HomeMy WebLinkAboutStaff Report 13915
City of Palo Alto (ID # 13915)
City Council Staff Report
Meeting Date: 4/11/2022 Report Type: Consent Calendar
City of Palo Alto Page 1
Title: Approval of the Economic Recovery Advisory Report as Recommended
by the Policy and Services Committee
From: City Manager
Lead Department: City Auditor
Recommendation
Policy & Services Committee, City Auditor and staff recommends that City council
review and approve the corresponding report:
1) Economic Recovery Advisory report and corresponding recommendations for
improvement and approve.
Background
Discussion
The OCA performed a review of the City’s long-term financial planning models and
inputs and offer recommendations for improvement. This model is known as the Long
Range Financial Forecast (LRFF). This audit activity was conducted in accordance with
the FY2021 Audit Plan approved by City Council.
Baker Tilly, in its capacity serving as the Office of the City Auditor (OCA), performed a
citywide risk assessment that assessed a wide range of risk areas, including strategic,
financial, operational, compliance, technological, and reputation risks. The purpose of
the assessment was to identify and prioritize risks to develop the annual audit plan.
During the FY2021 risk assessment, the OCA identified the followings risks which led to
this project:
• COVID-19 Response
• Financial Performance/Revenue Generation
• Tax Revenue & Economic Recovery
City of Palo Alto Page 2
Through the audit activity, the OCA identified four (4) recommendations. The
Administrative Services Department concurred or partially concurred with each finding
and has drafted responses and action plans for each item.
The Office of City Auditor will perform periodic follow up procedures to validate that
those recommendations have been addressed as indicated in management responses.
The analysis, observations, and recommendations are summarized in the attached
Economic Recovery Advisory report. Please note that the OCA has attached an
additional report, intended to be for informational purposes only (i.e., no
recommendations), that was derived as a result of our work on this project. This
supplemental report is entitled the Economic Resiliency Review.
The report was unanimously approved by the Policy & Services Committee.
Timeline, Resource Impact, Policy Implications
Stakeholder Engagement
Environmental Review
Attachments:
• Office of the City Auditor - Economic Recovery Advisory - Final Draft
• Office of the City Auditor - Economic Resiliency Review - Final Draft
The objectives of the audit activity were to:
1) Review the City’s long-term financial planning models and inputs and offer
recommendations for improvement.
2) Identify and evaluate key revenue source categories that present long term risk
to the City's financial sustainability and perform scenario analysis.
3) Offer ad hoc advisory assistance during the FY22 budget process.
The timeline for implementation of corrective action plans is identified within the
attached report. All corrective actions are scheduled to be implemented by Fall of 2022
as part of the next Long Range Financial Forecast as well as the remaining FY 2023
annual budget process to the extent appropriate.
The Office of the City Auditor worked primarily with the Administrative Services
Department and engaged with additional stakeholders, including the City Manager’s
Office and City Attorney’s Office, as necessary.
Environmental review is not applicable to this activity.
1
City of Palo Alto
Office of the City Auditor
Economic Recovery Advisory
March 15, 2022
2
Executive Summary
Purpose of the Audit Activity
The purpose of this audit activity was to review the City of Palo Alto’s (“City”) long-term financial planning models and
inputs, offer recommendations for improvement, identify and evaluate key revenue source categories that present long
term risk to the City's financial sustainability and perform scenario analysis. The Office of the City Auditor (“OCA”) also
offered ad hoc advisory assistance during the FY22 budget process.
In addition to the above, the OCA included economic development subject matter experts to conduct a high-level review
of economic resiliency as a resource to the City and any future City employees focused on economic development
initiatives. The report highlights below summarize all that is included in the informational economic resiliency report as
well as the Revenue Trends and Models Review.
Report Highlights
Revenue Trends and Models Review
Page 8 The review of the City’s forecasting models focused on three major revenue
sources with inputs that may be more affected by current economic trends:
• Transient Occupancy Tax (TOT)
• Sales Tax
• Property Tax
The analysis of the City’s revenues also focused on Documentary Transfer Tax
and Utility User Tax.
This section reviews the revenue sources in light of historical performance,
historical averages, and corresponding economic indicators. It also provides a
description of models used, some industry best practices where applicable,
and corresponding observations and recommendations.
For each major revenue source, the OCA presents the data from all
correlations and analyses completed, presenting the information with all factors
considered. Each section has specific notes regarding the OCA’s thoughts on
trends, but the OCA also found that the City’s current LRFF aligns with the
OCA’s considerations.
The OCA also analyzed data and assumptions used for models, evaluated
economic factors impacting each revenue source, and determine whether the
current financial forecast model uses the most relevant economic factors for
predictive purposes. While the OCA provides specific recommendations and
considerations, the City’s models are overall aligned with best practices. The
OCA does recommend that workbooks are notated with more thoroughness
and that clarity is provided where assumptions are made.
3
Economic Resiliency Review
Page 33 The Economic Resiliency Review is released as an independent informational
report, but this section reviews the scope of the document, including categories
of recommendations and research.
The OCA provides detailed recommendations in this separate report, include
recommendations regarding business and industry attraction, retention, and
diversification strategies, housing and workforce findings impacted by COVID-
19 pandemic, and enhancements to livability for residents and business
engagement and retention.
4
Table of Contents
Executive Summary ........................................................................................................................................................... 2
Purpose of the Audit Activity ...................................................................................................................................... 2
Report Highlights ........................................................................................................................................................... 2
Objective ........................................................................................................................................................................... 5
Background ..................................................................................................................................................................... 5
Scope ................................................................................................................................................................................. 5
Methodology .................................................................................................................................................................... 5
Compliance Statement .................................................................................................................................................. 6
Organizational Strengths ............................................................................................................................................. 6
Introduction to City Financials ....................................................................................................................................... 7
Revenue Analysis and Models Review ........................................................................................................................ 8
Economic Resiliency Review ........................................................................................................................................ 33
Appendices ........................................................................................................................................................................ 34
Appendix A: Management Response – Revenue Trends and Model Review .............................................. 34
5
Introduction
Objective The objectives of this audit activity were to:
1) Review the City’s long-term financial planning models and inputs and offer
recommendations for improvement.
2) Identify and evaluate key revenue source categories that present long term risk to
the City's financial sustainability and perform scenario analysis.
3) Offer ad hoc advisory assistance during the FY22 budget process.
Background The OCA performed a citywide risk assessment that assessed a wide range of risk areas,
including strategic, financial, operational, compliance, technological, and reputation risks.
The purpose of the assessment was to identify and prioritize risks to develop the annual
audit plan.
During the FY2021 risk assessment, the OCA identified the followings risks which led to this
project.
• COVID-19 Response
• Financial Performance/Revenue Generation
• Tax Revenue & Economic Recovery
• Current Planning Practices
Additionally, during the risk assessment, Baker Tilly included some examples of potential
risks in the future related to this audit:
• Large businesses moving to other locations or decreasing the focus on in-person
interactions at headquarters, lowering the daytime population and visitors
• Decreasing real estate values due to external factors decreases City revenues from
property taxes
• Lost revenue for the City to fund City services with Prop 13 in place
• High taxation on residents due to increased property values, especially long term
Palo Alto residents, in the absence of Prop 13
Scope The project team analyzed each revenue source. The analysis, which focused on a subset
of high risk revenue sources, includes the following:
• Historical trends
• Distribution of revenue sources by revenue type: o Source(s) o Concentration/distribution of revenue received to identify:
Largest payors
Geographic location o Historical relationship between economic factors and other relevant factors
to revenue amounts o Perform a sensitivity analysis to determine the range of likely variability
based on relevant drivers of sensitivity o Comparison of per-capita revenues by type to other similar cities
Methodology To achieve the engagement objectives, Baker Tilly conducted an analysis that will
encompass the steps listed below.
• Audit Planning & Management
− Gather information to understand the environment under review
− Assess the audit risk
− Write audit planning memo and audit program
6
[1] Government auditing standards require an external peer review at least once every three (3) years. The last peer review of the
Palo Alto Office of the City Auditor was conducted in 2017. The Palo Alto City Council approved a contract from October 2020
through June 2022 with Baker Tilly US, LLP (Baker Tilly) and appointed Kyle O’Rourke, Senior Consulting Manager in Baker Tilly's
Public Sector practice, as City Auditor. Given the transition in the City Audit office, a peer review was not conducted in 2020 and will
be conducted in the second year of Baker Tilly’s contract.
− Announce initiation of audit and conduct kick-off meeting with key
stakeholders
• Information Gathering
− Request and review background information
− Conduct interviews with key stakeholders
− Conduct research to identify relevant information to assess risks
• Analyze
− Historical trends
− Distribution of revenue sources by revenue type
− Review analysis with City staff
− Modify analysis incorporating City staff recommendations as
appropriate
• Reporting
− Develop findings, conclusions and recommendations
− Validate findings with appropriate individuals
− Draft audit report and obtain written management responses
− Review report with member of City Council and/or the appropriate
committee
− Present the final report to City Council and/or the appropriate committee
Compliance Statement
This audit activity was conducted from September 2021 to January 2022 in accordance with
generally accepted government auditing standards, except for the requirement of an
external peer review[1]. Those standards require that we plan and perform the audit to obtain
sufficient, appropriate evidence to provide a reasonable basis for our findings and
conclusions based on our audit objectives. We believe that the evidence obtained provides a
reasonable basis for our findings and conclusions based on our audit objectives.
Organizational Strengths
During this audit activity, we observed certain strengths of the City. Key strengths include:
• Detailed and thorough data collection and financial analysis
• Strategic mindset and intentionality in economic analysis and planning, including
proactively reaching out to industry experts
• High level of competency among City personnel
Additionally, the OCA commends the City for its response to COVID-19. In particular, we
greatly admire all efforts taken to support the health and wellbeing of Palo Alto citizens and
Stanford students, as well as the support of essential workers during this time of heightened
risk.
The Office of the City Auditor greatly appreciates the support of the Administrative Services Department in conducting this audit activity.
Thank you!
7
Introduction to City Financials
Citywide Revenue
Over the last five fiscal years
(2016 – 2020) the City’s overall
revenue collection has increased
by $100 million dollars. However,
the COVID-19 pandemic also
resulted in a $60 million dollar
decrease from FY 2019 – 2020.
The City's total revenues for FY
2020 were $581.1 million dollars
which includes both government
and enterprise type funds.
General Fund Revenue
Even though the City has
managed to maintain accurate
projections for upcoming fiscal
years, the COVID-19 pandemic
has posed a challenge to
forecasting model/revenue
projection methods. Although the
enterprise funds comprise a
larger portion of the City’s total
revenues, the OCA maintained
focus on revenues in the general
fund for this analysis which were
$209.7 million in FY 2020,
making up 36% of all City
revenues. The following sources
were selected from the General
Fund for in depth analysis by the
OCA due to their size and
potential influence on City
operations because of their
flexible uses: Property Tax,
Sales Tax, Transient Occupancy
Tax, Utility User Tax, and
Documentary Transfer Tax.
Forecasting Accuracy
Through the last 5 years of significant
growth, the City’s ability to forecast the
upcoming year’s revenue collections has
strayed no more than 9% from their
estimates. See the chart to the right for
specifics pulled from the archived budget
documents on the City’s website.
Total City Revenue
Fiscal Year Actual Revenues City Projections % Difference
2016 $479,746 $487,295 -2%
2017 510,037 546,318 -7%
2018 585,572 590,236 -1%
2019 640,090 617,307 4%
2020 581,165 632,159 -9%
Charges for Services, 4.6%Charges to Other Funds,
2.1%
Documentary Transfer Tax, 1.2%
From other Agencies, 2.2%
Net Sales, 58.3%
Other Revenue, 4.2%
Other Taxes and Fines,
0.2%
Permits and Licenses, 1.8%
Property Taxes, 9.6%
Rental Income, 2.9%
Return on Investments, 1.8%
Sales Tax, 5.3%
Transit Occupancy Tax,
3.2%Utility Users Tax, 2.8%
Total Citywide Revenue by Category
Charges for Services,
11.2%
Charges to Other Funds, 5.3%
Documentary
Transfer Tax, 3.3%
From Other
Agencies, 0.7%
Other Revenue, 1.4%
Other Taxes and Fines, 0.6%
Permits and Licenses, 3.8%
Property Taxes, 24.4%
Rental Income, 7.6%
Return on Investments, 0.7%
Sales Taxes, 14.6%
Transient Occupancy Tax, 8.8%
Utility Users Tax, 7.7%
Operating Transfers-in, 9.9%
Total General Fund Revenue by Category
8
Revenue Analysis and Models Review
Overview
The City has a robust forecasting process. The Administrative Services Department ensures a smooth process for
inputting all relevant information into Long Range Financial Plans (LRFF). The Administrative Services Department
updates the financial forecasting model on an annual basis prior to the start of the budget process. These forecasting
exercises inform the creation of the budget. The Department includes multiple scenarios to present to Council to inform
decisions regarding the formation of the budget. The Administrative Services Department also reviews trends compared
to the budget on a quarterly basis. These quarterly reviews are presented to Council.
