2550 Ventura Avenue Santa Rosa CA 95403-2859 (707) 565-1900 www.PermitSonoma.org
MEMO
DATE: May 20, 2022
TO: Amy Lyle, City of Santa Rosa
FROM: Brian Oh, County of Sonoma
SUBJECT: Transfer of 6th Cycle Regional Housing Needs Allocation
For its Housing Element update, the County of Sonoma will be building off a decade of smart growth land-use policies such as a 100% density bonus on all eligible unincorporated Sonoma County parcels.
Other efforts such as establishing Specific Plans in the Airport Area, the Springs and redevelopment of the Sonoma Developmental Center in Sonoma Valley will be maximizing the county’s limited urban lands for potentially 1400 units of additional housing pending Board of Supervisor adoption of the plans later this year. Additionally, the County has identified 59 additional sites being considered for higher density housing that are most appropriate for smart growth development. These sites met the criteria by being located within existing services, within 2000 feet of transit and/or a job center and without environmental and cultural constraints. Furthermore, additional policies currently being explored through the Housing Element update such as incentives for senior and missing middle housing, a 3 for 1 density program, as well as incentives for proposals that include at least 20% of its units as affordable housing, thereby further maximizing the limited unincorporated county lands. Despite maximizing these efforts on limited unincorporated land, the County cannot maintain its commitment to smart growth without establishing partnerships with our Sonoma County jurisdictions.
The County proposes a transfer agreement of 1,800 units with the City of Santa Rosa to align the county’s 6th Cycle Regional Housing Needs Allocation with the following county and city policies centered on directing smart growth within city centers and existing infrastructure.
1. Each of the County’s 10 cities have adopted voter-approved Urban Growth Boundaries to direct future growth inside of cities and city UGBs.
2. LAFCO policy generally prohibits the expansion of urban services outside of adopted Urban Growth Boundaries and Spheres of Influence prior to annexation of the subject lands into the City.
3. Most recently affirmed in 2016, Community Separators exist throughout the county.
4. These open spaces, urban growth boundaries and community separators approved by City and County voters benefit all of its residents, but prevent the unincorporated county from developing housing outside of currently designated Spheres of Influence and Urban Growth Boundaries.
5. The city desires to focus its future residential growth within its priority growth areas, namely the Downtown Station Area Specific Plan area where an additional 7,000 units of new housing are planned.
Sonoma County Permit and Resource Management Department 2550 Ventura Avenue Santa Rosa CA 95403-2859 (707) 565-1900 www.PermitSonoma.org
6. City and County both recognize that a joint City-County planning effort must take place in the South Santa Rosa community.
7. In 2020, the Sonoma County Board of Supervisors committed $10 million to the Renewal Enterprise District (RED), a City-County JPA for the furtherance of affordable and workforce housing that will, throughout the 6th cycle planning period, benefit housing projects located within the City of Santa Rosa.
Looking ahead, the County commits to a joint effort in ensuring a long-range, equitable plan for the South Santa Rosa community. Such planning will build on other partnerships such as the Renewal Enterprise District and its commitment to bring affordable housing to the county with its initial $10 million commitment to housing in downtown Santa Rosa. Furthermore, County investments of its County Fund for Housing revenues into city projects would require a share of City RHNA credits to the County RHNA as a way to maintain its commitment to smart growth in city centers. The County collects Transient Occupancy Tax, in-lieu & workforce housing fees from unincorporated projects and funds the County Fund for Housing (CFH) to finance development and preservation of affordable housing units countywide. Through regional land-use coordination, Sonoma County can ensure the sustainable and equitable growth of its communities.
Sincerely,
Brian Oh
Permit Sonoma
Comprehensive Planning Manager
[email protected]
Attachment: ABAG RHNA Allocation Report, December 2021
ASSOCIATION OF BAY AREA GOVERMENTS EXECUTIVE BOARD
Jesse Arreguin, President
Mayor, City of Berkeley
Belia Ramos, Vice President
Supervisor, County of Napa
David Rabbitt,
Supervisor, County of Sonoma —
Immediate Past President
Susan Adams
Councilmember, City of Rohnert
Park
Candace Andersen
Supervisor, County of Contra Costa
Nikki Fortunato Bas
Councilmember, City of Oakland
London Breed
Mayor, City and County of
San Francisco
Tom Butt
Mayor, City of Richmond
David Canepa
Supervisor, County of San Mateo
Cindy Chavez
Supervisor, County of Santa Clara
Pat Eklund
Mayor, City of Novato
Maya Esparza
Councilmember, City of San Jose
Carroll Fife
Councilmember, City of Oakland
Neysa Fligor
Mayor, City of Los Altos
Leon Garcia
Mayor, City of American Canyon
Liz Gibbons
Mayor, City of Campbell
Giselle Hale
Vice Mayor, Redwood City
Barbara Halliday
Mayor, City of Hayward
Rich Hillis
Planning Director, City and County
of San Francisco
Dave Hudson
Mayor, City of San Ramon
Otto Lee
Supervisor, County of Santa Clara
Matthew Mahan
Councilmember, City of San Jose
Rafael Mandelman
Supervisor, City and County of
San Francisco
Gordon Mar
Supervisor, City and County of
San Francisco
Nathan Miley
Supervisor, County of Alameda
Karen Mitchoff
Supervisor, County of Contra Costa
Stephanie Moulton-Peters
Supervisor, County of Marin
Raul Peralez
Councilmember, City of San Jose
Carlos Romero
Mayor, East Palo Alto
James Spering
Supervisor, County of Solano
Loren Taylor
Councilmember, City of Oakland
Vacant
City and County of San Francisco
(Mayor)
Vacant
County of Alameda
Vacant
County of San Mateo
Lori Wilson
Mayor, City of Suisun City
Jayne Battey
Board Member, San Francisco Bay
Regional Water Quality Control
Board — Advisory Member
ABAG-MTC STAFF:
Matt Maloney
Section Director,
Regional Planning
Dave Vautin
Assistant Director, Major Plans
Gillian Adams
Principal Planner
Eli Kaplan
Regional Housing
Policy Analyst
Aksel Olsen
Senior Planner
Leah Zippert
Senior Public Information Officer
Julie Teglovic
Public Information Analyst
Michele Stone
Graphic Design
2 HOUSING METHODOLOGY COMMITTEE
LOCAL JURISDICTION
ELECTED OFFICIALS*
Jesse Arreguín, Chair
Mayor, City of Berkeley, Alameda
County
Julie Pierce
Mayor, City of Clayton, Contra Costa
County
Pat Eklund
Mayor, City of Novato, Marin
County
Diane Dillon
Supervisor, County of Napa
Rick Bonilla
Deputy Mayor, City of San Mateo,
San Mateo County
Neysa Fligor
Mayor, City of Los Altos, Santa Clara
County
Monica Brown
Supervisor, Solano County
John Vasquez
Supervisor, Solano County
(Alternate)
Susan Adams
Councilmember, City of Rohnert
Park, Sonoma County
LOCAL JURISDICTION
STAFF
Alameda County
Ellen Clark
Planning Manager,
City of Pleasanton
Darin Ranelletti
Policy Director for Housing
Security, City of Oakland
Contra Costa County
Forrest Ebbs
Community Development Director,
City of Antioch
Mindy Gentry
Planning Manager, City of Concord
Andrea Ouse
Community Development Director,
City of Concord (Alternate)
Marin County
Elise Semonian
Planning Director, Town of
San Anselmo
Ethan Guy
Principal Analyst, City of San Rafael
(Alternate)
Napa County
Vin Smith
Community Development Director,
City of Napa
San Francisco County
Paolo Ikezoe‡
Senior Planner, City and County of
San Francisco
James Pappas
‡
Senior Planner, City and County of
San Francisco
San Mateo County
Josh Abrams+
Baird + Driskell Community
Planning, Staff to 21 Elements
Nell Selander
+
Deputy Director, Economic &
Community Development, City of
South San Francisco
Santa Clara County
Michael Brilliot
Deputy Director for Citywide
Planning, City of San Jose
Aarti Shrivastava
Assistant City Manager/Community
Development Director, City of
Mountain View
Jennifer Carman
Development Services Director, City
of Morgan Hill (Alternate)
Andrew Crabtree
Community Development Director,
City of Santa Clara (Alternate)
Solano County
Matt Walsh
Principal Planner, Solano County
David Feinstein
Principal Planner, City of Fairfield
(Alternate)
Sonoma County
Jane Riley
Comprehensive Planning Manager,
Sonoma County
Milan Nevajda
Deputy Planning Director, Sonoma
County (Alternate)
Noah Housh
Community Development Director,
City of Cotati (Alternate)
REGIONAL
STAKEHOLDERS
Social Equity
Victoria Fierce
California Renter Legal Advocacy
and Education Fund (CaRLA)
Jeffrey Levin
East Bay Housing Organizations
(EBHO)
Fernando Marti
Council of Community Housing
Organizations
* The City and County of San Francisco did not appoint an elected official representative
+ Both served on HMC, Selander replaced Abrams
‡ Both served on HMC, Pappas replaced Ikezoe
HMC ROSTER RHNA 3
Business Community
Russell Hancock
Joint Venture Silicon Valley
Matt Regan
Bay Area Council
Xiomara Cisneros
Bay Area Council (Alternate)
Non-Profit Housing
Welton Jordan
EAH Housing
Rodney K. Nickens, Jr.
Non-Profit Housing Association of
Northern California (NPH)
For-Profit Housing
Paul Campos
Building Industry Association of the
Bay Area (BIA)
Jonathan Fearn
Greystar Development
Open Space/Agriculture
Amanda Brown-Stevens
Greenbelt Alliance
Public Education
Brandon Kline
San Francisco State University
Public Health
Anita Addison
La Clinica de la Raza
Philanthropy
Rupinder (Ruby) Bolaria
Shifrin
Chan Zuckerberg Initative
Public/Alternative
Transportation
Bob Planthold
Government and Community
Advocates Strategies, Inc.
RPC Housing
Subcommittee
Carlos Romero
Urban Ecology
Labor
Scott Littlehale
Northern California Carpenters
Regional Council
State Partner
Tawny Macedo
California Department of Housing &
Community Development (HCD)
Tom Brinkhuis
HCD (Alternate)
Megan Kirkeby
HCD (Alternate)
© Martin Klimek. All Rights Reserved.
6 very low-income households all the way to market-
rate housing. This was developed by the California
Department of Housing and Community Development
(HCD) in 2020 and is known as the Regional Housing
Needs Determination (RHND).
•
The role of the region is to allocate a share of the
RHND to each local government in the region. As the
Council of Governments (COG) for the nine-county Bay
Area, ABAG is required to develop the methodology
for sharing the RHND among all cities, towns and
counties in the region. During 2019 and 2020, ABAG
developed the RHNA methodology in conjunction with
a committee of elected officials, city and county staff, and stakeholders called the Housing Methodology
Committee (HMC).
•
The role of local governments is to participate in the
development of the allocation methodology and to
update their Housing Elements to show how they will
accommodate their share of the RHND, following the
adoption of the final RHNA allocations. The Housing
Element must include an inventory of sites that have
been zoned for sufficient capacity to accommodate the
jurisdiction’s RHNA allocation for each income category.
RHNA Public Engagement and Outreach
ABAG has employed a variety of strategies to encourage
public participation to ensure the perspectives and
input of local governments, stakeholders, and members
of the public were represented throughout the RHNA
development process. ABAG provided opportunities
to learn about RHNA and provide input through regular
© Tim Griffith courtesy SAHA. All Rights Reserved.
8 © 2012 Clark Mishler courtesy BRIDGE Housing. All Rights Reserved.
ABAG meetings that were open to the public, outreach to local government elected officials and staff, and electronic news blasts and postings to the ABAG website to notify
interested parties at decision points throughout the
process. ABAG's outreach and engagement activities are described in more detail below.
ABAG Housing Methodology Committee
As it has for the past several RHNA cycles, ABAG
convened a Housing Methodology Committee (HMC) to
guide development of the methodology used to allocate
a share of the region’s total housing need to every local government in the Bay Area. The HMC was comprised
of local elected officials, jurisdiction staff, and other stakeholders from throughout the Bay Area.
ABAG’s HMC approach stands out compared to most
other large Councils of Governments, going beyond the
legal requirements to facilitate dialogue and information- sharing among local government representatives and
stakeholders from across the Bay Area with crucial
expertise to address the region’s housing challenges. As ABAG strives to advance equity and affirmatively further fair housing, the agency sought to ensure a breadth
of voices in the methodology process, and expanded
the HMC to include additional members representing
social equity, labor, and philanthropy. Additionally, HMC representatives were recruited via increased outreach.
The HMC held 12 meetings starting in October 2019
to formulate a recommended RHNA methodology.
Information about the topics discussed at the meetings is available on the ABAG website.
10 • General Assemblies in February 2020 and June 2020 that provided information designed for elected officials about RHNA, Housing Elements, and Plan Bay Area
2050.
• Webinars in December 2020 and January 2021 about
the Plan Bay Area 2050 Final Blueprint and Draft RHNA
Methodology.
Public
All meetings of the HMC, RPC, and Executive Board were
open to the public. Representatives of many housing
and land use stakeholder groups actively participated in RHNA discussions. The public also had the opportunity
to provide input during the public comment period at the meetings described above. Members of the public were
also invited to participate in the two webinars ABAG held about the Draft RHNA Methodology.
ABAG also engaged Bay Area residents from traditionally under-represented groups through a series of seven
focus groups organized in partnership with community-
based organizations throughout the region. In January
and February 2020 focus groups were held with Acterra,
Community Resources for Independent Living, Green
Hive, Sacred Heart Community Service, Sound of Hope
Radio Network, and West Oakland Environmental
Indicators Project. Focus group participants were asked questions about regional housing issues in an interactive setting and encouraged to discuss thoughts freely with
other participants. A summary of participants’ comments was shared with the HMC and RPC to inform development
of the RHNA methodology.
Public Comment Period and Developing the Final
Methodology
The ABAG Executive Board approved release of the
proposed RHNA methodology for public comment on
October 15, 2020. As required by law, ABAG held a
public comment period from October 25 to November
27 and conducted a public hearing at the November 12
meeting of the ABAG Regional Planning Committee. The
comments received provided perspectives from over 200
local government staff and elected officials, advocacy
organizations, and members of the public.
In response to feedback received during the public
comment period, the RPC and Executive Board voted
to incorporate the “equity adjustment” as part of the
draft RHNA methodology approved in January 2021.
As required by law, ABAG submitted the draft RHNA
methodology to HCD for its review on February 11, 2021.
On April 12, 2021, HCD sent ABAG a letter confirming the draft RHNA methodology furthers the RHNA objectives
(see Appendix 1 for the letter ABAG received from
HCD). The Executive Board approved the final RHNA
methodology and draft allocations (shown in Appendix 7) at its meeting on May 20, 2021.
11 The Regional Housing Needs Determination4
In consultation with ABAG, HCD determined that the
Bay Area must plan for 441,176 new housing units from
2023 to 2031. This determination is based on population projections produced by the California Department of
Finance (see Appendix 2 for the letter ABAG received
from HCD). Details of the RHND by income category
are shown in Table 1. This determination is based on
population projections produced by the California
Department of Finance and the application of specific
adjustments to determine the total amount of housing
needs for the region. The adjustments are a result of
recent legislation that sought to incorporate an estimate of existing housing need by
requiring HCD to apply factors
related to a target vacancy rate,
the rate of overcrowding, and
the share of cost-burdened
households.
5 The new laws
governing the methodology for
how HCD calculates the RHND
resulted in a significantly higher
number of housing units for which
the Bay Area must plan compared
to previous RHNA cycles.
Table 1: ABAG Regional Housing Needs Determination
from HCD
INCOME CATEGORY PERCENT HOUSING UNIT NEED
Very Low* 25.9% 114,442
Low 14.9% 65,892
Moderate 16.5% 72,712
Above
Moderate 42.6% 188,130
TOTAL 100% 441,176
* Extremely Low 15.5% Included in “Very Low”
Income Category
© Bernard Andre. All Rights Reserved.
ABOUT THE REGIONAL HOUSING NEEDS ALLOCATION RHNA
12 As noted previously, the purpose of the RHNA
methodology is to divide the RHND among Bay
Area jurisdictions. The methodology is a formula
that calculates the number of housing units
assigned to each city and county, and the formula
also distributes each jurisdiction’s housing unit
allocation among four affordability levels.
RHNA Statutory Objectives and Factors
Development of the RHNA methodology was guided
by the statutory requirements that the RHNA meet
five objectives
6 and be consistent with the forecasted
development pattern from Plan Bay Area 2050.7 The five
statutory objectives of RHNA can be summarized as:
Objective 1: Increase housing supply and mix of housing types, tenure and affordability in all cities and counties in an equitable manner.
Objective 2: Promote infill development and socio-
economic equity, protect environmental and agricultural resources, encourage efficient development patterns and achieve greenhouse gas emissions reduction targets.
Objective 3: Promote improved intraregional jobs-
housing relationship, including balance between low-
wage jobs and affordable housing.
Objective 4: Balance disproportionate household income
distributions (more high-income RHNA to lower-income
areas and vice-versa).
Objective 5: Affirmatively further fair housing.
Since the last RHNA cycle (2015 to 2023), the State has made several changes to the laws that govern the RHNA
process, including modifications to the objectives that the RHNA allocation must meet. Changes include highlighting the importance of specifically addressing the balance
between low-wage jobs and homes affordable to low-
wage workers (known as jobs-housing fit) when looking
at improving the jobs-housing relationship as part of
Objective 3 as well as considering achievement of the
region’s greenhouse gas emissions reduction target when promoting infill development and socioeconomic equity as part of Objective 2. However, the most notable addition is Objective 5, the new requirement to “affirmatively further fair housing,” which focuses on overcoming patterns
of segregation and fostering inclusive communities.
8 This new requirement applies to RHNA as well as local government Housing Element updates. While RHNA has
always focused on increasing access to housing for all, the new statutory requirements make this commitment to fair housing a more explicit aspect of the RHNA process and
Housing Element updates.
In addition to meeting the objectives outlined above,
State Housing Element Law requires ABAG to consider
a specific set of factors in the development of the RHNA methodology. The law also requires ABAG to survey its
member jurisdictions to gather information on the factors that must be considered for inclusion in the methodology. 9 As part of the new requirement related to affirmatively furthering fair housing, ABAG included questions in the survey about local governments’ issues, strategies and
actions related to achieving fair housing goals.
THE FINAL RHNA METHODOLOGY
THE FINAL RHNA METHODOLOGY RHNA 13
As a complement to these survey questions, ABAG staff
also reviewed the fair housing reports that jurisdictions submit to the federal government if they receive block
grant funding from the U.S. Department of Housing and
Urban Development. ABAG opened an online survey
to all jurisdictions in the region from January-February 2020 and received 72 responses, a response rate of 66
percent.
10 ABAG staff reviewed the survey responses as
well as other relevant data to inform the development of a methodology that achieves the objectives outlined in
state statute.
Housing Element Law also identifies several criteria that cannot be used as the basis for a determination of a
jurisdiction’s share of the regional housing need.
These include:
1. Any ordinance, policy, voter-approved measure or
standard of a city or county that directly or indirectly limits the number of residential building permits issued by a city or county.
2. Prior underproduction of housing in a city or county from the previous regional housing need allocation.
3. Stable population numbers in a city or county from the previous regional housing needs cycle.
More information about how the final RHNA methodology
furthers the objectives and addresses the methodology
factors in Housing Element Law is provided in the RHNA
Statutory Objectives and Factors section.
MIchele Stone, MTC
14 Final RHNA Methodology Performance
Evaluation
As noted previously, Housing Element Law requires that
the RHNA methodology meet five statutory objectives
and that it be consistent with the forecasted development pattern from Plan Bay Area 2050. In January 2021, the
Plan Bay Area 2050 Final Blueprint was approved by the
ABAG Executive Board and Metropolitan Transportation
Commission (MTC) as the Preferred Alternative for the
Environmental Impact Report.
Working with the HMC, ABAG-MTC staff developed a set
of performance evaluation metrics that provide feedback about how well methodology options addressed the five
statutory objectives for RHNA and furthered regional
planning goals. Each metric corresponds to one of the
five RHNA statutory objectives and the metrics selected were primarily based on the analysis conducted by HCD
in evaluating the RHNA methodologies completed by
other regions in California.
11 Appendix 3 describes the
evaluation metrics in more detail and demonstrates that the final RHNA methodology performs well in advancing
the five statutory objectives of RHNA.
ABAG-MTC staff also developed a framework for
evaluating consistency between RHNA and Plan Bay
Area 2050. This approach compares the 8-year RHNA
allocations to the 35-year housing growth from the Plan Bay Area 2050 Final Blueprint at the county and sub-
county geographies used in the plan. If the 8-year growth level from RHNA does not exceed the 35-year housing
growth level at either of these geographic levels, then RHNA and Plan Bay Area 2050 will be determined to be
consistent. Staff evaluated the final RHNA methodology
using this approach and determined that the RHNA
allocation is consistent with Plan Bay Area.
12 © Bruce Damonte courtesy MidPen Housing. All Rights Reserved.
THE FINAL RHNA METHODOLOGY RHNA 17
Table 3: Allocation Factor Data and Assumptions
ACCESS TO HIGH OPPORTUNITY AREAS
Overview The Access to High Opportunity Areas factor received the most consistent support from the HMC throughout the methodology development process. This factor allocates more housing units to jurisdictions with a higher percentage of households living in areas labelled High Resource or Highest Resource on the 2020 Opportunity Map produced by HCD and the California Tax Credit Allocation Committee (TCAC). 14 The Opportunity Map
stems from HCD’s policy goals to avoid further segregation and concentration of poverty and to encourage access to opportunity through affordable housing programs. The map uses publicly available data sources to identify areas in the state whose characteristics have been shown by research to support positive economic, educational, and health outcomes for low-income families and their children. The Access to High Opportunity Areas factor directly addresses the RHNA objective to affirmatively further fair housing by increasing access to opportunity and replacing segregated living patterns. 15 Although this
factor does not explicitly incorporate racial demographics, it has the potential to expand housing opportunities for low-income households and people of color in more places where these communities have historically lacked access. Another practical strength of this factor is that HCD has consistently used the Opportunity Map to assess whether other regions’ RHNA methodologies meet the objective to affirmatively further fair housing.
Impact More housing units allocated to jurisdictions with the most access to opportunity.
Definition
The percentage of a jurisdiction’s households living in census tracts labelled High Resource or Highest Resource based on opportunity index scores.
Data Source HCD/TCAC 2020 Opportunity Maps
Note: The original Opportunity Map methodology required that 40 percent of tracts designated as rural within each county are labelled as High or Highest Resource.
