HomeMy WebLinkAboutStaff Report 4287
City of Palo Alto (ID # 4287)
Regional Housing Mandate Committee Staff Report
Report Type: Meeting Date: 12/12/2013
City of Palo Alto Page 1
Summary Title: Staff Response to Questions on Housing Methodology and
ABAG
Title: Staff Response to Questions on Housing Methodology and ABAG
From: City Manager
Lead Department: Planning and Community Environment
This is an informational report and no action is required.
Recommendation
Staff is providing responses to Councilmember questions; no action is recommended.
Executive Summary
At the last Regional Housing Mandate Committee meeting, members of the committee
requested information regarding the relationship between the Regional Housing Needs
Allocation (RHNA) methodology and office development. Members also requested clarification
regarding the implications if the City were no longer part of the Association of Bay Area
Governments (ABAG).
In short, the RHNA methodology for 2014-2022 did consider job growth as one of the factors in
distributing the regional housing number to local jurisdictions. However it would be difficult to
draw a direct line between office development and RHNA allocations because so many factors
(other than job growth) were involved. Also, the City of Palo Alto would continue to receive a
RHNA allocation and would still be required to meet State housing requirements even if the City
were not a member of ABAG. If it were not a member, however, the City would lose the ability
to participate in future discussions about the housing methodology and other issues.
Background
City of Palo Alto Page 2
Please see the attached materials summarizing the methodology that was used by the
Association of Bay Area Governments (ABAG) to determine the Regional Housing Needs
Allocation (RHNA) for Bay Area jurisdictions for the planning period from 2014-2022. Also see
the article summarizing implications of Corte Madera’s decision to withdraw from ABAG. As
noted above, the City of Palo Alto would continue to receive a RHNA allocation and would still
be required to meet State housing requirements even if the City were not a member of ABAG.
If it were not a member, however, the City would lose the ability to participate in future
discussions about the housing methodology and other issues.
Discussion
ABAG is responsible for updating the methodology it uses to determine the Regional Housing
Needs Allocation (RHNA) for Bay Area jurisdictions prior to each housing cycle. For the current
cycle, the methodology included factors related to job growth, and was built upon a framework
derived from what SB 375 called the Sustainable Communities Strategy. Future methodologies
may vary, however they must comply with the substantive and procedural requirements in
statute (Government Code Sec. 65584 et seq.).
Attachments:
Attachment A: Step by Step RHNA Methodology (PDF)
Attachment B: Final RHNA Methodology Model Spreadsheet (PDF)
Attachment C: Implications for Withdrawing from ABAG - CalWatchDog article (PDF)
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2014-2022 Regional Housing Need Allocation (RHNA) Methodology
Technical Documentation
The spreadsheet (Final RHNA Methodology Model) shows the steps for calculating a jurisdiction’s
2014-2022 Regional Housing Need Allocation (RHNA) using the methodology adopted by the ABAG
Executive Board on July 19, 2012.
It includes several tabs that display the components of the RHNA methodology by jurisdiction. The tabs
(shown at the bottom of the screen) include:
SCS Input: data from the Jobs-Housing Connection Strategy (SCS)
Fair Share Factors and Scoring:
o Past RHNA Performance
o Employment
o Transit
o Scoring Summary: adjustments based on the Fair Share Factors
RHNA Model: each step of the RHNA methodology, including the Sustainability Component and
Fair Share Component
Income Distribution: the income allocation by jurisdiction
Draft Summary: the Draft RHNA numbers by jurisdiction compared to previous RHNA cycles
Draft RHNA: the Draft RHNA numbers by jurisdiction
Final RHNA: the Final RHNA numbers by jurisdiction
All of these topics are described in more detail below.
A. Data From the Jobs-Housing Connection Strategy (“SCS Input” tab)
This table shows the total amount of housing unit growth for 2014-2022, based on the forecast from
the Jobs-Housing Connection Strategy (SCS), adopted by ABAG and MTC on July 19, 2012. The SCS
includes housing unit totals for 2010 and 2040, as well as for the interim years of 2015, 2020, 2025,
2030, and 2035, plus the RHNA years of 2014 and 2022. The results are summed by jurisdiction and
grouped into Priority Development Area (PDA) and non-PDA totals.
Figures have been adjusted based on the jurisdiction’s Sphere of Influence (SOI) and the county-
specific SOI rules of the RHNA methodology. These rules are:
1. In Napa, San Mateo, Santa Clara, Solano, and Sonoma counties, the allocation of housing need
generated by the unincorporated SOI was assigned to the cities.
2. In Alameda and Contra Costa counties, the allocation of housing need generated by the
unincorporated SOI was assigned to the county.
3. In Marin County, 62.5% of the allocation of housing need generated by the unincorporated SOI
was assigned to the city and 37.5% was assigned to the county.
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B. Fair Share Factors and Scoring
The RHNA Methodology includes three “Fair Share” factors: past RHNA performance (affordable
units), total 2010 employment outside of PDAs, and transit coverage and frequency. The
methodology for scoring each of these factors is described in detail below:
a. Factor: Past RHNA Performance (“Past RHNA Performance” tab)
This factor evaluates a jurisdiction’s performance in issuing permits to meet its RHNA allocations
for very low- and low-income units for the 1999-2006 RHNA period. The scores were calculated
using information in ABAG’s report A Place to Call Home: Housing in the San Francisco Bay Area
(August 2007).
The factor is based on the total number of very low- and low-income units permitted. Each
jurisdiction’s permit data for the two income categories is shown on the “RHNA Performance”
tab (Columns B – H). Columns J – K show the combined totals for the two income categories.
Each jurisdiction in the region is ranked from 1 to 109 based on the total number of permits
issued for very low- and low-income units from 1999-2006 (Column L). The jurisdiction’s rank for
the factor is then normalized to a scale of -100% to 100% (Column M). The Score Adjustment
moves a jurisdiction’s allocation up or down by modifying its Non-PDA Growth Total. Those
jurisdictions that have permitted less of their past RHNA numbers will receive a higher RHNA
allocation for this period.
b. Factor: 2010 Employment (“Jobs” tab)
The employment factor is based on National Establishment Time Series (NETS) data for 2010. The
NETS data is gathered by individual business and includes number of jobs, industry type, and
location. This data was used instead of Census data because it is location-specific, which allows
for calculation of the number of jobs within PDAs and the number outside of PDAs.
The data for each jurisdiction is shown on the “Jobs” tab. Columns B – D show the employment
data for each jurisdiction, separated into jobs located within PDAs and jobs located outside PDAs.
Each jurisdiction in the region is ranked from 1 to 109 based on the total number of non-PDA jobs
(Column F). The jurisdiction’s rank for the factor is then normalized to a scale of -100% to 100%
(Column G). The Score Adjustment moves a jurisdiction’s allocation up or down by modifying its
Non-PDA Growth Total. Those jurisdictions that have a higher number of jobs outside of PDAs will
receive a higher RHNA allocation.
c. Factor: Transit (“Transit” tab)
The transit factor is based on measures of service frequency and overall coverage for an entire
jurisdiction. Service frequency is measured by average daily headways (time in minutes between
transit arrivals over a 24-hour weekday period) in 2009 by jurisdiction. The data is from the
Metropolitan Transportation Commission. The calculation is done at the intersection-level based
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on how frequently a transit vehicle arrives at that location; therefore, the average headway only
takes into account intersections within a jurisdiction that have transit stops.
Transit coverage is measured by the percent of intersections within a jurisdiction that have
transit stops. This information helps avoid overstating the overall availability of transit
jurisdiction-wide based on the fact that some jurisdictions have a small number of stops, but
happen to have frequent transit.
The data for each jurisdiction is shown on the “Transit” tab. Frequency calculations are shown in
Columns B – D. Column B shows the total number of transit arrivals within a jurisdiction in a 24-
hour period. Column C shows the average stops per hour, while Column D converts this average
into an average headway for the 24-hour period. The jurisdiction’s score is normalized to a scale of
-100% to 100% (Column E).
Coverage calculations are shown in Columns G – J. Column G shows the total number of
intersections within the jurisdiction that have transit stops. Column H shows the total number of
intersections within the jurisdiction, and Column I shows the percent of intersections with
transit. The jurisdiction’s score is normalized to a scale of -100% to 100% (Column J).
The Score Adjustments for frequency and coverage are averaged to create a composite transit
score (Column L). Each element is weighted equally. This Score Adjustment moves a
jurisdiction’s allocation up or down by modifying its Non-PDA Growth Total. Those jurisdictions
that have better transit service and coverage will receive a higher RHNA allocation. The score
was normalized to fit the range of 1 to -1 in Column M.
Jurisdictions’ transit scores were not ranked from 1 to 109 because the impact of “outlier”
jurisdictions on the adjustments was not particularly significant.
d. Scoring Summary for Three Factors (“Scoring Summary” tab)
Each jurisdiction’s results and Score Adjustments for each Fair Share Factor are shown on the
“Scoring Summary” tab of the spreadsheet. Column C shows the total number of very low- and
low-income units the jurisdiction permitted during the 1999-2006 RHNA period. Column D
shows the jurisdiction’s “Score Adjustment” based on this factor. Column F shows the
jurisdiction’s total employment outside of PDAs. Column G shows the jurisdiction’s “Score
Adjustment” based on this factor. Column I shows the jurisdiction’s transit frequency score.
Column J shows the jurisdiction’s transit coverage score. Column K shows the jurisdiction’s
combined Score Adjustment based on its transit coverage and frequency.
Each of the three Fair Share Factors is given equal weight—in this case 33% for each (highlighted
in red in Columns M – O). The Score Adjustment for each factor (from Columns
C – K) is multiplied by the weight, and the results for each weighted factor are shown in
Columns M – O. These weighted Adjustment Factors are what gets applied to the Adjusted
Non-PDA Growth Total in the RHNA Model tab.
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C. Sustainability Component (“RHNA Model” tab)
This table shows the steps of the RHNA methodology.
Step 1: The Sustainability Split – Blue Heading (columns D – G)
To determine the Sustainability Split, the regional housing need determination received from HCD
(187,990) is multiplied by 70%. This results in a Sustainability Split of 131,593. This step directs most
of the housing need to jurisdictions with PDAs, consistent with the sustainability principles of the
Jobs-Housing Connection Strategy.
PDA Growth
Each jurisdiction that has a PDA is assigned a portion of the Sustainability Split, based on its PDAs’
share of the region’s total PDA growth. This is calculated using the following steps:
Sum the growth in each of a jurisdiction’s PDAs to determine the jurisdiction’s PDA Growth
Total. The PDA total for each jurisdiction is shown in the “in PDAs” column on the SCS Input
tab.
Divide this total by the total amount of PDA growth in the region (131,593) to determine the
jurisdiction’s Share of PDA Growth (Column D)
Multiply this share by the Sustainability Split to determine each jurisdiction’s share of the
housing need assigned to the Sustainability Split (Column E)
In summary:
Jurisdiction’s PDA Growth Total
Total Regional PDA Growth X Sustainability Split =
Jurisdiction’s PDA
Growth Scaled to
Sustainability Split
Non-PDA Growth
Non-PDA Growth represents the amount of growth that is expected to occur outside of PDAs. The
amount of Non-PDA Growth is 56,397.
The process for determining each jurisdiction’s Non-PDA Growth Total parallels the process for
identifying each jurisdiction’s PDA Growth Total:
Divide the jurisdiction’s Non-PDA Growth Total by the total amount of Non-PDA Growth in
the region to determine the jurisdiction’s Share of Non-PDA Growth (Column F). The
jurisdiction’s Non-PDA Growth Total is shown in the “Not in PDAs” column on the SCS Input
tab.
Multiply this share by the Non-PDA portion of the Sustainability Split to determine each
jurisdiction’s share of the housing need assigned to Non-PDA Growth (Column G).
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Step 2: The Upper Threshold – Green Heading (columns I – J)
The jurisdiction’s PDA Growth Total (Column E) is divided by the jurisdiction’s household formation
growth (Column I) to determine the jurisdiction’s proportion of PDA growth to household formation
growth. See Section F for a description of how household formation growth is derived.
Those jurisdictions where the PDA Growth Total meets or exceeds 110% of household formation
growth are highlighted in green (Column J). These jurisdictions retain their PDA Growth Total, but do
not receive additional growth based on the Fair Share factors, so their total Non-PDA Growth Total
(Column G) must be redistributed to other jurisdictions throughout the region.
Step 3: Growth Redistribution from Jurisdictions where the PDA Growth Total Exceeds the Upper
Threshold – Purple Heading (columns L – R)
The extra growth from jurisdictions that meet or exceed the upper threshold is redistributed to
other jurisdictions based on a jurisdiction’s share of the region’s total household formation growth.
This share is calculated by dividing the jurisdiction’s household formation growth (Column I) by the
total household growth for the region. The results are shown in (Column L). Column M shows the
shares of this growth that must be redistributed (for those that have met the 110% threshold).
These shares are excluded from the regional total, and the shares for other jurisdictions are
recalculated (Column N).
The total amount of growth that must be redistributed is shown in Column P. This is the total
amount of Non-PDA Growth for jurisdictions that meet the upper threshold. The total amount that
must be redistributed throughout the region is summed at the bottom of Column P. This total is
multiplied by each jurisdiction’s Redistributed Share of Growth (Column N) to determine the total
number of households that the jurisdiction will receive as part of the redistribution. This total is
shown in Column Q. The jurisdiction’s Adjusted Non-PDA Growth Total (Column R) is the sum of the
jurisdiction’s original Non-PDA Growth Total plus its portion of the redistributed total.
D. Fair Share Component (“RHNA Model” tab)
Step 4: Application of the Fair Share Factors – Teal Heading (columns T – AB)
Columns T – Y show the impact of each of the three Fair Share Factors on a jurisdiction’s Non-PDA
Growth Total (Column R). For each of the factors, there is a “Score Adjustment,” which is a percent
between -100% and 100% that is applied to a jurisdiction’s Non-PDA Growth Total. The Score
Adjustment is based on the jurisdiction’s performance on the Fair Share Factor. This information
comes from the “Scoring Summary” tab (described in greater detail in Section B).
Column T shows a jurisdiction’s Score Adjustment based on its past RHNA performance, while
Column U shows the impact this Score Adjustment has on the jurisdiction’s Non-PDA Growth Total.
Column V shows a jurisdiction’s Score Adjustment based on its total 2010 employment outside of
PDAs, while Column W shows the impact this Score Adjustment has on the jurisdiction’s Non-PDA
Growth Total.
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Column X shows a jurisdiction’s Score Adjustment based on its transit frequency and coverage, while
Column Y shows the impact this Score Adjustment has on the jurisdiction’s Non-PDA Growth Total.
Column Z shows the “Combined Adjustment,” which is the sum of the effects of each of the three
factors on the jurisdiction’s Non-PDA Growth Total. Column AA shows the jurisdiction’s Factor
Adjusted Non-PDA Growth Total, which is calculated by adding the Combined Adjustment (Column
Z) to the Adjusted Non-PDA Growth Total (Column R).
After the scoring adjustments have been applied (Column AA), the scores must be scaled to ensure
they match the regional non-PDA total that results from the Sustainability Split (56,397). This final
modification is made by multiplying the jurisdiction’s share of the Factor Adjusted Non-PDA Growth
(the jurisdiction’s number in Column AA divided by the sum for all jurisdictions in Column AA) by the
total non-PDA growth for the region. This result is shown in Column AB, the Draft Non-PDA Growth
Total.
Step 5: Application of the 40% Minimum Housing Floor – Orange Heading (columns AD – AG)
Column AD shows the draft RHNA allocation that is the sum of the jurisdiction’s PDA Growth Total
(Column E) and Non-PDA Growth Total (Column AB). Column AE shows how the draft RHNA
allocation compares to the jurisdiction’s household formation growth (Column I). See Section F for a
description of how household formation growth is derived. Jurisdictions where the draft allocation is
less than the 40% minimum housing floor are highlighted in red.
The allocation for these jurisdictions must be increased so that it meets the minimum housing floor.
This is accomplished by adjusting other jurisdictions’ allocations. Column AF identifies the
jurisdictions that have met or exceeded the minimum housing floor of 40%. Jurisdictions have a zero
in Column AF have either exceeded the upper housing threshold or did not meet the minimum
housing floor of 40%. Column AG shows the adjusted number at which the jurisdictions’ allocations
are set for those who have met or exceeded the minimum housing floor. Column AH shows the
adjusted number at which the jurisdictions’ allocations are set for those who have either exceeded
the upper housing threshold or did not meet the minimum housing floor.
In the rebalancing in Column AG, the allocations for the rest of the jurisdictions in the region need to
be rebalanced so the allocations to the jurisdictions that did not meet the minimum housing floor
can be increased. The sum of Column AG is the total amount of housing excluded from rebalancing
(because these jurisdictions have a fixed allocation, as noted above). The allocations for jurisdictions
that do not have a set allocation are rebalanced based on the jurisdiction’s share of the total RHNA
allocation, excluding the total for jurisdictions with set allocations.
E. Application of Final Rebalance and Reallocation (“RHNA Model” tab)
Steps 5 and 6: Application of Final Rebalance and Reallocation – Pink Heading (columns AJ - AY)
The jurisdictions’ Pre-Final RHNA is shown in Column AJ. A comparison of the jurisdiction’s RHNA to
its household formation growth is shown in Column AK. The jurisdiction’s share of the region’s total
RHNA is shown in Column AL. A comparison to the jurisdiction’s 2007-2014 RHNA is shown in
Columns AN – AO.
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A jurisdiction’s RHNA is limited to no more than 150% of its allocation for the 2007-2014 RHNA.
Column AS shows the jurisdiction’s Pre-Final RHNA. Column AT shows the maximum RHNA for
jurisdictions whose Pre-Final RHNA exceeds 150% of the 2007-2014 RHNA. The excess housing units
for these jurisdictions (Column AU) are redistributed equally among those jurisdictions whose pre-
final RHNA allocations (Column AS) are lower than the allocation the jurisdiction received for the
2007-2014 RHNA. Column AV shows the initial share of each jurisdiction prior to redistribution (with
jurisdictions that exceeded the 150% mark set at 0%) while Column AW shows the final share of
each jurisdiction and excludes the jurisdictions that exceeded the 150% mark. Column AX shows
number that needs to be added to each jurisdiction because of the rebalancing. Column AY shows
the final RHNA.