The OCA conducted a Revenue Trends and Models Review to understand the current state of the City’s forecasting
practices. First, the OCA reviewed forecasting and financial models used by the City as well as the inputs those models
utilize for their forecasts. This review was specific to Property Tax, Sales Tax, and Transient Occupancy Tax (TOT).
These revenue sources alone account for almost three quarters of the total revenue base.
Following the review of the City’s models, the OCA conducted a deeper review of revenue trends and data analysis for
major tax revenue sources of the City, specifically Property Tax, Sales Tax, TOT, Documentary Transfer Tax, and Utility
User Tax. The OCA reviewed the revenue sources for the historical performance which contribute to estimates of future
performance. The section below describes the methodology and results of these reviews.
Methodology
In review of the City’s models, the OCA first completed a data request to review all existing spreadsheets, documents,
and reference materials used by the City in their economic forecasting processes. The OCA also conducted interviews
and walkthroughs with members of Administrative Services to ensure full understanding of these documents. The OCA
conducted independent best practice research and analysis for each of these revenue categories. Finally, the OCA
documented the full understanding of current state of the models and inputs in the tables below, documented all best
practices, and detailed any gaps for each revenue category.
In review of these revenue trends, the OCA reviewed economic and financial planning spreadsheets and documents
provided by the City, including ACFRs, OpenGov and the internal documents already requested during the review of the
models. The OCA also conducted independent research and analysis around state and national projections/economic
indicators for each of these revenue categories. Finally, the OCA documented their understanding of each revenue source
in the sections below. The sections are broken out by each revenue category and offer historical revenue analysis and
future projections where possible.
Analysis
In completing this audit activity, there were a number of elements considered in the analysis. The OCA completed a
thorough review of qualitative and quantitative analysis. In regards to qualitative information, the OCA completed
interviews with City staff to walk through current-state practices and ensure understanding of documentation. Additionally,
the OCA reviewed qualitative best practices from research and internal institutional expertise.
In regards to quantitative information, the OCA also completed a number of analyses and correlations to inform the audit.
For all revenue sources, the OCA completed correlation calculations with the following factors:
• Unemployment (State and City)
• Personal Income (State and County)
• Inflation (Nation, State, Region)
• Consumer Price Index (Nation, State, Region)
• Population (County, City)
• Enrollment (Palo Alto Schools, Stanford)
• Permits for new construction (City)
• Mortgage Rates
• Prime Rates
• Federal Funds Rates
9
Results
The detailed results of the analysis of revenues are in the sections shown below. There is a section for each revenue
source analyzed containing historical performance notes, historical averages, economic indicators with high correlation,
and qualitative factors for consideration. Each section also contains graphs showing the revenue sources historical
performance and projections based on historical averages and economic indicators. Please see the sections below for a
detailed analysis of Property Tax, Sales Tax, Transient Occupancy Tax, Documentary Transfer Tax, and Utility User Tax.
The results from the review of the City’s models are also in the sections below1. The review of the City’s models are only
for Property Tax, Sales Tax, and Transient Occupancy Tax.
Overall, the City's forecasting process is thoughtful and thorough. Additionally, the City has a number of highly skilled and
sought-after advisors informing their forecasting decisions. The OCA's review confirmed that the City is consistent with
industry best practices and uses reliable information. Additionally, the City’s LRFF projections are aligned with Baker
Tilly’s recommendations for projections. This may point to Baker Tilly and the Administrative Services Department’s
collaboration during the preliminary analysis in March of 2020, both parties completing analysis that are complimentary to
one another. Even with the alignment to best practices, there are always opportunities for improvement in the forecasting
process. In particular, the OCA has two overarching recommendations for improvement:
1. Naming conventions and workbook narratives. In order to develop an accurate and thorough forecasting model, the
City considers a number of data elements from various workbooks. The input from multiple parties is also involved. At the
moment, the City doesn't have a consistent naming convention for workbooks, nor does it have narrative or instructions
within the workbook itself to describe the flow of information. The City relies on the institutional knowledge of employees
to understand how the forecasting information is pulled together. Without consistent naming conventions and information
narratives, the City is at risk if key stakeholders in this process were to leave the organization. The OCA recommends
developing a consistent naming convention and adding narrative in workbooks, including the purpose of the sheet, from
where information is pulled, to where information is pushed, and with whom does the ownership of the spreadsheet lie.
2. Assumptions and "guess-timates". In some cases, the City uses assumptions and rough estimates to consider various
scenarios such as percentage growth in situations when supporting data is sparse. This can be a necessary action if no
better alternatives exist. However, the OCA learned anecdotally that some of these rough estimates are highly specific,
oftentimes having estimated numbers to the hundredths place. If a certain series of inputs are rough estimates, these
inputs should not be made to look as though they were calculated. Instead those estimates should be clearly labeled with
a short description as to the thought process behind how the estimator arrived at that number. This will help to clarify
which values were calculated at one point vs. which values are estimated without a specific calculation.
1 FY20 numbers were used in analyses as FY21 numbers were unavailable for most of the audit period.
10
Property Taxes
Property Tax Overview
The City and the Bay Area’s historically stable property values contribute to the City’s revenues. While these property
values are high, the City cannot collect on the full value of these properties based on Proposition 13’s tax caps which
imposes a maximum annual tax increase of 2% unless a change in property ownership or new construction. However, the
revenue source is highly important to the City’s fiscal health even with Proposition 13 in place.
Total Revenue: $51.1M in FY202, $56.6M in FY213
Proportion of Total Revenue: 28% in FY20, 34% in FY21
Property Tax Observations and Data Analysis
The OCA analyzed the historical performance of property tax revenue and estimating future years. The first part of the
analysis consisted of reviewing historical revenus (FY 2000 – 2020) for patterns and/or anomalies and identifying their
causes if possible. The second part required using historical averages and economic indicators to make future projections
from FY 2021 – 2040 to help identify where revenues might trend in future years. These analytical insights were combined
with less quantifiable factors to get the best possible theory of where revenues may trend in the coming years.
Historical Performance
The table and chart below display the historic property tax revenues from FY 2000 – 2020 and a trendline with the linear
equation.
The information shows how property tax revenues for the City have grown rapidly over the previous 20 years. They
started at roughly $10.7M in FY 2000 and have increased to around $51M in FY 2020. Over the years there have been
large variances in year over year percentage changes ranging from -1.13% in 2011 to +21.53% in 2005.
2 Palo Alto’s FY20 ACFR: https://www.cityofpaloalto.org/civicax/filebank/documents/79645
3 Palo Alto’s FY21 ACFR: https://www.cityofpaloalto.org/files/assets/public/administrative-services/city-budgets/fy-2021-city-
budget/city-of-palo-alto-acfr-fy2021-final.pdf
y = 1866.1x + 6039.3
R² = 0.9562
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Fiscal Year
Historical Property Tax RevenuesYear
Historical Property
Tax Revenues Dollar Change
Percentage
Change
2000 $10,770 N/A N/A
2001 $12,110 $1,340 12.44%
2002 $13,231 $1,121 9.26%
2003 $13,821 $590 4.46%
2004 $13,706 ($115)-0.83%
2005 $16,657 $2,951 21.53%
2006 $18,730 $2,073 12.45%
2007 $21,467 $2,737 14.61%
2008 $23,107 $1,640 7.64%
2009 $25,445 $2,338 10.12%
2010 $25,982 $537 2.11%
2011 $25,688 ($294)-1.13%
2012 $26,494 $806 3.14%
2013 $28,742 $2,248 8.48%
2014 $30,587 $1,845 6.42%
2015 $34,117 $3,530 11.54%
2016 $36,607 $2,490 7.30%
2017 $39,381 $2,774 7.58%
2018 $42,839 $3,458 8.78%
2019 $47,327 $4,488 10.48%
2020 $51,089 $3,762 7.95%
11
Historical Averages
The table and chart below show the property tax revenue projections for the 5-, 10-, and 20-year growth averages applied
through 2040.
Growth averages:
• 5 Year: 8.41%
• 10 Year: 7.00%
• 20 Year: 8.09%
The historic averages were applied to the latest revenue collections of $51M and projected forward for the next 20 years.
The chart above shows how the collections would look if each of the averages were applied in future years.
Fiscal Year 5 yr avg 10 yr avg 20 yr avg
2021 $55,386 $54,663 $55,225
2022 60,044 58,487 59,695
2023 65,094 62,578 64,527
2024 70,569 66,956 69,751
2025 76,504 71,640 75,397
2026 82,938 76,651 81,501
2027 89,914 82,014 88,098
2028 97,476 87,751 95,230
2029 105,674 93,889 102,938
2030 114,562 100,457 111,271
2031 124,197 107,485 120,279
2032 134,643 115,004 130,015
2033 145,967 123,049 140,540
2034 158,243 131,657 151,917
2035 171,553 140,867 164,214
2036 185,981 150,721 177,508
2037 201,623 161,265 191,877
2038 218,580 172,546 207,409
2039 236,964 184,617 224,199
2040 256,894 197,532 242,348
Property Tax Projections using Historical Averages
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Fiscal Year
Property Tax Projections using Historical
Averages
5 yr avg 10 yr avg 20 yr avg
12
Economic Indicators
The chart and graph below illustrate the property tax revenue projections using economic indicators to create projections.
The method section below describes how the calculations were done.
Economic Indicators with High Correlation:
• Personal Income (99.13% for California and 98.60% for Santa Clara County)
• Consumer Price Index (CPI) (98.50% for the U.S., 96.49% for California, and 99.42% for San Francisco)
• Population (88.95% for Palo Alto and 95.77% for Santa Clara County)
• Stanford Enrollment (93.90%)
Method:
In an effort to validate the historical projections, the OCA
examined economic indicators with strong correlations. If a
correlation was determined to be greater than 85%, future
estimates for the indicator were obtained from a reputable
source and if necessary, the OCA calculated the average
annual change and used that to project future years of the
indicator and the revenue.
After establishing a plausible future projection with the
economic indicator, the OCA completed a regression analysis
between the historical tax revenue collections and historical
economic indicator data. The OCA then used the regression to
calculate the linear equation and project out possible property
tax revenues.
Fiscal
Year
PT Projection
based on US
CPI
PT Projection
based on CA
CPI
PT Projection based
on CA Personal
Income
PT Projection
based on Santa
Clara Population
2021 $46,789 $49,420 $52,825 $49,516
2022 48,794 51,747 52,999 51,473
2023 51,067 54,716 56,128 53,515
2024 53,550 58,056 59,442 55,473
2025 55,925 60,316 62,419 57,483
2026 58,347 62,622 65,515 59,498
2027 60,818 64,973 68,734 61,560
2028 63,338 67,372 72,082 63,521
2029 65,908 69,818 75,564 65,477
2030 68,530 72,314 79,186 67,479
2031 71,205 74,859 82,952 69,400
2032 73,932 77,455 86,869 71,260
2033 76,715 80,103 90,942 73,092
2034 79,553 82,804 95,179 74,909
2035 82,448 85,560 99,585 76,702
2036 85,400 88,370 104,167 78,495
2037 88,412 91,236 108,933 80,266
2038 91,484 94,160 113,889 81,961
2039 94,617 97,142 119,043 83,566
2040 97,813 100,184 124,404 85,183
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Fiscal Year
Property Tax Projections Using Economic Indicators
PT Projection with US CPI PT Projection with CA CPI
Projection based on CA Personal Income Property Tax Projection based on Santa Clara Population
13
Property Tax Interpretation
The graph below is a combination of the historical property taxes, projections made from historical averages, and
economic indicators.
The property tax revenues for the City have performed extraodinarily well over the last 20 years. They have grown steadly
with a 20 year average of 8.09% and have been fairly insulated during times of national economic decline. This
phenomenon proved true again again during the COVID-19 pandemic, where property values ramained high and even
increased in some categories (Economic Resiliency Review). In fact there have only been two years where revenues
declined, from FY 2003-2004 and FY 2010-2011.
Looking purely at the historial average projections it shows an optimistic future. On the high end, the 5 year average
increases collections upwards of $250M in FY 2040 and on the low end, the 10-year average projects collections around
$200M by then. The rate of growth property tax revenues have experienced in the City appears to be an exponential
curve when projected forward. However, it is not likely revenues will continue to grow at the historical rates for another 20-
years. Contributing factors being, the City has neared it’s limit on population growth without an increase in density, gaining
only 6,500 residents since 2000, and there are current concerns of corporate headquarters moving out of state (Hoover
Institute)4.
