HomeMy WebLinkAboutItem 7.1 Regional Housing Needs Alloc Update
C I T Y C LE R K
File # D[3]~QJ-[z]~
AGENDA STATEMENT
CITY COUNCIL MEETING DATE: January 16, 2007
SUBJECT:
ATTACHMENTS:
RECOMMEiON:
~
FINANCIAL STATEMENT:
None.
PROJECT DESCRIPTION:
Background
Update on Regional Housing Needs Allocation (RHNA)
Methodology for Income Allocations for the 2007-2014 Housing
Element Cycle
Report prepared by Mamie R. Nuccio, Associate Planner
1)
2)
City Council Agenda Statement dated December 19,2006.
Association of Bay Area Governments Memo dated January
4, 2007 regarding Alternative Income Allocation Method.
Draft letter to ABAG regarding the proposed alternative
income allocation methodology.
3)
1)
Receive report and direct Staff to forward comments
(Attachment 3) to ABAG regarding the proposed Alternative
Income Allocation Methodology for the 2007-2014 Housing
Element cycle, OR
Accept the proposed Alternative Income Allocation
Methodology for the 2007-2014 Housing Element cycle.
2)
On November 16,2006, the Association of Bay Area Governments (ABAG) Executive Board adopted a
Resolution authorizing the release of the proposed Regional Housing Needs Allocation (RHNA)
methodology for public review and comment. The comment period is still currently open and will close
on January 18, 2007. The ABAG Executive Board will also meet on January 18th to adopt the final
RHNA methodology.
At the December 19, 2006 City Council meeting, Staff presented an informational report on the RHNA
methodology and the City Council accepted the proposed methodology. The methodology included the
allocation of the regional housing need as well as the allocatioJ). of housing units by income level (See
COPY TO:
Page 1 of 4
ITEMNO.~
G:\General Plan\Housing Element\RHNA 2007-2014\01-16-07 CCSR RHNA Method Alternatives.doc
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Attachment 1). The proposed methodologies for the 2007-2014 RHNA period were as follows (See
Tables 1 and 2 below):
Committee Recommendation for the RHNA Methodolo
Household Growth from 2007-2014
Existing Em loyment in 2007
Em loyment Growth from 2007-2014
Household Growth near transit from 2007-2014
Employment Growth near transit from 2007-2014
40%
20%
20%
10%
10%
Table 2. Recommended Re ional Income Allocation based on Affordabili
Very Low Income
Low Income
Moderate Income
Above Moderate Income
Total
U to 50% AMI
50%-80% AMI
80%-120% AMI
Above 120% AMI
23%
16%
19%
42%
100%
Housing Methodology Committee (HMC) Meeting of January 4, 2007
Since the opening of the public review and comment period, ABAG has received comments from various
jurisdictions regarding the proposed RHNA methodology.
Allocation ofthe Region's Total Housing Need
The most prevalent concern expressed by local jurisdictions regarding the proposed methodology for
allocating the region's total housing need was regarding the transit factor. The proposed methodology
includes two transit factors: household growth near transit and employment growth near transit. Both
factors are weighted at 10% (See Table 1 above). Local jurisdictions expressed concern regarding the use
of both existing and planned transit as factors in the methodology based on a belief that this would
unfairly burden those jurisdictions with either existing or planned transit and those cities with multiple
transit stations. In response to these comments, ABAG staff developed several new alternative
methodologies for consideration. The HMC reviewed these new alternatives at the January 4, 2007
meeting and by majority vote recommended that the proposed methodology remain unchanged (See Table
1 above).
Allocation of Housing Units by Income Level
Local jurisdictions also expressed concern to ABAG regarding the allocation of housing units by income
level. Some jurisdictions believe that by applying the "equal share" concept, the methodology does not do
enough to alleviate existing concentrations of lower income housing. These jurisdictions indicated that
they would be unfairly burdened with additional lower income housing further perpetuating regional,
social and economic inequities. In response to these comments, ABAG staff prepared several new
alternative methodologies for consideration (See Attachment 2). The HMC reviewed the new alternatives
and by majority vote recommended a new methodology that takes into consideration existing
concentrations of lower income housing.
ANALYSIS:
Since the HMC did not recommend to ABAG any modifications to the proposed methodology for the
allocation of total housing units, which the City Council reviewed at the December 19, 2006 meeting,
Page 2 of 4
Staffs analysis focuses only on the proposed changes to the methodology for allocating units by income
level.
Allocation of Housing Units by Income Level
The previously proposed methodology for the allocation of housing units by income level applied an
"equal share" approach whereby all jurisdictions within the region would be required to provide the same
percentage of lower income housing based on the distribution of lower income households within the
region (See Table 2 above). By taking the "equal share" approach, every jurisdiction would contribute to
the region's need for lower income housing; however, this approach did not take into consideration
existing concentrations of lower income housing.
The proposed alternative methodology attempts to factor into the allocation existing concentrations of
lower income housing. . Census 2000 data for local household income distribution and regional household
income distribution would be used by ABAG to determine where existing concentrations of lower income
housing currently exist and where additional lower income housing is needed. A comparison of a
jurisdiction's existing household income distribution to the region's household income distribution would
determine whether the jurisdiction has more, or less, lower income households than the region as a whole.
If a jurisdiction has more lower income households (i.e. Oakland), then they would be assigned a smaller
percentage of lower income housing units for the 2007-2014 RHNA cycle. If a jurisdiction has less lower
income households (i.e. Piedmont), they would be assigned a larger percentage of lower income housing
units for the 2007-2014 RHNA cycle. For example, under the previously proposed methodology, Oakland
and Piedmont would both be required to set aside 23% of their housing unit allocation for very-low
income households. Under the currently proposed methodology, Oakland would be assigned 13% (to
correct for existing concentrations of lower income housing) whereas Piedmont would be assigned 33%
(to compensate for an existing lack of lower income housing). The actual number of lower income
housing units assigned to a jurisdiction would be a percentage of that jurisdiction's total housing unit
allocation.
The multiplier used by ABAG in calculating the proposed alternative methodology is 175%. This means
that each jurisdiction is allocated 175% of the difference between its 2000 household income distribution
and the 2000 regional household income distribution. Other multipliers of 100%, 125% or 150% could
also be used to address existing concentrations of low income housing. ABAG proposed a multiplier of
150% to the HMC; however, this was not supported by a majority.
Implications for Dublin
Under the previously proposed methodology, Dublin would have been required to provide the following:
Very Low Income
Low Income
Moderate Income
Above Moderate Income
Previous Proposed
Methodology
23%
16%
19%
42%
Currently Proposed
Methodology
33%
20%
19%
28%
Based on the proposed alternative methodology now recommended by the HMC, which utilizes a 175%
multiplier, Dublin's percentage of very low and low income units would increase by 10% and 4%
respectively, an increase of 343 very low income units and 121 low income units (please note that the
number of units stated are provided for illustrative purposes only and do not necessarily reflect the actual
Page 3 of 4
allocation); the percentage of moderate income units would remain proportionately the same; and, above
moderate income units would be reduced by 14% (See Table 3 below).
Total
Need
Dublin 3,440*
Percentage of
Total
*The Total Need number reflects the 1999-2006 RHNA and is not the official RHNA number for the 2007-2014 RHNA cycle.
Similarly, the numbers shown for very low, low, moderate and above moderate incomes are based on the 1999-2006 RHNA
and are used for illustrative purposes only; they do not reflect Dublin's actual allocation for the 2007-2014 RHNA.