F. Household Formation Growth
Household formation growth is an estimate of the future number of households without taking into
account financial, zoning or land availability constraints. Household formation growth is calculated
based on the expected population growth and the rates at which different age and ethnic groups
form households. Population growth is forecast based on natural increase, migration, and jobs.
1. Job growth: Expected number of jobs as a share of the national job growth, considering historic
trends, performance by industry, international competitiveness, and labor skills.
2. Net migration: total number of people moving into the region minus people moving out of the
region. This can be related to economic, social, or political reasons. The largest share of net
migration is based on jobs, which means that a growing economy will attract more people and a
declining economy will push people out of the region.
3. Natural increase: total number of expected births minus deaths.
4. Population: Sum of natural increase and net migration.
5. Household formation rates: The expected number of households formed per 100 residents over
20 years of age by age and ethnic group. If a 50% rate is applied to one million residents, it will
result in 500,000 households. These rates vary by age and ethnicity. For example, many 25- to
35-year-old residents live with their parents or friends so this group will form fewer households
than older groups. Similarly, many Latino and Asian households include more grandparents or
cousins than White families, thus they will form fewer households. These rates are based on
historic trends.
Job Growth
Net Migration
Natural
Increase Population Growth Household
Formation Growth
Household
Formation Rates
+
=
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6. Household formation growth: Total expected growth in households derived from household
formation rates applied to population growth.
Household formation growth by local jurisdiction for the San Francisco Bay Area: The process
described above is developed at the regional and county levels. Then, the county total household
formation growth is distributed based on each city’s share of county current population.
G. Income Allocation (“Income Distribution” tab)
The Income Distribution tab shows the steps for distributing each jurisdiction’s total RHNA into the
four required income categories:
Very low income: 0-50% of Area Median Income (AMI)
Low income: 51-80% of AMI
Moderate income: 81-120% of AMI
Above Moderate: More than 120% of AMI
The total regional housing need determination from HCD is broken into these four categories as
follows:
Income Category Percent Regional Housing Need
Very low income 24.8% 46,680
Low income 15.4% 28,940
Moderate income 17.8% 33,420
Above Moderate 42.0% 78,950
Total 100.0% 187,990
For the income allocation, each jurisdiction is given 175% of the difference between its household
income distribution and the region-wide household income distribution (shown above). This income
allocation method gives jurisdictions that have a relatively higher proportion of households in a
certain income category a smaller allocation of housing units in that same category. Conversely,
jurisdictions that have a lower proportion of households in an income category would receive a
larger allocation of housing units in that same category.
Columns D – G show the jurisdiction’s existing income distribution, based on household income data
from the 2005-2009 American Community Survey. Columns I – L show the jurisdiction’s income
distribution after it has been adjusted by the 175% shift.
The first step in determining the jurisdiction’s Adjusted Income Distribution is to calculate the
difference between the jurisdiction’s existing proportion of households in an income category and
the region’s proportion of households in that income category. This difference is then multiplied by
175%. Finally, the result is added to the jurisdiction’s initial proportion of households in that income
category.
The result is the share of the jurisdiction’s total RHNA allocation that will be in that particular
income category. These steps are completed for each of the four income categories.
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The jurisdiction’s adjusted share for a particular income category (Columns I – L) is then multiplied
by the jurisdiction’s total draft RHNA allocation (Column B) to determine the jurisdiction’s allocation
for each of the four income categories (Columns N – R).
H. Draft Jurisdiction Allocation with Previous RHNA Cycles Totals (“Draft Summary” tab)
This table shows the Draft RHNA for each jurisdiction by income category. Because formulas were
used to create each number, the figures are not whole numbers (integers), but all contain fractions.
When added together, they may not round to the actual total. The rounding error has been
corrected with the Draft RHNA given to the ABAG Executive Board on July 19, 2012.
This table also shows the total RHNA for each jurisdiction for 1999-2006 and 2007-2014.
I. Draft Jurisdiction Allocation (“Draft RHNA” tab)
This table shows the Draft RHNA for each jurisdiction by income category. The rounding errors have
been fixed. This table was presented to the ABAG Executive Board on July 19, 2012 when the RHNA
Methodology adopted by the Board.
J. Final Jurisdiction Allocation (“Final RHNA” tab)
This table shows the Final RHNA for each jurisdiction by income category. The table shows the final
adjustments made after the appeal hearing with three appeals approved by the ABAG Executive
Board on May 16, 2013. This table will be sent to the ABAG Executive Board for adoption on July 18,
2013.
2010 Employment
PDA Jobs Non‐PDA Jobs Total Jobs
Non‐PDA Jobs
Rank
Non‐PDA Jobs
Score
Adjustment
Maximum 462,444.0 145,721.0 550,386.0 109.0 1.0
Minimum 0.0 464.0 464.0 1.0 ‐1.0
Alameda County
Alameda 2,595 23,888 26,483 29 48%
Albany 2,883 2,184 5,067 90 ‐65%
Berkeley 21,700 52,082 73,782 6 91%
Dublin 4,943 12,538 17,481 47 15%
Emeryville 11,487 4,865 16,352 70 ‐28%
Fremont 41,842 47,438 89,280 8 87%
Hayward 9,652 54,252 63,904 5 93%
Livermore 8,779 27,536 36,315 24 57%
Newark 2,059 14,757 16,816 42 24%
Oakland 157,455 36,399 193,854 10 83%
Piedmont 0 2,101 2,101 93 ‐70%
Pleasanton 9,871 42,054 51,925 9 85%
San Leandro 11,681 27,667 39,348 23 59%
Union City 839 18,417 19,256 34 39%
Alameda County Unincorporated 4,427 30,605 35,032 17 70%
Contra Costa County
Antioch 3,931 15,490 19,421 38 31%
Brentwood 0 8,286 8,286 56 ‐2%
Clayton 0 2,242 2,242 88 ‐61%
Concord 16,316 31,305 47,621 16 72%
Danville 0 12,535 12,535 48 13%
El Cerrito 3,492 2,222 5,714 89 ‐63%
Hercules 2,922 1,173 4,095 103 ‐89%
Lafayette 6,183 4,052 10,235 75 ‐37%
Martinez 6,821 18,099 24,920 35 37%
Moraga 1,199 2,978 4,177 79 ‐44%
Oakley 1,608 2,107 3,715 92 ‐69%
Orinda 2,755 2,447 5,202 84 ‐54%
Pinole 4,543 1,487 6,030 100 ‐83%
Pittsburg 8,210 7,184 15,394 59 ‐7%
Pleasant Hill 6,332 11,635 17,967 50 9%
Richmond 15,401 15,868 31,269 37 33%
San Pablo 5,874 2,025 7,899 94 ‐72%
San Ramon 22,012 19,635 41,647 33 41%
Walnut Creek 7,412 36,294 43,706 11 81%
Contra Costa County Unincorporated 7,212 33,584 40,796 12 80%
Marin County
Belvedere 0 464 464 109 ‐100%
Corte Madera 0 6,812 6,812 60 ‐9%
Fairfax 0 2,376 2,376 85 ‐56%
Larkspur 1,549 5,002 6,551 68 ‐24%
Mill Valley 0 5,912 5,912 64 ‐17%
Novato 0 22,450 22,450 30 46%
Ross 0 507 507 108 ‐98%
San Anselmo 0 3,901 3,901 76 ‐39%
San Rafael 14,695 26,291 40,986 26 54%
Sausalito‐Marin City 0 7,392 7,392 58 ‐6%
Tiburon 54 2,702 2,756 81 ‐48%
Marin County Unincorporated 4,061 10,691 14,752 53 4%
Napa County
American Canyon 1,037 1,443 2,480 102 ‐87%
Calistoga 0 2,299 2,299 86 ‐57%
Napa 0 28,741 28,741 21 63%
St. Helena 0 4,393 4,393 73 ‐33%
Yountville 0 1,445 1,445 101 ‐85%
Napa County Unincorporated 0 22,391 22,391 31 44%
San Francisco County
San Francisco 462,444 87,942 550,386 2 98%
2010 Employment
PDA Jobs Non‐PDA Jobs Total Jobs
Non‐PDA Jobs
Rank
Non‐PDA Jobs
Score
Adjustment
Maximum 462,444.0 145,721.0 550,386.0 109.0 1.0
Minimum 0.0 464.0 464.0 1.0 ‐1.0
San Mateo County
Atherton 0 2,282 2,282 87 ‐59%
Belmont 4,057 3,345 7,402 78 ‐43%
Brisbane 442 5,827 6,269 66 ‐20%
Burlingame 10,522 15,354 25,876 40 28%
Colma 1,961 574 2,535 106 ‐94%
Daly City 6,201 13,166 19,367 46 17%
East Palo Alto 1,077 1,590 2,667 99 ‐81%
Foster City 0 13,385 13,385 45 19%
Half Moon Bay 0 4,944 4,944 69 ‐26%
Hillsborough 0 2,109 2,109 91 ‐67%
Menlo Park 9,617 31,703 41,320 15 74%
Millbrae 5,205 1,702 6,907 98 ‐80%
Pacifica 0 5,691 5,691 67 ‐22%
Portola Valley 0 1,783 1,783 97 ‐78%
Redwood City 25,408 32,965 58,373 14 76%
San Bruno 7,955 4,154 12,109 74 ‐35%
San Carlos 9,684 6,364 16,048 61 ‐11%
San Mateo 24,045 26,597 50,642 25 56%
South San Francisco 8,805 29,682 38,487 19 67%
Woodside 0 2,626 2,626 82 ‐50%
San Mateo County Unincorporated 0 11,113 11,113 51 7%
Santa Clara County
Campbell 9,935 14,014 23,949 43 22%
Cupertino 9,976 11,014 20,990 52 6%
Gilroy 4,087 13,642 17,729 44 20%
Los Altos 5,153 8,141 13,294 57 ‐4%
Los Altos Hills 0 2,957 2,957 80 ‐46%
Los Gatos 1,681 17,218 18,899 36 35%
Milpitas 23,435 15,391 38,826 39 30%
Monte Sereno 0 525 525 107 ‐96%
Morgan Hill 1,383 14,983 16,366 41 26%
Mountain View 24,048 21,642 45,690 32 43%
Palo Alto 25,903 49,478 75,381 7 89%
San Jose 218,010 145,721 363,731 1 100%
Santa Clara 23,993 72,350 96,343 4 94%
Saratoga 917 8,933 9,850 55 0%
Sunnyvale 38,641 25,214 63,855 27 52%
Santa Clara County Unincorporated 151 3,359 3,510 77 ‐41%
Solano County
Benicia 4,401 9,756 14,157 54 2%
Dixon 0 4,491 4,491 71 ‐30%
Fairfield 7,479 75,366 82,845 3 96%
Rio Vista 0 2,009 2,009 95 ‐74%
Suisun City 1,670 1,837 3,507 96 ‐76%
Vacaville 3,898 28,388 32,286 22 61%
Vallejo 4,663 30,129 34,792 18 69%
Solano County Unincorporated 0 5,838 5,838 65 ‐19%
Sonoma County
Cloverdale 981 862 1,843 105 ‐93%
Cotati 563 2,607 3,170 83 ‐52%
Healdsburg 0 6,326 6,326 62 ‐13%
Petaluma 2,707 25,170 27,877 28 50%
Rohnert Park 135 12,463 12,598 49 11%
Santa Rosa 37,444 33,229 70,673 13 78%
Sebastopol 3,833 1,150 4,983 104 ‐91%
Sonoma 0 6,086 6,086 63 ‐15%
Windsor 1,177 4,451 5,628 72 ‐31%
Sonoma County Unincorporated 9,684 28,745 38,429 20 65%
Source:
Establishment Time Series (NETS) data for 2010
Transit Frequency & Coverage
Total Stops
2009
Average
Stops Per
Hour 2009
Average
Combined
Headway
2009
Score
Adjustment ‐
Frequency
Intersections
with Transit Intersections
Percent
Intersections
with Transit
Score
Adjustment ‐
Coverage
Adjustment
Weighted
Average
Final Transit
Score
Adjustment
Weight: 50.00% Weight: 50.00%
Maximum 225.0 1.0 26.8% 1.0 1.0 1.0
Minimum 6.9 ‐1.0 0.0%‐1.0 ‐0.6 ‐1.0
Alameda County
Alameda 92.7 3.9 15.5 92% 192 1,508 12.7%‐5% 44% 32%
Albany 104.5 4.4 13.8 94% 37 262 14.1% 6% 50% 39%
Berkeley 93.9 3.9 15.3 92% 423 1,826 23.2% 73% 83% 79%
Dublin 22.8 0.9 63.2 48% 123 1,224 10.0%‐25% 12%‐7%
Emeryville 113.0 4.7 12.7 95% 49 215 22.8% 70% 82% 79%
Fremont 42.0 1.8 34.3 75% 488 5,125 9.5%‐29% 23% 7%
Hayward 64.6 2.7 22.3 86% 361 2,570 14.0% 5% 45% 34%
Livermore 28.9 1.2 49.8 61% 259 2,646 9.8%‐27% 17%‐1%
Newark 47.1 2.0 30.6 78% 135 915 14.8% 10% 44% 32%
Oakland 94.3 3.9 15.3 92% 1,385 7,221 19.2% 43% 68% 61%
Piedmont 42.9 1.8 33.6 76% 45 277 16.2% 21% 48% 37%
Pleasanton 25.7 1.1 56.0 55% 228 2,095 10.9%‐19% 18% 1%
San Leandro 65.8 2.7 21.9 86% 224 1,699 13.2%‐1% 42% 30%
Union City 80.7 3.4 17.8 90% 163 1,342 12.1%‐9% 40% 28%
Alameda County Unincorporated 52.9 2.2 27.2 81% 292 2,929 10.0%‐26% 28% 13%
Contra Costa County
Antioch 51.1 2.1 28.2 80% 170 2,491 6.8%‐49% 16%‐2%
Brentwood 26.6 1.1 54.1 57% 70 1,476 4.7%‐65%‐4%‐26%