4 Hoover Report: https://www.hoover.org/research/why-company-headquarters-are-leaving-california-unprecedented-numbers
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Fiscal Year
Property Tax Revenue Summary
Property Tax PT Projection with US CPI
PT Projection with CA CPI Projection based on CA Personal Income
Property Tax Projection based on Santa Clara Population Historical 5 yr avg
Historical 10 yr avg Historical 20 yr avg
Linear (Property Tax)
14
Property Tax Current State Model Description
Property taxes make up the largest portion of the City's General Fund revenues and approximately 25% of total revenues
(staff report6); fortunately, they were not as negatively impacted by the COVID-19 pandemic as sales tax and TOT. The
City expects property tax revenues to grow around 10.2% from FY21 to FY22 and is projecting consistent property tax
revenues for the next 3 years.
The following factors determine property tax revenue; net assessed values, transfer of ownership, new
construction/declines, and Proposition 13 inflation adjustment. In determining the various scenarios for property taxes the
City calculates their base projection which takes into account the major factors outlined. Once a baseline is established,
the Treasury Department applies a growth factor and summarized.
Due to the unique economic situation COVID has created, the City crafted several scenarios for their revenue projections.
To determine the various scenarios for Property Tax, the City determines the base projections for the revenues in the
workbooks described above, and adjusts the appropriate multipliers to reflect the expected revenues in a 2-3 year
recovery period, 3-5 year recovery period (base), and 5-10 year recovery period. These scenarios were presented to
Council along with Sales and TOT projections.
The City uses the following inputs when forecasting Property Taxes:
1) Tax rolls received from the County Controller's Office
2) New growth and known decreases, including Proposition 13
3) Historical data on prior year assessed values and collections
4) Regional data on property sales and values (including a review of Zillow)
5) Property tax consultants input Property Tax Industry Best Practices
The City's property tax projection methods align with what we see with other Baker Tilly clients. They rely heavily on
historical data, current tax rolls from the County, and upcoming changes within the local market. Our review confirmed
that the City is using reliable information and their practices are consistent with other communities.
Property Tax Observations and Recommendations
In our review, the OCA attempted to find correlations between assessed values and several factors such as the 30-year
mortgage rates, Prime Rates, and Federal Funds rates. The strongest correlations the OCA found were between the
City's total assessed values and both the Santa Clara County Per Capita Personal Income and California Total Personal
Income. There is 99% correlation between total AVs and California's total personal income followed closely by a 98%
correlation between the total assessed value and Santa Clara County's per capita personal income.
Based on these correlations, the OCA ran a preliminary regression analysis which showed a projected decrease in total
assessed values in 2022 before rebounding back in 2023 and 2024. After running the regression analysis, we looked at
the FY16 Financial Report5 which contained historical information back through the Great Recession, which showed total
AVs remaining flat from 2009-2013. Taking all of that into account, our prediction is that AVs will remain flat for the next 3-
4 years and the City's base case reflects that outcome.
The most conservative long term assumption, however, would be a projection of linear growth into future years. The
conservative assumption appears to align closely to the projections shown in the Long Range Financial Forecast (LRFF)
dated December 7th, 2021.
5 Palo Alto’s FY16 ACFR: https://www.cityofpaloalto.org/civicax/filebank/documents/54744
15
Sales Tax
Sales Tax Overview
Palo Alto has a number of factors drawing visitors to the City. Similar to TOT, those in Palo Alto for business, education,
or leisure contribute to the City’s revenues through the sales tax associated with their spending.
Total Revenue: $30.6M in FY20, $29.1M in FY21
Proportion of Total Revenue: 17% in FY20, 17% in FY21
Sales Tax Observations and Data Analysis
The OCA performed an analysis of the historical performance of sales tax revenue and estimated future years. The
historical analysis required examination of past performance to gain an understanding of the revenue volatility, factors
creating noticeable change and reactions to economic downturns. In addition, the OCA used economic indicators to
estimate revenue out through FY 2040.
Historical Performance
The table and graph below depict the historical sales tax revenues from FY 2000 – 2020 and shows a trend line with the
linear equation.
Sales taxes can be a volatile revenue source and the City’s revenues are no exception. In the last twenty years there
have been year over year percentage changes as low as -22.11% (FY 2001-2002) and up to +17.42% (FY 2018-2019).
The revenues in FY 2000 were $22.8M and have increased to $30.5M in FY 2020. That is roughly $8M in growth at a rate
of 1.46% annually.
Fiscal
Year
Historical
Sales Tax
Revenues
Dollar
Change
Percentage
Change
2000 $22,867 N/A N/A200125,786 $2,919 12.77%2002 20,085 (5,701)-22.11%2003 18,041 (2,044)-10.18%2004 18,151 110 0.61%
2005 19,132 981 5.40%
2006 20,143 1,011 5.28%
2007 22,130 1,987 9.86%
2008 22,472 342 1.55%
2009 20,005 (2,467)-10.98%2010 17,868 (2,137)-10.68%
2011 20,591 2,723 15.24%2012 22,046 1,455 7.07%
2013 25,606 3,560 16.15%
2014 29,424 3,818 14.91%
2015 29,675 251 0.85%
2016 30,018 343 1.16%
2017 29,923 (95)-0.32%
2018 31,091 1,168 3.90%
2019 36,508 5,417 17.42%2020 30,563 (5,945)-16.28%
y = 669.98x + 17017
R² = 0.5907
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16
Historic Average Projections
The chart and table below depict the sales tax revenue projected out through FY 2040 using historic averages.
Growth averages:
4 Year: 5.32%
5 Year: 0.59% (you can see pandemic hit hard when comparing 4 yr. and 5 yr. averages)
10 Year: 5.51%
20 Year: 1.46%
The historical averages of the revenue show that the 5-year average contains the large drop in revenues described
above, making it a mere 0.59%. When applying the annual averages, the 10-year average is projecting revenues to be
around $90M in FY 2040, whereas the 5- and 20-year averages paint a picture of roughly $40M in revenue. The 10-year
average may be slightly skewed because its first year of collections is after a large dip, seemingly linked to the 2008
market crash. Therefore, the average captures this rebound and only one major year of decline due to the COVID-19
pandemic.
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Sales Tax Projections using Historical
Averages
5 yr avg 10 yr avg 20 yr avg
Fiscal
Year 5 yr avg 10 yr avg 20 yr avg
2021 $30,744 $32,248 $31,010
2022 30,926 34,027 31,463
2023 31,109 35,903 31,922
2024 31,292 37,883 32,389
2025 31,478 39,972 32,862
2026 31,664 42,176 33,342
2027 31,851 44,502 33,829
2028 32,039 46,956 34,323
2029 32,229 49,545 34,825
2030 32,420 52,278 35,334
2031 32,611 55,160 35,850
2032 32,804 58,202 36,374
2033 32,998 61,412 36,905
2034 33,193 64,798 37,444
2035 33,390 68,372 37,991
2036 33,587 72,142 38,547
2037 33,786 76,120 39,110
2038 33,986 80,318 39,681
2039 34,187 84,747 40,261
2040 34,389 89,420 40,849
17
Economic Indicators
The table and graph below display the sales tax revenue projections using economic indicators with a high correlation.
The calculations were done using the same method described in the Property Tax section.
Economic Indicators with High Correlation:
• Personal Income (Santa Clara County)
• Stanford Enrollment
• TOT Revenues (Correlation >85%)
The only projectable economic indicator with a high correlation is Personal Income for Santa Clara County. The two have
an R2 value roughly 93% which is enough to say there is a correlation but not enough for total confidence in their
relationship moving forward. The OCA projected out the Personal Income for Santa Clara County using growth
percentages for 2021-2024 obtained from CA States Personal income projections published by the California Department
of Finance in the ‘California Economic Forecast MR 2021-2022’. A growth factor of 4% was then applied to years 2025-
2040.
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Sales Tax Projections Using Economic Indicators
Projection using Santa Clara Personal Income
Fiscal
Year
Projection
using Santa
Clara Personal
Income
2021 $35,726
2022 35,795
2023 37,034
2024 38,346
2025 39,525
2026 40,750
2027 42,025
2028 43,351
2029 44,730
2030 46,164
2031 47,655
2032 49,206
2033 50,819
2034 52,496
2035 54,241
2036 56,055
2037 57,942
2038 59,905
2039 61,945
2040 64,068
18
Sales Tax Interpretation
The graph below is a combination of the historical sales taxes, projections made from historical averages, and economic
indicators.
The OCA looked at all data sources made available by the City and outside organizations to help interpret the meaning of
the data trends identified above. Historical data provided by the City shows that during times of national economic
recession (Dotcom bubble, 9/11, and 2008 housing crash) sales tax revenue falls off drastically with -10% to -20%
reductions and takes roughly 3-5 years to recover.
The COVID-19 pandemic appears to have continued this trend as it hit sales tax hard with a revenue reduction of -16.28%
when comparing FY 2019 to 2020. Much of this can be attributed to the forced business closure, work from home policies,
and other new social norms the pandemic introduced. While there may be no way to tell when or what might cause an
economic recession, knowing the range of revenue decrease (10%-20%) can help the City budget for worse case
scenarios.
Continuing to use the context learned during data analysis, the OCA created the graph above, combining historical sales
tax revenues, projections using historical averages, and economic indicators. The Santa Clara Personal Income projection
shows a more aggressive growth than the 5- and 20-year averages and less growth than the 10 yr. average. As stated
above, the 10-year average may be skewed due to the timing of recessions and rebounds while the 5-year average is
affected by the COVID-19 Pandemic. Additionally, while the details of the information is confidential, the 10-year average
includes substantial Sales Tax changes from a small number businesses, contributing to the skewed data according to the
Administrative Services Department. The City’s LRFF estimates $43 million in sales tax revenues in FY 2032, which is
between the projections using Santa Clara Personal Income and the 20-year average.
y = 669.98x + 17017R² = 0.5907
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Sales Tax Summary
Actual Sales Tax Revenues 5 yr avg 10 yr avg
20 yr avg Santa Clara Personal Income Linear (Actual Sales Tax Revenues)
19
Sales Tax Current State Model Description
Sales Tax receipts have taken a significant hit because of COVID-19 and the health regulations implemented as a direct
result. The total year over year change from 2019 to 2020 was -16.3% and an addition decrease of -4.7% in 2021. Recent
sales tax revenues point to the yearly revenue being $1.5 million, or 5.2%, over 2021 actuals.
The City uses several major factors in their sales tax projections; actual collections received, tax consultant provides a
report, previous recessions, unemployment, and local economists.
First, the City receives monthly sales tax information from the State. Their sales tax consultant also provides a report
summarizing monthly revenues and providing future projections. The projections are inputted by Treasury and OMB
factors in more locally focused insights to get an estimate for current year collections. Once the current year base
scenario is established, a growth multiplier is added using inputs described above. Finally, the data is cleaned up and
becomes the deliverable for Council.
To determine the various scenarios for sales tax, the City determines the base projections for the revenues in the
workbooks described above, and adjusts the appropriate multipliers to reflect the expected revenues/operating margins in
a 2-3 year recovery period, 3-5 year recovery period (base), and 5-10 year recovery period. These scenarios were
presented to Council along with TOT and Property Tax projections.
Inputs considered when forecasting Sales Tax revenues include:
1) Consultant report - MuniServices 2020Q3
2) Data from the previous recession
3) Unemployment rates
4) State collections - sent monthly
5) Input from local economists
Sales Tax Industry Best Practices
The City's sales tax projection methods align with what we see with other Baker Tilly clients. They rely heavily on historical
data, monthly sales tax revenues, and upcoming changes within the local market. Our review confirmed that the City is
using reliable information and their practices are consistent with other communities.
Sales Tax Observations and Recommendations
During our review, we looked for correlations between historical sales tax revenues and the following data: Unemployment
rates, Santa Clara County Per Capita Personal Income, California Total Personal Income, and historical TOT taxes. The
strongest correlations came from Santa Clara County per capita income 85.3%, California total personal income 83.6%,
and historical TOT revenues 82.0%. While none of these correlations are in the 90% percentile, they could give insight on
future years by leveraging projections from reputable state and national entities to check for consistency with current
models. Since TOT and Sales Tax have a reasonably strong correlation, when modeling future years the City may check
that both revenue projections are aligned in future years.
As far as historical averages, the 1.46% 20-year average is a conservative estimate, showing $40M in revenue by FY
2040. The Santa Clara Personal Income splits the middle between the 10-year average and the 20-year average with
around 3.8% growth. The City’s LRFF estimate of $43 million in sales tax revenues in FY 2032 is plausible based on our
analysis.
20
Transient Occupancy Tax
Transient Occupancy Tax Overview
The City has a number of travelers for local businesses/corporations, Stanford University affiliates, and leisure. These
travelers contribute to a large portion of the City’s revenue through the Transient Occupancy Tax (TOT).