However, all non-rural tracts in a region are compared to each other, not just to other tracts in the same county, and the tracts with opportunity index scores in the top 40 percent among all non-rural tracts are labelled High or Highest Resource. Staff from UC Berkeley’s Othering and Belonging Institute, who prepared the opportunity index data for TCAC and HCD, issued a recalculation of the opportunity index to ABAG/MTC staff for use in the RHNA methodology. In the recalculation, all Bay Area census tracts are compared to each other, so rural areas are now compared to all other tracts in the region instead of solely to other rural tracts in the same county. This recalculation mostly affected Solano and Sonoma Counties, which had fewer tracts classified as High or Highest Resource as a result. Table 3 continued on next page
18 Table 3: Allocation Factor Data and Assumptions (continued)
JOB PROXIMITY
Overview The two factors based on job proximity (Job Proximity – Auto and Job Proximity – Transit) consider the relationship between jobs and transportation. Job Proximity – Auto is based on jobs that can be accessed from a jurisdiction by a 30-minute auto commute, while Job Proximity – Transit is based on jobs that can be accessed from a jurisdiction within a 45-minute transit commute. These factors encourage more housing in jurisdictions with easier access to the region’s job centers. Additionally, these factors use a commute shed to measure job access rather than solely considering the jobs present within a jurisdiction’s boundaries. Using a commute shed intends to better capture the lived experience of accessing jobs irrespective of jurisdiction boundaries. Housing and job markets extend beyond jurisdiction boundaries—in most cities, a majority of workers work outside their jurisdiction of residence, and demand for housing in a particular jurisdiction is substantially influenced by its proximity and accessibility to jobs in another community.
Impact More housing allocated to jurisdictions with easier access to region’s job centers.
Definition • Job Proximity – Auto: Share of region’s total jobs that can be accessed from a jurisdiction by a 30-minute auto commute during the morning peak period.
Assumes single-occupant vehicle drivers who decline the use of Express Lanes. • Job Proximity – Transit: Share of region’s total jobs that can be accessed from a jurisdiction by a 45-minute transit commute during the morning peak period.
Assumes transit users can choose from all modes available to them to get between home and work.
Data Source MTC, Travel Model One, Model Run 2015_06_002 (Source: Plan Bay Area 2040, 2017) other jurisdictions in the region. A jurisdiction with an above-average score on a factor would get an upwards
adjustment, whereas a city with a below-average score on a factor would get a downwards adjustment relative to the baseline allocation.
By design, the factors are placed on the same scale so
a factor can modify the baseline in the range from 50
percent to 150 percent: Jurisdictions scoring at the top for the region will get baseline share times 1.5, while jurisdictions scoring at the bottom for the region will get baseline share times 0.5. This scaling approach helps
distribute RHNA units throughout the region by ensuring that even a jurisdiction with a low score gets an allocation from each factor and placing a limit on how many units
20 percent of existing households, it would receive at least two percent of the very low- and low-income RHNA units.
The composite score is calculated by adding together
the jurisdiction’s divergence index score16 (which
measures segregation by looking at how much local racial demographics differ from the region) and the percent
of the jurisdiction’s households with household incomes above 120 percent of the area median income (AMI).
Jurisdictions with a composite score greater than the
median score for the region are included in the group of “exclusionary” jurisdictions. Accordingly, a jurisdiction does not necessarily need to have an extremely high
divergence score or percent of households above
120 percent AMI to be considered “exclusionary,” as a
jurisdiction’s composite score only needs to be in the top half for all Bay Area jurisdictions.
The equity adjustment excludes five jurisdictions who have composite scores above the region's median, but median
incomes in the bottom quartile for the region. These
jurisdictions were excluded from the equity adjustment
to avoid directing additional lower-income RHNA units to jurisdictions with racial demographics that are different than the rest of the region but that already have a high share of lower-income households.
© Karl Nielsen. All Rights Reserved.
21 The equity adjustment is the last step in the allocation methodology, and is applied after the methodology's
factors and weights are used to determine a jurisdiction's allocation by income category. If the allocation of lower- income RHNA units to one of the 49 jurisdictions identified by the equity adjustment's composite score does not meet the equity adjustment's proportionality threshold, then lower-income units are redistributed from the remaining 60 jurisdictions in the region to increase that jurisdiction’s lower-income allocation until it is proportional. Each
jurisdiction in this group has its allocation of lower-income units reduced in proportion to its share of the total lower- income units among the jurisdictions in the group of
60. The equity adjustment does not have any effect on
moderate- and above moderate-income units.
Appendix 6 shows the calculations for the composite
score used to identify the 49 jurisdictions that exhibit racial and socioeconomic demographics that differ from the
regional average. It also shows the effects of the equity adjustment on each jurisdiction’s allocation of lower-
income units. Of the 49 jurisdictions, 31 receive allocations that meet the equity adjustment’s proportionality
threshold based on the methodology’s factors and
weights that emphasize access to high opportunity
areas. The allocations for these 31 jurisdictions do not change as a result of the equity adjustment. The other 60 jurisdictions in the region see reductions in their lower- income allocations (and thus their total allocations) as units are shifted to the 18 jurisdictions whose allocations are increased as a result of the equity adjustment.
© Julio Cesar courtesy BRIDGE Housing. All Rights Reserved.
THE FINAL RHNA METHODOLOGY RHNA
22 RHNA APPEALS PROCESS
Government Code Section 65584.05 identifies a limited
and narrow set of circumstances where a jurisdiction
can appeal a draft RHNA allocation. On May 20, 2021,
following approval of the final RHNA methodology and
draft allocations, the ABAG Executive Board approved
the ABAG 2023-2031 RHNA Appeals Procedures. The
Executive Board also delegated authority to the ABAG
Administrative Committee to conduct the required public hearing for considering RHNA appeals and to make the
final determinations on the appeals.
The Appeals Procedures provide an overview of the
statutory procedures and bases for an appeal and outline ABAG’s policies for conducting the public hearing. This document and other materials related to the appeals
process are on the ABAG website at https://abag.
ca.gov/our-work/housing/rhna-regional-housing-needs-
allocation/2023-2031-rhna-appeals-process.
On May 25, 2021, ABAG notified each local jurisdiction, HCD, and members of the public about adoption of
the draft RHNA allocations and initiation of the appeals period. The email to jurisdictions included a link to the ABAG 2023-2031 RHNA Appeals Procedures.
Appeals Submitted by Local Jurisdictions
ABAG received appeals from 27 Bay Area jurisdictions
(with one jurisdiction submitting two separate appeals) during the 45-day appeals period from May 25, 2021 to
July 9, 2021. On July 16, 2021, ABAG posted all appeal
materials received from local jurisdictions on its website and distributed them to each local jurisdiction, HCD and members of the public consistent with Government Code
Section 65584.05(c).
Public Comments on Appeals
During the public comment period from July 16, 2021 to
August 30, 2021, ABAG received nearly 450 comments
from local jurisdictions, HCD, regional stakeholders, and members of the public on the 28 appeals submitted. On
September 1, 2021, ABAG posted all comments received
during the comment period on its website.
Notice of Appeals Public Hearing
Also on September 1, 2021, ABAG distributed the public
comments and the public hearing schedule to each local
jurisdiction, HCD and members of the public. This ensured © Noah Berger. All Rights Reserved.
23 that each jurisdiction that submitted an appeal received notice of the public hearing schedule at least 21 days
in advance, consistent with Government Code Section
65584.05(d). Between August 29 and September 3, 2021,
legal notices announcing the public hearing schedule were posted on the ABAG website and published in multiple
languages in newspapers in all nine Bay Area counties.
Appeals Public Hearing at ABAG
Administrative Committee
The ABAG Administrative Committee conducted the
public hearing to consider the RHNA appeals. Each
jurisdiction’s appeal was considered at one of six meetings on the following dates:
•
September 24, 2021
• September 29, 2021
• October 8, 2021
• October 15, 2021
• October 22, 2021
• October 29, 2021.
The jurisdiction, HCD, other local jurisdictions and the public could submit comments related to the appeal. Per ABAG’s adopted appeals procedures, during the hearing,
the jurisdiction that submitted the appeal could present the bases for the appeal and information to support
the arguments to the committee. The jurisdiction’s
presentation was followed by a response from ABAG-
MTC staff, consistent with the information provided in the written staff report. Then, the applicant could respond to the arguments or evidence that ABAG-MTC staff
presented. After these presentations, members of the
public could provide comments prior to discussion by
the Administrative Committee. Committee members
considered all documents submitted and all public
comments prior to taking a preliminary vote on the
jurisdiction’s appeal.
Final Determinations on Appeals
The ABAG Administrative Committee met on November
12, 2021, to take final action to ratify the preliminary decisions it made on each appeal during the public
hearing. Documents related to the final determination for all appeals is available on the ABAG website.
The ABAG Administrative Committee denied all appeals
submitted by local jurisdictions, with the exception of the appeal submitted by the County of Contra Costa.
The Administrative Committee partially granted the
County's appeal because an area annexed to Pittsburg
in 2018 (LAFCO 17-08) was incorrectly included as part
of unincorporated Contra Costa County in the Plan
Bay Area 2050 Final Blueprint. Reducing the County’s
total households in 2050 (the baseline allocation for
RHNA methodology) by the 412 households that the
Final Blueprint forecasted in that area results in a
reduction in the County’s total RHNA of 35 units. As
allowed by Government Code Section 65584.05(e)(1),
the Administrative Committee determined that these 35
RHNA units should be transferred to the City of Pittsburg.
This transfer of units is reflected in the final RHNA
allocations issued by ABAG on November 19, 2021.
RHNA APPEALS PROCESS RHNA
24 Jurisdiction
VERY LOW INCOME
(<50% of Area
Median Income)
LOW INCOME
(50-80% of Area
Median Income)
MODERATE INCOME
(80-120% of Area
Median Income)
ABOVE MODERATE
INCOME
(> 120% of Area
Median Income)
TOTAL
ALAMEDA COUNTY
Alameda 1,421 818 868 2,246 5,353
Albany 308 178 175 453 1,114
Berkeley 2,446 1,408 1,416 3,664 8,934
Dublin 1,085 625 560 1,449 3,719
Emeryville 451 259 308 797 1,815
Fremont 3,640 2,096 1,996 5,165 12,897
Hayward 1,075 617 817 2,115 4,624
Livermore 1,317 758 696 1,799 4,570
Newark 464 268 318 824 1,874
Oakland 6,511 3,750 4,457 11,533 26,251
Piedmont 163 94 92 238 587
Pleasanton 1,750 1,008 894 2,313 5,965
San Leandro 862 495 696 1,802 3,855
Unincorporated Alameda County 1,251 721 763 1,976 4,711 Union City 862 496 382 988 2,728
The ABAG Executive Board adopted the final RHNA allocations on December 16, 2021. The final allocations reflect the ABAG Administrative Committee's decision to partially grant the appeal filed by the County of Contra Costa, which affected the final allocations for the County and the City of Pittsburg.
THE FINAL RHNA ALLOCATION
Table 4: Final RHNA Allocations
29 Jurisdiction
VERY LOW INCOME
(<50% of Area
Median Income)
LOW INCOME
(50-80% of Area
Median Income)
MODERATE INCOME
(80-120% of Area
Median Income)
ABOVE MODERATE
INCOME
(> 120% of Area
Median Income)
TOTAL
SOLANO COUNTY
Jurisdictions in Solano County formed a subregion to complete a separate RHNA process on behalf of all jurisdictions in the county. The Solano Subregion adopted its final RHNA allocations on November 18, 2021. For more information see https://www.solanocounty.com/ depts/rm/planning/regional_housing_needs_allocation_and_housing_element.asp Solano Subregion 2,803 1,612 1,832 4,745 10,992
SONOMA COUNTY
Cloverdale 74 43 45 116 278
Cotati 60 34 39 101 234
Healdsburg 190 109 49 128 476
Petaluma 499 288 313 810 1,910
Rohnert Park 399 230 265 686 1,580
Santa Rosa 1,218 701 771 1,995 4,685
Sebastopol 55 31 35 92 213
Sonoma 83 48 50 130 311
Unincorporated Sonoma 1,036 596 627 1,622 3,881
Windsor 385 222 108 279 994
TOTAL 114,442 65,892 72,712 188,130 441,176
FINAL ALLOCATION RHNA
32 RHNA STATUTORY OBJECTIVES
AND FACTORS
As noted previously, Housing Element Law
requires the RHNA methodology to further
five objectives that recognize the importance
of comprehensively planning for housing in
ways that also promote equity, strengthen the
economy, improve connections between jobs
and housing, and protect the environment.
The statutory objectives, and the ways in which
the Bay Area’s final RHNA methodology meets
them, are described below. See also Appendix
1 for HCD's findings about how the RHNA
methodology furthers the RHNA objectives.
RHNA Objectives
OBJECTIVE 1 — “increasing the housing supply and
the mix of housing types, tenure, and affordability in
all cities and counties within the region in an equitable manner, which shall result in each jurisdiction receiving an allocation of units for low- and very low-income
households.”
The methodology furthers this objective by allocating
a share of the region’s housing need across all income
categories to all jurisdictions in the Bay Area. As a result, all jurisdictions receive an allocation of very low- and low-income units. The methodology allocates these
units equitably, as the methodology allocation factors
direct very low- and low-income units based primarily
on a jurisdiction’s access to opportunity. Accordingly, jurisdictions with the most residents living in census tracts designated as High Resource or Highest Resource on the
California Tax Credit Allocation Committee (TCAC) 2020
Opportunity Map receive a higher share of their allocation as lower-income units than other jurisdictions in the region (see Appendix 3).
As shown in Appendix 3, jurisdictions with the highest
housing costs also receive a higher share of their allocation as lower-income units than other jurisdictions in the
region. Because jurisdictions must zone at higher densities to accommodate their allocations of low- and very-low-
income units, the methodology will result in both greater affordability and a more diverse range of housing types throughout the region, particularly in the jurisdictions that currently lack affordable housing opportunities.
OBJECTIVE 2 — “Promoting infill development and
socioeconomic equity, the protection of environmental
and agricultural resources, the encouragement of
efficient development patterns, and the achievement of
the region’s greenhouse gas reductions targets provided by the State Air Resources Board pursuant to Section
65080.”
The intent of this objective is consistent with many of the strategies integrated into Plan Bay Area 2050. The
final RHNA methodology incorporates the Plan Bay Area
2050 Final Blueprint as the data source for the baseline allocation used to assign each jurisdiction a beginning RHNA STATUTORY OBJECTIVES AND FACTORS RHNA 33
share of the RHND, using each jurisdiction’s share of
the region’s households in the year 2050. In effect, this baseline allocation takes into consideration a jurisdiction’s existing total number of households plus its household
growth from the Final Blueprint.
The Plan Bay Area 2050 uses the Bay Area UrbanSim
2.0 17 model to analyze a wide variety of land use data, such as access to jobs, services, and other destinations as informed by Plan Bay Area 2050 transportation
investments. Therefore, the Final Blueprint prioritizes housing growth in three types of growth geographies,
Priority Development Areas nominated by local
jurisdictions, Transit-Rich Areas with lower greenhouse gas emissions potential, and High-Resource Areas with
excellent access to jobs, schools, and more. The growth geographies in the Final Blueprint also exclude areas
with high wildfire risk and areas outside urban growth
boundaries. Accordingly, the methodology’s use of Plan
Bay Area 2050 results in an allocation that promotes infill development, protects environmental and agricultural
resources, and reduces the region’s greenhouse gas
emissions.
The inclusion of job proximity by both automobile and
transit as factors in the RHNA methodology complements
the use of Plan Bay Area 2050 as the baseline allocation to further this objective. These factors direct more housing to the jurisdictions with the most jobs that can be accessed with a 30-minute commute by automobile or a 45-minute
commute by transit. The inclusion of the Job Proximity – Transit factor encourages growth that capitalizes on the Bay Area’s existing transit infrastructure, while the Job Proximity – Auto factor recognizes that most people in
the region commute by automobile. Encouraging shorter
commutes for all modes of travel is an important strategy for reducing greenhouse gas emissions.
© Noah Berger. All Rights Reserved.
34 As shown in Appendix 3, the final RHNA methodology
results in jurisdictions with the most access to jobs and transit as well as jurisdictions with the lowest vehicle miles traveled per resident experiencing higher growth
rates from their RHNA allocations than other jurisdictions in the region. Therefore, the methodology furthers the
sustainability goals represented by this objective. The final RHNA methodology also promotes socioeconomic equity
by expanding the range of housing choices available in
all jurisdictions throughout the Bay Area with a particular emphasis on adding homes affordable to lower-income
residents in jurisdictions with high resource areas to
promote socioeconomic mobility.
OBJECTIVE 3 — “Promoting an improved intraregional
relationship between jobs and housing, including an
improved balance between the number of low-wage jobs
and the number of housing units affordable to low-wage
workers in each jurisdiction.”
The final RHNA methodology directly incorporates the
forecasted development pattern from the Plan Bay Area
2050 Final Blueprint as the baseline allocation. The Final Blueprint emphasizes growth near job centers and in
locations near transit, as well as in high-resource areas, with the intent of reducing greenhouse gas emissions.
The strategies incorporated into the Final Blueprint help improve the region’s jobs-housing balance, leading to
shorter commutes—especially for low-income workers.
Moreover, the allocation factors in the final RHNA
methodology focus entirely on job proximity and access
to opportunity. Seventy percent of very low- and low-
income units are allocated based on jurisdictions’ access to opportunity according to the TCAC 2020 Opportunity
Map methodology, which incorporates proximity to jobs
filled by workers with less than a bachelor’s degree.
The remaining 30 percent of the lower-income units
are allocated based on jurisdictions’ proximity to jobs.
Furthermore, 60 percent of the region’s moderate- and
above moderate-income units are allocated based on
jurisdictions’ proximity to jobs.
As a result of differences in how units are distributed across income categories in the RHND, the final RHNA
methodology allocates 48 percent of all units based on the factors related to job proximity. Thus, the methodology promotes an improved intraregional relationship between jobs and housing. As noted previously, the final RHNA
methodology results in jurisdictions with the most access to jobs experiencing higher growth rates from their RHNA allocations than other jurisdictions in the region.
© Martin Klimek. All Rights Reserved.
RHNA STATUTORY OBJECTIVES AND FACTORS RHNA 35
Also, as shown in Appendix 3, the final RHNA
methodology results in jurisdictions with the most
imbalanced jobs-housing fit (or, ratio between the number of low-wage jobs and the number of housing units
affordable to low-wage workers) receiving a higher share of lower-income units than other jurisdictions.
OBJECTIVE 4 — “Allocating a lower proportion of housing need to an income category when a jurisdiction already
has a disproportionately high share of households in
that income category, as compared to the countywide
distribution of households in that category from the most recent American Community Survey.”
The final RHNA methodology allocates 70 percent of very low- and low-income units based on a jurisdiction’s access to opportunity according to the TCAC 2020 Opportunity
Map methodology, which scores jurisdictions partially
based on their poverty rates and median home values.
Consequently, jurisdictions with the most households
in High Resource or Highest Resource census tracts
have disproportionately large shares of higher-income
residents and relatively small shares of lower-income
residents. The final RHNA methodology furthers Objective 4 by allocating lower-income units directly to these
jurisdictions with the most access to resources. As a
result, the jurisdictions with the largest percentage of households with incomes above 120 percent of the area
median income receive a significantly higher share of their RHNA as lower-income units than the jurisdictions with the largest percentage of households with incomes below 80
percent of area median income (see Appendix 3).
OBJECTIVE 5 — “Affirmatively furthering fair housing,
which means taking meaningful actions, in addition
to combating discrimination, that overcome patterns
of segregation and foster inclusive communities free
from barriers that restrict access to opportunity based on protected characteristics. Specifically, affirmatively furthering fair housing means taking meaningful actions that, taken together, address significant disparities in housing needs and in access to opportunity, replacing
segregated living patterns with truly integrated and
balanced living patterns, transforming racially and
ethnically concentrated areas of poverty into areas of
opportunity, and fostering and maintaining compliance
with civil rights and fair housing laws.”
The final RHNA methodology affirmatively furthers fair
housing by emphasizing access to opportunity based
on the data from the TCAC 2020 Opportunity Map. The
Access to High Opportunity Areas factor assigns 70
percent of the region’s very low- and low-income units
and 40 percent of the region’s moderate- and above
moderate-income units.
The equity adjustment included in the final RHNA
methodology also helps affirmatively further fair housing.
This adjustment ensures that the 49 jurisdictions identified as exhibiting racial and socioeconomic demographics
that differ from the regional average receive a share
of the region’s lower-income RHNA units that is at
least proportional to the jurisdiction’s share of existing households. Most of these 49 jurisdictions receive
allocations that meet this proportionality threshold based on the final RHNA methodology’s emphasis on access to
36 high opportunity areas. However, the equity adjustment ensures that the other 18 jurisdictions that might exhibit racial and economic exclusion but do not have significant shares of households living in high opportunity areas also receive proportional allocations.
Additionally, the final RHNA methodology’s emphasis on
affirmatively furthering fair housing is supported by the inclusion of High-Resource Areas as one of the growth
geographies in the Plan Bay Area 2050 Final Blueprint.
In the Final Blueprint, High-Resource Areas are defined as the Census tracts identified as High and Highest
Resource in the State’s Opportunity Map if they were
inside a Priority Development Area (PDA) or if they were near transit in a jurisdiction that designated less than 50 percent of its PDA-eligible land as PDAs.
18 As shown in Appendix 3, the allocations from the final RHNA methodology result in the jurisdictions with the
highest percentage of residents living in High Resource or Highest Resource tracts in the TCAC 2020 Opportunity Map receiving a larger share of the region’s lower-income units than other jurisdictions. With the equity adjustment, jurisdictions exhibiting above-average levels of racial and economic exclusion receive a share of the region’s lower- income units that is 19 percent greater than their share of the region’s households, and, as noted above, all of the 49 jurisdictions achieve the proportionality threshold.
Thus, the methodology will require jurisdictions with the most access to opportunity and those with a pattern of
excluding people of color and lower-income households
to zone for a broader range of housing types, particularly housing that is affordable to lower-income households.
RHNA Methodology Factors
Housing Element Law also identifies factors that ABAG
must consider in developing its RHNA methodology,
to the extent sufficient data is available. The statutory factors, and the ways in which the Bay Area’s final
RHNA methodology meets them, are described below.
Additionally, these factors were considered as part of the local jurisdiction survey conducted by ABAG. A summary
of the results of the local jurisdiction survey, which
helped provide local context on local conditions during the development of the methodology, is included as
Appendix 8.
1. Each member jurisdiction’s existing and projected jobs and housing relationship. This shall include an estimate based on readily available data on the number of
low-wage jobs within the jurisdiction and how many
housing units within the jurisdiction are affordable
to low-wage workers as well as an estimate based on
readily available data, of projected job growth and
projected household growth by income level within
each member jurisdiction during the planning period.
The final RHNA methodology directly incorporates
each jurisdiction’s existing and projected jobs-housing relationship in both the baseline allocation and the
allocation factors. Forecasts from Plan Bay Area 2050
inform the baseline allocation, and Plan Bay Area 2050
emphasizes growth near job centers and includes
strategies related to increased housing densities and
office development subsidies to address jobs-housing
imbalances in the region. The strategies incorporated into RHNA STATUTORY OBJECTIVES AND FACTORS RHNA 37
the Final Blueprint help improve the region’s jobs-housing balance, leading to shorter commutes—especially for low- income workers.