Clayton 13.2 0.6 109.1 6% 30 350 8.6%‐36%‐15%‐39%
Concord 30.5 1.3 47.3 63% 266 2,866 9.3%‐31% 16%‐2%
Danville 26.5 1.1 54.4 56% 33 1,216 2.7%‐80%‐12%‐36%
El Cerrito 74.4 3.1 19.3 89% 110 636 17.3% 29% 59% 50%
Hercules 44.8 1.9 32.2 77% 51 601 8.5%‐37% 20% 3%
Lafayette 20.5 0.9 70.2 42% 36 732 4.9%‐63%‐11%‐34%
Martinez 35.1 1.5 41.1 69% 92 1,018 9.0%‐32% 18% 1%
Moraga 26.8 1.1 53.7 57% 21 353 5.9%‐56% 1%‐20%
Oakley 31.8 1.3 45.3 65% 49 928 5.3%‐61% 2%‐19%
Orinda 20.8 0.9 69.4 43% 44 557 7.9%‐41% 1%‐20%
Pinole 66.5 2.8 21.7 86% 54 430 12.6%‐6% 40% 27%
Pittsburg 61.5 2.6 23.4 85% 95 1,508 6.3%‐53% 16%‐2%
Pleasant Hill 43.2 1.8 33.3 76% 90 966 9.3%‐30% 23% 6%
Richmond 62.5 2.6 23.0 85% 279 2,350 11.9%‐11% 37% 23%
San Pablo 67.8 2.8 21.3 87% 89 551 16.2% 21% 54% 44%
San Ramon 30.7 1.3 46.9 63% 91 1,204 7.6%‐44% 10%‐9%
Walnut Creek 34.3 1.4 42.0 68% 164 1,639 10.0%‐25% 21% 4%
Contra Costa County Unincorporated 40.0 1.7 36.0 73% 320 5,142 6.2%‐53% 10%‐9%
Marin County
Belvedere 21.5 0.9 67.0 45% 2 62 3.2%‐76%‐15%‐40%
Corte Madera 28.4 1.2 50.8 60% 22 281 7.8%‐41% 9%‐10%
Fairfax 73.7 3.1 19.5 88% 13 192 6.8%‐49% 20% 2%
Larkspur 43.7 1.8 32.9 76% 21 314 6.7%‐50% 13%‐6%
Mill Valley 62.8 2.6 22.9 85% 20 440 4.5%‐66% 10%‐10%
Novato 46.6 1.9 30.9 78% 117 1,366 8.6%‐36% 21% 4%
Ross 68.0 2.8 21.2 87% 4 79 5.1%‐62% 12%‐6%
San Anselmo 79.1 3.3 18.2 90% 20 389 5.1%‐62% 14%‐4%
San Rafael 61.0 2.5 23.6 85% 111 1,296 8.6%‐36% 24% 8%
Sausalito 69.9 2.9 20.6 87% 22 223 9.9%‐26% 31% 16%
Tiburon 25.3 1.1 56.8 54% 18 225 8.0%‐40% 7%‐13%
Marin County Unincorporated 40.4 1.7 35.6 74% 109 2,151 5.1%‐62% 6%‐14%
Napa County
American Canyon 28.3 1.2 51.0 60% 16 411 3.9%‐71%‐6%‐28%
Calistoga 36.5 1.5 39.5 70% 2 154 1.3%‐90%‐10%‐34%
Napa 27.8 1.2 51.8 59% 196 1,837 10.7%‐20% 19% 2%
St. Helena 29.1 1.2 49.6 61% 20 201 10.0%‐26% 18% 0%
Yountville 55.5 2.3 25.9 83% 6 111 5.4%‐60% 11%‐7%
Napa County Unincorporated 35.0 1.5 41.2 69% 21 884 2.4%‐82%‐7%‐30%
San Francisco County
San Francisco 208.5 8.7 6.9 100% 2,436 9,102 26.8% 100% 100% 100%
Transit Frequency Transit Coverage Combined Score
Transit Frequency & Coverage
Total Stops
2009
Average
Stops Per
Hour 2009
Average
Combined
Headway
2009
Score
Adjustment ‐
Frequency
Intersections
with Transit Intersections
Percent
Intersections
with Transit
Score
Adjustment ‐
Coverage
Adjustment
Weighted
Average
Final Transit
Score
Adjustment
Weight: 50.00% Weight: 50.00%
Maximum 225.0 1.0 26.8% 1.0 1.0 1.0
Minimum 6.9 ‐1.0 0.0%‐1.0 ‐0.6 ‐1.0
Transit Frequency Transit Coverage Combined Score
San Mateo County
Atherton 44.2 1.8 32.6 76% 30 292 10.3%‐23% 27% 11%
Belmont 55.8 2.3 25.8 83% 60 498 12.0%‐10% 36% 23%
Brisbane 31.5 1.3 45.8 64% 19 206 9.2%‐31% 17%‐1%
Burlingame 72.3 3.0 19.9 88% 49 571 8.6%‐36% 26% 10%
Colma 110.9 4.6 13.0 94% 15 62 24.2% 81% 88% 85%
Daly City 75.4 3.1 19.1 89% 189 1,088 17.4% 30% 59% 51%
East Palo Alto 59.7 2.5 24.1 84% 51 326 15.6% 17% 51% 40%
Foster City 28.1 1.2 51.2 59% 87 579 15.0% 12% 36% 22%
Half Moon Bay 35.4 1.5 40.7 69% 36 345 10.4%‐22% 23% 7%
Hillsborough 101.0 4.2 14.3 93% 2 422 0.5%‐96%‐2%‐23%
Menlo Park 43.1 1.8 33.4 76% 100 821 12.2%‐9% 33% 19%
Millbrae 43.6 1.8 33.0 76% 56 433 12.9%‐3% 36% 23%
Pacifica 46.0 1.9 31.3 78% 97 641 15.1% 13% 45% 34%
Portola Valley 6.4 0.3 225.0 ‐100% 15 159 9.4%‐30%‐65%‐100%
Redwood City 47.6 2.0 30.2 79% 160 1,637 9.8%‐27% 26% 10%
San Bruno 49.6 2.1 29.0 80% 104 721 14.4% 8% 44% 32%
San Carlos 62.3 2.6 23.1 85% 42 621 6.8%‐49% 18% 0%
San Mateo 55.7 2.3 25.9 83% 191 1,708 11.2%‐16% 33% 19%
South San Francisco 63.8 2.7 22.6 86% 143 1,161 12.3%‐8% 39% 26%
Woodside 12.0 0.5 120.0 ‐4% 14 261 5.4%‐60%‐32%‐60%
San Mateo County Unincorporated 41.7 1.7 34.5 75% 140 1,882 7.4%‐44% 15%‐3%
Santa Clara County
Campbell 58.7 2.4 24.5 84% 87 856 10.2%‐24% 30% 15%
Cupertino 57.8 2.4 24.9 83% 80 1,297 6.2%‐54% 15%‐3%
Gilroy 49.4 2.1 29.1 80% 83 961 8.6%‐35% 22% 5%
Los Altos 39.9 1.7 36.1 73% 58 1,001 5.8%‐57% 8%‐11%
Los Altos Hills 60.8 2.5 23.7 85% 6 325 1.8%‐86%‐1%‐22%
Los Gatos 39.3 1.6 36.6 73% 56 888 6.3%‐53% 10%‐9%
Milpitas 60.6 2.5 23.8 85% 143 1,272 11.2%‐16% 34% 20%
Monte Sereno — — —— 0 98 0.0%‐100% — —
Morgan Hill 54.2 2.3 26.6 82% 49 962 5.1%‐62% 10%‐9%
Mountain View 49.4 2.1 29.1 80% 155 1,494 10.4%‐22% 29% 13%
Palo Alto 71.1 3.0 20.3 88% 182 1,525 11.9%‐11% 38% 25%
San Jose 78.3 3.3 18.4 89% 1,506 17,615 8.5%‐36% 27% 11%
Santa Clara 61.9 2.6 23.2 85% 233 2,449 9.5%‐29% 28% 13%
Saratoga 27.7 1.2 52.0 59% 59 1,012 5.8%‐56% 1%‐20%
Sunnyvale 54.3 2.3 26.5 82% 248 2,637 9.4%‐30% 26% 10%
Santa Clara County Unincorporated 72.2 3.0 19.9 88% 209 2,895 7.2%‐46% 21% 4%
Solano County
Benicia 14.4 0.6 99.8 15% 26 635 4.1%‐69%‐27%‐55%
Dixon 22.0 0.9 65.5 46% 1 479 0.2%‐98%‐26%‐53%
Fairfield 33.7 1.4 42.7 67% 200 2,682 7.5%‐44% 11%‐8%
Rio Vista 16.0 0.7 90.0 24% 9 386 2.3%‐83%‐29%‐57%
Suisun City 45.5 1.9 31.6 77% 65 631 10.3%‐23% 27% 12%
Vacaville 32.9 1.4 43.8 66% 146 1,932 7.6%‐44% 11%‐8%
Vallejo 40.6 1.7 35.4 74% 240 2,894 8.3%‐38% 18% 0%
Solano County Unincorporated 27.2 1.1 53.0 58% 13 939 1.4%‐90%‐16%‐41%
Sonoma County
Cloverdale 15.6 0.6 92.5 22% 28 250 11.2%‐16% 3%‐18%
Cotati 35.5 1.5 40.6 69% 23 196 11.7%‐12% 28% 13%
Healdsburg 44.9 1.9 32.1 77% 12 326 3.7%‐72% 2%‐19%
Petaluma 40.1 1.7 35.9 73% 94 1,430 6.6%‐51% 11%‐8%
Rohnert Park 41.1 1.7 35.0 74% 86 729 11.8%‐12% 31% 16%
Santa Rosa 55.7 2.3 25.8 83% 401 3,674 10.9%‐18% 32% 18%
Sebastopol 26.0 1.1 55.5 55% 26 233 11.2%‐17% 19% 2%
Sonoma 44.8 1.9 32.1 77% 31 288 10.8%‐20% 29% 13%
Windsor 28.9 1.2 49.8 61% 65 685 9.5%‐29% 16%‐2%
Sonoma County Unincorporated 36.6 1.5 39.3 70% 252 4,949 5.1%‐62% 4%‐16%
Source:
MTC Regional Transit Database
Factor Scores
County Jurisdiction Permits Issued
Permits Issued
Ranked
Adjustment Non‐PDA Jobs
Non‐PDA Jobs
Score
Adjustment
Average
Combined
Headway 2009
Percent
Intersections
with Transit
Final Transit
Score
Adjustment
(Equal Weight)
Alameda Alameda 336 ‐38% 23,888 48%15.5 12.7%32%
Alameda Albany 15 78% 2,184 ‐65%13.8 14.1%39%
Alameda Berkeley 496 ‐51% 52,082 91%15.3 23.2%79%
Alameda Dublin 506 ‐55% 12,538 15%63.2 10.0%‐7%
Alameda Emeryville 187 ‐10% 4,865 ‐28%12.7 22.8%79%
Alameda Fremont 503 ‐53% 47,438 87%34.3 9.5%7%
Alameda Hayward 57 44% 54,252 93%22.3 14.0%34%
Alameda Livermore 461 ‐46% 27,536 57%49.8 9.8%‐1%
Alameda Newark 0 100% 14,757 24%30.6 14.8%32%
Alameda Oakland 1,300 ‐94% 36,399 83%15.3 19.2%61%
Alameda Piedmont 0 100% 2,101 ‐70%33.6 16.2%37%
Alameda Pleasanton 530 ‐61% 42,054 85%56.0 10.9%1%
Alameda San Leandro 108 18% 27,667 59%21.9 13.2%30%
Alameda Union City 232 ‐23% 18,417 39%17.8 12.1%28%
Alameda Alameda County Unincorporated 303 ‐31% 30,605 70%27.2 10.0%13%
Contra Costa Antioch 838 ‐87% 15,490 31%28.2 6.8%‐2%
Contra Costa Brentwood 614 ‐72% 8,286 ‐2%54.1 4.7%‐26%
Contra Costa Clayton 84 31% 2,242 ‐61%109.1 8.6%‐39%
Contra Costa Concord 286 ‐29% 31,305 72%47.3 9.3%‐2%
Contra Costa Danville 141 8% 12,535 13%54.4 2.7%‐36%
Contra Costa El Cerrito 5 94% 2,222 ‐63%19.3 17.3%50%
Contra Costa Hercules 164 1% 1,173 ‐89%32.2 8.5%3%
Contra Costa Lafayette 17 76% 4,052 ‐37%70.2 4.9%‐34%
Contra Costa Martinez 0 100% 18,099 37%41.1 9.0%1%
Contra Costa Moraga 21 68% 2,978 ‐44%53.7 5.9%‐20%
Contra Costa Oakley 461 ‐46% 2,107 ‐69%45.3 5.3%‐19%
Contra Costa Orinda 0 100% 2,447 ‐54%69.4 7.9%‐20%
Contra Costa Pinole 40 51% 1,487 ‐83%21.7 12.6%27%
Contra Costa Pittsburg 628 ‐74% 7,184 ‐7%23.4 6.3%‐2%
Contra Costa Pleasant Hill 164 1% 11,635 9%33.3 9.3%6%
Contra Costa Richmond 1,293 ‐91% 15,868 33%23.0 11.9%23%
Contra Costa San Pablo 284 ‐27% 2,025 ‐72%21.3 16.2%44%
Contra Costa San Ramon 564 ‐70% 19,635 41%46.9 7.6%‐9%
Contra Costa Walnut Creek 179 ‐5% 36,294 81%42.0 10.0%4%
Contra Costa Contra Costa County Unincorporated 549 ‐63% 33,584 80%36.0 6.2%‐9%
Marin Belvedere 0 100% 464 ‐100%67.0 3.2%‐40%
Marin Corte Madera 0 100% 6,812 ‐9%50.8 7.8%‐10%
Marin Fairfax 0 100% 2,376 ‐56%19.5 6.8%2%
Marin Larkspur 13 85% 5,002 ‐24%32.9 6.7%‐6%
Marin Mill Valley 97 25% 5,912 ‐17%22.9 4.5%‐10%
Marin Novato 824 ‐85% 22,450 46%30.9 8.6%4%
Marin Ross 0 100% 507 ‐98%21.2 5.1%‐6%
Marin San Anselmo 0 100% 3,901 ‐39%18.2 5.1%‐4%
Marin San Rafael 112 14% 26,291 54%23.6 8.6%8%
Marin Sausalito 22 66% 7,392 ‐6%20.6 9.9%16%
Marin Tiburon 7 91% 2,702 ‐48%56.8 8.0%‐13%
Marin Marin County Unincorporated 204 ‐16% 10,691 4%35.6 5.1%‐14%
Napa American Canyon 174 ‐1% 1,443 ‐87%51.0 3.9%‐28%
Napa Calistoga 18 74% 2,299 ‐57%39.5 1.3%‐34%
Napa Napa 528 ‐59% 28,741 63%51.8 10.7%2%
Napa St. Helena 20 70% 4,393 ‐33%49.6 10.0%0%
Napa Yountville 2 98% 1,445 ‐85%25.9 5.4%‐7%
Napa Napa County Unincorporated 75 35% 22,391 44%41.2 2.4%‐30%
San Francisco San Francisco 5,304 ‐98% 87,942 98%6.9 26.8%100%
RHNA Performance (Very Low
+ Low)Employment Transit Coverage & Frequency
Factor Scores
County Jurisdiction Permits Issued
Permits Issued
Ranked
Adjustment Non‐PDA Jobs
Non‐PDA Jobs
Score
Adjustment
Average
Combined
Headway 2009
Percent
Intersections
with Transit
Final Transit
Score
Adjustment
(Equal Weight)
RHNA Performance (Very Low
+ Low)Employment Transit Coverage & Frequency
San Mateo Atherton 0 100% 2,282 ‐59%32.6 10.3%11%
San Mateo Belmont 44 48% 3,345 ‐43%25.8 12.0%23%
San Mateo Brisbane 8 89% 5,827 ‐20%45.8 9.2%‐1%
San Mateo Burlingame 0 100% 15,354 28%19.9 8.6%10%
San Mateo Colma 73 38% 574 ‐94%13.0 24.2%85%
San Mateo Daly City 33 59% 13,166 17%19.1 17.4%51%
San Mateo East Palo Alto 212 ‐20% 1,590 ‐81%24.1 15.6%40%
San Mateo Foster City 88 27% 13,385 19%51.2 15.0%22%
San Mateo Half Moon Bay 106 20% 4,944 ‐26%40.7 10.4%7%
San Mateo Hillsborough 15 78% 2,109 ‐67%14.3 0.5%‐23%
San Mateo Menlo Park 0 100% 31,703 74%33.4 12.2%19%
San Mateo Millbrae 0 100% 1,702 ‐80%33.0 12.9%23%
San Mateo Pacifica 10 87% 5,691 ‐22%31.3 15.1%34%
San Mateo Portola Valley 15 78% 1,783 ‐78%225.0 9.4%‐100%
San Mateo Redwood City 106 20% 32,965 76%30.2 9.8%10%
San Mateo San Bruno 325 ‐33% 4,154 ‐35%29.0 14.4%32%
San Mateo San Carlos 0 100% 6,364 ‐11%23.1 6.8%0%
San Mateo San Mateo 210 ‐18% 26,597 56%25.9 11.2%19%
San Mateo South San Francisco 192 ‐14% 29,682 67%22.6 12.3%26%
San Mateo Woodside 0 100% 2,626 ‐50%120.0 5.4%‐60%
San Mateo San Mateo County Unincorporated 31 63% 11,113 7%34.5 7.4%‐3%
Santa Clara Campbell 37 57% 14,014 22%24.5 10.2%15%
Santa Clara Cupertino 48 46% 11,014 6%24.9 6.2%‐3%
Santa Clara Gilroy 516 ‐57% 13,642 20%29.1 8.6%5%
Santa Clara Los Altos 40 51% 8,141 ‐4%36.1 5.8%‐11%
Santa Clara Los Altos Hills 32 61% 2,957 ‐46%23.7 1.8%‐22%
Santa Clara Los Gatos 86 29% 17,218 35%36.6 6.3%‐9%
Santa Clara Milpitas 701 ‐76% 15,391 30%23.8 11.2%20%
Santa Clara Monte Sereno 19 72% 525 ‐96%— 0.0%—
Santa Clara Morgan Hill 556 ‐68% 14,983 26%26.6 5.1%‐9%
Santa Clara Mountain View 123 10% 21,642 43%29.1 10.4%13%
Santa Clara Palo Alto 344 ‐40% 49,478 89%20.3 11.9%25%
Santa Clara San Jose 8,301 ‐100% 145,721 100%18.4 8.5%11%
Santa Clara Santa Clara 758 ‐78% 72,350 94%23.2 9.5%13%
Santa Clara Saratoga 61 42% 8,933 0%52.0 5.8%‐20%
Santa Clara Sunnyvale 112 14% 25,214 52%26.5 9.4%10%
Santa Clara Santa Clara County Unincorporated 483 ‐48% 3,359 ‐41%19.9 7.2%4%
Solano Benicia 182 ‐8% 9,756 2%99.8 4.1%‐55%
Solano Dixon 0 100% 4,491 ‐30%65.5 0.2%‐53%
Solano Fairfield 249 ‐25% 75,366 96%42.7 7.5%‐8%
Solano Rio Vista 39 55% 2,009 ‐74%90.0 2.3%‐57%
Solano Suisun City 80 33% 1,837 ‐76%31.6 10.3%12%
Solano Vacaville 778 ‐83% 28,388 61%43.8 7.6%‐8%
Solano Vallejo 553 ‐66% 30,129 69%35.4 8.3%0%
Solano Solano County Unincorporated 71 40% 5,838 ‐19%53.0 1.4%‐41%
Sonoma Cloverdale 163 5% 862 ‐93%92.5 11.2%‐18%
Sonoma Cotati 114 12% 2,607 ‐52%40.6 11.7%13%
Sonoma Healdsburg 188 ‐12% 6,326 ‐13%32.1 3.7%‐19%
Sonoma Petaluma 451 ‐42% 25,170 50%35.9 6.6%‐8%