Total Revenue: $18.6M in FY20, $5.2M in FY21
Proportion of Total Revenue: 10% in FY20, 3% in FY21
Transient Occupancy Tax Observation and Data Analysis
The OCA analyzed the historical performance of TOT revenues and estimated future years. The historical analysis
required examination of past performance to identify patterns in the data as well as irregularities. The future analysis
consisted of identifying highly correlated economic indicators and using them to project out revenues through fiscal year
2040. In both the historical and future analysis, qualitative factors are considered, and their potential impacts explained.
Historical Performance
The table and graph below displays the historical Transient Occupancy tax revenues from fiscal year 2000 – 2020 and
includes a trend line with the linear equation.
Transient Occupancy Tax Revenues for the City have had an interesting plateau effect over the past twenty years. In
2000 the City collected $8.2M in revenues which dropped down to $5.3M by 2003. After that initial decline it took until
2011 for the revenues to get back around the $8M mark. After 2011 the revenues increased dramatically with year over
year increases north of 36%. This was in part because of passed ballot measures to increase the TOT rates in 2014 and
2018. The revenue collections plateaued again after 2016 where they grew around 4% per year until the pandemic took
full effect creating a -27.7% reduction (2019-2020).
Fiscal
Year
TOT
Revenues
Dollar
Change
Percentage
Change
2000 $8,293 N/A N/A
2001 $9,359 $1,066 12.85%
2002 $6,615 ($2,744)-29.32%
2003 $5,333 ($1,282)-19.38%
2004 $5,489 $156 2.93%
2005 $5,686 $197 3.59%
2006 $6,393 $707 12.43%
2007 $6,708 $315 4.93%
2008 $7,976 $1,268 18.90%
2009 $7,111 ($865)-10.85%
2010 $6,858 ($253)-3.56%
2011 $8,082 $1,224 17.85%
2012 $9,664 $1,582 19.57%
2013 $10,794 $1,130 11.69%
2014 $12,255 $1,461 13.54%
2015 $16,699 $4,444 36.26%
2016 $22,366 $5,667 33.94%
2017 $23,477 $1,111 4.97%
2018 $24,937 $1,460 6.22%
2019 $25,649 $712 2.86%
2020 $18,553 ($7,096)-27.67%
y = 933.99x + 1549.8
R² = 0.6841
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21
Historic Average Projections
The chart below shows that 5-, 10-, and 20-year averages applied to the TOT revenues for the next 20 years.
Growth averages:
• 5 Year: 2.13%
• 10 Year: 10.46%
• 20 Year: 2.13%
The 10-year average is abnormally high as that is when the revenues increased dramatically. The OCA believes the 5-
and 20-year averages are more reliable estimates for long term projections.
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TOT Projections using Historical Averages
5 yr avg 10 yr avg 20 yr avg
Fiscal
Year 5 yr avg 10 yr avg 20 yr avg
2021 $18,948 $20,494 $19,315
2022 $19,351 $22,639 $20,109
2023 $19,763 $25,008 $20,935
2024 $20,183 $27,625 $21,795
2025 $20,613 $30,516 $22,690
2026 $21,051 $33,709 $23,622
2027 $21,499 $37,236 $24,593
2028 $21,957 $41,133 $25,603
2029 $22,424 $45,437 $26,655
2030 $22,901 $50,192 $27,750
2031 $23,389 $55,444 $28,890
2032 $23,886 $61,245 $30,077
2033 $24,395 $67,654 $31,313
2034 $24,914 $74,734 $32,599
2035 $25,444 $82,554 $33,938
2036 $25,985 $91,193 $35,333
2037 $26,538 $100,735 $36,784
2038 $27,103 $111,277 $38,295
2039 $27,680 $122,921 $39,869
2040 $28,269 $135,784 $41,507
22
Economic Indicator Projections
The following graph depicts the TOT revenues projected through 2040 using highly correlated economic indicators.
Economic Indicators with High Correlation:
• Personal Income (California and Santa Clara County)
• Population (Palo Alto)
• Stanford Enrollment
There were three economic indicators used to project out TOT revenues through 2040, California Personal Income, Santa
Clara County’s Personal Income and Santa Clara County’s Population. All three showed higher revenues than the 20-year
average with the population being the closest. The most likely scenario is still revenue somewhere around the $30-$40M
revenue mark in 2040.
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TOT Projections using Econmic Indicators
Santa Clara Population CA Personal Income
SC Personal Income
Fiscal
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Santa Clara
Population
CA
Personal
Income
SC
Personal
Income
2021 $22,475 $25,701 $26,903
2022 $23,501 $25,793 $26,992
2023 $24,556 $27,447 $28,606
2024 $25,694 $29,198 $30,316
2025 $26,881 $30,771 $31,851
2026 $28,019 $32,407 $33,448
2027 $29,188 $34,109 $35,109
2028 $30,360 $35,878 $36,836
2029 $31,558 $37,719 $38,632
2030 $32,699 $39,633 $40,500
2031 $33,836 $41,623 $42,442
2032 $35,000 $43,693 $44,463
2033 $36,117 $45,846 $46,564
2034 $37,198 $48,085 $48,749
2035 $38,263 $50,413 $51,022
2036 $39,319 $52,835 $53,386
2037 $40,362 $55,354 $55,844
2038 $41,405 $57,973 $58,400
2039 $42,434 $60,697 $61,059
2040 $43,419 $63,530 $63,824
Projections
23
Transient Occupancy Tax Interpretation
The following chart displays a combination of the historical TOT revenues and projections using historical averages as
well as economic indicators.
The OCA used the data provided by the City and outside sources to get as wide of a view as possible for TOT revenues
and possible influencing factors. When looking at the historical revenue graph, it is hard to miss how the COVID-19
pandemic affected the TOT revenues with a -27.7% decline from 2019-2020. When looking at other major economic
downturns the revenues also dipped, Dotcom bubble showed a -22.11% decline (FY 2001-2002) and the housing market
crash showed a -10.85% decline (FY 2008-2009). Although TOT revenues declined in both scenarios, revenues took 6-7
years to rebound after what is presumed to be the dotcom bubble and only took two years to rebound after 2008. Unlike
the City’s Sales Tax, which has historically taken 3-5 years to rebound post-recession, the TOT has had different recovery
lengths making it difficult to say how TOT will bounce back after the pandemic since its effects are still lingering.
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TOT Summary
Actual TOT Revenues Santa Clara Population CA Personal Income
Historical 5 yr avg Historical 10 yr avg Historical 20 yr avg
SC Personal Income Linear (Actual TOT Revenues)
24
Transient Occupancy Tax Current State Model Description
The TOT was heavily impacted by the COVID-19 pandemic. The City assumes that TOT revenue will continue to suffer
until the virus is under control and travel resumes. In particular, the reduction of "business and other non-leisure travel is a
driving impact" (Staff Reports)6.
TOT revenue is comprised of four factors; the number of hotels in the City, the number of rooms at each hotel, the
average occupancy of rooms, and the average room rate. Applying a 15.5% TOT to those four factors makes up the
revenue the City receives from these hotels. The City is expecting TOT to be 57% below pre-pandemic levels in FY22,
which is up from FY21 actuals and but still 80% below pre-pandemic levels (prior to FY21). As reported by hotels, room
rates have decreased by nearly 50% and occupancy rates have decreased from 80% to 44%.
When forecasting TOT into the future, the City uses a number of factors to determine likely scenarios. First, the City
assesses the current state of TOT. This includes detailed information on each hotel, including historical data and
information on room rates, occupancy rates, and number of rooms. The City then includes assumptions and analyses
from the Treasury department.
Treasury considers a few factors, including historical trends of TOT, regional trends of TOT, and behaviors of tax
revenues after past recessions. Treasury's analysis is used to create the City Council Staff Report6 and Presentation7 with
various scenarios of economic recovery.
To determine the various scenarios for TOT, the City determines the base projections for the revenues and adjusts the
appropriate multipliers to reflect the expected revenues/operating margins in a 2-3 year recovery period, 3-5 year recovery
period (base), and 5-10 year recovery period. These scenarios were presented to the Council along with Sales and
Property Tax projections. In summary, the inputs considered when forecasting TOT are the following:
1) Historical data of number of hotels, number of rooms, average room rate, and average occupancy rate
2) Future hotels according to City Planning
3) Historical recovery data from the past recessions
4) Regional (Northern California) hotel/motel trends
5) Discussions with economists on potential future trends in travel
Transient Occupancy Tax Industry Best Practices
Traditionally, the City's forecasting methods are directly in-line with what we've seen with past clients. The analysis relies
heavily on historical data, projected growth in rooms, and adjusting for economic conditions. Throughout our research, we
also saw similar methods being used in neighboring communities such as Mountain View, San Mateo, and others.
However, through our research, we've also identified additional factors that are worthy of consideration. One such factor is
being used by the City of San Jose8. While much of San Jose's TOT forecasts are similar to the City's methods, San Jose
also considers the activity and growth of local events/conventions in their TOT forecasting.
Accounting firm, Ernst and Young (“EY”), also wrote a report about forecasting hotel trends in the age of COVID9. Their
thesis discussed the importance of the type of travel a community attracts.
For example, the pace of recovery for a highly professional community will vary from a tourist destination. EY also
considers the national unemployment rate as an indicator of travel levels as well as the rise in virtual alternatives.
CBRE writes a report10 similar to EY, and both reports agree that the hotel quality matters a great deal in forecasting. In
CBRE's analysis, luxury hotels are seeing less fluctuation than mid-segment or budget hotels. Finally, Moody's, the credit
6 2/8/21 City Council Staff Report: https://www.cityofpaloalto.org/civicax/filebank/documents/80088
7 2/8/21 City Council Presentation: https://www.cityofpaloalto.org/civicax/filebank/documents/80198
8 San Jose’s Five Year Forecast: https://www.sanjoseca.gov/home/showpublisheddocument?id=69842
9 EY Hotel Forecasting Report: https://www.ey.com/en_us/real-estate-hospitality-construction/how-to-better-forecast-recovery-in-
the-hotel-industry
10 CBRE Hotel Forecasting Report: https://www.hotelmanagement.net/operate/cbre-adjusts-us-hotel-industry-
forecast#:~:text=According%20to%20the%20recently%20released,during%20the%20year's%20second%20half.
25
rating agency, produces a report on their outlook on hotels11. They believe that leisure and personal travelers will be the
first to return. They also believe that business travelers will lag on a macro level, but that it will depend greatly on industry.
Moody's in general is more on the pessimistic side, believing that a full recovery within 3-5 years is unlikely.
In summary, additional inputs found in research not captured in the City's forecasting process are as follows:
1) Size and frequency of local conventions and events
2) National unemployment
3) Hotel market segments and property-specific attributes
4) Demographics of travelers
Transient Occupancy Tax Observations and Recommendations
In regards to forecasting of revenue trends, the OCA concluded that Santa Clara Population, 20-year average, and the
linear trend line are a conservative forecast. Those projections are all grouped very closely together and have a
reasonable growth pattern. The difficulty is predicting how quickly the revenue will recover to the pre-pandemic levels
especially with a varied recovery pattern from historic recessions.
The LRFF dated December 7, 2021, shows a 5-6 year recovery period (FY 2019 – 2025), which is in between the
recovery periods for the previous economic downturns, and estimates $37.2 million in revenues for FY 2032. These
estimates are just slightly higher than the OCA’s most reasonable estimates described above, which show revenues of
$30M -$35M for FY 2032.
The OCA also recommends considering the four factors listed above as additional forecasting inputs, namely; size and
frequency of local conventions/events, national unemployment, hotel market segments and property-specific attributes,
and demographics of travelers. While some of these inputs may not be possible due to lack of data or lack of applicability
to the City, the OCA recommends considering these other inputs to determine if improvements to forecasting can be
made. Additionally, where data is available, the City can determine correlations between various factors to determine if
there is a relationship between TOT and a given input. Finally, in some cases, the City may find that these factors do not
apply specifically to Palo Alto and may disregard certain factors in future forecasts. For example, while San Jose includes
analysis around local conventions/events, this is also because they have a much larger convention center. If Palo Alto
does not have the same ability to gather data around local conventions/events, it may not be a factor that can be included
in a broader analysis.
The City should also reach out to larger hotels to understand if they forecast as well as large businesses and Stanford
University to understand their expectations for conferences, business travel, student activity, etc. If a relationship with
Stanford to provide such information does not exist, this would be a useful relationship to develop for future analyses that
may also benefit the University.
11 Moody’s Hotel Industry Assessment Report: “Consumer comfort vital for travel, tourism dependent sectors’ eventual recovery”
26
Documentary Transfer Tax
Documentary Transfer Tax Overview
The OCA analyzed the Documentary Transfer Tax (DTT) revenues and provided estimates of future performance based
on historical averages. The analysis shed light on the volatility of the revenue source and proved difficult to find any
correlating economic indicators that could be used to project future revenues.
