appendix 6) 17 Conservation priorities adopted by ABAG in 2008. - 24 - Housing growth in this scenario starts with local plans at the county, city, and PDA level. Five factors are then applied to forecast growth in alignment with regional goals: 1) local transit service, 2) vehicle-miles traveled, 3) employment by 2040, 4) low-wage workers commuting from outside each place, and 5) housing cost. Housing growth is next adjusted to acknowledge suburban growth supported by existing infrastructure, including that on presently undeveloped land, and to ensure that no county or city’s proposed growth substantially deviates from local plans. This Jobs-Housing Connection Scenario accounts for the current high vacancy rate by city by assuming a standard vacancy rate of four percent. It also assumes an increase in group housing, recognizing prevailing demographic and social trends. (see Appendix 4) Growth Distribution Priority Development Areas In the Jobs-Housing Connection Scenario, more than two-thirds of all regional growth by 2040 is allocated within Priority Development Areas which represent about 4 percent of the region’s total land area. PDAs represent 74 percent or over 500,000 units of new housing production, and 67 percent of new jobs, or almost 747,000. Between 2010 and 2040, the share of housing in PDAs shifts from 26% to 37% and jobs from 47.6% to 52.3%. Figure 6. Share of Jobs and Housing in PDAs by County, 2040 0% 20% 40% 60% 80% 100% San Francisco Santa Clara Alame da San Mateo Sonoma Contra Costa Sol a n o Napa Mar i n Jobs Households - 25 - Cities Within cities the Place Types identified as Regional Centers, Mixed-Use Corridors and Urban Neighborhoods account for the majority of growth. The three major cities with Regional Centers San Jose, San Francisco, and Oakland take 36% of the total housing growth and 35% of total job growth by 2040. El Camino Real/The Grand Boulevard, San Pablo Corridor, and East 14th – International Boulevard corridor connects a variety of PDAs and also represents a major share of both housing and job growth. Medium size cities such as Fremont, Dublin, Pleasanton, Santa Rosa, Richmond, Walnut Creek, and Concord also play a major role in accommodating new jobs and housing - together they represent about 15% of total housing growth and 12% of total job growth. Small cities, single family neighborhoods, and rural areas have a very small share of the overall growth by 2040 and are expected to retain the same scale and character over the next 28 years. Place Type Examples For example, the following PDAs by Place Type illustrate the broad spectrum of growth that could occur by 2040. Regional Centers – High Growth Regional Centers like North San Jose represent major downtown areas for employment growth. Employment is expected to increase by over 45,000, or from 90,000 to over 135,500 by 2040. San Jose envisions high- density infill development of up to 15 stories or more for the area, representing millions of new commercial square feet and thousands of new homes. City Centers – Moderate Growth City Centers include areas like Central Richmond that represent more modest areas of growth. Job creation, a major city goal, is projected to increase from just over 6,500 to 8,700, or by 34% by 2040. The City hopes to restore the vitality that the district had during WWII and the post-war years by capitalizing on the wealth of transportation options available, and increase transit ridership by creating a safe, vibrant, walkable neighborhood with 3-4 story infill development that includes housing, neighborhood-serving retail, and other employment activities around the BART station. - 26 - Transit Town Centers – Low Growth Transit Town Centers are well represented by places like the City of Cloverdale, located at the northern end of Sonoma County in the idyllic Alexander Valley. Only 550 new jobs are forecast by 2040, with virtually all growth occurring within the downtown area. Cloverdale envisions a livable downtown where residents, workers, and visitors can take advantage of rail and bus service around the local SMART transit station. New development, mostly 1-2 stories, will be clustered within the downtown area to limit development on sensitive habitats including nearby rivers and creeks. Growth by County With more than one worker per average household, increases in jobs are greater than housing units. By 2040, Santa Clara and Alameda counties absorb the most housing unit growth in the region, with 30% and 23% respectively, followed by Contra Costa (13%), San Francisco (12%), and San Mateo (9%). Solano and Sonoma take on about 5% each, while Marin and Napa take on only 1% each. In terms of employment by 2040, most jobs are concentrated in Santa Clara, Alameda, and San Francisco with 26%, 23%, and 16% percent respectively. They are followed by Contra Costa and San Mateo with 11% and 10% respectively. Figure 7. Growth of Jobs and Housing by County, 2010‐2040 0 50 100 150 200 250 300 350 San t a C l a r a Alam e d a San Fra n cisco Con t r a Cos t a San Mateo Sonoma Sola n o Nap a Marin Th o u s a n d s Jobs Housing Units - 27 - San Francisco San Francisco is one of California’s largest cities and home to many of the region’s landmarks. Like many port cities, the convergence of various cultures in one location resulted in a diverse population. Over time the city has emerged as a major financial and cultural center, as well as a primary tourist and convention destination in North America. In recent years San Francisco has emerged as a leading center for innovative companies and enterprises. Also, recent residential development has significantly expanded the city’s ability to accommodate population growth. Surrounded by water, San Francisco’s population and employment growth over the decades was accommodated with more intense development throughout the city’s varied neighborhoods. As a result, the city has the highest residential and commercial densities in the region. San Francisco is one of the region’s largest employment hubs, and accommodates nearly one half million commuters each day many of whom travel using the region’s most extensive public transit system. From 2010 to 2040 housing and jobs projection, San Francisco is estimated to absorb 93,470 additional households or 13% of the total regional household growth. In terms of employment, the projections estimate an increase of 175,000 additional jobs or 16% of total regional growth. Contra Costa County Located across from San Francisco and Marin County, Contra Costa County has grown to be one of the third most populous areas in the Bay Area region; the county’s natural beauty and its strategic location between the San Francisco Bay and California’s Central Valley have long attracted residents and businesses. Auto-oriented growth spurts during the 1940s and then again from the 1980s through early 2000 pushed development eastward. Over one-third of Contra Costa County’s most recent population growth took place in the eastern portion of the county. Growth is expected to continue as the county supports major thoroughfares and BART, connecting city centers, employment centers, transit neighborhoods, and transit town centers to regional employment hubs and affordable housing options. From 2010 to 2040 forecasts, Contra Costa County is projected to experience 13% of the total regional housing growth or an estimated 90,000 additional households within its boundaries. West County, the area in and surrounding the San Pablo Avenue PDA, will take on a significant portion of the county’s housing growth. Contra Costa is predicted to absorb 11% of the total regional employment growth.. - 28 - The added growth has fueled concerns amongst county residents over sprawl development, congestion, open an Mateo ange divides cisco, me residential and office development has appeared in recent years. The wntow areas and transit-served neighborhoods have been the primary focus for growth in San Mateo along El Camino Real are working together to transform the corridor from an ario rce areas in the hills and coastside. space preservation, jobs and economic development, and quality of life. These concerns are particularly germane to Contra Costa as 33% of the new household growth is projected to be very low income. The Preferred Scenario incorporates feedback from local jurisdictions in the county to build a local and regional policy framework with emphasis on focused growth within existing centers and that identifies areas for future growth. S San Mateo County is strategically located between San Francisco and Silicon Valley. The Coast R the county into two distinct parts: the bayside and coast. Ninety percent of development in the county is located on the bayside. The communities along the bayside of the Peninsula are home to Fortune 500 headquarters, globally significant firms and research entities as well as many charming town centers and residential neighborhoods. The downtowns of many of the county’s cities, including South San Fran San Bruno, Millbrae, Burlingame, San Mateo, Belmont, San Carlos, Redwood City, and Menlo Park, are clustered near a Caltrain station, often encompassing or bordering El Camino Real. In contrast, the coast is primarily agricultural, although so do n County. Local governments auto-oriented commercial strip into a grand boulevard that includes a mix of homes, stores, parks, and services, and links the transit town centers and city center nodes along its length. Currently, there are 40 PDAs, and 3 additional proposed, that encompass the Grand Boulevard and other key areas well suited to account for many of the 58,250 additional housing units projected in the Jobs-Housing Connection Scen through 2040. These additional units represent 9% of the total regional housing unit growth. Concurrently, the County is expected to support 10% of the total employment growth for the region. Local feedback was incorporated to ensure that the Jobs-Housing Connection Scenario appropriately concentrates growth given local plans for Priority Development Areas. San Mateo and Redwood City are expected to house the largest concentration of jobs and housing in the County. The concentration of growth in these bayside communities will reduce growth pressures on the coast, allowing the county to retain its agricultural, scenic, and natural resou - 29 - Alameda County Located just across the bay from San Francisco, Alameda County is the most centrally located county in t region. Its centrality, access to economic opportunities, unique communities, and diverse array of natural amenities make it an attractive choice for residents and business. The University of California, Berkeley, Lawrence Livermore Laboratory, and Disney Pixar Studio are among the highly esteemed campuses located within its he boundaries. nty is home to the City of Oakland, one of the largest cities in the region, The Port of Oakland, tly ional al and d ers Rail th Sonoma County. Alameda Cou one of the country’s busiest container ports, nineteen BART stations, and an enviable park system. Alameda County has long been a major hub of economic activity in the Bay Area and is projected to grow significan within the 2010 to 2040 estimates, taking on 23% of total regional household growth, or 154,000 addit units, and 23% of total regional job growth or 252,000 jobs. Local feedback was incorporated on growth projections in PDA corridors. Accordingly, the Jobs Housing Connection Scenario supports new homes and jobs into neighborhoods along major transportation corridors in Oakland, Emeryville, Dublin, and Fremont. Marin County Located north of San Francisco and south of Sonoma County, Marin County is recognized for its natur agricultural landscapes, which support local farming and ranching, tourism, recreation, wildlife habitat, an water supply. More than 50 percent of the county is protected open space and the Marin Agricultural Land Trust and the Marin County Department of Parks and Open Space have worked for decades to protect and preserve the county’s iconic landscapes. Plan Bay Area will support continued protection of the many Priority Conservation Areas in Marin County. For decades, Marin County has managed growth through city-centered growth policies and focused development along the urbanized U.S. Route 101 Highway corridor. Golden Gate Transit bus service off connections throughout the county and to surrounding areas. Ferry terminals in Sausalito, Tiburon, and Larkspur also connect residents to jobs in San Francisco. In the future, the proposed Sonoma-Marin Area Transit (SMART) rail connection will link the Larkspur ferry terminal wi - 30 - Feedback received from several jurisdictions related to the Initial Vision Scenario and Alternative Scenarios s es e Marin County takes one percent of the regional housing growth by 2040 and two ercent of the region’s job growth. cities along the U.S. 101 corridor, which has een supported by voter-approved urban growth boundaries and other policies that encourage separation s Windsor, Santa Rosa, Rohnert Park, Cotati, and Petaluma re largely located within Priority Development Areas and will provide improved connections among the y and to employment opportunities in San Francisco. he county’s forecasted household growth. r , more than 0 percent of unincorporated county land falls within those designations. The County seeks to continue to has been taken into account in the development of the Jobs-Housing Connection Scenario. Some jurisdictions indicated that levels of household and employment growth were appropriate, while other citie expressed concern that the distributions were too high. The Jobs-Housing Connection Scenario recogniz Marin County’s relatively limited role in the region’s growing economy and focuses Marin’s growth along th Highway 101 corridor. p Sonoma County Sonoma County is the largest, northernmost county in the San Francisco Bay Area and contains coastal areas, redwood forests and oak woodlands, rivers, wetlands and baylands, vineyards, grasslands, and small farms. Urban development in Sonoma County is concentrated within b between cities and scenic landscapes to maintain the county’s rural character and economy. The existing bu service in the county will be enhanced by the introduction of Sonoma-Marin Area Rail Transit (SMART). The stations planned in Cloverdale, Healdsburg, a cities in the count Local feedback from Sonoma County was utilized to tailor the housing and job estimates in the draft Jobs Housing Connection Scenario. Sonoma County assumes six percent of regional housing unit growth by 2040, and six percent of the total regional job growth. Household and job growth are focused in Santa Rosa, the largest jurisdiction in the county, and other jurisdictions along the SMART corridor. The PDAs in Sonoma County will encompass 56 percent of t Napa County Napa County is internationally acclaimed for its winemaking, and the picturesque Napa Valley wine region is a major draw for San Francisco Bay Area visitors. The valley is bounded by mountains, and the Napa Rive empties into San Pablo Bay through the narrow Mare Island Strait. Napa County has strong policies to prioritize agricultural uses and to protect farmlands, watersheds, and open space. Consequently 9 - 31 - protect these lands and encourage recreation through its ten Priority Conservation Areas. Most non- agricultural development is clustered in the four cities and one town connected by Highway 29, wh parallels the Napa River in the western part of the county. Local feedback provided information on constraints to growth in Napa County. The Jobs-Housing Connection Scenario recognizes the focus on agricultural and watershed protection in the County by allocating only one percent of the reg ich ion’s housing growth and two percent of the region’s job growth. The ities of Napa and American Canyon assume most of the County’s household growth, while the City of rated Napa County assume most of the job growth in the County. The PDAs in the nd . olano County’s historical growth was in part attributable to military bases. The county’s location between f near g Connection Scenario for Solano County recognizes city centered growth and thus, focuses he C Napa and unincorpo Cities of Napa and American Canyon help focus 27 percent of the County’s household growth. Solano County Solano County has the distinction of containing nearly half the San Francisco Bay Area’s important farmla and more than half the region’s wetlands, according to the State Farmland Mapping and Monitoring Program. The Sacramento River flows along the southeastern portion of Solano County emptying into the Sacramento-San Joaquin River Delta, the largest estuary on the U.S.’s West Coast, and into the Suisun Bay. Five Priority Conservation Areas have been identified in the county to protect important natural resources S the metropolitan centers of San Francisco and Sacramento and its lower land prices relative to other parts o the Bay Area made it an attractive place for increased housing development in response to the demand for lower cost housing. Solano County’s Orderly Growth Initiative, adopted in 1994, encourages city-centered growth and supports the agricultural economy. This policy has focused jobs and commercial areas in and the county’s major urban areas. The Jobs-Housin the majority of household and job growth in the cities of Fairfield, Vacaville, and Vallejo. The scenario also recognizes existing greenfield development capacity within urban growth boundaries in a few communities including Fairfield. Solano County takes five percent of the region’s housing growth and five percent of t region’s job growth. The PDAs in Solano County jurisdictions help focus 43 percent of the County’s household growth. - 32 - Policies and Strategies - Building Complete Communities sis cenario’s enefits, current proposed regional programs, and the framework for a larger policy discussion at the State This scenario will guide growth to strengthen the economic performance of the region through appropriate access to jobs, affordable housing and amenities. he retention of these lands by supporting the continuation of rural communities. Jobs-Housing Connection Scenario Benefits The Jobs-Housing Connection Scenario does not offer immediate policy prescriptions for remedying a cri caused by conditions outside our region. Rather, it focuses on the Bay Area recovery, once the national economy has been restored. This section provides an overview of the Jobs-Housing Connection S b and Federal levels.  Strengthening the character of places: The Bay Area encompasses a wide range of places that vary in character, scale, activities, population, and access. The Jobs-Housing Connection Scenario pursues a development pattern that enhances the qualities of each place and provides diverse housing and transportation choices.  Supporting a healthy economy: The region has great assets to support a healthy economy.  Preserving open space and agricultural land: In contrast to previous trends, greenfield development is minimized to retain the open space and agricultural land of the region. This Scenario proposes growth within the region’s urban footprint around the regional transportation network for a more efficient use of infrastructure as well as water resources. The Bay Area’s greenbelt of agricultural, natural resource, and open space lands is a treasured asset that contributes to the region’s quality of life, and supports economic development. This Scenario supports t agricultural activities in  Location of future housing and jobs next to transit, amenities, and services: This scenario recognizes the need to produce affordable housing, maximize the use of existing infrastructure, and reduce reliance on the automobile. Schools, shops, parks, health services, and restaurants close to residents and workers increase walking, biking, and transit while reducing time spent driving. This location pattern strengthens the identity and diversity of places and reduces greenhouse gas emissions. - 33 -  Build quality multifamily housing for a range of incomes and household sizes: The concentration of housing in PDAs support the increaseddemand for multifamily housing.  orridors to provide access to jobs and services: The Jobs-Housing Connection Scenario emphasizes growth along transit corridors to increase transportation options, improve mobility, and expand access to jobs and services. It the pr ide a unique urban evelopment pattern and transportation network. The implementation of Plan Bay Area panded as growth to locations where it lture and vacant ion. PCAs, ommunity. communities ent e and implementing their land use plans. Also, this scenario links jobs, wages and population to define the housing needs by income level. Strengthening regional transit c recognizes the complementary functions of different nodes along the corridor, and importance of cultivating and connecting diverse place types that ov quality with a particular mix of shops, services, or amenities. Regional Land Use Programs The Plan Bay Area Jobs-Housing Connection Scenario builds upon the FOCUS program to identify a sustainable land use d is achieved through a range of existing programs and incentives, many of which are enhanced and ex well as new regional programs. The comprehensive set of transportation policies, strategies, and investments will be released in April and are not a part of this document. FOCUS Program FOCUS is a regional development and conservation strategy that seeks to guide can best be served by existing infrastructure and amenities, and preserve areas important for agricu open space. New development is directed away from neighborhoods that are exclusively residential to land and low density underutilized commercial and industrial parks with better access to transportat Simply put, these are the areas most appropriate for new development. Priority Conservation Areas, or are areas important for agriculture or open space. These areas are also designated by the local c Several incentives have been developed to support regional and local goals for creating complete through the PDA framework. Incentives include grants to create land use plans that support the developm of PDAs as complete communities, infrastructure grants to support the implementation of land us transit plans, and technical assistance to evaluate specific issues or hurdles local jurisdictions face in - 34 - One Bay Area Grant egion to million for 50% in orth Bay counties) in PDAs to support the development of those areas as complete communities. n to support jurisdictions in creating PDA land use plans. The One Bay Area Grant proposal is a new approach to spending transportation funding in this r support implementation of the SCS by focusing incentives in PDAs and PCAs. It distributes $250 among county congestion management agencies (CMAs) based on a county’s population, plans accommodating housing, and housing production. CMAs will spend 70% of these county funds ( N The One Bay Area Grant proposal increases fundi g The plans engage communities to determine appropriate land uses, transportation and infrastructure improvements, along with other planning elements including a programmatic environmental impact report, to create a long-term development vision for the area. The One Bay Area grant proposal also creates a regional pilot program to fund Priority Conservation Areas in North Bay counties. Other counties will also be able to spend a portion of grant funds on PCAs if they so choose. Transit Oriented Affordable Housing Fund In 2011, the Metropolitan Transportation Commission approved $10 million to match outside funds and establish a $50 million revolving loan fund for affordable housing developers to finance land acquisition in Priority Development Areas.18 The TOAH Fund is available for experienced nonprofit and for-profit developers, municipal agencies and joint ventures of these entities. Projects must be located in PDAs. 18 Stanley and Citi Community Capital, each of which provided $12.5 million; The Ford Foundatio Living Cities, a collaborative of foun community development financial Other investors in the Bay Area Transit Oriented Affordable Housing (TOAH) Fund include Morgan n and dations and financial institutions, which invested $3 million each; six institutions (CDFIs), which combined to contribute $8.5 million; and Foundation, which provided $500,000 plus the 2007 seed funding to develop the fund’s business plan. the San Francisco - 35 - Disaster Resilience Initiative tainability into the long-term vision of the region is anticipating, preparing able to recover from major disasters. ABAG, local agencies around the region, and community ears, and Bike and pedestrian paths committed to plan, fund, develop, and improve cle and pedestrians facilities within the Bay Area. California passed the Complete Streets Act of 2008 which requires all cities and counties to include complete streets policies as part of their general plans so that clists, pedestrians, transit riders,  Encourage local cities and counties to develop and regularly update bicycle and pedestrian plans, policies, and design guidelines that support the development of bicycle and pedestrian facilities.  Develop regional policies and dedicate funding to support the development of bicycle and pedestrian infrastructure that facilitates travel within communities, job centers, and other major activity areas and connections to schools and major transit nodes. Part of successfully integrating sus for, and being partners are collaborating on a Regional Disaster Resilience Initiative focusing on restoring lifeline and critical infrastructure after a major disaster. While the region already has a robust and well-organized system of disaster response and preparedness, recovery actions and responsibilities in the subsequent months, y decades are much less defined. Planning for recovery ensures that the Bay Area rebuilds and reshapes its region in a proactive and sustainable way that aligns with our goals for the future. The development of a cohesive and comprehensive system of bicycle and pedestrian facilities is critical for sustainable growth and a high quality of life in the nine-county Bay Area. A significant amount of effort and resources has already been bicy roadways are designed to safely accommodate all users, including bicy children, seniors, and the disabled, as well as motorists. Existing bicycle and pedestrian path policies include:  ABAG and MTC will continue to encourage the development of safe, accessible, and useable bicycle and pedestrian facilities through regional planning and funding.  