The final RHNA methodology amplifies the Plan Bay
Area 2050 Final Blueprint’s emphasis on improving jobs- housing balance by using factors related to job proximity to allocate nearly half of the RHND. These factors direct housing units to those jurisdictions with the most jobs that can be accessed with a 30-minute commute by
automobile and/or a 45-minute commute by transit. The
combination of the Access to High Opportunity Areas
factor and job proximity factors for allocating lower-
income RHNA units intends to enable more Bay Area
workers to reside closer to their jobs, with an emphasis on providing more affordable housing in jurisdictions with the largest imbalance between low-wage jobs and housing
affordable to low-wage workers.
The final RHNA methodology helps to create a more
balanced relationship between housing and jobs by
directing RHNA units to job-rich jurisdictions and
jurisdictions with the most imbalanced jobs-housing fit.
As shown in Appendix 3, jurisdictions with the largest
share of the Bay Area’s jobs receive allocations that result in the highest growth rates compared to the rest of the jurisdictions in the region. Additionally, the jurisdictions with the worst jobs-housing fit receive a larger share of their RHNA as affordable housing than other jurisdictions and receive a share of the RHND that is 22 percent greater than their share of the region’s households. This outcome is supported by inclusion of the equity adjustment in the RHNA methodology, which directed additional lower-
income units to jurisdictions with an imbalanced jobs-
housing fit.
2. The opportunities and constraints to development
of additional housing in each member jurisdiction,
including all of the following:
a. Lack of capacity for sewer or water service due
to federal or state laws, regulations or regulatory
actions, or supply and distribution decisions made
by a sewer or water service provider other than the
local jurisdiction that preclude the jurisdiction from
providing necessary infrastructure for additional
development during the planning period.
b. The availability of land suitable for urban
development or for conversion to residential
use, the availability of underutilized land, and
opportunities for infill development and increased
residential densities. The council of governments
may not limit its consideration of suitable housing
sites or land suitable for urban development to
existing zoning ordinances and land use restrictions
of a locality, but shall consider the potential for
increased residential development under alternative
zoning ordinances and land use restrictions. The
determination of available land suitable for urban
development may exclude lands where the Federal
Emergency Management Agency (FEMA) or the
Department of Water Resources has determined
that the flood management infrastructure designed
to protect that land is not adequate to avoid the risk
of flooding.
38 c. Lands preserved or protected from urban
development under existing federal or state
programs, or both, designed to protect open space,
farmland, environmental habitats, and natural
resources on a long-term basis, including land
zoned or designated for agricultural protection
or preservation that is subject to a local ballot
measure that was approved by the voters of that
jurisdiction that prohibits or restricts conversion to
nonagricultural uses.
d. County policies to preserve prime agricultural
land, as defined pursuant to Section 56064,
within an unincorporated area and land within
an unincorporated area zoned or designated for
agricultural protection or preservation that is
subject to a local ballot measure that was approved
by the voters of that jurisdiction that prohibits or
restricts its conversion to nonagricultural uses.
The opportunities and constraints to housing development are addressed through the incorporation of the Plan Bay Area 2050 Final Blueprint as the baseline allocation in the final RHNA methodology. In developing the Plan Bay Area 2050 Final Blueprint, ABAG-MTC staff worked with local
governments to gather information about local plans,
zoning, physical characteristics and potential development opportunities and constraints for each jurisdiction. This information is an input into the UrbanSim 2.0 model that uses a simulation of buyers and sellers in local real estate markets to estimate housing feasibility. In assessing
feasibility, the UrbanSim 2.0 model also integrates
the higher cost of building on parcels with physical
development constraints, e.g., steep hillsides. Protected park land and open space are excluded from development
in the model.
However, the Final Blueprint does not limit a jurisdiction’s housing allocation based on local plans or zoning. The
UrbanSim 2.0 model is used to forecast expanded growth
potential in growth geographies identified in the Final Blueprint, such as Transit-Rich Areas and High Resource Areas. This allows additional feasible growth within
the urban footprint by increasing allowable residential densities and expanding housing into select areas
currently zoned for commercial and industrial uses.
The Plan Bay Area 2050 Final Blueprint maintains all
existing urban growth boundaries, without any expansion, over the lifespan of the long-range plan. Existing urban growth boundaries, which take a variety of forms across the region but are relatively common in the Bay Area,
help not only to protect prime agricultural lands from
development, but also parks and open space as well.
Land outside urban growth boundaries also tends not to
have urban services such as sewer and water. The Final
Blueprint also incorporates strategies to protect high- value conservation lands, including matching funds to
help conserve high-priority natural and agricultural lands.
Including the Plan Bay Area 2050 Final Blueprint in the RHNA methodology addresses concerns about natural
hazards, as the Final Blueprint excludes areas with
unmitigated high hazard risk from Growth Geographies.
The Final Blueprint Growth Geographies exclude CAL FIRE designated “Very High” fire severity areas in incorporated jurisdictions, and "High" and "Very High" fire severity areas RHNA STATUTORY OBJECTIVES AND FACTORS RHNA 39
as well as county-designated wildland-urban interfaces
(WUIs) where applicable in unincorporated areas.19 The
Final Blueprint strategies focus future growth away from the highest fire risk zones, support increased wildland management programs, and support residential building
upgrades that reduce the likelihood for damage when
fires occur in the wildland urban interface.
The Final Blueprint also incorporates strategies to
mitigate the impacts of sea level rise, protecting nearly all communities at risk from two feet of permanent inundation.
Riverine flooding is not yet integrated into the Final
Blueprint because existing research does not provide
guidance on how to model impacts of temporary riverine
flooding to buildings and land value. Communities can
choose to take these risks into consideration with where and how they site future development, either limiting
growth in areas of higher hazard or by increasing building standards to cope with the hazard.
3. The distribution of household growth assumed
for purposes of a comparable period of regional
transportation plans and opportunities to maximize
the use of public transportation and existing
transportation infrastructure.
As noted above, the final RHNA methodology’s
baseline allocation directly incorporates the forecasted development pattern from Plan Bay Area 2050, the
Bay Area’s Regional Transportation Plan/Sustainable
Communities Strategy. The growth geographies in
Plan Bay Area 2050 emphasize access to transit, both
in locally nominated Priority Development Areas and
in regionally identified Transit-Rich Areas. This land use pattern is developed with complementary transportation
investments in an effort to ensure past and future
transportation investments are maximized.
The final RHNA methodology builds on the transit-
focused development pattern from Plan Bay Area 2050
by also allocating 15 percent of the region’s very low- and low-income units based on a jurisdiction’s proximity to jobs that can be accessed by public transit. Thus, the
methodology will encourage higher-density housing in
jurisdictions with existing transit infrastructure, which can maximize the use of public transportation in these
communities.
Similarly, the results in Appendix 3 demonstrate that the jurisdictions with the largest share of the region’s Transit Priority Area (TPA)
20 acres experience significantly higher
growth rates from the final RHNA methodology than
other jurisdictions. The 25 jurisdictions with the most TPA acreage grow by 18 percent on average as a result
of allocations from the final RHNA methodology. All
other jurisdictions grow by 12 percent on average. The
jurisdictions with the most access to public transit receive the most growth from the final RHNA methodology,
which will encourage the use of public transportation and existing transportation infrastructure.
4. Agreements between a county and cities in a county to direct growth toward incorporated areas of the county
and land within an unincorporated area zoned or
designated for agricultural protection or preservation
that is subject to a local ballot measure that was
approved by the voters of the jurisdiction that prohibits or restricts conversion to nonagricultural uses.
40 Use of the Plan Bay Area 2050 Final Blueprint as the RHNA baseline integrates several key strategies related to agricultural preservation. First, the growth pattern in the Final Blueprint is significantly driven by the urban growth boundaries strategy which maintains all existing urban growth boundaries, without any expansion, over
the lifespan of the long-range plan. Second, this strategy is supported by an agricultural land preservation strategy that helps to acquire land for permanent agricultural use.
At the same time, because urban growth boundaries
often extend outside of existing city limits, there
remains a limited amount of unincorporated county
growth in the Plan Bay Area 2050 Final Blueprint. ABAG- MTC will continue discussions with local jurisdictions
about opportunities to direct additional RHNA units to
incorporated areas, including the use of the provisions in Housing Element Law that allow a county transfer a
portion of its RHNA allocation to a city or town after it receives its RHNA allocation from ABAG.
21 5. The loss of units contained in assisted housing
developments, as defined in paragraph (9) of
subdivision (a) of Section 65583, that changed to
non-low-income use through mortgage prepayment,
subsidy contract expirations, or termination of use
restrictions.
Comprehensive data about the loss of assisted housing
units is not available for all Bay Area jurisdictions in a consistent format. Jurisdictions that provided information on this topic as part of the survey of local jurisdictions often relied on internal data sources. Twenty-seven
percent of survey respondents stated their jurisdiction had lost subsidized affordable housing units in the past 10 years, and 32 percent noted they expected to lose
units in the next 10 years. Given the lack of consistent data, this topic was not included as a specific factor in the final RHNA methodology. The loss of assisted housing
units for lower-income households is an issue that would best be addressed by local jurisdictions when preparing their Housing Elements. ABAG included available data
in its preapproved data package as a starting point for supporting local jurisdictions in addressing this issue.
6. The percentage of existing households at each of the income levels listed in subdivision (e) of Section 65584 that are paying more than 30 percent and more than
50 percent of their income in rent.
During the consultation process for the RHND, ABAG
worked with HCD to compare the Bay Area’s share of
cost-burdened households to comparable regions
throughout the United States. The comparison used data
© UNKNOWN courtesy SAHA. All Rights Reserved.
RHNA STATUTORY OBJECTIVES AND FACTORS RHNA 41
from the 2012-2016 Comprehensive Housing Affordability
Strategy (CHAS) to evaluate cost burden for lower-income and higher-income households. The averages of these
cost burdens by income group formed the basis for an
adjustment that was included in the RHND.
22 The data analysis prepared for the RHND indicated that approximately 66 percent of Bay Area households earning less than 80 percent of the Area Median Income (AMI) are cost-burdened, while 16 percent of households earning
above 80 percent AMI are cost-burdened. The prevalence
of cost burden as a concern for many Bay Area households was confirmed by the results of the survey sent to local jurisdictions, where 51 respondents (72 percent) indicated that high housing costs and high rates of cost burden
affect housing needs in their jurisdictions.
The UrbanSim 2.0 model used to develop the Plan Bay
Area 2050 Final Blueprint considers both housing costs
and relative incomes when forecasting future growth.
Moreover, Plan Bay Area 2050 incorporates multiple
strategies to address housing unaffordability, including allowing a greater mix of housing types and densities
in the plan’s growth geographies, reducing barriers to
housing near transit and in areas of high opportunity,
transforming aging malls and office parks into mixed-
income neighborhoods, raising additional funding for
affordable housing, requiring 10 to 20 percent of new
housing to be affordable, and strengthening renter
protections beyond current state regulations.
The final RHNA methodology further addresses cost-
burdened households in the Bay Area – particularly the
high percentage of cost-burdened households earning
less than 80 percent of AMI – by allocating lower-income units to all jurisdictions, particularly those with the most access to opportunity. The methodology allocates 70
percent of the region’s lower-income units based on
jurisdictions’ access to opportunity according to the TCAC 2020 Opportunity Map.
As shown in Appendix 3, the jurisdictions with the highest housing costs receive a larger percentage of their RHNA as lower-income units than other jurisdictions in the
region, and the jurisdictions with the most households
in High or Highest Resource census tracts also receive
a larger percentage of their allocations as lower-income units than other jurisdictions.
Local governments will have an opportunity to address
jurisdiction-specific issues related to cost-burdened
households when they update their housing elements.
ABAG-MTC staff included data on jurisdiction-specific rates of housing cost burden as part of housing data packets
prepared to assist with housing element updates.
7. The rate of overcrowding.
During the consultation process for the RHND, ABAG
worked with HCD to compare the Bay Area’s rate of
overcrowding to comparable regions throughout the
United States. The comparison used data from the 2014-
2018 American Community Survey (ACS) to evaluate
overcrowding. The Bay Area’s overcrowding rate of 6.73
percent is nearly double the rate of comparable regions.
Consequently, ABAG’s RHND includes an overcrowding
adjustment.
23 42
Overcrowding rates are inputs into the Plan Bay Area
2050 regional growth forecast, which informs the baseline allocation in the final RHNA methodology. As noted
earlier, Plan Bay Area 2050 also directly incorporates
multiple strategies to address housing affordability, and these strategies also seek to reduce overcrowding.
Like housing cost burden, overcrowding indicates a lack of adequate housing supply, especially housing units
affordable for lower-income households. The final RHNA
methodology seeks to expand the housing supply, and
especially the supply of affordable units, within the most expensive parts of the region, which can help reduce
the rates of overcrowding experienced by Bay Area
households. As shown in Appendix 3, the final RHNA
methodology results in the jurisdictions with the highest housing costs receiving a larger percentage of their RHNA as lower-income units than other jurisdictions and a share of the region’s total RHNA that is 8 percent larger than their share of the region’s households.
Local governments will have an opportunity to address
jurisdiction-specific issues related to overcrowded
households when they update their housing elements.
ABAG-MTC staff included data on jurisdiction-specific
rates of overcrowding as part of housing data packets
prepared to assist with housing element updates.
8. The housing needs of farmworkers.
ABAG included questions about housing needs for the
region’s farmworkers in its survey of local jurisdictions, however consistent data is not available for all Bay
Area jurisdictions. ABAG’s final RHNA methodology
incorporates this factor through its emphasis on proximity to jobs, which includes agricultural jobs. As shown in
Appendix 3, the final RHNA methodology also results in
jurisdictions with the most low-wage jobs per housing
unit affordable to low-wage workers receiving higher
percentages of affordable housing compared to other
jurisdictions in the region. This outcome is supported
by inclusion of the equity adjustment in the RHNA
methodology, which directed additional lower-income
units to jurisdictions with an imbalanced jobs-housing fit.
As a result, jurisdictions with larger farmworker housing need will be expected to provide more very low- and low- income units to meet this demand.
© Karl Nielsen. All Rights Reserved.
RHNA STATUTORY OBJECTIVES AND FACTORS RHNA 43
9. The housing needs generated by the presence of a
private university or a campus of the California State
University or the University of California within any
member jurisdiction.
Responses to questions from ABAG’s Local Jurisdiction
Survey about housing demand created by postsecondary
educational institutions indicate a need for better data collection on this issue. Despite the lack of precise data on this topic at the local level, the housing needs generated by postsecondary institutions are incorporated into
Plan Bay Area 2050, which directly informs the baseline allocation of the final RHNA methodology. The Regional
Growth Forecast projects the number of households and
group quarters residents, some of whom are students.
Additionally, the local growth patterns developed for
the Plan Bay Area 2050 Final Blueprint using UrbanSim
consider the presence of major universities as well as
these institutions’ residential and non-residential pipeline projects.
Moreover, the RHNA methodology allocates nearly half of all units based on proximity to jobs, and postsecondary education institutions tend to be significant job centers.
Therefore, the methodology will allocate more housing
to jurisdictions near community colleges or public and
private universities, which will result in additional housing units that can enable these jurisdictions to address the housing needs of students, faculty, and staff at these
institutions.
© Bruce Damonte courtesy CHP . All rights reserved.
44 10. The housing needs of individuals and families
experiencing homelessness.
Comprehensive jurisdiction-level data about individuals and families experiencing homelessness is not available for all Bay Area jurisdictions in a consistent format. As a result, this topic was not included as a specific factor in the final RHNA methodology. However, the methodology
does consider the housing needs of individuals and
families experiencing homelessness by allocating very
low- and low-income units to all jurisdictions throughout the region. As the RHNA methodology focuses on access
to opportunity and proximity to jobs, the methodology
can help ensure that housing targeted toward people
experiencing homelessness can enable them to access
employment and other essential resources for stability and economic mobility. Furthermore, ABAG will encourage
all local jurisdictions to adequately plan for the needs of those experiencing homelessness in their housing
elements.
11. The loss of units during a state of emergency that was declared by the Governor pursuant to the California
Emergency Services Act (Chapter 7 (commencing
with Section 8550) of Division 1 of Title 2), during the planning period immediately preceding the relevant
revision pursuant to Section 65588 that have yet to be
rebuilt or replaced at the time of the analysis.
ABAG received two responses in the survey of local
jurisdictions that identified the number of units lost during declared states of emergency. The City of Santa Rosa
indicated that 3,043 housing units were lost on October 8, 2017 and that, as of February 2020 when the survey was
conducted, 2,323 units had been completed or were in the construction/permitting process. The County of Sonoma
stated the unincorporated county lost 2,200 units in the 2017 Sonoma Complex Fires and 1,235 units had been
rebuilt or were under construction as of February 2020.
The County also lost 176 units in the 2019 Kincade fire and 4 were in the process of being rebuilt as of February 2020. Unincorporated Napa County also reported to the
California Department of Finance (DOF) that it lost 587 housing units during the wildfires that took place in 2017.
In developing the RHND, HCD analyzed Bay Area
jurisdictions’ annual reports to DOF and found that the ten-year annual average rate of demolitions for the Bay Area is 0.40 percent of the housing stock. The RHND
included HCD’s minimum replacement adjustment of 0.5
percent, which exceeds the region’s demolition rate. This adjustment added 15,120 housing units to the RHND.
Since the demolition adjustment in the RHND included
significantly more units than were lost, it was not necessary to include a specific factor in the final RHNA methodology to address the loss of units.
12. The region’s greenhouse gas emissions targets
provided by the State Air Resources Board pursuant
to Section 65080.
Plan Bay Area 2050, which is used as the baseline
allocation in the final RHNA methodology, includes a
diverse range of strategies to reduce greenhouse gas
emissions, including:
45 • Focusing more housing growth in areas near high- quality public transit and in high-resource communities near job centers
• Redeveloping aging malls and office parks in mixed- income communities
• Vastly expanding the amount of funding for production and preservation of affordable housing
• Focusing more job growth near high-quality public
transit, especially in housing-rich communities to
address jobs-housing imbalance
•
Investing in new local and regional rail lines,
express buses, local bus systems, and more to serve
communities across the Bay Area
•
Investing in world-class bicycle and pedestrian
infrastructure in all communities to enable
neighborhood trips to be completed without a car.
The greenhouse gas reduction forecasts in Plan Bay Area 2050 are subject to the review of the State Air Resources Board. The Final Blueprint meets and exceeds the 19
percent per-capita target set for this planning cycle.
Additionally, the final RHNA methodology’s allocation
factors focus on locating housing near jobs. As a result, as shown in Appendix 3, jurisdictions with the most access to jobs and transit as well as those with the lowest VMT per resident experience higher growth rates resulting from the final RHNA methodology’s allocations.
13. Any other factors adopted by the council of
governments, that further the objectives listed in
subdivision (d) of Section 65584, provided that
the council of governments specifies which of the
objectives each additional factor is necessary to
further.
No other planning factors were adopted by ABAG to
review as a specific local planning factor.
ABAG-MTC Photo Archive.
RHNA STATUTORY OBJECTIVES AND FACTORS RHNA
46 Housing Element Law allows two or more
jurisdictions to form a “subregion” to conduct
a parallel RHNA process to allocate the
subregion’s housing need among its members.
A subregion is responsible for conducting
its own RHNA process that meets all of the
statutory requirements related to process and
outcomes, including developing its own RHNA
methodology, allocating a share of need to each
member jurisdiction, and conducting its own
appeals process. The subregion’s final allocation
must meet the same requirements as the
regional allocation: it must further the statutory
objectives, have considered the statutory factors,
and be consistent with the development pattern
of Plan Bay Area 2050.
For the 2023 to 2031 RHNA, a subregion was formed in
Solano County that includes City of Benicia, City of Dixon, City of Fairfield, City of Rio Vista, City of Suisun City, City of Vacaville, City of Vallejo, and County of Solano.
ABAG must assign each subregion a share of the Bay
Area’s RHND, which represents the total number of units, by income category, the subregion must allocate to its
member jurisdictions. Each subregion’s portion of the
RHND has been removed from the units allocated by
ABAG’s process for the rest of the region’s jurisdictions.
The ABAG Executive Board approved the release of Draft
Subregional Shares for public comment on October
15, 2020. ABAG received no comments on the Draft
Subregional Shares during the public comment period.
The Final Subregional Shares, as shown in Table 5 (below), were approved by the ABAG Executive Board on January
21, 2021.
The Solano Subregion adopted its final RHNA allocations on November 18, 2021. See website https://www.
solanocounty.com/depts/rm/planning/regional_housing_
needs_allocation_and_housing_element.asp for more
information.
RHNA SUBREGIONS
Table 5: Final Subregional Shares, Total Units by Income Category Subregion VERY LOW LOW MODERATE ABOVE MODERATE TOTAL
Solano County 2,803 1,612 1,832 4,745 10,992
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-203148
ENDNOTES
1 Government Code Section 65580 covers all facets of Housing Element Law.
The RHNA process is covered in Section 65584. RHNA factors are covered in Section 65584.04; objectives are covered in 65584(d).
2 The four income categories included in the RHND are:
• Very Low Inc ome: 0-50% of Area Median Income
• Low Income : 50-80% of Area Median Income
• Moderate I ncome: 80-120% of Area Median Income
• Above Mode rate Income: 120% or more of Area Median Income 3 Read more on the HCD Regional Housing Needs Allocation and Housing Elements web page.
4 More details about the RHND is available on the ABAG RHNA website (scroll to bottom of page). At this time, the RHND has been finalized by the State for the Bay Area’s RHNA process.
5 Government Code Section 65584.01.
6 Government Code Section 65584(d).
7 Government Code Section 65584.04(m)(1).
8 According to Government Code Section 65584(e), affirmatively furthering fair housing means “For purposes of this section, “affirmatively furthering fair housing” means taking meaningful actions, in addition to combating discrimination, that overcome patterns of segregation and foster inclusive communities free from barriers that restrict access to opportunity based on protected characteristics. Specifically, affirmatively furthering fair housing means taking meaningful actions that, taken together, address significant disparities in housing needs and in access to opportunity, replacing segregated living patterns with truly integrated and balanced living patterns, transforming racially and ethnically concentrated areas of poverty into areas of opportunity, and fostering and maintaining compliance with civil rights and fair housing laws.”
9 See State of California Government Code Section 65584.04(b)(1). 10 A summary of the Local Jurisdiction Survey responses is available on the ABAG website.
11 For letters HCD sent to other regions, see the January 2020 HMC meeting agenda packet.
12 The final RHNA methodology and Plan Bay Area 2050 are consistent for all nine counties and in 33 of 34 superdistricts (i.e., sub-county areas) using the methodology developed during the HMC process. In the one superdistrict flagged during the consistency check, the Final Blueprint reflects the loss of more than 1,000 homes in wildfires since 2015. Anticipated reconstruction of these units during the RHNA period does not yield significant net growth in housing units, making these allocations consistent with the Final Blueprint long-range projections.
13 Plan Bay Area 2050 is the long-range regional plan for the San Francisco Bay Area, serving as the 2021 Regional Transportation Plan/Sustainable Communities Strategy for the Bay Area
14 For more information on the Opportunity Map, see pages 10-13 of this document from the March 2020 HMC meeting’s agenda packet. 15 See Government Code Section 65584(e).