Documentary Transfer Tax Observations and Data Analysis
Historical Performance
The chart below depicts the historical Documentary Transfer Tax performance from 2000 – 2020 and includes a trend line
with the linear equation.
Documentary Transfer Tax revenues have been very volatile over the last 20 years. Swings ranging from -42.55% to
+59.35%. However, the overall growth from 2000-2020 was only $2.5M, which averages out to around 2.26% annually.
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Historical Documentary Transfer Tax
Fiscal
Year
Historical
Documentary
Transfer Tax
Dollar
Change
Percentage
change
2000 $4,413 N/A N/A
2001 $3,807 ($606)-13.73%
2002 $2,874 ($933)-24.51%
2003 $3,513 $639 22.23%
2004 $5,598 $2,085 59.35%
2005 $5,144 ($454)-8.11%
2006 $5,726 $582 11.31%
2007 $5,837 $111 1.94%
2008 $5,382 ($455)-7.80%
2009 $3,092 ($2,290)-42.55%
2010 $3,707 $615 19.89%
2011 $5,167 $1,460 39.38%
2012 $4,821 ($346)-6.70%
2013 $6,810 $1,989 41.26%
2014 $8,143 $1,333 19.57%
2015 $10,051 $1,908 23.43%
2016 $6,266 ($3,785)-37.66%
2017 $7,491 $1,225 19.55%
2018 $9,229 $1,738 23.20%
2019 $6,923 ($2,306)-24.99%
2020 $6,903 ($20)-0.29%
27
Historical Average Projections
The graph below displays the DTT revenue projections using historical averages.
Growth averages:
• 5 Year: -7.24%
• 10 Year: 6.41%
• 20 Year: 2.24%
The 5-, 10-, and 20-year averages all paint a very different picture from one another. The 10 yr. average shows growth of
6.41% while the 5-year average shows -7.24% growth and the 20-year average splits it all down the middle with 2.26%
growth.
Fiscal
Year 5 yr avg 10 yr avg 20 yr avg
2021 $6,403 $7,346 $7,059
2022 $5,940 $7,817 $7,219
2023 $5,510 $8,318 $7,382
2024 $5,111 $8,852 $7,549
2025 $4,741 $9,420 $7,720
2026 $4,398 $10,024 $7,895
2027 $4,079 $10,667 $8,073
2028 $3,784 $11,351 $8,256
2029 $3,510 $12,080 $8,443
2030 $3,256 $12,854 $8,634
2031 $3,020 $13,679 $8,829
2032 $2,802 $14,556 $9,029
2033 $2,599 $15,490 $9,233
2034 $2,411 $16,484 $9,442
2035 $2,236 $17,541 $9,655
2036 $2,074 $18,666 $9,874
2037 $1,924 $19,864 $10,097
2038 $1,785 $21,138 $10,326
2039 $1,656 $22,494 $10,559
2040 $1,536 $23,937 $10,798
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Documentary Transfer Tax Projections using
Historical Averages
5 yr avg 10 yr avg 20 yr avg
28
Documentary Transfer Tax Interpretation
The graph below is a combination of historic performance and all projections which were only the historic average
projections.
Due to the lack of correlation with any economic indicators, the only projections the OCA were able to model were the
historical averages. As stated in the observations and data analytics section, all three averages show vastly different
outcomes when projected forward. In this case it was determined that using the 20-year average is the most reliable and
conservative projections going forward. The LRFF Documentary Transfer Tax projections align with this assumption.
y = 225.6x + 3275.4
R² = 0.5207
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Documentary Transfer Tax Projection Summary
Historical Document Transfer Tax 5 yr avg
10 yr avg 20 yr avg
Linear (Historical Document Transfer Tax)
29
Utility User Tax
Utility Users Tax Overview
An analysis was performed by the OCA for the historical utility user tax revenues and future years were projected. The
analysis provided insight into the steady increase in revenues over the past 20 years and future projections were
calculated using historical averages and several economic indicators. These projections along with nationwide utility
estimates were used to provide a best estimate of UUT’s future performance.
Utility Users Tax Observations and Data Analysis
Historical Performance
The chart below shows the historical revenues for Utility User Tax from 2000 – 2020.
Utility user tax revenues have had a steady increase over the last 20 years. It has grown just over $10M since the year
2000 which averages out to be 5.2% annually. There are a few years where revenues had jumped, but overall, it has not
been rapid growth.
y = 486.38x + 5150.1R² = 0.9153
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Fiscal Year
Historical Utility Users Tax RevenuesFiscal
Year
Historical
Utility
Dollar
Change
Percentage
Change
2000 $5,861 N/A N/A
2001 $6,895 $1,034 17.64%
2002 $6,456 ($439)-6.37%
2003 $7,067 $611 9.46%
2004 $7,152 $85 1.20%
2005 $7,269 $117 1.64%
2006 $8,760 $1,491 20.51%
2007 $9,356 $596 6.80%
2008 $10,285 $929 9.93%
2009 $11,030 $745 7.24%
2010 $11,296 $266 2.41%
2011 $10,851 ($445)-3.94%
2012 $10,833 ($18)-0.17%
2013 $10,860 $27 0.25%
2014 $11,008 $148 1.36%
2015 $10,861 ($147)-1.34%
2016 $12,469 $1,608 14.81%
2017 $14,240 $1,771 14.20%
2018 $15,414 $1,174 8.24%
2019 $16,402 $988 6.41%
2020 $16,140 ($262)-1.60%
30
Historical Growth Averages
The graph below displays the UUT’s revenue projections using the growth averages shown below.
Growth averages:
• 5 Year: 8.24%
• 10 Year: 3.63%
• 20 Year: 5.20%
The historical data was used to compute growth averages over the last 5, 10, and 20 years. Those averages were applied
to the 2020 revenues and carried forward until 2040.
Fiscal
Year 5 yr avg 10 yr avg 20 yr avg
2021 $17,471 $16,726 $16,979
2022 $18,911 $17,334 $17,861
2023 $20,470 $17,964 $18,789
2024 $22,158 $18,616 $19,765
2025 $23,985 $19,293 $20,792
2026 $25,962 $19,994 $21,872
2027 $28,103 $20,720 $23,008
2028 $30,420 $21,473 $24,203
2029 $32,928 $22,253 $25,461
2030 $35,643 $23,061 $26,784
2031 $38,581 $23,899 $28,175
2032 $41,762 $24,767 $29,639
2033 $45,205 $25,667 $31,179
2034 $48,933 $26,600 $32,799
2035 $52,967 $27,566 $34,503
2036 $57,334 $28,567 $36,295
2037 $62,061 $29,605 $38,181
2038 $67,178 $30,681 $40,164
2039 $72,716 $31,795 $42,251
2040 $78,712 $32,950 $44,446
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Utility Users Tax Projections using Historical
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5 yr avg 10 yr avg 20 yr avg
31
Economic Indicator Projections
The chart below depicts the UUT projections using highly correlated economic indicators.
Economic Indicators with High Correlation:
• Personal Income (California and Santa Clara County)
• Consumer Price Index (U.S., California, and San Francisco)
• Population (Palo Alto and Santa Clara County)
• Stanford Enrollment
The OCA found several economic indicators that had strong correlations to the historical UUT revenues. The indicators
we were able to project forward include California CPI, US CPI, and California Personal Income
Fiscal
Year CA CPI US CPI
CA
Personal
Income
2021 $16,513 $15,826 $17,252
2022 $17,126 $16,354 $17,296
2023 $17,907 $16,952 $18,101
2024 $18,786 $17,606 $18,953
2025 $19,380 $18,231 $19,718
2026 $19,987 $18,869 $20,514
2027 $20,606 $19,520 $21,342
2028 $21,237 $20,184 $22,203
2029 $21,881 $20,861 $23,098
2030 $22,537 $21,551 $24,029
2031 $23,207 $22,255 $24,997
2032 $23,890 $22,974 $26,004
2033 $24,587 $23,706 $27,052
2034 $25,298 $24,454 $28,141
2035 $26,023 $25,216 $29,274
2036 $26,762 $25,993 $30,452
2037 $27,516 $26,787 $31,677
2038 $28,285 $27,596 $32,951
2039 $29,070 $28,421 $34,277
2040 $29,871 $29,262 $35,655
Projections
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UUT Projections Using Economic Indicators
CA CPI US CPI CA Personal Income
32
Utility Users Tax Interpretation
The chart below shows a combination of the historical UUT and the projections using historical averages and correlated
economic indicators.
The OCA found some conflict between the historical averages, economic indicators, Utility Financial Forecasts and
outside resources. Our internal data would suggest that the 10-year and 20-year average projections give a solid
indication of where revenues might go as they are very close together. In addition, all three economic indicators created
projections that were very close to the 10-year average. Based on this alone it could be suggested that revenues ending
up in the $30-$35M by 2040 is plausible. The 10-year average and economic indicators also align with the Utility Users
Tax projections for FY 2032 shown in the LRFF.
The OCA found information in the City Utility Department’s FY 2022 Preliminary Financial Forecasts11 and FY 2021
Financial Plans12 complimentary to the historical average projections. The plans show load forecasts with decreasing
consumption for electric, water, and gas utilities as well as slight rate increases to offset some of the declining
consumption leading to percentage increases close to the 10- and 20-year historical averages.
However, the OCA found some conflicting information from the Annual Energy Outlook 2021 published by the U.S.
Energy Information Administration. This showed that the average annual electricity usage is anticipated to increase across
the US by slightly less than 1% through 2050. That is more conservative than all of the historical factors and would have
revenues only reaching around $20M by 2040.
Due to conflicting opinions and data the City’s best option may be to continue leveraging their utility departments
knowledge and forecasts to keep a close watch on their energy usage in the coming years and see if it aligns closer to the
very minimal growth or remains closer to the historical averages.
11 FY 2022 Preliminary Financial Forecasts: https://www.cityofpaloalto.org/files/assets/public/agendas-minutes-reports/reports/city-manager-reports-cmrs/2021/id-
11864.pdf
12 FY 2021 Financial Plans:
Electric: https://www.cityofpaloalto.org/files/assets/public/agendas-minutes-reports/reports/city-manager-reports-cmrs/attachments/attachment-b-fy2021-electric-
utility-financial-plan.pdf
Gas: https://www.cityofpaloalto.org/files/assets/public/agendas-minutes-reports/reports/city-manager-reports-cmrs/attachments/attachment-e-2021-gas-plan.pdf
Water: https://www.cityofpaloalto.org/files/assets/public/agendas-minutes-reports/reports/city-manager-reports-cmrs/attachments/attachment-j-fy-2021-water-
financial-plan.pdf
y = 486.38x + 5150.1
R² = 0.9153$0
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UUT Summary
Utility User Tax 5 yr avg 10 yr avg 20 yr avg
CA CPI US CPI CA Personal Income Linear (Utility User Tax)
33
Economic Resiliency Review
Overview
The OCA conducted an review of the City’s economic resiliency in parallel with the Revenue Trends and Models Review.
The OCA included Economic Development subject matter specialists (SMSs) to complete an informational report of the
Cities current state of economic resiliency. The City is currently in search for new position(s) focused on economic
development. To aid in these efforts, the OCA will provide this Economic Resiliency Review in the form of a separate
report.
Methodology
To complete a review of the City’s economic resiliency, the Economic Development SMSs conducted two main tasks. The
first task was focused on data analysis of key economic indicators while the second task was the resiliency analysis itself.
In regards to the data analysis, the City has had a recent focus on economic development, given the impacts from the
Covid-19 pandemic, such as the reduction in retail sales tax, recent business turnover, and site redevelopment
opportunities. The OCA conducted an analysis to assess the City’s economic resiliency current state and uncover
opportunities for adjustment or improvement.
Results
The Economic Development SMSs produced a memo specific for informational purposes. This memo was provided as
guidance as the City searches for internal economic development personnel. The memo includes three high-level
strategic recommendations supported by the economic resiliency analysis. Recommendations focus on the following
areas:
− Business/industry diversification strategies
− Housing and workforce findings impacted by Covid-19 pandemic
− Enhancements to livability for resident and business engagement and retention, which could include high quality
service delivery, recommendations on placemaking or other strategies to build community, and/or business
programming
34
Appendices
Appendix A: Management Response – Revenue Trends and Model Review
35
Recommendation Responsible
Department(s)
Agree, Partially Agree, or Do Not Agree and Target Date
and Corrective Action Plan
Finding: Workbook Organization and Clarity
In order to develop an accurate and thorough forecasting model, the City considers a
number of data elements from various workbooks. The input from multiple parties is also
involved. At the moment, the City doesn't have a consistent naming convention for
workbooks, nor does it have narrative or instructions within the workbook itself to describe
the flow of information. The City relies on the institutional knowledge of employees to
understand how the forecasting information is pulled together. Without consistent naming
conventions and information narratives, the City is at risk if key stakeholders in this process
were to leave the organization. The OCA recommends developing a consistent naming
convention and adding narrative in workbooks, including the purpose of the sheet, from
where information is pulled, to where information is pushed, and with whom does the
ownership of the spreadsheet lie.