Prioritize the completion of the regional bicycle network in MTC’s Regional Bicycle Plan and the regional trails that provide regional linkages between Bay Area communities, job centers, parks, and other major activity centers, and continue to dedicate funding that prioritizes the completion of these regional bicycle and pedestrian facilities. - 36 - Proposed Policy Framework he majority of new job and housing growth must be guided into PDAs. New housing must also be more residents. The policies needed to make this vision a licies. truction and rehabilitation of housing for all income ese policies include increasing funding for affordable housing programs such as Low Income existing housing programs arating ndle parking); adopting parking maximums and more flexible parking standards; recognizing changing household out How do we address these regional economic, demographic, housing, and transportation challenges? To achieve the broader goals of the Plan Bay Area requires numerous policy changes, which no one level of government can accomplish alone. Policy changes and new programs from the Bay Area’s 101 incorporated cities and 9 counties, and from regional agencies such as MTC and ABAG, will be required, as will changes at the Federal and State levels. Only such a multi-pronged initiative can succeed in achieving the goals proposed in the SCS. T affordable, so that new jobs can be filled by Bay Area reality fall under four general, somewhat overlapping, categories including: 1) Job Production, 2) Housing Production, 3) Infrastructure and Community Services, and 4) Federal and State po Job production policies seek to increase the quantity, quality and spatial concentration of jobs in the region. These policies include tax credits for businesses and lower income working households; sectoral job strategies that promote growing industries as well as building the skills necessary to connect residents with expanding sectors; small and micro-business technical and tax-filing assistance; and efforts to ensure a sufficient supply of affordable incubator space to encourage business starts. Housing production policies act to increase the cons levels. Th Housing Tax Credits (LIHTC) and issuing affordable housing bonds; modifying to prioritize PDAs as locations for housing production; local regulatory reform and streamlining; sep the cost of parking from new development to allow residents and businesses to maximize savings (unbu demographics and allowing and encouraging secondary units that can support more households with changing neighborhood character and also supplement incomes (particularly for older residents on fixed incomes living alone); and encouraging larger family sized units, particularly in PDAs. - 37 - - 38 - s ensure that the infrastructure and public amenities needed ies es eral and State policies concern the funding, taxation, and regulation of Bay Area communities. These conservation. Infrastructure and community services policie to make PDAs better neighborhoods and employment centers occur as jobs and housing grow. These polic include increased funding for schools; addressing our aging water infrastructure prone to disruption in tim of emergency, protecting our limited water supply with state of the art water conservation and storage technology; increasing funding for city parks and rural open spaces; and planting trees greening streets, and building infrastructure in ways that also improve air quality and reduce pollution from storm water runoff. Fed include more favorable tax policies that promote the production of all types of housing, especially affordable housing and rental units; transfer some taxing authority from Sacramento to the local level for improved accountability and local control (correcting an unintended consequence of Prop 13); and augmenting existing grant, loan, and bond programs for infrastructure, housing, commercial investment, and energy Next Steps By April 2012, ABAG will incorporate a more complete set of policies and strategies that can be applied at the local level through case studies addressing different place types. ABAG will also suggest a framework to address policy changes needed at the Federal and State levels to fully implement Plan Bay Area. Appendices - 39 - 1 Housing Distribution by Jurisdiction Housing Units Households 2010-2040 2010-2040 PDA Share Alameda County Jurisdictions 2010 2040 Total % 2010 2040 Total % 2010 2040 Alameda 32,350 38,020 5,670 18%30,120 36,500 6,380 21% 7%20% Albany 7,890 8,840 950 12%7,400 8,430 1,030 14% 23%23% Berkeley 49,450 57,940 8,490 17%46,030 55,630 9,600 21% 16%25% Dublin 15,780 36,560 20,770 132%14,910 32,810 17,900 120% 35%52% Emeryville 6,650 12,430 5,780 87%5,690 11,930 6,240 110% 62%80% Fremont 73,990 94,600 20,610 28%71,010 89,430 18,430 26% 36%42% Hayward 48,300 62,080 13,780 29%45,370 59,590 14,230 31% 12%26% Livermore 30,340 41,820 11,480 38%29,130 40,150 11,010 38% 5%29% Newark 13,410 18,870 5,450 41%12,970 17,740 4,760 37% 5%19% Oakland 169,710 208,660 38,950 23%153,790 200,310 46,520 30% 72%76% Piedmont 3,920 3,980 60 2%3,800 3,820 20 1% 0%0% Pleasanton 26,050 31,710 5,660 22%25,250 30,440 5,200 21% 5%16% San Leandro 32,420 40,150 7,730 24%30,720 38,550 7,830 25% 26%36% Union City 21,260 23,920 2,660 13%20,430 22,960 2,530 12% 5%8% Unincorporated 51,020 56,970 5,950 12%48,520 54,690 6,170 13% 25%29% Housing Units Households 2010-2040 2010-2040 PDA Share Contra Costa County Jurisdictions 2010 2040 Total % 2010 2040 Total % 2010 2040 Antioch 34,850 41,110 6,260 18%32,250 39,460 7,210 22% 5%15% Brentwood 17,520 18,790 1,270 7%16,490 18,040 1,540 9% 0%0% Clayton 4,090 4,190 110 3%4,010 4,070 60 1% 0%0% Concord 47,130 66,860 19,730 42%44,280 64,180 19,900 45% 10%31% Danville 15,930 17,810 1,880 12%15,420 17,100 1,680 11% 0%0% El Cerrito 10,720 12,160 1,440 13%10,140 11,670 1,530 15% 12%20% Hercules 8,550 13,360 4,810 56%8,120 12,830 4,710 58% 20%45% Lafayette 9,650 11,050 1,400 15%9,220 10,610 1,390 15% 20%27% Martinez 14,980 16,600 1,630 11%14,290 15,940 1,650 12% 5%9% Moraga 5,750 7,030 1,270 22%5,570 6,750 1,180 21% 8%16% Oakley 11,480 18,140 6,660 58%10,730 16,640 5,920 55% 19%32% Orinda 6,800 7,440 630 9%6,550 7,140 590 9% 5%7% Pinole 7,160 8,270 1,110 16%6,780 7,940 1,160 17% 27%33% Pittsburg 21,130 29,450 8,320 39%19,530 28,270 8,740 45% 27%43% Pleasant Hill 14,320 15,940 1,620 11%13,710 15,300 1,590 12% 14%16% Richmond 39,330 46,440 7,110 18%36,090 44,580 8,490 24% 27%31% San Pablo 9,570 11,690 2,120 22%8,760 11,220 2,460 28% 33%41% San Ramon 26,220 30,220 3,990 15%25,280 29,010 3,720 15% 2%10% Walnut Creek 32,680 35,110 2,430 7%30,510 33,710 3,200 10% 4%10% Unincorporated 62,400 73,890 11,490 18%57,640 70,940 13,300 23% 12%21% - 40 - Housing Units Households 2010-2040 2010-2040 PDA Share Marin County Jurisdictions 2010 2040 Total % 2010 2040 Total % 2010 2040 Belvedere 1,050 1,070 20 2%930 1,020 90 10% 0%0% Corte Madera 4,030 4,230 210 5%3,790 4,060 270 7% 0%0% Fairfax 3,590 3,890 310 9%3,380 3,740 360 11% 0%0% Larkspur 6,380 6,520 140 2%5,910 6,260 350 6% 0%0% Mill Valley 6,530 7,110 570 9%6,080 6,820 740 12% 0%0% Novato 21,160 22,050 890 4%20,280 21,170 890 4% 0%0% Ross 880 960 80 9%800 930 130 16% 0%0% San Anselmo 5,540 5,990 460 8%5,240 5,750 510 10% 0%0% San Rafael 24,010 26,830 2,820 12%22,760 25,760 2,990 13% 19%26% Sausalito 4,540 4,910 380 8%4,110 4,720 600 15% 0%0% Tiburon 4,030 4,360 330 8%3,730 4,190 460 12% 0%0% Unincorporated 29,500 31,440 1,940 7%26,190 29,990 3,800 15% 16%16% Housing Units Households 2010-2040 2010-2040 PDA Share Napa County Jurisdictions 2010 2040 Total % 2010 2040 Total % 2010 2040 American Canyon 5,980 7,910 1,920 32%5,660 7,590 1,930 34% 7%26% Calistoga 2,320 2,370 50 2%2,020 2,290 270 13% 0%0% Napa 30,150 33,460 3,310 11%28,170 32,120 3,950 14% 3%5% St. Helena 2,780 2,830 60 2%2,400 2,740 340 14% 0%0% Yountville 1,250 1,280 30 2%1,050 1,240 190 18% 0%0% Unincorporated 12,280 12,560 280 2%9,580 12,150 2,570 27% 0%0% Housing Units Households 2010-2040 2010-2040 PDA Share San Francisco County Jurisdictions 2010 2040 Total % 2010 2040 Total % 2010 2040 San Francisco 376,940 457,580 80,640 21%345,810 439,280 93,470 27% 53%61% - 41 - Housing Units Households 2010-2040 2010-2040 PDA Share San Mateo County Jurisdictions 2010 2040 Total % 2010 2040 Total % 2010 2040 Atherton 2,530 2,860 330 13%2,330 2,750 420 18% 0%0% Belmont 11,030 12,070 1,040 9%10,580 11,580 1,010 10% 8%15% Brisbane 1,930 7,030 5,100 264%1,820 6,750 4,930 271% 0%69% Burlingame 13,030 16,940 3,910 30%12,360 16,260 3,900 32% 58%65% Colma 590 840 250 42%560 800 240 43% 96%98% Daly City 32,590 36,360 3,780 12%31,090 34,910 3,820 12% 24%31% East Palo Alto 7,820 8,880 1,060 14%6,940 8,520 1,580 23% 14%21% Foster City 12,460 13,780 1,320 11%12,020 13,230 1,210 10% 0%0% Half Moon Bay 4,400 4,790 390 9%4,150 4,590 440 11% 0%0% Hillsborough 3,910 4,260 350 9%3,690 4,090 400 11% 0%0% Menlo Park 13,090 15,050 1,970 15%12,350 14,450 2,100 17% 23%28% Millbrae 8,370 10,690 2,320 28%7,990 10,270 2,270 28% 35%44% Pacifica 14,520 15,420 890 6%13,970 14,800 830 6% 0%0% Portola Valley 1,900 2,070 180 9%1,750 2,010 270 15% 0%0% Redwood City 29,170 37,290 8,130 28%27,960 35,800 7,850 28% 20%33% San Bruno 15,360 19,460 4,100 27%14,700 18,680 3,980 27% 36%49% San Carlos 12,020 14,130 2,120 18%11,520 13,570 2,040 18% 29%34% San Mateo 40,010 49,610 9,600 24%38,230 47,630 9,390 25% 33%44% South San Francisco 21,810 28,120 6,310 29%20,940 27,000 6,060 29% 33%48% Woodside 2,160 2,290 140 6%1,980 2,200 230 12% 0%0% Unincorporated 22,350 27,310 4,960 22%20,910 26,220 5,300 25% 30%42% Housing Units Households 2010-2040 2010-2040 PDA Share Santa Clara County Jurisdictions 2010 2040 Total % 2010 2040 Total % 2010 2040 Campbell 16,950 20,090 3,140 19%16,160 19,280 3,120 19% 12%26% Cupertino 21,030 25,820 4,790 23%20,170 24,780 4,620 23% 15%23% Gilroy 14,850 18,010 3,160 21%14,180 17,290 3,120 22% 18%27% Los Altos 11,200 16,820 5,620 50%10,740 16,150 5,410 50% 7%32% Los Altos Hills 3,000 3,040 40 1%2,790 2,920 130 5% 0%0% Los Gatos 13,050 14,190 1,140 9%12,360 13,620 1,270 10% 11%10% Milipitas 19,810 29,590 9,790 49%19,180 28,410 9,230 48% 6%30% Monte Sereno 1,290 1,410 120 9%1,210 1,360 140 12% 0%0% Morgan Hill 12,860 17,750 4,890 38%12,330 17,830 5,500 45% 4%11% Mountain View 33,880 44,060 10,170 30%31,960 42,290 10,340 32% 58%64% Palo Alto 28,220 35,340 7,130 25%26,360 33,930 7,570 29% 25%40% San Jose 314,040 430,910 116,870 37%301,390 413,680 112,290 37% 33%49% Santa Clara 45,150 56,450 11,310 25%43,020 54,200 11,170 26% 9%19% Saratoga 11,120 12,010 890 8%10,730 11,530 800 7% 2%2% Sunnyvale 55,790 74,430 18,640 33%53,370 71,450 18,090 34% 39%50% Unincorporated 29,690 33,200 3,510 12%28,230 31,870 3,640 13% 1%1% - 42 - Housing Units Households 2010-2040 2010-2040 PDA Share Solano County Jurisdictions 2010 2040 Total % 2010 2040 Total % 2010 2040 Benicia 11,310 12,930 1,620 14%10,690 12,410 1,720 16% 5%12% Dixon 6,170 6,780 610 10%5,860 6,510 650 11% 12%15% Fairfield 37,180 52,610 15,420 41%34,480 51,770 17,290 50% 10%28% Rio Vista 3,890 4,260 370 10%3,450 4,090 640 19% 9%17% Suisun City 9,450 11,010 1,550 16%8,920 10,570 1,650 18% 12%21% Vacaville 32,810 38,300 5,480 17%31,090 37,810 6,720 22% 3%4% Vallejo 44,430 47,800 3,370 8%40,560 45,890 5,330 13% 2%4% Unincorporated 7,450 8,720 1,280 17%6,710 8,370 1,660 25% 0%0% Housing Units Households 2010-2040 2010-2040 PDA Share Sonoma County Jurisdictions 2010 2040 Total % 2010 2040 Total % 2010 2040 Cloverdale 3,430 4,280 850 25%3,180 4,110 930 29% 33%44% Cotati 3,140 3,710 570 18%2,980 3,570 590 20% 28%35% Healdsburg 4,800 5,090 290 6%4,390 4,890 500 11% 0%0% Petaluma 22,740 25,940 3,200 14%21,740 24,900 3,160 15% 3%10% Rohnert Park 16,550 20,550 3,990 24%15,810 19,720 3,920 25% 9%23% Santa Rosa 67,400 87,180 19,790 29%63,590 85,190 21,600 34% 28%40% Sebastopol 3,470 3,920 460 13%3,280 3,760 490 15% 72%74% Sonoma 5,540 5,720 170 3%4,960 5,540 580 12% 0%0% Windsor 9,540 11,790 2,250 24%8,960 11,520 2,560 29% 15%22% Unincorporated 67,970 73,540 5,570 8%56,950 70,600 13,650 24% 12%14% - 43 - 2 Employment Distribution by Jurisdiction Employment 2010-2040 PDA Share Alameda County Jurisdictions 2010 2040 Total % 2010 2040 Alameda 24,030 33,160 9,130 38% 15% 36% Albany 4,210 5,660 1,450 34% 45% 45% Berkeley 77,020 99,100 22,080 29% 30% 33% Dublin 16,760 28,060 11,300 67% 28% 53% Emeryville 16,040 23,620 7,580 47% 70% 78% Fremont 89,900 120,250 30,360 34% 46% 48% Hayward 69,100 90,180 21,070 30% 16% 20% Livermore 38,370 52,560 14,190 37% 38% 43% Newark 17,870 23,560 5,690 32% 6% 11% Oakland 190,250 270,860 80,610 42% 82% 83% Piedmont 1,930 2,410 490 25% 0% 0% Pleasanton 54,230 71,840 17,610 32% 18% 24% San Leandro 39,900 52,890 12,990 33% 33% 40% Union City 20,560 25,410 4,850 24% 2% 11% Unincorporated 34,270 47,340 29,490 38% 45% 52% Employment 2010-2040 PDA Share Contra Costa County Jurisdictions 2010 2040 Total % 2010 2040 Antioch 19,070 25,740 6,670 35% 21% 31% Brentwood 8,650 11,410 2,760 32% 0% 0% Clayton 1,540 1,910 380 24% 0% 0% Concord 47,520 67,980 20,450 43% 17% 39% Danville 13,440 17,670 4,230 31% 0% 0% El Cerrito 5,880 7,600 1,720 29% 60% 58% Hercules 3,880 6,450 2,580 66% 71% 76% Lafayette 10,640 13,970 3,330 31% 56% 57% Martinez 18,300 22,550 4,250 23% 22% 23% Moraga 4,740 6,070 1,330 28% 0% 0% Oakley 3,740 6,890 3,150 84% 47% 69% Orinda 5,530 7,370 1,840 33% 58% 58% Pinole 6,740 8,470 1,730 26% 78% 78% Pittsburg 14,130 20,020 5,880 42% 50% 58% Pleasant Hill 17,360 23,010 5,650 33% 41% 45% Richmond 30,670 41,280 10,610 35% 51% 53% San Pablo 7,460 9,720 2,260 30% 77% 81% San Ramon 43,880 57,820 13,930 32% 50% 56% Walnut Creek 41,650 55,400 13,740 33% 18% 21% Unincorporated 40,100 56,520 29,490 41% 18% 21% - 44 - Employment 2010-2040 PDA Share Marin County Jurisdictions 2010 2040 Total % 2010 2040 Belvedere 430 500 70 16% 0% 0% Corte Madera 7,940 8,380 440 6%0% 0% Fairfax 1,490 1,870 370 25% 0% 0% Larkspur 7,190 7,940 750 10% 0% 0% Mill Valley 5,980 7,140 1,160 19% 0% 0% Novato 20,890 24,280 3,390 16% 0% 0% Ross 510 620 110 22% 0% 0% San Anselmo 3,740 4,610 870 23% 0% 0% San Rafael 37,620 43,810 6,190 16% 37% 39% Sausalito 6,220 7,730 1,510 24% 0% 0% Tiburon 2,340 2,880 540 23% 0% 0% Unincorporated 16,380 20,270 3,890 24% 14% 15% Employment 2010-2040 PDA Share Napa County Jurisdictions 2010 2040 Total % 2010 2040 American Canyon 2,920 4,150 1,230 42% 44% 51% Calistoga 2,220 2,750 530 24% 0% 0% Napa 33,950 44,320 10,370 31% 32% 30% St. Helena 5,340 6,510 1,170 22% 0% 0% Yountville 1,600 2,030 430 27% 0% 0% Unincorporated 24,630 30,460 5,830 24% 0% 0% Employment 2010-2040 PDA Share San Francisco County Jurisdictions 2010 2040 Total % 2010 2040 San Francisco 568,720 743,790 175,060 31% 83% 85% - 45 - Employment 2010-2040 PDA Share San Mateo County Jurisdictions 2010 2040 Total % 2010 2040 Atherton 2,610 3,360 750 29% 0% 0% Belmont 8,220 10,390 2,160 26% 15% 24% Brisbane 7,220 22,760 15,540 215%8% 67% Burlingame 30,420 39,020 8,600 28% 41% 47% Colma 2,790 3,210 420 15% 76% 75% Daly City 21,000 26,280 5,280 25% 31% 37% East Palo Alto 2,720 3,810 1,090 40% 30% 32% Foster City 13,890 17,590 3,710 27% 0% 0% Half Moon Bay 5,110 6,190 1,080 21% 0% 0% Hillsborough 2,190 2,650 460 21% 0% 0% Menlo Park 28,990 35,020 6,030 21% 25% 28% Millbrae 6,950 9,130 2,180 31% 73% 78% Pacifica 5,920 7,430 1,510 25% 0% 0% Portola Valley 1,510 1,890 390 26% 0% 0% Redwood City 58,340 77,250 18,910 32% 37% 38% San Bruno 12,930 17,070 4,140 32% 65% 72% San Carlos 16,170 19,860 3,690 23% 63% 64% San Mateo 52,930 73,100 20,180 38% 48% 61% South San Francisco 46,170 57,070 10,900 24% 16% 23% Woodside 1,770 2,190 430 24% 0% 0% Unincorporated 17,350 22,640 5,290 30% 37% 42% Employment 2010-2040 PDA Share Santa Clara County Jurisdictions 2010 2040 Total % 2010 2040 Campbell 27,230 34,920 7,700 28% 41% 42% Cupertino 25,990 33,260 7,270 28% 40% 42% Gilroy 17,600 22,000 4,390 25% 27% 30% Los Altos 14,700 19,710 5,020 34% 39% 47% Los Altos Hills 3,580 4,450 870 24% 0% 0% Los Gatos 23,580 29,120 5,540 23% 9% 9% Milipitas 45,060 56,460 11,400 25% 12% 18% Monte Sereno 450 590 140 32% 0% 0% Morgan Hill 17,520 22,770 5,250 30% 9% 13% Mountain View 47,800 63,560 15,750 33% 73% 79% Palo Alto 89,370 118,650 29,270 33% 37% 39% San Jose 375,360 515,450 140,090 37% 66% 74% Santa Clara 112,460 144,460 32,000 28% 22% 23% Saratoga 11,870 14,560 2,690 23% 10% 12% Sunnyvale 74,610 94,850 20,240 27% 72% 77% Unincorporated 39,060 48,040 8,970 23% 40% 48% - 46 - Employment 2010-2040 PDA Share Solano County Jurisdictions 2010 2040 Total % 2010 2040 Benicia 14,240 18,980 4,740 33% 65% 73% Dixon 4,460 5,800 1,340 30% 12% 14% Fairfield 39,300 55,380 16,070 41% 15% 21% Rio Vista 1,790 2,390 600 34% 37% 42% Suisun City 3,080 4,550 1,470 48% 34% 44% Vacaville 29,800 41,930 12,130 41% 12% 13% Vallejo 31,660 43,430 11,770 37% 12% 14% Unincorporated 8,010 10,860 2,850 36% 4% 8% Employment 2010-2040 PDA Share Sonoma County Jurisdictions 2010 2040 Total % 2010 2040 Cloverdale 1,570 2,290 720 46% 53% 58% Cotati 2,920 3,870 950 32% 22% 31% Healdsburg 6,440 8,300 1,860 29% 0% 0% Petaluma 28,830 38,570 9,740 34% 11% 22% Rohnert Park 11,730 16,360 4,640 40% 30% 39% Santa Rosa 75,460 105,760 30,300 40% 59% 59% Sebastopol 5,650 7,320 1,670 29% 96% 97% Sonoma 6,650 8,930 2,280 34% 0% 0% Windsor 5,610 8,010 2,400 43% 18% 23% Unincorporated 47,150 62,950 15,800 34% 23% 32% - 47 - - 48 - 3 Maps of Priority Development Areas by County AlamedaCounty Newark UnionCity Pleasanton Livermore Hayward SanLeandro Oakland Alameda Fremont Dublin Piedmont Albany Santa ClaraCounty Contra CostaCounty San MateoCounty Berkeley Emeryville %&p( %&n( %&n( ?É %&p( %&t( %&n( ?Þ %&t( !"c$ ?½ ?½ %&p( %&t( %&p( %&p( ?Ù %&n( %&n( !"c$ Future Place Type for Priority Development Areas in Alameda County Within Urban Footprint Within Urban Growth Limits Priority Conservation Area Future Priority Development Area Place Type Protected Open Space Source: Street Base Map © 2006 TeleAtlas, Inc. All rights reserved. Protected areas data from California Protected Areas Database (www.calands.org), 2011 ABAG GIS/February 2012 Scale: 0 3 61.5 Kilometers 0 2 41 Miles Mixed-Use Corridor Transit Neighborhood Urban Neighborhood Transit Town Center Suburban Center City Center Regional Center Contra CostaCounty Brentwood ElCerrito Lafayette Danville Concord WalnutCreek Orinda Moraga Hercules Antioch SanRamon Pinole Richmond Martinez Clayton Pittsburg Oakley PleasantHill AlamedaCounty SolanoCounty SacramentoCounty San Pablo !"c$ %&n( ?î %&n( %&n( %&t( ?î %&n( !"c$ %&p( %&p( ?Ù ?Ù %&p( ?Ù Future Place Type for Priority Development Areas in Contra Costa County Within Urban Footprint Within Urban Growth Limits Priority Conservation Area Future Priority Development Area Place Type Protected Open Space Source: Street Base Map © 2006 TeleAtlas, Inc. All rights reserved.Protected areas data from California Protected Areas Database (www.calands.org), 2011 ABAG GIS/February 2012 Scale: 0 3 61.5 Kilometers 0 2 41 Miles Employment Center Mixed-Use Corridor Transit Neighborhood Transit Town Center Suburban Center City Center Regional Center IÆ ?Ô %&n( IÆ ?Ô IÆ San Pablo Bay San Francisco Bay Pacific Ocean MarinCounty SonomaCounty Contra CostaCounty Novato San Rafael Ross SanAnselmo Sausalito Belvedere Tiburon MillValley CorteMadera Larkspur Fairfax Future Place Type for Priority Development Areas in Marin County Within Urban Footprint Within Urban Growth Limits Priority Conservation Area Future Priority Development Area Place Type Protected Open Space Source: Street Base Map © 2006 TeleAtlas, Inc. All rights reserved.Protected areas data from California Protected Areas Database (www.calands.org), 2011 ABAG GIS/February 2012 Scale: 0 3 61.5 Kilometers 0 2 41 Miles Transit Neighborhood Transit Town Center City Center IÆ IÆ !"c$ IÆ Aç ?Ý Aà ?õ ?õ Aç ?õ !"c$ ?Ý %&m( Aà AçNapaCounty SonomaCounty SolanoCounty YoloCounty Calistoga SaintHelena Yountville Napa AmericanCanyon Future Place Type for Priority Development Areas in Napa County Within Urban Footprint Within Urban Growth Limits Priority Conservation Area Future Priority Development Area Place Type Protected Open Space Source: Street Base Map © 2006 TeleAtlas, Inc. All rights reserved. Protected areas data from California Protected Areas Database (www.calands.org), 2011 ABAG GIS/February 2012 Scale: 0 4 82 Kilometers 0 2.5 51.25 Miles Rural Corridor Mixed-Use Corridor Rural Town Center SanFrancisco San MateoCounty AlamedaCounty MarinCounty IÆ %&j( IÆ%&j( IÆ ?Ô !"c$ San Francisco Bay Pacific Ocean San Francisco Bay Future Place Type for Priority Development Areas in San Francisco Within Urban Footprint Within Urban Growth Limits Priority Conservation Area Future Priority Development Area Place Type Protected Open Space Source: Street Base Map © 2006 TeleAtlas, Inc. Allrights reserved. Protected areas data from California Protected AreasDatabase (www.calands.org), 2011 ABAG GIS/February 2012 Scale: 0 1 20.5 Kilometers 0 0.5 10.25 Miles Regional Center Mixed-Use Corridor Transit Neighborhood Urban Neighborhood Transit Town Center Santa ClaraCounty MenloPark Colma SanBruno San MateoCounty AlamedaCountyPacifica Woodside HalfMoonBay DalyCity Belmont Brisbane Atherton SanMateo RedwoodCity Hillsborough PortolaValley FosterCity East PaloAlto Millbrae Burlingame South SanFrancisco SanCarlos %&n( ?É %&t( %&n(%&t( ?½ IÆ IÆ ?¾ ?¾ %&j( %&t( ?Ô ?Ô ?Ô ?½ ?þ ?É IÆ %&j( ?þ %&j( ?½ San Francisco Bay Pacific Ocean Future Place Type for Priority Development Areas in San Mateo County Within Urban Footprint Within Urban Growth Limits Priority Conservation Area Future Priority Development Area Place Type Protected Open Space Source: Street Base Map © 2006 TeleAtlas, Inc. Allrights reserved. Protected areas data from California Protected Areas Database (www.calands.org), 2011 ABAG GIS/February 2012 Scale: 0 3 61.5 Kilometers 0 2 41 Miles Rural Corridor Mixed-Use Corridor Transit Neighborhood Transit Town Center Suburban Center City Center Santa ClaraCounty MountainView MilpitasPaloAlto MorganHill LosAltos Gilroy SanMateoCounty StanislausCounty Santa CruzCounty Saratoga Cupertino SantaClara LosAltosHills MonteSereno LosGatos SanJose San BenitoCounty IÆ IÆ IÆ %&p( ?¾ IÆ ?¾ ?â %&j(%&t( ?Â IÆ %&t( Aþ Aþ Aþ %&j( Future Place Type for Priority Development Areas in Santa Clara County Campbell Sunnyvale Within Urban Footprint Within Urban Growth Limits Priority Conservation Area Future Priority Development Area Place Type Protected Open Space Source: Street Base Map © 2006 TeleAtlas, Inc. All rights reserved.Protected areas data from California Protected Areas Database (www.calands.org), 2011 ABAG GIS/February 2012 Scale: 0 4 82 Kilometers 0 2.5 51.25 Miles Employment Center Mixed-Use Corridor Transit Neighborhood Urban Neighborhood Transit Town Center Suburban Center City Center Regional Center SolanoCounty RioVista Dixon Vacaville Vallejo Benicia Fairfield SuisunCity NapaCounty YoloCounty SacramentoCounty SonomaCounty ?õ Contra CostaCounty!"c$ %&r( %&p( ?õ !"c$ !"c$ ?Ý ?Ý AÚ !"c$ %&m( Aà Aç ?½ Future Place Type for Priority Development Areas in Solano County Within Urban Footprint Within Urban Growth Limits Priority Conservation Area Future Priority Development Area Place Type Protected Open Space Source: Street Base Map © 2006 TeleAtlas, Inc. Allrights reserved. Protected areas data from California Protected AreasDatabase (www.calands.org), 2011 ABAG GIS/February 2012 Scale: 0 3 61.5 Kilometers 0 2 41 Miles Employment Center Mixed-Use Corridor Transit Neighborhood Rural Town Center Transit Town Center Suburban Center SonomaCounty RohnertPark Healdsburg Sebastopol Sonoma Petaluma Cloverdale SantaRosa NapaCounty LakeCounty MarinCounty Windsor Cotati IÆ ?Ô ?Ô IÆ IÆ AÜ AÜ Aç ?Ý Aà ?õ ?õ IÆ Aà ?Ô Aç Pacific Ocean Future Place Types for Priority Development Areas in Sonoma County Within Urban Footprint Within Urban Growth Limits Priority Conservation Area Future Priority Development Area Place Type Protected Open Space Source: Street Base Map © 2006 TeleAtlas, Inc. All rights reserved. Protected areas data from California Protected AreasDatabase (www.calands.org), 2011 ABAG GIS/February 2012 Scale: 0 5 102.5 Kilometers 0 3 61.5 Miles Rural Corridor Mixed-Use Corridor Transit Neighborhood Rural Town Center Transit Town Center Suburban Center City Center 4 Summary of Regional Projection Economic and Demographic Assumptions Population Profile The age and ethnic composition of the region’s future growth comes from: State of California, Department of Finance, Population Projections for California and Its Counties 2000- 2050, Sacramento, California, July 2007. For each decade, the growth shares by age and ethnic composition are added to the 2010 base population profile from Census 2010 to get future year age and ethnic total population profiles. The net migration assumption for the Department of Finance forecast averages 177,000 statewide over the 50-year period, or approximately 35% of the growth. Housing Units A thirty-year average housing production level of 22,000 is assumed. This is based upon an analysis of past production and future policy supports, acknowledging that high housing costs and limited production is a factor constraining the ability of the region to accommodate future job growth. Vacant Units Vacant units are calculated by an assumed future vacancy rate of 4% of total housing units in future years, due to regular turnover of the housing stock. Persons per Household Existing headship rates – the ratio of household population to heads of households – by age and ethnic group are derived from the 2005-2009 American Community Survey 5-year average estimate. The existing headship rates by age and ethnic group are applied to the future year household population profile to get the future persons per household for the Bay Area. Changes in headship are not assumed – the change in the overall persons per household over time is solely a result of the changing population profile of the region. Household Population Total household population is calculated by multiplying the future persons per household by the future total households. Group Quarters Population The future group quarters population is calculated as a share of total population. The share is calculated using Census 2010 rates of group quarter population by age applied to the future year population profile. Population Total population is calculated by adding household population and group quarters population. Non-Institutionalized Population Similar to the group quarters population, non-institutionalized population is calculated as a share of total population. The share is calculated using Census 2010 rates of non-institutionalized population by age applied to the future year population profile. Labor Force Participation Rates For future labor force participation rates, we rely on: United States Department of Labor, Bureau of Labor Statistics, Labor force participation rates, 2008-2018 and Labor force participation rates, to 2050. The future national labor force participation rates by age and ethnic group are applied to the future non-institutionalized population profile. The overall rate is then adjusted based upon the difference in 2010 between national and regional labor force participation to get the future labor force participation rate for the Bay Area. - 58 - Labor Force Labor force is calculated by multiplying the future year non-institutionalized population by the future labor force participation rate. Unemployment Rate The assumption is for full employment levels in future years. This is assumed as a 5.1% unemployment rate per the Bureau of Labor Statistics. Employed Residents Employed residents are calculated by subtracting the unemployed residents from the labor force. Unemployed residents are calculated by multiplying the labor force by the unemployment rate. Employed Residents per Job This ratio is influenced by levels of in-commuting and out-commuting as well as the number of employed residents holding multiple jobs. We have assumed that this ratio holds at the 2010 level, implying the rates of net in-commuting and multiple job-holding remain constant. This implies a small increase in in-commuting proportionate to the increase in total jobs in the region, but halts the trend of increasing rates of in- commuting into the region seen in recent decades, due to road capacity constraints and additional housing production supports within the region. Jobs Total potential jobs in the Bay Area are provided by Center for Continuing Study of the California Economy, based on an analysis of the Bay Area’s share of national jobs by job sector and the region’s competitiveness in these sectors. The forecast jobs are calculated from employed residents, holding the 2010 employed resident per job ratio of 0.966 constant. This assumption holds the rates of net in-commuting and multiple job holding constant into the future, as opposed to the increases experienced in the 80’s and 90’s. The resulting forecast jobs are about 100,000 jobs lower than the potential jobs in the economic forecast from the Center for Continuing Study of the California Economy. - 59 - 5 Housing Distribution Methodology The housing distribution takes into account local input and key sustainability, equity, and economic factors, including new data that help to better identify sustainable locations for growth and planned levels of development. The housing distribution is linked to existing and future transit service and expected level of greenhouse gas emissions from each area of the region, with the goal of utilizing the existing transit infrastructure efficiently and directing growth to places that can provide the best opportunity for emissions reductions. However, growth in each place is tied directly to locally-defined housing potential. Data Sources 2010 Census Summary File 1 (U. S. Census Bureau) The U.S. Census counts every resident in the United States. It is mandated by Article I, Section 2 of the Constitution and takes place every 10 years. National and state population totals from the 2010 Decennial Census were released on December 21, 2010. Redistricting data, which include additional state, county and local counts, were released starting in February 2011. Decennial Census population, housing unit, and household data for the region were obtained from the 2010 Census Summary File 1: http://factfinder2.census.gov/main.html Longitudinal Employment and Household Dynamics (U. S. Census Bureau) The Longitudinal Employment and Household Dynamics (LEHD) program uses statistical and computing techniques to combine federal and state administrative data on employers and employees with core Census Bureau censuses and surveys. The program provides employment statistics on employment, job creation, turnover, and earnings by industry, age and sex at the local, state, county and sub-county. More information on the LEHD data is available at: http://lehd.did.census.gov/led/ Regional Travel Demand Model (MTC) Vehicle miles traveled (VMT) data at the Transportation Analysis Zone (TAZ) level from the Alternative Scenarios were obtained via MTC’s Regional Travel Demand Model. UrbanSim (UCBerkeley, Purdue University) UrbanSim is a software-based simulation urban development model incorporating land use, transportation, economic, and environmental factors. Housing development potential data was obtained via the model’s land use database, which includes current local general plan land use and zoning designations. http://www.urbansim.org/Main/WebHome National Establishment Times-Series (Walls & Associates / Dun and Bradstreet) Walls & Associates converts Dun and Bradstreet archival establishment data into a time-series database of establishment information called the National Establishment Times-Series (NETS) Database. The NETS data is gathered by individual business and includes number of jobs, industry type, and location. ABAG has analyzed the NETS data to provide information on the spatial distribution of jobs at the jurisdiction and PDA level by employment sector, as well as changes in spatial distribution at these geographies from 1989- 2009. More information on the NETS data is available at: http://www.youreconomy.org/nets/?region=Walls Housing Distribution Factors - 60 - Locally-based Development Potential Housing development potential was used as the basis for distributing household growth to each area. The potential for housing development up to 2040 for each place was determined from existing and future land use data and local growth potential information from the following three sources: 1. Local input on SCS scenarios Local feedback on the SCS scenarios through letters, emails, meetings, and the SCS Basecamp forum, the PDA Assessment, and new applications for PDA designation provided detailed information on planned growth in specific PDAs and jurisdictions. 