569*
16%
661*
19%
1,432*
42%
690*
20%
669*
19%
The most significant impact of the newly proposed alternative methodology to Dublin would be providing
1,121 very low income units (343 additional units from the previously proposed methodology) within the
2007-2014 RHNA cycle. While Staff supports the methodology in concept, it may be unrealistic to expect
that such a large number of very low income units could be provided within a short 7 year time frame.
Additionally, the types of support services utilized most frequently by lower income households are
currently located within the more urban areas ofthe region and not in the Tri-Valley area.
Therefore, Staff recommends that the City Council forward a letter (Attachment 3) to ABAG requesting
consideration of the use of a different multiplier to achieve more realistic goals for 2007-2014 RHNA
cycle while continuing to take into consideration existing concentrations of lower income housing. As
shown in Attachment 2, ABAG has calculated an alternative methodology using a 150% multiplier which
would increase Dublin's percentage of very low and low income units by 6% and 3% respectively; an
increase of 235 very low income units and 79 low income units (please note that the number of units
stated are provided for illustrative purposes only and do not necessarily reflect the actual allocation).
While this results in a slightly lower income allocation for very low and low income units, it is still
somewhat aggressive. Staffs recommendation is that ABAG consider using a less aggressive multiplier
of 100% or 125% thereby taking a more incremental approach to addressing existing concentrations of
lower income housing which is more realistic and more likely to be achievable. It would also give service
providers more of an opportunity to redirect resources to the outlying suburban areas of the region as the
service population grows.
NEXT STEPS:
Since the public review and comment period for the 2007-2014 RHNA methodology closes on January
18,2007, Staff has prepared a draft letter to ABAG for the City Council's consideration which expresses
concern over the proposed alternative methodology for the allocation of housing units by income level
(See Attachment 3).
STAFF RECOMMENDATION:
Staff recommends that the City Council: 1) Receive report and direct Staff to forward comments
(Attachment 3) to ABAG regarding the proposed Alternative Income Allocation Methodology for the
2007-2014 Housing Element cycle, OR 2) Accept the proposed Alternative Income Allocation
Methodology for the 2007-2014 Housing Element cycle.
Page 4 of 4
\ Db '..h.D
CITY CLERK
File # DOO(C]-~CJ
AGENDA STATEMENT
CITY COUNCIL MEETING DATE: December 19,2006
SUBJECT:
ATTACHMENTS:
RECOMMENDATION: . 1)
,/I ~ ,).",-
r:J( '1 ~',j\
FINANCIAL STATEMENT:
PROJECT DESCRIPTION:
Background
Informational Report on the Regional Housing Needs Allocation
(RHNA) Methodology for the 2007-2014 Housing Element Cycle
Report prepared by Mamie R. Nuccio, Associate Planner
1)
Association of Bay Area Governments (ABAG) memo to the
ABAG Executive Board dated October 26, 2006 regarding
Draft Regional Housing Needs Allocation Methodology.
Association of Bay Area Governments (ABAG) memo to the
Housing Methodology Committee (HMC) dated October 11,
2006 regarding Scenarios for Allocating Units by Income
2)
2)
Receive report and accept the 2007-2014 RHNA
methodology for the allocation of total housing units and
allocation of units by income level, OR
Direct Staff to forward comments to ABAG regarding the
methodology.
None.
State law requires that all governing bodies (i.e. the City Council) adopt a comprehensive, long term
General Plan for the physical development of the City. The General Plan must include the 7 State
mandated elements; one of which is the Housing Element. The Housing Element establishes specific
goals, policies and objectives to meet the current and future housing needs of the City. The Housing
Element must be updated approximately every 5 years and submitted for review to the State Department
of Housing and Community Development (HCD) for compliance with State law. The City completed the
most recent update to the Housing Element in June 2003 and received State certification in July 2003.
The next update to the Housing Element is due in June 2009.
COPY TO:
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The updating of the Housing Element begins with the State issuing Regional Housing Needs Allocation
(RHNA) numbers to the local Council of Governments. The Association of Bay Area Governments
(ABAG) is the Council of Governments for the 9 county San Francisco Bay Area region. In accordance
with State law, ABAG is required to determine the existing and projected housing need for the San
Francisco Bay Area and to develop a methodology by which to allocate the region's housing need to
individual counties and cities within the region. The State, in collaboration with ABAG, assigns the
RHNA numbers which must then be incorporated into each jurisdictions Housing Element update.
1999-2006 RHNA Period
Regional Housing Need
The methodology adopted by ABAG for allocating the regions total housing need during the 1999-2006
Housing Element cycle included two factors, 1) Household Growth and 2) Employment Growth. Both
factors were weighted equally at 50% (See Table 1 below).
Table 1. 1999-2006 RHNA Methodolo
Factor
Household Growth from 1999-2006
Em loyment Growth from 1999-2006
Total
Percent e
50%
50%
100%
The number of housing units allocated to each jurisdiction was based on the projected growth in
households and employment for the 1999-2006 RHNA period. Based on this methodology, the City of
Dublin was assigned a RHNA of 5,436 housing units.
State law requires that local jurisdictions remove constraints and adequately zone land to allow for the
construction of their RHNA. During the 1999-2006 RHNA period, the City of Dublin successfully zoned
land to allow for the construction of over 5,436 housing units. While local jurisdictions are not required
to actually construct their allocation of housing units, the City issued building permits for the construction
of 3,585 new housing units during the 1999-2006 RHNA period. Dublin's success in enabling the
construction of new housing units surpasses that of many jurisdictions within the Bay Area.
Affordabi1ity
In addition to adopting a methodology for allocating the regions total housing need to individual
jurisdictions, ABAG also adopts a methodology for the distribution of affordable housing units. When
allocating affordable housing units, the methodology attempts to ensure that all communities contribute
equally in providing affordable housing and responding to existing needs for affordable housing without
over concentrating lower income households within a particular community.
In an effort to move the Bay Area region towards a more equitable distribution of affordable housing
units, the methodology adopted for the 1999-2006 RHNA period started with each jurisdictions existing
income distribution and then moved it 50% towards the regional average. Based on this methodology, the
City of Dublin received an allocation of 796 very low income units, 531 low income units, 1,441
moderate income units, and 2,668 above moderate income units (see Table 2 below).
Page 2 of7
Table 2. Ci of Dublin 1999-2006 RHNA
Income 1Jj~l!1 1999-2006 RHNA **
30:04lo
Very Low Income
( <50% AMI*
Low Income
>50% to <80% AMI* )
Moderate Income
(>80% to <120% AMI*)
Above Moderate Income
(> 120% AMI*
Total 5,436 3,585
* AMI: Area Median Income. The Area Median Income for a family of four living in Alameda County in 2006 is $86,300.
**Local jurisdictions are required by State law to remove constraints and adequately zone land to allow for the construction of
their housing unit allocations.
***Permitted is defmed as the issuance of Building Permits for the construction of a residential unit.
796
T otalT:Jni
1999-20fJ6iJlfll1iflik
257 (32%)
531
239 (45%)
1,441
369 (26%)
2,668
2,720 (102%)
2007-2014 RHNA Period
In order to determine how to allocate the Bay Area's share of housing need for the 2007-2014 RHNA
period, ABAG assembled a Housing Methodology Committee (HMC) comprised of elected officials, city
and county members and stakeholder representatives to advise ABAG staff on a methodology for the
allocation. City Staff attended the HMC meetings which were held monthly from May to October 2006.
ABAG discussed with the HMC various methodologies and a variety of factors were considered
including, household growth; existing employment; employment growth; household growth near transit;
and, employment growth near transit. In all, the HMC reviewed 10 different methodologies.