16 Jurisdictions with above-average levels of racial and economic exclusion were identified based on their divergence index scores and their percentage of households above 120 percent Area Median Income. The divergence index score is a calculation of how different a jurisdiction’s racial demographics are from the region’s demographics. If a jurisdiction has the same racial distribution as the region, the jurisdiction’s divergence index is scored at 0. The more a jurisdiction’s demographics diverge from the regional distribution, the higher the divergence index score. A high score does not necessarily indicate that the jurisdiction is racially homogenous, only that its demographic profile differs markedly from the region’s racial demographics. Given the multitude of racial and ethnic groups in the Bay Area, the Othering and Belonging Institute at UC Berkeley has identified the Divergence Index as the best measure of segregation in the region in part because this measure captures segregation for multiple racial groups simultaneously.
17 Bay Area UrbanSim 2.0 is a spatially explicit economic model that forecasts future business and household locations. It forecasts future land use change (e.g., development or redevelopment) starting from an integrated base year database containing information on the buildings, households, businesses and land use policies within the region. During the simulation, Bay Area UrbanSim 2.0 forecasts the choices real estate developers make on how much, what, and where to build, based upon future-focused public policy inputs (strategies & growth geographies adopted for use in Plan Bay Area 2050). This adds additional housing units and commercial space in profitable locations (i.e., land use policies at the site allow the construction of a building that is profitable under forecast demand). Additional documentation for Bay Area UrbanSim 2.0 is available at: https://github. com/UDST/bayarea_urbansim
18 For purposes of designating High-Resource Areas in the Final Blueprint, “near transit” was defined as within 1/2 mile of a rail station, ferry terminal or bus stop with peak headways of 15 minutes or less, or within 1/4 mile of a bus stop with peak headways of 30 minutes or less.
19 The only exception is for locally-nominated Priority Development Areas (PDAs). The only PDA affected was the Urbanized Corridor PDA in Marin County.
20 Transit Priority Areas are defined in the California Public Resources Code, Section 21099 as areas within 1/2 mile of a Major Transit stop, which could be any of the following:
• Existing r ail stations
• Planned ra il stations in an adopted Regional Transportation Plan • Existing f erry terminals with bus or rail
• Planned fe rry terminals with bus or rail service in an adopted Regional Transportation Plan
• Intersec tion of at least two existing or planned bus routes with headways of 15 minutes or better during both the morning and evening peak periods
21 Government Code Section 65584.07.
22 See the June 9, 2020 letter in which HCD provided the RHND for the Bay Area. 23 See the June 9, 2020 letter in which HCD provided the RHND for the Bay Area. 24 Health and Safety Code Section 50515.05
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 A2
APPENDIX 1
STATE OF CALIFORNIA - BUSINESS CONSUMER SERVICES AND HOUSING AGENCY GAVIN NEWSOM Governor
DEPARTMENT OF HOUSING AND COMMUNITY DEVELOPMENT
DIVISION OF HOUSING POLICY DEVELOPMENT
2020 W. El Camino Avenue, Suite 500
Sacramento, CA 95833
(916) 263-2911 / FAX (916) 263-7453
www hcd ca.gov
April 12, 2021
Therese W. McMillan, Executive Director
Association of Bay Area Governments
375 Beale Street, Suite 700
San Francisco, CA 94105
Dear Executive Director Therese W. McMillan:
RE: Review of Draft Regional Housing Need Allocation (RHNA) Methodology
Thank you for submitting the draft Association of Bay Area Governments (ABAG) Sixth Cycle Regional Housing Need Allocation (RHNA) Methodology. Pursuant to Government Code Section 65584.04(i), the California Department of Housing and Community Development (HCD) is required to review draft RHNA methodologies to determine whether a methodology furthers the statutory objectives described in Government Code Section 65584(d).
In brief, the draft ABAG RHNA methodology begins with the total regional determination provided by HCD of 441,176 units and uses a baseline allocation to assign each jurisdiction a beginning share of the units. The baseline allocation is based on each jurisdiction’s share of the region’s total households in the year 2050 from the Plan Bay Area Final Blueprint. The methodology then applies one set of factors and weights to adjust the baseline allocation for the very low and low units, and another set for moderate and above moderate units to address the statutory objectives.
For the low- and very low-income allocations, the methodology uses three adjustments:
access to high opportunity areas (70 percent), job proximity by auto (15 percent), and job proximity by transit (15 percent). For the moderate and above moderate allocations, the methodology uses two adjustments: access to high opportunity areas (40 percent) and job proximity by auto (60 percent).
Lastly, the methodology applies an equity adjustment that identifies 49 jurisdictions that exhibit higher racial segregation and higher median incomes than regional averages. The adjustment ensures each jurisdiction receives an allocation of lower income units that is proportional to its share of the region’s total households in 2020.
--continued on next page--
A3
APPENDIX 1
--continued from previous page--
HCD has completed its review of the methodology and finds that the draft ABAG RHNA Methodology furthers the statutory objectives described in Government Code 65584(d).1 HCD acknowledges the complex task of developing a methodology to allocate RHNA to 109 jurisdictions while furthering the five statutory objectives of RHNA. This methodology largely distributes more RHNA near jobs, transit and resources linked to long-term improvements of life outcomes. In particular, HCD applauds the use of objective factors specifically linked to the statutory objectives.
HCD commends ABAG for a robust methodology development process, with exceptional stakeholder engagement, through its Housing Methodology Committee (HMC). The HMC consisted of nine elected officials and 12 planning staff, with representation from all six ABAG counties. It also consisted of 16 diverse regional stakeholders. This combination of elected officials, local government staff, and regional stakeholders met 12 times over the course of a nearly one calendar year.
Below is a brief summary of findings related to each statutory objective described within Government Code Section 65584(d):
1. Increasing the housing supply and the mix of housing types, tenure, and affordability in all cities and counties within the region in an equitable manner, which shall result in each jurisdiction receiving an allocation of units for low- and very low-income households.
On a per capita basis, the methodology allocates larger shares of RHNA to higher income jurisdictions, resulting in an allocation larger than their existing share of households. Jurisdictions with more expensive housing units – an indicator of higher housing demand – receive larger allocations on a per capita basis. For example, Palo Alto and Menlo Park have some of the highest housing costs in the region, according to American Community Survey Data. Both jurisdictions receive a share of the regional RHNA that is larger than their share of the region's population, putting them in the top 15 per capita allocations. Additionally, jurisdictions with higher rates of home ownership and single-family homes receive slightly larger lower-income allocations as a percentage of their total RHNA (supporting a mix of housing types).
2. Promoting infill development and socioeconomic equity, the protection of environmental and agricultural resources, the encouragement of efficient development patterns, and the achievement of the region’s greenhouse gas reductions targets provided by the State Air Resources Board pursuant to Section 65080.
The draft ABAG methodology encourages a more efficient development pattern by allocating nearly twice as many RHNA units to jurisdictions with higher jobs access, on a per capita basis. Jurisdictions with higher jobs access via transit also receive more RHNA on a per capita basis.
--continued on next page--
1 While HCD finds this methodology compliant, applying this methodology to another region or cycle may not necessarily further the statutory objectives as housing conditions and circumstances may differ.
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 A4
APPENDIX 1
--continued from previous page--
Jurisdictions with the lowest vehicle miles traveled (VMT) per capita, relative to the region, receive more RHNA per capita than those with the highest per capita VMT.
ABAG’s largest individual allocations go to its major cities with low VMT per capita and better access to jobs. For example, San Francisco – which has the largest allocation – has the lowest per capita VMT and is observed as having the highest transit accessibility in the region. As a major employment center, San Jose receives a substantial RHNA allocation despite having a higher share of solo commuters and a lower share of transit use than San Francisco. However, to encourage lower VMT in job-rich areas that may not yet be seeing high transit ridership, ABAG’s Plan Bay Area complements more housing in these employment centers (which will reduce commutes by allowing more people to afford to live near jobs centers) with strategies to reduce VMT by shifting mode share from driving to public transit.
3. Promoting an improved intraregional relationship between jobs and housing, including an improved balance between the number of low-wage jobs and the number of housing units affordable to low-wage workers in each jurisdiction.
The draft ABAG methodology allocates more RHNA units to jurisdictions with more jobs.
Jurisdictions with a higher jobs/housing imbalance receive higher RHNA allocations on a per capita basis. For example, jurisdictions within the healthy range of 1.0 to 1.5 jobs for every housing unit receive, on average, a RHNA allocation that is 61% of their current share of households. Jurisdictions with the highest imbalances – 6.2 and higher – receive an average allocation 1.21 times their current share of households. Lastly, higher income jurisdictions receive larger lower income allocations relative to their existing lower income job shares.
4. Allocating a lower proportion of housing need to an income category when a jurisdiction already has a disproportionately high share of households in that income category, as compared to the countywide distribution of households in that category from the most recent American Community Survey.
On average, cities with a larger existing share of lower income units receive smaller allocations of low- and very-low income units as a percentage of their total RHNA. For example, East Palo Alto’s current percentage of households that are lower income is the highest in the ABAG region and it receives the lowest lower income allocation as a percentage of its total RHNA. San Pablo’s percentage of households that are lower income is the second highest in the region and its lower income allocation as a percentage of its total RHNA is lower than 92% of other jurisdictions. Cities with smaller shares of existing lower income units receive larger allocations of low- and very low- income units as a percentage of their total RHNA.
5. Affirmatively furthering fair housing, which means taking meaningful actions, in addition to combating discrimination, that overcome patterns of segregation and foster inclusive communities free from barriers that restrict access to opportunity based on protected characteristics. Specifically, affirmatively furthering fair housing means taking meaningful actions that, taken together, address significant disparities in housing needs and in access
d
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 A6
A6
APPENDIX 2
STATE OF CAL FORNIA - BUSINESS, CONSUMER SERVICES AND HOUSING AGENCY GAVIN NEWSOM, Governor
DEPARTMENT OF HOUSING AND COMMUNITY DEVELOPMENT
DIVISION OF HOUSING POLICY DEVELOPMENT
2020 W. El Camino Avenue, Suite 500
Sacramento, CA 95833
(916) 263-2911 / FAX (916) 263-7453
www.hcd.ca gov
June 9, 2020
Therese W. McMillan, Executive Director
Association of Bay Area Governments
375 Beale Street. Suite 700
San Francisco, CA 94105
Dear Therese W. McMillan,
RE: Final Regional Housing Need Determination
This letter provides the Association of Bay Area Governments (ABAG) its final Regional Housing Need Determination. Pursuant to state housing element law (Government Code section 65584, et seq.), the Department of Housing and Community Development (HCD) is required to provide the determination of ABAG’s existing and projected housing need.
In assessing ABAG’s regional housing need, HCD and ABAG staff completed an extensive consultation process from March 2019 through May 2020 covering the methodology, data sources, and timeline for HCD’s determination of the Regional Housing Need. HCD also consulted with Walter Schwarm with the California Department of Finance (DOF) Demographic Research Unit.
Attachment 1 displays the minimum regional housing need determination of 441,176 total units among four income categories for ABAG to distribute among its local governments. Attachment 2 explains the methodology applied pursuant to Gov. Code section 65584.01. In determining ABAG’s housing need, HCD considered all the information specified in state housing law (Gov. Code section 65584.01(c)).
As you know, ABAG is responsible for adopting a methodology for RHNA allocation and RHNA Plan for the projection period beginning June 30, 2022 and ending December 31, 2030. Pursuant to Gov. Code section 65584(d), the methodology to prepare ABAG’s RHNA plan must further the following objectives:
(1) Increasing the housing supply and mix of housing types, tenure, and affordability (2) Promoting infill development and socioeconomic equity, protecting environmental and agricultural resources, and encouraging efficient development patters (3) Promoting an improved intraregional relationship between jobs and housing (4) Balancing disproportionate household income distributions (5) Affirmatively furthering fair housing
Pursuant to Gov. Code section 65584.04(d), to the extent data is available, ABAG shall include the factors listed in Gov. Code section 65584.04(d)(1-13) to develop its RHNA
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 A8
A8
APPENDIX 2
ATTACHMENT 1
HCD REGIONAL HOUSING NEED DETERMINATION
ABAG: June 30, 2022 through December 31, 2030
Income Category Percent Housing Unit Need
Very-Low* 25.9% 114,442
Low 14.9% 65,892
Moderate 16.5% 72,712
Above-Moderate 42.6% 188,131
Total 100.0% 441,176
* Extremely-Low 15.5% Included in Very-Low Category
Notes:
Income Distribution:
Income categories are prescribed by California Health and Safety Code (Section 50093, et. seq.). Percents are derived based on Census/ACS reported household income brackets and county median income, then adjusted based on the percent of cost-burdened households in the region compared with the percent of cost burdened households nationally.
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 A10
A10
APPENDIX 2
8. Occupied Units: This figure reflects DOF’s estimate of occupied units at the start of the projection period (June 30, 2022).
9. Cost Burden Adjustment: HCD applies an adjustment to the projected need by comparing the difference i cost -burden by income group for the region to the cost- burden by income group for the comparable regions, as determined by ABAG The very-low and low income RHNA is increased by the percent difference (66.64%-
66.00%=.64%) between the region and the comparable region cost burden rate for households earning 80% of area median income and below, then this difference is applied to very low- and low-income RHNA proportionate to the share of the population these groups currently represent. The moderate and above-moderate income RHNA is increased by the percent difference (16.25%-13.10%=3.15%) between the region and the comparable region cost burden rate for households earning above 80% Area Median Income, then this difference is applied to moderate and above moderate income RHNA proportionate to the share of the population these groups currently represent. Data is from 2012-2016 CHAS.
7. Replacement Adjustment: HCD applies a replacement adjustment between .5% and 5% to the total housing stock based on the current 10-year annual average percent of demolitions the region’s local government annual reports to Department of Finance (DOF). For ABAG the 10-year annual average multiplied by the length of the projection period is .40%, and the minimum .50% adjustment is applied.
ATTACHMENT 2
HCD REGIONAL HOUSING NEED DETERMINATION:
ABAG June 30, 2021 through December 31, 2030
ABAG: PROJE
ION PER OD (8 5 yea s)
D m ned Pop lati n, Ho s hold H i g Unit Ne d
p Take o Cal la giona o i ed A o n
jecti n djus d 6 m nth to D c mber 31 2 3
Q P i De
3 20 30 pro ec ion ad ust d + mont s o Decembe 31 2030)
usehold (HH Populat on 33 655
je te Household 3,023 735
aca cy Adjus ent 3 27 98,7
erc owdi g justm 3.13%) 4 6 5
pla em n dju me ( 5 + 5 20
ccupied Uni s (HHs est ma e J u e 30 20 2 -2 800 185 os -bu den Adjustmen +9 10
Cy le Regional Housing Nee Asse sment (RHN ) 441 17
a for h s art is availa le p r que t
regions overcrowding rate (6.7 %) exceeds he comparable
(Continued)
APPENDICES RHNA A11
APPENDIX 3
Overview of Performance Evaluation Metrics
1 See California Government Code Section 65584(d).
2 For copies of letters HCD sent to other regions, see this document from the January 2020 HMC meeting agenda packet.
The RHNA allocation methodology must meet five objectives identified in Housing Element Law.1 To help ensure that any proposed methodology would meet the statutory
RHNA objectives and receive approval from the California Department of Housing and Community Development
(HCD), ABAG-MTC staff developed a set of evaluation
metrics to assess different methodology options. These
metrics are based largely on the analytical framework used by HCD in evaluating the draft methodologies completed
by other regions in California, as evidenced by the approval letters HCD provided to the Sacramento Area Council
of Governments (SACOG), San Diego Association of
Governments (SANDAG), and Southern California Association of Governments (SCAG).
2 Other metrics reflect input from
members of the Housing Methodology Committee (HMC).
In the evaluation metrics, each statutory objective has been reframed as a question that reflects the language Housing Element Law uses to define the objectives. Each statutory objective is accompanied by quantitative metrics for
evaluating the allocation produced by a methodology. The metrics are generally structured as a comparison between the allocations to the top jurisdictions in the region for a particular characteristic – such as jurisdictions with the most expensive housing costs – and the allocations to the rest of the jurisdictions in the region.
Metrics Based on Lower-Income Unit Percentage vs.
Metrics Based on Total Allocation
Several of the metrics focus on whether jurisdictions with certain characteristics receive a significant share of their RHNA as lower-income units. These metrics reflect HCD’s analysis in its letters evaluating RHNA methodologies from other regions. However, HMC members advocated for metrics that also examine the total number of units assigned to a jurisdiction. These HMC members asserted that it is ultimately less impactful if a jurisdiction receives a high share of its RHNA as lower-income units if that same jurisdiction receives few units overall. Accordingly, each metric that focuses on the share of lower-income units assigned to jurisdictions with certain characteristics is paired with a complementary metric that examines whether those jurisdictions also receive a share of the regional housing need that is at least proportional to their share of the region’s households. A value of 1.0 for these complementary metrics means that the group of jurisdictions’ overall share of RHNA is proportional relative to its overall share of households in 2020, while a value below 1.0 is less than proportional.
Evaluation of Final RHNA Methodology
The graphs below show how well the final RHNA
methodology performs in achieving the five statutory RHNA objectives based on the evaluation metrics.