2. PDA Place Types Locally-selected place types by PDA served as a reference on the scale of growth proposed in each PDA. 3. UrbanSim Land Use Data (new) The UrbanSim forecasting model includes a land use database with current local zoning designations and general plan land use designations. Development potential up to 2040 for each area within the region was determined via analysis of the local zoning and land use designations. Sustainability, Equity and Economic Factors 1. Transit Each area throughout the region was identified by its highest level of transit service. Growth was distributed based on transit tiers, with the goal of utilizing the existing transit infrastructure more efficiently; places with high levels of transit service were directed commensurately more growth. Transit Tiers: Tier 1: BART, Muni Metro, VTA Light Rail, Caltrain Tier 2: ACE, Amtrak Capital Corridor, SMART, eBART, Bus Rapid Transit corridors Tier 3: All other transit (bus, ferry, etc.) 2. Vehicle Miles Traveled per Household (new) Vehicle Miles Traveled (VMT) data1 for each PDA and non-PDA area is available from MTC’s Regional Travel Demand Model. The 2040 VMT per household measure modeled from the best-performing SCS Alternative Scenario was used in the distribution to identify the places that are expected to result in the lowest greenhouse gas emissions (the VMT per household measure is highly correlated with greenhouse gas emissions). Each place was categorized by VMT tier, shown below. VMT per Household Tiers: Tier 1: 0-25 vmt/hh Tier 2: 25-35 vmt/hh Tier 3: 35-45 vmt/hh Tier 4: 45+ vmt/hh 3. Current housing vacancy data (new) To account for current vacant housing units, identified via the 2010 U.S. Census, vacancy absorption was factored into the housing distribution. Vacancy absorption is the number of existing vacant units that are available to accommodate new households in an area; it reduces the total number of new units that will have to be built in an area to accommodate growth to 2040. The vacancy absorption calculation allows for up to 4% vacancy in each area. 1 VMT by place of residence for all home-based trips was used for the housing distribution. - 61 - 4. Employment Factor (revised) To link housing growth more closely to job centers, the initial housing distribution was adjusted by an employment adjustment factor for each area, based on the Jobs-Housing Connection Scenario 2040 employment for each jurisdiction. 5. Net Low-income In-commuting Factor To shift growth to places that are importing many low-income workers, a net low-income in-commuting factor was used to adjust the initial housing distribution. U.S. Census Bureau LEHD data was used to determine the number of workers commuting to and from the jurisdiction by income category in 2009 and previous years. 6. Housing Value Factor To shift housing growth to places that offer high quality services (schools, infrastructure, parks, etc.), the initial housing distribution was adjusted by a housing value factor, based on jurisdictional median home value. Methodology 1. Housing unit growth was added to each PDA’s and non-PDA area’s 2010 housing unit value based on each area's housing development potential, adjusted by Transit-VMT Tier growth adjustment rates and distributed via the steps described below. Transit-VMT Tier Adjustment Rates Transit Tier VMT Tier Growth Adjustment Rate 1 1 1.1 1 2 1.25 1 3 1.2 1 4 1.15 2 1 1.25 2 2 1.2 2 3 1.15 2 4 1 3 1 1.2 3 2 1 3 3 1 3 4 0.75 - 62 - Housing Distribution Steps Step Area Base Housing Unit Growth Growth Adjustment 1 Any VMT Tier 1 area PDAs: Local feedback level of growth Other areas: UrbanSim development potential Maximum of Base Growth or Transit‐VMT Tier Rate x Base Growth. No adjustment for PDA areas if planned level of growth exceeds Place Type mid‐point unit level. 2 All remaining PDAs (excluding Employment Centers): VMT Tiers 2, 3, 4 Local feedback level of growth Maximum of Base Growth or Transit‐VMT Tier Rate x Base Growth. No adjustment for PDA areas if planned level of growth exceeds Place Type mid‐point unit level. 3 All remaining non‐PDA areas (excluding areas outside of Urban Growth Boundaries/Urb an Limit Lines Remainder of Regional Control Total x Core Constrained Alternative Scenario Share of Growth x Transit‐ VMT Tier Rate (less vacant housing units for places with vacancy >10%) 2. Growth in all areas was then adjusted plus or minus 10 percent based on the combined adjustment factors: a. Housing Value (weight = 3) b. Net Low-income In-commuting (weight = 2) c. 2040 Employment (weight = 1) 3. Vacancy absorption was factored in for each area to obtain household growth. 4. The jurisdictional level of growth was adjusted up or down based on feedback, ensuring that growth in each place meets at least 5% of existing units. Growth from areas exceeding 115% of their locally- identified level of growth was re-balanced to areas under 75% of their locally-identified level of growth. Feedback and Issues from Alternative Scenarios Housing Distribution  Many jurisdictions commented that regional growth numbers were high, mainly compared to growth experienced over past couple of decades  Methodology appeared to push more growth to PDAs and jurisdictions at a rate much greater than housing has historically been produced - 63 - - 64 -  In general, larger jurisdictions were more comfortable with the levels of growth in their cities, while smaller jurisdictions felt the distributions were too high.  The final distribution must be a combination of previous scenarios, as “no single scenario adequately meets the aspirations and conditions of [all] jurisdictions” as noted by the Contra Costa Transportation Authority. Changes to methodology used in the Alternative Scenarios  Revised methodology to adjust housing growth by transit type and VMT per household tiers, instead of transit type and job tiers. The rationale for this methodology is to direct growth to areas commensurate with each area’s existing level of transit infrastructure and service and expected level of greenhouse gas emissions  Revised methodology to base housing growth on local plans and development potential instead of Place Type minimum levels of growth  Linked the housing distribution to the 2040 job distribution rather than the 2010 job distribution.  Did not apply the 40 percent household formation growth threshold to jurisdictions  Did not cap PDA growth to 95 percent of jurisdictional growth  Incorporated housing unit vacancy absorption into the analysis  Applied consistent methodology across all areas of the region, both PDAs and areas outside of PDAs 6 Employment Distribution Methodology The employment distribution takes into account employment growth by sector and is linked to transit infrastructure and local input. Employment growth is organized under three major groups: knowledge-sector jobs, population-serving jobs, and all other jobs. The knowledge-sector jobs are expected to grow based on current concentration, specialization, and past growth as well as transit service and access. Population-serving jobs, such as retail stores are expected to grow based on the number of residents per place. All other jobs are expected to grow according to the existing distribution of jobs in each of these sectors. Data Sources California Department of Transportation Sector Forecast (Caltrans) Caltrans uses an econometric model to project employment by industry out to 2040 for each county in California. The agency’s model uses variables and assumptions taken from the UCLA Anderson Forecast and historic employment data from EDD. The most recent projections were released in August 2011, titled California County-Level Economic Forecast: 2011-2040. In comparison, the most recent EDD and BLS projections available date from 2008 and 2009. A complete description of the 2011 Caltrans projection methodology and data out to 2040 is available at: http://www.dot.ca.gov/hq/tpp/offices/eab/socio_economic.html. Center for Continuing Study of the California Economy (CCSCE) Stephen Levy at CCSCE uses national short-term and long-term economic and demographic forecasts to prepare long-term regional economic projections by industry sector. Details on the CCSCE methodology and analysis are provided in a report, Bay Area Job Growth to 2040: Projections and Analysis. Walls & Associates / Dun and Bradstreet (NETS) Walls & Associates converts Dun and Bradstreet archival establishment data into a time-series database of establishment information called the National Establishment Times-Series (NETS) Database. ABAG has analyzed the NETS data to provide information on the spatial distribution of jobs at the jurisdiction and PDA level by employment sector, as well as changes in spatial distribution at these geographies from 1989- 2009. More information on the NETS data is available at: http://www.youreconomy.org/nets/?region=Walls Methodology 2010 Employment Distribution Current employment is based on total jobs by sector as detailed in the CCSCE report. This is derived from California Employment Development Department wage and salary job estimates plus estimates for self employed workers developed from the 1990 and 2000 Census and American Community Survey annual estimates. The distribution to the counties is based upon 2010 sector totals by county from the Caltrans forecast. NETS data is used to distribute jobs by PDA and jurisdiction for each sector within each county. 2040 Employment Distribution Total regional employment The 2040 total job number was established from an analysis of economic and demographic trends, housing production, and policy direction to reduce reliance upon in-commuting to provide additional workforce for future Bay Area jobs. The 2040 job, population, and household totals provide a consistent set of demographic projections that accounts for: future age and ethnic demographic changes (DoF forecast), labor force participation rates (BLS), headship rates (HCD/DOF/ACS), group quarter and institutional shares of population (ACS), and normalized future unemployment and vacancy rates (5.1% and 4%, respectively). - 65 - Employment by economic sector and county The composition of employment in 2040 by different industry sectors is based upon Bay Area Job Growth to 2040: Projections and Analysis, prepared by Stephen Levy at the Center for Continuing Study of the California Economy. This report uses a shift-share methodology (calculating regional growth as a share of national growth by industry sector) to project the future composition of Bay Area employment among the broad 2- digit NAICS industry sectors. The distribution of 2040 employment among the nine counties for each industry sector is based upon county shares of regional employment in Caltrans’ California County-Level Economic Forecast: 2011-2040. The agency’s econometric model uses variables and assumptions taken from the UCLA Anderson Forecast and historic employment data from EDD. The distribution of employment by jurisdiction and Priority Development Area was then calculated as a share of county growth for each industry sector. Employment by jurisdiction and Priority Development Area The distribution of employment at the jurisdiction and Priority Development Area geographies relies upon three basic approaches depending upon the type of job: 1. Population-serving jobs: For jobs that provide services to households, employment location is dependent upon where people live. As a result, growth of these jobs was distributed at the jurisdiction and PDA geography based upon the spatial distribution of household growth in the region. Residential construction jobs were also included in this category, as they will be located where new housing is built. Based upon an analysis of Bay Area employment at the 4-digit NAICS categories, this included 14% of new Construction jobs, 48% of new Retail jobs, 60% of Health and Education jobs, and 36% of Leisure and Hospitality sector jobs. 2. Knowledge-sector jobs: For jobs in Professional and Business Services, Information, and Finance, a Knowledge Strength Index was used to weight the distribution of jobs at the jurisdiction level. The index weights jurisdiction growth based upon the following factors: Average total employment 1990- 2010 (10%); average knowledge-sector employment 1990-2010 (10%); Knowledge-sectors county location quotient 2010 (20%); share of county’s jobs 2010 (10%); share of knowledge-sector job growth in county 1990-2000 (10%); employees per square mile 2010 (15%); average combined headway 2009 (20%); and share of intersections in jurisdiction with transit (5%) [employment data from NETS, transit data from MTC]. This index reflects the tendency of these jobs to prefer locations with already high concentrations of similar companies and a shared labor pool. The maximum deviations for any jurisdiction from existing shares in these sectors based upon the index weighting was +9% and -3% of county growth. Priority Development Areas received a 10% increase in share of jurisdiction growth in these sectors over existing shares. 3. All other jobs: For the remaining sectors, employment growth was distributed based upon the existing distribution in 2010 as derived from analysis of NETS establishment data. This data provides employment information by location of a business establishment. This is a high level of geographical resolution, which allows us to capture the employment by PDA more accurately than previous zip code data. Following the distribution outlined above, staff reviewed job capacity information for Priority Development Areas provided by local jurisdictions (either directly as feedback on the Initial Vision or Alternative Scenarios, in PDA application materials, or in regional land use data collected by ABAG). Where there was additional job growth in a jurisdiction and capacity identified for that growth in Priority Development Areas, the PDA - 66 - employment numbers were increased to reflect the local plans. Additionally, shifts among PDAs within a jurisdiction were made to better reflect where growth was planned for by local jurisdictions Feedback and Issues from Alternative Scenarios Employment Distribution  Methodology distorted existing distribution, primarily affecting numbers in Solano County  Many jurisdictions commented that regional growth numbers were high, mainly compared to growth experienced over past couple of decades  In general, Marin jurisdictions said growth was too high within the county, Solano, Sonoma, and eastern Contra Costa jurisdictions that it was too low within their respective part of the region  PDA geography not the most appropriate for discussing employment centers where knowledge- sector jobs concentrate  PDA employment growth still did not reflect local plans Changes to methodology used in the Alternative Scenarios  Revised total job growth by sector reflecting economic and demographic data (consultant reports and demographic analysis)  Revised distribution of growth by sector among counties to better differentiate growth rates among counties (Caltrans California County-Level Economic Forecast: 2011-2040)  Maintain methodology for population-serving employment based upon distribution of household growth in the scenario (but include additional ‘Health and Education’ and ‘Leisure and Hospitality’ jobs based upon analysis of population-serving shares of these sectors, as well as the share of ‘Construction’ jobs that are for residential construction)  Revise knowledge-sector employment growth distribution using an index weighting jurisdictions based upon concentrations of employment, specialization in knowledge-sector jobs, past growth, and transit service and access  Maintain methodology for remaining sectors based upon existing share  Priority Development Area distribution revised as a calculated share of jurisdictional growth  For areas where local plans show additional capacity in PDAs and there was additional growth within the jurisdiction, the job allocation to the PDAs was increased. Additionally, shifts among PDAs within a jurisdiction were made to better reflect where growth was planned for by local jurisdictions. - 67 - 7 Resources American Farmland Trust, Greenbelt Alliance, and Sustainable Agriculture Education (SAGE), Sustaining Our Agricultural Bounty: An Assessment of the Current State of Farming and Ranching in the San Francisco Bay Area, January 2011. http://www.farmland.org/documents/SustainingOurAgriculturalBountyMARCH2011.pdf. Bay Area Council Economic Institute, in partnership with UCLA Anderson Forecast, 5th Annual Regional Economic Forecast Conferences, December 2011. http://www.bayareaeconomy.org/economic-forecasts/. Chapple, Karen and Jacob Wegmann, Affordable Housing Needs and Strategies, 2012. Will be made available on ABAG website shortly. East Bay Economic Development Alliance, Building on Our Assets: Economic Development & Job Creation in the East Bay, October 2011. http://www.edab.org/research_facts_figures/building_on_our_assets_2011_Report.htm. Joint Venture, in partnership with Silicon Valley Community Foundation, 2012 Index of Silicon Valley, 2012. http://www.jointventure.org/index.php?option=com_content&view=article&id=157&Itemid=567. Levy, Stephen, Bay Area Job Growth to 2040: Projections and Analysis, Center for Continuing Study of the California Economy, February 2012. Will be made available on ABAG website shortly. Mendez, Michael, “Latino New Urbanism: Building on Cultural Preferences,” Opolis: An International Journal of Suburban and Metropolitan Studies, 1.1 (2005). Metropolitan Transportation Commission, Transit-Oriented Development Demand Analysis, prepared by Center for Transit-Oriented Development and Strategic Economics, July 2005. http://www.reconnectingamerica.org/assets/Uploads/4d.pdf Myers, Dowell, Attrition of Homeownership in California in the 2000s: Now Seeking Generational Replacements, USC Population Dynamics Research Group, July 2011. http://www.usc.edu/schools/price/research/popdynamics/pdf/2011_Myers-etal_California-Roller- Coaster.pdf. Nelson, Arthur C., The New California Dream: How Demographic and Economic Trends May Shape the Housing Market, A Land Use Scenario for 2020 and 2035, Urban Land Institute, 2011. Pitkin, John and Dowell Myers, The 2010 Census Benchmark for California’s Growing and Changing Population, USC Population Dynamics Research Group, February 2011. http://www.usc.edu/schools/price/research/popdynamics/pdf/2011_Pitkin-Myers_CA-2010-New- Benchmark.pdf. Retsinas, Nicolas P. and Eric S Belsky, ed. Revisiting Rental Housing: Policies, Programs, and Priorities, Brookings Institution Press, Washington, D.C., 2008. Sonoma County Economic Development Board, Economic Development Strategy and Jobs Plan, November 2011. http://edb.sonoma-county.org/. State of California, Department of Finance, Population Projections for California and Its Counties 2000-2050, by Age, Gender and Race/Ethnicity, Sacramento, California, July 2007. http://www.dof.ca.gov/research/demographic/reports/projections/p-3/. - 68 - - 69 - State of California, Department of Housing and Community Development, Raising the Roof – California Housing Development Projections and Constraints 1997-2020, Sacramento, California, 2000. http://www.hcd.ca.gov/hpd/hrc/rtr/index.html. U.S. Census Bureau, 2010 Decennial Census. http://2010.census.gov/2010census/. U.S. Census Bureau, 2005-2009 American Community Survey 5-Year Estimates. http://www.census.gov/acs/www/. U.S. Department of Transportation, Summary of Travel Trends: 2009 National household Travel Survey, June 2011. http://nhts.ornl.gov/2009/pub/stt.pdf. Jobs - Housing Connection Scenario Draft MTC Planning/ABAG Administrative Committee March 9, 2012 2 Jobs -Housing Connection Prepares the region for Job Growth Provides housing and transportation choices for future Bay Area residents and families Aligns transportation investments, housing growth, and land use planning Houses the region’s population at all income levels 2. Past Trends & Future Projections I B~ C3LI-_~~-------------------------------------------------------- Regional Growth 2010 2040 Growth 2010 - 2040 Jobs 3,385,000 4,505,000 1,120,000 Population 7,152,000 9,299, 000 2,147,000 Housing Units 2,786,000 3,446,000 660,000 Source: California Department of Finance, US Census, Center for Continuing Study of the California Economy, United States Department of Labor, Bureau of Labor Statistics, ABAG ‐400 ‐200 0 200 400 600 800 80‐90 90‐00 00‐10 10‐20 20‐30 30‐40 1.1 million jobs by 2040 Employment Growth Th o u s a n d s 6 Locations for new employment Renewed regional centers Office parks Downtown areas and transit corridors Industrial and agricultural land Population Growth Th o u s a n d s 900 800 700 600 500 400 300 200 100 o I 80-90 I 90-00 00-10 I-- I-- I-- I-- I-- I-- I-- I I I I 10-20 20-30 30-40 I BayArea Cl~_~------------------------------------------------- 0 0.5 1 1.5 2 2.5 3 0‐24 25‐44 45‐64 65+ 2010 2040 Mi l l i o n s Population by Age and Decade +19% +15% +1%+131% Housing Unit Forecast •Market analysis •Local Plan Capacity •Access to employment centers •Enhanced transit service •Pedestrian environment •Neighborhood amenities •Local Government Input •Priority Development Areas •Place types •Use of existing infrastructure and transit •Absorbs current vacancies (6.4%) to 4% in 2040 •Increases group housing to recognize projected growth in the senior population Housing Production Th o u s a n d s 660,000 Housing Units by 2040 3. Regional Employment and Housing Distribution Share of Bay Area’s Housing Units and Jobs in 2040 0% 5% 10% 15% 20% 25% 30% Jobs Housing Units• • % of 2010-2040 Employment & Housing Growth in PDAs 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Jobs Housing Units• • BayArea an ~---------------------- % of Employment located in PDAs in 2010 and 2040 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 2010 2040• • BayArea an ~---------------------- 4. Key Challenges & Opportunities 44%of Bay Area Jobs are within a ½ Mile of Regional Transit Stations or ¼ Mile of Frequent Local Bus Employment Data Source: National Establishment Time Series (NETS) Maps produced by Mark Shorett, Arup 75 % of Bay Area Jobs are within a ½Mile of Highway Off-Ramps Employment Data Source: National Establishment Time Series (NETS) Maps produced by Mark Shorett, Arup Job Growth Issues Housing Production is critical Significant employment concentrations are not well served by transit Regional investments are needed to foster job growth in PDAs and improve work accessibility A variety of strategies should support lower income workers servicing the knowledge based employment sector “Knowledge based” jobs have grown in PDAs Job Growth Opportunities Knowledge-based industries are clustering close to amenities, services, and transit easier access to jobs and services Concentrated housing and office jobs reducing pressures on industrial and agricultural land Housing in Complete Communities will support local serving jobs City commitment to support local businesses at selected locations 21 Housing Production Challenges Affordability gap has increased for very low, low and moderate income households Multifamily housing is expensive, with a complex entitlement process and high risk financing Current housing crisis created high vacancies and number of foreclosures 22 Increase in multi-family development throughout Bay Area in recent years Multi-family developments in 2010 were 65% of all permits, versus 44% in 2000, and 25% in 1990. Increase in housing in transit-accessible cities from 1990 to 2010: San Francisco, Berkeley, Walnut Creek, San Mateo, and San Jose. Growth of senior population in residential care facilities Young professional preferences for urban housing Housing Market Opportunities 23 Jobs Housing Connection Scenario reduces GHG by 9% Plan Bay Area Transportation Investments to be released in April Other Policy Initiatives to be released in April Total To Be Determined Per Capita GHG Reductions by 2035 6. Regional Housing Need Allocation (RHNA) Brief history of RHNA •AB 2853 (Roos), passed in 1980, required that Councils of Governments (COGs) determine the existing and projected regional housing needs for persons at all income levels. •RHNA cycles: • 1981 – 1988 • 1988 – 1995 • Temporary suspension of RHNA requirement • 1999 – 2006 • 2007 – 2014 RHNA - The allocation process • State determines need for region based upon Department of Finance population forecasts and population forecasts used for the regional transportation plan. • State and Council of Governments (COG) negotiate allocation numbers and reach agreement on final allocation for region. • COG distributes Regional Housing Need Allocation numbers to cities/counties. • Cities/counties incorporate local RHNA numbers into their general plans. Proposed Methodology Sustainability Component –70% allocated based on growth in PDAs Fair Share Component –30% allocated based on growth outside PDAs Increase diversity of housing affordability in all jurisdictions Sustainability Component PDAs as Complete Communities Cities with PDAs are not overburdened Fair Share Component Factors –Transit –Jobs –RHNA past performance Cities without PDAs address minimum amount of housing need –Minimum housing floor (40%) Draft Regional Housing Need Determination from HCD Bay Area’s total housing need for 8-year RHNA period HCD’s methodology accounts for recession and vacant / foreclosed units 2007-2014 RHND 2014-2022 RHND 214,500 187,990 6. Timeline I B~ C3LI-_~~-------------------------------------------------------- March 9,2012: Release Draft Jobs –Housing Connection Scenario, Preliminary RHNA Methodology April 13, 2012: Release transportation scenarios and performance targets May 2012: Approval of Draft Preferred Scenario; RHNA Methodology, and OneBayArea Grant Through September 2012: Additional Community Input November 2012: Release Draft SCS/RTP and Draft EIR April 2013: Adopt SCS SCS Schedule SCS Jobs-Housing Connection Scenario Distribution Spreadsheet Description This spreadsheet details the employment distribution used in the Jobs-Housing Connection Scenario. It contains six tabs, three summarizing the employment distribution at the county, jurisdiction, and PDA geographies, two detailing growth by distribution method and employment sector by SSA and PDA, and one tab providing details on the Knowledge Strength Index used in the knowledge-sector distribution method. The two detailed tabs show total employment, and employment broken out into eleven industry sectors. These correspond to the primary activity of the employer, not the occupations of particular employees. The eleven sectors are: Agriculture and Natural Resources; Construction; Manufacturing and Wholesale; Retail; Transportation, Utilities, and Warehousing; Information; Financial Activities; Professional Services; Health and Education; Leisure, Hospitality, and Other Services; and Government. The detailed tabs also break out the employment growth distribution into the component methods used. They show for each sector the 2010 and 2040 numbers, as well as the growth due to the various distribution components. The components are as follows: Existing: This is the baseline distribution,utilizing the Dun and Bradstreet (NETS) data on the existing distribution of jobs within each county. The growth distribution for these sectors is the same as the existing 2010 distribution –each geography gets its share of the industry sector growth based upon its existing share of that industry sector jobs. Local: This is the distribution of new population-serving jobs based upon household growth. For 14% of new Construction jobs, 48% of new Retail jobs, 60% of new Health and Education jobs, and 36% of new Leisure and Hospitality jobs,employment was distributed based upon household growth in the scenario. Knowledge: The distribution of new Knowledge-sector jobs at the jurisdiction level was weighted using the Knowledge Strength Index, which is included in this spreadsheet as a separate tab. The index reflects the tendency of these jobs to prefer locations with already high concentrations of similar companies and a shared labor pool. Priority Development Areas received a 10% increase in share of jurisdiction growth in these sectors over existing shares. Adjust/Plans: At the SSA level, two additional adjustments were made and documented in this column. An adjustment from San Francisco to Brisbane reflecting the development potential of the Baylands site, and a shift among the unincorporated areas of Santa Clara and Sonoma counties to reflect the local plan PDA growth in these unincorporated areas. At the PDA level, the Plans column reflects adjustments based upon PDA growth capacity from local plans. In cases where additional jurisdiction growth was planned in the PDAs or the plans distributed growth differently amongst the PDAs within a jurisdiction, adjustments were made in this column. PDAs draft 4/9/2012 12 3 4 5 6 7 8 9 10 11 12 13 1415 16 17 18 19 2021 22 23 24 25 2627 28 29 30 31 32 33 34 35 36 37 38 3940 41 42 43 44 4546 47 48 49 50 5152 53 54 55 56 57 58 59 60 61 62 63 6465 66 67 68 69 7071 72 73 74 75 7677 78 79 80 81 82 83 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z AA AB AC AD AE AF AG AH LOCAL Absorption PDA TOTALS County Key Juris KeyName KeyPlaceType HH HU VacHU Vacancy Rate 2040 VMT 2040 VMT/ household LOCAL Housing Unit Growth (2010-2040) HH Vacancy Absorption Transit- VMT Tier Transit- VMT Tier Adj. Rate JOBS Factor Adj. Rate Home Value Factor Adj. Rate In- Commute Factor Adj. Rate Combined Factor Adj. Rate HU Growth 2010-2040 HH Growth 2010-2040 HU 2040 HH 2040 % change Housing Units 2010-2040 % of PDA Housing Units Growth % of County Housing Units Growth % of Regional Housing Units Growth Alameda ALA1 Alameda Naval Air Station Transit Town Center 1,088 1,462 374 25.6%371,686 45 4,212 316 33 