The HMC was also asked to recommend a methodology for determining the Regional Income Allocations.
The Regional Income Allocations set forth the number of RHNA housing units which must be set aside
for very low, low, moderate and above moderate income levels.
Regional Housing Need
The HMC recommended that the following five RHNA factors be used in allocating the regional housing
need:
1) Household Growth;
2) Existing Employment;
3) Employment Growth;
4) Household Growth near Transit; and
5) Employment Growth near Transit (See Table 3 below).
Projections 2007 is used to determine what each jurisdictions household growth and employment growth
will be for the 2007-2014 RHNA period. Because Projections 2007 looks at data in 5 year increments (i.e.
2005,2010, 2015, etc.) ABAG will look at growth between 2005 and 2010 and between 2010 and 2015
and average the growth on a yearly basis to estimate the 2007 and 2014 numbers. For example, Dublin's
number of households in 2005 was 13,440 and is projected to be 16,600 in 2010 for a total household
growth of 3,160 households. By dividing the household growth (3,160 households) by 5 (the number of
years between 2005 and 2010) you obtain an estimated yearly household growth rate of 632 households.
In order to estimate Dublin's 2007 household population you would add the total number of households in
2005 (13,440) plus 632 households for 2006 and another 632 households for 2007. The result is an
Page 3 of7
estimated 14,704 households for 2007. The same formula would be used to e~~~~old
population in 2014 as well as existing employment and employment growth.
Each factor is weighted based upon the relative importance ofthe factor. For example, the HMC felt that
household growth over the next 7 years (2007-2014) should receive the greatest weighting at 40% since
this factor directly affects the need for additional housing units. Existing Employment in 2007 is included
as a factor and weighted 20% in order to improve the existing jobs/housing balance within the region.
Employment Growth is also included as a factor and weighted 20% in order to maintain a jobs/housing
balance over the next 7 years. Lastly, the HMC felt that Household Growth and Employment Growth near
Transit were important factors to include but gave them a lower weighting of 10% each. To determine
Household Growth and Employment Growth near Transit, ABAG will focus on growth within a 12 mile
radius of existing or planned transit stations. Including growth near transit as a factor is intended to focus
future development near transit stations to help alleviate traffic congestion within the region.
Table 3. HMC Recommendation for the RHNA Methodolo
Factor
Household Growth from 2007-2014
Existin Em 10 ent in 2007
Em 10 ent Growth from 2007-2014
Household Growth near transit from 2007-2014
Em 10 ent Growth near transit from 2007-2014
Percenta e
40%
20%
20%
10%
10%
Once the State of California releases the regional housing needs number in March 2007, ABAG will apply
the methodology shown in Table 3 above to distribute the housing units among the individual counties
and cities within the region.
Affordability
ABAG presented the HMC with four different scenarios for allocating housing units by income level
(Attachment 2). However, the HMC did not recommend any of these scenarios because they felt that the
allocation of housing units by income level based on the regional income distribution was the best
solution (See Table 4 below). The regional income distribution is proposed to be based on household
income data from Census 2000 or data from the U.S. Department of Housing and Urban Development that
specifies household incomes by household size.
Table 4. Recommended Re ional Income Allocation based on Affordabili
Income Level Area Median Income* (AM
Ve Low Income U to 50% AMI
Low Income 50%-80% AMI
Moderate Income 80%-120% AMI
Above Moderate Income Above 120% AMI
Total
*In 2006, the Area Median Income for a family of four living in Alameda County is $86,300.
Percenta e
23%
16%
19%
42%
100%
By assigning each community an equal share of the region's affordable housing units, the methodology
recognizes that the need for affordable housing is a problem that is shared by the region as a whole and it
is consistent with the idea that every jurisdiction must contribute its "fair share" to providing affordable
housing. The proposed methodology also promotes a more equitable income distribution by moving each
jurisdiction to the same standardized income distribution and is more likely to avoid the over
concentration of income groups within a particular jurisdiction.
Page 40f7
ABA G Executive Board Meeting
5" C!b4la
At the October 26, 2006 ABAG Executive Board Meeting, ABAG staff presented the HMC's
recommendation for the RHNA methodology and the Regional Income Allocation methodology (See
Tables 3 and 4 above). The Executive Board adopted a Resolution authorizing the release of the draft
methodology for public review and comment. The public review and comment period is November 16,
2006 to January 18, 2007. On January 18th, ABAG staff will return to the Executive Board with a
recommendation on the final methodology including responses to all comments received during the public
review and comment period.
ANALYSIS:
2007-2014 Regional Housing Need Allocation (RHNA)
Based on the recommended methodology (See Table 3 above), and if the total RHNA were the same as
last cycle, City Staff estimates that Dublin's total housing allocation would be reduced by approximately
39% from the previous 1999-2006 Housing Element cycle. Because the RHNA numbers for the 2007-
2014 period have not been released yet by the State of California, and in order to illustrate the proposed
methodology, ABAG has applied the draft methodology to the total 1999-2006 RHNA resulting in a
hypothetical allocation of3,326 housing units (See Table 5 below).
Table 5. Comparison of RHNA for 1999-2006 and 2007-2014
RHNA Period
1999-2006
2007-2014
*Not the official RHNA number for 2007-2014.
Housinl! Units
5,436
3,326*
The primary reason for the anticipated reduction in RHNA is based on the new methodology which has
been designed to direct growth to the more urbanized areas ofthe region (i.e. San Francisco, Oakland, and
San Jose) and by including existing employment centers as a factor and not just employment growth. By
taking into consideration existing employment centers, jurisdictions which currently have a greater
imbalance between jobs and housing would be assigned a larger number of housing units in order to try
and correct the imbalance.
Since Dublin currently has a jobs/housing balance of approximately 1.39 jobs per housing unit and
planned future development that will provide both jobs and housing, Dublin is not anticipated to have a
tremendous imbalance.
2007-2014 Regional Income Allocation based on Affordability
One of the goals of the RHNA process is to ensure that local governments consider the housing needs of
persons at all income levels. In allocating affordable units to individual jurisdictions, the idea is that each
locality must contribute their fair share in planning for some of the region's need for very low and low
income units and at the same time avoiding or mitigating the over concentration of income groups in a
jurisdiction. To meet these goals, the proposed methodology for the 2007-2014 period would assign each
jurisdiction's need based on the regional average. In order to illustrate this, ABAG applied the proposed
methodology (See Table 3 above) to the 1999-2006 RHNA numbers (See Table 6 below).
Page 5 of7
Table 6. Com arison of RHNA b Income Cate or
1999-2006 RHNA
La ~ t..\ l.D
2007-2014 RHNA
Income Level Dwellin Units % of total Dwellin Units
Ve Low Income 796 15% 765*
Low Income 531 9% 532*
Moderate Income 1,441 26% 632*
Above Moderate Income 2,668 50% 1,397*
Total 5,436 100% 3,326*
*These numbers are based on the 1999-2006 RHNA and are not the official RHNA numbers for 2007-2014.
% of total
23%
16%
19%
42%
100%
Again, it should be noted that the number of total housing units and corresponding breakdown of units by
income category are illustrative only. Until the State determines the overall housing need for the entire
Bay Area region, Staffwill not know what Dublin's allocation will be.
While Dublin's total allocation of housing units for the 2007-2014 RHNA cycle is anticipated to be 39%
less than the previous RHNA, the percentage of very low and low income units is expected to be higher in
order to meet the region's need for housing units in these income categories. Though the percentage of
very low and low income units may be higher, the actual dwelling unit count in these categories is
expected to be relatively the same as the previous RHNA. Conversely, the percentage of moderate and
above moderate units is expected to be lower and the actual dwelling unit count significantly less than the
previous RHNA in these income categories.