Administrative Services Concurrence: Partially Agree
Target Date: Fall 2022
Action Plan:
The City partially agrees with the recommendation and recognizes
the potential for “key personnel” risk. There are four main
workbooks that are used to monitor and forecast General Fund
major tax revenue that each pull data from multiple sources. Staff
believe the file names of the workbooks are appropriately named;
however, staff agrees with the recommendation that the source
of information can be improved to ensure sufficient informational
narratives that assist in identifying data used from a detail level to
summarized level for forecasting activities. The addition of
information narratives would help improve the ability for other
staff to both navigate the supporting workbooks as well as
summarizing the forecasts for public narratives such as the Long
Range Financial Forecast or budget documents.
Staff believe the ownership of these workbooks is clear, the City’s
Manager of Treasury, Debt, and Investments is responsible for
these forecast activities; support from a team of staff including
senior managers and divisions in the department such as the
Office of Management and Budget collaborate on the public
reporting and financial planning.
In some cases, the City uses assumptions and rough estimates to consider various
scenarios such as percentage growth in situations when supporting data is sparse. This can
be a necessary action if no better alternatives exist. However, the OCA learned anecdotally
that some of these rough estimates are highly specific, oftentimes having estimated
numbers to the hundredths place. If a certain series of inputs are rough estimates, these
inputs should not be made to look as though they were calculated. Instead those estimates
should be clearly labeled with a short description as to the thought process behind how the
estimator arrived at that number. This will help to clarify which values were calculated at
one point vs. which values are estimated without a specific calculation.
Administrative Services Concurrence: Agree
Target Date: Fall 2022
Action Plan:
Staff agrees that labeling estimates with short descriptions and
selecting consistent rounding (i.e. hundredths place) will improve
documentation of forecast assumptions and will implement this
as part of the remaining FY 2023 annual budget process and as
part of the FY 2024-2033 Long Range Financial Forecast.
36
Finding: Model Improvements
The OCA recommends considering the following four factors as additional forecasting
inputs for TOT;
- size and frequency of local conventions/events,
- national unemployment,
- hotel market segments and property-specific attributes, and
- demographics of travelers.
While some of these inputs may not be possible due to lack of data or lack of applicability to
the City, the OCA recommends considering these other inputs to determine if improvements
to forecasting can be made. Where data is available for these factors, the City can use
correlations factors to determine the strength of the relationship between TOT and a given
input.
Administrative Services Concurrence: Partially Agree
Target Date: Fall 2022
Action Plan:
Administrative Services staff agree that these variables are
informative inputs to forecasting when data is readily available for
these factors. Staff review some of these factors including
national unemployment, historical trends driving events and
travelers on a macro level when developing the City’s financial
reporting documents with projections including but not limited to
the Long Range Financial Forecast and annual budget process.
Incorporating these additional factors at a more precise individual
revenue level, as noted in the recommendation, is not feasible as
the data is not readily available or applicable for Palo Alto at this
time. For example, since Palo Alto does not have assets such as a
destination convention center like neighboring jurisdictions San
Jose and Santa Clara, the size and frequency of local
conventions/events is not as closely impactful in Palo Alto.
Staff does have and references historical data such as
demographics of travelers studied as part of the 2015 economic
analysis completed as part of the development of the current
Comprehensive Plan. Factors such as these are typically provided
by organizations such as a convention and visitors bureau which
the City is currently not a participant in. Staff will continue to
review available data as part of the annual Long Range Financial
Forecast.
The City should reach out to larger hotels to understand if they forecast as well as large
businesses and Stanford University to understand their expectations for conferences,
business travel, student activity, etc. If a relationship with Stanford to provide such
information does not exist, this would be a useful relationship to develop for future analyses
that may also benefit the University.
Administrative Services Concurrence: Partially Agree
Target Date: Fall 2022
Action Plan:
The City does review expected special events based on historical
trends as part of the annual forecast, however, staff agree that
seeking feedback from the local community would provide
additional insights specific to Palo Alto if such organizations are
able and willing to provide and disclose such information to the
City. Historically staff have received anecdotal feedback on an ad
hoc basis. In the coming Long Range Financial Forecast, staff will
reach out to applicable organizations for this feedback and
include feedback to the extent it is received.
City of Palo Alto
Office of the City Auditor
Economic Resiliency Review
March 15, 2022
Executive Summary
Purpose of the Economic Resiliency Review
The Economic Resiliency Review was a component of a broader Economic Recovery Advisory audit activity. The purpose
of the Economic Recovery Advisory audit activity was to review the City of Palo Alto’s (“City”) long-term financial
planning models and inputs, offer recommendations for improvement, identify and evaluate key revenue source
categories that present long term risk to the City's financial sustainability and perform scenario analysis. The Office of
the City Auditor (“OCA”) also offered ad hoc advisory assistance during the FY22 budget process.
In this particular report, the OCA included economic development subject matter specialists to conduct an Economic
Resiliency Review as a resource to the City and any future City employees focused on economic development initiatives.
This report supplements the Revenue Trends and Models Review included in the broader Economic Recovery Advisory
report.
Results
The Economic Development SMSs produced this separate report specific to the Economic Resiliency Analysis. This report
was provided as guidance as the City searches for internal economic development personnel. The report includes three
high-level strategic considerations supported by the economic resiliency analysis. Recommendations focus on the
following areas:
− Business/industry attraction, retention, and diversification strategies
− Housing and workforce findings impacted by Covid-19 pandemic
− Enhancements to livability for resident and business engagement and retention, which could include high quality
service delivery, recommendations on placemaking or other strategies to build community, and/or business
programming
Table of Contents
Executive Summary ................................................................................................................................................................. 2
Purpose of the Economic Resiliency Review ....................................................................................................................... 2
Results ............................................................................................................................................................................. 2
Objective ............................................................................................................................................................................. 5
Background ......................................................................................................................................................................... 5
Scope ................................................................................................................................................................................... 5
Methodology ....................................................................................................................................................................... 5
Compliance Statement ........................................................................................................................................................ 6
Organizational Strengths ..................................................................................................................................................... 6
Economic Resiliency Review – Palo Alto ................................................................................................................................. 7
Summary Presentation of Demographic Data & Business Baseline ................................................................................... 7
Population, Income, and Educational Attainment .......................................................................................................... 7
Business Size Profile ........................................................................................................................................................ 9
Employment and Industry Mix ...................................................................................................................................... 10
Labor Force and Commuting Patterns .......................................................................................................................... 13
Retail – Shopping Centers and Key Corridors ............................................................................................................... 14
Economic Resiliency Analysis ................................................................................................................................................ 15
Development Incentives .................................................................................................. Error! Bookmark not defined.
Economic Trends ........................................................................................................................................................... 15
Business Engagement During Covid-19 ........................................................................................................................ 15
Considerations ...................................................................................................................................................................... 17
Business and Industry Attraction, Retention, and Diversification Strategies ............................................................... 17
Housing and workforce findings impacted by Covid-19 pandemic .............................................................................. 18
Enhancements to livability for resident and business engagement and retention ...................................................... 19
Appendix A: Management Response ....................................................................................... Error! Bookmark not defined.
Index of Figures
Figure 1: Zillow Home Value Index, Palo Alto, 2012-2021 ...................................................................................................... 8
Figure 2: Redfin Median Sale Price, Palo Alto and Santa Clara County, 2016-2021 ............................................................... 8
Figure 3 Palo Alto Top Largest Employers, Good City Company ............................................................................................ 9
Figure 4 California Business Percentage, Esmi/DatabaseUSA.com ...................................................................................... 10
Figure 5 Palo Alto Business Percentage, Emsi/DatabaseUSA.com ....................................................................................... 10
Figure 6 Palo Alto Change in Industry Employment, 2015-2020, US Bureau of Labor Statistics .......................................... 10
Figure 7 Palo Alto 2020 Industry Location Quotient, 2-digit NAICS, US Bureau of Labor Statistics ..................................... 11
Figure 8 Palo Alto 2020 Industry Location Quotient, 6-digit NAICS, US Bureau of Labor Statistics ..................................... 12
Figure 9 Commuting Patterns, US Census, OntheMap, 2019 ............................................................................................... 13
Figure 10 Employed and Living in Palo Alto, US Census, OntheMap, 2019 .......................................................................... 13
Figure 11 Inflow/Outflow Job Counts, US Census, OntheMap, 2019 ................................................................................... 13
Figure 12 Change in Employment by Occupations, US Bureau of Labor Statistics, 2015-2020 ............................................ 15
Figure 13 Location Quotient by Occupations, US Bureau of Labor Statistics, 2020 ............................................................. 15
Introduction
1 Discussed in Palo Alto Online, “Layoffs. Red tape. Anxiety. Small businesses fight to survive pandemic” 4/17/2020;
https://www.paloaltoonline.com/news/2020/04/17/as-costs-mount-small-businesses-fight-to-survive. Accessed 01/05/22
Objective The objectives of the Economic Recovery Advisory audit activity was to:
1) Review the City’s long-term financial planning models and inputs and offer
recommendations for improvement.
2) Identify and evaluate key revenue source categories that present long term risk to
the City's financial sustainability and perform scenario analysis.
3) Offer ad hoc advisory assistance during the FY22 budget process.
The Economic Resiliency Review is a component of these broader objectives.
Background The OCA performed a citywide risk assessment that assessed a wide range of risk areas,
including strategic, financial, operational, compliance, technological, and reputation risks.
The purpose of the assessment was to identify and prioritize risks to develop the annual
audit plan.
During the FY2021 risk assessment, the OCA identified the followings risks which led to this
project.
• COVID-19 Response
• Financial Performance/Revenue Generation
• Tax Revenue & Economic Recovery
• Current Planning Practices
Additionally, during the risk assessment, Baker Tilly included some examples of potential
risks in the future related to this audit:
• Large businesses moving to other locations or decreasing the focus on in-person
interactions at headquarters, lowering the daytime population and visitors
• Decreasing real estate values due to external factors decreases City revenues from
property taxes
• Lost revenue for the City to fund City services with Prop 13 in place
• High taxation on residents due to increased property values, especially long term
Palo Alto residents, in the absence of Prop 13
Scope To complete a review of the City’s economic resiliency, the Economic Development SMSs
conducted two main tasks. The first task was focused on data analysis of key economic
indicators while the second task was the resiliency analysis itself.
Methodology The City has recently focused on economic development, given the impacts from the Covid-
19 pandemic, such as the reduction in retail sales tax revenue, recent business turnover1,
and resulting site and/or storefront revitalization opportunities. As there is some available
data from previous studies, the Economic Development SMSs conducted a mix of data
review and alignment from existing studies and additional analysis, including:
− Economic Growth & Employment
− Demographics
− Business/Industry
− Visual review and locational analysis using maps and other tools
The Economic Resiliency Analysis involved calculating risk to a community’s key economic
assets and developing a strategy to buffer risk to minimize or avoid shocks to the economy.
The OCA conducted an analysis to assess the City’s economic resiliency current state and
uncover opportunities for adjustment or improvement, including:
− Economic incentives scan
− Regional economic trend scan, including employment and industry location
quotient
− Economic resiliency readiness check, including evaluation of steady state and
responsiveness factors
Compliance Statement
This audit activity was conducted in accordance with the Annual Audit Plan. The audit
activity was not performed in compliance with the generally accepted government auditing
standards (GAGAS). The audit activity was not performed in compliance with GAGAS for
two primary reasons:
− The individuals conducting the activity did not meet the CPE requirements. As
subject matter experts in construction risk, the team members are not required to
obtain government audit CPE. Rather, multiple team members are required to be
technically competent construction risk professionals and obtain CPE in
construction risk topics.
o Mitigating factor – City Auditor Kyle O’Rourke and Manager Chiemi Perry
both adhere to CPE requirements
− The City of Palo Alto Office of the City Auditor has not undergone an External Peer
Review in the required 3 year cycle as required by Standards.
o Note – the Office of the City Auditor will undergo a peer review at the
conclusion of FY22.
We planned and performed the activity to obtain sufficient, appropriate evidence to
provide a reasonable basis for our recommendations based on our objectives. We believe
that the evidence obtained provides a reasonable basis for our findings and conclusions
based on our audit the objectives.
Organizational Strengths
During this review, we observed certain strengths of the City regarding economic resiliency.