Sonoma Rohnert Park 760 ‐81% 12,463 11%35.0 11.8%16%
Sonoma Santa Rosa 1,929 ‐96% 33,229 78%25.8 10.9%18%
Sonoma Sebastopol 5 94% 1,150 ‐91%55.5 11.2%2%
Sonoma Sonoma 179 ‐5% 6,086 ‐15%32.1 10.8%13%
Sonoma Windsor 332 ‐35% 4,451 ‐31%49.8 9.5%‐2%
Sonoma Sonoma County Unincorporated 989 ‐89% 28,745 65%39.3 5.1%‐16%
Factor Scores
County Jurisdiction
Alameda Alameda
Alameda Albany
Alameda Berkeley
Alameda Dublin
Alameda Emeryville
Alameda Fremont
Alameda Hayward
Alameda Livermore
Alameda Newark
Alameda Oakland
Alameda Piedmont
Alameda Pleasanton
Alameda San Leandro
Alameda Union City
Alameda Alameda County Unincorporated
Contra Costa Antioch
Contra Costa Brentwood
Contra Costa Clayton
Contra Costa Concord
Contra Costa Danville
Contra Costa El Cerrito
Contra Costa Hercules
Contra Costa Lafayette
Contra Costa Martinez
Contra Costa Moraga
Contra Costa Oakley
Contra Costa Orinda
Contra Costa Pinole
Contra Costa Pittsburg
Contra Costa Pleasant Hill
Contra Costa Richmond
Contra Costa San Pablo
Contra Costa San Ramon
Contra Costa Walnut Creek
Contra Costa Contra Costa County Unincorporated
Marin Belvedere
Marin Corte Madera
Marin Fairfax
Marin Larkspur
Marin Mill Valley
Marin Novato
Marin Ross
Marin San Anselmo
Marin San Rafael
Marin Sausalito
Marin Tiburon
Marin Marin County Unincorporated
Napa American Canyon
Napa Calistoga
Napa Napa
Napa St. Helena
Napa Yountville
Napa Napa County Unincorporated
San Francisco San Francisco
Weightings:
RHNA Employment Transit
33% 33% 33%
‐13% 16% 11%
26%‐22% 13%
‐17% 30% 26%
‐18% 5%‐2%
‐3%‐9% 26%
‐18% 29% 2%
15% 31% 11%
‐15% 19% 0%
33% 8% 11%
‐31% 28% 20%
33%‐23% 12%
‐20% 28% 0%
6% 20% 10%
‐8% 13% 9%
‐10% 23% 4%
‐29% 10%‐1%
‐24%‐1%‐9%
10%‐20%‐13%
‐10% 24%‐1%
3% 4%‐12%
31%‐21% 17%
0%‐30% 1%
25%‐12%‐11%
33% 12% 0%
23%‐15%‐7%
‐15%‐23%‐6%
33%‐18%‐7%
17%‐28% 9%
‐25%‐2%‐1%
0% 3% 2%
‐30% 11% 8%
‐9%‐24% 15%
‐23% 14%‐3%
‐2% 27% 1%
‐21% 27%‐3%
33%‐33%‐13%
33%‐3%‐3%
33%‐19% 1%
28%‐8%‐2%
8%‐6%‐3%
‐28% 15% 1%
33%‐33%‐2%
33%‐13%‐1%
5% 18% 3%
22%‐2% 5%
30%‐16%‐4%
‐5% 1%‐5%
0%‐29%‐9%
25%‐19%‐11%
‐20% 21% 1%
23%‐11% 0%
33%‐28%‐2%
12% 15%‐10%
‐33% 33% 33%
Factor Scores
County Jurisdiction
San Mateo Atherton
San Mateo Belmont
San Mateo Brisbane
San Mateo Burlingame
San Mateo Colma
San Mateo Daly City
San Mateo East Palo Alto
San Mateo Foster City
San Mateo Half Moon Bay
San Mateo Hillsborough
San Mateo Menlo Park
San Mateo Millbrae
San Mateo Pacifica
San Mateo Portola Valley
San Mateo Redwood City
San Mateo San Bruno
San Mateo San Carlos
San Mateo San Mateo
San Mateo South San Francisco
San Mateo Woodside
San Mateo San Mateo County Unincorporated
Santa Clara Campbell
Santa Clara Cupertino
Santa Clara Gilroy
Santa Clara Los Altos
Santa Clara Los Altos Hills
Santa Clara Los Gatos
Santa Clara Milpitas
Santa Clara Monte Sereno
Santa Clara Morgan Hill
Santa Clara Mountain View
Santa Clara Palo Alto
Santa Clara San Jose
Santa Clara Santa Clara
Santa Clara Saratoga
Santa Clara Sunnyvale
Santa Clara Santa Clara County Unincorporated
Solano Benicia
Solano Dixon
Solano Fairfield
Solano Rio Vista
Solano Suisun City
Solano Vacaville
Solano Vallejo
Solano Solano County Unincorporated
Sonoma Cloverdale
Sonoma Cotati
Sonoma Healdsburg
Sonoma Petaluma
Sonoma Rohnert Park
Sonoma Santa Rosa
Sonoma Sebastopol
Sonoma Sonoma
Sonoma Windsor
Sonoma Sonoma County Unincorporated
Weightings:
RHNA Employment Transit
33% 33% 33%
33%‐20% 4%
16%‐14% 8%
30%‐7% 0%
33% 9% 3%
13%‐31% 28%
20% 6% 17%
‐7%‐27% 13%
9% 6% 7%
7%‐9% 2%
26%‐22%‐8%
33% 25% 6%
33%‐27% 8%
29%‐7% 11%
26%‐26%‐33%
7% 25% 3%
‐11%‐12% 11%
33%‐4% 0%
‐6% 19% 6%
‐5% 22% 9%
33%‐17%‐20%
21% 2%‐1%
19% 7% 5%
15% 2%‐1%
‐19% 7% 2%
17%‐1%‐4%
20%‐15%‐7%
10% 12%‐3%
‐25% 10% 7%
24%‐32% —
‐23% 9%‐3%
3% 14% 4%
‐13% 30% 8%
‐33% 33% 4%
‐26% 31% 4%
14% 0%‐7%
5% 17% 3%
‐16%‐14% 1%
‐3% 1%‐18%
33%‐10%‐18%
‐8% 32%‐3%
18%‐25%‐19%
11%‐25% 4%
‐28% 20%‐3%
‐22% 23% 0%
13%‐6%‐14%
2%‐31%‐6%
4%‐17% 4%
‐4%‐4%‐6%
‐14% 17%‐3%
‐27% 4% 5%
‐32% 26% 6%
31%‐30% 1%
‐2%‐5% 4%
‐12%‐10%‐1%
‐30% 22%‐5%
SCS Jobs‐Housing Connection Strategy
Upper Limit: 110%
Lower Limit: 40%
Growth in PDA: 70%
Growth in non‐PDA: 30%
Factors: 3
STEP 1 =R10+Z10
=D10*$E$8 =F10*$G$8 =E10/I10 =I10/I$137 =(G10‐P10)+Q10 =U10+W10+Y10 =AA10/AA$137*
PDA: 131,593 Non‐PDA: 56,397
County Jurisdiction
Share of PDA
Growth
PDA Growth
Scaled to Split
Share of Non‐
PDA Growth
Non‐PDA
Growth Scaled
to Split
Score
Adjustment
Effect on Non‐
PDA Growth
Total
Score
Adjustment
Effect on Non‐
PDA Growth
Total
Score
Adjustment
Effect on Non‐
PDA Growth
Total
Alameda Alameda 0.95% 1,250 0.84% 473 2,825 44% 1.1% 1.1% 0 7 480 ‐13%‐60 16% 77 11% 50 67 547 492
Alameda Albany 0.05% 67 0.46% 257 622 11% 0.2% 0.2% 0 1 258 26% 68 ‐22%‐56 13% 33 45 304 273
Alameda Berkeley 1.28% 1,686 1.83% 1,030 3,918 43% 1.5% 1.5% 0 9 1,040 ‐17%‐175 30% 314 26% 274 413 1,453 1,305
Alameda Dublin 1.21% 1,589 1.68% 948 1,802 88% 0.7% 0.7% 0 4 952 ‐18%‐174 5% 47 ‐2%‐23 ‐150 802 721
Alameda Emeryville 1.12% 1,469 0.35% 200 372 395%0.1% 0.1% 0.0% 200 0 0 ‐3% 0 ‐9% 0 26% 0 0 0 0
Alameda Fremont 2.38% 3,138 4.09% 2,309 7,879 40% 3.1% 3.1% 0 18 2,327 ‐18%‐409 29% 675 2% 51 317 2,645 2,376
Alameda Hayward 1.95% 2,570 1.88% 1,061 5,485 47% 2.1% 2.1% 0 13 1,074 15% 158 31% 331 11% 121 610 1,683 1,513
Alameda Livermore 1.54% 2,029 1.38% 778 2,943 69% 1.1% 1.1% 0 7 785 ‐15%‐121 19% 150 0%‐2 27 812 730
Alameda Newark 0.51% 665 0.54% 307 1,615 41% 0.6% 0.6% 0 4 310 33% 103 8% 25 11% 33 162 472 424
Alameda Oakland 9.27% 12,204 4.53% 2,554 15,483 79% 6.0% 6.0% 0 36 2,590 ‐31%‐808 28% 719 20% 526 438 3,028 2,721
Alameda Piedmont 0.00% 0 0.04% 22 408 0% 0.2% 0.2% 0 1 23 33% 8 ‐23%‐5 12% 3 5 28 25
Alameda Pleasanton 0.71% 937 2.09% 1,180 2,549 37% 1.0% 1.0% 0 6 1,185 ‐20%‐242 28% 337 0% 3 97 1,282 1,152
Alameda San Leandro 1.15% 1,514 1.15% 647 3,016 50% 1.2% 1.2% 0 7 654 6% 40 20% 129 10% 66 234 888 798
Alameda Union City 0.16% 212 1.28% 722 2,711 8% 1.1% 1.1% 0 6 728 ‐8%‐55 13% 94 9% 67 107 835 750
Alameda Alameda County Unincorporated 1.13% 1,488 0.39% 221 5,382 28% 2.1% 2.1% 0 13 233 ‐10%‐24 23% 55 4% 10 40 274 246
23.42% 30,818 22.53% 12,708 57,010 22.1% 200 132 12,640 ‐1,692 2,893 1,212 2,413 13,525
Contra Costa Antioch 0.82% 1,083 0.92% 518 3,357 32% 1.3% 1.3% 0 8 526 ‐29%‐153 10% 55 ‐1%‐4 ‐102 425 381
Contra Costa Brentwood 0.00% 0 0.41% 233 1,861 0% 0.7% 0.7% 0 4 237 ‐24%‐57 ‐1%‐1 ‐9%‐21 ‐79 158 142
Contra Costa Clayton 0.00% 0 0.05% 28 346 0% 0.1% 0.1% 0 1 29 10% 3 ‐20%‐6 ‐13%‐4 ‐7 22 20
Contra Costa Concord 1.87% 2,461 1.81% 1,021 4,043 61% 1.6% 1.6% 0 9 1,031 ‐10%‐100 24% 248 ‐1%‐6 142 1,173 1,054
Contra Costa Danville 0.16% 205 0.53% 299 1,365 15% 0.5% 0.5% 0 3 302 3% 8 4% 13 ‐12%‐36 ‐15 287 258
Contra Costa El Cerrito 0.21% 275 0.20% 110 736 37% 0.3% 0.3% 0 2 112 31% 35 ‐21%‐24 17% 19 30 142 128
Contra Costa Hercules 0.83% 1,091 0.43% 241 789 138%0.3% 0.3% 0.0% 241 0 0 0% 0 ‐30% 0 1% 0 0 0 0
Contra Costa Lafayette 0.20% 259 0.33% 188 761 34% 0.3% 0.3% 0 2 190 25% 48 ‐12%‐23 ‐11%‐22 3 193 173
Contra Costa Martinez 0.14% 186 0.32% 181 1,150 16% 0.4% 0.4% 0 3 183 33% 61 12% 23 0% 0 84 267 240
Contra Costa Moraga 0.07% 89 0.28% 156 517 17% 0.2% 0.2% 0 1 157 23% 35 ‐15%‐23 ‐7%‐11 1 158 142
Contra Costa Oakley 0.67% 879 1.34% 757 1,099 80% 0.4% 0.4% 0 3 759 ‐15%‐117 ‐23%‐173 ‐6%‐48 ‐338 421 378
Contra Costa Orinda 0.03% 46 0.29% 166 555 8% 0.2% 0.2% 0 1 167 33% 56 ‐18%‐30 ‐7%‐11 14 182 163
Contra Costa Pinole 0.14% 186 0.23% 129 623 30% 0.2% 0.2% 0 1 130 17% 22 ‐28%‐36 9% 12 ‐2 128 115
Contra Costa Pittsburg 1.31% 1,725 0.87% 492 2,095 82% 0.8% 0.8% 0 5 496 ‐25%‐123 ‐2%‐12 ‐1%‐3 ‐138 358 322
Contra Costa Pleasant Hill 0.08% 104 0.41% 234 1,097 10% 0.4% 0.4% 0 3 236 0% 1 3% 7 2% 5 13 249 224
Contra Costa Richmond 0.89% 1,170 2.86% 1,616 3,273 36% 1.3% 1.3% 0 8 1,623 ‐30%‐495 11% 180 8% 127 ‐187 1,436 1,290
Contra Costa San Pablo 0.31% 406 0.30% 170 1,004 40% 0.4% 0.4% 0 2 172 ‐9%‐15 ‐24%‐41 15% 25 ‐32 140 126
Contra Costa San Ramon 0.54% 705 1.64% 924 1,979 36% 0.8% 0.8% 0 5 929 ‐23%‐216 14% 126 ‐3%‐29 ‐119 810 727
Contra Costa Walnut Creek 0.61% 801 2.26% 1,274 2,129 38% 0.8% 0.8% 0 5 1,279 ‐2%‐23 27% 347 1% 19 343 1,622 1,458
Contra Costa Contra Costa County Unincorporated 0.45% 598 1.41% 795 5,207 11% 2.0% 2.0% 0 12 807 ‐21%‐171 27% 214 ‐3%‐25 18 825 742
9.32% 12,271 16.90% 9,530 33,986 13.2% 241 77 9,366 ‐1,200 844 ‐12 ‐369 8,084
Marin Belvedere 0.00% 0 0.01% 6 39 0% 0.0% 0.0% 0 0 6 33% 2 ‐33%‐2 ‐13%‐1 ‐1 6 5
Marin Corte Madera 0.00% 0 0.10% 59 177 0% 0.1% 0.1% 0 0 59 33% 20 ‐3%‐2 ‐3%‐2 16 75 67
Marin Fairfax 0.00% 0 0.10% 54 151 0% 0.1% 0.1% 0 0 55 33% 18 ‐19%‐10 1% 0 9 63 57
Marin Larkspur 0.00% 0 0.19% 104 324 0% 0.1% 0.1% 0 1 105 28% 30 ‐8%‐8 ‐2%‐2 19 125 112
Marin Mill Valley 0.00% 0 0.21% 118 317 0% 0.1% 0.1% 0 1 118 8% 10 ‐6%‐7 ‐3%‐4 ‐1 118 106
Marin Novato 0.00% 0 0.47% 266 1,018 0% 0.4% 0.4% 0 2 269 ‐28%‐76 15% 41 1% 4 ‐31 238 214
Marin Ross 0.00% 0 0.02% 13 45 0% 0.0% 0.0% 0 0 13 33% 4 ‐33%‐4 ‐2% 0 0 13 12
Marin San Anselmo 0.00% 0 0.12% 68 261 0% 0.1% 0.1% 0 1 68 33% 23 ‐13%‐9 ‐1%‐1 13 81 73
Marin San Rafael 0.47% 613 0.63% 357 1,155 53% 0.4% 0.5% 0 3 360 5% 17 18% 64 3% 10 91 451 405
Marin Sausalito 0.00% 0 0.13% 71 143 0% 0.1% 0.1% 0 0 71 22% 16 ‐2%‐1 5% 4 18 89 80
Marin Tiburon 0.00% 5 0.11% 61 192 3% 0.1% 0.1% 0 0 61 30% 19 ‐16%‐10 ‐4%‐3 6 67 61
Marin Marin County Unincorporated 0.08% 106 0.16% 90 962 11% 0.4% 0.4% 0 2 92 ‐5%‐5 1% 1 ‐5%‐4 ‐8 84 75
0.55% 724 2.25% 1,267 4,784 1.9% 0 11 1,278 77 54 1 131 1,266
Napa American Canyon 0.29% 386 0.26% 146 219 176%0.1% 0.1% 0.0% 146 0 0 0% 0 ‐29% 0 ‐9% 0 0 0 0
Napa Calistoga 0.00% 0 0.02% 13 66 0% 0.0% 0.0% 0 0 13 25% 3 ‐19%‐3 ‐11%‐1 ‐1 12 11
Napa Napa 0.18% 236 1.18% 665 1,021 23% 0.4% 0.4% 0 2 668 ‐20%‐132 21% 140 1% 5 13 681 612
Napa St. Helena 0.00% 0 0.03% 16 76 0% 0.0% 0.0% 0 0 16 23% 4 ‐11%‐2 0% 0 2 18 16
Napa Yountville 0.00% 0 0.01% 7 43 0% 0.0% 0.0% 0 0 7 33% 2 ‐28%‐2 ‐2% 0 0 7 7
Napa Napa County Unincorporated 0.00% 0 0.31% 173 310 0% 0.1% 0.1% 0 1 174 12% 21 15% 26 ‐10%‐17 29 203 183
0.47% 622 1.81% 1,020 1,735 0.7% 146 4 878 ‐102 160 ‐14 43 828
San Francisco San Francisco 17.90% 23,559 8.22% 4,636 24,026 98% 9.3% 9.4% 0 56 4,691 ‐33%‐1,530 33% 1,535 33% 1,564 1,568 6,260 5,624
17.90% 23,559 8.22% 4,636 24,026 9.3% 0 56 4,691 ‐1,530 1,535 1,564 1,568 5,624
2014‐2022 RHNA Methodology Model
Total to
Redistribute
Redistributed
Share of
Growth
Transit
Jurisdiction
Share of
Total to
Redistibute
Upper Housing
Threshold for PDA
Redistribution of Non‐PDA Growth for Jurisdictions where
PDAs Meet or Exceed Upper Threshold
RHNA Performance
Adjusted Non‐
PDA Growth
Total
Household
Formation
Growth
PDA % of
Household
Formation
Growth
Jurisdiction
Share of HH
Formation
Growth
Application of the Fair Share Factors
Employment
Combined
Adjustment
Factor
Adjusted Non‐
PDA Growth
Share of
Growth to
Redistribute
Draft Non‐PDA
Growth Total
STEP 1 STEP 2 STEP 3 STEP 4
Sustainability Split
2014‐2022 RHNA Methodology Model
SCS Jobs‐Housing Connection Strategy
Upper Limit: 110%
Lower Limit: 40%
Growth in PDA: 70%
Growth in non‐PDA: 30%
Factors: 3
STEP 1 =R10+Z10
=D10*$E$8 =F10*$G$8 =E10/I10 =I10/I$137 =(G10‐P10)+Q10 =U10+W10+Y10 =AA10/AA$137*
PDA: 131,593 Non‐PDA: 56,397
County Jurisdiction
Share of PDA
Growth
PDA Growth
Scaled to Split
Share of Non‐
PDA Growth
Non‐PDA
Growth Scaled
to Split
Score
Adjustment
Effect on Non‐
PDA Growth
Total
Score
Adjustment
Effect on Non‐
PDA Growth
Total
Score
Adjustment
Effect on Non‐
PDA Growth
Total
2014‐2022 RHNA Methodology Model
Total to
Redistribute
Redistributed
Share of
Growth
Transit
Jurisdiction
Share of
Total to
Redistibute
Upper Housing
Threshold for PDA
Redistribution of Non‐PDA Growth for Jurisdictions where