NEXT STEPS:
Regional Housing Needs Allocation Timeline (See Table 7)
As mentioned previously in this report, the public review and comment period for the 2007-2014 RHNA
methodology will end on January 18, 2007. Also, on January 18th ABAG staff will return to the ABAG
Executive Board to present the final methodology and all comments received during the public review and
comment period. It is expected that the State of California will release the draft RHNA numbers to
ABAG in March 2007 and ABAG anticipates issuing the draft RHNA numbers to individual jurisdiction's
by June 30, 2007. Following the release of the draft RHNA numbers there will be a revision period at
which time local jurisdictions will have an opportunity to review and request changes to their allocations.
At the close of the revision period, the fmal allocations will be assigned and an appeal period begins
(approximately from November 2007 to April 2008). It is expected that the final RHNA numbers will be
allocated by ABAG no later than June 30, 2008. The RHNA timeline is set forth in Table 7 below.
Table 7. RHNA Timeline
Date
November 16, 2006
November 16,2006 - Janua
January 18, 2007
March I, 2007
June 30, 2007
June 2007 - October 2007
November 2007 - A ri12008
June 30, 2008
June 30, 2009
ABAG Executive Board releases proposed methodology for
ub1ic review and comment
60-da Public Review and Comment Period on methodolo
ABAG Executive Board to ado t final methodology
State of California determination of regional housing need
ABAG issues draft RHNA numbers
Revision Period
A eals Period
Final RHNA numbers issued b ABAG
Housin Element U date due to State of California
Page 60f7
CONCLUSION:
., ool..\~
The allocation of RHNA marks the beginning of the next Housing Element update and the methodology,
which will be adopted by the ABAG Executive Board on January 18, 2007, will determine how the 2007-
2014 RHNA is distributed to local jurisdictions. ABAG is currently accepting comments on the
methodology for the allocation of total housing units and affordable housing units.
Staff recommends that the five proposed factors used for the methodology - household growth, existing
employment, employment growth, household growth near transit and employment growth near transit - be
accepted because the factors consider expected household and employment growth as well as existing
employment to ensure an adequate jobslhousing balance for the region. Staff also recommends that the
use of the regional income allocations to distribute affordable housing units to individual jurisdictions be
accepted because the methodology recognizes that the need for affordable housing is a problem that is
shared by the region as a whole and requires that all jurisdictions contribute to providing affordable
housing in order to reduce the over concentration of affordable units within a particular jurisdiction.
STAFF RECOMMENDATION:
Staff recommends that the City Council: 1) Receive report and accept the 2007-2014 RHNA
methodology for the allocation oftotal housing units and allocation of units by income level, OR 2) Direct
Staff to forward comments to ABAG regarding the methodology.
Page 70f7
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ASSOCIA nON OF BAY AREA GOVERNMENTS
o
Representing City and County Governments of the San Francisco Bay Area
ASAG
MEMO
To:
From:
Date:
Re:
ABAG Executive Board
ABAG Staff
October 26, 2006
Draft Regional Housing Needs Allocation Methodology
Recommendation
Staff recommends that the ABAG Executive Board adopt Resolution 13-06 authorizing release of the
Housing Methodology Committee's recommendation for a proposed Regional Housing Needs Allocation
Methodology (RHNA) for the 2007-2014 period. Upon release, a not less than 60-day public comment
period on the methodology will begin. The comment period will close on January 18,2007. On that date,
staff will bring to the Executive Board recommendations for the final methodology. The fmal
methodology shall include responses to all comments received on the draft RHNA methodology and
reasons for any significant changes.
Background
As the region's Council of Governments, ABAG is responsible for allocating the state-defermined
regional housing need to all jurisdictions in the Bay Area. The HMC was established in May 2006 to
assist staff in developing a recommended methodology for allocating the regional need for adoption by
the ABAG Executive Board. The HMC was comprised of local elected officials, city and county staff,
and stakeholder representatives.
Regional Housing Needs Allocation Process
The Regional Housing Needs (RHN) process is a state mandate regarding planning for housing in
Califomia. The state, regional and local governments each have a role to play. Local governments have
autonomy in planning for exactly how and where housing will be developed in their individual
communities. The amount of housing cities and counties must plan for, however, is determined through
the interplay of state, regional and local housing policy.
The State of California requires that all jurisdictions in the state update the Housing Elements of their
General Plans. Housing Elements serve as the local plan for how a jurisdiction win meet its share of the
region's housing need. The State of California, via the Housing and Community Development
Department (HCD), determines each region's need for housing, primarily based on estimated population
growth. COGs then allocate that need, for all income groups, amongst jurisdictions. jurisdictions then
plan for that need in their housing elements, which are state-certified by HCD.
RHNA Methodology Recommendation
The regional housing needs allocation methodology is the tool used to assign each jurisdiction in the Bay
Area its share of the region's total housing need. The actual tool is a mathematical equation that consists
of weighted factors. There are also a set of "rules" that dictate how units will be allocated by income,
within spheres of influence, voluntary transfer of units, and subregions. The HMC's recommendation
encompasses these distinct components of the methodology.
In their recommendation, the HMC members considered local land use plans and policies, regional
growth policies and the state's housing polices, as expressed in the state mandated RHNA objectives.
Additional information on how these recommendations were derived is contained in the attached report.
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Attachment 1
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Draft RHNA Allocation Methodology 10/26/06
Page 2
1. Weighted Factors
Factors in the allocation methodology are the mathematical variables that allocate shares of the
regional housing need (RHN). The factors reflect: 1) state mandated RHNA objectives; 2) RHNA
statutory requirements; 3) local policy and 4) regional policy. In the methodology, each factor is
given priority relative to the others. Priority is established through "weighting" in the formula. For
example, if one of the factors, household growth, is determined to be more important than another
factor, e.g., transit, the methodology can give household growth a higher weight than transit in the
formula. The methodology may also equally weight the factors, therefore ensuring that all the factors
are of equal priority.
A. Household Growth, 40 Percent
Each local jurisdiction should plan for housing according to regionally projected household growth
within its boundaries during the RHNA planning period (2007 - 2014). Household growth should be
weighted 40 percent in the allocation. Household growth is used as a factor, as opposed to existing
households or total households, to ensure that additional housing is not allocated where there are
existing concentrations of homes in the region, but rather where growth is anticipated to occur. In this
way household growth as a factor in the methodology ensures that the allocation is consistent with
both local plans for growth and with regional growth policies, as those areas that are planning for
household growth would receive a higher allocation than those areas not planning for growth.
Household growth in ABAG's Projections is most influenced by local land use plans and policies,
including planned and protected agricultural lands, open space and parks, city-centered growth
policies, urban growth boundaries, and any physical or geological constraints.
Regional policies have been incorporated into Projections since 2002, are assumed to go into effect
by 2010, and therefore have some effect on regional housing growth estimates in the 2007-2014
RHNA period. Regional policies assume that there will be increased housing growth in existing
urbanized areas, near transit stations and along major public transportation corridors. These regional
policies are consistent with state housing policies to promote infill development, environmental and
agricultural protection and efficient development patterns.