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 APPENDICES RHNA A16 A17
A17
APPENDIX 4
A16
Factor Scores by Jurisdiction
Jurisdiction
BASELINE ALLOCATION:
Share of Bay Area
Households in
Year 2050
(A)
FACTOR: ACCESS TO HIGH OPPORTUNITY AREAS (AHOA) FACTOR: JOB PROXIMITY - AUTO (JPA) FACTOR: JOB PROXIMITY - TRANSIT (JPT)
FACTOR PREPARATION
FACTOR
DISTRIBUTION:
ADJUSTED BASELINE
RESCALED TO 100%
(A * B) / SUM(A * B)
FACTOR PREPARATION
FACTOR
DISTRIBUTION:
ADJUSTED BASELINE
RESCALED TO 100%
(A * B) / SUM(A * B)
FACTOR PREPARATION
FACTOR
DISTRIBUTION:
ADJUSTED BASELINE
RESCALED TO 100%
(A * B) / SUM(A * B)
RAW AHOA
FACTOR SCORE
AHOA
FACTOR SCORE
RESCALED TO
0.5-1.5 RANGE
(B)
BASELINE ADJUSTED
BY
AHOA FACTOR
(A * B)
RAW JPA FACTOR
SCORE
JPA FACTOR SCORE
RESCALED TO
0.5-1.5 RANGE
(B)
BASELINE
ADJUSTED BY JPA
FACTOR
(A * B)
RAW JPT FACTOR
SCORE
JPT FACTOR SCORE
RESCALED TO
0.5-1.5 RANGE
(B)
BASELINE
ADJUSTED BY JPT
FACTOR
(A * B)
Alameda 1.100% 0.658 1.158 1.274% 1.372% 16.458 1.013 1.114% 1.075% 2.570 0.677 0.744% 0.995% Albany 0.206% 0.845 1.345 0.278% 0.299% 16.532 1.015 0.210% 0.202% 5.333 0.866 0.179% 0.239% American Canyon 0.176% 0.000 0.500 0.088% 0.095% 4.492 0.638 0.113% 0.109% 0.000 0.500 0.088% 0.118% Antioch 1.270% 0.000 0.500 0.635% 0.684% 1.670 0.549 0.698% 0.673% 0.050 0.503 0.639% 0.855% Atherton 0.072% 0.414 0.914 0.066% 0.071% 21.084 1.158 0.083% 0.080% 1.827 0.625 0.045% 0.060% Belmont 0.305% 1.000 1.500 0.457% 0.492% 19.019 1.093 0.333% 0.322% 0.754 0.552 0.168% 0.225% Belvedere 0.032% 1.000 1.500 0.048% 0.052% 3.208 0.597 0.019% 0.019% 0.000 0.500 0.016% 0.022% Berkeley 1.701% 0.730 1.230 2.093% 2.253% 18.029 1.062 1.807% 1.744% 7.622 1.023 1.741% 2.328% Brentwood 0.647% 0.000 0.500 0.323% 0.348% 1.290 0.537 0.348% 0.335% 0.000 0.500 0.323% 0.432% Brisbane 0.423% 0.000 0.500 0.211% 0.228% 26.701 1.334 0.564% 0.544% 0.111 0.508 0.215% 0.287% Burlingame 0.546% 1.000 1.500 0.820% 0.883% 21.877 1.183 0.646% 0.624% 0.770 0.553 0.302% 0.404% Calistoga 0.052% 0.000 0.500 0.026% 0.028% 0.499 0.513 0.027% 0.026% 0.000 0.500 0.026% 0.035% Campbell 0.563% 0.657 1.157 0.652% 0.702% 23.847 1.245 0.701% 0.677% 3.067 0.711 0.400% 0.535% Clayton 0.111% 1.000 1.500 0.167% 0.179% 6.175 0.690 0.077% 0.074% 0.016 0.501 0.056% 0.074% Cloverdale 0.120% 0.000 0.500 0.060% 0.065% 0.400 0.509 0.061% 0.059% 0.000 0.500 0.060% 0.080% Colma 0.052% 0.000 0.500 0.026% 0.028% 25.758 1.304 0.068% 0.066% 5.495 0.877 0.046% 0.062% Concord 1.725% 0.112 0.612 1.057% 1.138% 6.800 0.710 1.225% 1.182% 0.382 0.526 0.908% 1.214% Corte Madera 0.138% 1.000 1.500 0.207% 0.223% 7.987 0.747 0.103% 0.100% 0.728 0.550 0.076% 0.102% Cotati 0.092% 0.000 0.500 0.046% 0.050% 4.449 0.636 0.059% 0.057% 0.001 0.500 0.046% 0.062% Cupertino 0.724% 1.000 1.500 1.086% 1.169% 27.568 1.361 0.985% 0.951% 0.866 0.559 0.405% 0.541% APPENDIX 4
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 APPENDICES RHNA A18 A19
A19
APPENDIX 4
A18
Factor Scores by Jurisdiction
Jurisdiction
BASELINE ALLOCATION:
Share of Bay Area
Households in
Year 2050
(A)
FACTOR: ACCESS TO HIGH OPPORTUNITY AREAS (AHOA) FACTOR: JOB PROXIMITY - AUTO (JPA) FACTOR: JOB PROXIMITY - TRANSIT (JPT)
FACTOR PREPARATION
FACTOR
DISTRIBUTION:
ADJUSTED BASELINE
RESCALED TO 100%
(A * B) / SUM(A * B)
FACTOR PREPARATION
FACTOR
DISTRIBUTION:
ADJUSTED BASELINE
RESCALED TO 100%
(A * B) / SUM(A * B)
FACTOR PREPARATION
FACTOR
DISTRIBUTION:
ADJUSTED BASELINE
RESCALED TO 100%
(A * B) / SUM(A * B)
RAW AHOA
FACTOR SCORE
AHOA
FACTOR SCORE
RESCALED TO
0.5-1.5 RANGE
(B)
BASELINE ADJUSTED
BY
AHOA FACTOR
(A * B)
RAW JPA FACTOR
SCORE
JPA FACTOR SCORE
RESCALED TO
0.5-1.5 RANGE
(B)
BASELINE
ADJUSTED BY JPA
FACTOR
(A * B)
RAW JPT FACTOR
SCORE
JPT FACTOR SCORE
RESCALED TO
0.5-1.5 RANGE
(B)
BASELINE
ADJUSTED BY JPT
FACTOR
(A * B)
Daly City 0.945% 0.273 0.773 0.730% 0.786% 26.874 1.339 1.266% 1.222% 6.054 0.916 0.865% 1.157% Danville 0.424% 1.000 1.500 0.636% 0.685% 9.019 0.780 0.330% 0.319% 0.025 0.502 0.213% 0.284% Dublin 0.705% 1.000 1.500 1.057% 1.139% 8.733 0.771 0.543% 0.524% 0.222 0.515 0.363% 0.486% East Palo Alto 0.206% 0.000 0.500 0.103% 0.111% 30.667 1.458 0.301% 0.290% 1.896 0.630 0.130% 0.174% El Cerrito 0.405% 0.110 0.610 0.247% 0.266% 14.763 0.960 0.389% 0.375% 2.914 0.700 0.284% 0.379% Emeryville 0.493% 0.000 0.500 0.246% 0.265% 19.602 1.111 0.548% 0.528% 13.124 1.401 0.690% 0.923% Fairfax 0.098% 1.000 1.500 0.148% 0.159% 3.296 0.600 0.059% 0.057% 0.288 0.520 0.051% 0.068% Foster City 0.327% 1.000 1.500 0.491% 0.529% 18.052 1.063 0.348% 0.336% 0.227 0.516 0.169% 0.226% Fremont 2.434% 0.920 1.420 3.456% 3.722% 12.595 0.892 2.170% 2.094% 0.516 0.535 1.303% 1.742% Gilroy 0.461% 0.166 0.666 0.307% 0.331% 1.289 0.537 0.248% 0.239% 0.035 0.502 0.231% 0.310% Half Moon Bay 0.149% 0.000 0.500 0.074% 0.080% 0.200 0.503 0.075% 0.072% 0.000 0.500 0.074% 0.099% Hayward 1.571% 0.000 0.500 0.786% 0.846% 11.689 0.863 1.357% 1.309% 0.661 0.545 0.857% 1.146% Healdsburg 0.121% 0.000 0.500 0.061% 0.065% 3.132 0.595 0.072% 0.070% 0.020 0.501 0.061% 0.081% Hercules 0.264% 0.000 0.500 0.132% 0.142% 8.488 0.763 0.202% 0.195% 0.450 0.531 0.140% 0.188% Hillsborough 0.097% 1.000 1.500 0.146% 0.157% 15.669 0.988 0.096% 0.093% 0.019 0.501 0.049% 0.065% Lafayette 0.382% 1.000 1.500 0.572% 0.616% 13.389 0.917 0.350% 0.338% 0.578 0.540 0.206% 0.275% Larkspur 0.189% 1.000 1.500 0.284% 0.306% 6.557 0.702 0.133% 0.128% 0.659 0.545 0.103% 0.138% Livermore 1.269% 0.373 0.873 1.108% 1.193% 4.970 0.653 0.828% 0.799% 0.103 0.507 0.643% 0.860% Los Altos 0.301% 1.000 1.500 0.451% 0.486% 30.664 1.458 0.438% 0.423% 0.862 0.559 0.168% 0.225% Los Altos Hills 0.076% 1.000 1.500 0.114% 0.123% 29.816 1.432 0.109% 0.105% 0.000 0.500 0.038% 0.051% APPENDIX 4
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 APPENDICES RHNA A20 A21
A21
APPENDIX 4
A20
Factor Scores by Jurisdiction
Jurisdiction
BASELINE ALLOCATION:
Share of Bay Area
Households in
Year 2050
(A)
FACTOR: ACCESS TO HIGH OPPORTUNITY AREAS (AHOA) FACTOR: JOB PROXIMITY - AUTO (JPA) FACTOR: JOB PROXIMITY - TRANSIT (JPT)
FACTOR PREPARATION
FACTOR
DISTRIBUTION:
ADJUSTED BASELINE
RESCALED TO 100%
(A * B) / SUM(A * B)
FACTOR PREPARATION
FACTOR
DISTRIBUTION:
ADJUSTED BASELINE
RESCALED TO 100%
(A * B) / SUM(A * B)
FACTOR PREPARATION
FACTOR
DISTRIBUTION:
ADJUSTED BASELINE
RESCALED TO 100%
(A * B) / SUM(A * B)
RAW AHOA
FACTOR SCORE
AHOA
FACTOR SCORE
RESCALED TO
0.5-1.5 RANGE
(B)
BASELINE ADJUSTED
BY
AHOA FACTOR
(A * B)
RAW JPA FACTOR
SCORE
JPA FACTOR SCORE
RESCALED TO
0.5-1.5 RANGE
(B)
BASELINE
ADJUSTED BY JPA
FACTOR
(A * B)
RAW JPT FACTOR
SCORE
JPT FACTOR SCORE
RESCALED TO
0.5-1.5 RANGE
(B)
BASELINE
ADJUSTED BY JPT
FACTOR
(A * B)
Los Gatos 0.335% 1.000 1.500 0.503% 0.542% 20.659 1.145 0.384% 0.371% 0.120 0.508 0.170% 0.228% Martinez 0.383% 0.298 0.798 0.306% 0.329% 8.951 0.778 0.298% 0.288% 0.149 0.510 0.196% 0.262% Menlo Park 0.481% 0.848 1.348 0.648% 0.697% 30.389 1.450 0.697% 0.672% 1.426 0.598 0.287% 0.384% Mill Valley 0.164% 1.000 1.500 0.246% 0.265% 6.629 0.705 0.116% 0.112% 0.268 0.518 0.085% 0.114% Millbrae 0.350% 1.000 1.500 0.526% 0.566% 26.434 1.326 0.465% 0.448% 0.810 0.556 0.195% 0.260% Milpitas 1.257% 0.623 1.123 1.412% 1.520% 25.695 1.302 1.637% 1.580% 2.588 0.678 0.852% 1.139% Monte Sereno 0.032% 1.000 1.500 0.048% 0.051% 21.405 1.168 0.037% 0.036% 0.007 0.500 0.016% 0.021% Moraga 0.204% 1.000 1.500 0.306% 0.330% 12.396 0.886 0.181% 0.174% 0.267 0.518 0.106% 0.141% Morgan Hill 0.410% 0.000 0.500 0.205% 0.221% 4.420 0.635 0.261% 0.252% 0.153 0.511 0.209% 0.280% Mountain View 1.754% 0.925 1.425 2.499% 2.690% 31.814 1.494 2.621% 2.529% 1.737 0.619 1.086% 1.452% Napa 0.769% 0.028 0.528 0.406% 0.437% 3.017 0.591 0.455% 0.439% 0.243 0.517 0.397% 0.531% Newark 0.609% 0.114 0.614 0.374% 0.402% 9.202 0.785 0.478% 0.461% 0.393 0.527 0.321% 0.429% Novato 0.672% 0.252 0.752 0.505% 0.544% 3.815 0.616 0.414% 0.400% 0.058 0.504 0.339% 0.453% Oakland 6.338% 0.243 0.743 4.708% 5.069% 19.810 1.118 7.086% 6.838% 7.035 0.983 6.231% 8.332% Oakley 0.450% 0.000 0.500 0.225% 0.242% 1.361 0.540 0.243% 0.234% 0.000 0.500 0.225% 0.301% Orinda 0.235% 1.000 1.500 0.352% 0.379% 18.135 1.065 0.250% 0.241% 0.069 0.505 0.118% 0.158% Pacifica 0.356% 1.000 1.500 0.534% 0.575% 10.511 0.826 0.294% 0.284% 0.108 0.507 0.181% 0.242% Palo Alto 0.935% 1.000 1.500 1.402% 1.510% 30.656 1.458 1.363% 1.315% 0.937 0.564 0.527% 0.705% Petaluma 0.716% 0.077 0.577 0.413% 0.445% 3.584 0.609 0.437% 0.421% 0.047 0.503 0.361% 0.482% Piedmont 0.098% 1.000 1.500 0.146% 0.158% 19.883 1.120 0.109% 0.105% 4.836 0.832 0.081% 0.109% APPENDIX 4
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 APPENDICES RHNA A22 A23
A23
APPENDIX 4
A22
Factor Scores by Jurisdiction
Jurisdiction
BASELINE ALLOCATION:
Share of Bay Area
Households in
Year 2050
(A)
FACTOR: ACCESS TO HIGH OPPORTUNITY AREAS (AHOA) FACTOR: JOB PROXIMITY - AUTO (JPA) FACTOR: JOB PROXIMITY - TRANSIT (JPT)
FACTOR PREPARATION
FACTOR
DISTRIBUTION:
ADJUSTED BASELINE
RESCALED TO 100%
(A * B) / SUM(A * B)
FACTOR PREPARATION
FACTOR
DISTRIBUTION:
ADJUSTED BASELINE
RESCALED TO 100%
(A * B) / SUM(A * B)
FACTOR PREPARATION
FACTOR
DISTRIBUTION:
ADJUSTED BASELINE
RESCALED TO 100%
(A * B) / SUM(A * B)
RAW AHOA
FACTOR SCORE
AHOA
FACTOR SCORE
RESCALED TO
0.5-1.5 RANGE
(B)
BASELINE ADJUSTED
BY
AHOA FACTOR
(A * B)
RAW JPA FACTOR
SCORE
JPA FACTOR SCORE
RESCALED TO
0.5-1.5 RANGE
(B)
BASELINE
ADJUSTED BY JPA
FACTOR
(A * B)
RAW JPT FACTOR
SCORE
JPT FACTOR SCORE
RESCALED TO
0.5-1.5 RANGE
(B)
BASELINE
ADJUSTED BY JPT
FACTOR
(A * B)
Pinole 0.183% 0.000 0.500 0.091% 0.098% 8.067 0.750 0.137% 0.132% 0.414 0.528 0.096% 0.129% Pittsburg 0.787% 0.000 0.500 0.393% 0.423% 5.046 0.655 0.515% 0.497% 0.330 0.523 0.411% 0.550% Pleasant Hill 0.368% 0.636 1.136 0.418% 0.450% 9.503 0.795 0.292% 0.282% 0.189 0.513 0.189% 0.252% Pleasanton 1.135% 1.000 1.500 1.703% 1.833% 8.215 0.754 0.856% 0.826% 0.507 0.535 0.607% 0.812% Portola Valley 0.045% 1.000 1.500 0.067% 0.072% 13.912 0.933 0.042% 0.040% 0.000 0.500 0.022% 0.030% Redwood City 0.984% 0.473 0.973 0.958% 1.032% 21.781 1.180 1.161% 1.121% 0.668 0.546 0.537% 0.719% Richmond 1.227% 0.000 0.500 0.614% 0.661% 11.673 0.863 1.059% 1.022% 0.764 0.552 0.678% 0.907% Rohnert Park 0.625% 0.000 0.500 0.312% 0.336% 4.447 0.636 0.398% 0.384% 0.070 0.505 0.315% 0.422% Ross 0.022% 1.000 1.500 0.032% 0.035% 4.210 0.629 0.014% 0.013% 0.592 0.541 0.012% 0.016% San Anselmo 0.167% 1.000 1.500 0.250% 0.270% 3.554 0.608 0.102% 0.098% 0.229 0.516 0.086% 0.115% San Bruno 0.730% 0.244 0.744 0.543% 0.585% 25.955 1.311 0.957% 0.924% 0.798 0.555 0.405% 0.542% San Carlos 0.455% 1.000 1.500 0.683% 0.735% 21.435 1.169 0.532% 0.514% 1.310 0.590 0.269% 0.359% San Francisco 14.304% 0.544 1.044 14.936% 16.082% 31.995 1.500 21.455% 20.705% 14.561 1.500 21.455% 28.689% San Jose 14.426% 0.347 0.847 12.212% 13.149% 20.319 1.134 16.358% 15.786% 2.396 0.665 9.587% 12.819% San Leandro 1.137% 0.000 0.500 0.569% 0.612% 18.689 1.083 1.231% 1.188% 3.221 0.721 0.820% 1.097% San Mateo 1.419% 0.611 1.111 1.576% 1.697% 20.527 1.140 1.618% 1.562% 1.250 0.586 0.831% 1.112% San Pablo 0.248% 0.000 0.500 0.124% 0.134% 12.425 0.886 0.220% 0.212% 1.304 0.590 0.146% 0.196% San Rafael 1.048% 0.211 0.711 0.745% 0.803% 4.974 0.653 0.684% 0.661% 0.016 0.501 0.525% 0.703% San Ramon 0.975% 1.000 1.500 1.462% 1.574% 8.182 0.753 0.734% 0.709% 0.159 0.511 0.498% 0.666% Santa Clara 2.135% 0.639 1.139 2.431% 2.618% 27.4 41 1.357 2.898% 2.796% 3.493 0.740 1.580% 2.112% Santa Rosa 1.745% 0.067 0.567 0.990% 1.066% 4.165 0.627 1.095% 1.057% 0.416 0.529 0.922% 1.234% APPENDIX 4
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 APPENDICES RHNA A24 A25
A25
APPENDIX 4
A24
Factor Scores by Jurisdiction
Jurisdiction
BASELINE ALLOCATION:
Share of Bay Area
Households in
Year 2050
(A)
FACTOR: ACCESS TO HIGH OPPORTUNITY AREAS (AHOA) FACTOR: JOB PROXIMITY - AUTO (JPA) FACTOR: JOB PROXIMITY - TRANSIT (JPT)
FACTOR PREPARATION
FACTOR
DISTRIBUTION:
ADJUSTED BASELINE
RESCALED TO 100%
(A * B) / SUM(A * B)
FACTOR PREPARATION
FACTOR
DISTRIBUTION:
ADJUSTED BASELINE
RESCALED TO 100%
(A * B) / SUM(A * B)
FACTOR PREPARATION
FACTOR
DISTRIBUTION:
ADJUSTED BASELINE
RESCALED TO 100%
(A * B) / SUM(A * B)
RAW AHOA
FACTOR SCORE
AHOA
FACTOR SCORE
RESCALED TO
0.5-1.5 RANGE
(B)
BASELINE ADJUSTED
BY
AHOA FACTOR
(A * B)
RAW JPA FACTOR
SCORE
JPA FACTOR SCORE
RESCALED TO
0.5-1.5 RANGE
(B)
BASELINE
ADJUSTED BY JPA
FACTOR
(A * B)
RAW JPT FACTOR
SCORE
JPT FACTOR SCORE
RESCALED TO
0.5-1.5 RANGE
(B)
BASELINE
ADJUSTED BY JPT
FACTOR
(A * B)
Saratoga 0.280% 1.000 1.500 0.420% 0.453% 23.686 1.239 0.347% 0.335% 0.193 0.513 0.144% 0.192% Sausalito 0.125% 1.000 1.500 0.187% 0.202% 17.729 1.053 0.132% 0.127% 0.683 0.547 0.068% 0.091% Sebastopol 0.086% 0.000 0.500 0.043% 0.046% 3.671 0.612 0.053% 0.051% 0.000 0.500 0.043% 0.057% Sonoma 0.133% 0.000 0.500 0.067% 0.072% 0.838 0.523 0.070% 0.067% 0.000 0.500 0.067% 0.089% South San Francisco 0.929% 0.208 0.708 0.658% 0.709% 26.058 1.314 1.221% 1.178% 1.079 0.574 0.534% 0.713% St. Helena 0.068% 0.000 0.500 0.034% 0.036% 1.078 0.531 0.036% 0.035% 0.000 0.500 0.034% 0.045% Sunnyvale 2.088% 0.702 1.202 2.510% 2.703% 29.357 1.417 2.959% 2.855% 2.221 0.653 1.362% 1.822% Tiburon 0.126% 1.000 1.500 0.190% 0.204% 4.756 0.646 0.082% 0.079% 0.027 0.502 0.063% 0.085% Unincorporated Alameda 1.419% 0.279 0.779 1.106% 1.191% 6.426 0.698 0.991% 0.957% 0.025 0.502 0.712% 0.952% Unincorporated Contra Costa 2.203% 0.359 0.859 1.893% 2.038% 5.598 0.672 1.481% 1.429% 0.012 0.501 1.103% 1.475% Unincorporated Marin 0.822% 0.761 1.261 1.036% 1.116% 1.379 0.540 0.444% 0.428% 0.017 0.501 0.412% 0.551% Unincorporated Napa 0.279% 0.132 0.632 0.176% 0.190% 1.882 0.556 0.155% 0.150% 0.003 0.500 0.140% 0.187% Unincorporated San Mateo 0.809% 0.447 0.947 0.766% 0.825% 2.242 0.567 0.459% 0.443% 0.043 0.503 0.407% 0.544% Unincorporated Santa Clara 0.815% 0.420 0.920 0.750% 0.807% 9.501 0.795 0.647% 0.625% 0.068 0.505 0.411% 0.550% Unincorporated Sonoma 1.540% 0.059 0.559 0.860% 0.926% 1.752 0.552 0.850% 0.820% 0.013 0.501 0.771% 1.031% Union City 0.727% 0.126 0.626 0.455% 0.490% 9.138 0.783 0.569% 0.549% 1.094 0.575 0.418% 0.559% Walnut Creek 1.148% 0.922 1.422 1.632% 1.757% 9.192 0.785 0.901% 0.870% 0.386 0.527 0.604% 0.808% Windsor 0.260% 0.000 0.500 0.130% 0.140% 3.758 0.615 0.160% 0.154% 0.000 0.500 0.130% 0.174% Woodside 0.058% 0.981 1.481 0.085% 0.092% 17. 348 1.041 0.060% 0.058% 0.036 0.502 0.029% 0.039% Yountville 0.029% 0.000 0.500 0.015% 0.016% 1.820 0.554 0.016% 0.016% 0.080 0.506 0.015% 0.020% REGION TOTAL 92.873% 100.000% 103.624% 100.000% 74.786% 100.000% APPENDIX 4
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 APPENDICES RHNA A38 A39
Equity Adjustment STEP 1: IDENTIFY JURISDICTIONS EXHIBITING RACIAL
AND ECONOMIC EXCLUSION
STEP 2: COMPARE JURISDICTION'S LOWER-INCOME ALLOCATION FROM FACTORS/WEIGHTS TO LOWER-INCOME ALLOCATION NEEDED TO BE PROPORTIONAL TO JURISDICTION'S SHARE OF 2020 HOUSEHOLDS STEP 3: IDENTIFY CHANGE IN UNITS BY
INCOME CATEGORY3
STEP 4: FINAL VERY LOW- AND LOW-INCOME
ALLOCATIONS
2020 HOUSEHOLDS
UNMODIFIED ALLOCATION FROM
FACTORS/WEIGHTS
HYPOTHETICAL
VERY
LOW-INCOME
PROPORTIONAL
HYPOTHETICAL
LOW-INCOME
PROPORTIONAL
BEGINNING ALLOCATION PLUS
EQUITY ADJUSTMENT
DIVERGENCE
INDEX SCORE
SHARE OF
HOUSEHOLDS
ABOVE
120% AMI1
EQUITY
ADJUSTMENT
COMPOSITE
SCORE2 TOTAL
SHARE OF
REGION
VERY LOW-
INCOME UNITS
LOW-INCOME
UNITS
VERY LOW-
INCOME SHARE
LOW-INCOME
SHARE TOTAL
VERY LOW-
INCOME UNITS
LOW-INCOME
UNITS VERY LOW-INCOME UNITS LOW-INCOME UNITS
More Exclusionary – Subject to Adjustment (the more exclusionary jurisdictions whose allocations based on factors/weights need to be increased to meet the equity adjustment's proportionality threshold) Atherton 0.246 0.821 1.066 2,273 0.083% 81 47 0.071% 0.071% 94 54 20 13 7 94 54 Daly City 0.273 0.445 0.718 32,167 1.168% 1,039 598 0.908% 0.908% 1,336 769 468 297 171 1,336 769 Gilroy 0.310 0.479 0.790 16,116 0.585% 359 207 0.314% 0.314% 669 385 488 310 178 669 385 Half Moon Bay 0.207 0.562 0.768 4,363 0.158% 93 54 0.081% 0.082% 181 104 138 88 50 181 104 Healdsburg 0.346 0.454 0.800 4,576 0.166% 78 45 0.068% 0.068% 190 109 176 112 64 190 109 Hercules 0.208 0.571 0.779 8,278 0.300% 179 104 0.156% 0.158% 344 198 259 165 94 344 198 Hillsborough 0.198 0.847 1.045 3,733 0.136% 153 88 0.134% 0.134% 155 89 3 2 1 155 89 Livermore 0.133 0.579 0.712 31,696 1.151% 1,240 714 1.084% 1.084% 1,317 758 121 77 44 1,317 758 Mill Valley 0.455 0.659 1.115 6,298 0.229% 252 144 0.220% 0.219% 262 151 17 10 7 262 151 Monte Sereno 0.278 0.811 1.090 1,265 0.046% 51 30 0.045% 0.046% 53 30 2 2 0 53 30 Pleasant Hill 0.149 0.550 0.699 13,626 0.495% 451 261 0.394% 0.396% 566 326 180 115 65 566 326 Portola Valley 0.387 0.735 1.122 1,768 0.064% 70 40 0.061% 0.061% 73 42 5 3 2 73 42 Ross 0.607 0.765 1.372 826 0.030% 33 19 0.029% 0.029% 34 20 2 1 1 34 20 St. Helena 0.338 0.401 0.739 2,477 0.090% 43 24 0.038% 0.036% 103 59 95 60 35 103 59 Unincorporated Marin 0.292 0.577 0.869 26,491 0.962% 1,063 611 0.929% 0.927% 1,100 634 60 37 23 1,100 634 Unincorporated Napa 0.256 0.521 0.777 8,889 0.323% 210 121 0.183% 0.184% 369 213 251 159 92 369 213 Union City 0.233 0.525 0.758 20,751 0.753% 582 335 0.509% 0.508% 862 496 441 280 161 862 496 Windsor 0.264 0.500 0.763 9,272 0.337% 168 97 0.147% 0.147% 385 222 342 217 125 385 222 APPENDIX 6APPENDIX 6