PDAs Meet or Exceed Upper Threshold
RHNA Performance
Adjusted Non‐
PDA Growth
Total
Household
Formation
Growth
PDA % of
Household
Formation
Growth
Jurisdiction
Share of HH
Formation
Growth
Application of the Fair Share Factors
Employment
Combined
Adjustment
Factor
Adjusted Non‐
PDA Growth
Share of
Growth to
Redistribute
Draft Non‐PDA
Growth Total
STEP 1 STEP 2 STEP 3 STEP 4
Sustainability Split
San Mateo Atherton 0.00% 0 0.11% 60 260 0% 0.1% 0.1% 0 1 61 33% 20 ‐20%‐12 4% 2 10 71 64
San Mateo Belmont 0.18% 240 0.16% 93 903 27% 0.3% 0.4% 0 2 95 16% 15 ‐14%‐14 8% 7 9 104 94
San Mateo Brisbane 0.00% 0 0.14% 76 137 0% 0.1% 0.1% 0 0 76 30% 23 ‐7%‐5 0% 0 17 94 84
San Mateo Burlingame 0.68% 898 0.76% 427 998 90% 0.4% 0.4% 0 2 429 33% 143 9% 40 3% 15 197 627 563
San Mateo Colma 0.05% 66 0.02% 9 56 118%0.0% 0.0% 0.0% 9 0 0 13% 0 ‐31% 0 28% 0 0 0 0
San Mateo Daly City 0.71% 937 0.66% 374 3,712 25% 1.4% 1.4% 0 9 382 20% 75 6% 21 17% 64 161 543 488
San Mateo East Palo Alto 0.17% 229 0.06% 31 1,149 20% 0.4% 0.4% 0 3 34 ‐7%‐2 ‐27%‐9 13% 5 ‐7 27 24
San Mateo Foster City 0.00% 0 0.54% 303 1,058 0% 0.4% 0.4% 0 2 305 9% 27 6% 19 7% 22 69 374 336
San Mateo Half Moon Bay 0.00% 0 0.16% 92 457 0% 0.2% 0.2% 0 1 93 7% 6 ‐9%‐8 2% 2 1 93 84
San Mateo Hillsborough 0.00% 0 0.18% 103 390 0% 0.2% 0.2% 0 1 104 26% 27 ‐22%‐23 ‐8%‐8 ‐4 100 90
San Mateo Menlo Park 0.25% 330 0.49% 279 1,114 30% 0.4% 0.4% 0 3 282 33% 94 25% 70 6% 18 181 463 416
San Mateo Millbrae 0.53% 694 0.39% 217 759 91% 0.3% 0.3% 0 2 219 33% 73 ‐27%‐58 8% 17 31 251 225
San Mateo Pacifica 0.00% 0 0.29% 162 1,371 0% 0.5% 0.5% 0 3 165 29% 48 ‐7%‐12 11% 18 54 219 197
San Mateo Portola Valley 0.00% 0 0.07% 39 158 0% 0.1% 0.1% 0 0 39 26% 10 ‐26%‐10 ‐33%‐13 ‐13 26 24
San Mateo Redwood City 2.22% 2,923 1.40% 789 2,675 109% 1.0% 1.0% 0 6 796 7% 54 25% 201 3% 26 282 1,078 968
San Mateo San Bruno 0.78% 1,032 0.63% 354 1,511 68% 0.6% 0.6% 0 4 358 ‐11%‐40 ‐12%‐42 11% 38 ‐44 314 282
San Mateo San Carlos 0.23% 308 0.45% 253 998 31% 0.4% 0.4% 0 2 255 33% 85 ‐4%‐9 0% 0 76 331 297
San Mateo San Mateo 1.65% 2,171 1.49% 841 3,346 65% 1.3% 1.3% 0 8 849 ‐6%‐52 19% 157 6% 53 159 1,007 905
San Mateo South San Francisco 1.35% 1,774 0.56% 315 2,221 80% 0.9% 0.9% 0 5 321 ‐5%‐15 22% 71 9% 28 84 404 363
San Mateo Woodside 0.00% 0 0.05% 27 200 0% 0.1% 0.1% 0 0 28 33% 9 ‐17%‐5 ‐20%‐6 ‐1 27 24
San Mateo San Mateo County Unincorporated 0.21% 280 0.08% 43 2,299 12% 0.9% 0.9% 0 5 48 21% 10 2% 1 ‐1% 0 11 59 53
9.03% 11,883 8.67% 4,887 25,772 10.0% 9 60 4,938 612 373 288 1,274 5,580
Santa Clara Campbell 0.30% 392 0.82% 463 1,878 21% 0.7% 0.7% 0 4 467 19% 89 7% 35 5% 23 146 614 551
Santa Clara Cupertino 0.46% 610 0.78% 439 2,578 24% 1.0% 1.0% 0 6 445 15% 69 2% 8 ‐1%‐5 72 517 465
Santa Clara Gilroy 0.39% 507 0.50% 281 2,665 19% 1.0% 1.0% 0 6 287 ‐19%‐55 7% 20 2% 5 ‐30 257 231
Santa Clara Los Altos 0.00% 0 0.64% 363 1,404 0% 0.5% 0.5% 0 3 367 17% 62 ‐1%‐5 ‐4%‐14 43 410 368
Santa Clara Los Altos Hills 0.00% 0 0.06% 33 474 0% 0.2% 0.2% 0 1 34 20% 7 ‐15%‐5 ‐7%‐3 ‐1 33 30
Santa Clara Los Gatos 0.00% 0 0.39% 222 1,519 0% 0.6% 0.6% 0 4 226 10% 22 12% 26 ‐3%‐7 41 267 240
Santa Clara Milpitas 1.46% 1,922 3.03% 1,707 3,181 60% 1.2% 1.2% 0 7 1,715 ‐25%‐436 10% 169 7% 116 ‐151 1,563 1,405
Santa Clara Monte Sereno 0.00% 0 0.06% 32 198 0% 0.1% 0.1% 0 0 33 24% 8 ‐32%‐11 0% 0 ‐3 30 27
Santa Clara Morgan Hill 0.28% 372 1.34% 754 2,011 18% 0.8% 0.8% 0 5 759 ‐23%‐171 9% 66 ‐3%‐23 ‐129 630 566
Santa Clara Mountain View 1.73% 2,281 1.08% 610 3,369 68% 1.3% 1.3% 0 8 618 3% 20 14% 88 4% 27 135 753 677
Santa Clara Palo Alto 0.17% 226 3.13% 1,763 3,517 6% 1.4% 1.4% 0 8 1,771 ‐13%‐235 30% 525 8% 149 439 2,211 1,986
Santa Clara San Jose 22.65% 29,810 10.56% 5,957 47,314 63% 18.3% 18.4% 0 110 6,067 ‐33%‐2,022 33% 2,022 4% 222 222 6,290 5,651
Santa Clara Santa Clara 1.68% 2,214 3.44% 1,942 5,280 42% 2.0% 2.1% 0 12 1,954 ‐26%‐511 31% 615 4% 82 186 2,141 1,923
Santa Clara Saratoga 0.00% 0 0.39% 220 1,464 0% 0.6% 0.6% 0 3 223 14% 31 0% 0 ‐7%‐15 16 239 215
Santa Clara Sunnyvale 3.05% 4,013 3.21% 1,810 6,320 64% 2.4% 2.5% 0 15 1,825 5% 85 17% 315 3% 63 463 2,288 2,056
Santa Clara Santa Clara County Unincorporated 0.00% 0 0.20% 115 695 0% 0.3% 0.3% 0 2 117 ‐16%‐19 ‐14%‐16 1% 2 ‐33 84 75
32.18% 42,349 29.64% 16,714 83,867 32.5% 0 195 16,909 ‐3,058 3,853 623 1,419 16,466
Solano Benicia 0.17% 226 0.26% 145 762 30% 0.3% 0.3% 0 2 147 ‐3%‐4 1% 1 ‐18%‐27 ‐29 117 105
Solano Dixon 0.05% 64 0.12% 70 485 13% 0.2% 0.2% 0 1 71 33% 24 ‐10%‐7 ‐18%‐13 4 76 68
Solano Fairfield 2.03% 2,669 1.41% 795 3,001 89% 1.2% 1.2% 0 7 802 ‐8%‐66 32% 257 ‐3%‐20 171 973 874
Solano Rio Vista 0.07% 90 0.03% 16 242 37% 0.1% 0.1% 0 1 17 18% 3 ‐25%‐4 ‐19%‐3 ‐4 12 11
Solano Suisun City 0.20% 258 0.22% 125 785 33% 0.3% 0.3% 0 2 127 11% 14 ‐25%‐32 4% 5 ‐13 114 102
Solano Vacaville 0.15% 193 1.59% 894 2,669 7% 1.0% 1.0% 0 6 900 ‐28%‐248 20% 183 ‐3%‐23 ‐88 812 730
Solano Vallejo 0.16% 216 0.89% 501 3,352 6% 1.3% 1.3% 0 8 509 ‐22%‐111 23% 116 0% 1 6 514 462
Solano Solano County Unincorporated 0.00% 0 0.13% 72 379 0% 0.1% 0.1% 0 1 73 13% 10 ‐6%‐5 ‐14%‐10 ‐5 68 61
2.82% 3,717 4.64% 2,619 11,675 4.5% 0 27 2,646 ‐379 510 ‐90 41 2,414
Sonoma Cloverdale 0.14% 184 0.09% 51 280 66% 0.1% 0.1% 0 1 52 2% 1 ‐31%‐16 ‐6%‐3 ‐18 33 30
Sonoma Cotati 0.08% 101 0.08% 46 268 38% 0.1% 0.1% 0 1 46 4% 2 ‐17%‐8 4% 2 ‐4 42 38
Sonoma Healdsburg 0.00% 0 0.10% 57 384 0% 0.1% 0.1% 0 1 58 ‐4%‐2 ‐4%‐2 ‐6%‐4 ‐8 49 44
Sonoma Petaluma 0.32% 423 0.50% 284 1,824 23% 0.7% 0.7% 0 4 289 ‐14%‐40 17% 48 ‐3%‐7 0 289 260
Sonoma Rohnert Park 0.54% 712 0.47% 263 1,376 52% 0.5% 0.5% 0 3 267 ‐27%‐72 4% 10 5% 15 ‐47 219 197
Sonoma Santa Rosa 2.29% 3,017 3.33% 1,877 5,612 54% 2.2% 2.2% 0 13 1,890 ‐32%‐603 26% 490 6% 111 ‐2 1,888 1,696
Sonoma Sebastopol 0.07% 96 0.05% 27 250 39% 0.1% 0.1% 0 1 27 31% 9 ‐30%‐8 1% 0 0 28 25
Sonoma Sonoma 0.00% 0 0.15% 84 338 0% 0.1% 0.1% 0 1 85 ‐2%‐2 ‐5%‐4 4% 4 ‐2 83 74
Sonoma Windsor 0.22% 296 0.38% 215 840 35% 0.3% 0.3% 0 2 217 ‐12%‐26 ‐10%‐23 ‐1%‐2 ‐50 167 150
Sonoma Sonoma County Unincorporated 0.63% 823 0.20% 112 3,989 21% 1.5% 1.6% 0 9 122 ‐30%‐36 22% 26 ‐5%‐7 ‐17 105 94
4.29% 5,651 5.35% 3,016 15,161 5.9% 0 35 3,051 ‐769 513 109 ‐148 2,609
100.00% 131,593 100.00% 56,397 258,016 100.0% 0.6% 100.0% 596 596 56,397 ‐8,041 10,733 3,680 6,373 62,770 56,397
2014‐2022 RHNA Methodology Model
SCS Jobs‐Housing Connection Strategy
Upper Limit: 110%
Lower Limit: 40%
Growth in PDA: 70%
Growth in non‐PDA: 30%
Factors: 3
County Jurisdiction
Alameda Alameda
Alameda Albany
Alameda Berkeley
Alameda Dublin
Alameda Emeryville
Alameda Fremont
Alameda Hayward
Alameda Livermore
Alameda Newark
Alameda Oakland
Alameda Piedmont
Alameda Pleasanton
Alameda San Leandro
Alameda Union City
Alameda Alameda County Unincorporated
Contra Costa Antioch
Contra Costa Brentwood
Contra Costa Clayton
Contra Costa Concord
Contra Costa Danville
Contra Costa El Cerrito
Contra Costa Hercules
Contra Costa Lafayette
Contra Costa Martinez
Contra Costa Moraga
Contra Costa Oakley
Contra Costa Orinda
Contra Costa Pinole
Contra Costa Pittsburg
Contra Costa Pleasant Hill
Contra Costa Richmond
Contra Costa San Pablo
Contra Costa San Ramon
Contra Costa Walnut Creek
Contra Costa Contra Costa County Unincorporated
Marin Belvedere
Marin Corte Madera
Marin Fairfax
Marin Larkspur
Marin Mill Valley
Marin Novato
Marin Ross
Marin San Anselmo
Marin San Rafael
Marin Sausalito
Marin Tiburon
Marin Marin County Unincorporated
Napa American Canyon
Napa Calistoga
Napa Napa
Napa St. Helena
Napa Yountville
Napa Napa County Unincorporated
San Francisco San Francisco
=$E10+AB10 =IF(OR(AE10<$C$3,$J10>=$C$2),0,AD10)
*R$137 =AD10/$I10 =IF(AE10<$C$3,$I10*$C$3,IF($J10>=$C$2,$E10,0))
Region Total: 214,500
Jurisdiction
Share of RHNA
Jurisdiction
Total
Jurisdiction
Share
1,742 62% 1,742 1,689 0 1,689 60% 0.90% 2,046 0.95% 1,689 0 0 0.90% 0.96% 27 1,716
339 55% 339 329 0 329 53% 0.18% 276 0.13% 329 0 0 0.18% 0.19% 5 334
2,991 76% 2,991 2,900 0 2,900 74% 1.54% 2,431 1.13% 2,900 0 0 1.54% 1.64% 46 2,946
2,310 128% 2,310 2,240 0 2,240 124% 1.19% 3,330 1.55% 2,240 0 0 1.19% 1.27% 35 2,275
1,469 395% 0 0 1,469 1,469 395% 0.78% 1,137 0.53% 1,469 0 0 0.78% 0.83% 23 1,492
5,514 70% 5,514 5,347 0 5,347 68% 2.84% 4,380 2.04% 5,347 0 0 2.84% 3.03% 84 5,432
4,082 74% 4,082 3,959 0 3,959 72% 2.11% 3,393 1.58% 3,959 0 0 2.11% 2.24% 62 4,021
2,759 94% 2,759 2,675 0 2,675 91% 1.42% 3,394 1.58% 2,675 0 0 1.42% 1.51% 42 2,717
1,090 67% 1,090 1,057 0 1,057 65% 0.56% 863 0.40% 1,057 0 0 0.56% 0.60% 17 1,073
14,925 96% 14,925 14,473 0 14,473 93% 7.70% 14,629 6.82% 14,473 0 0 7.70% 8.19% 228 14,701
25 6%0 0 163 163 40% 0.09% 40 0.02% 163 60 103 0.00% 0.00% 0 60
2,089 82% 2,089 2,026 0 2,026 79% 1.08% 3,277 1.53% 2,026 0 0 1.08% 1.15% 32 2,058
2,312 77% 2,312 2,242 0 2,242 74% 1.19% 1,630 0.76% 2,242 0 0 1.19% 1.27% 35 2,277
962 35%0 0 1,084 1,084 40% 0.58% 1,944 0.91% 1,084 0 0 0.58% 0.61% 17 1,101
1,734 32%0 0 1,734 1,734 32% 0.92% 2,167 1.01% 1,734 0 0 0.92% 0.98% 27 1,762
44,343 40,153 38,936 4,451 43,387 23.08% 44,937 20.95% 43,387 60 103 22.99% 24.46% 682 43,965
1,464 44% 1,464 1,420 0 1,420 42% 0.76% 2,282 1.06% 1,420 0 0 0.76% 0.80% 22 1,442
142 8%0 0 744 744 40% 0.40% 2,705 1.26% 744 0 0 0.40% 0.42% 12 756
20 6%0 0 138 138 40% 0.07% 151 0.07% 138 0 0 0.07% 0.08% 2 141
3,515 87% 3,515 3,408 0 3,408 84% 1.81% 3,043 1.42% 3,408 0 0 1.81% 1.93% 54 3,462
462 34%0 0 546 546 40% 0.29% 583 0.27% 546 0 0 0.29% 0.31% 9 555
403 55% 403 391 0 391 53% 0.21% 431 0.20% 391 0 0 0.21% 0.22% 6 397
1,091 138% 0 0 1,091 1,091 138% 0.58% 453 0.21% 1,091 680 412 0.00% 0.00% 0 680
432 57% 432 419 0 419 55% 0.22% 361 0.17% 419 0 0 0.22% 0.24% 7 426
426 37%0 0 460 460 40% 0.24% 1,060 0.49% 460 0 0 0.24% 0.26% 7 467
232 45% 232 224 0 224 43% 0.12% 234 0.11% 224 0 0 0.12% 0.13% 4 228
1,258 114% 1,258 1,220 0 1,220 111% 0.65% 775 0.36% 1,220 1,163 57 0.00% 0.00% 0 1,163
209 38%0 0 222 222 40% 0.12% 218 0.10% 222 0 0 0.12% 0.13% 4 226
301 48% 301 291 0 291 47% 0.16% 323 0.15% 291 0 0 0.16% 0.16% 5 296
2,047 98% 2,047 1,985 0 1,985 95% 1.06% 1,772 0.83% 1,985 0 0 1.06% 1.12% 31 2,016
328 30%0 0 439 439 40% 0.23% 628 0.29% 439 0 0 0.23% 0.25% 7 446
2,461 75% 2,461 2,386 0 2,386 73% 1.27% 2,826 1.32% 2,386 0 0 1.27% 1.35% 38 2,424
532 53% 532 516 0 516 51% 0.27% 298 0.14% 516 447 69 0.00% 0.00% 0 447
1,433 72% 1,433 1,389 0 1,389 70% 0.74% 3,463 1.61% 1,389 0 0 0.74% 0.79% 22 1,411
2,259 106% 2,259 2,191 0 2,191 103% 1.17% 1,958 0.91% 2,191 0 0 1.17% 1.24% 35 2,225
1,339 26%0 0 1,339 1,339 26% 0.71% 3,508 1.64% 1,339 0 0 0.71% 0.76% 21 1,361
20,355 16,336 15,841 4,980 20,821 11.08% 27,072 12.62% 20,821 2,289 538 9.57% 10.18% 284 20,567
5 13%0 0 16 16 40% 0.01% 17 0.01% 16 0 0 0.01% 0.01% 0 16
67 38%0 0 71 71 40% 0.04% 244 0.11% 71 0 0 0.04% 0.04% 1 72
57 38%0 0 60 60 40% 0.03% 108 0.05% 60 0 0 0.03% 0.03% 1 61
112 35%0 0 130 130 40% 0.07% 382 0.18% 130 0 0 0.07% 0.07% 2 132
106 33%0 0 127 127 40% 0.07% 292 0.14% 127 0 0 0.07% 0.07% 2 129
214 21%0 0 407 407 40% 0.22% 1,241 0.58% 407 0 0 0.22% 0.23% 6 414
12 26%0 0 18 18 40% 0.01% 27 0.01% 18 0 0 0.01% 0.01% 0 18
73 28%0 0 104 104 40% 0.06% 113 0.05% 104 0 0 0.06% 0.06% 2 106
1,018 88% 1,018 987 0 987 85% 0.53% 1,403 0.65% 987 0 0 0.53% 0.56% 16 1,003
80 56% 80 78 0 78 54% 0.04% 165 0.08% 78 0 0 0.04% 0.04% 1 79
66 34%0 0 77 77 40% 0.04% 117 0.05% 77 0 0 0.04% 0.04% 1 78
181 19%0 0 181 181 19% 0.10% 773 0.36% 181 0 0 0.10% 0.10% 3 184
1,990 1,098 1,065 1,191 2,256 1.20% 4,882 2.28% 2,256 0 0 1.20% 1.28% 36 2,291
386 176% 0 0 386 386 176% 0.21% 728 0.34% 386 0 0 0.21% 0.22% 6 392
11 17%0 0 26 26 40% 0.01% 94 0.04% 26 0 0 0.01% 0.01% 0 27
848 83% 848 822 0 822 80% 0.44% 2,024 0.94% 822 0 0 0.44% 0.47% 13 835