B. Existing Employment, 20 Percent; Employment Growth, 20 Percent
Each local jurisdiction should plan for housing to accommodate existing employment (2007) and
regionally projected employment growth within its boundaries during the RHNA planning period
(2007 - 2014). This would ensure that the need allocation gives jurisdictions with both existing
concentrations of jobs and planned job growth a share of the regional housing need. This would direct
housing to existing job centers and to areas with anticipated employment growth. These jobs
allocation factors may address regional jobs-housing imbalance and facilitate access by proximity, for
housing would be directed to communities with jobs and planned jobs, which may reduce vehicle
miles traveled due to reduced inter- and intra-regional commuting.
C. Household Growth near Transit, 10 Percent; Employment Growth near Transit, 10 Percent
Each local jurisdiction with an existing or planned transit station should plan for more housing near
such stations. Current regional policy places incrementally more growth along major transportation
corridors and at transit stations. Therefore, a housing need allocation that uses regional housing
growth and employment as factors would be inclusive of "transit" as a policy issue. Using transit as a
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direct factor in the methodology would give transit a greater .degree of policy weight. Those
jurisdictions with transit, existing and planned, would receive a relatively higher proportion of the
housing needs allocation than those jurisdictions without existing or planned transit. The inclusion of
"planned" transit in the methodology could potentially give a relatively higher allocation to an area
where the planned transit does not materialize. However, if housing is built at appropriate densities
before transit is put in place, the transit investment may become more financially feasible, for
projected ridership would be higher.
2. Regional Income Allocations
Each local jurisdiction should plan for income-based housing in the same ratio as the regional average
income distnbution. A methodology that assigns each jurisdiction's regional housing need based on
the regional average income distribution would be an "equal share" approach because it applies the
same income distribution to each jurisdiction. Although considered an equitable approach, it does not
consider existing concentrations of poverty.
3. Spheres of Influence
Each local jurisdiction with the land-use permitting authority in a "Sphere of Influence" should plan
for the housing needed to accommodate housing growth, existing employment and employment
growth in such "Sphere of Influence" areas. A 100 percent allocation of the housing need to the
jurisdiction that has land use control over the area would ensure that the jurisdiction that plans for
accommodating the housing units also receives credit for any built units during the RHNA period.
4. Transfer of Units
After the initial allocation of the regional housing need, a local jurisdiction may request approval to
transfer units with willing partner(s), in a way that maintains total need allocation amongst all transfer
parties, maintains income distribution of both retained and transferred units, and includes package of
incentives to facilitate production of housing units. This transfer rule would allow the transfer of
allocated housing need between willing jurisdictions in conjunction with fmancial resources, while
maintaining the integrity of the state's RHNA objectives by preventing any jurisdiction from
abdicating its responsibility to plan for housing across all income categories. Transfers done in this
manner may facilitate increased housing production in the region.
5. Subregions
The County of San Mateo, in partnership with all twenty cities in the county, has formed a subregion,
as allowed by state statute. ABAG .will assign a share of the regional need to the subregion "in a
proportion consistent with the distribution of households" in Projections 2007. The subregion is then
responsible for completing its own RHNA process that is parallel to, but separate from, the regional
RHNA process. The subregion will create its own methodology, issue draft allocations, handle the
revision and appeal processes, and then issue fmal allocations to the members of the subregion. The
rules on how to handle the subregion allocation in the event the subregion fails are contained in the
attached RHNA technical document.
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Technical Documentation
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Introduction
The Regional Housing Needs Allocation (RHNA) process is a state mandate on planning for
housing in California. The state, regional and local governments each have a role to play. Local
governments have autonomy in planning for exactly how and where housing will be developed in
their individual communities. The amount of housing cities and counties must plan for, however,
is determined through the interplay of state, regional and local housing policy.
Periodically, the State of California requires that all jurisdictions in the state update the Housing
Element of their General Plans. Within these Housing Elements, the state mandates that local
governments plan for their share of the region's housing need, for people of all income
categories. In the case of the San Francisco Bay Area, ABAG, as the region's Council of
Governments, and the State Housing and Community Development Department (RCD),
determines the region's need for housing. This determination of need is primarily based on
estimated population growth. ABAG then allocate that need, for all income groups, amongst
jurisdictions. The jurisdictions then plan for that need in their local housing elements, which are
eventually state-certified by HCD.
This technical document details the process for developing the draft Regional Housing Needs
Allocation, describes the Housing Methodology Committee's allocation methodology
recommendations and rationale for each component, and offers information on ABAG's
Projections.
I. RHNA Schedule
II. RHNA State Goals & Regional Policy
Ill. Statutory Factors & Survey of Factors
IV. The Housing Methodology Committee
V. Draft Allocation Methodology
VI. Regional Projections
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I. RHNA Schedule
On September 29, 2006, ABAG received approval of a two-year extension for completing the
RHNA process from the Department of Housing and Community Development (HCD). The
following milestones are consistent with that two-year extension:
· November 16,2006: Adoption of draft allocation methodology by ABAG Executive
Board; start of a 60-day public comment period
· January 18, 2007: ABAG Executive Board adopts [mal methodology
. March 1, 2007: Determination of regional housing need
. June 30,2007: Release of draft allocations
. June 30,2008: Release offmal allocations
. June 30, 2009: Housing element revisions due to HCD
II. RHN A State Goals & Regional Policy
There are four statutory objectives of RHNA. As shown below, these objectives include
increasing housing supply, affordability, and housing types; encouraging efficient
development and infill; promoting jobs-housing balance; and reducing concentrations of
poverty .
These objectives are consistent with the Bay Area's regional policies regarding growth.
Following the Smart Growth Strategy/Regional Livability Footprint Project that was
completed in 2002, ABAG's Executive Board resolved to use theses regional policies as the
basis for Projections. Since that decision, Projections assumes that, over time, local land use
policies will move the region closer toward regional policies.
The shift to policy-based Projections has important implications for growth and development
in the region. Projections now forecasts more growth in existing urbanized areas and near
transit, and less in agricultural areas. This is consistent with the RHNA objectives that call
for an increase in the supply of housing, jobs-housing balance, more infill development, and
protection of the environment, and efficient development patterns. Since the Projections
forecast is the basis for the RHNA allocations, these same regional policies will influence
how housing units are distributed within the region.
RHNA Objectives
(1) Increase 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.
(2) Promote infill development and socioeconomic
equity, the protection of environmental and
agricultural resources, and the encouragement
of efficient development patterns.
(3) Promote an improved intraregional relationship
November 2006, Page 2
Regional Policies
· Support existing communities
· Create compact, healthy communities with a
diversity of housing, jobs, activities, and
services to meet the daily needs of residents
· Increase housing affordability, supply and
choices
· Increase transportation efficiency and choices
· Protect and steward natural habitat, open space,
and agricultural land
· Improve social and economic equity
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Draft Regional Housing Needs Allocation, 4th Revision
between jobs and housing.
(4) Allocate 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 decennial United States census.
· Promote economic and fiscal health
· Conserve resources, promote sustainabiJity, and
improve environmental quality
· Protect public health and safety
TII. Statutory Factors & Survey of Factors
1. Statutory Factors
The RHNA statutes delineate specific factors that the HMC had to consider for inclusion
in the allocation methodology, including:
. Water and sewer capacity
· Land suitable for urban development or conversion to residential use
· Protected open space - lands protected by state and federal government
· County policies to protect prime agricultura1land
· Distribution of household growth
· Market demand for housing
· City-centered growth policies
· Loss of affordable units contained in assisted housing
· High housing cost burdens
· Housing needs of farm workers
· Impact of universities and colleges on housing needs in a community
With the advice of the HMC, ABAG staff considered how to incorporate the statutory
factors into the allocation methodology, how to allocate units by income, and how to address
issues such as spheres of influence, the relationship to subregions, and voluntary transfers of
housing units between jurisdictions. Their goal has been to develop an allocation
methodology that is consistent with the RHNA objectives and statutory requirements while
also reflecting local conditions and the regional goals for growth.