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 APPENDICES RHNA A40 A41
APPENDIX 6
Equity Adjustment STEP 1: IDENTIFY JURISDICTIONS EXHIBITING RACIAL
AND ECONOMIC EXCLUSION
STEP 2: COMPARE JURISDICTION'S LOWER-INCOME ALLOCATION FROM FACTORS/WEIGHTS TO LOWER-INCOME ALLOCATION NEEDED TO BE PROPORTIONAL TO JURISDICTION'S SHARE OF 2020 HOUSEHOLDS STEP 3: IDENTIFY CHANGE IN UNITS BY
INCOME CATEGORY3
STEP 4: FINAL VERY LOW- AND LOW-INCOME
ALLOCATIONS
2020 HOUSEHOLDS
UNMODIFIED ALLOCATION FROM
FACTORS/WEIGHTS
HYPOTHETICAL
VERY
LOW-INCOME
PROPORTIONAL
HYPOTHETICAL
LOW-INCOME
PROPORTIONAL
BEGINNING ALLOCATION PLUS
EQUITY ADJUSTMENT
DIVERGENCE
INDEX SCORE
SHARE OF
HOUSEHOLDS
ABOVE
120% AMI1
EQUITY
ADJUSTMENT
COMPOSITE
SCORE2 TOTAL
SHARE OF
REGION
VERY LOW-
INCOME UNITS
LOW-INCOME
UNITS
VERY LOW-
INCOME SHARE
LOW-INCOME
SHARE TOTAL
VERY LOW-
INCOME UNITS
LOW-INCOME
UNITS VERY LOW-INCOME UNITS LOW-INCOME UNITS
More Exclusionary – Not Subject to Adjustment (the more exclusionary jurisdictions whose allocations based on factors/weights already meet the equity adjustment's proportionality threshold) Belmont 0.104 0.627 0.731 10,516 0.382% 488 281 0.426% 0.426% 437 252 - - - 488 281 Belvedere 0.611 0.709 1.320 933 0.034% 49 28 0.043% 0.042% 39 22 - - - 49 28 Clayton 0.287 0.691 0.978 4,005 0.145% 170 97 0.149% 0.147% 166 96 - - - 170 97 Corte Madera 0.360 0.665 1.026 4,066 0.148% 213 123 0.186% 0.187% 169 97 - - - 213 123 Cupertino 0.432 0.700 1.132 19,998 0.726% 1,193 687 1.042% 1.043% 831 478 - - - 1,193 687 Danville 0.298 0.694 0.992 15,474 0.562% 652 376 0.570% 0.571% 643 370 - - - 652 376 Dublin 0.110 0.705 0.815 22,021 0.799% 1,085 625 0.948% 0.949% 915 527 - - - 1,085 625 Fairfax 0.409 0.536 0.946 3,294 0.120% 149 86 0.130% 0.131% 137 79 - - - 149 86 Foster City 0.150 0.702 0.852 12,449 0.452% 520 299 0.454% 0.454% 517 298 - - - 520 299 Fremont 0.243 0.627 0.871 74,488 2.704% 3,640 2,096 3.181% 3.181% 3,094 1,782 - - - 3,640 2,096 Lafayette 0.274 0.661 0.936 9,503 0.345% 599 344 0.523% 0.522% 395 227 - - - 599 344 Larkspur 0.399 0.514 0.913 5,954 0.216% 291 168 0.254% 0.255% 247 142 - - - 291 168 Los Altos 0.213 0.767 0.980 11,114 0.403% 501 288 0.438% 0.437% 462 266 - - - 501 288 Los Altos Hills 0.215 0.837 1.053 2,915 0.106% 125 72 0.109% 0.109% 121 70 - - - 125 72 Los Gatos 0.225 0.617 0.842 12,821 0.465% 537 310 0.469% 0.470% 533 307 - - - 537 310 Menlo Park 0.093 0.625 0.718 13,076 0.475% 740 426 0.647% 0.647% 543 313 - - - 740 426 Millbrae 0.148 0.577 0.725 8,124 0.295% 575 331 0.502% 0.502% 337 194 - - - 575 331 Milpitas 0.397 0.600 0.997 21,814 0.792% 1,685 970 1.472% 1.472% 906 522 - - - 1,685 970 APPENDIX 6
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 APPENDICES RHNA A42 A43
APPENDIX 6
Equity Adjustment STEP 1: IDENTIFY JURISDICTIONS EXHIBITING RACIAL
AND ECONOMIC EXCLUSION
STEP 2: COMPARE JURISDICTION'S LOWER-INCOME ALLOCATION FROM FACTORS/WEIGHTS TO LOWER-INCOME ALLOCATION NEEDED TO BE PROPORTIONAL TO JURISDICTION'S SHARE OF 2020 HOUSEHOLDS STEP 3: IDENTIFY CHANGE IN UNITS BY
INCOME CATEGORY3
STEP 4: FINAL VERY LOW- AND LOW-INCOME
ALLOCATIONS
2020 HOUSEHOLDS
UNMODIFIED ALLOCATION FROM
FACTORS/WEIGHTS
HYPOTHETICAL
VERY
LOW-INCOME
PROPORTIONAL
HYPOTHETICAL
LOW-INCOME
PROPORTIONAL
BEGINNING ALLOCATION PLUS
EQUITY ADJUSTMENT
DIVERGENCE
INDEX SCORE
SHARE OF
HOUSEHOLDS
ABOVE
120% AMI1
EQUITY
ADJUSTMENT
COMPOSITE
SCORE2 TOTAL
SHARE OF
REGION
VERY LOW-
INCOME UNITS
LOW-INCOME
UNITS
VERY LOW-
INCOME SHARE
LOW-INCOME
SHARE TOTAL
VERY LOW-
INCOME UNITS
LOW-INCOME
UNITS VERY LOW-INCOME UNITS LOW-INCOME UNITS
Moraga 0.220 0.667 0.887 5,703 0.207% 318 183 0.278% 0.278% 237 136 - - - 318 183 Orinda 0.260 0.761 1.021 6,789 0.246% 372 215 0.325% 0.326% 282 162 - - - 372 215 Palo Alto 0.154 0.649 0.804 27,667 1.004% 1,556 896 1.360% 1.360% 1,149 662 - - - 1,556 896 Piedmont 0.275 0.799 1.074 3,910 0.142% 163 94 0.142% 0.143% 162 94 - - - 163 94 Pleasanton 0.098 0.674 0.773 27, 283 0.990% 1,750 1,008 1.529% 1.530% 1,133 653 - - - 1,750 1,008 San Anselmo 0.501 0.610 1.110 5,318 0.193% 253 145 0.221% 0.220% 221 127 - - - 253 145 San Carlos 0.212 0.686 0.898 11,702 0.425% 739 425 0.646% 0.645% 486 280 - - - 739 425 San Ramon 0.151 0.696 0.847 28,004 1.017% 1,497 862 1.308% 1.308% 1,163 670 - - - 1,497 862 Saratoga 0.267 0.710 0.977 10,800 0.392% 454 261 0.397% 0.396% 449 258 - - - 454 261 Sausalito 0.494 0.570 1.064 4,142 0.150% 200 115 0.175% 0.175% 172 99 - - - 200 115 Sunnyvale 0.101 0.618 0.719 57,888 2.101% 2,968 1,709 2.593% 2.594% 2,405 1,385 - - - 2,968 1,709 Tiburon 0.447 0.675 1.122 3,893 0.141% 193 110 0.169% 0.167% 162 93 - - - 193 110 Woodside 0.382 0.754 1.136 2,034 0.074% 90 52 0.079% 0.079% 84 49 - - - 90 52 Other Jurisdictions (the jurisdictions not identified as exclusionary whose lower-income allocations are shifted to the group of more exclusionary jurisdictions whose allocations need to be increased) Alameda 0.047 0.490 0.537 31,829 1.155% 1,455 837 1.271% 1.270% 1,322 761 -53 -34 -19 1,421 818 Albany 0.065 0.444 0.509 6,434 0.234% 315 182 0.275% 0.276% 267 154 -11 -7 -4 308 178 American Canyon 0.065 0.489 0.553 5,967 0.217% 115 67 0.100% 0.102% 248 143 -5 -3 -2 112 65 Antioch 0.193 0.347 0.540 34,096 1.238% 811 467 0.709% 0.709% 1,416 815 -30 -19 -11 792 456 Berkeley 0.075 0.439 0.514 47,718 1.732% 2,504 1,441 2.188% 2.187% 1,982 1,141 -91 -58 -33 2,446 1,408 APPENDIX 6
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 APPENDICES RHNA A44 A45
APPENDIX 6
Equity Adjustment STEP 1: IDENTIFY JURISDICTIONS EXHIBITING RACIAL
AND ECONOMIC EXCLUSION
STEP 2: COMPARE JURISDICTION'S LOWER-INCOME ALLOCATION FROM FACTORS/WEIGHTS TO LOWER-INCOME ALLOCATION NEEDED TO BE PROPORTIONAL TO JURISDICTION'S SHARE OF 2020 HOUSEHOLDS STEP 3: IDENTIFY CHANGE IN UNITS BY
INCOME CATEGORY3
STEP 4: FINAL VERY LOW- AND LOW-INCOME
ALLOCATIONS
2020 HOUSEHOLDS
UNMODIFIED ALLOCATION FROM
FACTORS/WEIGHTS
HYPOTHETICAL
VERY
LOW-INCOME
PROPORTIONAL
HYPOTHETICAL
LOW-INCOME
PROPORTIONAL
BEGINNING ALLOCATION PLUS
EQUITY ADJUSTMENT
DIVERGENCE
INDEX SCORE
SHARE OF
HOUSEHOLDS
ABOVE
120% AMI1
EQUITY
ADJUSTMENT
COMPOSITE
SCORE2 TOTAL
SHARE OF
REGION
VERY LOW-
INCOME UNITS
LOW-INCOME
UNITS
VERY LOW-
INCOME SHARE
LOW-INCOME
SHARE TOTAL
VERY LOW-
INCOME UNITS
LOW-INCOME
UNITS VERY LOW-INCOME UNITS LOW-INCOME UNITS
Brentwood 0.084 0.522 0.606 20,067 0.728% 411 237 0.359% 0.360% 834 480 -14 -9 -5 402 232 Brisbane 0.009 0.536 0.545 1,890 0.069% 324 187 0.283% 0.284% 79 45 -11 -7 -4 317 183 Burlingame 0.082 0.595 0.677 12,386 0.450% 883 509 0.772% 0.772% 515 296 -32 -20 -12 863 497 Calistoga 0.280 0.322 0.602 2,067 0.075% 32 19 0.028% 0.029% 86 49 -1 -1 0 31 19 Campbell 0.041 0.572 0.613 16,855 0.612% 770 444 0.673% 0.674% 700 403 -28 -18 -10 752 434 Cloverdale 0.228 0.336 0.564 3,328 0.121% 76 44 0.066% 0.067% 138 80 -3 -2 -1 74 43 Colma 0.090 0.470 0.560 499 0.018% 45 26 0.039% 0.039% 21 12 -2 -1 -1 44 25 Concord 0.074 0.397 0.471 45,297 1.644% 1,322 762 1.155% 1.156% 1,882 1,083 -48 -30 -18 1,292 744 Cotati 0.295 0.341 0.636 3,002 0.109% 61 35 0.053% 0.053% 125 72 -2 -1 -1 60 34 East Palo Alto* 0.452 0.337 0.789 7, 274 0.264% 169 97 0.148% 0.147% 302 174 -6 -4 -2 165 95 El Cerrito 0.059 0.501 0.561 10,332 0.375% 342 197 0.299% 0.299% 429 247 -13 -8 -5 334 192 Emeryville 0.084 0.505 0.589 6,667 0.242% 462 265 0.404% 0.402% 277 159 -17 -11 -6 451 259 Hayward 0.147 0.383 0.530 48,286 1.753% 1,100 632 0.961% 0.959% 2,006 1,155 -40 -25 -15 1,075 617 Martinez 0.161 0.516 0.677 14,339 0.520% 358 206 0.313% 0.313% 596 343 -13 -8 -5 350 201 Morgan Hill 0.097 0.560 0.657 14,688 0.533% 268 155 0.234% 0.235% 610 351 -10 -6 -4 262 151 Mountain View 0.038 0.609 0.647 34,445 1.250% 2,838 1,635 2.480% 2.481% 1,431 824 -103 -65 -38 2,773 1,597 Napa 0.271 0.393 0.664 28,655 1.040% 516 298 0.451% 0.452% 1,190 685 -19 -12 -7 504 291 Newark 0.061 0.547 0.608 14,304 0.519% 475 274 0.415% 0.416% 594 342 -17 -11 -6 464 268 APPENDIX 6
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 APPENDICES RHNA A46 A47
APPENDIX 6
Equity Adjustment STEP 1: IDENTIFY JURISDICTIONS EXHIBITING RACIAL
AND ECONOMIC EXCLUSION
STEP 2: COMPARE JURISDICTION'S LOWER-INCOME ALLOCATION FROM FACTORS/WEIGHTS TO LOWER-INCOME ALLOCATION NEEDED TO BE PROPORTIONAL TO JURISDICTION'S SHARE OF 2020 HOUSEHOLDS STEP 3: IDENTIFY CHANGE IN UNITS BY
INCOME CATEGORY3
STEP 4: FINAL VERY LOW- AND LOW-INCOME
ALLOCATIONS
2020 HOUSEHOLDS
UNMODIFIED ALLOCATION FROM
FACTORS/WEIGHTS
HYPOTHETICAL
VERY
LOW-INCOME
PROPORTIONAL
HYPOTHETICAL
LOW-INCOME
PROPORTIONAL
BEGINNING ALLOCATION PLUS
EQUITY ADJUSTMENT
DIVERGENCE
INDEX SCORE
SHARE OF
HOUSEHOLDS
ABOVE
120% AMI1
EQUITY
ADJUSTMENT
COMPOSITE
SCORE2 TOTAL
SHARE OF
REGION
VERY LOW-
INCOME UNITS
LOW-INCOME
UNITS
VERY LOW-
INCOME SHARE
LOW-INCOME
SHARE TOTAL
VERY LOW-
INCOME UNITS
LOW-INCOME
UNITS VERY LOW-INCOME UNITS LOW-INCOME UNITS
Novato 0.184 0.482 0.666 20,606 0.748% 583 336 0.509% 0.510% 856 493 -21 -13 -8 570 328 Oakland 0.189 0.352 0.541 164,296 5.964% 6,665 3,838 5.824% 5.825% 6,825 3,930 -242 -154 -88 6,511 3,750 Oakley 0.143 0.483 0.626 12,363 0.449% 286 165 0.250% 0.250% 514 296 -11 -7 -4 279 161 Pacifica 0.049 0.573 0.622 13,774 0.500% 551 317 0.481% 0.481% 572 329 -20 -13 -7 538 310 Petaluma 0.259 0.435 0.694 23,027 0.836% 511 295 0.447% 0.448% 957 551 -19 -12 -7 499 288 Pinole 0.029 0.457 0.486 6,907 0.251% 124 71 0.108% 0.108% 287 165 -5 -3 -2 121 69 Pittsburg 0.216 0.325 0.540 22,067 0.801% 518 298 0.453% 0.452% 917 528 -19 -12 -7 506 291 Redwood City 0.084 0.543 0.628 30,346 1.102% 1,141 658 0.997% 0.999% 1,261 726 -41 -26 -15 1,115 643 Richmond 0.248 0.287 0.535 37, 271 1.353% 860 496 0.751% 0.753% 1,548 891 -31 -20 -11 840 485 Rohnert Park 0.180 0.277 0.457 16,722 0.607% 408 235 0.357% 0.357% 695 400 -14 -9 -5 399 230 San Bruno 0.046 0.511 0.556 15,573 0.565% 721 415 0.630% 0.630% 647 372 -27 -17 -10 704 405 San Francisco 0.029 0.517 0.546 373,404 13.554% 21,359 12,294 18.664% 18.658% 15,511 8,931 -772 -492 -280 20,867 12,014 San Jose 0.066 0.519 0.585 324,692 11.786% 15,444 8,892 13.495% 13.495% 13,488 7,766 -561 -356 -205 15,088 8,687 San Leandro 0.070 0.361 0.431 30,476 1.106% 882 507 0.771% 0.769% 1,266 729 -32 -20 -12 862 495 San Mateo 0.021 0.559 0.580 38,872 1.411% 1,819 1,047 1.589% 1.589% 1,615 930 -66 -42 -24 1,777 1,023 San Pablo 0.434 0.161 0.595 9,088 0.330% 177 102 0.155% 0.155% 378 217 -6 -4 -2 173 100 San Rafael 0.175 0.462 0.637 23,154 0.840% 877 504 0.766% 0.765% 962 554 -32 -20 -12 857 492 Santa Clara 0.060 0.570 0.631 46,387 1.684% 2,940 1,692 2.569% 2.568% 1,927 1,109 -107 -68 -39 2,872 1,653 APPENDIX 6
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 APPENDICES RHNA A48 A49
APPENDIX 6
Equity Adjustment STEP 1: IDENTIFY JURISDICTIONS EXHIBITING RACIAL
AND ECONOMIC EXCLUSION
STEP 2: COMPARE JURISDICTION'S LOWER-INCOME ALLOCATION FROM FACTORS/WEIGHTS TO LOWER-INCOME ALLOCATION NEEDED TO BE PROPORTIONAL TO JURISDICTION'S SHARE OF 2020 HOUSEHOLDS STEP 3: IDENTIFY CHANGE IN UNITS BY
INCOME CATEGORY3
STEP 4: FINAL VERY LOW- AND LOW-INCOME
ALLOCATIONS
2020 HOUSEHOLDS
UNMODIFIED ALLOCATION FROM
FACTORS/WEIGHTS
HYPOTHETICAL
VERY
LOW-INCOME
PROPORTIONAL
HYPOTHETICAL
LOW-INCOME
PROPORTIONAL
BEGINNING ALLOCATION PLUS
EQUITY ADJUSTMENT
DIVERGENCE
INDEX SCORE
SHARE OF
HOUSEHOLDS
ABOVE
120% AMI1
EQUITY
ADJUSTMENT
COMPOSITE
SCORE2 TOTAL
SHARE OF
REGION
VERY LOW-
INCOME UNITS
LOW-INCOME
UNITS
VERY LOW-
INCOME SHARE
LOW-INCOME
SHARE TOTAL
VERY LOW-
INCOME UNITS
LOW-INCOME
UNITS VERY LOW-INCOME UNITS LOW-INCOME UNITS
Santa Rosa 0.173 0.327 0.500 66,051 2.398% 1,247 718 1.090% 1.090% 2,744 1,580 -46 -29 -17 1,218 701 Sebastopol* 0.372 0.367 0.738 3,372 0.122% 56 32 0.049% 0.049% 140 81 -2 -1 -1 55 31 Sonoma* 0.378 0.390 0.768 5,030 0.183% 85 49 0.074% 0.074% 209 120 -3 -2 -1 83 48 South San Francisco 0.132 0.484 0.616 21,409 0.777% 892 514 0.779% 0.780% 889 512 -33 -21 -12 871 502 Unincorporated Alameda 0.034 0.431 0.465 48,899 1.775% 1,281 738 1.119% 1.120% 2,031 1,170 -47 -30 -17 1,251 721 Unincorporated Contra Costa 0.056 0.484 0.540 60,527 2.197% 2,131 1,227 1.862% 1.862% 2,514 1,448 -77 -49 -28 2,082 1,199 Unincorporated San Mateo 0.101 0.585 0.686 21,461 0.779% 830 479 0.725% 0.727% 892 513 -30 -19 -11 811 468 Unincorporated Santa Clara 0.063 0.542 0.604 26,299 0.955% 848 488 0.741% 0.741% 1,092 629 -31 -20 -11 828 477 Unincorporated Sonoma* 0.328 0.387 0.715 54,387 1.974% 1,060 610 0.926% 0.926% 2,259 1,301 -38 -24 -14 1,036 596 Walnut Creek 0.191 0.490 0.681 32,363 1.175% 1,696 976 1.482% 1.481% 1,344 774 - 61 -39 -22 1,657 954 Yountville*4 0.396 0.328 0.724 1,030 0.037% 19 11 0.017% 0.017% 43 25 0 0 0 19 11 Solano Subregion5 -105 -66 -39
APPENDIX 6
Sources: U.S. Census Bureau, American Community Survey 5-Year Data (2014-2018), Table B19013 for median household income; Table B19001 for households by income group; Table B03002 for population by race / ethnicity. State of California, Department of Finance, E-5 Population and Housing Estimates for Cities, Counties and the State — January 1, 2011-2020.
Sacramento, California, May 2020.
* These jurisdictions were excluded from being subject to the equity adjustment because they had average incomes in the bottom quartile for the region. 1 According to American Community Survey (ACS 2014-2018 PUMS) data, 120% of the area median income (AMI) for Bay Area households was $120,840. Due to the way the income categories are structured in the ACS summary files needed for jurisdiction tabulations, the information reported here includes households with incomes greater than $100,000. 2 Bay Area Median Composite Score: 0.694
3 Total units to shift from 60 least exclusive jurisdictions to 18 jurisdictions subject to equity adjustment: 3,068 units 4 The proportional reduction in Yountville's allocation of lower-income units was less than a unit, so the equity adjustment did not affect its final allocation. 5 Jurisdictions in Solano County formed a subregion to complete a separate RHNA process on behalf of all jurisdictions in the county. The Solano Subregion adopted its final RHNA allocations (developed using its own RHNA methodology) on November 18, 2021. The unit reductions from the equity adjustment shown here were incorporated into the final subregional share assigned by ABAG to the Solano Subregion.
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 A50
Jurisdiction
VERY LOW INCOME
(<50% of Area
Median Income)
LOW INCOME
(50-80% of Area
Median Income)
MODERATE INCOME
(80-120% of Area
Median Income)
ABOVE MODERATE
INCOME
(> 120% of Area
Median Income)
TOTAL
ALAMEDA COUNTY
Alameda 1,421 818 868 2,246 5,353
Albany 308 178 175 453 1,114
Berkeley 2,446 1,408 1,416 3,664 8,934
Dublin 1,085 625 560 1,449 3,719
Emeryville 451 259 308 797 1,815
Fremont 3,640 2,096 1,996 5,165 12,897
Hayward 1,075 617 817 2,115 4,624
Livermore 1,317 758 696 1,799 4,570
Newark 464 268 318 824 1,874
Oakland 6,511 3,750 4,457 11,533 26,251
Piedmont 163 94 92 238 587
Pleasanton 1,750 1,008 894 2,313 5,965
San Leandro 862 495 696 1,802 3,855
Unincorporated Alameda County 1,251 721 763 1,976 4,711 Union City 862 496 382 988 2,728
Draft RHNA Allocations
On May 25, 2021, ABAG notified the city/town manager or county administrator and planning or community development director of each local jurisdiction, HCD, and members of the public about the adoption of the draft RHNA allocations and the initiation of the appeals period.
APPENDIX 7
A55
Jurisdiction
VERY LOW INCOME
(<50% of Area
Median Income)
LOW INCOME
(50-80% of Area
Median Income)
MODERATE INCOME
(80-120% of Area
Median Income)
ABOVE MODERATE
INCOME
(> 120% of Area
Median Income)
TOTAL
SOLANO COUNTY*
Benicia 203 117 135 351 806
Dixon 91 53 57 146 347
Fairfield 778 447 508 1,314 3,047
Rio Vista 127 73 76 197 473
Suisun City 156 90 101 264 611
Unincorporated Solano 237 137 149 385 908
Vacaville 487 279 305 791 1,862
Vallejo 724 416 501 1,297 2,938
SONOMA COUNTY
Cloverdale 74 43 45 116 278
Cotati 60 34 39 101 234
Healdsburg 190 109 49 128 476
Petaluma 499 288 313 810 1,910
Rohnert Park 399 230 265 686 1,580
Santa Rosa 1,218 701 771 1,995 4,685
Sebastopol 55 31 35 92 213
Sonoma 83 48 50 130 311
Unincorporated Sonoma 1,036 596 627 1,622 3,881
Windsor 385 222 108 279 994
TOTAL 114,442 65,892 72,712 188,130 441,176
* Jurisdictions in Solano County have formed a subregion and are developing their own methodology to allocate units among the members. The draft allocations shown here are what jurisdictions would receive from ABAG only in the event the subregion is unable to complete its allocation process. The final allocations identified by the Solano County subregion will be reflected in the Final RHNA Plan to be adopted by the end of 2021.