16 21%0 0 30 30 40% 0.02% 121 0.06% 30 0 0 0.02% 0.02% 0 31
7 15%0 0 17 17 40% 0.01% 87 0.04% 17 0 0 0.01% 0.01% 0 17
183 59% 183 177 0 177 57% 0.09% 651 0.30% 177 0 0 0.09% 0.10% 3 180
1,449 1,030 999 460 1,459 0.78% 3,705 1.73% 1,459 0 0 0.78% 0.83% 23 1,482
29,183 121% 29,183 28,299 0 28,299 118% 15.05% 31,193 14.54% 28,299 0 0 15.05% 16.01% 446 28,745
29,183 29,183 28,299 0 28,299 15.05% 31,193 14.54% 28,299 0 0 15.05% 16.01% 446 28,745
2014‐2022 RHNA Methodology Model
Final RHNA
Application of Final Rebalance and Reallocation
Jurisdictions
RHNA Max if
1.5 Times Last
RHNA
Pre‐Final
RHNA
Jurisdictions
That Get
Rebalanced
Share to
Redistribute
Redistributed
Shares
Difference
That Will Get
Rebalanced
Pre‐Final RHNA Allocation 2007‐2014 RHNA
Allocation
Pre‐Final
RHNA
Allocation
RHNA as % of
HH Formation
Growth
STEP 6
Set Min/Max
Allocations
Rebalance
Allocations for
Other
Jurisdictions
Rebalance
Allocations
Jurisdictions
>= 40% HH
Form Growth
Draft RHNA
Allocation
(PDA + Non‐
PDA)
RHNA as % of
HH Formation
Growth
Application of 40% Minimum Housing Floor
STEP 5
2014‐2022 RHNA Methodology Model
SCS Jobs‐Housing Connection Strategy
Upper Limit: 110%
Lower Limit: 40%
Growth in PDA: 70%
Growth in non‐PDA: 30%
Factors: 3
County Jurisdiction
San Mateo Atherton
San Mateo Belmont
San Mateo Brisbane
San Mateo Burlingame
San Mateo Colma
San Mateo Daly City
San Mateo East Palo Alto
San Mateo Foster City
San Mateo Half Moon Bay
San Mateo Hillsborough
San Mateo Menlo Park
San Mateo Millbrae
San Mateo Pacifica
San Mateo Portola Valley
San Mateo Redwood City
San Mateo San Bruno
San Mateo San Carlos
San Mateo San Mateo
San Mateo South San Francisco
San Mateo Woodside
San Mateo San Mateo County Unincorporated
Santa Clara Campbell
Santa Clara Cupertino
Santa Clara Gilroy
Santa Clara Los Altos
Santa Clara Los Altos Hills
Santa Clara Los Gatos
Santa Clara Milpitas
Santa Clara Monte Sereno
Santa Clara Morgan Hill
Santa Clara Mountain View
Santa Clara Palo Alto
Santa Clara San Jose
Santa Clara Santa Clara
Santa Clara Saratoga
Santa Clara Sunnyvale
Santa Clara Santa Clara County Unincorporated
Solano Benicia
Solano Dixon
Solano Fairfield
Solano Rio Vista
Solano Suisun City
Solano Vacaville
Solano Vallejo
Solano Solano County Unincorporated
Sonoma Cloverdale
Sonoma Cotati
Sonoma Healdsburg
Sonoma Petaluma
Sonoma Rohnert Park
Sonoma Santa Rosa
Sonoma Sebastopol
Sonoma Sonoma
Sonoma Windsor
Sonoma Sonoma County Unincorporated
=$E10+AB10 =IF(OR(AE10<$C$3,$J10>=$C$2),0,AD10)
*R$137 =AD10/$I10 =IF(AE10<$C$3,$I10*$C$3,IF($J10>=$C$2,$E10,0))
Region Total: 214,500
Jurisdiction
Share of RHNA
Jurisdiction
Total
Jurisdiction
Share
2014‐2022 RHNA Methodology Model
Final RHNA
Application of Final Rebalance and Reallocation
Jurisdictions
RHNA Max if
1.5 Times Last
RHNA
Pre‐Final
RHNA
Jurisdictions
That Get
Rebalanced
Share to
Redistribute
Redistributed
Shares
Difference
That Will Get
Rebalanced
Pre‐Final RHNA Allocation 2007‐2014 RHNA
Allocation
Pre‐Final
RHNA
Allocation
RHNA as % of
HH Formation
Growth
STEP 6
Set Min/Max
Allocations
Rebalance
Allocations for
Other
Jurisdictions
Rebalance
Allocations
Jurisdictions
>= 40% HH
Form Growth
Draft RHNA
Allocation
(PDA + Non‐
PDA)
RHNA as % of
HH Formation
Growth
Application of 40% Minimum Housing Floor
STEP 5
64 25%0 0 104 104 40% 0.06% 83 0.04% 104 0 0 0.06% 0.06% 2 106
334 37%0 0 361 361 40% 0.19% 399 0.19% 361 0 0 0.19% 0.20% 6 367
84 61% 84 82 0 82 60% 0.04% 401 0.19% 82 0 0 0.04% 0.05% 1 83
1,461 146% 1,461 1,417 0 1,417 142% 0.75% 650 0.30% 1,417 975 442 0.00% 0.00% 0 975
66 118% 0 0 66 66 118% 0.04% 65 0.03% 66 0 0 0.04% 0.04% 1 67
1,425 38%0 0 1,485 1,485 40% 0.79% 1,207 0.56% 1,485 0 0 0.79% 0.84% 23 1,508
253 22%0 0 460 460 40% 0.24% 630 0.29% 460 0 0 0.24% 0.26% 7 467
336 32%0 0 423 423 40% 0.23% 486 0.23% 423 0 0 0.23% 0.24% 7 430
84 18%0 0 183 183 40% 0.10% 276 0.13% 183 0 0 0.10% 0.10% 3 186
90 23%0 0 156 156 40% 0.08% 86 0.04% 156 129 27 0.00% 0.00% 0 129
745 67% 745 723 0 723 65% 0.38% 993 0.46% 723 0 0 0.38% 0.41% 11 734
919 121% 919 891 0 891 117% 0.47% 452 0.21% 891 678 213 0.00% 0.00% 0 678
197 14%0 0 548 548 40% 0.29% 275 0.13% 548 413 136 0.00% 0.00% 0 413
24 15%0 0 63 63 40% 0.03% 74 0.03% 63 0 0 0.03% 0.04% 1 64
3,891 145% 3,891 3,773 0 3,773 141% 2.01% 1,856 0.87% 3,773 2,784 989 0.00% 0.00% 0 2,784
1,314 87% 1,314 1,274 0 1,274 84% 0.68% 973 0.45% 1,274 0 0 0.68% 0.72% 20 1,294
605 61% 605 587 0 587 59% 0.31% 599 0.28% 587 0 0 0.31% 0.33% 9 596
3,076 92% 3,076 2,983 0 2,983 89% 1.59% 3,051 1.42% 2,983 0 0 1.59% 1.69% 47 3,030
2,138 96% 2,138 2,073 0 2,073 93% 1.10% 1,635 0.76% 2,073 0 0 1.10% 1.17% 33 2,106
24 12%0 0 80 80 40% 0.04% 41 0.02% 80 62 19 0.00% 0.00% 0 62
334 15%0 0 334 334 15% 0.18% 1,506 0.70% 334 0 0 0.18% 0.19% 5 339
17,463 14,234 13,803 4,263 18,066 9.61% 15,738 7.34% 18,066 5,040 1,826 5.96% 6.34% 177 16,417
943 50% 943 915 0 915 49% 0.49% 892 0.42% 915 0 0 0.49% 0.52% 14 929
1,075 42% 1,075 1,042 0 1,042 40% 0.55% 1,170 0.55% 1,042 0 0 0.55% 0.59% 16 1,059
739 28%0 0 1,066 1,066 40% 0.57% 1,615 0.75% 1,066 0 0 0.57% 0.60% 17 1,083
368 26%0 0 562 562 40% 0.30% 317 0.15% 562 476 86 0.00% 0.00% 0 476
30 6%0 0 190 190 40% 0.10% 81 0.04% 190 122 68 0.00% 0.00% 0 122
240 16%0 0 608 608 40% 0.32% 562 0.26% 608 0 0 0.32% 0.34% 10 617
3,326 105% 3,326 3,225 0 3,225 101% 1.72% 2,487 1.16% 3,225 0 0 1.72% 1.83% 51 3,276
27 14%0 0 79 79 40% 0.04% 41 0.02% 79 62 18 0.00% 0.00% 0 62
938 47% 938 909 0 909 45% 0.48% 1,312 0.61% 909 0 0 0.48% 0.51% 14 924
2,958 88% 2,958 2,868 0 2,868 85% 1.53% 2,599 1.21% 2,868 0 0 1.53% 1.62% 45 2,913
2,212 63% 2,212 2,145 0 2,145 61% 1.14% 2,860 1.33% 2,145 0 0 1.14% 1.21% 34 2,179
35,461 75% 35,461 34,387 0 34,387 73% 18.29% 34,721 16.19% 34,387 0 0 18.29% 19.46% 542 34,929
4,138 78% 4,138 4,012 0 4,012 76% 2.13% 5,873 2.74% 4,012 0 0 2.13% 2.27% 63 4,075
215 15%0 0 586 586 40% 0.31% 292 0.14% 586 438 148 0.00% 0.00% 0 438
6,069 96% 6,069 5,886 0 5,886 93% 3.13% 4,426 2.06% 5,886 0 0 3.13% 3.33% 93 5,978
75 11%0 0 75 75 11% 0.04% 1,090 0.51% 75 0 0 0.04% 0.04% 1 77
58,815 57,120 55,390 3,165 58,555 31.15% 60,338 28.13% 58,555 1,097 320 30.39% 32.33% 901 59,136
332 44% 332 322 0 322 42% 0.17% 532 0.25% 322 0 0 0.17% 0.18% 5 327
132 27%0 0 194 194 40% 0.10% 728 0.34% 194 0 0 0.10% 0.11% 3 197
3,543 118% 3,543 3,436 0 3,436 114% 1.83% 3,796 1.77% 3,436 0 0 1.83% 1.94% 54 3,490
101 42% 101 98 0 98 40% 0.05% 1,219 0.57% 98 0 0 0.05% 0.06% 2 99
361 46% 361 350 0 350 45% 0.19% 610 0.28% 350 0 0 0.19% 0.20% 6 355
923 35%0 0 1,068 1,068 40% 0.57% 2,901 1.35% 1,068 0 0 0.57% 0.60% 17 1,084
678 20%0 0 1,341 1,341 40% 0.71% 3,100 1.45% 1,341 0 0 0.71% 0.76% 21 1,362
62 16%0 0 62 62 16% 0.03% 99 0.05% 62 0 0 0.03% 0.03% 1 63
6,131 4,337 4,205 2,664 6,869 3.65% 12,985 6.05% 6,869 0 0 3.65% 3.89% 108 6,978
214 76% 214 207 0 207 74% 0.11% 417 0.19% 207 0 0 0.11% 0.12% 3 210
139 52% 139 135 0 135 50% 0.07% 257 0.12% 135 0 0 0.07% 0.08% 2 137
44 12%0 0 154 154 40% 0.08% 331 0.15% 154 0 0 0.08% 0.09% 2 156
682 37%0 0 730 730 40% 0.39% 1,945 0.91% 730 0 0 0.39% 0.41% 12 741
909 66% 909 881 0 881 64% 0.47% 1,554 0.72% 881 0 0 0.47% 0.50% 14 895
4,713 84% 4,713 4,570 0 4,570 81% 2.43% 6,534 3.05% 4,570 0 0 2.43% 2.59% 72 4,642
121 49% 121 118 0 118 47% 0.06% 176 0.08% 118 0 0 0.06% 0.07% 2 120
75 22%0 0 135 135 40% 0.07% 353 0.16% 135 0 0 0.07% 0.08% 2 137
446 53% 446 432 0 432 51% 0.23% 719 0.34% 432 0 0 0.23% 0.24% 7 439
917 23%0 0 917 917 23% 0.49% 1,364 0.64% 917 0 0 0.49% 0.52% 14 932
8,260 6,541 6,343 1,936 8,279 4.40% 13,650 6.36% 8,279 0 0 4.40% 4.68% 131 8,409
187,990 170,032 164,880 23,110 187,990 100.00% 214,500 100.00% 187,990 8,486 2,787 94.00% 100.00% 2,787 187,990
2014‐2022 RHNA Methodology Model
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
25% 15% 18% 42%
Regional Total by Income:46,680 28,940 33,420 78,950
25% 15% 18% 42%
Income Shift:175%
RHNA
Allocation
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Total
Alameda 1,716 24% 17% 20% 40% 26% 14% 16% 43% 442 247 282 744 1,716
Albany 334 27% 15% 19% 40% 24% 16% 17% 43% 80 53 57 145 334
Berkeley 2,946 35% 16% 15% 34% 18% 15% 20% 47% 530 440 581 1,396 2,948
Dublin 2,275 12% 10% 17% 62% 35% 20% 19% 27% 793 444 423 613 2,274
Emeryville 1,492 34% 17% 18% 31% 18% 14% 17% 50% 275 210 258 750 1,492
Fremont 5,432 17% 13% 18% 53% 31% 17% 18% 34% 1,707 922 974 1,825 5,428
Hayward 4,021 30% 20% 21% 30% 21% 12% 16% 51% 862 490 625 2,042 4,018
Livermore 2,717 18% 13% 17% 52% 31% 17% 18% 34% 835 472 494 919 2,720
Newark 1,073 18% 15% 22% 45% 31% 15% 15% 39% 328 166 157 421 1,073
Oakland 14,701 40% 17% 16% 27% 14% 14% 19% 53% 2,050 2,066 2,803 7,784 14,702
Piedmont 60 6% 6% 8% 80% 40% 23% 25% 13% 24 14 15 8 60
Pleasanton 2,058 12% 11% 15% 62% 35% 19% 20% 27% 713 389 405 551 2,058
San Leandro 2,277 29% 20% 21% 30% 22% 12% 15% 51% 502 269 350 1,156 2,277
Union City 1,101 20% 14% 18% 47% 29% 16% 17% 38% 316 179 191 416 1,101
Alameda County Unincorporated 1,762 26% 19% 19% 36% 24% 13% 17% 46% 428 226 294 814 1,762
43,965 9,886 6,586 7,908 19,582 43,962
Antioch 1,442 26% 17% 22% 35% 24% 14% 15% 47% 348 204 213 676 1,441
Brentwood 756 18% 14% 20% 49% 31% 16% 16% 37% 233 123 122 278 756
Clayton 141 10% 12% 12% 66% 36% 18% 22% 24% 51 25 31 33 141
Concord 3,462 28% 19% 20% 33% 23% 13% 16% 48% 794 442 556 1,669 3,462
Danville 555 12% 9% 12% 68% 35% 20% 22% 22% 195 111 124 124 555
El Cerrito 397 25% 15% 18% 42% 25% 16% 17% 42% 100 63 69 165 397
Hercules 680 16% 13% 22% 50% 32% 17% 15% 36% 219 117 100 244 680
Lafayette 426 13% 10% 13% 64% 34% 20% 21% 25% 146 83 90 106 426
Martinez 467 23% 15% 19% 42% 26% 15% 17% 41% 123 72 78 193 467
Moraga 228 15% 11% 12% 62% 33% 19% 22% 27% 75 43 50 61 228
Oakley 1,163 22% 16% 22% 40% 27% 15% 15% 43% 316 173 174 500 1,163
Orinda 226 9% 8% 10% 74% 37% 21% 24% 18% 84 47 53 40 226
Pinole 296 22% 14% 23% 41% 27% 16% 14% 43% 80 48 42 126 296
Pittsburg 2,016 33% 19% 21% 28% 19% 13% 16% 52% 390 253 315 1,059 2,017
Pleasant Hill 446 24% 15% 16% 45% 26% 15% 19% 40% 117 69 84 177 446
Richmond 2,424 35% 19% 19% 27% 18% 13% 17% 53% 436 304 408 1,274 2,422
San Pablo 447 42% 20% 19% 19% 12% 12% 17% 59% 55 53 75 264 447
San Ramon 1,411 10% 10% 15% 66% 36% 20% 20% 24% 514 278 281 339 1,411
Walnut Creek 2,225 23% 15% 19% 44% 27% 16% 17% 40% 601 353 379 893 2,225
Contra Costa County Unincorporated 1,361 22% 15% 18% 46% 27% 16% 18% 39% 372 217 242 530 1,361
20,567 5,249 3,076 3,486 8,753 20,564
Belvedere 16 21% 10% 10% 59% 28% 19% 24% 29%4 3 4 5 16
Corte Madera 72 18% 12% 18% 52% 30% 18% 18% 34% 22 13 13 25 72
Fairfax 61 23% 12% 17% 48% 27% 18% 18% 37% 16 11 11 23 61
Larkspur 132 18% 16% 20% 46% 30% 15% 16% 39% 40 20 21 51 132
Mill Valley 129 16% 11% 15% 59% 32% 19% 20% 29% 41 24 26 38 129
Novato 414 23% 15% 18% 44% 27% 16% 17% 40% 111 65 72 166 414
Ross 18 14% 8% 11% 68% 34% 21% 23% 22%6 4 4 4 18
San Anselmo 106 17% 14% 18% 51% 31% 16% 18% 35% 33 17 19 37 106
San Rafael 1,003 27% 16% 18% 39% 24% 15% 18% 44% 239 147 180 437 1,003
Sausalito 79 15% 12% 15% 57% 32% 18% 20% 30% 26 14 16 24 79
Tiburon 78 17% 9% 9% 65% 31% 20% 24% 24% 24 16 19 19 78
Marin County Unincorporated 184 19% 12% 15% 54% 30% 18% 20% 33% 55 32 37 60 184
2,291 617 366 421 888 2,292
American Canyon 392 19% 18% 22% 42% 30% 14% 15% 42% 116 54 58 164 392
Calistoga 27 30% 26% 21% 24% 22% 8% 15% 55% 6 2 4 15 27
Napa 835 29% 19% 19% 33% 22% 13% 17% 48% 185 106 141 403 835
St. Helena 31 26% 16% 18% 41% 25% 15% 18% 43% 8 5 5 13 31
Yountville 17 24% 21% 17% 38% 26% 12% 18% 44%4 2 3 8 17
Napa County Unincorporated 180 21% 14% 18% 48% 28% 17% 18% 37% 51 30 32 66 180
1,482 370 198 244 669 1,482