See Section IV. 1. Weighted Factors for a detailed description of how the factors are
included in the recommended methodology.
2. Survey of Factors
On September 15, 2006, ABAG sent a memorandum and survey form to each planning
director of every local jurisdiction in the region. The memorandum explained the use of
factors in the RHNA allocation methodology, described the status of the HM:C's
deliberations, set forth the criteria for using a factor in the methodology, and solicited local
input on the statutory factors and suggestions for additional factors. ABAG received
responses from 42 local jurisdictions (A detailed summary of survey responses is available at
http://W\vw.abag.ca.l!.ov/planning/housingneeds or by contacting ABAG staff.)
November 2006, Page 3
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The Governor signed AB 2572 into law on September 29, 2006. The legislation adds a
statutory factor: housing needs generated by the presence of a private university or a campus
of the Califomia State University or the University of California.
The HMC concluded that student populations need not be added as an explicit factor in the
allocation methodology. The reason is that the existence of universities and resulting student
populations are included in ABAG's household population estimates. Therefore, ABAG will
circulate its explanation of the effects of this factor and a survey form for this factor during
the review period of the draft methodology. Responses will be due in time for ABAG staff to
evaluate them and to make any necessary changes to the methodology.
The 42 survey responses varied widely. Many commented on the HMC deliberations,
supporting or opposing specific measures under consideration, and offering alternative
methodological approaches. Others commented on the existing and near-term market
conditions for housing in their jurisdictions.
The comments that focused on how specific factors should be explicitly considered in the
methodology can be summarized as follows:
s s
ummarv urvev ResDonses
1. Jobs/Housing Relationship
(a) use employed residents to. measure jobs/housing balance 3
(b) take into account home based businesses/employment 1
(c) use commute shed to assess jobslhousing balance 2
2. Constraints due to SewerlWater/Land Capacity
(a) respondents identified specific sewer/water constraints 2
3. Public TransitITransportation Infrastructure
(a) respondents confirmed they were planning for TOO 5
4. Market Demand for Housing 0
5. City-Centered Development
(a) described local city-centered policies 6
(b) described specific policies, agreements, etc. on development in spheres of influence 7
(SOl)
(c) stated there were no written agreements on SOls 1
6. Loss of Assisted Housing Units
(a) identified at risk units at varying degrees of specificity 10
(b) do not use as a factor 1
7. High Housing Cost Burden
(a) use CHAS data 1
8. Housing Needs of Fannworkers
(a) identified local efforts for farmworker housing 4
9. Others
(a) use congestion levels 1
(b) reward past performance in meeting RHNA goals 1
(c) RHNA allocation should at least equal planned growth 1
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Several of the possible allocation factors proposed in the surveys were considered by the
HMC, but not explicitly incorporated in the draft methodology. These factors include those
related to:
.
Jobs-housingbalance: l(a)-(c)
Sewer/water constraints: 2
City-centered development: Sea) - (c)
Loss of assisted housing units: 6
High housing cost burdens: 7
Housing needs of farm workers: 8
· Traffic congestion: 9(a)
Rewards for past RHNA performance: 9(b)
.
.
.
.
.
.
The HMC has included the following suggested REINA factors as explicit components of the
draft methodology but may not have used them in precisely the way suggested by the
respondents:
.
Public transit/transportation infrastructure: 3
The HMC did not consider 9( c).
In each instance where a respondent described specific localized data in support of its
response to a survey question, e.g. 2, 6(a) and 8(a), the respondent did not identify sources for
comparable data for other jurisdictions. Therefore, staff could not conclude that the proffered
factor met the statutory requirement for comparability and availability. Consequently, the
proposed factor was not used.
IV. Housing Methodology Committee
As the region's Council of Governments, ABAG is responsible for allocating the state-
determined regional housing need to all jurisdictions in the Bay Area. The lIMC was established
in May 2006 to assist staff in developing a recommended methodology for allocating the regional
need for adoption by the ABAG Executive Board. The HMC was comprised of local elected
officials, city and county staff, and stakeholder representatives from each county in the region. It
includes members from each county so that it adequately represents the entire region.
The members of the Housing Methodology Committee were:
Barbara Kondylis, Supervisor, District 1 (Solano), ABAG Executive Board
Scott Haggerty, Supervisor, District I (Alameda), ABAG Executive Board
Jerffery Levine, Housing Department, City of Oakland, Alameda
Jennifer Hosterman, Mayor, City of Pleasant on, Alameda
Dan Marks, Director of Planning & Development, City of Berkeley, Alameda
Julie Pierce, Council Member, City of Clayton, Contra Costa
Phillip Woods, Principal Planner, City of Concord, Contra Costa
Gwen Regalia, Council Member, City of Walnut Creek, Contra Costa
Linda Jackson, Principal Planner, City of San Rafael, Marin
Paul Kennoyan, Community Development Dir., City of Sausalito, Marin
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Stacy Lauman, Assistant Planner, County of Marin, Marin
Jean Hasser, Senior Planner, City of Napa, Napa
Diane Dillon, Supervisor, County of Napa, Napa
Howard Siegel, Community Partnership Mgr., County of Napa, Napa
Amit Ghosh, Assistant Planning Director, San Francisco, San Francisco
Doug Shoemaker, Mayor's Office of Housing, City of San Francisco, San Francisco
Amy Tharp, Director of Planning, City of San Francisco, San Francisco
Duane Bay, Housing Director, San Mateo County, San Mateo
Andrea Ouse, City Planner, Town of Co 1m a, San Mateo
Mark Duino, Planner, San Mateo County, San Mateo
Laurel Prevetti Deputy Dir., Planning, Building and Code Enforcement, City of San Jose, Santa
Clara
Regina Brisco, Housing Planner, City of Gilroy, Santa Clara
Steve Piasecki, Planning Director, City of Cupertino, Santa Clara
Matt Walsh, Principal Planner, Solano County, Solano
Chuck Dimmick, Councilmember (Vacaville) Solano City/County Coord. Council, Solano
Eve Somjen, Assistant Director, City of Fairfield, Solano
Mike Moore, Community Development Dir., City ofPetaluma, Sonoma
Jake MacKenzie, Council Member, City of Rohnert Park, Sonoma
Jennifer Barrett, Deputy Director - Planning, County of Sonoma, Sonoma
Geeta Rao, Policy Director, Nonprofit Housing of Northern California, Stakeholder
Kate O'Hara, Regional Issues Organizer, Greenbelt Alliance, Stakeholder
Margaret Gordon, Community Liaison, West Oakland Indicators Project, Stakeholder
Andrew Michael, Vice President, Bay Area Council, Stakeholder
Paul B. Campos, VP, Govt. Affairs & Gen. Counsel, Home Builders Association, Stakeholder
V. The Regional Needs Allocation Methodology
The RENA methodology assigns each jurisdiction in the Bay Area its share of the region's total
housing need. The methodology includes an allocation tool that is a mathematical equation that
consists of weighted factors. There are also "rules" regarding allocation of units by income, how
to handle units in spheres of influence, voluntary transfers of units, and subregions. The draft
methodology encompasses these distinct components of the methodology.
In their recommendation, the HMC members considered local land use plans and policies,
regional growth policies and the state's housing polices, as expressed in the state mandated
RHNA objectives.