Key strengths include:
− A high-level of responsiveness and a problem-solving approach to business needs
during the Covid-19 pandemic
− A strong baseline for the city’s residential property tax base and supporting
demographics to continue to support future growth
− A healthy mix of business sizes, industry specialization, and geographic location of
retail and business centers across the city to support the city’s economic resiliency
profile
The Office of the City Auditor greatly appreciates the support of the Administrative Services Department in conducting this audit activity.
Thank you!
Economic Resiliency Review
Introduction
The purpose of the economic resiliency review is to provide a high-level background of baseline conditions and core
indicators that the City could consider as an economic resiliency threshold. This review is primarily drawn from
secondary research, data, and reports. Unfortunately, there is a lag of data available to truly elucidate the disruption of
the Covid-19 pandemic to the core conditions of the local and regional economy.
The intent of this review is to summarize past baseline conditions to prepare the City and its economic development
partners to move forward with a new set of challenges and conditions given the ongoing pandemic and long-term
changes to remote work and an online retail economy. With that in mind, the City considers itself as embedded within
the business community, aligned with other groups that provide support and resources to local businesses, such as the
Palo Alto Chamber of Commerce, the downtown business district, the California Avenue business district, property
owners and brokers, the Stanford Research Park, and Stanford University. As such, the City should rely on its many
partners in economic development to act on strategies, policies, and programming to support economic growth.
Summary Presentation of Demographic Data & Business Baseline
To develop a working understanding of the economic baseline, the economic development SMSs reviewed key indicator
demographics, including population data, income, educational attainment, housing market data, business, and
employment information. These demographic indicators determined a strong profile of economic resiliency.
Population, Income, and Educational Attainment
The City’s population was 68,572 as of the 20202 census data. Between the 2010 and 2020 censuses, the population
grew by a steady but modest pace of 6.47% that was above the state average of 6.13%, but below the national average
of 7.35% and below the 8.67% increase experienced in Santa Clara County as a whole3. The population is significantly
less diverse than Santa Clara County and California. 54.9% identified as “white alone, not Hispanic or Latino” while that
number was 30.6% in Santa Clara County and 36.5% statewide4. The City’s younger age brackets are comparable to
county and state averages; however, the city has a higher proportion of its population that is 65 years or older at 19.4%
while the county and state percentages are 13.9% and 14.8% respectively5.
Palo Alto has very strong personal and family income when compared to the county and state averages. Median
household income was $158,271 while Santa Clara County was $124,055 and California was $75,235. Per capita income
was even more significant at $92,590 with the county and state averages being $56,248 and $36,955 respectively.
Poverty prevalence was significantly lower as well, with the percent of persons in poverty at 6.10% compared to 7.52%
in Santa Clara County and 11.8% in California6.
The city has extremely high education attainment rates. 97.4% of persons age 25 year or older are high school graduates
or higher, and 82.8% have a bachelor’s degree or higher7. The city’s extremely high rate of college degrees is of note,
being more than double the state average. By comparison, Santa Clara County’s rates are 88.4% and 52.4% respectively,
and California’s rates are 83.3% and 33.9% respectively.
2 US Census Bureau, Quick Facts; Palo Alto, CA; 2020 Census
3 US Census Bureau, 2020 Census.
4 Ibid
5 Ibid
6 Ibid
7 US Census Bureau, QuickFacts; Palo Alto, CA; 2015-2019 data
Housing Market and Property Tax Revenue
The city has a very strong housing market with much higher property values than county and state averages. According
to the US Census Bureau, the median value of owner-occupied housing units was $2,000,000+ in Palo Alto, $984,000 in
Santa Clara County, and $505,000 in California8.
According to the more recent Zillow Home Value Index9, the
typical home value in The City was $3,560,805 as of their
October 2021 data. This value is seasonally adjusted and
reflects the typical value for homes in the 35th to 65th
percentile range and represents an 11.6% increase over the
previous year. The market has rebounded from the 2008
Financial Crisis and following recession, with Zillow’s Home
Value Index showing a value of $1,500,000 in Dec 2011. Over
the last five years, growth has continued but the pace has
moderated somewhat.
According to Redfin’s November 2021 data, the housing
market is considered very competitive, with a Redfin Compete Score of 87 out of 100. The median sale price was
$3,525,000, representing a 21.5% increase year-over-year. The average home is on the market for 11 days and sells for
8% above list price, with some homes selling for 17% above list price. Approximately 64.6% of homes sold above list
price, a 31.7% increase year-over-year.
With the rapid increase in housing prices during the post-recession recovery period, housing costs have increased
considerably to nearly three times the national average for owners and more than double the national average for
renters. According to data from the US Census
Bureau10, median monthly owner costs with a
mortgage were $4,000+, significantly higher than the
$3,381 in Santa Clara County and $2,357 in California.
Median gross rent (2015-2019) in Palo Alto was
similarly high when compared with the county and
state averages of $2,268 and $1,503 respectively.
As the residential property market has experienced
increased activity in recent years, the City’s property
values have seen increases across all categories of real
estate. The Palo Alto Property Tax Summary for 2019-
2020 reflects pre-pandemic numbers. According to the
summary, the year-over-year increase in property
values for all real estate categories was $3.055 billion,
an increase of 7.8%. Residential values experienced a
6.3% increase and accounted for 56% of all growth for
the period. Commercial properties had an increase of 9.7% which accounted for approximately 24.8% of all growth for
the period. Industrial properties saw the greatest increase at 28.4% after having declined by 10% in the previous year.
The significant jump in industrial values was primarily related to improvements to certain larger parcels. Fifty vacant
parcels were developed and transferred to other uses.
8 Ibid
9 Zillow Home Value Index; Palo Alto, CA, October 2021 Report
10 US Census Bureau, QuickFacts; Palo Alto, CA; 2015-2019 data
Figure 1: Zillow Home Value Index, Palo Alto, 2012-2021
Figure 2: Redfin Median Sale Price, Palo Alto and Santa Clara County, 2016-
2021
While there was uncertainty surrounding real estate and the overall economy at the outset of the pandemic, the trend
towards higher property values and increased real estate transactions has continued. Any negative impact to
commercial or industrial properties is likely to be offset by the strong growth in residential property sales during the
pandemic which was buoyed by historically low interest rates. Even with the Federal Reserve considering multiple
interest rate increases in 202211, interest rates are anticipated to remain comparatively low relative to historic trends,
which should sustain the residential real estate market and related property tax revenues.
While residential property values are a benefit in terms of property tax revenue, they can pose a barrier to local
employment growth. When comparing Palo Alto’s housing data to Santa Clara County and the state, high housing prices
combined with limited land available for new housing, could be a driver of the unusually high commute patterns
identified in the labor force summary below. (US Census 2019, Commute Patterns). Employers in Palo Alto import 93%
of their employment base from outside of the city. If the standard rule follows that mortgage/rent payments should not
exceed more than 30% of a household budget, a theoretical person purchasing a house at the current median home
value of $2,000,000 (US Census) to $3,500,000 (based on recent sales data) with a median income of $158,271 would
spend a much higher percentage of their income on housing-related costs. While the housing market, income levels, and
household budget allocations are more complex than this example, economic development strategies to support
businesses and the workforce may be needed to mitigate some of the issues related to housing costs.
However, since commute data is not available from 2020- to current, which includes the pandemic, it is unknown to
what the extent the trend toward remote work will impact the residential and commercial real estate market and
property tax revenue. With remote work becoming more prevalent as a result of the pandemic, particularly as it relates
to business class/white-collar jobs, there may be a trend for workers to relocate to more suburban areas with lower
costs of living, larger residential properties, and access to outdoor space. Additionally, many employers are downsizing
office size and shifting to more remote and shared-work spaces. It is not known if this trend is temporary or permanent
and how it may impact the city in the long term. However, based on current real estate market data, it does not appear
to be negatively impacting residential prices.
Business Size Profile
The city’s employment base is characterized by several
factors that are noticeably different from California as a
whole. A high percentage of overall employment is from
larger companies. The percentage of employers with 500+
employees is four times the state average. Similarly, the
percentage of employers with between 250 to 499 employees
is double the state average. At the other end of the spectrum,
companies in the size class of 1 to 4 employees make up a
higher percentage of businesses in Palo Alto, with a rate of
41.4% when compared to the state average of 34.7%.
Figure 3 above shows the largest employers in the city. While there is a heavy concentration of firms in the information
industry sector, healthcare and social services account for half of the top employers, and three out of the top four on
the list are in the healthcare and social services industry. There is a significant economic impact of Stanford University
School of Medicine and its related medical research and technology, and Stanford Health Care (among the top hospitals
in the nation) that add to the health care economy core strength. Stanford University (4,500 employees) is a strong asset
11 Timiraos, N. (2022, January 5). Fed minutes point to possible rate increase in March. The Wall Street Journal. Retrieved January 7,
2022, from https://www.wsj.com/articles/fed-minutes-reflect-growing-unease-over-high-inflation-11641409628
Palo Alto Employers Employees
Stanford Healthcare 5,500
Lucile Packard Children's Hospital 5,700
Stanford University 4,500
Veteran's Affairs Palo Alto Healthcare System 3,900
VMWare, Inc.3,500
SAP Labs Inc.3,500
Space Systems/Loral*2,800
Hewlett-Packard Company 2,500
Palo Alto Medical Foundation 2,200
Varian Medical Systems 1,400
Figure 3: Palo Alto Top Largest Employers, Good City Company
Figure 3 Palo Alto Top Largest Employers, Good City Company
and economic driver on multiple levels, as a top tier education and research institution, as well as driver of new
residents and visitors to the community.
Employment and Industry Mix
We looked at industry employment based on percentage change from 2015 to 2020 and industry employment by
location quotient to determine both the growth in employment and the concentration of employment as it compares to
the national average.
In the top growing industries, the largest change in industry employment was in the Information sector with a 5-year
increase of 32%, or 6,697 new jobs. Government saw a 5-year increase of 12% or 910 jobs. Health Care and Technical
Assistance saw a 5-year increase of 11% increase or 2,575 jobs. And Professional and Scientific, and Technical Services
saw a 5-year increase of 10% or 1,726 jobs.
Figure 4: Palo Alto Business Percentage, Emsi/DatabaseUSA.com
Figure 4 Palo Alto Business Percentage, Emsi/DatabaseUSA.com
Figure 5 California Business Percentage, Esmi/DatabaseUSA.com
Figure 5 Palo Alto Change in Industry Employment, 2015-2020, US Bureau of Labor Statistics
Next, we looked at location quotient (LQ), which is a ratio that measures a region’s industrial specialization relative to
the US. An LQ greater than 1 indicates an industry with a greater share of local area employment than the national base.
Palo Alto has several industries, such as information; health care and social assistance; educational services; and
professional, scientific, and technical services, which have a LQ over 1. The Information industry with a NAICS code of 51,
includes jobs in software development, data communications, data processing, and other jobs related to publishing of
information and data.
Location quotients help to understand regional economic strengths and opportunities. They are also useful in
forecasting regional economic trends based on trends for specific market sectors. It is no surprise that the city has
significant employment in the Information industry with a location quotient of 11.42. This high location quotient tells us
there is a significant information technology ecosystem that can be leveraged to support other industry linkages as well
as attract sub industry sector businesses. This robust ecosystem drives the local economy with extensive reach both
domestically and internationally.
Analyzing location quotients at the more granular 6-digit NAICS versus a 2-digit NAICS allows for more precise reporting
of the employment and overall growth of a region’s industry sectors. More precise industry data allows workforce
training providers to design programs to better fit the existing and future skill needs of those industries, and for
communities to leverage this projected growth. To get a closer look at the city’s industry mix, we looked at the 6-digit
industry NAICS location quotient by employment. Internet Publishing and Broadcasting and Web Search Portal had a
location quotient of 86.72, while Blank Magnetic and Optical Recording Media Manufacturing had a location quotient of
56.52 and Radio and Television Broadcasting and Wireless Communications Equipment Manufacturing12 had a location
quotient of 35.36. All these LQ specializations reveal strengths with the technology industry.
12 NAICS 334220 includes manufacturing radio and television broadcast and wireless communications equipment. Examples of
products made by these establishments are transmitting and receiving antennas, cable television equipment, GPS equipment,
pagers, cellular phones, mobile communications equipment, and radio and television studio and broadcasting equipment.
Figure 6 Palo Alto 2020 Industry Location Quotient, 2-digit NAICS, US Bureau of Labor Statistics
However, often a significant industry concentration needs to be addressed in terms of economic diversification. A
diversified economy is more able to withstand downturns in their primary economic driving activity leading to a more
resilient economy. Industry diversification also provides for skill set diversification which can allow employees to transfer
more quickly into other occupations and employers to customize job training programs should there be downturns in
the primary industry.