1.00 4.0% 0.1%-8.4% -2.1%4,138 4,288 5,600 5,376 283%0.81% 2.69% 0.63% Alameda ALA2 Alameda Northern Waterfront Transit Neighborhood 986 1,073 87 8.1%39,296 28 800 44 32 1.00 4.0% 0.1%-8.4% -2.1%786 799 1,859 1,785 73%0.15% 0.51% 0.12% Alameda ALB1 Albany San Pablo Avenue & Solano AvenueMixed-Use Corridor 1,690 1,812 122 6.7%62,512 34 215 50 22 1.20 -4.4% -1.0% -3.2% -2.3%253 292 2,065 1,982 14%0.05% 0.16% 0.04% Alameda ALC1 Alameda County UnincorporatedCastro Valley BART Transit Neighborhood 1,396 1,482 86 5.8%49,879 30 550 27 12 1.25 6.2%-2.5%0.0%-0.2%688 687 2,170 2,083 46%0.14% 0.45% 0.10% Alameda ALC2 Alameda County UnincorporatedEast 14th Street and Mission Boulevard Mixed Use CorridorMixed-Use Corridor 6,743 7,190 447 6.2%197,832 26 1,604 159 21 1.25 6.2%-2.5%0.0%-0.2%1,989 2,069 9,179 8,812 28%0.39% 1.29% 0.30% Alameda ALC3 Alameda County UnincorporatedHesperian Corridor Transit Neighborhood 2,737 2,862 125 4.4%103,881 34 715 11 32 1.00 6.2%-2.5%0.0%-0.2%716 698 3,578 3,435 25%0.14% 0.46% 0.11% Alameda ALC4 Alameda County UnincorporatedMeekland Corridor Transit Neighborhood 1,297 1,396 99 7.1%66,309 32 477 43 32 1.00 6.2%-2.5%0.0%-0.2%477 501 1,873 1,798 34%0.09% 0.31% 0.07% Alameda BER1 Berkeley Adeline Street Mixed-Use Corridor 623 687 64 9.3%18,253 24 226 37 11 1.10 9.2% 1.9% 8.6% 5.3%263 289 950 912 38%0.05% 0.17% 0.04% Alameda BER2 Berkeley Downtown City Center 2,566 2,688 122 4.5%67,150 10 4,050 14 11 1.10 9.2% 1.9% 8.6% 5.3%4,280 4,123 6,968 6,689 159%0.84% 2.78% 0.65% Alameda BER3 Berkeley San Pablo Avenue Mixed-Use Corridor 1,278 1,455 177 12.2%37,974 25 741 119 21 1.25 9.2% 1.9% 8.6% 5.3%783 870 2,238 2,148 54%0.15% 0.51% 0.12% Alameda BER3_BER6Berkeley SUB-AREA: San Pablo Avenue / University AvenueMixed-Use Corridor 166 176 10 5.7%4,443 24 105 3 21 1.25 9.2% 1.9% 8.6% 5.3%111 109 287 275 63%0.02% 0.07% 0.02% Alameda BER4 Berkeley South Shattuck Mixed-Use Corridor 312 344 32 9.3%8,117 22 90 18 31 1.20 9.2% 1.9% 8.6% 5.3%115 128 459 440 33%0.02% 0.07% 0.02% Alameda BER5 Berkeley Telegraph Avenue Mixed-Use Corridor 992 1,110 118 10.6%27,600 23 353 74 21 1.25 9.2% 1.9% 8.6% 5.3%373 432 1,483 1,424 34%0.07% 0.24% 0.06% Alameda BER6 Berkeley University Avenue Mixed-Use Corridor 1,394 1,484 90 6.1%40,077 26 444 31 22 1.20 9.2% 1.9% 8.6% 5.3%564 572 2,048 1,966 38%0.11% 0.37% 0.09% Alameda DUB1 Dublin Downtown Specific Plan AreaSuburban Center 793 831 38 4.6%53,421 45 1,634 5 13 1.20 2.4% 0.7% 4.0% 2.1%2,008 1,932 2,839 2,725 242%0.39% 1.30% 0.30% Alameda DUB2 Dublin Transit Center Suburban Center 624 671 47 7.0%69,079 54 3,400 20 14 1.15 2.4% 0.7% 4.0% 2.1%4,003 3,863 4,674 4,487 597%0.79% 2.60% 0.61% Alameda DUB3 Dublin Town Center Suburban Center 3,750 4,134 384 9.3%379,155 58 5,949 219 34 0.75 2.4% 0.7% 4.0% 2.1%6,091 6,066 10,225 9,816 147%1.20% 3.96% 0.92% Alameda EME1 Emeryville Mixed-Use Core City Center 3,525 4,154 629 15.1%88,284 12 4,635 463 21 1.25 1.4%-6.6%7.8%-0.5%5,785 6,016 9,939 9,541 139%1.14% 3.76% 0.88% Alameda FRE1 Fremont Centerville Transit Neighborhood 10,361 10,849 488 4.5%438,922 37 2,200 54 23 1.15 9.8%-0.3%1.8% 2.1%2,591 2,541 13,440 12,902 24%0.51% 1.68% 0.39% Alameda FRE2 Fremont City Center City Center 6,867 7,310 443 6.1%392,249 27 2,332 151 12 1.25 9.8%-0.3%1.8% 2.1%2,986 3,017 10,296 9,884 41%0.59% 1.94% 0.45% Alameda FRE3 Fremont Irvington District Transit Town Center 6,909 7,281 372 5.1%311,340 35 2,400 81 12 1.25 9.8%-0.3%1.8% 2.1%3,072 3,030 10,353 9,939 42%0.60% 2.00% 0.47% Alameda FRE4 Fremont South Fremont/Warm SpringsSuburban Center 1,652 2,330 149 6.4%78,529 43 3,000 585 33 1.00 9.8%-0.3%1.8% 2.1%3,072 3,534 5,402 5,186 132%0.60% 2.00% 0.47% Alameda HAY1 Hayward The Cannery Transit Neighborhood 331 343 12 3.5%13,213 34 770 -2 22 1.20 8.8%-5.2%4.4% 0.3%775 742 1,118 1,073 226%0.15% 0.50% 0.12% Alameda HAY2 Hayward Downtown City Center 2,096 2,287 191 8.4%91,351 22 3,001 100 11 1.10 8.8%-5.2%4.4% 0.3%3,323 3,290 5,610 5,386 145%0.65% 2.16% 0.50% Alameda HAY3_a Hayward South Hayward BARTMixed-Use Corridor 172 184 12 6.5%30,891 31 1,202 5 12 1.25 8.8%-5.2%4.4% 0.3%1,210 1,166 1,394 1,338 657%0.24% 0.79% 0.18% Alameda HAY3_b Hayward South Hayward BARTUrban Neighborhood 1,658 1,796 138 7.7%161,562 50 2,403 66 14 1.15 8.8%-5.2%4.4% 0.3%2,782 2,737 4,578 4,395 155%0.55% 1.81% 0.42% Alameda HAY4 Hayward Mission Corridor Mixed-Use Corridor 1,229 1,482 253 17.1%56,633 30 1,884 194 22 1.20 8.8%-5.2%4.4% 0.3%1,896 2,014 3,378 3,243 128%0.37% 1.23% 0.29% Alameda LIV1 Livermore Downtown Suburban Center 917 1,018 101 9.9%91,342 41 1,819 60 23 1.15 5.6%-1.0%2.2% 1.2%2,123 2,098 3,141 3,015 209%0.42% 1.38% 0.32% Alameda LIV2 Livermore Vasco Road Station Planning AreaSuburban Center 85 96 11 11.5%70,114 62 4,534 7 24 1.00 6.2%-2.5%0.0%-0.2%4,578 4,402 4,674 4,487 4769%0.90% 2.97% 0.69% Alameda LIV3 Livermore Isabel Avenue/BART Station Planning AreaSuburban Center 466 531 65 12.2%45,086 61 3,666 44 34 0.75 6.2%-2.5%0.0%-0.2%3,720 3,615 4,251 4,081 701%0.73% 2.42% 0.56% Alameda NEW1 Newark Dumbarton Transit Oriented DevelopmentTransit Town Center 138 142 4 2.8%26,943 48 2,500 -2 24 1.00 0.6%-2.5% -0.8% -1.4%2,473 2,372 2,615 2,510 1741%0.49% 1.61% 0.37% Alameda NEW2 Newark Old Town MIxed Use AreaTransit Neighborhood 576 595 19 3.2%34,731 43 387 -5 33 1.00 0.6%-2.5% -0.8% -1.4%383 363 978 939 64%0.08% 0.25% 0.06% Alameda OKD1 Oakland Transit Oriented Development CorridorsMixed-Use Corridor 60,971 67,370 6,399 9.5%1,720,743 23 10,493 3,704 31 1.20 10.2%-3.6%0.0%-0.1%10,516 13,799 77,886 74,770 16%2.07% 6.83% 1.59% Alameda OKD2 Oakland Coliseum BART Station AreaTransit Town Center 3,436 3,874 438 11.3%156,953 23 1,856 283 11 1.10 10.2%-3.6%0.0%-0.1%2,046 2,247 5,920 5,683 53%0.40% 1.33% 0.31% Alameda OKD3 Oakland Downtown & Jack London SquareRegional Center 10,626 11,908 1,282 10.8%224,324 8 8,815 806 11 1.10 10.2%-3.6%0.0%-0.1%9,718 10,135 21,626 20,761 82%1.91% 6.31% 1.47% Alameda OKD4 Oakland Eastmont Town CenterUrban Neighborhood 5,960 6,851 891 13.0%185,722 28 420 617 32 1.00 10.2%-3.6%0.0%-0.1%421 1,021 7,272 6,981 6%0.08% 0.27% 0.06% Alameda OKD5 Oakland Fruitvale & Dimond AreasUrban Neighborhood 12,835 14,209 1,374 9.7%397,295 22 4,084 806 11 1.10 10.2%-3.6%0.0%-0.1%4,502 5,128 18,711 17,963 32%0.88% 2.92% 0.68% Alameda OKD6 Oakland MacArthur Transit VillageUrban Neighborhood 8,025 8,818 793 9.0%202,246 17 2,921 440 11 1.10 10.2%-3.6%0.0%-0.1%3,220 3,532 12,038 11,557 37%0.63% 2.09% 0.49% Alameda OKD7 Oakland West Oakland Transit Town Center 9,025 10,825 1,800 16.6%299,209 15 4,367 1,367 11 1.10 10.2%-3.6%0.0%-0.1%4,815 5,989 15,640 15,014 44%0.95% 3.13% 0.73% Alameda PLE1 Pleasanton Hacienda Suburban Center 1,269 1,311 42 3.2%70,480 39 2,897 -10 13 1.20 8.0% 4.1% 8.4% 6.2%3,702 3,544 5,013 4,813 282%0.73% 2.40% 0.56% Alameda SLE1 San Leandro Bay Fair BART Transit VillageTransit Town Center 627 656 29 4.4%27,207 29 740 3 12 1.25 6.0%-3.8%3.8% 0.4%931 897 1,587 1,524 142%0.18% 0.60% 0.14% Alameda SLE2 San Leandro East 14th Street Mixed-Use Corridor 3,493 3,848 355 9.2%120,225 22 803 201 21 1.25 6.0%-3.8%3.8% 0.4%1,010 1,171 4,858 4,664 26%0.20% 0.66% 0.15% Alameda SLE3 San Leandro Downtown Transit Oriented DevelopmentCity Center 1,001 1,076 75 7.0%43,555 11 3,122 82 11 1.10 6.0%-3.8%3.8% 0.4%3,457 3,400 6,592 6,328 321%0.68% 2.24% 0.52% Alameda SLE3_SLE2San Leandro SUB-AREA: Downtown Transit Oriented Development / E14Mixed-Use Corridor 2,928 3,135 207 6.6%88,296 47 309 32 11 1.10 6.0%-3.8%3.8% 0.4%342 360 1,418 1,361 11%0.07% 0.22% 0.05% Alameda UNI1 Union City Intermodal Station DistrictCity Center 1,027 1,055 28 2.7%40,018 31 676 -14 12 1.25 1.8%-1.9% -8.0% -3.3%819 772 1,874 1,799 78%0.16% 0.53% 0.12% Contra CostaANT1 Antioch Hillcrest eBART StationSuburban Center 154 163 9 5.5%32,577 47 2,501 2 24 1.00 2.0%-6.2%-10.0%-6.1%2,356 2,264 2,519 2,418 1446%0.46% 2.76% 0.36% Contra CostaANT2 Antioch Rivertown WaterfrontTransit Town Center 1,434 1,596 162 10.2%87,292 35 2,000 98 32 1.00 2.0%-6.2%-10.0%-6.1%1,884 1,907 3,480 3,341 118%0.37% 2.21% 0.29% Contra CostaCCC1 Contra Costa County UnincorporatedContra Costa CentreMixed-Use Corridor 1,717 1,910 128 6.7%68,166 32 388 116 12 1.25 6.4%-1.1%0.0% 0.5%489 585 2,399 2,303 26%0.10% 0.57% 0.07% Contra CostaCCC2 Contra Costa County UnincorporatedNorth Richmond Transit Neighborhood 1,027 1,237 210 17.0%80,911 46 2,350 161 34 0.75 6.4%-1.1%0.0% 0.5%2,369 2,435 3,606 3,462 192%0.47% 2.78% 0.36% Contra CostaCCC3_a Contra Costa County UnincorporatedPittsburg/Bay Point BART StationTransit Neighborhood 1,022 1,168 146 12.5%64,545 37 3,945 99 13 1.20 6.4%-1.1%0.0% 0.5%3,976 3,916 5,144 4,938 340%0.78% 4.66% 0.60% Contra CostaCCC3_b Pittsburg Pittsburg/Bay Point BART StationTransit Town Center 0 0 0 0.0%7,204 57 0 0 14 1.15 0.2%-7.4% -9.6% -6.8%1,408 1,351 1,408 1,351 #DIV/0!0.28% 1.65% 0.21% Contra CostaCCC4 Contra Costa County UnincorporatedDowntown El SobranteMixed-Use Corridor 1,672 1,809 137 7.6%82,974 47 487 65 34 0.75 6.4%-1.1%0.0% 0.5%491 536 2,300 2,208 27%0.10% 0.58% 0.07% Contra CostaCON1_a Concord Community Reuse AreaRegional Center 74 148 74 50.0%54,253 57 2,900 68 14 1.15 7.6%-4.3%4.8% 0.7%3,370 3,303 3,518 3,377 2277%0.66% 3.95% 0.51% Contra CostaCON1_b Concord Community Reuse AreaTransit Neighborhood 0 0 0 0.0%79,949 57 9,300 0 34 0.75 7.6%-4.3%4.8% 0.7%9,397 9,021 9,397 9,021 #DIV/0!1.85%11.02%1.42% Contra CostaCON2 Concord Downtown BART Station PlanningCity Center 4,204 4,599 395 8.6%122,882 18 2,911 211 11 1.10 7.6%-4.3%4.8% 0.7%3,235 3,317 7,834 7,521 70%0.64% 3.79% 0.49% Contra CostaELC1_a El Cerrito San Pablo Avenue CorridorMixed-Use Corridor 633 700 67 9.6%28,502 29 412 39 12 1.25 -3.8% -1.6% -6.4% -3.6%498 517 1,198 1,150 71%0.10% 0.58% 0.08% Contra CostaELC1_b El Cerrito San Pablo Avenue CorridorMixed-Use Corridor 591 640 49 7.7%24,607 26 454 23 12 1.25 -3.8% -1.6% -6.4% -3.6%549 551 1,189 1,142 86%0.11% 0.64% 0.08% Contra CostaHER1 Hercules Central Hercules Transit Neighborhood 401 411 10 2.4%38,462 48 2,646 -6 24 1.00 -5.4% -3.6% -7.2% -5.1%2,518 2,411 2,929 2,812 613%0.49% 2.95% 0.38% Contra CostaHER2 Hercules Waterfront District Transit Town Center 643 685 42 6.1%50,744 51 1,105 15 24 1.00 -5.4% -3.6% -7.2% -5.1%1,052 1,024 1,737 1,667 154%0.21% 1.23% 0.16% Contra CostaLAF1 Lafayette Downtown Transit Town Center 1,892 2,028 136 6.7%100,622 45 760 55 13 1.20 -1.6%10.0%2.8% 5.7%967 983 2,995 2,875 48%0.19% 1.13% 0.15% Contra CostaMAR1 Martinez Downtown Transit Neighborhood 748 816 68 8.3%31,152 34 600 35 22 1.20 3.6%-2.7%0.8%-0.5%719 725 1,535 1,473 88%0.14% 0.84% 0.11% Contra CostaMOR1 Moraga Moraga Center Transit Town Center 429 441 12 2.7%26,034 56 647 -6 34 0.75 -6.6%9.0%-4.0%2.1%663 630 1,104 1,059 150%0.13% 0.78% 0.10% Contra CostaOAK1 Oakley Employment Area Suburban Center 556 579 23 4.0%50,436 54 1,003 0 34 0.75 -6.4% -6.8% -9.0% -7.5%930 893 1,509 1,449 161%0.18% 1.09% 0.14% Contra CostaOAK2 Oakley Downtown Transit Town Center 516 560 44 7.9%33,669 55 1,304 22 34 0.75 -6.4% -6.8% -9.0% -7.5%1,210 1,184 1,770 1,700 216%0.24% 1.42% 0.18% Contra CostaOAK3 Oakley Potential Planning AreaTransit Neighborhood 976 1,057 81 7.7%64,756 54 1,392 39 34 0.75 -6.4% -6.8% -9.0% -7.5%1,291 1,278 2,348 2,254 122%0.25% 1.51% 0.20% Contra CostaORI1 Orinda Downtown Transit Town Center 326 341 15 4.4%16,396 45 148 1 13 1.20 -5.0%10.0%-4.2%2.8%183 177 524 503 54%0.04% 0.21% 0.03% Contra CostaPIN1 Pinole Old Town & San Pablo AvenueMixed-Use Corridor 1,295 1,433 138 9.6%74,319 46 118 81 24 1.00 -3.2% -4.2% -7.0% -5.0%112 189 1,545 1,484 8%0.02% 0.13% 0.02% Contra CostaPIN2 Pinole Appian Way CorridorMixed-Use Corridor 524 561 37 6.6%32,059 48 633 15 24 1.00 -3.2% -4.2% -7.0% -5.0%603 594 1,164 1,118 108%0.12% 0.71% 0.09% Contra CostaPIT1 Pittsburg Railroad Avenue eBART StationTransit Town Center 3,582 3,932 330 8.4%181,413 34 3,248 193 22 1.20 0.2%-7.4% -9.6% -6.8%3,642 3,689 7,574 7,271 93%0.72% 4.27% 0.55% Contra CostaPIT2 Pittsburg Downtown Transit Neighborhood 1,595 1,874 279 14.9%98,469 44 2,011 204 33 1.00 0.2%-7.4% -9.6% -6.8%1,879 2,008 3,753 3,603 100%0.37% 2.20% 0.28% Contra CostaPLH1 Pleasant Hill Diablo Valley CollegeTransit Neighborhood 333 359 26 7.2%15,753 41 300 12 33 1.00 1.6%-0.8%7.4% 2.4%308 307 667 640 86%0.06% 0.36% 0.05% Contra CostaPLH2 Pleasant Hill Buskirk Avenue CorridorMixed-Use Corridor 1,618 1,730 112 6.5%69,038 32 86 43 32 1.00 1.6%-0.8%7.4% 2.4%88 128 1,818 1,746 5%0.02% 0.10% 0.01% Contra CostaRIC1 Richmond Central Richmond & 23rd StreetCity Center 4,700 5,243 543 10.4%170,873 23 500 333 11 1.10 5.2%-6.5% -9.8% -5.7%520 833 5,763 5,533 10%0.10% 0.61% 0.08% Contra CostaRIC2_a Richmond South Richmond Transit Neighborhood 3,247 3,585 338 9.4%177,442 38 1,500 195 33 1.00 5.2%-6.5% -9.8% -5.7%1,419 1,557 5,004 4,804 40%0.28% 1.66% 0.22% Contra CostaRIC2_b Richmond South Richmond Mixed-Use Corridor 637 686 49 7.1%28,977 27 893 22 22 1.20 5.2%-6.5% -9.8% -5.7%845 833 1,531 1,470 123%0.17% 0.99% 0.13% Contra CostaSAR1 San Ramon City Center Suburban Center 479 494 15 3.0%41,082 48 652 -5 34 0.75 6.6% 3.5% 5.4% 4.7%684 652 1,178 1,131 139%0.13% 0.80% 0.10% Contra CostaSAR2 San Ramon North Camino RamonTransit Town Center 42 129 87 67.4%11,478 37 1,500 82 33 1.00 6.6% 3.5% 5.4% 4.7%1,575 1,594 1,704 1,636 1221%0.31% 1.85% 0.24% Contra CostaSPA1 San Pablo San Pablo Avenue Mixed-Use Corridor 2,525 2,779 254 9.1%70,335 24 1,300 143 21 1.25 -2.0% -8.3% -8.2% -7.2%1,512 1,594 4,291 4,119 54%0.30% 1.77% 0.23% Contra CostaSPA2 San Pablo Rumrill Road Employment Center 397 432 35 8.1%11,478 25 60 18 31 1.20 -2.0% -8.3% -8.2% -7.2%67 82 499 479 15%0.01% 0.08% 0.01% 2010 Adjustment FactorsMTC Transp. Model 5119.xlsPDAs 1 PDAs draft 4/9/2012 2 3 4 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z AA AB AC AD AE AF AG AH LOCAL Absorption PDA TOTALS County Key Juris KeyName KeyPlaceType HH HU VacHU Vacancy Rate 2040 VMT 2040 VMT/ household LOCAL Housing Unit Growth (2010-2040) HH Vacancy Absorption Transit- VMT Tier Transit- VMT Tier Adj. Rate JOBS Factor Adj. Rate Home Value Factor Adj. Rate In- Commute Factor Adj. Rate Combined Factor Adj. Rate HU Growth 2010-2040 HH Growth 2010-2040 HU 2040 HH 2040 % change Housing Units 2010-2040 % of PDA Housing Units Growth % of County Housing Units Growth % of Regional Housing Units Growth 2010 Adjustment FactorsMTC Transp. Model 84 85 86 87 Contra CostaWAL1 Walnut Creek West Downtown City Center 1,277 1,523 257 16.9%36,969 21 2,471 185 11 1.10 6.8%-0.5%8.8% 3.8%2,062 2,165 3,585 3,441 135%0.41% 2.42% 0.31% Contra CostaWCC1_a Contra Costa County UnincorporatedSUB-AREA: West Contra Costa Transportation Advisory Committee: San Pablo Avenue CorridorMixed-Use Corridor 1,591 1,744 153 8.8%75,018 43 487 83 23 1.15 6.4%-1.1%0.0% 0.5%564 625 2,308 2,216 32%0.11% 0.66% 0.09% Contra CostaWCC1_c Richmond San Pablo Avenue CorridorMixed-Use Corridor 1,710 1,866 156 8.4%75,113 35 1,446 81 22 1.20 5.2%-6.5% -9.8% -5.7%1,642 1,658 3,508 3,368 88%0.32% 1.93% 0.25% Contra CostaWCC1_g Hercules SUB-AREA: West Contra Costa Transportation Advisory Committee: San Pablo Avenue CorridorMixed-Use Corridor 595 624 29 4.6%46,605 51 770 4 24 1.00 -5.4% -3.6% -7.2% -5.1%733 708 1,357 1,303 117%0.14% 0.86% 0.11% 5119.xlsPDAs 2 I I I I I I I I I I I I I , "" '''' .. PDAs draft 4/9/2012 2 3 4 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z AA AB AC AD AE AF AG AH LOCAL Absorption PDA TOTALS County Key Juris KeyName KeyPlaceType HH HU VacHU Vacancy Rate 2040 VMT 2040 VMT/ household LOCAL Housing Unit Growth (2010-2040) HH Vacancy Absorption Transit- VMT Tier Transit- VMT Tier Adj. Rate JOBS Factor Adj. Rate Home Value Factor Adj. Rate In- Commute Factor Adj. Rate Combined Factor Adj. Rate HU Growth 2010-2040 HH Growth 2010-2040 HU 2040 HH 2040 % change Housing Units 2010-2040 % of PDA Housing Units Growth % of County Housing Units Growth % of Regional Housing Units Growth 2010 Adjustment FactorsMTC Transp. Model 88 89 90 91 92 9394 95 96 97 98 99100 101 102 103 104 105106 107 108 109 110 111 112 113 114 115 116 117 118119 120 121 122 123 124125 126 127 128 129 130131 132 133 134 135 136 137 138 139 140 141 142 143144 145 Marin MCO1 Marin County UnincorporatedUrbanized 101 CorridorTransit Neighborhood 4,291 4,578 287 6.3%142,727 34 437 104 32 1.00 0.4% 7.6% 0.0% 3.9%455 541 5,033 4,832 10%0.09% 5.59% 0.07% Marin SRA1 San Rafael Downtown City Center 2,419 2,608 189 7.2%82,956 30 1,090 85 22 1.20 5.4% 4.9% 7.6% 5.9%1,389 1,419 3,997 3,838 53%0.27%17.05%0.21% Marin SRA2 San Rafael Civic Center/North Rafael Town CenterTransit Town Center 1,898 1,988 90 4.5%48,279 25 811 10 21 1.25 5.4% 4.9% 7.6% 5.9%1,077 1,044 3,065 2,942 54%0.21%13.21%0.16% Napa ACA1 American CanyonHighway 29 CorridorMixed-Use Corridor 398 437 39 8.9%19,113 49 1,680 22 34 0.75 -7.4% -5.6% -6.6% -6.3%1,580 1,538 2,017 1,936 362%0.31%28.00%0.24% Napa NAP1 Napa Downtown Napa Rural Town Center 130 153 23 15.0%4,806 32 518 17 32 1.00 4.8%-2.8% -8.8% -3.5%501 498 654 628 328%0.10% 8.88% 0.08% Napa NAP2 Napa Soscol Gateway CorridorRural Corridor 602 643 41 6.4%29,423 48 458 15 34 0.75 4.8%-2.8% -8.8% -3.5%443 441 1,086 1,043 69%0.09% 7.85% 0.07% San FranciscoSFO1 San Francisco Bayview/Hunters Point Shipyard/Candlestick PointUrban Neighborhood 10,472 11,614 1,142 9.8%855,522 28 10,500 677 12 1.25 10.6%3.6% 9.4% 6.7%11,237 11,465 22,851 21,937 97%2.21%13.93%1.70% San FranciscoSFO2 San Francisco Balboa Park Transit Neighborhood 4,793 5,218 425 8.1%212,264 100 1,780 27 12 1.25 10.6%3.6% 9.4% 6.7%1,905 1,856 3,175 3,048 37%0.37% 2.36% 0.29% San FranciscoSFO3 San Francisco Downtown-Van Ness-GearyRegional Center 11,125 11,947 821 6.9%199,183 2 13,858 7,643 11 1.10 10.6%3.6% 9.4% 6.7%16,314 23,304 117,830 113,117 137%3.21%20.23%2.47% San FranciscoSFO4 San Francisco Eastern NeighborhoodsUrban Neighborhood 29,364 31,231 1,867 6.0%811,856 16 10,000 1,347 11 1.10 10.6%3.6% 9.4% 6.7%10,702 11,621 44,970 43,171 34%2.10%13.27%1.62% San FranciscoSFO5 San Francisco Mission Bay Urban Neighborhood 1,192 1,270 78 6.1%60,252 6 2,964 133 11 1.10 10.6%3.6% 9.4% 6.7%3,489 3,482 6,956 6,678 275%0.69% 4.33% 0.53% San FranciscoSFO6 San Francisco Port of San FranciscoMixed-Use Corridor 89,813 101,516 11,666 11.5% 1,413,214 248 1,600 -5 11 1.10 10.6%3.6% 9.4% 6.7%1,884 1,803 2,005 1,924 2%0.37% 2.34% 0.29% San FranciscoSFO7 San Francisco Transbay Terminal Regional Center 31,550 34,268 2,617 7.6%1,014,289 556 4,550 149 11 1.10 10.6%3.6% 9.4% 6.7%4,869 4,823 5,359 5,145 14%0.96% 6.04% 0.74% San FranciscoSFO8 San Francisco Treasure Island Transit Town Center 3,196 3,467 271 7.8%236,157 63 7,000 74 32 1.00 10.6%3.6% 9.4% 6.7%7,491 7,265 8,178 7,851 216%1.47% 9.29% 1.14% San FranciscoSFO9_b San Francisco San Francisco/San Mateo Bi-County Area (with City of Brisbane)Transit Neighborhood 122 121 8 6.6%91,010 8 5,060 55 12 1.25 10.6%3.6% 9.4% 6.7%5,415 5,253 7,044 6,762 4475%1.06% 6.72% 0.82% San FranciscoSFO10 San Francisco 19th Avenue Transit Town Center 322 490 296 60.4%16,481 2 5,730 216 31 1.20 10.6%3.6% 9.4% 6.7%6,132 6,103 11,350 10,896 1251%1.20% 7.60% 0.93% San FranciscoSFO11 San Francisco Market & Octavia Urban Neighborhood 586 687 101 14.7%133,193 8 5,980 344 11 1.10 10.6%3.6% 9.4% 6.7%6,400 6,487 18,347 17,613 932%1.26% 7.94% 0.97% San FranciscoSFO12 San Francisco Mission-San Jose CorridorMixed-Use Corridor 1,509 1,629 120 7.4%378,402 11 1,100 618 11 1.10 10.6%3.6% 9.4% 6.7%1,295 1,861 32,526 31,225 79%0.25% 1.61% 0.20% San MateoBEL1 Belmont Villages of Belmont Mixed-Use Corridor 888 923 35 3.8%50,683 40 800 -2 23 1.15 -2.6%6.9%-5.4%1.2%934 895 1,857 1,783 101%0.18% 1.60% 0.14% San MateoBUR1 Burlingame Burlingame El Camino RealTransit Town Center 7,165 7,614 449 5.9%349,424 35 2,470 144 12 1.25 4.4%10.0%8.2% 8.5%3,359 3,369 10,973 10,534 44%0.66% 5.77% 0.51% San MateoCCG1_a Daly City SUB-AREA: CCAG/Daly City: Mission BoulevardMixed-Use Corridor 3,792 3,996 204 5.1%173,979 30 0 44 12 1.25 2.6%-1.2%-10.4%-3.6%391 420 4,387 4,212 10%0.08% 0.67% 0.06% San MateoCCG1_b Colma SUB-AREA: CCAG/ColmaMixed-Use Corridor 542 564 22 3.9%38,272 31 200 -1 12 1.25 -8.2% -2.0%5.0%-0.7%249 238 813 780 44%0.05% 0.43% 0.04% San MateoCCG1_c South San FranciscoSUB-AREA: CCAG/South San FranciscoMixed-Use Corridor 5,343 5,671 222 3.9%271,525 35 2,295 101 12 1.25 7.0% 0.1% 7.2% 3.6%2,981 2,963 8,652 8,306 53%0.59% 5.12% 0.45% San MateoCCG1_d San Bruno SUB-AREA: CCAG/San Bruno: Transit CorridorsMixed-Use Corridor 1,099 1,153 54 4.7%65,278 40 462 8 13 1.20 -0.8% -0.2% -7.4% -2.7%541 528 1,694 1,627 47%0.11% 0.93% 0.08% San MateoCCG1_e Millbrae SUB-AREA: CCAG/Millbrae: Transit Station AreaMixed-Use Corridor 2,511 2,687 176 6.6%123,762 36 200 69 13 1.20 -2.4%7.9%-5.2%1.8%245 304 2,932 2,815 9%0.05% 0.42% 0.04% San MateoCCG1_f San Mateo SUB-AREA: CCAG/San Mateo: DowntownMixed-Use Corridor 10,887 11,485 598 5.2%393,789 31 800 139 12 1.25 8.6% 3.2%-5.0%1.3%1,017 1,114 12,502 12,001 9%0.20% 1.75% 0.15% San MateoCCG1_h San Carlos SUB-AREA: CCAG/San Carlos: Railroad CorridorMixed-Use Corridor 2,908 3,114 206 6.6%117,615 35 300 81 12 1.25 0.0% 7.8% 4.2% 5.3%396 462 3,510 3,370 13%0.08% 0.68% 0.06% San MateoCCG1_i Redwood City SUB-AREA: CCAG/Redwood City: DowntownMixed-Use Corridor 3,876 4,090 214 5.2%144,544 31 500 50 12 1.25 8.4% 4.7% 6.0% 5.7%663 687 4,753 4,563 16%0.13% 1.14% 0.10% San MateoCCG1_j Menlo Park SUB-AREA: CCAG/Menlo Park: El Camino Real Corridor and DowntownMixed-Use Corridor 1,837 1,927 90 4.7%74,768 34 200 13 12 1.25 5.8%10.0%5.2% 7.7%270 272 2,197 2,109 14%0.05% 0.46% 0.04% San MateoCCG1_k San Mateo County UnincorporatedSUB-AREA: CCAG/San Mateo County (Unincorporated Colma)Mixed-Use Corridor 169 247 78 31.6%24,261 22 25 68 11 1.10 1.2% 5.5% 0.0% 2.9%28 95 275 264 11%0.01% 0.05% 0.00% San MateoCCG1_l San Mateo County UnincorporatedSUB-AREA: CCAG/San Mateo County (North Fair Oaks)Mixed-Use Corridor 2,402 2,541 139 5.5%143,177 41 3,024 37 13 1.20 1.2% 5.5% 0.0% 2.9%3,747 3,634 6,288 6,036 147%0.74% 6.43% 0.57% San MateoCCG1_m San Mateo County UnincorporatedSUB-AREA: CCAG/San Mateo CountyMixed-Use Corridor 44 47 3 6.4%17,900 30 26 1 13 1.20 1.2% 5.5% 0.0% 2.9%33 32 80 76 69%0.01% 0.06% 0.00% San MateoDAL1 Daly City Mission Boulevard Mixed-Use Corridor 289 299 10 3.3%18,359 35 169 -2 12 1.25 2.6%-1.2%-10.4%-3.6%204 194 503 483 68%0.04% 0.35% 0.03% San MateoDAL1_CCG1_aDaly City SUB-AREA: CCAG/Daly City: Mission BoulevardMixed-Use Corridor 1,776 1,966 190 9.7%117,980 30 906 111 12 1.25 2.6%-1.2%-10.4%-3.6%876 952 2,842 2,728 45%0.17% 1.50% 0.13% San MateoDAL2 Daly City Bayshore Transit Town Center 1,550 1,590 40 2.5%121,165 38 2,124 -24 33 1.00 2.6%-1.2%-10.4%-3.6%2,053 1,947 3,643 3,497 129%0.40% 3.53% 0.31% San MateoEPA1 East Palo Alto Ravenswood Transit Town Center 972 1,028 56 5.4%73,192 49 927 15 24 1.00 -7.0% -2.5% -7.8% -5.0%883 863 1,911 1,835 86%0.17% 1.52% 0.13% San MateoMEN1 Menlo Park El Camino Real Corridor and DowntownTransit Town Center 196 213 17 8.0%7,568 32 135 8 12 1.25 5.8%10.0%5.2% 7.7%182 183 395 379 86%0.04% 0.31% 0.03% San MateoMEN1_CCG1_jMenlo Park SUB-AREA: CCAG/Menlo Park: El Camino Real Corridor and DowntownTransit Town Center 812 919 107 11.6%32,455 33 565 70 12 1.25 5.8%10.0%5.2% 7.7%764 803 1,683 1,615 83%0.15% 1.31% 0.12% San MateoMIL1 Millbrae Transit Station Area Mixed-Use Corridor 55 57 2 3.5%3,376 37 69 0 13 1.20 -2.4%7.9%-5.2%1.8%84 80 141 135 148%0.02% 0.14% 0.01% San MateoMIL1_CCG1_eMillbrae SUB-AREA: CCAG/Millbrae: Transit Station AreaMixed-Use Corridor 214 225 11 4.9%74,253 37 1,431 2 13 1.20 -2.4%7.9%-5.2%1.8%1,462 1,406 1,687 1,620 650%0.29% 2.51% 0.22% San MateoRWC1 Redwood City Downtown City Center 307 336 29 8.6%62,222 17 3,127 16 11 1.10 8.4% 4.7% 6.0% 5.7%3,316 3,199 3,652 3,506 987%0.65% 5.70% 0.50% San MateoRWC1_CCG1_iRedwood City SUB-AREA: CCAG/Redwood City: DowntownCity Center 680 725 45 6.2%27,521 19 1,105 16 11 1.10 8.4% 4.7% 6.0% 5.7%1,290 1,254 2,015 1,934 178%0.25% 2.21% 0.20% San MateoRWC2 Redwood City Veterans Corridor Mixed-Use Corridor 732 772 40 5.2%67,569 32 1,036 9 32 1.00 8.4% 4.7% 6.0% 5.7%1,099 1,064 1,871 1,796 142%0.22% 1.89% 0.17% San MateoSBR1 San Bruno Transit Corridors Mixed-Use Corridor 1,082 1,135 53 4.7%56,873 36 1,074 8 12 1.25 -0.8% -0.2% -7.4% -2.7%1,310 1,265 2,445 2,347 115%0.26% 2.25% 0.20% San MateoSBR1_CCG1_dSan Bruno SUB-AREA: CCAG/San Bruno: Transit CorridorsMixed-Use Corridor 3,053 3,192 139 4.4%139,830 28 1,740 11 12 1.25 -0.8% -0.2% -7.4% -2.7%2,122 2,049 5,314 5,102 66%0.42% 3.64% 0.32% San MateoSCA1_CCG1_hSan Carlos SUB-AREA: CCAG/San Carlos: Railroad CorridorTransit Town Center 439 460 21 4.6%28,622 38 629 3 13 1.20 0.0% 7.8% 4.2% 5.3%797 768 1,257 1,207 173%0.16% 1.37% 0.12% San MateoSFO9_a Brisbane San Francisco/San Mateo Bi-County Area (with San Francisco)Suburban Center 0 0 0 0.0%31,422 41 3,991 0 13 1.20 -2.8%1.5% 3.6% 1.5%4,874 4,679 4,874 4,679 #DIV/0!0.96% 8.37% 0.74% San MateoSMA1 San Mateo Downtown City Center 93 97 4 4.1%7,503 27 143 0 12 1.25 8.6% 3.2%-5.0%1.3%182 175 279 268 188%0.04% 0.31% 0.03% San MateoSMA1_CCG1_fSan Mateo SUB-AREA: CCAG/San Mateo: DowntownCity Center 408 441 33 7.5%22,786 26 375 15 12 1.25 8.6% 3.2%-5.0%1.3%476 473 917 881 108%0.09% 0.82% 0.07% San MateoSMA2 San Mateo Rail Corridor Transit Neighborhood 138 144 6 4.2%17,029 29 551 0 12 1.25 8.6% 3.2%-5.0%1.3%699 672 843 810 486%0.14% 1.20% 0.11% San MateoSMA2_CCG1_fSan Mateo SUB-AREA: CCAG/San Mateo: Rail CorridorTransit Neighborhood 358 372 14 3.8%141,140 30 4,543 -1 12 1.25 8.6% 3.2%-5.0%1.3%4,619 4,433 4,991 4,791 1242%0.91% 7.93% 0.70% San MateoSMA3_CCG1_fSan Mateo El Camino Mixed-Use Corridor 835 880 45 5.1%49,120 27 977 10 12 1.25 8.6% 3.2%-5.0%1.3%1,241 1,202 2,121 2,037 141%0.24% 2.13% 0.19% San MateoSMC1 San Mateo County UnincorporatedMidcoast Rural Corridor 3,674 3,898 224 5.7%222,274 62 1,000 68 34 0.75 1.2% 5.5% 0.0% 2.9%1,032 1,059 4,930 4,733 26%0.20% 1.77% 0.16% San MateoSSF1 South San FranciscoDowntown Transit Town Center 1,509 1,587 78 4.9%87,629 27 3,090 15 12 1.25 7.0% 0.1% 7.2% 3.6%3,211 3,097 4,798 4,606 202%0.63% 5.51% 0.49% 5119.xlsPDAs 3 PDAs draft 4/9/2012 2 3 4 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z AA AB AC AD AE AF AG AH LOCAL Absorption PDA TOTALS County Key Juris KeyName KeyPlaceType HH HU VacHU Vacancy Rate 2040 VMT 2040 VMT/ household LOCAL Housing Unit Growth (2010-2040) HH Vacancy Absorption Transit- VMT Tier Transit- VMT Tier Adj. Rate JOBS Factor Adj. Rate Home Value Factor Adj. Rate In- Commute Factor Adj. Rate Combined Factor Adj. Rate HU Growth 2010-2040 HH Growth 2010-2040 HU 2040 HH 2040 % change Housing Units 2010-2040 % of PDA Housing Units Growth % of County Housing Units Growth % of Regional Housing Units Growth 2010 Adjustment FactorsMTC Transp. Model 146 147 148 149 150 151152 153 154 155 156 157158 159 160 161 162 163164 165 166 167 168 169 170 171 172 173 174 175 176177 178 179 180 181 182183 184 185 186 187 188189 190 191 192 193 194 195 196 197 198 199 200 201202 203 204205 206 207 208 209 210211 212 213 214 215 216217 218 219 220 221 222 223 Santa ClaraCAM1 Campbell Central Redevelopment AreaTransit Neighborhood 1,257 1,344 87 6.5%80,567 33 1,169 33 12 1.25 3.4% 1.1% 7.0% 3.5%1,516 1,489 2,860 2,746 113%0.30% 0.75% 0.23% Santa ClaraGIL1 Gilroy Downtown Transit Town Center 882 978 96 9.8%58,916 31 1,576 57 12 1.25 1.0%-1.1%3.0% 0.6%1,988 1,965 2,966 2,847 203%0.39% 0.99% 0.30% Santa ClaraMOH1 Morgan Hill Downtown Transit Town Center 508 568 60 10.6%41,093 37 1,192 37 13 1.20 0.8% 1.4% 3.2% 1.9%1,462 1,441 2,030 1,949 257%0.29% 0.73% 0.22% Santa ClaraMPT1 Milpitas Transit Area Suburban Center 730 793 40 5.0%167,719 31 7,100 31 12 1.25 7.2%-1.2%5.8% 2.5%7,303 7,042 8,096 7,772 921%1.43% 3.63% 1.11% Santa ClaraMVW1 Mountain View Whisman Station Transit Neighborhood 703 665 19 2.9%38,150 36 984 -65 13 1.20 8.2% 2.9% 8.0% 5.5%1,041 935 1,706 1,638 157%0.20% 0.52% 0.16% Santa ClaraMVW2 Mountain View Downtown Transit Town Center 4,780 5,240 447 8.5%188,637 32 900 251 12 1.25 8.2% 2.9% 8.0% 5.5%1,190 1,393 6,430 6,173 23%0.23% 0.59% 0.18% Santa ClaraMVW3 Mountain View San Antonio Center Transit Town Center 3,415 3,592 177 4.9%127,056 28 2,690 33 12 1.25 8.2% 2.9% 8.0% 5.5%2,846 2,766 6,438 6,181 79%0.56% 1.41% 0.43% Santa ClaraMVW4 Mountain View El Camino Real CorridorMixed-Use Corridor 8,738 9,192 454 4.9%332,205 30 1,590 86 22 1.20 8.2% 2.9% 8.0% 5.5%2,019 2,025 11,211 10,763 22%0.40% 1.00% 0.31% Santa ClaraMVW5 Mountain View East Whisman Employment Center 675 715 28 3.9%58,849 34 0 12 12 1.25 8.2% 2.9% 8.0% 5.5%0 12 715 686 0%0.00% 0.00% 0.00% Santa ClaraMVW6 Mountain View North Bayshore Suburban Center 345 364 19 5.2%30,467 33 1,110 4 12 1.25 8.2% 2.9% 8.0% 5.5%1,468 1,414 1,832 1,759 403%0.29% 0.73% 0.22% Santa ClaraPAL1 Palo Alto California Avenue Transit Neighborhood 694 799 45 5.6%44,674 32 800 73 12 1.25 9.6%10.0%9.2% 9.7%880 918 1,679 1,612 110%0.17% 0.44% 0.13% Santa ClaraSCL1 Santa Clara El Camino Real Focus AreaMixed-Use Corridor 1,646 1,840 194 10.5%71,164 30 1,000 121 22 1.20 10.0%-0.3%9.0% 4.5%1,258 1,328 3,098 2,974 68%0.25% 0.63% 0.19% Santa ClaraSCL2 Santa Clara Santa Clara Station Focus AreaCity Center 450 476 26 5.5%20,910 27 3,350 7 12 1.25 10.0%-0.3%9.0% 4.5%3,511 3,378 3,987 3,828 738%0.69% 1.75% 0.53% Santa ClaraSJO1 San Jose Greater Downtown Regional Center 3,720 4,593 927 20.2%88,433 14 6,500 690 11 1.10 10.4%-0.4%-10.6%-2.0%7,028 7,437 11,621 11,156 153%1.38% 3.49% 1.06% Santa ClaraSJO2 San Jose Cottle Transit VillageSuburban Center 1,787 1,907 120 6.3%138,674 482 0 0 12 1.25 10.4%-0.4%-10.6%-2.0%8,735 8,385 8,735 8,385 458%1.72% 4.34% 1.32% Santa ClaraSJO3 San Jose North San Jose Regional Center 1,061 1,093 32 2.9%62,515 1 32,000 30 11 1.10 10.4%-0.4%-10.6%-2.0%31,456 30,227 42,337 40,643 2878%6.18%15.63%4.77% Santa ClaraSJO4 San Jose Downtown "Frame"City Center 2,337 2,430 93 3.8%86,060 3 10,400 562 21 1.25 10.4%-0.4%-10.6%-2.0%10,223 10,376 28,345 27,211 421%2.01% 5.08% 1.55% Santa