1. Weighted Factors
Factors in the allocation methodology are the mathematical variables that partly determine
how the regional housing need (RHN) is allocated to local jurisdictions. The factors reflect:
1) state mandated RHNA objectives; 2) RHNA statutory requirements; 3) local policy and 4)
regional policy.
In the methodology, each factor is given priority relative to the others. Priority is established
through "weighting" in the formula. For example, if one of the factors, e.g., household
growth, is determined to be more important than another factor, e.g., transit, the methodology
can give household growth a higher weight than transit in the formula. The methodology may
also equally weight the factors, therefore ensuring that all the factors are of equal priority.
November 2006, Page 6
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The factors and weights (expressed in parenthesis) recommend by the HMC are:
.
Household growth (40%)
Existing employment (20%)
Employment growth (20%)
Household growth near existing and planned transit (10%) .
Employment growth near existing and planned transit (10%)
.
.
.
.
Household growth, existing employment, and employment growth are estimated in ABAG's
regional household and employment forecasts, Projections.
A. Household Growth, 40 percent
Each local jurisdiction should plan for housing according to regionally projected household
growth within its boundaries during the RHNA planning period (2007 - 2014). Household
growth should be weighted 40 percent in the allocation.
The use of housing as a RHNA factor represents consistency with local, regional, and state
policies regarding where housing growth will and should occur in the region. Where and how
much housing growth will occur in the region is estimated by ABAG's forecasting model, as
documented in Projections. Specifically, household growth is based on: 1) local land use
policies and plans; 2) demographic and economic trends, such as migration, birth and death
rates, housing prices, and travel costs; and 3) regional growth policies.
Household growth in ABAG's Projections is most influenced by local land use plans and
policies, including planned and protected agricultural lands, open space and parks, city-
centered growth policies, urban growth boundaries, and any physical or geological
constraints.
Regional policies incorporated into Projections since 2002, are assumed to go into effect by
2010, and therefore have some effect on regional housing growth estimates in the 2007-2014
RHNA period. Regional policies assume that there will be increased housing growth in
existing urbanized areas, near transit stations and along major public transportation corridors.
These regional policies are consistent with state housing policies to promote infill
development, environmental and agricultural protection and efficient development patterns.
The impacts of regional policy assumptions in Projections are: a) potential environmental and
agricultural resource protection by directing growth away from existing open and agricultural
lands; b) the encouragement of efficient development patterns through increased infill
development and higher densities in existing communities; and c) the potential for increased
transportation choices, e.g. walking and public transit, through more housing development
near transit and jobs.
The household estimates in Projections account for all people living in housing units,
including students. Thus, the portion of the student population that occupies part of a local
jurisdiction's housing stock is counted as such and as a source of future household formation.
The portion of the student population that occupies "group quarters," such as college
dormitories, are not included in household population counts. This is consistent with state
policy regarding RHNA that excludes "group quarters" from being counted as housing units.
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Household growth is used as a factor, as opposed to existing units or total units, to ensure that
additional housing is not planned where there are existing concentrations of homes in the
region, but rather where growth is anticipated to occur. In this way household growth as a
factor in the methodology ensures that the allocation is consistent with both local plans for
growth and with regional growth policies, as those areas that are planning for household
growth would receive a higher allocation than those areas not planning for growth.
B. Employment, 40 percent (Existing Employment, 20 percent; Growth, 20 percent)
Each local jurisdiction should plan for housing to accommodate existing employment (2007)
and regionally projected employment growth (2007-2014) within its boundaries during the
RHNA planning period.
This would ensure that the need allocation gives jurisdictions with both existing
concentrations of jobs and planned job growth a share of the regional housing need. This
would direct housing to existing job centers and to areas with anticipated employment
growth. These jobs allocation factors may be effective in addressing regional jobs-housing
imbalance. These factors would also facilitate access by proximity, for housing would be
directed to communities with jobs and planned jobs, which may reduce vehicle miles traveled
due to reduced inter- and intra-regional commuting.
As a factor, employment has the ability to assign regional housing needs to jurisdictions in a
way that provides a better balance between housing and employment. In the Bay Area, as in
many metropolitan areas, employment centers have historically not produced enough housing
to match job growth. Limited housing production near existing jobs and in areas with
continued employment growth has escalated Bay Area housing costs and has triggered
increased housing production in outlying Bay Area communities and in surrounding counties,
including San Joaquin, Stanislaus, and San Benito. This has led to longer commutes on
increasingly congested freeways, inefficient use of public transportation infrastructure and
land capacity, and negative impacts on health, equity, air quality, the environment and overall
quality ofIife in the Bay Area.
In the allocation methodology, employment can be used in varying degrees of aggressiveness
to address regional jobs-housing imbalance. The HMC considered three options:
1) employment growth, 2) existing jobs (2007) and 3) total jobs in the RHNA period (existing
jobs in 2007 and growth from 2007 to 2014). Employment growth as a factor would assure
that jurisdictions that are planning for employment growth also plan for commensurate
housing. However, this would be ineffective in addressing historic regional jobs-housing
imbalances, and therefore it is the least aggressive option. Existing jobs as an allocation
factor would give relatively higher allocations to existing job centers and would therefore be
the most aggressive toward historic jobs-housing imbalances; however it does not take into
account future job growth. Total jobs as a factor would give relatively higher allocations to
both jurisdictions that are currently job centers and those with planned job growth. Therefore,
this is a moderately aggressive approach relative to the other two options.
The HMC recommends a balance between the least and most aggressive options by
separately weighting employment growth and existing employment. This would attempt to
address historic jobs-housing imbalances and would seek to avert future imbalances. While
an aggressive approach, it is relatively less aggressive than the use of total jobs as a factor. A
total jobs factor would primarily direct growth to existing job centers, which would receive
November 2006, Page 8
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the entire 40 percent weight for employment, as opposed to the 20 percent recommended
weight.
Existing Employment, 20 Percent
The location and amount of existing jobs in the region is estimated by ABAG's forecasting
model, as documented in Projections. Specifically, existing employment is based on:
1) existing regional and local job data, and 2) regional and local economic trends,
attractiveness of commercial/industriallocations, including labor force costs, housing prices,
travel costs, access to potential employees, markets, and similar businesses.
The inclusion of existing employment as a RHNA factor ensures that regional housing need
is allocated in a manner consistent with regional policies and state RHNA objectives.
Planning for more housing in communities with existing jobs can address historic jobs-
housing imbalances. More housing in existing job centers may also encourage infill and
efficient development pattems through higher densities in existing communities. There is also
the potential for reduced 'inter- and intra-regional vehicle miles traveled and shorter
commutes, as more housing would be planned in proximity to existing jobs. More housing
near jobs may also encourage alternative modes of travel, including walking and public
transportation, as most existing jobs centers in the region are also transit rich. Planning for
housing near existing jobs also places less development pressure on outlying areas, especially
in rural areas with agricultural lands and protected open space.
Employment Growth, 20 Percent
The location and amount of employment growth in the region is projected by ABAG's
forecasting model, as documented in Projections. Specifically, employment growth is based
on: 1) local land use policies and plans; 2) economic trends, such as national and regional
industrial assumptions, attractiveness of commercial/industrial locations, including labor
force costs, housing prices, travel costs, access to potential employees, markets, and similar
businesses; and 3) regional policy.
Inclusion of local land use policies and plans and economic trends in ABAG's employment
growth forecast ensures that the use of employment growth as a RHN A factor is consistent
with local policies, plans, and local capacity for job growth. Employment growth in
Projections considers all the land protection and growth policies, physical constraints, and the
employment-related factors identified by the state and the HMC for inclusion in the allocation
methodology, including existing jobs centers, home-based businesses, employed residents,
housing prices, household income and employment at private universities, and campuses of
the California State University and the University of California.