APPENDIX 7
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031A56 APPENDIX 8
Summary of Local Jurisdiction Survey Results
This appendix provides information from reports presented to the Housing Methodology Committee (HMC) in March
and April 2020. These reports summarized responses to the Local Jurisdiction Survey, and these summaries intended to inform the HMC’s development of the RHNA methodology.
Though the HMC has concluded its work, this appendix
makes reference to factors that the HMC could consider for the methodology, as the HMC was beginning to develop
the RHNA methodology when the Local Jurisdiction Survey summary reports were completed.
OVERVIEW OF SURVEY PROCESS
Housing Element Law requires each Council of Government (COG) to survey its member jurisdictions during the Regional Housing Needs Allocation (RHNA) process to gather
information on factors that must be considered for inclusion in the methodology.
1 Recent legislation also requires ABAG
to collect information on jurisdictions’ fair housing issues and strategies for achieving fair housing goals.
2 ABAG staff
presented the Housing Methodology Committee with a draft of the survey in November 2019. Staff revised the survey to incorporate feedback from HMC members, local jurisdiction staff, and other stakeholders, and the ABAG Regional
Planning Committee approved the survey in December
2019. The survey became available online on January 8,
2020. A survey link was emailed to city managers, county administrators, community development and planning
directors, and housing staff in all 109 ABAG jurisdictions. The deadline for completing the survey was February 5, 2020, at which point ABAG received 72 responses, a response rate of 66%. Table 1 shows the response rates for each of the nine Bay Area counties.
SURVEY ORGANIZATION
The survey consisted of 53 questions in two sections. Section 1 included 36 questions related to the statutory housing and land use factors. These questions were divided into four topics: Relationship Between Jobs and Housing, Housing
Opportunities and Constraints, Housing Affordability and Overcrowding, and Housing Demand. Section 2 included 14 questions that collected information on local jurisdictions’ fair housing issues as well as strategies and actions for achieving fair housing goals. These questions were divided into three topics: Fair Housing Planning and Data Sources; Diversity/ Segregation, Access to Opportunity, and Housing Needs; and Fair Housing Goals and Actions.
Table 1. Local jurisdiction survey response rate by county.
COUNTY RESPONSES RESPONSE RATE
Alameda 9 60%
Contra Costa 14 70%
Marin 8 73%
Napa 3 50%
San Francisco 1 100%
San Mateo 14 67%
Santa Clara 13 81%
Solano 4 50%
Sonoma 7 70%
1 See State of California Government Code Section 65584.04(b)(1). 2 See State of California Government Code Section 65584.04(b)(2).
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031A60 • Increased housing density
• Policies to incentivize affordable housing production, such as density bonuses
• Funding to acquire and preserve affordable housing that currently exists on the market without subsidy
Topic 2: Relationship Between Jobs and Housing
The seven questions within this topic area focused on factors within jurisdictions that create opportunities or constraints for developing more housing. These questions also focus specifically on opportunities and constraints for encouraging jobs and housing near transit, developing housing near job centers, and minimizing greenhouse gas emissions.
Key Takeaways from Respondents’ Comments
Opportunities and constraints for developing housing:
Jurisdictions’ constraints for developing new housing
centered on issues related to costs and land. Nearly all respondents (87%) cited construction costs as a constraint (see Figure 4 on page A57). Other constraints reported
by more than 50% of jurisdictions were the availability of vacant land, funding for affordable housing, availability of construction workforce, land suitability, and availability of surplus public land. There was less of a regional consensus around opportunities for developing housing, with no single factor being cited as an opportunity by most respondents.
Factors considered to be opportunities related largely to infrastructure and community amenities, with the most
common opportunities being the availability of schools, availability of parks, water capacity, and sewer capacity. These four factors were also the only factors listed more commonly as opportunities than as constraints.
Opportunities and constraints for encouraging housing near transit and jobs: 57 jurisdictions (80%) stated they encounter opportunities or constraints in encouraging jobs and housing near existing transportation infrastructure, while 50 (70%) reported having opportunities or constraints for encouraging housing near job centers. In their responses to these
questions, jurisdictions reported a mix of both opportunities and constraints for developing housing near jobs and transit, with some respondents noting that both opportunities
and constraints exist simultaneously in their jurisdictions.
Jurisdictions in Alameda, Contra Costa, Marin, Napa, and San Mateo counties noted that specific plans for areas around bus and rail transit centers provide opportunities for greater density and mixed-use development near transportation
infrastructure, which can encourage housing near jobs
and transit. Similarly, jurisdictions in Santa Clara County discussed how rezoning efforts near job centers can create opportunities for more housing near jobs.
Some of the obstacles listed by jurisdictions echo what was mentioned in the previous questions related to opportunities and constraints for developing housing in general: limited vacant land, high construction costs, and construction
labor shortage. Additionally, jurisdictions throughout the region stated that a lack of existing transit service prevents them from encouraging jobs and housing near public
transportation infrastructure. Likewise, respondents across the region also noted that their jurisdictions lack job centers, which prevents them from locating housing near jobs. Lastly, some jurisdictions noted that while they do have job centers, the land near these jobs is not zoned to allow for residential construction.
APPENDIX 8
APPENDICES RHNA A65
Assessment, Santa Clara County Planning Department survey, and the California Employment Development Department. The most common barriers to meeting demand for farmworker
housing are similar to barriers to developing affordable housing generally. Among the 16 respondents with a need for farmworker housing, the most common barriers are a lack of financing and limited availability of land (see Figure 9 on page A62).
Housing demand created by postsecondary educational
institutions: Responses to questions about housing demand created by postsecondary educational institutions indicate a need for better data collection on this issue. Only 8 respondents (11%) were able to provide an estimate for this housing need. Several more jurisdictions indicated there is significant housing demand created by nearby postsecondary educational institutions, but the number of housing units needed to meet this demand is unknown. The eight
jurisdictions that were able to estimate the housing demand created by postsecondary educational institutions stated that the data for their estimates came from surveys conducted by these institutions, but several more jurisdictions indicated they have not been able to obtain this information from local colleges and universities.
Loss of subsidized affordable housing: 19 respondents
(27%) stated their jurisdictions had lost subsidized affordable housing units in the past 10 years due to expiring affordability contracts or other issues facing at-risk affordable housing units. Most of the data for these responses came from internal sources. Jurisdictions noted their awareness of affordable housing built with redevelopment funds that converted to market-rate due to expiring regulatory agreements, and
respondents also stated they were aware of below-market-rate units built through inclusionary housing programs that had lapsing affordability requirements.
A larger number of respondents expected to lose affordable housing units in the next 10 years, with 23 respondents (32%) noting that they anticipated these future losses.
These respondents also referred to internal city records that indicated the pending expiration of regulatory agreements.
Notably, one jurisdiction stated that 68% of existing below- market-rate rental units in its Below Market Rate Housing Program are set to expire in 10 years. Additionally, another respondent commented that the number of affordable
units owned by for-profit owners in their jurisdiction is high according to research by the California Housing Partnership, which indicates a high risk for losing these affordable units in the future.
5 These survey responses indicate that helping cities prevent the loss of affordable housing because of expiring affordability requirements could be a potential focus of ABAG’s Regional Early Action Planning grants program. Additionally, the variety of data on at-risk affordable units collected by both individual jurisdictions and the California Housing Partnership points to a need to compile this data if the HMC were to consider using the loss of affordable units as a RHNA methodology factor.
Loss of housing units due to state-declared emergencies:
Only six respondents (8%) stated their jurisdiction had lost housing units during a state-declared emergency (such as a fire or other natural disaster) that have not been rebuilt. These 5 For more information on the California Housing Partnership’s research on at-risk affordable housing in California, see https://1p08d91kd0c03rlxhmhtydpr-wpengine.netdna-ssl.com/wp-content/uploads/2020/02/2020-Affordable-Homes-at-Risk_CHPC-Final.pdf.
APPENDIX 8
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 A66
jurisdictions are in Napa, San Mateo, Santa Clara, Solano, and Sonoma counties. Two jurisdictions in Sonoma County were able to provide precise data on the number of units lost in recent fires. Another Sonoma County jurisdiction noted that they did not lose any housing in the fire but have experienced increased demand in housing because of lost units in
surrounding communities. Additionally, two jurisdictions in Marin County noted that, while they have not lost units recently, they expect that units lost in the future due to sea level rise and increased flooding may not be replaced.
SECTION 2: SUMMARY OF RESPONSES TO FAIR
HOUSING QUESTIONS
The data and information collected in the Local Jurisdiction Survey can help Bay Area jurisdictions understand the
framework needed for assessing fair housing issues, which state law now requires for the next Housing Element update in 2022. Notably, several jurisdictions reported in the survey that they lack data on segregation patterns and have not previously set goals in their Housing Elements related to removing barriers to housing choice. However, this type of analysis will likely be needed for the upcoming Housing Element update.
Accordingly, the survey results can help ABAG staff identify assistance that they can offer through the Regional Early Action Planning (REAP) grants program to help local jurisdictions comply with new Housing Element requirements. Additionally, both the Local Jurisdiction Survey and the review of Bay Area jurisdictions’ fair housing reports to HUD identified regional themes regarding both barriers to fair housing choice and strategies to further fair housing. This knowledge can inform how ABAG designs technical assistance and grant programs in the future to help local jurisdictions implement successful fair housing strategies.
Topic 1: Fair Housing Planning and Data Sources
The eight questions in this topic area centered on
jurisdictions’ processes for assessing fair housing issues in their communities. Federal law obligates jurisdictions receiving block grant funding from HUD to submit a
Consolidated Plan to HUD every five years, and this process requires jurisdictions to assess local fair housing issues (see Section 3 for more details on federally mandated fair housing reporting). While the Local Jurisdiction Survey did ask whether jurisdictions currently submit fair housing reports to HUD, all questions on the survey could be applicable to jurisdictions regardless of whether they participate in federal fair housing reporting. This portion of the survey also asked about the data jurisdictions use for fair housing planning and the efforts they have made to elicit public participation in their fair housing planning processes.
Key Takeaways from Respondents’ Comments
Fair housing reporting to HUD: According to the results of the local jurisdiction survey, 37 respondents (51%) have submitted a fair housing report to HUD. Because these
reports are submitted as part of five-year planning cycles, most of these jurisdictions recently submitted a report for the years 2020-2025 or are currently working on a report for this cycle, though a few jurisdictions’ Consolidated Plans are on a different timeline. While some reports are submitted to HUD by individual jurisdictions, this reporting can also be completed as a collaborative effort between a county government and local jurisdictions within the county.
APPENDIX 8
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 A70
residents. Two other factors ranked in the top five for three out of four of the fair housing issues: availability of larger affordable units and land use/zoning laws. These five factors are highlighted in Table 3 on following pages.
The survey results show the most consensus around factors contributing to limited access to housing in jurisdictions as well as disparities in housing cost burdens and overcrowding. 32 respondents (44%) indicated that the availability of larger affordable units contributes to a lack of access to housing in their jurisdiction. Additionally, displacement due to increased rents, displacement of low-income residents and/or residents of color, and community opposition to development were all listed by more than one-third of jurisdictions as contributing to limited housing access. These same four factors were also the most commonly indicated causes of disparities in housing cost burdens and overcrowding, with 42% of respondents
stating that displacement due to increased rents contributes to these disparities.
For the issues of segregated housing patterns/concentrated areas of poverty and disparities in access to opportunity areas, no contributing factor was selected by more than 12 respondents (17%). However, respondents did report similar causes for these fair housing issues: displacement due to increased rents, displacement of low-income residents and/ or residents of color, community opposition to development, location of affordable housing, and availability of larger affordable units.
Respondents were also asked to select the top three factors contributing to fair housing issues in their jurisdiction and to describe the reason for these selections. Below are the factors most commonly listed by jurisdictions as the main contributors to fair housing issues as well as a summary of why respondents selected these factors. The factors appear in order of how frequently they were cited by respondents as top contributors to fair housing issues, with the most frequently listed factors first.
• Displacement: Respondents noted that displacement
disproportionately affects low-income residents and
residents of color, which can result in disproportionate overcrowding for these populations. Additionally, the rising housing costs in communities affected by displacement
limit opportunities for racial and socioeconomic diversity and integration.
• Community opposition to development: Respondents
reported that residents commonly oppose denser housing, affordable housing, or housing with supportive services for formerly homeless residents. This opposition can
significantly increase the time to approve new development and drives up costs for both affordable and market-rate projects.
• Lack of affordable housing, especially larger units:
Respondents described how rising housing costs
and a limited supply of affordable housing cause the
displacement of low-income residents and prevent low-
income households from moving into communities.
• Land use and zoning laws: Some respondents noted
APPENDIX 8
A71
Table 3. Which of the following factors contribute to fair housing issues in your jurisdiction? Check all that apply. (Question 45) Factors Contributing to Fair Housing Issues
FAIR HOUSING ISSUES
Disparities in access to
opportunity areas
Segregated housing patterns
or concentrated areas of
poverty
Disparities in access to
opportunity areas
Disparities in housing
cost burdens and
overcrowding
Access to financial services 5 1 1 1
Access to grocery stores and healthy food
options 3 4 7 2
Access to healthcare facilities and medical
services 3 2 2 2
**Availability of larger affordable units 32 9 9 18
Availability, frequency, and reliability of public
transit 20 5 8 6
CEQA and the land use entitlement process 14 4 6 6
**Community opposition to development 24 10 9 15
Creation and retention of high-quality jobs 8 0 5 7
Deteriorated/abandoned properties 2 2 0 3
**Displacement due to increased rents 30 11 9 30
Displacement due to natural hazards 3 1 1 4
**Displacement of low-income/POC residents 25 12 11 24
Foreclosure patterns 2 3 2 4
Impacts of natural hazards 8 1 2 3
Lack of community revitalization strategies 2 3 2 3
Lack of private investments in low-income/POC
communities 6 6 6 5
Lack of public investments in low-income/POC
communities 4 3 4 2
Continued next page
* Factors highlighted in bold with asterisks (**) are among the five most commonly selected across fair housing issues.
APPENDIX 8
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 A72
that their jurisdictions are zoned primarily or entirely for single-family housing, and respondents also mentioned
restrictions on multi-family development created by
minimum lot sizes, density caps, height limits, and/or
minimum parking requirements. These respondents
reported that low-density zones cannot accommodate
affordable housing, and current land use restrictions result in limited sites for multi-family projects. Consequently, affordable development is nearly impossible in some
jurisdictions, while in other jurisdictions affordable
developments are concentrated in the few areas with denser zoning. As a result, current land use and zoning codes
perpetuate the segregation created by decisions of the past. • Barriers to development: In addition to community
opposition and land use laws, respondents described other barriers to development such as the availability of land suitable for development, the California Environmental
Quality Act (CEQA) and the land use entitlement
process, and the high cost of construction. Respondents discussed how their jurisdictions’ approval processes for development and CEQA inhibit housing production. These
respondents noted that CEQA slows down the entitlement
Table 3. Which of the following factors contribute to fair housing issues in your jurisdiction? Check all that apply. (Question 45) Factors Contributing to Fair Housing Issues
FAIR HOUSING ISSUES
Disparities in access to
opportunity areas
Segregated housing patterns
or concentrated areas of
poverty
Disparities in access to
opportunity areas
Disparities in housing
cost burdens and
overcrowding
Lack of regional cooperation 7 2 6 6
**Land use and zoning laws 20 10 7 9
Lending discrimination 2 2 2 4
Location of affordable housing 16 11 8 7
Location of employers 8 2 3 8
Location of environmental health hazards 2 2 0 2
Location of proficient schools and school assign-
ment policies 3 5 6 4
Occupancy standards limiting number of people
per unit 4 0 0 3
Private discrimination 4 2 2 3
Range of job opportunities available 7 0 5 5
Other 2 0 1 1
APPENDIX 8
A73
process and enables groups opposed to development to
threaten litigation and create additional delays. The project costs created by CEQA and lengthy entitlement processes can make housing development financially infeasible,
particularly for affordable projects. Survey responses
indicated that these barriers to development inhibit access to these communities generally and especially for lower- income populations.
• Location of employers: Respondents discussed how
limited job options within their jurisdictions and lack of access to job centers increase the costs of living there, as residents need to travel farther for work. Additionally, some mentioned that a lack of high-quality jobs within the jurisdiction prevents local jobholders from affording the high cost of housing.
• Public transit availability: Respondents suggested that a lack of public transit options inhibits those living in their jurisdiction from accessing jobs and services if they do not own a car, which makes the jurisdiction less accessible to a diverse range of households.
Topic 3: Fair Housing Goals and Actions
The four questions within this topic area discussed the actions jurisdictions have taken to remove barriers to equal housing opportunity and prevent the displacement of low-income
households. Respondents were also asked to reflect on their goals for fair housing policies and whether the strategies they have implemented achieve these goals. For more
information on the strategies to further fair housing that Bay Area jurisdictions have detailed in their fair housing reports to HUD, see Section 3.
Key Takeaways from Respondents’ Comments
Policies and initiatives to further fair housing: The survey results indicate that there are eight actions that a majority of respondents have taken to address existing segregation and enable equal housing choice (see Figure 13 on page A72).
Most of these actions center on increasing the number of affordable housing units. For example, 69% of respondents have supported the development of affordable housing
for special needs populations such as seniors, people with disabilities, people experiencing homelessness, and/or
those with mental health issues. The survey responses also indicate that most respondents have sought to increase the supply of affordable housing through inclusionary zoning, land use changes, developing affordable housing near transit, encouraging the construction of larger affordable units, using publicly owned land for affordable development, and establishing local funding sources for affordable housing construction. Other common strategies to advance fair
housing focus on low-income homeownership, with 53% of
respondents funding home rehabilitation and improvements for low-income homeowners and 49% of respondents
providing resources to support low-income homebuyers.
Goals for fair housing policies: Many of the jurisdictions’ survey responses noted that a goal of their fair housing policies is facilitating equal housing opportunities by removing barriers to affordable housing. Specifically,
respondents discussed the following objectives for their fair housing policies related to increasing the affordable housing supply:
APPENDIX 8
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031 A76
Table 4. Describe the reasons for the success or lack of success of your jurisdiction’s actions to overcome historical patterns of segregation or remove barriers to equal housing opportunity. (Question 49)
FACTORS ENABLING SUCCESS IN
ACHIEVING FAIR HOUSING POLICY
GOALS:
FACTORS PREVENTING SUCCESS IN
ACHIEVING FAIR HOUSING POLICY
GOALS:
• Creation of new local funding
sources for affordable housing
• Construction of 100% affordable
housing developments with local
financial support
• Streamlined approvals processes
for development, particularly for
affordable housing and ADUs
• Production of new
affordable housing through
inclusionary zoning
• Affordable housing opportunities
are not limited to low-income
neighborhoods
• Rezoning and other policies
implemented through Housing
Element updates resulting in
increased development of both
market-rate and affordable units
• Ongoing funding for fair housing
services providers
• Strong leadership, political will,
and community support for
policies that advance fair
housing goals
• Available funding inadequate
for meeting the demand for
affordable housing and other
housing services
• Land prices, land availability,
and construction costs hamper
affordable housing construction
• Development of affordable
housing cannot keep pace with
the need
• Longer timeframe required
to see the effects of efforts to
deconcentrate poverty and make
affordable housing available
throughout all neighborhoods
• Lack of private investment,
particularly in historically
marginalized communities
• Lack of staff to work on
policy development
and implementation
• Community opposition to
policies related to furthering
fair housing
• Programs and land use regulations to preserve affordable housing in mobile home parks
• Just cause eviction protections
• Downpayment assistance programs for residents
• Partnering with land trusts to acquire foreclosed homes and other for-sale properties to make them available for low- and moderate-income homebuyers
• Assisting landlords with low-cost loans and grants for property improvements in return for keeping long-time
residents in place
SECTION 3: SUMMARY OF BAY AREA LOCAL FAIR
HOUSING REPORTS
Federally Mandated Fair Housing Reports
Federal law obligates state and local jurisdictions receiving block grant funding from the HUD to submit a Consolidated Plan every five years, and this process requires conducting an Analysis of Impediments to Fair Housing Choice (AI). 1 In
2015, HUD released a final rule on affirmatively furthering fair housing (AFFH), which provided updated guidelines for assessing fair housing issues and created a new Assessment of Fair Housing (AFH) tool to replace the AI process. HUD’s intent for this new process was to improve community planning
around fair housing issues, as this new tool required public participation and increased data analysis.
2 In 2018, however,
HUD suspended the AFH tool and reinstated the previous
6 See https://www.hudexchange.info/programs/consolidated-plan/consolidated- plan-process-grant-programs-and-related-hud-programs/ or more information on the Consolidated Plan process.
7 See https://files.hudexchange.info/resources/documents/AFFH-Fact-Sheet.pdf and https://www.hudexchange.info/programs/affh/overview/ for more information on the 2015 AFFH rule and AFH tool.
APPENDIX 8
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031A78 requirement to complete an AI report.3 In response to HUD’s decision, the California legislature passed Assembly Bill 686 in 2018, which states that AFFH obligations must be interpreted in a manner consistent with HUD’s 2015 AFFH rule, regardless of subsequent amendments to or suspensions of the rule. 4 As
a result, some reports submitted by Bay Area jurisdictions for the 2020-2025 cycle are labeled AFH reports, while others are AI reports, but the content and format of reports submitted since the passage of Assembly Bill 686 are likely to be similar, regardless of whether the report is labeled an AI or AFH.
Bay Area Reports
Currently, 41 Bay Area cities and counties participate in the Consolidated Plan process and have submitted AI or AFH
reports to HUD. Because these reports are submitted as
part of five-year planning cycles, most of these jurisdictions recently submitted a report for the years 2020-2025 or
are currently working on a report for this cycle, though reporting in some jurisdictions occurs on a different timeline.
While some reports are submitted to HUD by individual
jurisdictions, this reporting can also be completed as a collaborative effort between a county government and local jurisdictions within the county.
Below is a summary of the 16 AI and AFH reports, which are the most recently submitted fair housing documents from Bay Area jurisdictions available to the public. These reports cover the following jurisdictions:
8 See https://files.hudexchange.info/resources/documents/FR-Notice-AFFH-AI-Notice.pdf for the 2018 HUD notice. 9 See https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=201720180AB686 for text of Assembly Bill 686. 10 See https://www.hudexchange.info/programs/consolidated-plan/consolidated-plan-process-grant-programs-and-related-hud-programs/ or more information on the Consolidated Plan process.
11 See https://files.hudexchange.info/resources/documents/AFFH-Fact-Sheet.pdf and https://www.hudexchange.info/programs/affh/overview/ for more information on the 2015 AFFH rule and AFH tool.