ClaraSJO5 San Jose Berryessa Station Transit Neighborhood 4,633 4,849 216 4.5%192,059 43 4,800 -40 13 1.20 10.4%-0.4%-10.6%-2.0%4,718 4,489 6,599 6,335 97%0.93% 2.35% 0.71% Santa ClaraSJO6 San Jose Communications HillTransit Town Center 650 683 33 4.8%58,549 5 2,800 -8 12 1.25 5.0% 2.6% 0.0% 2.1%3,441 3,295 10,248 9,838 504%0.68% 1.71% 0.52% Santa ClaraSJO7 San Jose West San Carlos and Southwest Expressway CorridorsMixed-Use Corridor 1,672 1,783 111 6.2%67,881 4 8,500 412 22 1.20 5.0% 2.6% 0.0% 2.1%8,525 8,596 19,674 18,887 478%1.67% 4.24% 1.29% Santa ClaraSJO8 San Jose East Santa Clara/Alum Rock CorridorMixed-Use Corridor 478 487 9 1.8%19,403 1 4,000 137 22 1.20 5.0% 2.6% 0.0% 2.1%3,934 3,913 11,109 10,664 808%0.77% 1.95% 0.60% Santa ClaraSJO9 San Jose Stevens Creek TOD CorridorMixed-Use Corridor 614 641 27 4.2%47,068 13 3,860 16 22 1.20 10.4%-0.4%-10.6%-2.0%3,794 3,659 6,418 6,162 592%0.75% 1.89% 0.57% Santa ClaraSJO10 San Jose Oakridge/Almaden Plaza Urban VillageSuburban Center 818 858 40 4.7%117,505 27 7,303 44 12 1.25 10.4%-0.4%-10.6%-2.0%7,179 6,935 9,086 8,722 837%1.41% 3.57% 1.09% Santa ClaraSJO11 San Jose Capitol/Tully/King Urban VillagesSuburban Center 795 849 54 6.4%38,672 24 2,250 -12 33 1.00 10.4%-0.4%-10.6%-2.0%2,212 2,112 3,305 3,173 261%0.43% 1.10% 0.34% Santa ClaraSJO12 San Jose Saratoga TOD CorridorMixed-Use Corridor 0 0 0 0.0%9,765 4 1,115 -4 32 1.00 10.4%-0.4%-10.6%-2.0%1,096 1,048 3,526 3,385 #DIV/0!0.22% 0.54% 0.17% Santa ClaraSJO13 San Jose Winchester Boulevard TOD CorridorMixed-Use Corridor 140 146 6 4.1%8,672 1 2,000 22 32 1.00 10.4%-0.4%-10.6%-2.0%1,966 1,909 6,815 6,542 1347%0.39% 0.98% 0.30% Santa ClaraSJO14 San Jose Bascom TOD CorridorMixed-Use Corridor 184 196 3 1.5%80,528 45 1,560 6 32 1.00 5.0% 2.6% 0.0% 2.1%1,551 1,495 2,234 2,145 791%0.30% 0.77% 0.24% Santa ClaraSJO15 San Jose Bascom Urban VillageMixed-Use Corridor 10,416 10,881 465 4.3%1,056,626 577 805 40 33 1.00 5.0% 2.6% 0.0% 2.1%792 800 2,575 2,472 7%0.16% 0.39% 0.12% Santa ClaraSJO16 San Jose Camden Urban VillageMixed-Use Corridor 16,835 18,122 1,147 6.3%658,719 1,326 1,000 -10 33 1.00 5.0% 2.6% 0.0% 2.1%988 938 1,475 1,416 5%0.19% 0.49% 0.15% Santa ClaraSJO17 San Jose Blossom Hill/Snell Urban VillageMixed-Use Corridor 1,846 1,881 35 1.9%175,324 140 1,083 1 13 1.20 10.4%-0.4%-10.6%-2.0%1,065 1,023 1,706 1,637 57%0.21% 0.53% 0.16% Santa ClaraSJO18 San Jose Capitol Corridor Urban VillagesMixed-Use Corridor 6,543 6,807 264 3.9%293,513 80 6,245 6 12 1.25 5.0% 2.6% 0.0% 2.1%6,139 5,899 6,997 6,717 90%1.21% 3.05% 0.93% Santa ClaraSJO19 San Jose Westgate/El Paseo Urban VillageSuburban Center 10,291 11,149 833 7.5%407,565 390 2,500 20 33 1.00 10.4%-0.4%-10.6%-2.0%2,457 2,379 3,306 3,174 22%0.48% 1.22% 0.37% Santa ClaraSJO20 San Jose Old Edenvale Employment AreaEmployment Center 6,751 7,175 424 5.9%394,710 1,547 0 0 12 1.25 10.4%-0.4%-10.6%-2.0%0 0 146 140 0%0.00% 0.00% 0.00% Santa ClaraSJO21 San Jose International Business Park AreaEmployment Center 2,503 2,624 121 4.6%122,520 54 0 4 12 1.25 10.4%-0.4%-10.6%-2.0%0 4 196 188 0%0.00% 0.00% 0.00% Santa ClaraSUN1 Sunnyvale Lawrence Station Transit VillageTransit Neighborhood 1,557 1,657 100 6.0%73,236 32 2,400 34 12 1.25 9.0% 1.3% 6.2% 4.2%2,509 2,443 4,166 3,999 151%0.49% 1.25% 0.38% Santa ClaraSUN2 Sunnyvale Downtown & Caltrain StationTransit Town Center 1,816 1,835 107 5.8%78,922 31 1,500 -55 12 1.25 9.0% 1.3% 6.2% 4.2%1,960 1,827 3,795 3,643 107%0.39% 0.97% 0.30% Santa ClaraSUN3 Sunnyvale El Camino Real CorridorMixed-Use Corridor 10,348 10,993 645 5.9%424,525 31 4,200 205 22 1.20 5.0% 2.6% 0.0% 2.1%4,391 4,421 15,384 14,769 40%0.86% 2.18% 0.67% Santa ClaraSUN4 Sunnyvale Moffett Park Employment Center 0 15 0 0.0%49 53 0 14 14 1.15 9.0% 1.3% 6.2% 4.2%0 14 15 14 0%0.00% 0.00% 0.00% Santa ClaraSUN5 Sunnyvale Peery Park Employment Center 129 125 15 12.0%15,256 34 0 -9 32 1.00 9.0% 1.3% 6.2% 4.2%0 -9 125 120 0%0.00% 0.00% 0.00% Santa ClaraSUN6 Sunnyvale East Sunnyvale ITR Urban Neighborhood 742 1,019 70 6.9%87,271 35 3,110 236 32 1.00 9.0% 1.3% 6.2% 4.2%3,251 3,357 4,270 4,100 319%0.64% 1.62% 0.49% Santa ClaraSUN7 Sunnyvale Reamwood Light Rail StationEmployment Center 0 0 0 0.0%102 23 429 0 11 1.10 9.0% 1.3% 6.2% 4.2%493 474 493 474 #DIV/0!0.10% 0.25% 0.07% Santa ClaraSUN8 Sunnyvale Tasman Station ITR Mixed-Use Corridor 1,389 1,439 50 3.5%48,440 25 1,730 -8 11 1.10 9.0% 1.3% 6.2% 4.2%1,809 1,729 3,248 3,118 126%0.36% 0.90% 0.27% Santa ClaraVTA1_a Campbell VTA City Cores, Corridors & Station AreasMixed-Use Corridor 749 799 50 6.3%53,097 36 0 18 12 1.25 3.4% 1.1% 7.0% 3.5%1,482 1,441 2,281 2,190 186%0.29% 0.74% 0.22% Santa ClaraVTA1_b Cupertino VTA City Cores, Corridors & Station AreasMixed-Use Corridor 2,980 3,159 179 5.7%123,739 39 0 53 13 1.20 3.0% 9.3% 6.8% 7.4%2,792 2,733 5,951 5,713 88%0.55% 1.39% 0.42% Santa ClaraVTA1_c Gilroy VTA City Cores, Corridors & Station AreasMixed-Use Corridor 1,732 1,880 148 7.9%149,488 33 0 73 12 1.25 1.0%-1.1%3.0% 0.6%0 73 1,880 1,805 0%0.00% 0.00% 0.00% Santa ClaraVTA1_d Los Altos VTA City Cores, Corridors & Station AreasMixed-Use Corridor 702 750 48 6.4%38,442 36 0 18 13 1.20 -0.2%10.0%1.4% 5.4%4,662 4,494 5,412 5,196 622%0.92% 2.32% 0.71% Santa ClaraVTA1_e Los Gatos VTA City Cores, Corridors & Station AreasMixed-Use Corridor 1,327 1,410 83 5.9%58,540 42 0 27 13 1.20 2.8%10.0%6.6% 7.7%0 27 1,410 1,354 0%0.00% 0.00% 0.00% Santa ClaraVTA1_f Milpitas VTA City Cores, Corridors & Station AreasMixed-Use Corridor 453 464 12 2.6%31,564 33 0 -7 12 1.25 7.2%-1.2%5.8% 2.5%254 237 718 690 55%0.05% 0.13% 0.04% Santa ClaraVTA1_h Palo Alto VTA City Cores, Corridors & Station AreasMixed-Use Corridor 5,923 6,378 385 6.0%257,252 29 0 200 12 1.25 9.6%10.0%9.2% 9.7%5,943 5,905 12,321 11,828 93%1.17% 2.95% 0.90% Santa ClaraVTA1_i San Jose VTA City Cores, Corridors & Station AreasMixed-Use Corridor 24,960 25,920 1,039 4.0%1,299,920 32 0 -77 12 1.25 5.0% 2.6% 0.0% 2.1%0 -77 25,920 24,883 0%0.00% 0.00% 0.00% Santa ClaraVTA1_j Santa Clara VTA City Cores, Corridors & Station AreasMixed-Use Corridor 1,901 2,080 112 5.4%165,507 36 0 96 13 1.20 10.0%-0.3%9.0% 4.5%1,289 1,334 3,369 3,235 62%0.25% 0.64% 0.20% Santa ClaraVTA1_k Santa Clara County UnincorporatedVTA City Cores, Corridors & Station AreasMixed-Use Corridor 151 165 14 8.5%8,283 56 0 7 14 1.15 5.0% 2.6% 0.0% 2.1%0 7 165 158 0%0.00% 0.00% 0.00% Santa ClaraVTA1_l Saratoga VTA City Cores, Corridors & Station AreasMixed-Use Corridor 168 183 15 8.2%9,548 58 0 8 14 1.15 -1.2%10.0%0.2% 4.9%100 104 283 272 55%0.02% 0.05% 0.02% Santa ClaraVTA1_m Sunnyvale VTA City Cores, Corridors & Station AreasMixed-Use Corridor 4,800 5,194 228 4.4%186,331 30 0 186 12 1.25 9.0% 1.3% 6.2% 4.2%825 978 6,019 5,778 16%0.16% 0.41% 0.12% Solano BEN1 Benicia Downtown Transit Neighborhood 527 604 77 12.7%26,572 48 979 53 34 0.75 -0.6%-2.7%-3.0%-2.5%957 972 1,561 1,499 159%0.19%3.22%0.15% Solano BEN2 Benicia Northern Gateway Employment Center 2 2 0 0.0%224 61 0 0 34 0.75 -0.6% -2.7% -3.0% -2.5%0 0 2 2 0%0.00% 0.00% 0.00% Solano DIX1 Dixon Downtown Dixon Rural Town Center 686 737 51 6.9%37,787 55 275 22 34 0.75 -6.2% -6.6% -3.4% -5.5%261 272 998 958 35%0.05% 0.88% 0.04% Solano FAI1 Fairfield Downtown South (Jefferson Street)Suburban Center 598 679 81 11.9%24,005 31 329 54 24 1.00 -2.2%4.5% 0.0% 1.9%432 468 1,111 1,066 64%0.08% 1.45% 0.07% Solano FAI2 Fairfield Fairfield-Vacaville Train StationTransit Town Center 89 408 319 78.2%22,809 66 6,392 303 24 1.00 -2.2%4.5% 0.0% 1.9%6,240 6,293 6,648 6,382 1529%1.23%21.01%0.95% Solano FAI3 Fairfield North Texas Street CoreMixed-Use Corridor 1,595 1,769 174 9.8%60,107 37 1,815 103 33 1.00 7.4%-6.7% -4.6% -3.6%1,754 1,787 3,523 3,382 99%0.34% 5.91% 0.27% Solano FAI4 Fairfield West Texas Street GatewayMixed-Use Corridor 1,017 1,115 98 8.8%50,891 49 2,600 53 34 0.75 7.4%-6.7% -4.6% -3.6%2,513 2,466 3,628 3,483 225%0.49% 8.46% 0.38% Solano RIV1 Rio Vista Downtown Rio Vista Rural Town Center 302 359 57 15.9%21,696 57 400 43 34 0.75 -9.4% -8.9% -2.4% -6.8%374 402 733 704 104%0.07% 1.26% 0.06% Solano SUI1 Suisun City Downtown & WaterfrontTransit Town Center 1,094 1,184 90 7.6%87,067 67 1,166 43 24 1.00 -7.6% -8.0% -9.2% -8.3%1,073 1,072 2,257 2,166 91%0.21% 3.61% 0.16% Solano VAC1 Vacaville Downtown Transit Town Center 223 246 23 9.3%16,221 54 754 13 34 0.75 3.8%-7.0% -8.6% -5.7%713 698 959 921 290%0.14% 2.40% 0.11% Solano VAC2 Vacaville Allison Area Suburban Center 553 606 53 8.7%24,925 44 107 29 33 1.00 3.8%-7.0% -8.6% -5.7%101 126 707 679 17%0.02% 0.34% 0.02% Solano VAL1 Vallejo Waterfront & DowntownSuburban Center 977 1,128 151 13.4%35,984 24 772 106 31 1.20 4.6%-7.5%-10.2%-6.4%870 941 1,998 1,918 77%0.17% 2.93% 0.13% Sonoma CLO1 Cloverdale Downtown/SMART Transit AreaTransit Town Center 1,039 1,148 109 9.5%52,380 51 760 63 24 1.00 7.8%-3.6%0.0%-0.5%744 777 1,892 1,816 65%0.15% 2.00% 0.11% Sonoma COT1 Cotati Downtown and Cotati DepotTransit Town Center 832 885 53 6.0%42,287 52 433 18 24 1.00 -7.8% -5.3% -2.6% -4.8%413 415 1,298 1,247 47%0.08% 1.11% 0.06% Sonoma PET1 Petaluma Central, Turning Basin/Lower ReachSuburban Center 749 811 62 7.6%35,740 41 1,615 30 23 1.15 4.2%-2.5% -5.8% -2.5%1,816 1,773 2,627 2,522 224%0.36% 4.89% 0.28% Sonoma ROH1 Rohnert Park Sonoma Mountain VillageSuburban Center 195 202 7 3.5%10,217 51 2,198 -1 24 1.00 -1.4% -6.5% -7.6% -6.0%2,072 1,989 2,274 2,184 1026%0.41% 5.58% 0.31% Sonoma ROH2 Rohnert Park Central Rohnert ParkTransit Town Center 1,299 1,361 62 4.6%48,500 38 1,052 8 33 1.00 -1.4% -6.5% -7.6% -6.0%992 960 2,353 2,259 73%0.19% 2.67% 0.15% Sonoma SCO1 Sonoma County UnincorporatedForestville Rural Town Center 891 988 97 9.8%56,900 63 416 57 34 0.75 7.8%-3.6%0.0%-0.5%415 456 1,403 1,347 42%0.08% 1.12% 0.06% Sonoma SCO2 Sonoma County UnincorporatedGraton Rural Town Center 533 566 33 5.8%35,285 67 453 10 34 0.75 7.8%-3.6%0.0%-0.5%452 444 1,018 977 80%0.09% 1.22% 0.07% Sonoma SCO3 Sonoma County UnincorporatedGuerneville Rural Town Center 373 455 82 18.0%21,293 56 425 64 34 0.75 7.8%-3.6%0.0%-0.5%424 471 879 844 93%0.08% 1.14% 0.06% Sonoma SCO4 Sonoma County UnincorporatedPenngrove Urban Service AreaRural Town Center 415 442 27 6.1%19,601 48 388 9 34 0.75 7.8%-3.6%0.0%-0.5%387 381 829 796 88%0.08% 1.04% 0.06% 5119.xlsPDAs 4 PDAs draft 4/9/2012 2 3 4 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z AA AB AC AD AE AF AG AH LOCAL Absorption PDA TOTALS County Key Juris KeyName KeyPlaceType HH HU VacHU Vacancy Rate 2040 VMT 2040 VMT/ household LOCAL Housing Unit Growth (2010-2040) HH Vacancy Absorption Transit- VMT Tier Transit- VMT Tier Adj. Rate JOBS Factor Adj. Rate Home Value Factor Adj. Rate In- Commute Factor Adj. Rate Combined Factor Adj. Rate HU Growth 2010-2040 HH Growth 2010-2040 HU 2040 HH 2040 % change Housing Units 2010-2040 % of PDA Housing Units Growth % of County Housing Units Growth % of Regional Housing Units Growth 2010 Adjustment FactorsMTC Transp. Model 224 225 226 227 228 229230 231 232 233 234 235236 237 238 239 240 241 242 243 244 245 246 247 248249 250 251 252 253 254255 256 257 258 259 260 261 262 263 264 265 266 Sonoma SCO5 Sonoma County UnincorporatedThe Springs Rural Corridor 4,700 5,107 407 8.0%260,695 56 1,125 203 34 0.75 7.8%-3.6%0.0%-0.5%1,123 1,280 6,230 5,980 22%0.22% 3.02% 0.17% Sonoma SEB1 Sebastopol Nexus Area Rural Town Center 2,356 2,505 149 5.9%111,173 48 400 49 34 0.75 7.8%-3.6%0.0%-0.5%398 431 2,903 2,787 16%0.08% 1.07% 0.06% Sonoma SRO1 Santa Rosa SUB-AREA: Downtown Station AreaCity Center 895 955 60 6.3%37,419 36 813 22 23 1.15 7.8%-3.6%0.0%-0.5%938 923 1,893 1,818 98%0.18% 2.53% 0.14% Sonoma SRO1_SRO2Santa Rosa SUB-AREA: Downtown Station AreaCity Center 876 946 70 7.4%28,478 21 1,109 32 21 1.25 9.4%-4.5%2.4% 0.1%1,392 1,368 2,338 2,244 147%0.27% 3.75% 0.21% Sonoma SRO1_SRO2_SRO3Santa Rosa SUB-AREA: Downtown Station AreaCity Center 123 135 12 8.9%7,188 21 1,379 7 21 1.25 9.4%-4.5%2.4% 0.1%1,731 1,668 1,866 1,791 1282%0.34% 4.66% 0.26% Sonoma SRO1_SRO3Santa Rosa SUB-AREA: Downtown Station AreaCity Center 182 198 16 8.1%10,806 33 2,452 8 23 1.15 7.8%-3.6%0.0%-0.5%2,939 2,829 3,137 3,011 1484%0.58% 7.91% 0.45% Sonoma SRO2 Santa Rosa SUB-AREA: Mendocino Avenue/Santa Rosa Avenue CorridorMixed-Use Corridor 5,807 6,233 426 6.8%251,487 42 275 177 23 1.15 7.8%-3.6%0.0%-0.5%317 481 6,550 6,288 5%0.06% 0.85% 0.05% Sonoma SRO2_SRO3Santa Rosa SUB-AREA: Mendocino Avenue/Santa Rosa Avenue CorridorMixed-Use Corridor 43 47 4 8.5%2,042 34 39 2 22 1.20 9.4%-4.5%2.4% 0.1%47 47 94 90 100%0.01% 0.13% 0.01% Sonoma SRO3 Santa Rosa SUB-AREA: Sebastopol Road CorridorMixed-Use Corridor 2,398 2,611 213 8.2%117,689 49 1,085 109 23 1.15 7.8%-3.6%0.0%-0.5%1,121 1,185 3,732 3,583 43%0.22% 3.02% 0.17% Sonoma SRO4 Santa Rosa North Santa Rosa StationSuburban Center 3,957 4,242 285 6.7%137,455 35 2,390 115 24 1.00 7.8%-3.6%0.0%-0.5%2,879 2,880 7,121 6,837 68%0.57% 7.75% 0.44% Sonoma SRO5 Santa Rosa Roseland Area Transit Neighborhood 3,397 3,569 172 4.8%148,558 44 3,000 29 33 1.00 7.8%-3.6%0.0%-0.5%4,385 4,775 7,954 8,172 123%0.86%11.80%0.66% Sonoma WIN1 Windsor Redevelopment AreaSuburban Center 1,365 1,434 69 4.8%78,136 56 1,300 12 24 1.00 -3.6% -3.8% -6.8% -4.8%1,242 1,204 2,676 2,569 87%0.24% 3.34% 0.19% 689,026 751,195 60,944 8.1%29,030,972 26 449,402 32,121 508,987 521,284 1,260,182 1,210,311 68%100%77%77% LOCAL Absorption PDA TOTALS County Key Juris KeyName KeyPlaceType HH HU VacHU Vacancy Rate 2040 VMT 2040 VMT/ household LOCAL Housing Unit Growth (2010-2040) HH Vacancy Absorption Transit- VMT Tier Transit- VMT Tier Adj. Rate JOBS Factor Adj. Rate Home Value Factor Adj. Rate In- Commute Factor Adj. Rate Combined Factor Adj. Rate HU Growth 2010-2040 HH Growth 2010-2040 HU 2040 HH 2040 % change Housing Units 2010-2040 % of PDA Housing Units Growth % of County Housing Units Growth % of Regional Housing Units Growth Regional Center 58,156 63,957 5,753 9.0%1,642,997 8 68,623 9,385 72,754 79,229 202,290 194,199 114% 14%11% City Center 36,033 39,282 3,260 8.3%1,447,180 15 49,042 2,294 53,855 53,995 110,888 106,453 137% 11%8% Urban Neighborhood 70,834 77,495 6,454 8.3%2,894,920 19 42,383 4,666 46,005 48,830 139,994 134,394 59%9%7% Transit Neighborhood 49,502 53,027 3,530 6.7%2,605,656 35 56,445 1,213 59,393 58,766 107,012 103,267 112% 12%9% Transit Town Center 64,772 71,436 6,846 9.6%3,531,599 28 71,485 3,800 79,070 79,707 158,578 152,234 111% 16%12% Mixed-Use Corridor 350,706 381,877 30,589 8.0%13,224,712 34 91,060 8,807 114,631 118,853 414,391 397,815 30% 23%17% Suburban Center 33,905 37,180 2,698 7.3%2,259,788 53 64,019 1,359 76,909 75,192 103,175 99,048 207% 15%12% Rural Town Center 5,686 6,205 519 8.4%308,542 54 3,275 271 3,212 3,355 9,417 9,041 52%1%0% Rural Corridor 8,976 9,648 672 7.0%512,392 58 2,583 286 2,598 2,781 12,246 11,757 27%1%0% Employment Center 10,456 11,088 623 5.6%603,188 117 489 40 560 577 2,191 2,103 5%0%0% 689,026 751,195 60,944 8.1%29,030,972 26 449,402 32,121 508,987 521,284 1,260,182 1,210,311 68%100%77% LOCAL Absorption PDA TOTALS County Key Juris KeyName KeyPlaceType HH HU VacHU Vacancy Rate 2040 VMT 2040 VMT/ household LOCAL Housing Unit Growth (2010-2040) HH Vacancy Absorption Transit- VMT Tier Transit- VMT Tier Adj. Rate JOBS Factor Adj. Rate Home Value Factor Adj. Rate In- Commute Factor Adj. Rate Combined Factor Adj. Rate HU Growth 2010-2040 HH Growth 2010-2040 HU 2040 HH 2040 % change Housing Units 2010-2040 % of PDA Housing Units Growth % of County Housing Units Growth % of Regional Housing Units Growth Alameda 186,640 206,191 19,022 9.2%7,104,125 25 103,802 11,303 113,904 120,652 320,095 307,292 55% 22% 74% 17% Contra Costa 45,162 49,882 4,646 9.3%2,362,552 35 55,227 2,725 57,812 58,224 107,694 103,386 116% 11% 68%9% Marin 8,608 9,174 566 6.2%273,961 31 2,338 199 2,922 3,004 12,096 11,612 32%1% 36%0% Napa 1,130 1,233 103 8.4%53,342 46 2,656 54 2,525 2,477 3,758 3,607 205%0% 45%0% San Francisco 184,043 203,458 19,412 9.5%5,421,823 18 70,122 11,277 77,133 85,324 280,591 269,367 38% 15% 96% 12% San Mateo 62,635 66,395 3,654 5.5%3,398,866 33 41,009 1,104 47,632 46,831 114,027 109,466 72%9% 82%7% Santa Clara 160,721 171,185 9,952 5.8%8,494,685 28 135,551 3,617 165,544 162,539 336,729 323,260 97% 33% 82% 25% Solano 7,663 8,837 1,174 13.3%408,287 45 15,589 821 15,287 15,496 24,124 23,159 173%3% 51%2% Sonoma 32,425 34,840 2,415 6.9%1,513,330 45 23,107 1,021 26,229 26,736 61,069 59,161 75%5% 71%4% 689,026 751,195 60,944 8.1%29,030,972 26 449,402 32,121 508,987 521,284 1,260,182 1,210,311 68%100%77%77% MTC Transp. Model Adjustment Factors 2010 2010 Adjustment Factors MTC Transp. Model 5119.xlsPDAs 5 PDAs draft 4/9/2012 12 3 4 5 6 7 8 9 10 11 12 13 1415 16 17 18 19 2021 22 23 24 25 2627 28 29 30 31 32 33 34 35 36 37 38 3940 41 42 43 44 4546 47 48 49 50 5152 53 54 55 56 57 58 59 60 61 62 63 6465 66 67 68 69 7071 72 73 74 75 7677 78 79 80 81 82 83 A B C D E County Key Juris KeyName KeyPlaceType Alameda ALA1 Alameda Naval Air Station Transit Town Center Alameda ALA2 Alameda Northern Waterfront Transit Neighborhood Alameda ALB1 Albany San Pablo Avenue & Solano AvenueMixed-Use Corridor Alameda ALC1 Alameda County UnincorporatedCastro Valley BART Transit Neighborhood Alameda ALC2 Alameda County UnincorporatedEast 14th Street and Mission Boulevard Mixed Use CorridorMixed-Use Corridor Alameda ALC3 Alameda County UnincorporatedHesperian Corridor Transit Neighborhood Alameda ALC4 Alameda County UnincorporatedMeekland Corridor Transit Neighborhood Alameda BER1 Berkeley Adeline Street Mixed-Use Corridor Alameda BER2 Berkeley Downtown City Center Alameda BER3 Berkeley San Pablo Avenue Mixed-Use Corridor Alameda BER3_BER6Berkeley SUB-AREA: San Pablo Avenue / University AvenueMixed-Use Corridor Alameda BER4 Berkeley South Shattuck Mixed-Use Corridor Alameda BER5 Berkeley Telegraph Avenue Mixed-Use Corridor Alameda BER6 Berkeley University Avenue Mixed-Use Corridor Alameda DUB1 Dublin Downtown Specific Plan AreaSuburban Center Alameda DUB2 Dublin Transit Center Suburban Center Alameda DUB3 Dublin Town Center Suburban Center Alameda EME1 Emeryville Mixed-Use Core City Center Alameda FRE1 Fremont Centerville Transit Neighborhood Alameda FRE2 Fremont City Center City Center Alameda FRE3 Fremont Irvington District Transit Town Center Alameda FRE4 Fremont South Fremont/Warm SpringsSuburban Center Alameda HAY1 Hayward The Cannery Transit Neighborhood Alameda HAY2 Hayward Downtown City Center Alameda HAY3_a Hayward South Hayward BARTMixed-Use Corridor Alameda HAY3_b Hayward South Hayward BARTUrban Neighborhood Alameda HAY4 Hayward Mission Corridor Mixed-Use Corridor Alameda LIV1 Livermore Downtown Suburban Center Alameda LIV2 Livermore Vasco Road Station Planning AreaSuburban Center Alameda LIV3 Livermore Isabel Avenue/BART Station Planning AreaSuburban Center Alameda NEW1 Newark Dumbarton Transit Oriented DevelopmentTransit Town Center Alameda NEW2 Newark Old Town MIxed Use AreaTransit Neighborhood Alameda OKD1 Oakland Transit Oriented Development CorridorsMixed-Use Corridor Alameda OKD2 Oakland Coliseum BART Station AreaTransit Town Center Alameda OKD3 Oakland Downtown & Jack London SquareRegional Center Alameda OKD4 Oakland Eastmont Town CenterUrban Neighborhood Alameda OKD5 Oakland Fruitvale & Dimond AreasUrban Neighborhood Alameda OKD6 Oakland MacArthur Transit VillageUrban Neighborhood Alameda OKD7 Oakland West Oakland Transit Town Center Alameda PLE1 Pleasanton Hacienda Suburban Center Alameda SLE1 San Leandro Bay Fair BART Transit VillageTransit Town Center Alameda SLE2 San Leandro East 14th Street Mixed-Use Corridor Alameda SLE3 San Leandro Downtown Transit Oriented DevelopmentCity Center Alameda SLE3_SLE2San Leandro SUB-AREA: Downtown Transit Oriented Development / E14Mixed-Use Corridor Alameda UNI1 Union City Intermodal Station DistrictCity Center Contra CostaANT1 Antioch Hillcrest eBART StationSuburban Center Contra CostaANT2 Antioch Rivertown WaterfrontTransit Town Center Contra CostaCCC1 Contra Costa County UnincorporatedContra Costa CentreMixed-Use Corridor Contra CostaCCC2 Contra Costa County UnincorporatedNorth Richmond Transit Neighborhood Contra CostaCCC3_a Contra Costa County UnincorporatedPittsburg/Bay Point BART StationTransit Neighborhood Contra CostaCCC3_b Pittsburg Pittsburg/Bay Point BART StationTransit Town Center Contra CostaCCC4 Contra Costa County UnincorporatedDowntown El SobranteMixed-Use Corridor Contra CostaCON1_a Concord Community Reuse AreaRegional Center Contra CostaCON1_b Concord Community Reuse AreaTransit Neighborhood Contra CostaCON2 Concord Downtown BART Station PlanningCity Center Contra CostaELC1_a El Cerrito San Pablo Avenue CorridorMixed-Use Corridor Contra CostaELC1_b El Cerrito San Pablo Avenue CorridorMixed-Use Corridor Contra CostaHER1 Hercules Central Hercules Transit Neighborhood Contra CostaHER2 Hercules Waterfront District Transit Town Center Contra CostaLAF1 Lafayette Downtown Transit Town Center Contra CostaMAR1 Martinez Downtown Transit Neighborhood Contra CostaMOR1 Moraga Moraga Center Transit Town Center Contra CostaOAK1 Oakley Employment Area Suburban Center Contra CostaOAK2 Oakley Downtown Transit Town Center Contra CostaOAK3 Oakley Potential Planning AreaTransit Neighborhood Contra CostaORI1 Orinda Downtown Transit Town Center Contra CostaPIN1 Pinole Old Town & San Pablo AvenueMixed-Use Corridor Contra CostaPIN2 Pinole Appian Way CorridorMixed-Use Corridor Contra CostaPIT1 Pittsburg Railroad Avenue eBART StationTransit Town Center Contra CostaPIT2 Pittsburg Downtown Transit Neighborhood Contra CostaPLH1 Pleasant Hill Diablo Valley CollegeTransit Neighborhood Contra CostaPLH2 Pleasant Hill Buskirk Avenue CorridorMixed-Use Corridor Contra CostaRIC1 Richmond Central Richmond & 23rd StreetCity Center Contra CostaRIC2_a Richmond South Richmond Transit Neighborhood Contra CostaRIC2_b Richmond South Richmond Mixed-Use Corridor Contra CostaSAR1 San Ramon City Center Suburban Center Contra CostaSAR2 San Ramon North Camino RamonTransit Town Center Contra CostaSPA1 San Pablo San Pablo Avenue Mixed-Use Corridor Contra CostaSPA2 San Pablo Rumrill Road Employment Center AI AJ AK AL AM % change Households 2010-2040 % of PDA Households Growth % of County Household Growth % of Regional Households Growth 394%0.82% 2.72% 0.61% 81%0.15% 0.51% 0.11% 17%0.06% 0.19% 0.04% 49%0.13% 0.44% 0.10% 31%0.40% 1.31% 0.30% 25%0.13% 0.44% 0.10% 39%0.10% 0.32% 0.07% 46%0.06% 0.18% 0.04% 161%0.79% 2.61% 0.59% 68%0.17% 0.55% 0.12% 66%0.02% 0.07% 0.02% 41%0.02% 0.08% 0.02% 44%0.08% 0.27% 0.06% 41%0.11% 0.36% 0.08% 244%0.37% 1.22% 0.28% 619%0.74% 2.45% 0.55% 162%1.16% 3.84% 0.87% 171%1.15% 3.81% 0.86% 25%0.49% 1.61% 0.36% 44%0.58% 1.91% 0.43% 44%0.58% 1.92% 0.43% 214%0.68% 2.24% 0.50% 224%0.14% 0.47% 0.11% 157%0.63% 2.08% 0.47% 678%0.22% 0.74% 0.17% 165%0.53% 1.73% 0.39% 164%0.39% 1.28% 0.29% 229%0.40% 1.33% 0.30% 5179%0.84% 2.79% 0.63% 776%0.69% 2.29% 0.52% 1719%0.46% 1.50% 0.34% 63%0.07% 0.23% 0.05% 23%2.65% 8.74% 1.97% 65%0.43% 1.42% 0.32% 95%1.94% 6.42% 1.45% 17%0.20% 0.65% 0.15% 40%0.98% 3.25% 0.73% 44%0.68% 2.24% 0.50% 66%1.15% 3.79% 0.86% 279%0.68% 2.24% 0.51% 143%0.17% 0.57% 0.13% 34%0.22% 0.74% 0.17% 340%0.65% 2.15% 0.49% 12%0.07% 0.23% 0.05% 75%0.15% 0.49% 0.11% 1470%0.43% 2.52% 0.32% 133%0.37% 2.12% 0.27% 34%0.11% 0.65% 0.08% 237%0.47% 2.70% 0.35% 383%0.75% 4.35% 0.56% #DIV/0!0.26% 1.50% 0.19% 32%0.10% 0.60% 0.08% 4464%0.63% 3.67% 0.47% #DIV/0!1.73%10.02%1.29% 79%0.64% 3.68% 0.47% 82%0.10% 0.57% 0.07% 93%0.11% 0.61% 0.08% 601%0.46% 2.68% 0.34% 159%0.20% 1.14% 0.15% 52%0.19% 1.09% 0.14% 97%0.14% 0.81% 0.10% 147%0.12% 0.70% 0.09% 161%0.17% 0.99% 0.13% 229%0.23% 1.31% 0.17% 131%0.25% 1.42% 0.18% 54%0.03% 0.20% 0.03% 15%0.04% 0.21% 0.03% 113%0.11% 0.66% 0.08% 103%0.71% 4.10% 0.53% 126%0.39% 2.23% 0.29% 92%0.06% 0.34% 0.04% 8%0.02% 0.14% 0.02% 18%0.16% 0.93% 0.12% 48%0.30% 1.73% 0.22% 131%0.16% 0.92% 0.12% 136%0.13% 0.72% 0.09% 3794%0.31% 1.77% 0.23% 63%0.31% 1.77% 0.23% 21%0.02% 0.09% 0.01% 5119.xlsPDAs 6 PDAs draft 4/9/2012 2 3 4 A B C D E County Key Juris KeyName KeyPlaceType Alameda ALA1 Alameda Naval Air Station Transit Town Center84 85 86 87 Contra CostaWAL1 Walnut Creek West Downtown City Center Contra CostaWCC1_a Contra Costa County UnincorporatedSUB-AREA: West Contra Costa Transportation Advisory Committee: San Pablo Avenue CorridorMixed-Use Corridor Contra CostaWCC1_c Richmond San Pablo Avenue CorridorMixed-Use Corridor Contra CostaWCC1_g Hercules SUB-AREA: West Contra Costa Transportation Advisory Committee: San Pablo Avenue CorridorMixed-Use Corridor AI AJ AK AL AM % change Households 2010-2040 % of PDA Households Growth % of County Household Growth % of Regional Households Growth 170%0.42% 2.40% 0.31% 39%0.12% 0.69% 0.09% 97%0.32% 1.84% 0.24% 119%0.14% 0.79% 0.10% 5119.xlsPDAs 7 I I PDAs draft 4/9/2012 2 3 4 A B C D E County Key Juris KeyName KeyPlaceType Alameda ALA1 Alameda Naval Air Station Transit Town Center88 89 90 91 92 9394 95 96 97 98 99100 101 102 103 104 105106 107 108 109 110 111 112 113 114 115 116 117 118119 120 121 122 123 124125 126 127 128 129 130131 132 133 134 135 136 137 138 139 140 141 142 143144 145 Marin MCO1 Marin County UnincorporatedUrbanized 101 CorridorTransit Neighborhood Marin SRA1 San Rafael Downtown City Center Marin SRA2 San Rafael Civic Center/North Rafael Town CenterTransit Town Center Napa ACA1 American CanyonHighway 29 CorridorMixed-Use Corridor Napa NAP1 Napa Downtown Napa Rural Town Center Napa NAP2 Napa Soscol Gateway CorridorRural Corridor San FranciscoSFO1 San Francisco Bayview/Hunters Point Shipyard/Candlestick PointUrban Neighborhood San FranciscoSFO2 San Francisco Balboa Park Transit Neighborhood San FranciscoSFO3 San Francisco Downtown-Van Ness-GearyRegional Center San FranciscoSFO4 San Francisco Eastern NeighborhoodsUrban Neighborhood San FranciscoSFO5 San Francisco Mission Bay Urban Neighborhood San FranciscoSFO6 San Francisco Port of San FranciscoMixed-Use Corridor San FranciscoSFO7 San Francisco Transbay Terminal Regional Center San FranciscoSFO8 San Francisco Treasure Island Transit Town Center San FranciscoSFO9_b San Francisco San Francisco/San Mateo Bi-County Area (with City of Brisbane)Transit Neighborhood San FranciscoSFO10 San Francisco 19th Avenue Transit Town Center San FranciscoSFO11 San Francisco Market & Octavia Urban Neighborhood San FranciscoSFO12 San Francisco Mission-San Jose CorridorMixed-Use Corridor San MateoBEL1 Belmont Villages of Belmont Mixed-Use Corridor San MateoBUR1 Burlingame Burlingame El Camino RealTransit Town Center San MateoCCG1_a Daly City SUB-AREA: CCAG/Daly City: Mission BoulevardMixed-Use Corridor San MateoCCG1_b Colma SUB-AREA: CCAG/ColmaMixed-Use Corridor San MateoCCG1_c South San FranciscoSUB-AREA: CCAG/South San FranciscoMixed-Use Corridor San MateoCCG1_d San Bruno SUB-AREA: CCAG/San Bruno: Transit CorridorsMixed-Use Corridor San MateoCCG1_e Millbrae SUB-AREA: CCAG/Millbrae: Transit Station AreaMixed-Use Corridor San MateoCCG1_f San Mateo SUB-AREA: CCAG/San Mateo: DowntownMixed-Use Corridor San MateoCCG1_h San Carlos SUB-AREA: CCAG/San Carlos: Railroad CorridorMixed-Use Corridor San MateoCCG1_i Redwood City SUB-AREA: CCAG/Redwood City: DowntownMixed-Use Corridor San MateoCCG1_j Menlo Park SUB-AREA: CCAG/Menlo Park: El Camino Real Corridor and DowntownMixed-Use Corridor San MateoCCG1_k San Mateo County UnincorporatedSUB-AREA: CCAG/San Mateo County (Unincorporated Colma)Mixed-Use Corridor San MateoCCG1_l San Mateo County UnincorporatedSUB-AREA: CCAG/San Mateo County (North Fair Oaks)Mixed-Use Corridor San MateoCCG1_m San Mateo County UnincorporatedSUB-AREA: CCAG/San Mateo CountyMixed-Use Corridor San MateoDAL1 Daly City Mission Boulevard Mixed-Use Corridor San MateoDAL1_CCG1_aDaly City SUB-AREA: CCAG/Daly City: Mission BoulevardMixed-Use Corridor San MateoDAL2 Daly City Bayshore Transit Town Center San MateoEPA1 East Palo Alto Ravenswood Transit Town Center San MateoMEN1 Menlo Park El Camino Real Corridor and DowntownTransit Town Center San MateoMEN1_CCG1_jMenlo Park SUB-AREA: CCAG/Menlo Park: El Camino Real Corridor and DowntownTransit Town Center San MateoMIL1 Millbrae Transit Station Area Mixed-Use Corridor San MateoMIL1_CCG1_eMillbrae SUB-AREA: CCAG/Millbrae: Transit Station AreaMixed-Use Corridor San MateoRWC1 Redwood City Downtown City Center San MateoRWC1_CCG1_iRedwood City SUB-AREA: CCAG/Redwood City: DowntownCity Center San MateoRWC2 Redwood City Veterans Corridor Mixed-Use Corridor San MateoSBR1 San Bruno Transit Corridors Mixed-Use Corridor San MateoSBR1_CCG1_dSan Bruno SUB-AREA: CCAG/San Bruno: Transit CorridorsMixed-Use Corridor San MateoSCA1_CCG1_hSan Carlos SUB-AREA: CCAG/San Carlos: Railroad CorridorTransit Town Center San MateoSFO9_a Brisbane San Francisco/San Mateo Bi-County Area (with San Francisco)Suburban Center San MateoSMA1 San Mateo Downtown City Center San MateoSMA1_CCG1_fSan Mateo SUB-AREA: CCAG/San Mateo: DowntownCity Center San MateoSMA2 San Mateo Rail Corridor Transit Neighborhood San MateoSMA2_CCG1_fSan Mateo SUB-AREA: CCAG/San Mateo: Rail CorridorTransit Neighborhood San MateoSMA3_CCG1_fSan Mateo El Camino Mixed-Use Corridor San MateoSMC1 San Mateo County UnincorporatedMidcoast Rural Corridor San MateoSSF1 South San FranciscoDowntown Transit Town Center AI AJ AK AL AM % change Households 2010-2040 % of PDA Households Growth % of County Household Growth % of Regional Households Growth 13%0.10% 4.84% 0.08% 59%0.27%12.69%0.20% 55%0.20% 9.34% 0.15% 387%0.30%16.62%0.22% 383%0.10% 5.38% 0.07% 73%0.08% 4.76% 0.06% 109%2.20%12.27%1.64% 39%0.36% 1.99% 0.27% 209%4.47%24.93%3.33% 40%2.23%12.43%1.66% 292%0.67% 3.73% 0.50% 2%0.35% 1.93% 0.26% 15%0.93% 5.16% 0.69% 227%1.39% 7.77% 1.04% 4323%1.01% 5.62% 0.75% 1898%1.17% 6.53% 0.87% 1107%1.24% 6.94% 0.93% 123%0.36% 1.99% 0.27% 101%0.17% 1.54% 0.13% 47%0.65% 5.78% 0.48% 11%0.08% 0.72% 0.06% 44%0.05% 0.41% 0.03% 55%0.57% 5.08% 0.42% 48%0.10% 0.91% 0.08% 12%0.06% 0.52% 0.04% 10%0.21% 1.91% 0.16% 16%0.09% 0.79% 0.07% 18%0.13% 1.18% 0.10% 