San Francisco 28,745 30% 14% 16% 40% 22% 16% 19% 43%6,207 4,621 5,437 12,482 28,747
Regional Income Distribution:
Existing Income Allocation Adjusted Income Distribution Allocation by Income
2014‐2022 RHNA
Income Distribution
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
25% 15% 18% 42%
Regional Total by Income:46,680 28,940 33,420 78,950
25% 15% 18% 42%
Income Shift:175%
RHNA
Allocation
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Total
Regional Income Distribution:
Existing Income Allocation Adjusted Income Distribution Allocation by Income
2014‐2022 RHNA
Income Distribution
Atherton 106 13% 2% 5% 80% 34% 25% 27% 13% 36 27 29 14 106
Belmont 367 17% 13% 17% 54% 31% 17% 18% 33% 116 63 67 121 367
Brisbane 83 19% 15% 17% 50% 30% 16% 18% 36% 25 13 15 30 83
Burlingame 975 20% 15% 20% 45% 29% 15% 16% 40% 280 149 158 388 975
Colma 67 19% 20% 23% 39% 30% 12% 14% 44% 20 8 9 30 67
Daly City 1,508 23% 19% 22% 37% 27% 13% 15% 45% 408 194 225 681 1,508
East Palo Alto 467 40% 21% 18% 22% 14% 12% 18% 57% 64 54 83 265 467
Foster City 430 13% 9% 18% 61% 34% 20% 18% 28% 148 87 76 119 430
Half Moon Bay 186 21% 13% 16% 50% 28% 17% 19% 36% 52 31 36 67 186
Hillsborough 129 7% 6% 6% 81% 39% 22% 26% 13% 50 29 34 16 129
Menlo Park 734 16% 12% 15% 58% 32% 18% 20% 30% 237 133 145 219 734
Millbrae 678 21% 16% 19% 44% 28% 15% 17% 40% 193 101 112 272 679
Pacifica 413 20% 14% 19% 48% 29% 17% 17% 37% 121 68 70 153 413
Portola Valley 64 15% 6% 11% 68% 33% 23% 23% 22% 21 15 15 14 64
Redwood City 2,784 25% 15% 17% 43% 25% 15% 18% 41% 706 429 502 1,148 2,784
San Bruno 1,294 21% 19% 20% 40% 28% 13% 16% 43% 365 166 208 556 1,295
San Carlos 596 15% 12% 17% 57% 33% 18% 19% 31% 195 107 111 182 596
San Mateo 3,030 21% 15% 18% 46% 28% 15% 17% 39% 859 469 530 1,173 3,030
South San Francisco 2,106 22% 18% 21% 39% 27% 14% 15% 44% 576 290 318 922 2,106
Woodside 62 9% 8% 9% 74% 37% 21% 24% 17% 23 13 15 11 62
San Mateo County Unincorporated 339 19% 12% 13% 56% 29% 18% 21% 31% 100 61 72 106 339
16,417 4,595 2,508 2,830 6,487 16,419
Campbell 929 22% 16% 20% 42% 27% 15% 16% 42% 252 137 150 390 929
Cupertino 1,059 14% 10% 12% 64% 33% 19% 22% 25% 354 206 230 268 1,059
Gilroy 1,083 30% 16% 15% 39% 22% 15% 20% 44% 235 159 215 473 1,083
Los Altos 476 12% 8% 10% 71% 35% 21% 24% 20% 168 99 112 96 476
Los Altos Hills 122 9% 5% 7% 80% 38% 23% 26% 13% 46 28 32 16 122
Los Gatos 617 15% 12% 13% 60% 32% 18% 21% 28% 200 112 132 173 617
Milpitas 3,276 18% 13% 19% 51% 31% 17% 17% 35% 1,000 568 563 1,143 3,274
Monte Sereno 62 8% 8% 14% 70% 38% 21% 21% 20% 23 13 13 13 62
Morgan Hill 924 19% 14% 15% 52% 29% 17% 20% 34% 272 153 184 314 924
Mountain View 2,913 22% 13% 17% 48% 28% 17% 18% 37% 810 490 525 1,088 2,913
Palo Alto 2,179 17% 10% 12% 62% 32% 20% 22% 27% 688 430 476 587 2,181
San Jose 34,929 24% 15% 18% 43% 26% 15% 18% 41% 9,193 5,405 6,161 14,172 34,932
Santa Clara 4,075 24% 13% 17% 46% 26% 17% 18% 39% 1,045 692 752 1,586 4,076
Saratoga 438 14% 7% 10% 70% 34% 22% 24% 21% 147 95 104 92 438
Sunnyvale 5,978 19% 14% 19% 49% 30% 17% 17% 36% 1,780 992 1,028 2,179 5,979
Santa Clara County Unincorporated 77 21% 14% 17% 48% 28% 16% 18% 37% 22 13 14 28 77
59,136 16,235 9,593 10,692 22,619 59,140
Benicia 327 20% 14% 19% 48% 29% 17% 17% 37% 94 54 56 122 327
Dixon 197 25% 20% 21% 34% 25% 12% 15% 48% 50 24 30 94 197
Fairfield 3,490 26% 19% 22% 34% 25% 13% 15% 48% 861 451 514 1,665 3,490
Rio Vista 99 38% 20% 20% 22% 15% 12% 16% 57% 15 12 16 57 99
Suisun City 355 19% 21% 26% 34% 29% 11% 12% 47% 105 40 42 169 355
Vacaville 1,084 23% 19% 20% 37% 26% 12% 16% 45% 286 134 173 490 1,084
Vallejo 1,362 31% 18% 21% 30% 21% 13% 15% 51% 283 178 211 690 1,362
Solano County Unincorporated 63 24% 17% 17% 42% 26% 14% 18% 42% 16 9 12 26 63
6,978 1,710 902 1,053 3,312 6,977
Cloverdale 210 34% 18% 22% 27% 19% 14% 15% 53% 39 29 31 111 210
Cotati 137 25% 18% 24% 33% 25% 13% 13% 48% 35 18 18 66 137
Healdsburg 156 32% 15% 19% 33% 20% 15% 17% 48% 31 24 26 75 156
Petaluma 741 23% 18% 20% 40% 27% 14% 16% 43% 198 102 120 321 741
Rohnert Park 895 32% 20% 23% 26% 20% 12% 14% 54% 180 107 126 483 895
Santa Rosa 4,642 32% 19% 20% 30% 20% 12% 16% 51% 943 579 756 2,360 4,639
Sebastopol 120 34% 17% 21% 28% 18% 14% 15% 52% 22 17 19 62 120
Sonoma 137 35% 14% 15% 36% 17% 17% 20% 46% 24 23 27 63 137
Windsor 439 22% 16% 21% 40% 27% 15% 15% 43% 120 65 67 188 440
Sonoma County Unincorporated 932 27% 18% 19% 36% 24% 14% 17% 46% 219 126 159 428 932
8,409 1,811 1,089 1,349 4,157 8,407
REGION 187,990 25% 15% 18% 42% 25% 15% 18% 42% 46,680 28,940 33,420 78,950 187,990
Note: This spreadsheet shows the calculations that was used to generate the RHNA by income category for each jurisdiction.
The allocation shown above may include rounding errors. Adjustments to the rounding errors have been made in the adopted version
DRAFT REGIONAL HOUSING NEED ALLOCATION
ADJUSTED w/ 50% Max
40% Minimum Not Used
In Unincorporated Areas
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Total
Alameda County
Alameda 442 247 282 744 1,716 2,046 2,162
Albany 80 53 57 145 334 276 277
Berkeley 530 440 581 1,396 2,948 2,431 1,269
Dublin 793 444 423 613 2,274 3,330 5,436
Emeryville 275 210 258 750 1,492 1,137 777
Fremont 1,707 922 974 1,825 5,428 4,380 6,708
Hayward 862 490 625 2,042 4,018 3,393 2,835
Livermore 835 472 494 919 2,720 3,394 5,107
Newark 328 166 157 421 1,073 863 1,250
Oakland 2,050 2,066 2,803 7,784 14,702 14,629 7,733
Piedmont 24 14 15 8 60 40 49
Pleasanton 713 389 405 551 2,058 3,277 5,059
San Leandro 502 269 350 1,156 2,277 1,630 870
Union City 316 179 191 416 1,101 1,944 1,951
Alameda County Unincorporated 428 226 294 814 1,762 2,167 5,310
9,886 6,586 7,908 19,582 43,962 44,937 46,793
Contra Costa County
Antioch 348 204 213 676 1,441 2,282 4,459
Brentwood 233 123 122 278 756 2,705 4,073
Clayton 51 25 31 33 141 151 446
Concord 794 442 556 1,669 3,462 3,043 2,319
Danville 195 111 124 124 555 583 1,110
El Cerrito 100 63 69 165 397 431 185
Hercules 219 117 100 244 680 453 792
Lafayette 146 83 90 106 426 361 194
Martinez 123 72 78 193 467 1,060 1,341
Moraga 75 43 50 61 228 234 214
Oakley 316 173 174 500 1,163 775 1,208
Orinda 84 47 53 40 226 218 221
Pinole 80 48 42 126 296 323 288
Pittsburg 390 253 315 1,059 2,017 1,772 2,513
Pleasant Hill 117 69 84 177 446 628 714
Richmond 436 304 408 1,274 2,422 2,826 2,603
San Pablo 55 53 75 264 447 298 494
San Ramon 514 278 281 339 1,411 3,463 4,447
Walnut Creek 601 353 379 893 2,225 1,958 1,653
Contra Costa County Unincorporated 372 217 242 530 1,361 3,508 5,436
5,249 3,076 3,486 8,753 20,564 27,072 34,710
Draft 2014‐2022 RHNA 2007‐
2014
RHNA
Total
1999‐
2006
RHNA
Total
175% Shift with Regional Median Household Income
Note: This draft 2014‐2022 RHNA by income category for each jurisdiction is based on the Jobs‐Housing Connection
Strategy, May 11, 2012. Totals may not add up due to rounding.
DRAFT REGIONAL HOUSING NEED ALLOCATION
ADJUSTED w/ 50% Max
40% Minimum Not Used
In Unincorporated Areas
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Total
Draft 2014‐2022 RHNA 2007‐
2014
RHNA
Total
1999‐
2006
RHNA
Total
175% Shift with Regional Median Household Income
Marin County
Belvedere 4 3 4 5 16 17 10
Corte Madera 22 13 13 25 72 244 179
Fairfax 16 11 11 23 61 108 64
Larkspur 40 20 21 51 132 382 303
Mill Valley 41 24 26 38 129 292 225
Novato 111 65 72 166 414 1,241 2,582
Ross 6 4 4 4 18 27 21
San Anselmo 33 17 19 37 106 113 149
San Rafael 239 147 180 437 1,003 1,403 2,090
Sausalito 26 14 16 24 79 165 207
Tiburon 24 16 19 19 78 117 164
Marin County Unincorporated 55 32 37 60 184 773 521
617 366 421 888 2,292 4,882 6,515
Napa County
American Canyon 116 54 58 164 392 728 1,323
Calistoga 6 2 4 15 27 94 173
Napa 185 106 141 403 835 2,024 3,369
St. Helena 8 5 5 13 31 121 142
Yountville 4 2 3 8 17 87 87
Napa County Unincorporated 51 30 32 66 180 651 1,969
370 198 244 669 1,482 3,705 7,063
San Francisco County
San Francisco 6,207 4,621 5,437 12,482 28,746 31,193 20,372
6,207 4,621 5,437 12,482 28,746 31,193 20,372
Note: This draft 2014‐2022 RHNA by income category for each jurisdiction is based on the Jobs‐Housing Connection
Strategy, May 11, 2012. Totals may not add up due to rounding.
DRAFT REGIONAL HOUSING NEED ALLOCATION
ADJUSTED w/ 50% Max
40% Minimum Not Used
In Unincorporated Areas
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Total
Draft 2014‐2022 RHNA 2007‐
2014
RHNA
Total
1999‐
2006
RHNA
Total
175% Shift with Regional Median Household Income
San Mateo County
Atherton 36 27 29 14 106 83 166
Belmont 116 63 67 121 367 399 317
Brisbane 25 13 15 30 83 401 426
Burlingame 280 149 158 388 975 650 565
Colma 20 8 9 30 67 65 74
Daly City 408 194 225 681 1,508 1,207 1,391
East Palo Alto 64 54 83 265 467 630 1,282
Foster City 148 87 76 119 430 486 690
Half Moon Bay 52 31 36 67 186 276 458
Hillsborough 50 29 34 16 129 86 84
Menlo Park 237 133 145 219 734 993 982
Millbrae 193 101 112 272 679 452 343
Pacifica 121 68 70 153 413 275 666
Portola Valley 21 15 15 14 64 74 82
Redwood City 706 429 502 1,148 2,784 1,856 2,544
San Bruno 365 166 208 556 1,295 973 378
San Carlos 195 107 111 182 596 599 368
San Mateo 859 469 530 1,173 3,030 3,051 2,437
South San Francisco 576 290 318 922 2,106 1,635 1,331
Woodside 23 13 15 11 62 41 41
San Mateo County Unincorporated 100 61 72 106 339 1,506 1,680
4,595 2,508 2,830 6,487 16,419 15,738 16,305
Santa Clara County
Campbell 252 137 150 390 929 892 777
Cupertino 354 206 230 268 1,059 1,170 2,720
Gilroy 235 159 215 473 1,083 1,615 3,746
Los Altos 168 99 112 96 476 317 261
Los Altos Hills 46 28 32 16 122 81 83
Los Gatos 200 112 132 173 617 562 402
Milpitas 1,000 568 563 1,143 3,274 2,487 4,348
Monte Sereno 23 13 13 13 62 41 76
Morgan Hill 272 153 184 314 924 1,312 2,484
Mountain View 810 490 525 1,088 2,913 2,599 3,423
Palo Alto 688 430 476 587 2,181 2,860 1,397
San Jose 9,193 5,405 6,161 14,172 34,932 34,721 26,114
Santa Clara 1,045 692 752 1,586 4,076 5,873 6,339
Saratoga 147 95 104 92 438 292 539
Sunnyvale 1,780 992 1,028 2,179 5,979 4,426 3,836
Santa Clara County Unincorporated 22 13 14 28 77 1,090 1,446
16,235 9,593 10,692 22,619 59,140 60,338 57,991
Note: This draft 2014‐2022 RHNA by income category for each jurisdiction is based on the Jobs‐Housing Connection
Strategy, May 11, 2012. Totals may not add up due to rounding.
DRAFT REGIONAL HOUSING NEED ALLOCATION
ADJUSTED w/ 50% Max
40% Minimum Not Used
In Unincorporated Areas
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Total
Draft 2014‐2022 RHNA 2007‐
2014
RHNA
Total
1999‐
2006
RHNA
Total
175% Shift with Regional Median Household Income
Solano County
Benicia 94 54 56 122 327 532 413
Dixon 50 24 30 94 197 728 1,464
Fairfield 861 451 514 1,665 3,490 3,796 3,812
Rio Vista 15 12 16 57 99 1,219 1,391
Suisun City 105 40 42 169 355 610 1,004
Vacaville 286 134 173 490 1,084 2,901 4,636
Vallejo 283 178 211 690 1,362 3,100 3,242
Solano County Unincorporated 16 9 12 26 63 99 2,719
1,710 902 1,053 3,312 6,977 12,985 18,681
Sonoma County
Cloverdale 39 29 31 111 210 417 423
Cotati 35 18 18 66 137 257 567
Healdsburg 31 24 26 75 156 331 573
Petaluma 198 102 120 321 741 1,945 1,144
Rohnert Park 180 107 126 483 895 1,554 2,124
Santa Rosa 943 579 756 2,360 4,639 6,534 7,654
Sebastopol 22 17 19 62 120 176 274
Sonoma 24 23 27 63 137 353 684
Windsor 120 65 67 188 440 719 2,071
Sonoma County Unincorporated 219 126 159 428 932 1,364 6,799
1,811 1,089 1,349 4,157 8,407 13,650 22,313
REGION 46,680 28,940 33,420 78,950 187,989 214,500 230,743
Actual Split 46,680 28,940 33,420 78,950
Difference 0 0 0 0
Note: This draft 2014‐2022 RHNA by income category for each jurisdiction is based on the Jobs‐Housing Connection
Strategy, May 11, 2012. Totals may not add up due to rounding.