Figure 7 Palo Alto 2020 Industry Location Quotient, 6-digit NAICS, US Bureau of Labor Statistics
Labor Force and Commuting Patterns
To further understand the geographic labor shed of the area, Baker Tilly analyzed the workforce’s place of work when
compared to the place of residence. Based on 2019 data from the Bureau of Labor Statistics, the City’s total primary
private employment was 109,220, with a net inflow
of 82,304 jobs. Of the 109,220 total primary private
jobs in 2019, only 24.6% of workers, or 26,916, were
living in the City of Palo Alto. Whereas 102,544
people were employed in the Palo Alto area but
living outside the area. Of the 26,916 workers living
in the city, only 6,676 are employed and living in the
Palo Alto. This data suggests a significant in-
migration of workers who could live in here if other
factors, such as available housing types, cost of
living, commute time, work flexibility, or other
factors were different. A further analysis of this
large in-migration of workers is warranted to
determine if providing a shorter commute or an
opportunity to live within the area in which they are
employed could create a positive impact for
attraction and retention of a highly skilled and
sought-after workforce.
Count Share
Employed in the Palo Alto area 109,220 100.0%
Living in the Palo Alto Area 26,916 24.6%
Net Job Inflow (+) or Outflow (-)82,304 -
Palo Alto (Private Primary Jobs)
2019
Figure 8 Commuting Patterns, US Census, OntheMap, 2019
Figure 10 Inflow/Outflow Job Counts, US Census, OntheMap, 2019 Figure 9 Employed and Living in Palo Alto, US Census, OntheMap,
2019
Retail – Shopping Centers and Key Corridors
The OCA’s economic recovery report
projects future growth in and sales tax over
time. The City’s largest sales tax
contributors are a diverse group of
businesses that conduct automobile sales,
medical sales, tech companies and data
management, and destination retail.
Geographically, the largest sales tax
contributors are located on automobile-
oriented corridors, at the Stanford
Shopping Center, or along small business-
scale or “walkable” retail corridors, such as
California Avenue and University Avenue.
While California Avenue and University
Avenue are well-known and contribute to a
sense of place in the city, the largest sales
tax performers are located in auto-oriented
retail destinations.
Numerous challenges have emerged on
small retail corridors during the Covid-19
pandemic, including temporary closures of
California Avenue to support outdoor
dining. Anecdotally, this appears to have
helped some businesses and hurt some
businesses. A broader analysis and
redevelopment strategy of the California
Avenue corridor could be conducted to
position it for the future economy and an
endemic status of Covid-19, where remote
work continues to impact localized retail corridors.
Business*Type**
Anderson Honda Auto
Lucile Packard Children's Hospital Medical
Tencent Tech/Data Management
Apple Stores Destination Retail
Macy's Department Store Destination Retail
Tesla Auto
Audi Palo Alto Magnussen's Auto
Toyota of Palo Alto Auto
Telsa Lease Trust Auto Lease
Bloomingdales Destination Retail
Hp Enterprise Services Tech/Data Management
Shell Service Stations Gas
McLaren San Francisco Auto
Tiffany & Company Destination Retail
Bon Appetit Management Co Catering
Neiman Marcus Department Store Destination Retail
Urban Outfitters Destination Retail
Hermes Destination Retail
Nest Labs Tech/Data Management
Varian Medical Systems Medical
Houzz Shop Destination Retail
Nordstrom Department Stores Destination Retail
Volvo Cars Palo Alto Auto
Integrative Archive Systems Tech/Data Management
Stanford Outpatient Clinic Pharmacy Medical
*Source for Business: Good City Company Presentation to Council, 6/1/21
Palo Alto, CA. Major Sales Tax Performers, 2020
**Source for Type: Baker Tilly
Economic Resiliency Analysis
The economic resiliency analysis involves calculating risk to a community’s key economic assets and developing a
strategy to buffer risk to minimize or avoid shocks to the economy. Baker Tilly analyzed the current state of economic
resiliency through a high-level review of development incentives, economic trends, and business engagement during the
COVID-19 pandemic to uncover opportunities for adjustment or improvement.
Economic Trends
Several of the city’s strong business
and professional occupations held
steady or experienced growth over
the past five years, such as
Business/Financial Operations,
Management, and Health Care
Practitioners/Technical, Educational,
and Sales. The city’s top growing
occupations can be found primarily in
the health-related industry and
Arts/Media fields. The Arts/Design/
Entertainment/Sports and Media
occupations grew by 43% from 2015 –
2020. In the same period, the
combined growth in occupations
related to the health care industry
grew by roughly 20%
The top occupations based on location
quotients are computer and
mathematical at 3.56, legal at 2.95,
art/design/entertainment/sports and
media at 1.92, and healthcare
practitioners and technical at 1.87.
These quotients indicate a higher
concentration of employment in these
occupations in the Palo Alto area than
the national average.
As we are shifting from an occupation-
based workforce to a skills-based workforce, knowing the concentration of skills within these occupations is important
to attract new businesses. Employers are often looking for skills versus occupations as they can retrain/upskill if the
employee has the basic industry skill sets. A valuable exercise would be to determine the skills sets within these industry
occupations to market the region’s workforce and to sustain existing business growth.
Business Engagement During Covid-19
The City was very involved in business recovery and engagement strategies during the Covid-19 pandemic, particularly
as it impacted retail and restaurants on key corridors, such as California Avenue and University Avenue. The city
developed an action team of decision makers and department heads, with hands-on involvement from the City Manager
to address and solve problems brought up by local businesses. The city developed weekly business check-in meetings
Figure 12: Bureau of Labor Statistics. 2015- 2020
Figure 13: Bureau of Labor Statistics. 2020
Figure 11 Change in Employment by Occupations, US Bureau of Labor Statistics, 2015-2020
Figure 12 Location Quotient by Occupations, US Bureau of Labor Statistics, 2020
and the Uplift Local program to quickly utilize outdoor spaces to help expand business activities outdoors for restaurants
and fitness businesses.
The Stanford Research Park is a powerhouse economic asset to the City and is an economic driver of jobs, salaries,
spending power, business growth, industry diversification, and property tax receipts. While there was some
collaboration during the COVID-19 pandemic with strategic support of the small businesses on California Avenue
corridor, a more proactive engagement strategy of business retention and attraction could be considered from the city
perspective, targeted to the larger industrial and tech businesses located there.
The City views itself as embedded within the business community when it comes to economic development, aligned
with other groups that provide support and resources to local businesses, such as the Palo Alto Chamber of Commerce,
the downtown business district, the California Avenue business district, property owners and brokers, the Stanford
Research Park, and Stanford University. The new economic development staff person is one of many participants within
the business development and economic development space, and can only act on the specific decisions, programs, or
action steps that can be executed by city staff.
Considerations
Business and Industry Attraction, Retention, and Diversification Strategies
The City and its economic development partners could explore the following considerations for business and industry
attraction, retention, and diversification to enhance long-term economic resiliency. Retail and small business corridors
warrant different considerations than considerations for larger businesses.
Retail and Small Business Corridor Considerations
While destination retail in the city is doing well, small business corridors, such as California Avenue and University
Avenue, have been more vulnerable to impacts of the COVID-19 pandemic and the changing retail environment. The
following considerations would support a more proactive approach by economic development partners to attraction and
retention of small businesses in walkable, “Main Street” corridors.
• A mix of coveted small-scale retail stores and California-based boutique brands can be found here, as well as
independently owned restaurants, wine bars, and other small business venues. A strategic approach to corridor
planning with intentional placemaking13 tactics could be utilized to address the multi-faceted set of issues on
key retail corridors.
• If it benefits the community’s identity and “placemaking” efforts, the City could consider if a more proactive
approach to support business retention and attraction on walkable, small business-focused corridors is a
priority. Ideas include simplifying the renovation and redevelopment process and fee structure, potential rent
subsidies for small businesses, and/or workforce support programs. If an incentive program is considered, the
City may require some evaluation criteria, such business plan information, revenue projections, market
analysis, customer potential, etc. to qualify for investment.
• Another approach to the California Avenue corridor could be to undertake a broader business and retail
analysis and develop a more divergent, future-focused redevelopment vision and plan. A new approach could
position the corridor for the future economy and an endemic status of Covid-19, where remote work continues
to impact localized retail corridors.
• The restrictions on big-box stores or retail chains could be reviewed to see if the current policy still meets the
City’s goals in the current retail environment.
• Economic development partners focused on corridor business attraction could collaborate on retail attraction
strategies with the owner/operator of Stanford Shopping Center. There may be an opportunity to capture an
overflow of retail prospects to locate on University Avenue or California Avenue.
• As the population ages, changing demographics that lean towards an online-focused, more diverse population
may not have the same brand allegiance to destination retail and/or may have different retail shopping habits.
Consider developing strategies that account for changing retail preferences and trends to ensure the future
success of destination retail.
Note: In contrast to the above, the City could choose a less proactive approach, including letting market forces
guide what happens in the University Avenue and California Avenue corridors. There may be consequences for the
small-scale, walkable businesses on these corridors and a lag in redevelopment efforts as market forces dictate
future opportunities.
13 “Placemaking” is used here to reference a process of creating quality places (unique, visually attractive, interesting, often including
public art and entertainment) to attract people and activities.
Larger Business Diversification Considerations
The following considerations summarize the findings on larger business diversification. While the City is doing well in
employment in total jobs and jobs within growth industries, continued business retention is an ongoing consideration,
especially with the rapidly changing business climate.
• The city’s industry mix is concentrated primarily in two industries, information and health care. Even though
these are growth industries, industry diversification can provide economic stabilization and help mitigate the
risk associated with the downturn in a concentrated industry.
• The percentage of employers with 500+ employees is four times the state average. This provides a workforce
that would attract other larger corporations. However, consideration of providing targeted support and
incentives to mid-size, small business and entrepreneurs would help diversify workforce and overall industry
mix.
• The top occupations based on location quotients by 2-digit NAICS are computer and mathematical at 3.56, legal
at 2.95, art/design/entertainment/sports and media at 1.92, and healthcare practitioners and technical at 1.87.
The location quotient by 6-digit industry NAICS are Internet Publishing and Broadcasting and Web Search Portal
at 86.72, Blank Magnetic and Optical Recording Media Manufacturing at 56.52 and Radio and Television
Broadcasting and Wireless at 35.36. These occupations support a highly educated and skilled workforce, which
can be used to support an effort to attract mid-sized businesses and aid in industry diversification.
Housing and workforce findings impacted by Covid-19 pandemic
Here are some brief short-term considerations in the areas of housing and workforce, as it relates to issues that
emerged and actions taken by the City during the Covid-19 pandemic.
• The housing market was stable and continued to grow throughout the pandemic. This helped buffer the City’s
revenue streams when retail sales tax, hotel stays, and other growth areas slumped.
• The continued growth of housing costs may need to be considered and further analyzed from a business
retention and workforce recruitment standpoint.
• Many businesses on the University Avenue and California Avenue retail corridors that depend on office workers
and commuters as customers struggled during the pandemic. Additionally, many businesses reported challenges
with workforce attraction and retention in city surveys. As the pandemic continues to disrupt daily life and long-
term remote work continues, a loss of foot traffic and daytime customers continues to negatively impact Palo
Alto’s retail and restaurants on key corridors. A broader strategy of coordination with partners to activate
corridor businesses and business support strategies could be explored by the City. A more rigorous analysis
using primary research of businesses on these corridors could be conducted to understand the long-term
impacts of remote work and new strategies to engage existing residents and businesses to rebuild the customer
base.
• As multi-family redevelopment sites become available, the City could consider additional financial and
placemaking benefit opportunities in supporting this development. For example, locating multi-family
development near transit networks, existing parking structures, or walkable retail corridors to increase foot
traffic to existing small businesses that struggled during the pandemic.
• The city was highly engaged with local businesses and did a good job with troubleshooting during the pandemic.
Some issues, such as parking and workforce attraction issues, emerged less as a crisis point as a result of the
pandemic and more as a systematic issue to address with a long-term operational approach. Therefore,
consideration of a strategic approach using urban planning and economic development expertise to address
some of these issues may be needed.
Enhancements to livability for resident and business engagement and retention
Palo Alto is a desirable community to live and to work in, which is supported by the strong economic indicators
discussed in this report. However, even a successful community needs to look to the future, and to continue to enhance
its position for businesses and residents. Some additional considerations to enhance livability to support positive
resident and business engagement and retention include:
• Create experiences in downtown spaces and retail locations to attract resident attention and support, increase
visitors, and prolong time spent in the walkable “Main Street” corridors, such as California Avenue and
University Avenue.
• Enhance opportunities for more and different events in coordination with Stanford, since Stanford enrollment
and visitor events have a positive impact on retail sales and hotel stays, as well as adding vibrancy and interest
for college students and residents.
• Protect and market natural assets and areas since open space and natural assets are at a premium in the Bay
Area and Southern Peninsula.
• Continue dialogue and ongoing engagement with current residents to guide delivery of relevant, high-quality
city services.