15%0.05% 0.47% 0.04% 56%0.02% 0.16% 0.01% 151%0.70% 6.24% 0.52% 74%0.01% 0.06% 0.00% 67%0.04% 0.33% 0.03% 54%0.18% 1.63% 0.14% 126%0.37% 3.34% 0.28% 89%0.17% 1.48% 0.12% 94%0.04% 0.31% 0.03% 99%0.15% 1.38% 0.11% 146%0.02% 0.14% 0.01% 657%0.27% 2.41% 0.20% 1042%0.61% 5.49% 0.46% 184%0.24% 2.15% 0.18% 145%0.20% 1.83% 0.15% 117%0.24% 2.17% 0.18% 67%0.39% 3.52% 0.29% 175%0.15% 1.32% 0.11% #DIV/0!0.90% 8.03% 0.67% 188%0.03% 0.30% 0.02% 116%0.09% 0.81% 0.07% 487%0.13% 1.15% 0.10% 1238%0.85% 7.61% 0.63% 144%0.23% 2.06% 0.17% 29%0.20% 1.82% 0.15% 205%0.59% 5.31% 0.44% 5119.xlsPDAs 8 PDAs draft 4/9/2012 2 3 4 A B C D E County Key Juris KeyName KeyPlaceType Alameda ALA1 Alameda Naval Air Station Transit Town Center146 147 148 149 150 151152 153 154 155 156 157158 159 160 161 162 163164 165 166 167 168 169 170 171 172 173 174 175 176177 178 179 180 181 182183 184 185 186 187 188189 190 191 192 193 194 195 196 197 198 199 200 201202 203 204205 206 207 208 209 210211 212 213 214 215 216217 218 219 220 221 222 223 Santa ClaraCAM1 Campbell Central Redevelopment AreaTransit Neighborhood Santa ClaraGIL1 Gilroy Downtown Transit Town Center Santa ClaraMOH1 Morgan Hill Downtown Transit Town Center Santa ClaraMPT1 Milpitas Transit Area Suburban Center Santa ClaraMVW1 Mountain View Whisman Station Transit Neighborhood Santa ClaraMVW2 Mountain View Downtown Transit Town Center Santa ClaraMVW3 Mountain View San Antonio Center Transit Town Center Santa ClaraMVW4 Mountain View El Camino Real CorridorMixed-Use Corridor Santa ClaraMVW5 Mountain View East Whisman Employment Center Santa ClaraMVW6 Mountain View North Bayshore Suburban Center Santa ClaraPAL1 Palo Alto California Avenue Transit Neighborhood Santa ClaraSCL1 Santa Clara El Camino Real Focus AreaMixed-Use Corridor Santa ClaraSCL2 Santa Clara Santa Clara Station Focus AreaCity Center Santa ClaraSJO1 San Jose Greater Downtown Regional Center Santa ClaraSJO2 San Jose Cottle Transit VillageSuburban Center Santa ClaraSJO3 San Jose North San Jose Regional Center Santa ClaraSJO4 San Jose Downtown "Frame"City Center Santa ClaraSJO5 San Jose Berryessa Station Transit Neighborhood Santa ClaraSJO6 San Jose Communications HillTransit Town Center Santa ClaraSJO7 San Jose West San Carlos and Southwest Expressway CorridorsMixed-Use Corridor Santa ClaraSJO8 San Jose East Santa Clara/Alum Rock CorridorMixed-Use Corridor Santa ClaraSJO9 San Jose Stevens Creek TOD CorridorMixed-Use Corridor Santa ClaraSJO10 San Jose Oakridge/Almaden Plaza Urban VillageSuburban Center Santa ClaraSJO11 San Jose Capitol/Tully/King Urban VillagesSuburban Center Santa ClaraSJO12 San Jose Saratoga TOD CorridorMixed-Use Corridor Santa ClaraSJO13 San Jose Winchester Boulevard TOD CorridorMixed-Use Corridor Santa ClaraSJO14 San Jose Bascom TOD CorridorMixed-Use Corridor Santa ClaraSJO15 San Jose Bascom Urban VillageMixed-Use Corridor Santa ClaraSJO16 San Jose Camden Urban VillageMixed-Use Corridor Santa ClaraSJO17 San Jose Blossom Hill/Snell Urban VillageMixed-Use Corridor Santa ClaraSJO18 San Jose Capitol Corridor Urban VillagesMixed-Use Corridor Santa ClaraSJO19 San Jose Westgate/El Paseo Urban VillageSuburban Center Santa ClaraSJO20 San Jose Old Edenvale Employment AreaEmployment Center Santa ClaraSJO21 San Jose International Business Park AreaEmployment Center Santa ClaraSUN1 Sunnyvale Lawrence Station Transit VillageTransit Neighborhood Santa ClaraSUN2 Sunnyvale Downtown & Caltrain StationTransit Town Center Santa ClaraSUN3 Sunnyvale El Camino Real CorridorMixed-Use Corridor Santa ClaraSUN4 Sunnyvale Moffett Park Employment Center Santa ClaraSUN5 Sunnyvale Peery Park Employment Center Santa ClaraSUN6 Sunnyvale East Sunnyvale ITR Urban Neighborhood Santa ClaraSUN7 Sunnyvale Reamwood Light Rail StationEmployment Center Santa ClaraSUN8 Sunnyvale Tasman Station ITR Mixed-Use Corridor Santa ClaraVTA1_a Campbell VTA City Cores, Corridors & Station AreasMixed-Use Corridor Santa ClaraVTA1_b Cupertino VTA City Cores, Corridors & Station AreasMixed-Use Corridor Santa ClaraVTA1_c Gilroy VTA City Cores, Corridors & Station AreasMixed-Use Corridor Santa ClaraVTA1_d Los Altos VTA City Cores, Corridors & Station AreasMixed-Use Corridor Santa ClaraVTA1_e Los Gatos VTA City Cores, Corridors & Station AreasMixed-Use Corridor Santa ClaraVTA1_f Milpitas VTA City Cores, Corridors & Station AreasMixed-Use Corridor Santa ClaraVTA1_h Palo Alto VTA City Cores, Corridors & Station AreasMixed-Use Corridor Santa ClaraVTA1_i San Jose VTA City Cores, Corridors & Station AreasMixed-Use Corridor Santa ClaraVTA1_j Santa Clara VTA City Cores, Corridors & Station AreasMixed-Use Corridor Santa ClaraVTA1_k Santa Clara County UnincorporatedVTA City Cores, Corridors & Station AreasMixed-Use Corridor Santa ClaraVTA1_l Saratoga VTA City Cores, Corridors & Station AreasMixed-Use Corridor Santa ClaraVTA1_m Sunnyvale VTA City Cores, Corridors & Station AreasMixed-Use Corridor Solano BEN1 Benicia Downtown Transit Neighborhood Solano BEN2 Benicia Northern Gateway Employment Center Solano DIX1 Dixon Downtown Dixon Rural Town Center Solano FAI1 Fairfield Downtown South (Jefferson Street)Suburban Center Solano FAI2 Fairfield Fairfield-Vacaville Train StationTransit Town Center Solano FAI3 Fairfield North Texas Street CoreMixed-Use Corridor Solano FAI4 Fairfield West Texas Street GatewayMixed-Use Corridor Solano RIV1 Rio Vista Downtown Rio Vista Rural Town Center Solano SUI1 Suisun City Downtown & WaterfrontTransit Town Center Solano VAC1 Vacaville Downtown Transit Town Center Solano VAC2 Vacaville Allison Area Suburban Center Solano VAL1 Vallejo Waterfront & DowntownSuburban Center Sonoma CLO1 Cloverdale Downtown/SMART Transit AreaTransit Town Center Sonoma COT1 Cotati Downtown and Cotati DepotTransit Town Center Sonoma PET1 Petaluma Central, Turning Basin/Lower ReachSuburban Center Sonoma ROH1 Rohnert Park Sonoma Mountain VillageSuburban Center Sonoma ROH2 Rohnert Park Central Rohnert ParkTransit Town Center Sonoma SCO1 Sonoma County UnincorporatedForestville Rural Town Center Sonoma SCO2 Sonoma County UnincorporatedGraton Rural Town Center Sonoma SCO3 Sonoma County UnincorporatedGuerneville Rural Town Center Sonoma SCO4 Sonoma County UnincorporatedPenngrove Urban Service AreaRural Town Center AI AJ AK AL AM % change Households 2010-2040 % of PDA Households Growth % of County Household Growth % of Regional Households Growth 118%0.29% 0.76% 0.21% 223%0.38% 1.00% 0.28% 284%0.28% 0.73% 0.21% 965%1.35% 3.59% 1.01% 133%0.18% 0.48% 0.13% 29%0.27% 0.71% 0.20% 81%0.53% 1.41% 0.40% 23%0.39% 1.03% 0.29% 2%0.00% 0.01% 0.00% 410%0.27% 0.72% 0.20% 132%0.18% 0.47% 0.13% 81%0.25% 0.68% 0.19% 751%0.65% 1.72% 0.48% 200%1.43% 3.79% 1.06% 469%1.61% 4.27% 1.20% 2849%5.80%15.39%4.32% 444%1.99% 5.28% 1.48% 97%0.86% 2.29% 0.64% 507%0.63% 1.68% 0.47% 514%1.65% 4.38% 1.23% 819%0.75% 1.99% 0.56% 596%0.70% 1.86% 0.52% 848%1.33% 3.53% 0.99% 266%0.41% 1.08% 0.30% #DIV/0!0.20% 0.53% 0.15% 1364%0.37% 0.97% 0.27% 813%0.29% 0.76% 0.21% 8%0.15% 0.41% 0.11% 6%0.18% 0.48% 0.13% 55%0.20% 0.52% 0.15% 90%1.13% 3.00% 0.84% 23%0.46% 1.21% 0.34% 0%0.00% 0.00% 0.00% 0%0.00% 0.00% 0.00% 157%0.47% 1.24% 0.35% 101%0.35% 0.93% 0.26% 43%0.85% 2.25% 0.63% #DIV/0!0.00% 0.01% 0.00% -7%0.00% 0.00% 0.00% 452%0.64% 1.71% 0.48% #DIV/0!0.09% 0.24% 0.07% 124%0.33% 0.88% 0.25% 192%0.28% 0.73% 0.21% 92%0.52% 1.39% 0.39% 4%0.01% 0.04% 0.01% 640%0.86% 2.29% 0.64% 2%0.01% 0.01% 0.00% 52%0.05% 0.12% 0.03% 100%1.13% 3.01% 0.84% 0%-0.01% -0.04% -0.01% 70%0.26% 0.68% 0.19% 5%0.00% 0.00% 0.00% 62%0.02% 0.05% 0.01% 20%0.19% 0.50% 0.14% 184%0.19%2.73%0.14% -4%0.00% 0.00% 0.00% 40%0.05% 0.76% 0.04% 78%0.09% 1.31% 0.07% 7071%1.21%17.64%0.90% 112%0.34% 5.01% 0.26% 243%0.47% 6.92% 0.35% 133%0.08% 1.13% 0.06% 98%0.21% 3.01% 0.15% 313%0.13% 1.96% 0.10% 23%0.02% 0.35% 0.02% 96%0.18% 2.64% 0.13% 75%0.15% 1.62% 0.11% 50%0.08% 0.86% 0.06% 237%0.34% 3.70% 0.25% 1020%0.38% 4.15% 0.28% 74%0.18% 2.00% 0.14% 51%0.09% 0.95% 0.07% 83%0.09% 0.93% 0.06% 126%0.09% 0.98% 0.07% 92%0.07% 0.79% 0.05% 5119.xlsPDAs 9 PDAs draft 4/9/2012 2 3 4 A B C D E County Key Juris KeyName KeyPlaceType Alameda ALA1 Alameda Naval Air Station Transit Town Center224 225 226 227 228 229230 231 232 233 234 235236 237 238 239 240 241 242 243 244 245 246 247 248249 250 251 252 253 254255 256 257 258 259 260 261 262 263 264 265 266 Sonoma SCO5 Sonoma County UnincorporatedThe Springs Rural Corridor Sonoma SEB1 Sebastopol Nexus Area Rural Town Center Sonoma SRO1 Santa Rosa SUB-AREA: Downtown Station AreaCity Center Sonoma SRO1_SRO2Santa Rosa SUB-AREA: Downtown Station AreaCity Center Sonoma SRO1_SRO2_SRO3Santa Rosa SUB-AREA: Downtown Station AreaCity Center Sonoma SRO1_SRO3Santa Rosa SUB-AREA: Downtown Station AreaCity Center Sonoma SRO2 Santa Rosa SUB-AREA: Mendocino Avenue/Santa Rosa Avenue CorridorMixed-Use Corridor Sonoma SRO2_SRO3Santa Rosa SUB-AREA: Mendocino Avenue/Santa Rosa Avenue CorridorMixed-Use Corridor Sonoma SRO3 Santa Rosa SUB-AREA: Sebastopol Road CorridorMixed-Use Corridor Sonoma SRO4 Santa Rosa North Santa Rosa StationSuburban Center Sonoma SRO5 Santa Rosa Roseland Area Transit Neighborhood Sonoma WIN1 Windsor Redevelopment AreaSuburban Center County Key Juris KeyName KeyPlaceType Regional Center City Center Urban Neighborhood Transit Neighborhood Transit Town Center Mixed-Use Corridor Suburban Center Rural Town Center Rural Corridor Employment Center County Key Juris KeyName KeyPlaceType Alameda Contra Costa Marin Napa San Francisco San Mateo Santa Clara Solano Sonoma AI AJ AK AL AM % change Households 2010-2040 % of PDA Households Growth % of County Household Growth % of Regional Households Growth 27%0.25% 2.67% 0.18% 18%0.08% 0.90% 0.06% 103%0.18% 1.92% 0.13% 156%0.26% 2.85% 0.20% 1356%0.32% 3.48% 0.24% 1555%0.54% 5.90% 0.40% 8%0.09% 1.00% 0.07% 110%0.01% 0.10% 0.01% 49%0.23% 2.47% 0.17% 73%0.55% 6.00% 0.41% 141%0.92% 9.95% 0.68% 88%0.23% 2.51% 0.17% 76%100%74%74% % change Households 2010-2040 % of PDA Households Growth % of County Household Growth % of Regional Households Growth 136% 15%11% 150% 10%8% 69%9%7% 119% 11%8% 123% 15%11% 34% 23%17% 222% 14%11% 59%1%0% 31%1%0% 6%0%0% 76%100%74% % change Households 2010-2040 % of PDA Households Growth % of County Household Growth % of Regional Households Growth 65% 23% 76% 17% 129% 11% 65%8% 35%1% 27%0% 219%0% 27%0% 46% 16% 91% 12% 75%9% 80%7% 101% 31% 83% 23% 202%3% 43%2% 82%5% 56%4% 76%100%74%74% 5119.xlsPDAs 10 CENTER FOR CONTINUING STUDY OF THE CALIFORNIA ECONOMY 132 HAMILTON AVENUE • PALO ALTO • CALIFORNIA • 94301 TELEPHONE: (650) 321-8550 FAX: (650) 321-5451 www.ccsce.com February 2012 Bay Area Job Growth to 2040 Projections and Analysis Prepared for: Association of Bay Area Governments Prepared by: Stephen Levy Introduction In September 2011, the Association of Bay Area Governments (ABAG) asked the Center for Continuing Study of the California Economy (CCSCE) to prepare regional job projections to 2040 and to assist ABAG staff in preparing population and household projections. This report is focused on the job projections prepared by CCSCE and includes a summary of the methodology, a description of the projections and an explanation of past, current and projected job growth in the region. The projections and this report were prepared by Stephen Levy, CCSCE’s Director. CCSCE acknowledges the assistance and support of Miriam Chion, Justin Fried, Ken Kirkey and Ezra Rapport from the ABAG staff who provided guidance and encouragement through the time we worked together. CCSCE also acknowledges Jon Haveman and Sean Randolph of the Bay Area Council Economic Institute. Jon provided assistance in interpreting the Council’s December 2011 economic forecast and Sean allowed CCSCE to use quotes and slides from the Institute’s upcoming Bay Area Economic Profile prepared with the assistance of McKinsey & Company. At the conclusion of the main report there is an appendix that describes the data, sources and methodology that provide the foundation for the report’s findings. 1 Summary The Bay Area is projected to add more than 1.2 million jobs between 2010 and 2040 and to grow slightly faster than the state and nation. Total Jobs (Thousands) 2010 2040 % Growth Bay Area 3,385.3 4,617.5 36.4% United States 141,821.3 183,310.7 29.3% Bay Area % of U.S. 2.39%2.52% Source: 2010‐U.S. Bureau of Labor Statistics BLS), the California Employment Development Department (EDD) and CCSCE; 2040‐CCSCE The region is expected to slowly recover the jobs lost during the recent recession and then experience moderate job growth to 2040. The Bay Area is projected to slightly outpace the state and nation in future job growth driven by the region’s large concentration and continuing competitive advantage in many areas of technology and the region’s position as a Pacific Rim trade and finance center. Still, in 2040 the region is expected to have a smaller share of U.S. jobs than in 1990 before the defense cutbacks or in 2000 before the dot.com bubble burst. The remainder of this report explains these findings and why the Bay Area is expected to reverse the lagging job growth of the past decade. 2 Bay Area Job Trends 1990-2010 Bay Area job levels experienced ups and downs during the two decades after 1990. Between 1990 and 1994 the Bay Area experienced a jobs recession that lasted longer than the nation’s although job losses were relatively modest. During this period the region was hit with defense related cutbacks lost more than 40,000 jobs associated with lower defense spending on aerospace and the closure of military bases. These losses deepened in the following years and were a permanent loss of part of the region’s economic base. During these years the region’s share of national jobs fell from 2.64% in 1990 to 2.50% in 1995 as the nation recovered more quickly from the 1990-91 recession and experienced a smaller impact from defense related job losses. During the late 1990s the regional economy roared back as technology and the dot.com boom took over. The Bay Area added more than 600,000 jobs between 1994 and 2000 while matching the previous record share of the nation’s total jobs. Regional job gains were led by computer services, information services related to the Internet and computer and electronics manufacturing. However, many of the jobs created during the dot.com boom quickly disappeared in the years after 2000 as the boom turned into the dot.com bubble bust. The region lost more than 300,000 jobs between 2000 and 2004 and the region’s share of U.S. jobs fell from 2.67% to 2.46%. The region lost 1/3 of the computer and electronics manufacturing jobs after 2000 and a larger share of the Internet related jobs while experiencing some job losses in professional, technical and 3 scientific services and temporary help agencies, all sectors serving the region’s technology firms. Between 2004 and 2007 the Bay Area once again outpaced the nation in job growth and was slowly recovering the job and share losses after 2000 when the recession created by the housing and financial crises hit the nation, state and region toward the end of 2007. Once again the region lost nearly 300,000 jobs and by early 2010 saw the region’s share of national jobs fall to the lowest since before 1990. However, this time the job losses had a different structure and told a different story. The largest job losses since 2007 were related to construction and finance with nearly 100,000 jobs lost as a result of the housing and financial sector crises. More than 50,000 jobs were lost in the retail trade and government sectors. While technology and trade sectors experienced job losses after 2007, these were modest and temporary with recovery starting in late 2010 and extending into 2011. While the recessions after 1990 and 2000 caused permanent job losses in the region’s economic base, the recent recession did not. While many indicators of this fact will be described below, venture capital trends show the region’s continuing strength in a single picture. Bay Area VC funding rebounded in 2010 and 2011 approaching pre-recession levels. At the same time the region’s share of national VC funding has been on a fairly steady uptrend since 2000 reaching record levels during recent years. These gains plus a surge in technology hiring from existing firms has pushed Bay Area job growth above the national average in 2011 with further gains expected in 2012 and 2013. 4 The Short-Term Outlook: 2011-2013 In December 2011 the San Jose metro area tied with Houston for the highest rate of job growth for all large metropolitan areas in the nation during the preceding 12 months. During that period the metro area saw a gain of 25,700 jobs for a 3.0% increase compared to the nation’s 1.3% gain. The San Francisco metro area also strongly outpaced the nation. Job growth in both metro areas was driven by gains in technology sectors. Job gains were recorded in Internet-related activities, computer and electronics manufacturing and especially in professional, scientific and technical services. Bay Area companies reported hiring gains driven by customer demand for their goods and services. Bay Area companies including Google, Apple, Facebook, LinkedIn and Zynga made business news headlines regularly reporting good news. Rents and building prices surged in tech centers including San Francisco, Palo Alto and San Jose as reported in the San Francisco and Silicon Valley Business Journals. In December 2011 the Bay Area Council Economic Institute released their regional economic forecast prepared in partnership with the Anderson Forecast Project at UCLA. The UCLA forecast highlights include:  Between the first quarter of 2011 and the fourth quarter of 2013, the region is expected to add more than 200,000 jobs for a gain of 7.5%. This gain is compared with a 4.5% increase expected in California  During that period the unemployment rate is forecast to drop from 10.5%% to 8.1%. By December 2011, the regional unemployment rate had declined to 8.6%.  Gains in personal income and taxable sales are forecast to outpace inflation.  New housing construction is forecast to start a recovery in 2013. In February 2012 Facebook announced their upcoming IPO, which together with the successful IPOs at LinkedIn, Zynga and other Bay Area companies confirmed that it was, once again, possible for entrepreneurs and workers to see a financial payoff from innovation and risk taking. 5 Job Growth and Trends to 2040 The Bay Area job projections were developed using three guiding principles: 1) The Bay Area projections were based on projections of job growth in the nation and state. The national and state projections provide the pool of job opportunities and the Bay Area projections reflect judgments about the share of national and state job growth that will locate in the Bay Area. 2) The Bay Area share of national and state job growth is determined by the industry composition of job growth and the projected share of job growth locating in the Bay Area. If national and state job growth is concentrated in sectors where the Bay Area has a competitive advantage, the region’s projected job growth will be higher than if national and state job growth is concentrated in sectors where the region has a below average share of jobs and a relatively poor competitive position. 3) The analysis of competitive advantage is focused on sectors in the Bay Area economic base. The region’s economic base consists of those sectors that sell a high proportion of goods and services to customers outside the region. They export goods and services to customers in world and national markets and markets throughout California. Key examples of economic base sectors in the Bay Area are manufacturing, information services related to the Internet, professional, scientific and technical services such as computer services and scientific R&D services, and foreign trade and tourism sectors. U.S. Job Growth to 2040 The U.S. job growth projections have three principal components: 1) A new, post-2010 Census set of population projections to 2040 2) Labor force participation rate projections that reflect longer working lives for older workers 3) Industry sector projections developed by CCSCE based on a review of existing national projections The population and labor force projections determine the amount of job growth projected between 2010 and 2040 and the industry projections identify the structure of job growth as an input to state and Bay Area job projections. 6 The resulting national projections of job growth are shown below. United States Total Jobs (Millions) 2010 2020 2040 141.8 163.2 183.3 2010‐2020 2020‐2040 2010‐2040 Change 21.3 20.1 41.5 % Change 15.1% 12.3% 29.3% Source: 2010‐U.S. Bureau of Labor Statistics (BLS) ; 2020 and 2040‐CCSCE The nation is expected to add 41.5 million jobs between 2010 and 2040 for an increase of 29.3%. Slightly more than half of the projected increase is expected to occur in the next ten years. The percentage increase in jobs (15.1%) between 2010 and 2020 is actually larger than the projected increase (12.3%) for the following 20 years. The concentration of job growth in the first ten years has two explanations, both of which apply to the state and Bay Area job projections: 1) A significant part of the job growth projected to 2020 includes the recovery of job losses incurred during the recession. The nation lost more than 8 million jobs during the recession. The national forecasts reviewed by CCSCE all have the nation regaining full employment by 2015 or 2016. As a result the 2020 projections include erasing the recession job losses plus added gains in the latter half of this decade. The job growth numbers look different when measured from the peak before the recession. Job growth between 2007 and 2020 is projected to be 13.1 million and the projected growth rate is 8.8% compared to the 21.3 million jobs and 15.1% growth rate measured from 2010. 2) After 2020 labor force and job growth slows as the tidal wave of baby boomer retirements takes effect. U.S population is projected to increase by 16.3% between 2020 and 2040, which is faster than the projected job growth (12.3%) and the reason is the retirement of the baby boom generation. 7 The Pattern of U.S. Industry Job Growth to 2040 Projecting industry growth 30 years into the future is a difficult task and although the projections shown below reflect the industry patterns expected by major national forecasting organizations, they come with a high degree of uncertainty in the years after 2020. The projected growth rates shown on the table are for the period from 2007 to 2040 and eliminate the fall and rise of job levels related to the recession and recovery—thus they illustrate the long-term trends. United States Jobs by Major Industry (Millions) 2007 2010 2020 2040 2007‐ 2040 Construction 7.6 5.5 7.4 8.4 10.6% Manufacturing 13.9 11.5 11.7 10.7 ‐23.2% Wholesale Trade 6.0 5.5 6.1 6.1 1.6% Retail Trade 15.5 14.4 15.4 15.9 2.7% Transp., Warehousing and Utilities 5.1 4.7 5.4 5.8 14.2% Information 3.0 2.8 3.0 3.2 5.2% Financial Activities 8.3 7.7 8.5 8.9 6.6% Professional and Business Services 17.9 16.7 21.4 27.0 50.3% Educational and Health Services 18.3 19.6 24.2 30.6 66.8% Leisure and Hospitality 13.4 13.0 15.7 18.3 36.6% Other Services 5.5 5.4 6.2 6.9 25.9% Government 22.2 22.5 23.7 25.9 16.6% Self Employed 11.3 10.6 12.5 14.0 23.6% Total Jobs 150.0 141.8 163.2 183.3 22.2% Source: 2007,2010‐U.S. Bureau of Labor Statistics (BLS) 2020 and 2040‐CCSCE However, the projections do show substantial differences in the expected growth rate among industries and these differences tell a story about where job growth is expected and where job levels will remain flat or decline. These differences directly influenced the Bay Area job projections described in a later section of this report. Agriculture and mining were excluded from the table as they are less important to the Bay Area economy, but jobs in these categories are in the totals. These projections also help identify which job growth is primarily a reflection of regaining jobs lost during the recession and which industries have long-term job growth potential. Some of the major trends include: 8  Construction job growth between 2010 and 2020 recovers jobs lost during the recession after which the industry will have modest growth.  Manufacturing job levels are expected to end the decade close to 2010 levels and decline thereafter, never reaching the pre-recession totals. Manufacturing production is projected to increase substantially between 2010 and 2040 as in recent decades although job growth will lag. The explanation is strong and continuing productivity growth in the sector. Put simply, over time manufacturing firms can produce more with fewer workers. The size of the U.S. market measured by population growth is below 1% per year while manufacturing productivity has been is close to 5% per year over the long term. Even with expanding manufacturing export markets and new advanced manufacturing opportunities, the sector will see a decline in overall job levels between 2010 and 2040.  By far the largest percentage job growth is expected in Professional and Business Services and Educational and Health Services. The Professional and Business Service sector includes the fast-growing, high wage professional, scientific and technical services industries and those sectors are critical for projecting Bay Area job growth. The largest percentage growth within these industries is in computer services, scientific research and development services and architectural and engineering services, all key components of the Bay Area economic base.  The largest and fastest-growing industries are within health and social services and are driven by the aging of the population.  Retail trade and financial services are sectors undergoing restructuring driven in different ways by technology. Retail trade growth is slowing as more customers take advantage of online shopping and that trend is expected to continue leading to below average job growth for retail trade. In finance, technology such as online banking and mobile phone technology for paying bills is reducing the demand for personnel in banks and technology also makes it easier to process financial transactions so job growth in this sector is also expected to be relatively small.  Leisure and Hospitality is the other fast-growing sector and includes amusements and hotels as well as the large restaurant sector.  The information sector is important for the Bay Area and the relatively slow job growth shown above is misleading because it consists of continuing job losses in telecommunications offset by the smaller but fast- growing software and Internet services sectors. 9 California Job Growth to 2040 The state is projected to experience job growth that is slightly faster than the nation’s job growth to 2040. California is expected to recover the recession job losses by 2015 or a year later and the unemployment rate will return to full employment levels between 2015 and 2017 according to the forecasts reviewed by CCSCE. In addition the state has a favorable industry composition given the expected U.S. job growth in technology, trade and tourism. California is outpacing the nation in job growth in 2011 and is forecast to outpace the nation in job growth in each year to 2020 in the latest long-term UCLA Anderson Forecast. These results are confirmed by CCSCE’s industry jobs analysis. California Total Jobs (Thousands) 2010 2020 2040 15701.4 18713.9 21155.5 2010‐2020 2020‐2040 2010‐2040 Change 3012.5 2441.6 5454.1 % Change 19.2% 13.0% 34.7% Source: 2010‐California Employment Development Department (EDD) and CCSCE; 2020 and 2040‐CCSCE California is projected to add nearly 5.5 million jobs between 2010 and 2040 with the largest absolute and percentage gains in the first decade as the recession job losses are regained and before the heart of the baby boom retirement wave. The state is projected to see a 34.7% increase in total jobs or slightly above the projected national increase of 29.3%. As with the national projections, the picture changes if job growth is measured from the pre-recession peak. The 2007-2020 gain is then 1.6 million jobs instead of 3.0 million and the percentage increase is 9.2% or slightly above the national job growth rate for this period. The chart on the next page shows the long-term trend of the state’s share of national jobs since 1990. While there are periods of share gain and periods of share losses, the overall pattern is that California job growth roughly matches the national growth rate and the state’s projected share of U.S. jobs in 2040 is approximately the same as the share in 1990. The state regains the share losses 10 in the recession by 2020 and then grows slightly faster than the nation between 2020 and 2040. Bay Area Job Growth to 2040 The Bay Area has a concentration and competitive advantage for most sectors in which technology is applied in the development of goods and services sold to customers around the state, nation and world. This strong position in technology, where job and export growth is expected, is the primary reason that the region is projected to experience job growth at a slightly faster pace than the nation. The Bay Area concentration can be seen in venture capital flows as shown on page 4 where the Bay Area is capturing 40% of the nation’s venture capital funding in recent years, above the shares captured during the dot.com boom. The Bay Area concentration can be seen in the technology sector job levels shown on the next page. The region with 2.4% of the nation’s total jobs in 2010 had 12.0% of computer and electronic manufacturing jobs, 5.8% of pharmaceutical jobs, 10.3% of software jobs, and 8.3% of Internet service jobs. The Bay Area advantage stands out in key fast-growing, high wage professional, scientific and technical services. In 2010 the region accounted for 3.3% of the nation’s architectural and engineering jobs, 7.0% of computer service jobs, 4.3% of management and technical consulting jobs and 8.1% of scientific R&D jobs— all above the 2.4% share of U.S. total jobs in the Bay Area. 11 Bay Area Share Advantage in Key Technology Sectors (2010 data) Jobs in Thousands Bay Area Share Bay Area U.S. of U.S. Computer & Electronics Manufacturing 132.5 1,100.1 12.0% Pharmaceuticals 16.0 276.5 5.8% Medical Equipment 11.1 359.0 3.1% Software 26.7 259.8 10.3% Internet-Related 31.8 383.5 8.3% Architectural & Engr. Services 42.1 1,276.7 3.3% Computer Services 100.9 1,441.5 7.0% Management & Tech.Services 41.7 991.4 4.2% Scientific R&D Services 50.0 620.3 8.1% Total Jobs 3,401.8 141,821.3 2.4% Source: BLS, EDD and CCSCE The Bay Area Council Economic Institute (BACEI) 2012 profile of the regional economy highlights the competitive advantage for innovation activities in the Bay Area. BACEI has graciously allowed CCSCE and ABAG to cite some of the material prepared for the profile by McKinsey & Company. Innovation highlights include:  The Bay Area is the dominant region for new patents. In 2010 regional organizations held 16,364 patents while the next largest center, New York, trailed with 6,383 followed by Los Angeles, Boston and Seattle.  