• Programs and land use regulations to preserve affordable housing in mobile home parks
• Just cause eviction protections
• Downpayment assistance programs for residents
• Partnering with land trusts to acquire foreclosed homes and other for-sale properties to make them available for low- and moderate-income homebuyers
• Assisting landlords with low-cost loans and grants for property improvements in return for keeping long-time
residents in place
SECTION 3: SUMMARY OF BAY AREA LOCAL FAIR
HOUSING REPORTS
Federally Mandated Fair Housing Reports
Federal law obligates state and local jurisdictions receiving block grant funding from the HUD to submit a Consolidated Plan every five years, and this process requires conducting an Analysis of Impediments to Fair Housing Choice (AI). 5 In
2015, HUD released a final rule on affirmatively furthering fair housing (AFFH), which provided updated guidelines for assessing fair housing issues and created a new Assessment of Fair Housing (AFH) tool to replace the AI process. HUD’s intent for this new process was to improve community planning
around fair housing issues, as this new tool required public participation and increased data analysis.
6 In 2018, however,
HUD suspended the AFH tool and reinstated the previous
APPENDIX 8
APPENDICES RHNA A79
requirement to complete an AI report.7 In response to HUD’s decision, the California legislature passed Assembly Bill 686 in 2018, which states that AFFH obligations must be interpreted in a manner consistent with HUD’s 2015 AFFH rule, regardless of subsequent amendments to or suspensions of the rule. 8 As
a result, some reports submitted by Bay Area jurisdictions for the 2020-2025 cycle are labeled AFH reports, while others are AI reports, but the content and format of reports submitted since the passage of Assembly Bill 686 are likely to be similar, regardless of whether the report is labeled an AI or AFH.
Bay Area Reports
Currently, 41 Bay Area cities and counties participate in the Consolidated Plan process and have submitted AI or AFH
reports to HUD. Because these reports are submitted as
part of five-year planning cycles, most of these jurisdictions recently submitted a report for the years 2020-2025 or
are currently working on a report for this cycle, though reporting in some jurisdictions occurs on a different timeline.
While some reports are submitted to HUD by individual
jurisdictions, this reporting can also be completed as a collaborative effort between a county government and local jurisdictions within the county.
Below is a summary of the 16 AI and AFH reports, which are the most recently submitted fair housing documents from Bay Area jurisdictions available to the public. These reports cover the following jurisdictions:
• Alameda County collaborative report: the cities of
12 See https://files.hudexchange.info/resources/documents/FR-Notice-AFFH-AI-Notice.pdf for the 2018 HUD notice. 13 See https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=201720180AB686 for text of Assembly Bill 686.
Alameda, Albany, Berkeley, Dublin, Emeryville, Fremont, Hayward, Livermore, Newark, Oakland, Piedmont,
Pleasanton, San Leandro, and Union City as well as
Alameda County
• Contra Costa County collaborative report: the cities of Antioch, Concord, Pittsburg, and Walnut Creek as well as Contra Costa County
• Marin County
• City and County of San Francisco
• San Mateo County collaborative report: the cities of Daly City, San Mateo, South San Francisco, Redwood City, as
well as San Mateo County
• Santa Clara County
• Sonoma County collaborative report: cities of Santa Rosa and Petaluma as well as Sonoma County
• City of Cupertino
• City of Fairfield
• City of Milpitas
• City of Mountain View
• City of Napa
• City of San Jose
• City of Sunnyvale
• City of Vacaville
• City of Vallejo
APPENDIX 8
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031A80 Reported Fair Housing Impediments, Strategies,
and Actions
This summary focuses on common impediments to fair
housing experienced by Bay Area jurisdictions, and it also lists specific strategies proposed and actions taken in response to these obstacles. While each AI or AFH report contains extensive city/county demographic information, housing
equity history, and details on how the report was produced, including community engagement efforts, this summary does not focus on the individual circumstances of each jurisdiction.
Rather, it collates these jurisdictions’ most significant barriers to affirmatively furthering fair housing, as self-reported, and lists the strategies they have taken to overcome them, in an attempt to draw out common themes at the regional level.
The top themes to emerge at the regional level are:
1. There is a severe lack of affordable housing amidst already- high housing costs regionwide.
2. The lack of affordable housing leads to displacement and gentrification, impacting access to employment,
transportation, and education for low-income people.
3. Communities often oppose new housing construction,
especially when it is dense, affordable housing. While
framed as an issue of “local control,” in some circumstances this opposition to housing may be rooted in implicit
discrimination based on race and class/income.
4. Jurisdictional zoning and approval policies and practices reflect this community opposition and contribute to the lack of affordable housing supply.
5. Lack of investment in specific neighborhoods is the result of longstanding explicit housing segregation, leading to racially-concentrated areas of poverty that persist today.
6. Outreach, education, and enforcement of fair housing activities are contracted out to nonprofits with insufficient resources.
7. There are significant accessibility barriers to housing for disabled, non-English-speaking, formerly incarcerated,
formerly homeless, and other specific populations.
8. Discrimination in the private housing market is prevalent, both in the rental market and in lending policies and
practices that impede home ownership.
9. There is much room for improvement in coordination and cooperation regionwide, both between jurisdictions and
among different housing advocacy groups.
Below are more details on these highly interrelated obstacles to fair housing in the Bay Area, as well as actions and strategies that may offer solutions. Nearly all of the reports considered each of the following nine impediments, but
they were inconsistent in clarifying whether the strategies noted have actually been implemented or are simply being considered. This high-level summary includes all strategies that local fair housing reports listed as potential solutions to these nine impediments. However, ABAG staff could
not determine from these reports how many jurisdictions had implemented each strategy versus how many were
considering the strategy but had not yet adopted it. The following list orders both the impediments and the strategies by approximate frequency and importance to the collective APPENDIX 8
APPENDICES RHNA A81
jurisdictions (i.e., the most frequently reported, most important ideas across reports are listed first), as interpreted by ABAG staff who compiled the summary after reviewing the reports.
IMPEDIMENT 1: Lack of Affordable Housing
A lack of affordable housing means a lack of racially and ethnically integrated and balanced communities. Every Bay Area jurisdiction examined in this summary reports a shortage of affordable housing for those who need it, in both rental and ownership markets. The inadequate supply of affordable housing creates a severe housing shortage for communities of color, which are disproportionately economically
disadvantaged.
9 Strategies and Actions for Overcoming this Impediment
1. Seek funding for new affordable housing construction • Pursue dedicated sources of funding for affordable
housing (citywide, countywide, or regionwide), including:
o Affordable housing bonds
o Local sales tax, transit occupancy tax, or vacant home tax o Housing trust funds for affordable housing development • Explore state and national funding, such as CA Senate Bill 2 • Increase in-lieu fees10 to reflect actual cost of affordable 14 For more information on economic disparities across racial/ethnic groups in the Bay Area, see An Equity Profile of the Nine-County San Francisco Bay Area Region, by PolicyLink and PERE, the Program for Environmental and Regional Equity at the University of Southern California. Read at: https://nationalequityatlas.org/sites/default/files/ Final_9_County_BayAreaProfile.pdf.
15 In-lieu fees are fees paid by developers of market rate housing to satisfy affordable housing requirements in jurisdictions with inclusionary housing ordinances. The fee is paid in-lieu of providing on-site affordable housing, and jurisdictions typically use the fee to finance affordable housing development at a different site. 16 Under the Project-Based Voucher program, a Public Housing Agency enters into anassistance contract with a development owner. This assistance subsidizes the rents for up to 25% of the units in the development for a specified term. Households living in units subsidized by PBVs pay 30% of their income toward rent, and the Public Housing Agency pays the development owner the difference between the rent the household pays and the gross rent for the unit. PBVs can enable an affordable housing development to charge more deeply affordable rents and better serve extremely low-income households. housing development
• Pool in-lieu fees among cities
• Adopt inclusionary housing policies to bolster funds to support affordable housing
2. Identify new sites for affordable housing
• Prepare and publicize available and easily obtainable maps of all incorporated and unincorporated vacant and
underutilized parcels
• Create a public database of potential sites that can be updated regularly
3. Incentivize developers to build new affordable units • Prioritize the production of affordable housing units in sizes appropriate for the population and based on family size • Reduce developer fees for affordable housing
• Encourage market rate housing to include affordable units, such as by promoting use of density bonuses
• Identify underutilized parcels to acquire, convert and develop into affordable housing
• Award higher points in housing developer applications to projects that offer units of 3+ bedrooms
• Support Project-Based Voucher (PBV) developments11
APPENDIX 8
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031A82 • Promote objective development and design standards for housing development projects that qualify for streamlined permit review
• Provide assistance to developers to secure entitlements and county funding for extremely low-income/special
needs units
• Coordinate use of housing subsidies to build affordable housing in high-opportunity areas in order to increase
low-income households’ access to designated opportunity areas with low poverty rates, healthy neighborhoods, and high-performing schools
• Explore the production of units that are affordable by design, such as Accessory Dwelling Units (ADUs) and
micro-units
4. Consider existing units: Protect currently affordable housing from becoming market-rate, and/or convert
currently market-rate housing to affordable housing
• Provide technical assistance and funding application assistance to retain affordable units at risk of converting to market rate
• Develop and implement a small site acquisition and
rehabilitation program that effectively channels fees paid to the city, leveraged with other public and private resources, to the preservation of small buildings serving low-income tenants
• Leverage financial resources from state and federal
17 For more information on gentrification, see https://www.urbandisplacement.org/gentrification-explained. 18 For more information on the impacts of displacement, see https://www.urbandisplacement.org/pushedout. 19 For more information on the statewide rent caps and just cause for eviction protections instituted by AB 1482, see https://sfrb.org/article/summary-ab-1482-california- tenant-protection-act-2019.
programs to rehabilitate existing affordable housing
projects nearing the end of their affordability restrictions and extend their subsidy into the future
• Donate municipally-owned, tax-foreclosed properties to nonprofit community land trusts to be rehabilitated, as needed, and preserved for long-term affordable housing
IMPEDIMENT 2: Displacement and Gentrification
As defined by the Urban Displacement Project at UC Berkeley, gentrification is a process of neighborhood change in a historically disinvested neighborhood that includes both economic and demographic change. These changes occur
as a result of both real estate investment and new higher- income residents moving in, which results in corresponding changes in the education level or racial makeup of residents. 12 Gentrification often causes displacement, which prevents long-term residents from benefitting from new investments in their neighborhood. Moreover, when low-income families are displaced from their homes, they typically move to lower- income neighborhoods, which generally lack options for high- quality employment, transportation, and schools.
13 Strategies and Actions for Overcoming this Impediment
1. Adopt tenant protections
• Adopt tenant protections, such as relocation costs,
increased noticing, just cause for eviction, and rent control ordinances
• Promote new fair housing laws, including AB 1482,14
APPENDIX 8
APPENDICES RHNA A83
including posting information on jurisdiction websites
• Collaborate with regional efforts such as established countywide homeless action plans/goals/programs that may provide one-time rent assistance to low-income people in jeopardy of being evicted due to life emergency or hardship • Commission market-based rent surveys to seek
adjustments to the fair market rents (FMRs) for the federal Housing Choice Voucher program
• Use eminent domain to block home foreclosures
• Fund and support multi-agency collaborative efforts for legal services, including organizations that do not receive Legal Services Corporation funding (federal funds) and are able to represent undocumented residents
2. Prioritize existing and new affordable housing,
specifically in gentrifying areas
• Develop displacement mitigation or replacement
requirements for any rezoning activities that could displace existing residents
• In tandem with investments in affordable housing
development in low-poverty areas, provide funds for
the preservation of affordable housing in areas that are undergoing gentrification or are at risk of gentrification, in particular in areas of high environmental health
• Donate municipally-owned, tax-foreclosed properties to nonprofit community land trusts to be rehabilitated, as needed, and preserved for long-term affordable housing
• Explore the development of policy that will allow a set- aside in affordable housing developments that prioritizes residents who are being displaced from low-income
neighborhoods undergoing displacement and/or
gentrification
• Offer minor home repair grants to help homeowners
remain in their homes
IMPEDIMENT 3: Community Opposition to New
Housing
Communities often prefer single-family homes in their
neighborhoods, which residents typically describe as based on fear of lowered property values, overcrowding, or changes in the character of the neighborhood. When communities
resist new housing, it often results in the exclusion of people of color and low-income households.
Strategies and Actions for Overcoming this Impediment
• Develop growth management programs intended to
concentrate urban development and preserve agriculture
and open space
• Provide ongoing community engagement to educate,
include and inform residents about the challenges
with housing, and to highlight the jurisdiction’s prior achievements in developing affordable housing and
addressing racial disparities in housing choice
• Develop strategies and talking points to address topics cited in opposition to housing development, including the impact on schools, water, transportation and traffic
• Include and expand the number of participants who
engage in discussions about barriers to fair housing and disparities in access and opportunities, and provide
APPENDIX 8
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031A84 opportunities to advance recommendations to address
housing challenges
IMPEDIMENT 4: Zoning Practices and Building
Approvals
Local land use controls, zoning regulations, and impact fees are major impediments to constructing and preserving affordable housing. Unlike many other impediments to fair housing, jurisdictions have the authority to directly address these issues.
Strategies and Actions for Overcoming this Impediment
1. Evaluate and update zoning
• Evaluate and update existing zoning to ensure compliance with state-mandated streamlining requirements
• Rezone and repurpose underdeveloped areas
• Modify current zoning and other local policies regulating housing development that pose a direct or indirect
constraint on the production of affordable housing
• Update zoning and programs to incentivize accessory
dwelling units (ADUs)
• Explore revisions to building codes or processes to reduce the costs of ADU construction and/or allow a greater
number of ADUs
• Encourage mixed-use transit-oriented development
for affordable housing sites that are located near
transportation facilities and employment centers by
appropriately zoning for higher density residential and mixed-use developments, maximizing the linkages
between employers and affordable housing
• Consider rezoning sites for affordable housing outside of racially segregated areas that are predominantly residents of color
• Consider reduced development standards, specifically parking requirements, to incentivize the development of specific housing types, including units with affordability covenants, units for special needs individuals, higher
density residential development, and developments near
public transit
2. Evaluate and update fees, processing times,
ordinances
• Review existing inclusionary housing in-lieu fees, housing impact fees, and jobs-housing linkage fee programs to
maximize number of units, as consistent with current
housing market conditions and applicable law
• Evaluate options for streamlined processing of affordable housing developments
• Discourage or eliminate live/work preferences in
inclusionary ordinances
APPENDIX 8
APPENDICES RHNA A85
IMPEDIMENT 5: Segregation, Lack of Investment in
Specific Areas, Racially/Ethnically Concentrated Areas
of Poverty (R/ECAPs)
Public and private disinvestment in certain areas has resulted in racially/ethnically concentrated areas of poverty (R/ECAPs).
In these neighborhoods, lack of tax revenue and funds for services has led to deteriorated and abandoned properties and areas where communities of color cannot access
amenities needed for a healthy life.
Strategies and Actions for Overcoming this Impediment
1. Target economic investment opportunities in R/ECAPS
while protecting against displacement
• Fund home-based childcare projects and microenterprise projects with Community Development Block Grant
(CDBG) funds
• Provide Family Self-Sufficiency program participants with job training referrals and career networking15
• Explore financially supporting economic development
activities and initiatives in and around R/ECAPs
• Prioritize economic development expenditures in and
around R/ECAPs
• Prioritize funding for job training programs in and around R/ECAPs, including industrial jobs
• Prioritize infrastructure and streetscaping improvements in R/ECAPs in order to facilitate local retail development 20 Family Self-Sufficiency is a program that enables HUD-assisted families to increase their earned income and reduce their need for welfare assistance and rental subsidies. • Engage with small business incubators to expand to R/ ECAPs or to provide technical assistance to start-up
incubators
• Explore methods for providing low-interest loans and below-market leases for tax-foreclosed commercial
properties to low-income residents seeking to start
businesses within R/ECAPs
2. Improve access to home renting and buying for
residents in R/ECAPS
• Work with communities to develop a community land
trust for low-income residents that creates opportunities for affordable housing and home ownership, with specific inclusion for residents of color with historic connections to the area
• Build affordable housing projects in middle- and upper- income neighborhoods to the maximum degree possible
• Create more standardized screening policies and
procedures for city-sponsored affordable housing
• First-time homebuyer down payment assistance programs IMPEDIMENT 6: Outreach, Education, Enforcement
Nearly all jurisdictions report contracting with nonprofit organizations (partly funded by city and county grants) to provide local fair housing services and education, including counseling, language services, and handling of fair housing complaints. Despite these efforts, the region lacks sufficient housing search assistance, voucher payment standards,
landlord outreach, mobility counseling, and education about APPENDIX 8
ABAG FINAL REGIONAL HOUSING NEEDS ALLOCATION (RHNA) PLAN: SAN FRANCISCO BAY AREA, 2023-2031A86 fair housing rights. Inadequate funding and organizational capacity of the nonprofits providing services plays a role.
Strategies and Actions for Overcoming this Impediment
1. Better fund all fair housing services
• Allocate more federal, state, and local funding for nonprofit organizations providing fair housing services
• Fund and support multi-agency collaborative efforts for legal services, including organizations that do not receive Legal Services Corporation funding (federal funds) and are able to represent undocumented residents
2. Promote better fair housing outreach and education
services
• Continue to contract with fair housing service providers to educate home seekers, landlords, property managers,
real estate agents, and lenders regarding fair housing law and recommended practices, including the importance
of reasonable accommodation under the Americans
with Disabilities Act; to mediate conflicts between home seekers, landlords, property managers, real estate agents, and lenders; and to continue fair housing testing and audits • Implement annual training programs for property
managers and residents
• Seek ways to increase resident access to fair housing services, such as improved marketing of services, improved landlord education, and improved tenant screening
services to avoid owner bias
• Educate tenants and landlords on new fair housing laws • Provide financial literacy and homebuyer education classes • Continue to fund housing placement services for people with disabilities to assist them in finding accessible housing • Develop and distribute informational brochure on
inclusionary leasing practices, including with licenses where applicable
• Continue and increase outreach and education activities for all protected classes
• Include education on new requirements of Assembly
Bill 2413 (Chiu), the Right to a Safe Home Act, in outreach activities to both landlords and the public
19 • Explore alternative formats for fair housing education workshops such as pre-taped videos and/or recordings,
which could serve persons with more than one job, families with young children and others who find it difficult to attend meetings in person
3. Better advertise affordable housing opportunities
• Create a database of all restricted housing units citywide/ countywide/regionwide that could be posted online to
provide user-friendly information about the location and application process for each development
• Advertise the availability of subsidized rental units via the jurisdictions’ websites and or apps, the 2-1-1 information and referral phone service, and other media outlets
APPENDIX 8
APPENDICES RHNA A87
IMPEDIMENT 7: Accessibility for Specific
Populations
Many jurisdictions report a lack of accessible
housing for persons with disabilities, non-English-
speaking people, formerly incarcerated people,
formerly homeless people, seniors, and other specific
populations—all direct fair housing issues.
Strategies and Actions for Overcoming this
Impediment
• Fund housing placement services for people with
disabilities to assist them in finding accessible
housing
• Offer landlord incentives, such as leasing bonuses,
for specific populations
• Conduct a research effort in collaboration with
an academic institution to better understand the
landlord population and create more evidence-
based policy initiatives
• Increase marketing efforts of affordable housing
units to people that typically face barriers and
discrimination in fair housing choice, such as
persons with disabilities, people of color, low-
income families, seniors, new immigrants, and
people experiencing homelessness
• To the extent practicable, use affordable housing
funds for the construction of permanent supportive
housing in developments in which 10-25% of
units are set aside for persons with disabilities.
Affirmatively market units to individuals with
intellectual and developmental disabilities, their
families, and service providers
• Explore methods for nonprofit partners to assist
in purchasing or master leasing affordable units
within inclusionary market-rate developments, and
set a portion of those units aside for persons with
disabilities
• Develop and disseminate a best practices guide
to credit screening in the rental housing context in
order to discourage the use of strict credit score
cut-offs and overreliance on eviction records
• For publicly-supported housing, develop
protocols to ensure responsiveness to reasonable
accommodation requests
IMPEDIMENT 8: Discrimination in Home
Ownership and Rental Markets
Over time explicit, legal discrimination has given way
to implicit, unwritten biases in mortgage access and
lending policies and practices for people of color—
specifically in high rates of denial of mortgages for
African American and Hispanic households. In the rental housing market, discrimination against low-income
people, minorities, immigrants, and LGBTQ people is
also prevalent. People using Housing Choice Vouchers
also face discrimination for their source of income.
Strategies and Actions for Overcoming this
Impediment
• Work with communities to develop a community
land trust for low-income residents that creates
opportunities for affordable housing and home
APPENDIX 8
From: Lyle, Amy
To: Montoya, Michelle
Cc: Hartman, Clare; Nicholson, Amy
Subject: FW: [EXTERNAL] Fwd: comment on Housing Element draft -- mobile homes Date: Monday, June 6, 2022 10:41:06 PM
Hi Michelle, can you include this in the late correspondence with the County’s RHNA comment letters please?
Thanks,
Amy
Amy Lyle (she,her)| Supervising Planner- Advance Planning Planning & Economic Development|100 Santa Rosa Avenue, Room 3 | Santa Rosa, CA 95404 Cell (707) 791-5533 | Office (707) 543-3410
| [email protected]
From: Warren Wiscombe <[email protected]>
Sent: Sunday, June 5, 2022 1:06 PM
To: Lyle, Amy <[email protected]>
Subject: [EXTERNAL] Fwd: comment on Housing Element draft -- mobile homes
---------- Forwarded message ---------
From: Warren Wiscombe <[email protected]>
Date: Sun, Jun 5, 2022 at 1:03 PM
Subject: comment on Housing Element draft -- mobile homes To: <[email protected]
>
Hi,
I searched the draft for instances of "trailer" (1) and "mobile" (many) to see if any new or creative policies were being proposed, and I didn't find any. It is of course nice to follow the law and not break up existing mobile home parks, but I had expected more focus on this housing type since it offers a very useful option for working our way out of the growing housing crisis.
I have watched as a variety of mobile housing types have been used to try to address the homeless crisis, and even the tents out at Los Guilicos, and it all seems like a kind of patchwork quilt with no guiding philosophy or plan, just a sort of knee- jerk reaction to the crisis of the moment.
I would have expected a more long term
view in The Draft, and I don't see one.
Right now, mobile home parks are scattered almost randomly around Santa Rosa.
There doesn't seem to be any plan. The Draft should offer such a plan, not just say "we are going to continue doing what we are doing". If ever Santa Rosa is to work its way out of the housing crisis, an organized plan for mobile homes, widely construed, has to be an integral part.
Note that the cost argument is central here. Mobile homes can be purchased for $50K or so, some more, some less, while the median price for a single-family house has soared to more than ten times that figure.
Single-family houses are now beyond the
reach of most middle-class people, and apartments are quickly following. Without a big plan to expand mobile homes -- beyond "just keep doing the same thing" -- we are dead on housing costs and unwittingly committing to an increasing traffic problem as people are forced to live out in the boonies, in more fire-prone areas, to escape skyrocketing SR housing costs.
I will close by noting that when I asked our old real estate agent about mobile homes, she said that hers and other realty companies don't even consider them houses, and don't include them in the multiple listings or in the assessments of median house prices.
They are just hidden from view (until they burn) and they are largely condemned to remain so in your current Draft.
Warren Wiscombe
Santa Rosa 95404