DRAFT REGIONAL HOUSING NEED ALLOCATION (2014‐2022)
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Total
REGION 46,680 28,940 33,420 78,950 187,990
Alameda County
Alameda 442 247 282 745 1,716
Albany 80 53 57 144 334
Berkeley 530 440 581 1,395 2,946
Dublin 793 444 423 615 2,275
Emeryville 275 210 258 749 1,492
Fremont 1,707 922 974 1,829 5,432
Hayward 862 490 625 2,044 4,021
Livermore 835 472 494 916 2,717
Newark 328 166 157 422 1,073
Oakland 2,050 2,066 2,803 7,782 14,701
Piedmont 24 14 15 7 60
Pleasanton 713 389 405 551 2,058
San Leandro 502 269 350 1,156 2,277
Union City 316 179 191 415 1,101
Alameda County Unincorporated 428 226 294 814 1,762
9,885 6,587 7,909 19,584 43,965
Contra Costa County
Antioch 348 204 213 677 1,442
Brentwood 233 123 122 278 756
Clayton 51 25 31 34 141
Concord 794 442 556 1,670 3,462
Danville 195 111 124 125 555
El Cerrito 100 63 69 165 397
Hercules 219 117 100 243 679
Lafayette 146 83 90 107 426
Martinez 123 72 78 194 467
Moraga 75 43 50 60 228
Oakley 316 173 174 500 1,163
Orinda 84 47 53 42 226
Pinole 80 48 42 126 296
Pittsburg 390 253 315 1,058 2,016
Pleasant Hill 117 69 84 176 446
Richmond 436 304 408 1,276 2,424
San Pablo 55 53 75 264 447
San Ramon 514 278 281 338 1,411
Walnut Creek 601 353 379 892 2,225
Contra Costa County Unincorporated 372 217 242 530 1,361
5,249 3,078 3,486 8,755 20,568
DRAFT REGIONAL HOUSING NEED ALLOCATION (2014‐2022)
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Total
Marin County
Belvedere 4 3 4 5 16
Corte Madera 22 13 13 24 72
Fairfax 16 11 11 23 61
Larkspur 40 20 21 51 132
Mill Valley 41 24 26 38 129
Novato 111 65 72 166 414
Ross 6 4 4 4 18
San Anselmo 33 17 19 37 106
San Rafael 239 147 180 437 1,003
Sausalito 26 14 16 23 79
Tiburon 24 16 19 19 78
Marin County Unincorporated 55 32 37 60 184
617 366 422 887 2,292
Napa County
American Canyon 116 54 58 164 392
Calistoga 6 2 4 15 27
Napa 185 106 141 403 835
St. Helena 8 5 5 13 31
Yountville 4 2 3 8 17
Napa County Unincorporated 51 30 32 67 180
370 199 243 670 1,482
San Francisco County
San Francisco 6,207 4,619 5,437 12,482 28,745
6,207 4,619 5,437 12,482 28,745
DRAFT REGIONAL HOUSING NEED ALLOCATION (2014‐2022)
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Total
San Mateo County
Atherton 36 27 29 14 106
Belmont 116 63 67 121 367
Brisbane 25 13 15 30 83
Burlingame 280 149 158 388 975
Colma 20 8 9 30 67
Daly City 408 194 225 681 1,508
East Palo Alto 64 54 83 266 467
Foster City 148 87 76 119 430
Half Moon Bay 52 31 36 67 186
Hillsborough 50 29 34 16 129
Menlo Park 237 133 145 219 734
Millbrae 193 101 112 272 678
Pacifica 121 68 70 154 413
Portola Valley 21 15 15 13 64
Redwood City 706 429 502 1,147 2,784
San Bruno 365 166 208 555 1,294
San Carlos 195 107 111 183 596
San Mateo 859 469 530 1,172 3,030
South San Francisco 576 290 318 922 2,106
Woodside 23 13 15 11 62
San Mateo County Unincorporated 100 61 72 106 339
4,595 2,507 2,830 6,486 16,418
Santa Clara County
Campbell 252 137 150 390 929
Cupertino 354 206 230 269 1,059
Gilroy 235 159 216 473 1,083
Los Altos 168 99 112 96 475
Los Altos Hills 46 28 32 15 121
Los Gatos 200 112 132 173 617
Milpitas 1,000 568 563 1,145 3,276
Monte Sereno 23 13 13 12 61
Morgan Hill 272 153 184 315 924
Mountain View 810 490 525 1,088 2,913
Palo Alto 688 430 476 585 2,179
San Jose 9,193 5,405 6,161 14,170 34,929
Santa Clara 1,045 692 752 1,586 4,075
Saratoga 147 95 104 92 438
Sunnyvale 1,780 992 1,027 2,179 5,978
Santa Clara County Unincorporated 22 13 14 28 77
16,235 9,592 10,691 22,616 59,134
DRAFT REGIONAL HOUSING NEED ALLOCATION (2014‐2022)
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Total
Solano County
Benicia 94 54 56 123 327
Dixon 50 24 30 93 197
Fairfield 861 451 514 1,664 3,490
Rio Vista 15 12 16 56 99
Suisun City 105 40 41 169 355
Vacaville 287 134 173 490 1,084
Vallejo 283 178 211 690 1,362
Solano County Unincorporated 16 9 12 26 63
1,711 902 1,053 3,311 6,977
Sonoma County
Cloverdale 39 29 31 111 210
Cotati 35 18 18 66 137
Healdsburg 31 24 26 75 156
Petaluma 198 102 120 321 741
Rohnert Park 180 107 126 482 895
Santa Rosa 943 579 756 2,364 4,642
Sebastopol 22 17 19 62 120
Sonoma 24 23 27 63 137
Windsor 120 65 67 187 439
Sonoma County Unincorporated 219 126 159 428 932
1,811 1,090 1,349 4,159 8,409
REGION 46,680 28,940 33,420 78,950 187,990
FINAL REGIONAL HOUSING NEED ALLOCATION (2014‐2022)
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Total
REGION 46,680 28,940 33,420 78,950 187,990
Alameda County
Alameda 444 248 283 748 1,723
Albany 80 53 57 145 335
Berkeley 532 442 584 1,401 2,959
Dublin 796 446 425 618 2,285
Emeryville 276 211 259 752 1,498
Fremont 1,714 926 978 1,837 5,455
Hayward 851 480 608 1,981 3,920
Livermore 839 474 496 920 2,729
Newark 330 167 158 423 1,078
Oakland 2,059 2,075 2,815 7,816 14,765
Piedmont 24 14 15 7 60
Pleasanton 716 391 407 553 2,067
San Leandro 504 270 352 1,161 2,287
Union City 317 180 192 417 1,106
Alameda County Unincorporated 430 227 295 817 1,769
9,912 6,604 7,924 19,596 44,036
Contra Costa County
Antioch 349 205 214 680 1,448
Brentwood 234 124 123 279 760
Clayton 51 25 31 34 141
Concord 798 444 559 1,677 3,478
Danville 196 111 124 126 557
El Cerrito 100 63 69 166 398
Hercules 220 118 100 244 682
Lafayette 138 78 85 99 400
Martinez 124 72 78 195 469
Moraga 75 44 50 60 229
Oakley 317 174 175 502 1,168
Orinda 84 47 54 42 227
Pinole 80 48 43 126 297
Pittsburg 392 254 316 1,063 2,025
Pleasant Hill 118 69 84 177 448
Richmond 438 305 410 1,282 2,435
San Pablo 56 53 75 265 449
San Ramon 516 279 282 340 1,417
Walnut Creek 604 355 381 895 2,235
Contra Costa County Unincorporated 374 218 243 532 1,367
5,264 3,086 3,496 8,784 20,630
FINAL REGIONAL HOUSING NEED ALLOCATION (2014‐2022)
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Total
Marin County
Belvedere 4 3 4 5 16
Corte Madera 22 13 13 24 72
Fairfax 16 11 11 23 61
Larkspur 40 20 21 51 132
Mill Valley 41 24 26 38 129
Novato 111 65 72 167 415
Ross 6 4 4 4 18
San Anselmo 33 17 19 37 106
San Rafael 240 148 181 438 1,007
Sausalito 26 14 16 23 79
Tiburon 24 16 19 19 78
Marin County Unincorporated 55 32 37 61 185
618 367 423 890 2,298
Napa County
American Canyon 116 54 58 164 392
Calistoga 6 2 4 15 27
Napa 185 106 141 403 835
St. Helena 8 5 5 13 31
Yountville 4 2 3 8 17
Napa County Unincorporated 51 30 32 67 180
370 199 243 670 1,482
San Francisco County
San Francisco 6,234 4,639 5,460 12,536 28,869
6,234 4,639 5,460 12,536 28,869
FINAL REGIONAL HOUSING NEED ALLOCATION (2014‐2022)
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Total
San Mateo County
Atherton 35 26 29 3 93
Belmont 116 63 67 222 468
Brisbane 25 13 15 30 83
Burlingame 276 144 155 288 863
Colma 20 8 9 22 59
Daly City 400 188 221 541 1,350
East Palo Alto 64 54 83 266 467
Foster City 148 87 76 119 430
Half Moon Bay 52 31 36 121 240
Hillsborough 32 17 21 21 91
Menlo Park 233 129 143 150 655
Millbrae 193 101 112 257 663
Pacifica 121 68 70 154 413
Portola Valley 21 15 15 13 64
Redwood City 706 429 502 1,152 2,789
San Bruno 358 161 205 431 1,155
San Carlos 195 107 111 183 596
San Mateo 859 469 530 1,242 3,100
South San Francisco 565 281 313 705 1,864
Woodside 23 13 15 11 62
San Mateo County Unincorporated 153 103 102 555 913
4,595 2,507 2,830 6,486 16,418
Santa Clara County
Campbell 253 138 151 391 933
Cupertino 356 207 231 270 1,064
Gilroy 236 160 217 475 1,088
Los Altos 169 99 112 97 477
Los Altos Hills 46 28 32 15 121
Los Gatos 201 112 132 174 619
Milpitas 1,004 570 565 1,151 3,290
Monte Sereno 23 13 13 12 61
Morgan Hill 273 154 185 316 928
Mountain View 814 492 527 1,093 2,926
Palo Alto 691 432 278 587 1,988
San Jose 9,233 5,428 6,188 14,231 35,080
Santa Clara 1,050 695 755 1,593 4,093
Saratoga 147 95 104 93 439
Sunnyvale 1,640 906 932 1,974 5,452
Santa Clara County Unincorporated 22 13 214 28 277
16,158 9,542 10,636 22,500 58,836
FINAL REGIONAL HOUSING NEED ALLOCATION (2014‐2022)
Very Low
0‐50%
Low
51‐80%
Moderate
81‐120%
Above
Moderate
120%+
Total
Solano County
Benicia 94 54 56 123 327
Dixon 50 24 30 93 197
Fairfield 779 404 456 1,461 3,100
Rio Vista 45 36 48 170 299
Suisun City 147 57 60 241 505
Vacaville 287 134 173 490 1,084
Vallejo 283 178 211 690 1,362
Solano County Unincorporated 26 15 19 43 103
1,711 902 1,053 3,311 6,977
Sonoma County
Cloverdale 39 29 31 112 211
Cotati 35 18 18 66 137
Healdsburg 31 24 26 76 157
Petaluma 199 103 121 322 745
Rohnert Park 181 107 127 484 899
Santa Rosa 947 581 759 2,375 4,662
Sebastopol 22 17 19 62 120
Sonoma 24 23 27 63 137
Windsor 120 65 67 188 440
Sonoma County Unincorporated 220 127 160 429 936
1,818 1,094 1,355 4,177 8,444
REGION 46,680 28,940 33,420 78,950 187,990
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Bay Area rebellion attacks housing mandate
April 13, 2012
Byadmin
April 13, 2012
By Dave Roberts
"The Mouse that Roared," a 1950s satirical novel and movie about a
tiny European country that declares war on the United States, has come
to life in the Bay Area. Corte Madera, a town of 9,200 people tucked
away in the Marin countryside, has rebelled against the Association of
Bav Area Governments over California's housing mandates.
Corte Madera's three square miles ofland is pretty much built out with
nearly 3,800 households, two schools (with a third on the way), two
shopping malls and a town park that hosts the annual Fourth of July
festivities. The small town is known as "the hidden jewel of Mm-in" -
and that's the way the residents want to keep it.
They feel like they've done their part to meet the state's affordable
housing mandates. They won national awards for a 79-unit affordable
development built in 2008. And they recently rezoned an industrial site, in the process losing jobs and tax revenue,
to accommodate a 180-unit development. The projects allowed the community to meet its state-mandated Regional
Ilousing Needs .Allocation requirement of 244 additional units.
But residents weren't happy about it.
"It's a lot of [new] housing in a community of9,200 people," said Mayor Bob Ravasio in a recent KSFO radio
interview. "As we were going through the final process on this, a lot of people in town made it very, very clear that
they were extremely upset. Rightfully so. We are 9,200 people; we are three square miles; we are built out. And we
are rezoning an industrial site in order to get the housing built."
But while there was a lot of anger and grumbling, it's what happened next that led to rebellion.
"We got information ... that they wanted us to add another 700 units and 49 percent more jobs by 2040," said
Ravasio. "And we all hit the roof."
The Corte MaderaTown Council studied the consequences of withdrawing in protest from ABAG, which oversees
the housing mandate for nine counties and 10 1 towns and cities. They learned that it would prevent them from
applying for government grants administered through ABAG. But that would not be a big loss.
"We went back the last 10 years and saw one government grant we received for about $60,000 for a bicycle path
improvement," said Ravasio.
l
"We had t9spend a fortune on consultants to comply with the conditions of the grant."
Downside
The other downside to withdrawing from ABAG, he said, "is you lose a seat at the table when you're discussing
things like the Regional Housing Needs Allocation. We went back through history, and we found that we really
hadn't been listened to in the past and didn't have a seat at the table anyway."
So, with little to lose, on March 6 the town council voted 4-1 to pull out of ABAG. The town will save $2,350 in
annual dues, but it will still be required to abide by state housing mandates controlled by ABAG. As a result, their
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Bay Area rebellion attacks,housing mandate I CalWatchDog
action may prove to be little more than sticking their heads out the window and shouting, "I'm as mad as hell, and
I'm not going to take this anymore!"
Those who spoke at the council meeting strongly backed that message, with many chanting the mantra of "local
control." One man expressed disdain over what he viewed as capitalism and socialism descending into fascism by
ABAG. A woman referred to attendees as "fellow abolitionists." Many were from surrounding communities who
said they hope their towns join Corte Madera's rebellion.
Other mayors have asked Ravasio to speak to their councils. He intends to do so after his council prepares a report
next month on the logistics of forming an ABAG-type organization for Marin communities, similar to the
Mendocino Counci I of Governments.
"You need to have something like this in order to be able to deal with the state and get control of your own Regional
Housing Needs Allocation requirements," he sail
In what may be a case of the squeaky wheel getting the grease, Corte Madera's housing requirement was recently
reduced to 270 additional units by 2040 -the largest decrease among Marin towns. Two others, Novato and
Larkspur, also had their requirements significantly reduced, while most other towns saw big increases -some like
Sausalito and Ross more than doubled. . /
This resulted in grumbling from those who got stuck with the increased housing in the latest growth plan.
"What apparently happened was radical shifting of the housing and jobs from those that complained to those that
didn't (some of whom as a result are now ready to complain), regardless of the sophisticated modeling
methodologies employed," observed the Marin County Council o1:'11a"ors ,md Council Members. "Is this plan
really a genuine effort to recommend the most rational plan, or just an effort to disperse the discontent as evenly as
possible?"
New numbers
ABAG responded that the earlier numbers were based on unrealistic estimates, and the revised numbers are based
upon "comprehensive forecasting methodology." it takes into account such factors as the town's proximity to
employment corridors and transit, real estate market conditions and development potential. "Political considerations
regarding 'discontent' were not used as a long term factor" ABAG's response states.
ABAG Senior Communications Officer Kathleen Cha acknowledged in an interview that "there definitely are those
frustrations relating to the housing numbers, and we understand that. In the end, all of this is a plan and it's a vision
for what can happen. In the end it has to come back to the local government. How that zoning changes or how they
come up with it rests back on them. It's not like everybody has to have this kind of density. It's 'look, these are what
the needs are -how can you meet them in your community and where could it, be?' That will have to be determined
by the local jurisdiction. It's not dictated by ABAG."
But a lot of cities are feeling like they're being dictated to. Last month, Palo Alto sent a 20-page complaint letter to
ABAG, arguing that the jobs and housing requirements are unrealistic, not accounting for market constraints, high
costs and the impacts of intensive development. City officials also believe that the plan will have a negligible impact
on greenhouse gas emissions in any case.
It's those emissions that are the driving force behind the discontent. ABAG and the Metropolitan Transportation
Commission are implementing the One Bav Area Plan, which is designed to reduce carbon dioxide emissions by 7
percent by 2020 and by 15 percent by 2035. It's authorized by SB 375, the Sustainable Communities and Climate
Protection Act of2008, and AB 32, the Global Warming Solutions Act of2006.
The goal is to supposedly save the planet. But many local officials and residents fear it's actually a case of
politicians and bureaucrats destroving their villages in order to save them.
"For us this is about local control," said Ravasio. "We are a small town. We want to remain a small town, which is
why people moved here in the first place. We should be allowed to do that and control growth and grow in a way
that makes sense for us. And not have it mandated to us by the state or a regional authority like ABAG. Which is
what's been happening, Which is why we took this step."
It remains to be seen whether the roar from this mouse echoes throughout the Bay Area and eventually the rest of
the state. If it does, it could be the first rebel yell in a new Civil War. Or perhaps it should be called the War of
Sacramento Aggression.
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Tags: AB 32, ,ABACi, Association orBa\, Area Governments, Bob Havasio, Corte Madera, Dave Roberts, Kathleen
Cha, SB 375
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