Innovation sectors in the Bay Area accounted for 18.4% of total employment, highest in the nation, followed closely by Boston, Seattle and the Raleigh Triangle with more than 16%. San Diego was next with 14.0% followed by Austin with 12.2%.  Seven of the top ten social media companies are headquartered in the Bay Area including Google, Facebook, Yahoo, Twitter, LinkedIn, Zynga and Yelp. 12  Nearly half of the top 100 clean-tech firms are in the Bay Area. The Bay Area innovation and technology advantage also comes from having the highest percentage of college graduates in the workforce of all major regional economies. The Bay Area’s 44% is followed by 43% in Boston and 37% in Seattle compared to the 28% national average Foreign trade and tourism are additional strengths in the region’s economic base, in part because the Bay Area is a major center for trade, investment and tourism with Pacific Rim countries. The top six export destinations—China, Japan, Taiwan, South Korea, Hong Kong, and Singapore—all represent fast-growing Asian markets. Bay Area exports are concentrated in high-value technology exports shipped by air. The Bay Area is the nation’s fourth-largest export center behind New York, Houston and Los Angeles. The BACEI-McKinsey regional profile has some other interesting findings relative to the region’s strengths:  The Bay Area has the second-largest concentration of Fortune 500 firms (30), trailing only New York (45) and ahead of the next highest concentration in Houston (22) and Dallas and Atlanta (10).  The Bay Area is home to 10 of the Fortune 500 global firms, the most of any U.S. region except New York—Chevron, H-P, McKesson, Wells Fargo, Apple, Intel, Safeway, Cisco, Google, and Oracle.  The Bay Area is a major travel and tourism center with 57 million flights annually, and 15.9 million tourists in 2010 who spent $8.3 billion. Projection Methodology and Key Findings Job projections to 2020 were developed based on detailed industry projections for the nation and state. The focus was on projecting job growth in the region’s economic base sectors and converting these projections to total jobs by projecting the population-serving jobs that would accompany the basic industry job growth and related population increase. The projections from 2020 to 2040 were developed by concentrating on major industry categories and projecting the Bay Area share of national and state growth based on the analysis of trends in the period from 2007 to 2020. The region is projected to experience job growth at a slightly faster rate than the state and nation. The primary reasons for this above average job growth is the 13 region’s above-average concentration in fast-growing sectors that apply technology to the development of goods and services that are sold to customers around the world. Information and professional services are where the largest job gains are projected for the region’s economic base. The Bay Area job growth is also strengthened by the region’s position as a major financial and trade center for Pacific Rim countries and as a region where Pacific Rim investors and workers continue to come to live and work. The Bay Area is projected to add more than 1.2 million jobs between 2010 and 2040 of which approximately 300,000 jobs represent a recovery of jobs lost since the pre-recession peak and just under 1 million jobs represent gains between 2007 and 2040. Between 2010 and 2020 the region is projected to add nearly700, 000 jobs of which approximately 300,000 represent the recovery of jobs lost during the recession. Job growth is expected to slow during the 20 years between 2020 and 2040 as baby boomer retirements slow labor force growth. The Bay Area is projected to increase the region’s share of California jobs with a gain from 21.6% in 2010 to 21.7% in 2020 and 21.8% in 2040. The Bay Area is also expected to outpace the nation in job growth with the region’s share of national jobs going from 2.39% in 2010 to 2.49% in 2020 and 2.52% in 2040. Bay Area Total Jobs (Thousands) 2007 2010 2020 2040 Bay Area Jobs 3652.0 3385.3 4068.5 4617.5 % of CA Jobs 21.3% 21.6%21.7%21.8% % of U.S. Jobs 2.43% 2.39%2.49%2.56% Source: 2007, 2010‐BLS, EDD and CCSCE 2020 and 2040‐CCSCE The region’s projected above average job growth is displayed graphically on the following page. 14 Major Industry Job Trends The major industry job trends in the Bay Area over the next 30 years mirror the national trends described on page 9. Construction job levels will almost regain pre-recession levels by 2020 and will increase slightly to 2040. Although this is a substantial gain measured from 2010 job levels, it is primarily driven by a slow return to more normal construction levels in the region. Manufacturing job levels are projected to increase slightly between 2010 and 2020 and then continue the long-term decline driven by the disparity between high productivity gains and slow increases in domestic demand as population growth slows and the population continues to age. These projections do not include major manufacturing job gains that might occur in the clean tech sector if regional firms develop products that attract worldwide customers. The largest job gains in absolute numbers and percentage increases are in Professional and Business Services and Education and Health Services. Within these larger categories the leading sectors are professional, scientific and technical services such as computer services and sectors associated with health care and social services for an aging population. The national trends of slow growth in retail trade and finance are also expected in the Bay Area. Above-average job growth is expected in the Information sector led by Internet- related services and in the number of self-employed residents as well as in the 15 Leisure and Hospitality sector, which includes amusements, hotels and restaurants. Bay Area Jobs by Major Industry (Thousands) 2007 2010 2020 2040 2007‐ 2040 Farm 23.2 20.7 21.7 19.3 ‐16.8% Natural Resources and Mining 2.4 1.9 2.3 2.0 ‐18.2% Construction 193.9 130.5 184.3 211.2 8.9% Manufacturing 348.0 308.3 319.1 291.3 ‐16.3% Wholesale Trade 129.2 113.6 134.9 136.3 5.5% Retail Trade 343.1 308.0 345.4 360.4 5.0% Transp., Warehousing & Utilities 102.2 90.5 111.1 119.4 16.8% Information 113.4 111.0 139.6 147.5 30.0% Financial Activities 201.4 170.6 210.4 219.2 8.8% Professional & Business Services 581.1 547.1 719.8 912.8 57.1% Educational and Health Services 385.6 410.5 516.5 655.0 69.9% Leisure and Hospitality 332.5 324.3 392.7 462.5 39.1% Other Services 112.1 109.3 139.2 156.8 39.9% Government 486.0 457.5 482.6 530.1 9.1% Self Employed 317.5 298.0 368.7 416.4 31.1% Total Jobs 3671.6 3401.8 4088.3 4640.1 26.4% Source: 2007, 2010: EDD and American Community Survey for self employed: 2020, 2040: CCSCE. Data includes San Benito County, which is part of the San Jose metro area. As a result the totals are slightly higher than the ABAG region totals cited above in the report. The Challenges to Achieving the Projected Job Growth ABAG asked CCSCE to develop what they called an “unconstrained” set of Bay Area job projections. CCSCE’s analysis assumes that over the next 30 years, many of the challenges facing the nation, state and region will be addressed. In addition this analysis assumes that at the regional level, the Bay Area will address challenges of housing, transportation and quality of life as well or better than other regions in the United States. Providing investors and families a high quality of life is essential to maintaining the Bay Area’s competitive advantage in the technology sectors that are expected to drive the region’s job growth. Up until now the region has done well in the competition for providing great places to live and work. A study of Silicon Valley high tech employers completed in 2011 reported: 16 ‘‘Silicon Valley’s top competitive advantage is its highly skilled pool of talent. Executives interviewed for the study say there is nowhere else in the world with such a concentration of highly skilled tech professionals, which is essential for businesses that require a steady stream of talent. The Valley’s high quality of life-----including beautiful weather, excellent schools, and the ability to live and work in the suburbs-----was another major advantage, making CEOs want to locate their companies there and attracting talented workers and their families.’’ On the other hand maintaining a high quality of life is increasingly difficult. A 2011 survey of Silicon Valley CEOs states the quality of life imperative succinctly. The Silicon Valley Leadership Group 2011 CEO Survey reported “a deteriorating state infrastructure in areas ranging from public education to public transportation has added to the difficulties of recruiting the best workforce, finding them housing and educating their children to be tomorrow’s world-class workforce”. The 2012 Bay Area Council Economic Institute Bay Area economic profile identifies a list of well-known Bay Area competitiveness challenges:  Housing affordability. Although median home prices have fallen and affordability is higher than it has been in several years, Bay Area median home prices and rents are still well above the national average.  K-12 and higher education. Both are facing continuing budget cuts throughout California as well as rising tuition levels at the state’s public and private colleges. Moreover, average test scores are at or below nationwide levels and high school dropout rates remain high. While immigration can continue to supply a part of the region’s workforce needs, most jobs will be filled by residents who are born, educated and trained in California.  Transportation infrastructure. Despite the ongoing work by MTC and local transit districts and the $billions planned for improving highway and public transit travel, the region does not yet have sufficient funding for all needed transportation infrastructure investments. Although transportation funding is a nationwide problem, it is an especially important challenge in a region that needs to be able to move people and goods efficiently to compete in the 21st century global economy  Governance challenges. California does not as yet have a plan to develop state and local budgets that are balanced and able to provide high quality public services. The unconstrained job growth analysis shows the competitive strength of the Bay Area economy going forward if these challenges can be met. 17 Sources and Methodology Appendix 1990-2010 Job Estimates The job estimates for the United States were published by the U.S. Bureau of Labor Statistics (BLS) at www.bls.gov. The job estimates used in developing the ABAG projections were those available in September 2011. BLS data and methodology are available at http://www.bls.gov/ces/. The wage and salary job estimates for California and the Bay Area were published by the California Employment Development Department. These are available at http://www.labormarketinfo.edd.ca.gov/Content.asp?pageid=166. The job estimates used in developing the ABAG projections were those available in September 2011. The Bay Area jobs data base includes the following metro areas as used by EDD: Oakland (Alameda and Contra Costa Counties); Napa (Napa County); San Francisco (Marin, San Francisco and San Mateo counties); San Jose (Santa Clara and San Benito counties); Santa Rosa (Sonoma County) and Vallejo (Solano County). For the ABAG region total job estimates and projections, San Benito County was excluded by the county is included in the table on page 16. Estimates for self employed workers were developed from the 1990 and 2000 Census and for recent years annual estimates are available from the American Community Survey at http://www.census.gov/acs/www/. The job estimates used in developing the ABAG projections were those available in September 2011. Methodology The job projections to 2040 developed for the ABAG region were based on a best-practice projection framework used by other regional planning agencies in California and by national forecasting firms that do long-term regional projections throughout the United States. A summary of the methodology is included in the Power Point presentation at the February 7, 2012 Regional Advisory Working Group Meeting and available at http://apps.mtc.ca.gov/events/agendaView.akt?p=1820. A more detailed description of the projection framework is available in Review of Best Practice State and Regional Projection Methodologies and Review of Recent Economic and Demographic Trends prepared by CCSCE for ABAG, SACOG and SCAG in April 2011. There are three major components common to regional and state long-term projections and these are the basis for the current ABAG methodology: 18 1) Population projections are developed based primarily on the projected rate of job growth. 2) State and regional job projections are developed based on projected national job growth and the share of national job growth expected to locate in a particular state or region. 3) Household projections are developed from population projections using varying combinations of demographic projections based on household formation rates and analyses of housing market conditions. The remainder of this section focuses on the job projection methodology. Job Projections for 2020-2040 All of the projections described in this report were developed by CCSCE in the fall of 2011. The first step in developing job projections for the ABAG region is to develop projections for national job growth in total and by major industry group (the industries shown on page 16). United States The U.S. job projections for 2020 were adapted from the 2018 projections published by BLS in November 2009 and described in the November 2009 Monthly Labor Review. The press release can be found at http://www.bls.gov/schedule/archives/all_nr.htm#ECOPRO and the articles can be found at http://www.bls.gov/opub/mlr/2009/11/home.htm. CCSCE modified the BLS projections to reflect the impact of the recession and changes in labor force participation trends that occurred after the 2009 projections were prepared. In February 2012 BLS produced a new set of projections to 2020 that can be found at http://www.bls.gov/emp/. The 2020 U.S. job projection used by CCSCE in developing the ABAG job was within 0.8% of the newly published BLS projection of total jobs for 2020. The projections for 2030 and 2040, as explained on page 6, were developed in three steps: 1) Projecting national population growth 2) Translating the projected population into total labor force and total jobs 3) Projecting job growth by major industry group 19 CCSCE used a set of U.S. population projections developed by John Pitkin and Dowell Myers that are based on 2010 Census estimates as a starting point and immigration assumptions developed by a panel of experts. The projection report and tables can be found at http://www.usc.edu/schools/price/futures/. The existing Census Bureau long-term population projections were developed before the 2010 Census results were released and are available at http://www.census.gov/population/www/projections/. The population projections developed by Pitkin and Myers and used by CCSCE have a lower U.S. population in 2040 than either the Census Bureau baseline or low projections series as a result of assumed lower immigration levels. The labor force projections were developed based on BLS projected labor force participation rates to 2050 that can be found at http://www.bls.gov/opub/mlr/2006/11/contents.htm. CCSCE modified the projections to increase the labor force participation of older workers after 2020. A national unemployment rate of 6% was assumed for 2020 to 2040. The population, labor force and unemployment projections combine to produce a projection of total jobs in the U.S. that was used in developing the ABAG projections. Jobs by industry for 2020 were developed based on the BLS projections adapted for trends emerging after they were published. The major industry projections for 2030 and 2040 were developed by CCCCE based on 1) the trends between 2010 and 2020 and a review by CCSCE of major industry job trends projected by other major national forecasting firms. California The California job projections were developed by CCSCE using a proprietary model that relates California job growth to U.S. job growth. Industries are categorized into economic base industries (those that sell a majority of goods and services outside the region, also known as export industries) and those that serve the local population. Growth in economic base industries, as explained on page 6 is related to the pool of job opportunities reflected in the national projections and the share locating in California based on analysis of historical trends and CCSCE judgment. Job growth in California’s economic base industries depends on how fast they are expected to grow nationwide and the state’s competitive position represented by the share of national jobs expected to locate in California. 20 Once the economic base jobs are projected, population serving jobs are added based on 1) the projected profile of these jobs in the nation and the extent to which California’s profile of population serving jobs differs from that in the nation. For the California projections developed as part of this project the principal findings were: 1) California is expected to have job growth that is slightly faster than the nation to 2040 based on the state’s industry structure, which has an above-average share of economic base jobs with high projected national growth. 2) In general the share of jobs in key industries is not expected to increase in California. It is the industry structure that pushes the overall job growth rate slightly above the national average. 3) The profile of population serving jobs in the state and nation are similar. Bay Area The Bay Area job projections were developed using the CCSCE model that is described above for California and as explained on page 6. As explained on pages 13 and 14 and supported elsewhere in text the pri8ncipal finding is that the Bay Area is projected to experience job growth that is slightly above the state average as a result of the region’s favorable economic base industry structure with an above average share of the sectors expected to post above-average job growth in the nation and state. Other Sources The venture capital funding graph on page 4 comes from data published by Price Waterhouse Coopers Lybrand and can be found at https://www.pwcmoneytree.com/MTPublic/ns/nav.jsp?page=notice&iden=B. The ranking of San Jose cited on page 5 as tied with Houston as the leading large metro area for job growth comes from a BLS press release that can be found at http://www.bls.gov/news.release/pdf/metro.pdf. The UCLA Bay Area forecast cited on page 5 can be found at http://www.bayareaeconomy.org/economic-forecasts/. The UCLA long-term forecast for California cited on page 10 was published in The UCLA Anderson Forecast for the Nation and California 2011-2021 in June 2011. UCLA forecast a 22.5% increase in total nonfarm jobs in California between 2010 and 2020 which is slightly higher than the CCSCE projection of 21 22 19.2% for total jobs during this period. UCLA forecast that California would outpace the nation in percentage job growth in each year through 2021. The McKinsey report cited on pages 12 and 13 and the Bay Area economic profile cited on page 17 will be published by the Bay Area Council in the spring of 2012. The Silicon Valley workforce report cited on page 16 can be found at http://www.novaworks.org/LaborMarketInfo/Reports/InformationTechnologyStudy .aspx. The Silicon Valley Leadership Group CEO Survey cited on page 17 can be found at http://svlg.org/press/library. Ci!JgfPaloNto Office of tile Mayor and City Council March 5, 2012 Mr. Mark Luce, President Association of Bay Area Govemments Joseph p, BOlt Metro Center P,O. Box 2050 Oakland, CA 94607-4756 Re: City of Palo Alto Comments on Sustainable Communities Strategy (SCS) Altemative Scenarios Dear Mr. Luce: AssoCiation of Bay Area Governments (ABAG) staff has requested local agency comments on its proposed land use Alternative Scenarios developed as a part of the One Bay Area Sustainable Community Strategy (SCS). We look forward to the fUlther discussion and refinement of the Preferred Scenario before the final Regional SCS is completed early in 2013. However, at this juncture we believe it is necessary for the City to express its concerns regarding the SCS Alternative Scenarios and the related regional jobs and housing forecasts, and to suggest altel11ative approaches to the Pre felTed Scenario. This letter provides the City of Palo Alto's (City) comments, which have been formulated following the City's considerable review and analysis of the Alternative Scenarios and related regional job and housing forecasts. The City acknowledges and appreciates the fact that the SCS process will continue following release of ABAG's Preferred Scenario, scheduled for March 8, 2012. In summary, the City's concerns are as follows: • • • The City of Palo Alto has been a national leader in implementing policies and programs that reduce greenhouse gas (GHG) emissions and the effectiveness of these efforts should be considered as a part of the SCS and achieving regional GHG emission reduction targets, The regional forecast of jobs and housing being considered as part of the SCS likely overstates future growth in the Bay Area and at a minimum is highly uncertain; ABAG should recognize the distinct possibility that actUlll growth rates in the Bay Area over the next 30 years may he lower and should phase job and housing allocations and implementation accordingly. Palo Alto's allocation of jobs and bousing units under all of tbe Alternative Scenarios is excessive by reference to its historical growth trends and development capacity; these allocations should more accurately consider policy constraints, market feasibility, and tbe high infrastructure costs and local fiscal impacts of such intensive redevelopment. P.O. Box 10250 P.lo Alto, CA 94303 650,329.2477 1 650,328.3631 fax • ABAG: SCS Alternative Scenarios March 5, 2012 The land use changes contemplated in the SCS Alternative Scenarios have a propottionately small contribution to achieving AB32/SB375 GHG reduction targets and there are very limited differences shown between the Alternative Scenarios; the considerable effort and investment needed to affect these land use changes should be re-directed to more cost effective regional and local GHG reduction measures. While the City retains serious concerns regarding the Regional Forecast of jobs and housing and also the allocation of future development under the Alternative Scenarios, Palo Alto is firmly committed to doing its share to achieve the State-mandated (AB32/SB375) GHG reduction targets. The measures already adopted by the City provide ample evidence of this commitment. Going forward, the City expects to cooperate with ABAG and our other regional pattners in the future efforts needed to achieve substantial reductions in GHG emissions through realistic and achievable regional and local policies, programs, and investments. The following items elaborate on the summary points listed above. 1. The City of Palo Alto has beell a lIatiollalleader ill implemelltillg policies alld programs that reduce GHG emissiolls. ' Over the past decade, the City of Palo Alto has adopted a range of policies, programs and projects to reduce GHG emissions, focused upon improving energy efficiency, enhancing multi modal transportation alternatives to the single-occupant vehicle, and creating walkable, mixed use districts. Implementing these policies, programs, and investments the City has become a national leader in reducing GHG emissions. Some of the key programs include: a. City of Palo Alto Climate Protection Plan (CPP): The CPP, adopted by the City Council in December 2007, includes goals for the reduction of CO2 from a 2005 baseline level as a result of changes in City operations and from CO2 reduction efforts within the community. • The City has surpassed its short term goal of a 5% reduction in emissions by 2009 for a total reduction of 3,266 metric tons of CO2. • By 2020, the City and community will reduce emissions by 15% from 2005 levels, equal to 119,140 metric tons of CO2, consistent with State emission reduction goals. b. Availability of Clean Energy: The City of Palo Alto is in a unique position as owner and operator of its electric utility to make available and provide clean energy to City customers. In this regard, the City Council adopted a goal to have 33% of the electricity supply to be provided by renewable electricity suppliers by 2015, five years in advance of the State requirement. • For FY2011, renewable electricity supplies account for 20% of the City's needs. • Contracts are in place with suppliers to provide 26% of the City's electrical needs as renewable by FY2014 with the potential to reach 30% from contracts that are still under negotiations. • CUiTently, 24% of the City's customers participate in the Palo Alto Green program, paying a surcharge on electric service to support renewable electricity supplies. • The City's Utilities Department is preparing a plan to be released later this year for the electric portfolio to be carbon neutral. 2 ABAG: SCS Alternative Scenarios March 5, 201:? c. Utility.Programs to Reduce Emissions: In addition to providing clean energy options for its customers, the Utilities Department offers programs such as rebates, assistance, information, and workshops to help customers increase electricity efficiency and cost savings that reduce emissions. These successes of these programs are measurable: In FY20 I I, Palo Alto customers reduced CO2 emissions by 12,557 tons through the use of electric and natural gas efficiency programs, incentives for solar photovoltaic (PY) and solar hot water systems, and other program efficiencies. d. Leadership in Green Building and Sustainable Design: In FY 2009, the City Council adopted the City's Green Building Ordinance to build a new generation of efficient buildings in Palo Alto that are environmentally responsible and healthy places in which to live and work. This program was one of the first in the nation to mandate green building requirements and certifications for virtually all public and private buildings. • In FY 2011, the City initiated thcfirst LEED-ND pilot program (LEED for Neighborhood Development) in the United States for assessing a development site's ability to qualify as a sustainable neighborhood project, including features that reduce dependence on automobile use, increase walkability, and encourage healthy living. • The City also rolled out energy use disclosure requirements for existing buildings undergoing small renovation work to belter understand the existing buildings' current performance and areas where education, policy, and programs can be influential in reducing energy usage. • The amount of CO2 diverted from the environment has increased since the adoption of the 2009 ordinance and programs. In 2010, the first full year of the ordinance, CO2 was reduced by approximately 1,013 tons. In 2011, C02 was reduced by approximately 2,818 tons, a 178% increase over the previous year. • Prior to the City's ordinance, as few as six green building projects existed throughout the City. By the end ofFY 2011, more than 240 green buildings have been completed or are under construction. 'e, Affordable Housing: The City of Palo Alto has been a leader in providing for affordable housing in one of the most expensive housing markets in the nation. • In 1974, Palo Alto became one of the first cities in the United States to adopt an inclusionary housing program that required the provision of below market rate (BMR) residential units in new multiple-family dwelling projects, • Today, the City oversees 239 ownership units and 198 rental BMR units that are available to qualified applicants. As the cost for housing in Palo Alto continues to increase, the value or these affordable units to provide housing within the urbanized Bay Area has also increased. BMR housing provides greater opportunity for lower income fami lies to live closer to their jobs and utilize public transit. • Recently, Palo Alto welcomed two new BMR housing projects, including the Tree House Apartments, a 35-unit complex with a Green Point Rating of 193 (the highest score in Palo Alto for multiple family housing) and Alta Torre, a 56-unit complex for very low income seniors. • Construction is underway at 801 Alma Street, a 50-unit family affordable prqject immediately adj acent to CalTrain and wilhin walking distance to shops and services on University Avenue in downtown. 3 ABAG: SCS Alternative Scenarfos March 5, 2012 f. Higher Density Land Uses Near Transit: The City's Comprehensive Plan designates two areas of the City (downtown and California Avenue) as appropriate for "Transit Oriented Residential," in a 2,OOO-foot radius of the City's two Cal train stations. These areas are identified for higher intensity residential and mixed-use development focused around a walkable, bicycle friendl y environment. • In 2006, two years before the adoption of SB375, the City Council adopted the Pedestrian and Transit Oriented DeVelopment combining district (PTOD) for the area around the California Avenue CalTrain station. This district allows for residential densities at 40 units per acre, increased floor area ratios, increased building heights, reduced parking requirements, and density bonuses for the provision of BMR housing in mixed-use and multiple family prqjects. • In 2007, Council supported the designation of this area as a PriOlity Development Area (PDA) as part of ABAG's FOCUS program that would eventually evolve into Plan Bay Area. This land use planning effort is one example of a pattern of early-adoption decisions made by the City Council to address growth, transit and greenhouse gas emissions within our community. • The Council has directed that housing sites proposed in the City's Housing Element should be focused in transit-proximate areas, and that increased height and intensity may be considered in those locations. g. Transportation Policies and Projects: The City of Palo Alto has developed transpOltation policies, programs and projects to implement a transit-oriented, walkable and bicycle-friendly vision that demonstrate leadership of the "Complete Streets" concept promoted by the Metropolitan Transportation Commission. Some of these key recent measures include: • Bicycle and Pedestrian Transportation Plan: The City is completing its updated plan to accommodate enhanced bicycle and pedestrian facilities and programs. and to elevate the City's Bicycle-Friendly Community slatus from Gold to Platinum level. • Stanford AvcnuefEl Camino Real Intersection Improvements: The City has recently completed improvements at this intersection to enhance safety for pedestrians and cyclists, including children who use the intersection as a route to school, and to upgrade the aesthetic qualities of the intersection and of El Camino Real. We expect the project will serve as a template for improving intersections throughout the Grand Boulevard corridor. • Safe Routes to School: The City's Safe Routes to School program has resulted in a phenomenal increase in school children bicycling and walking to school over the past decade. In the 2011 fiscal year, City staff coordinated lAO in-class bike and pedestrian safety education programs in 12 elementary schools, reaching 4,250 students. Recent surveys of how children usually get to elementary school showed an average of 42% choosing to walk, bike or skate to school, compared to a national average of only 13% (figures for middle schools and high schools are even greater). A rc{:cnt grant will allow the City to prepare Safe Routes maps for every elementary school in the city as well as to expand our education cuniculum into middle schools and to adults. • Traffic Calming on Residential Arterials: The City has an ambitious ITaffic calming program along "residential arterials" in efforts to support our Safe Routes to School program and to enhance bicycle and pedestrian safety. In particular, the ongoing 4 ABAG: SCS Alternative Scenarios March 5, 2012 Charleston Road-Arastradero Road Corridor project has provided substantial safety improvements and selective lane reductions to enhance bicycling and walking while maintaining efficient levels of vehicle throughput similar to those prior to the traffic calming improvements. • Bicycle Parking Corrals: The City has recently installed six green "bicycle parking corrals" in the Bay Area, with each corral providing for up to 10 bicycle parking spaces in highly visible, signed on-street areas in downtown. Up to a dozen more such installations are planned in the downtown and Califoll1ia A venue areas. • Califoll1ia Avenue Streetscape Improvements: A pending grant would support the substantial upgrade of Califoll1ia Avenue to a more pedestrian and bicycle-friendly roadway, incorporating "complete street" principles, and also enhancing access to the Califoll1ia A venue Cal train station. • Local Shuttles: The City, with support from the Caltrain JPB, offers local shuttle services for commuters, school children, seniors and others between points of interest within the city. These shuttles fUlther reduce the need for single-occupant vehicle trips and reduce traffic congestion and parking needs. Accordingly, the City of Palo Alto requests that ABAG consider the effectiveness of these local GHG emission reduction effOlts, incorporate them as a part of the SCS and related regional GHG reduction targets, and provide "credits" to those jurisdictions that have demonstrated implementation of meaningful GHG reduction measures. 2. The Regional Forecast of jobs alld hOl/sillg being considered as part of the SCS appears to overstate future growth in the Bay Area. The regional jobs and housing forecast used for the Altell1ative Scenarios is lower than the forecast for the Initial Vision Scenario and the Core Concentration Unconstrained Scenario, reflecting comments received from the local agencies following review of the Initial Vision Scenario. However, in the City of Palo Alto's view, the most recent jobs and housing forecasts for the three "constrained" Altell1ative Scenarios remain at the high end of plausible Bay Area jobs and housing growth over the next 30 years. The City is disappointed and dismayed at the minimal public discourse around the development of these projections, though we do appreciate Dr. Steven Levy's recent presentation of the analysis behind the projections. At this point, however, ABAG has provided insufficient justification for the methods and results of the regional job and housing forecasts used in the Altell1ative Scenarios. An evaluation of the Regional Forecast prepared late last year by Councilmember Greg Schmid provides an assessment of Califoll1ia growth forecasts (see Attachment A), indicating the tendency for forecasts to overstate growth as compared to actual figures from the Census, and discussing some of the key factors that will influence Califoll1ia's future growth. a. Jobs. Regarding the ABAG jobs forecast, a comparison with the last 20 years is noteworthy. Average job increases between 1990 and 2010 approximated 10,000 net new jobs annually. Excluding the three years that included the Great Recession where substantial jobs losses occurred (i.e. 2008-2010), the Bay Region added jobs at an average annual rate of 25,200 between 1990 and 2007. The ABAG jobs forecast used for the Altell1ative Scenmios assumes that the Region will add an average of over 33,000 jobs annually from 2010 to 2040, 5 ABAG: SCS Alternative Scenarios March S, 2012 a 32% increase over the pre-recession trend line. The method used to arrive at the jobs forecast assumes a "shift-share" of a national jobs growth forecast that itself is subject to question. As a palt of revisions to the regional jobs forecast, ABAG should consider a more fundamental economic assessment that identifies the key industries in the Bay Area that will drive job growth and also the distinct possibility that future jobs and housing may be closer to recent historical growth trends. b. Housing. Regarding the ABAG housing forecast used for the Alternative Scenarios, an additional 770,800 households are shown added to the Bay Area between 2010 and 2040 -an annual average growth of 25,700 households. The average annual housing growth rate between 1990 and 2010 was 21,000. At the present time, two years into ABAG's forecast period, the Bay Area, like much of the United States, remains in a weak housing market characterized by very limited new development, low pricing, slow sales of existing homes, tight credit, and an oversupply of homes resulting from a historically high number of foreclosed and distressed propelties. These conditions are expected to continue for several more years until the existing inventory is reduced and substantial improvement in the job market and related increases in household income occurs. In any event, the Bay Area will need to be in "catch-up" mode, meaning even higher additional households per year must be realized to meet the SCS forecast growth rates, once more normal housing market conditions emerge. Moreover, ABAG's regional housing forecast is based on a fixed share of a national population forecast prepared by the U.S. Census Bureau that presumed international in migration (primarily of Asian and Hispanic peoples) would continue and comprise approximately 80 percent of all population growth nationwide. c. Housing Affordability. In addition to questions regarding job growth (the ultimate cause of housing demand) there are a number of other questions regarding ABAG's housing forecast including those related to affordability. A presentation made by Karen Chapple of UC Berkeley at the ABAG's January Regional Advisory Working Group (RA WG) suggested that given likely wages paid by the new jobs expected, over 70 percent of all new households formed in the 2010 to 2040 period will be "moderate" income or below. In many Bay Area locations, especially the inner Bay Area urbanized areas that are the focus of growth under the SCS Alternative Scenarios, such "affordable" housing units must be subsidized in one fashion or another, either as "inclusionary" units burdened upon the market rate units constructed or by public subsidies such as (now eliminated) redevelopment agency funding and federal tax credits. Given the loss of redevelopment powers and funding and recent court cases affecting inclusionary programs (Palmer, Patterson) there is no assurance that adequate housing subsidy funding will be available. d. RHNA. Then there is the matter of the Regional Housing Needs Allocation (RHNA). As presently proposed, the regional forecast and related Preferred Scenario allocations to be released as a draft on March 8th will apparently serve as the basis of the future RHNA for each city and county, which will require a one-third of the overall Preferred Scenario housing allocation to cities in each 8-year "planning period" regardless of any assessment of realistic development capacity (note: recent information from ABAG indicates that less than one-third of the 2010-2040 forecast is likely for the 2015-2022 period, however, due to the economic 6 ABAG: SCS Alternative Scenarios March 5, 2012 housing downturn). This approach seems highly arbitrary, insensitive to local conditions and constraints, and far beyond what can realistically be expected from an economic perspective. Accordingly, givCll all of these concerns, the City strongly recommends that the jobs and housing forecasts for the Preferred Scenario be reduced to reflect more accurately current conditions, historical trends, and more fundamelllal assessment of economic (job) growth potential in the Bay Area. Developing a more realistic jobs and housing forecast would reduce the implied need to intensify land uses, reduce projected OliO emissions by lowering energy consumption, congestion and single occupancy vehicle trips, and require less costly transit and highway infrastructure investments. The SCS effort is to be revisited and updated every four years, so that there would be future opportunities to re-evaluate whether a higher forecast is appropriate and adjustments would be needed. 3. Palo Alto's allocation of jobs and 'lOusing Ill/its under the Altemative Scenario.I' is highly unrealistic and excessive relative to historical growth trends and develapment capacity. Santa Clara County dominates all other Bay Area counties in the aJiocation of ABAG's regional forecast of jobs and housing, absorbing 30 percent of the regional job forecast and 26 percelll of the regional housing forecast. Palo Alto is allocated new jobs ranging from 18,040 Outward Growth) to 26,070 (Focused Growth). Palo Alto households allocated range from 6,107 (Outward Growth) to 12,250 (Constrained Core Concentration and Focused Growth). These allocations have been made without regard to existing development capacity in Palo Alto (use of remaining vacant land and redevelopment of existing developed areas), the likely match between new household affordability and local housing prices, or a range of other potential local costs for achieving the required high density development. a. Jobs. The City presently contains approximately 62,300 jobs, according to ABAG. During the past decade (2000 to 2010), Palo Alto experienced a 14 percent decline in cmployment reflecting the combincd effect of the "dot-com" bust and the Great Recession. While economic conditions are expected to improve, there have been structural changes in technology industries that have ddven growth in the Silicon Valley over the past 50 years that portend only modest growth. The Alternative Scenarios assume that Palo Alto's job growth by 2040 will increase over the 20 10 estimate by between 27 percent and 40 percent. b. Housing. The housing projections in the Alternati ve Scenarios represent a 25-50 percent increase in housing units /fom 2010-2040, up to approximately 400 new units per year. The City has in the past 40 years (1970-2010) produced an average of 148 units per year. To more than double that output in a relatively built-out city is again entirely unrealistic and using such an assumption as the basis for growth scenarios and transportation investments will likely result in failure of the planning effort. c. Constraints. The City of Palo Alto is highly built out, and the existing limited number of vacant sites and redevelopment opportunity sites severely limit how the households and jobs allocated to Palo Alto in the SCS Alternative Scenarios could be accommodated. The "constrained" scenarios clearly do not appear to consider the many constraints to new development in Palo Alto, including limited school capacity and funding for infrastructure. 7 ABAG: SCS Alternative Scenarios March 5, 2012 Accordingly, the City requests that the allocations of jobs and housing units in Palo Alto should be lowered substantially to more accurately consider policy constraints, market feasibility, and infrastructure and local fiscal impacts of such intensive redevelopment. 4. The land use cilanges contemplated in the SCS Altemative Scenarios have a proportionately small c01ltribution to achieving AB321SB375 GHG reduction targets. The AB32/SB375 target for Califomia is a reduction to 85 million equivalent metric tons per year by 2050, an 80 percent reduction from current levels, To return to 1990 levels of 427 million tons, an 80 million ton reduction of projected 2020 levels is required. Of this 80 million ton reduction, approximately 96 percent is proposed to be achieved from improved fuel standards, energy efficiency, industrial measures, and other methods needed to curb emissions from the construction, manufacturing, and agricultural sectors. Only four percent, however, or 3.2 million tons, would be achieved by altering land usc patterns. This is shown effectively on the graph (Attachment B) prepared by the Contra Costa County Transportation Authority (CCTA) in their letter dated February 15, 2012. a, Negligible Difference Between Alternatives: The potential contributions of the land use changes contemplated in the SCS Alternative Scenarios show reductions in GHG emissions through 2040 range from 7.9 percent (Outward Growth) to 9.4 percent (Constrained Core Concentration). Compared to the initial Currenl Regional Plan Scenario, the Alternative Scenarios reduce GHG emissions by 0.9 percent to 2.4 percent of the remaining 4 percent affected by land use and transportation patterns. This is a negligible difference between the land use scenarios and argues for a more flexible approach that combines other GHG emission reduction strategies with a more realistic land use scenario. b, Regional Transportation Pricing and Policies: The MTC analysis of various transportation plicing and policy changes (e.g., telecommuting, electric vehicle strategies, parking pricing) may account for at least a 6.5% further reduction in GHG emissions, considerably more significant than the differences between the land use pattems in the Alternative Scenarios. c. Cost Effectiveness. Given the numerous challenges associated with fundamental changes in the way that Bay Area land use patterns would otherwise evolve, including wholesale changes to land use. regulations, presuming changes in market characteristics and preferences of homebuyers, and the need for substantial public investments and subsidies, we question the feasihility and cost-effectiveness of the Alternative Scenalios. Regarding market feasibility, there is no evidence that the resulting housing capacity and prototypes would match buyer preferences and affordability, Regarding cost-effectiveness, the comparable costs (mostly borne by local jurisdictions) of implementing the Altemative Scenados may be far higher than other altematives for achieving comparable GHG emission reductions, Accordingly, the City of Palo Alto recommends that a perfOlmance-based approach, involving establishing GHG reduction targets for the local jurisdictions along with a menu of options for achieving these targets (including feasible and realistic alterations in land use policy) should become the basis of the proposed SCS. B Conclusion ABAG: SCS Alternative Scenarios March 5, 201 2 In conclusion, the City of Palo Alto suggests that the Preferred Scenario for the Sustainability Communities Strategy should include: • A focus on GHG emission reductions, with the flexibility for each city and county to provide for a reasonable minimum amount of housing plus options for other commitments to GHG emission reductions; • Grant funding for transportation and planning oriented to Priority Development Areas (PDAs); • Realistic housing forecasts limited to each upcoming 8-year RHNA cycle, with review every four years to update projections; and • Longer range projections that are not allocated to cities and counties, but are used to provide context for regional transportation investments. Thank you again for the opportunity to comment in advance of your proposal for a Draft Preferred Scenario. If you have questions or need additional infonnation, please contact Curtis Williams, the City's Director of Planning and Community Environment, at (650) 329-2321 or curtis. williams@cityofpaloalto.org. Sincerely, /), ~ -{ Yiaway Yeh Mayor City of Palo Alto Attachments: Attachment A: November 15,2011 Memorandum: "California Demographic Forecasts: Why are the Numbers Overestimated," prepared by City of Palo Alto Councilmember Greg Schmid Attachment B: "Regional Land Use and Transportation SCS: Achieving Statewide GHG Reduction Rates," prepared for Contra Costa Transportation Authority cc: Adrienne J. Tissier, Chair, Metropolitan Transportation Commission Steve Heminger, Metropolitan Transportation Commission Ezra Rapport, Association of Bay Area Governments John Ristow, Valley Transportation Authority Palo Alto City Council 9 California Demographic Forecasts: Why are the numbers over estimated'l Prepared bV City of Palo Alto November 15, 2011 Actual California Population growth IAttachment A • 1 Over the last decade, the state of California added 3.4 million people, to reach a total of 37,3 million, This was an increase of 10% over the decade, This growth rate follows the gradual slowing that started after 1990, down dramatically from the very high rates of the post-World War II era, Note that the Department of Finance's (DOF) 2007 projections reflect a very high growth perspective, The DOF numbers are currently used as the population forecasts for all state and local projects-they are not scheduled to be revised until20D. Table 1. California's population growth over the last five decades (average growth from census to census) Census 1960s 29.2 1970s IS.5 19S0s 25,7 1990s 13,8 2000s 10.0 2010s 2020s 2030s Dept of Finance Projections (2007) 14.8 12.S 11.6 10.2 Source: US Census Bureau actual Census numbers; California Department of Finance 2007 Projections. Recent State forecasts have been consistently over-estimated Even after the sharp decline in growth during the 1990s, forecasters consistently tended to be overly optimistic about population growth rates through the 2000s. In 2005, the Public Policy Institute of California issued a report ("California 2025: Taking on the Future") that included the population projections of all the key demographic forecasters. The consensus forecast from this group was some 40% higher than the actual outcome for the state: Table 2. California Population Forecasts for 2010 made before 2005 (Percentage growth expected from ZOOO-2010) California Dept of Finance 1S.Z USC Population Dynamics 11.6 UC Berkeley (Lee, Miller) 13.9' Public Policy Institute of CA 1S.Z· CCSCE 17.2 UCLA Anderson Forecasting 16.6 Average of six 2005 forecasts 15.0 ·=center point of band Source: Public Policy Institute of California, "California 20ZS: Taking on the Future", 2005, Page 29. 2 The conse IlSUS forecast was some 50% above the actual numbers. The only forecaster who produced a number below the actual 10% growth was the UC Berkeley group who stated that there was a 5% chance that the growth rate would be lower than 7.1%. The 2005 PPIC Report stated that "Recent trends make population projections for California especially diflicult. .. For these reasons, planners should consider alternative population scenarios ... as useful alternatives for planners." (PPIC, 2005, pages 27- 28) Even as late as the end of 2009, on the eve of the decennial census, estimates by the California Dept. of Finance (the organization responsible forthe numbers that are used for all state allocation formulas) remained strikingly high at 14.1% which was 1.5 million or 44.7% above the above the contemporaneous and more accurate Census Bureau's Current Population Estimates. Critical Components of Change and the Future The Census data provide a nice detailed perspective on the actual components of change during the decade. While the 3.1 million people added through natural increase (births minus deaths) were the largest single growth factor, the 2 million net gain from foreign immigration was important in overcoming a net outflow of 1.6 million from native born emigration, primarily to other stales. Table 3. Components of Population Change In California, ZOOO-ZOlO (millions of people' Births Deaths +5.45 -2.35 Net Domestic migration -1.63 Foreign immigration +2.58 Foreign emigration -0.59 Military, etc -0.07 TOTAL +3.38 Source: USC, Population Dynamics Research Group, "What the Census would show", February 2011. 3 The challenge for projecting change in the future is the dramatic shifts in some of these base categories. With the aging population, we know that, even with slight increases in longevity, the aging population in California will raise the annual number of deaths in California from 271K in 2011 to 462K in 2039, while the number of births will rise slightly from 532K in 2011 to 551 in 2039. The natural increase will fall from some 260K today to 90K in 2040. Thus, over time any increase in California's population will increasingly rely on migration. Since net domestic migration has averaged a net outflow of some 160K per year since the early 1990s, any growth in population will be increasingly dependent on foreign migration. (Source: USC, Population Dynamics Groups, April 2011). There is little reason to See a major shift in domestic migration with California's high cost and high unemployment rate. That leaves foreign migration as the critical component source of long-term population growth. The most dynamic source for California'S growth has been immigration from Mexico, both legal and illegal. All observers (The Dept of Homeland Security, the Pew Charitable Trust Hispanic Center, and the Mexican Migration Project at Princeton) agree thaI net immigration from Mexico has been down dramatically in recent years with the stricter enforcement of border crossing and the prolonged recession in the US. Pew estimates that the illegal immigrant population in the US fell by some 7% between 2007 and 2010. The important debate about the future is whether this is a business cycle phenomenon or part of a longer term trend. The group that has the best data source and takes the longer term look is the Mexican Migration Project at Princeton. For decades they have been tracking migration patterns from Mexico and doing annual surveys of thousands of families from migration centers in Mexico. They found that the percent of first time immigrants from the Mexican communities of highest immigration fell from 1.2% of adults in 2000 to 0.6% in 2005 to zero in 2010. They identify that the changes are due to Mexican demographic and economic factors as much as from U,S, conditions. They identified five internal factors of change in Mexico: • Fertility rates are falling dramatically from 6,8 births per women in 1970 to 2.8 in 1995 to 2 in 2010 (replacement levell, • The number of young people entering the labor market has fallen from one million a year in the 1990s to 700K today and demographic factors will bring that down to about 300K in 2030, not enough to meet local job needs. 4 • The rate of college attendance and college completion has doubled over the last decade, raising the career path of an increasing share of young workers. • The wage disparity between Mexico and the U.S. is narrowing sharply with average wage gaps falling from 10:1 in the 1960s to some 3.7:1 in the early 2000s. • The cost of migration has risen dramatically for illegal entrants, further narrowing the earnings gap. All of these factors point to the need, at the least, of looking at alternative scenarios of population growth in California that are more sensitive to possible underlying changes in migration patterns, 5 Sources of Demographic projections about California US Bureau of the Census (responsible for the decennial Census and does updated estimates each year of state populations-has been much closer to actual numbers than the Cal Dept. of Finance) California Department of Finance (responsible for state population estimates between the Census years-forecasts used as key source for state government planning). Statewide estimates for 2010 (made in 2009) were 41% higher than the 2010 Census numbers for the state, 83% over for the nine Bay Area counties and 137% higher for the three West Bay counties. Ronald Lee, UC Berkeley, Center for Economics and Demographics of Aging, "Special Report: The Growth & Aging of California's Population", 2003 (an important report that identified the detailed assumptions that went into the Department of Finance's long-term projections). Hans Johnson, Public Policy Institute of California, "California 2025: Taking on the Future", Chapter 2 'California's Population in 2025' (a report that gathered projections from eight academic and government sources). Johnson concluded that "population projections for California are especially difficulLln addition to overweighting contemporary trends, forecasters are notoriously bad at predicting fundamental demographic shifts ... For these reasons, planners should consider alternative population scenarios." Pages 27·28. John Pitkin & Dowell Myers, USC Population Dynamics Research Group, "The 2010 Census Benchmark for California's Growing and Changing Population", February 2011; "Projections of the Population of (alifornia by Nativity and Year of Entry to the U.S.", April 2, 2011.IPitkin and Myers had the lowest of the forecasts in the 2005 study-though still overestimating growth by 16%. They are working with the (alifornia Department of Finance on components for a new longer·term forecast; they are still assuming a net immigration number of 160,000 holding steady in the future.) Steve Levy, Center for the Continuing Study of the California Economy UCLA Anderson Forecasting Project GregSchmid Dctobe r 2011 FIGURE 3: Regional Land Use and Transportation SCS ACHIEVING STATEWIDE GHG REDUCTION TARGETS Tons' Required Reductions: CD 2020 Emission, ................................. 507 Bay Area Regional Contribution Scenarios .-_--...... 0% Year Tons· By 2020 ........... 80 By 2050········· 715" 850 800 750 700 650 600 C 550 C!l ~500 '5 450 0-W '"' 400 o U 350 '" c 300 0 I- u 250 '£ CI) 200 :E c 150 ,Q ~ 100 50 427 80% 1990 1995 o Target 2020 Emission, 11990)················427 7.0'Yp Current Regional Plans ---- 7.'I"k Outward Growth----~rI: 8.2'7'0 Initial Va.sion/Core ConcenfTation @ Foreca,t 2050 Emission, ...................... 800" o Target 2050 Emissions [20"10 01 1990) ....... 85 ® Target 2035 Emission, r,nterpolated) ······275 9.1% FocuseC Growth --------' 9.4% Constrained Core Concentration "'Million Metric Tons C02 Equivc~nt .. Estimate based on Colifomio Council on Science end Technology Report, 2011 2000 2005 2010 2015 507 CD 27~® - 2020 2025 2030 2035 2040 2045 Projected Land Use & Transportation Emissions Reductions REGIONAL LAND USE & TRANSPORTATION (SCS) LOW CARBON FUEL STANDARDS IMPROVED FUEL EFFICIENCY [PAVLEY I & II) NON-TRANSPORTATION EMISSIONS REDUCTION ACTUAL/PROJECTED EMISSIONS 15% 5 0 2050 to