The inclusion of employment growth as a RHNA factor ensures that the regional housing
need is allocated to areas where job growth is forecasted to occur during the RHNA period.
These areas would have the responsibility of providing housing for the additional jobs that
are added to the region. These areas are typically served by the region's transit infrastructure.
Matching housing to jobs would still have the potential for reducing vehicle miles traveled
and. encouraging alternative modes of travel. This employment factor would place housing in
exi~ting communities, but would place less of the housing in the most urbanized cities in the
regIon.
As with household growth, inclusion of regional policies in ABAG' s Projections ensures that
the use of employment growth as a RHNA factor is consistent with both state and regional
polices regarding growth, infill development, and efficient use of land. This is because
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regional policies in Projections assume that relatively more job growth will occur in existing
urbanized communities and near transit, while less growth is projected in outlying
communities with no transit infrastructure, including those with agricultural areas and open
space. In addition, regional assumptions would promote greater use of public transportation
through increased job development near transit.
C. Household Growth near Transit, 10 Percent; Employment Growth near Transit,
10 Percent
Each local jurisdiction with an existing or planned transit station should plan for more
housing near such stations. As a factor, "household growth near transit" allocates 10 percent
of the regional housing need to jurisdictions based on their forecasted household growth near
existing or planned transit stations. As a factor, "employment growth near transit" allocates
10 percent of the regional housing need to jurisdictions based on their forecasted employment
growth near existing or planned transit stations.
Transit is defined as areas with fIXed-alignment public transit, both existing and planned. The
transit services included are: Altamont Commuter Express (ACE), Bay Area Rapid Transit
(BART), Caltrain, San Francisco MUNI light rail, and Santa Clara Valley Transportation
Authority (VT A) light rail, and ferries. Planned transit stations include all fixed transit
stations in the Metropolitan Transportation Commission's Regional Transportation Plan,
Track One.
Growth near transit is defined as household or employment growth within one-half mile of an
existing or planned transit station, but eliminating any overlap between stations located
within one mile of each other.
Incorporating a transit factor directly into the methodology would, in effect, give extra weight
to this state and regional objective. This is because a transit-based policy is already
incorporated into ABAG's policy-based Projections. Current regional policy places
incrementally more growth along major transportation corridors and at transit stations.
Therefore, a housing need allocation that uses regional housing growth and employment as
factors would indirectly include "transit" as a policy issue in the allocation methodology.
Using transit as a direct factor in the methodology would give transit a greater degree of
policy weight. Those jurisdictions with transit stations, existing and planned, would receive a
relatively higher proportion of the housing needs allocation than those jurisdictions without
existing or planned transit stations.
Despite some objections, the HMC recommends that transit be used as a direct factor. This
was due, in part, to the expectation that impacts of the policy assumptions in Projections will
not begin to take effect until 2010. Directing growth to areas with public transit in the
allocation methodology would ensure that this regional policy influences development
patterns during the 2007-2014 RHNA period.
Use of these factors would address the state RHNA objectives and regional goals of
encouraging the use of public transit and the efficient use of transportation infrastructure.
Directing housing need to areas near transit would also promote infill development, as
existing transit stations are primarily in existing urbanized areas in the region.
The effect of the addition of planned transit stations in the allocation methodology is that a
relatively higher share of the regional allocation is given to jurisdictions that will receive
November 2006, Page 10
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investments for public transportation. Inclusion of planned stations gives communities that
will have new transit stations an opportunity to ensure that they plan sufficient housing to
support the extension of transit services. In addition, given the long time-frame for
implementation of service extensions, it makes sense to begin the land use planning around
proposed stations before the transit stations are put in place. This is in support of both state
and regional policies to ensure efficient use of transportation infrastructure and to encourage
increased transit use. There is a multitude of data supporting the theory that higher population
densities have a net positive impact on transit ridership.
The HMC was divided in its support for including a transit, and particularly planned transit,
as part of tp.e allocation methodology. Many of the committee members believed that the
regional growth policies embedded in Projections sufficiently addressed both state and
regional policies promoting transit use and efficient use of transportation infrastructure. It
was felt by some members that having transit as a direct factor would give too much weight
to transit and would also unfairly burden communities with both existing and planned transit.
Planned transit was also contentious because some of the planned transit stations included in
MTC's Regional Transportation Plan may not be built, including many of the e-BART
stations planned for eastern Contra Costa County. However, others on the HMC felt that if
housing is built at appropriate densities before transit is put in place, the transit investment
may become more financially feasible, for projected ridership would be higher.
D. The Allocation Formula
The household growth, employment and transit factors are weighted together to create an
allocation formula. Each factor describes a jurisdiction's "share" of a regional total. For
example, if the region expects to grow by 100 households, and one city in the region is to
grow by 10 households in the same period, then that city's "share" of the region's growth is 10
percent.
A jurisdiction's share of the Regional Housing need is assigned according to its percentage
share of regional:
(Household Growth x .40) + (Employment Growth x .20) + (Existing Employment x .20)
+ (Household Growth near Transit x .10) + (Employment Growth near Transit x .10)
Growth is during the RHNA planning period (2007 - 2014). The transit factors refer to
growth that occurs within Y2 mile of planned or existing fixed transit stations in the
jurisdiction. Planned stations are those in the RTP 2005 - Track 1.
2. Regional Allocations of Housing Units based on Affordability
There are two primary goals of the RHNA process: 1) increase the supply of housing and
2) ensure that local governments consider the housing needs of persons at all income levels.
The HMC recommends that each local jurisdiction should plan for income-based housing in
the same ratio as the regional average income distribution (as described by the 2000 Census).
A methodology that allocates each jurisdiction's regional housing need based on the regional
average income distribution would be an "equal share" approach because it applies the same
income distribution to each jurisdiction. Although considered an equitable approach, it does
not consider existing concentrations of poverty.
November 2006, Page 11
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Draft Regional Housing Needs Allocation, 4th Revision
The following income allocation of regional housing needs to jurisdictions is recommended:
.
Very Low, 23 Percent
Households with income up' to 50 percent of the county's area median income (AMI)
Low, 16 Percent
Households with income between 50 and 80 percent of the county's AMI
Moderate, 19 Percent
Households with income between 80 and 120 percent of the county's AMI
Above-Moderate, 42 Percent
Households with income above 120 percent of the county's AMI
.
.
.
This recommendation is based on the recognition that the need for affordable housing is a
problem shared by the region as a whole, and is not localized to specific jurisdictions. By
assigning every community an equal share of the regional need for affordable units, the
methodology promotes the idea that every jurisdiction should do its "fair share" to provide
housing.
During the discussion of the income-based allocation, some HMC members expressed
concern that a potential drawback of the proposed "equal share" strategy is that it might
allocate affordable housing to jurisdictions that are less likely to build the units. If this were
the case, the income allocation would therefore hinder the region's ability to provide enough
housing affordable to meet the region's housing needs. However, there was general
agreement that the benefits of this approach outweighed the potential negative impact. In
addition, the HMC members felt that this issue could be worked out through the provisions in
the methodology that allow for voluntary transfer agreements between individual
jurisdictions.
The liMC discussed the possibility of using the proportion of households with a high housing
cost burden in a jurisdiction to adjust the income allocation for each jurisdiction. As a result,
areas with higher numbers of households with a cost burden would receive a larger share of
affordable units. This factor is based on the premise that directing more affordable housing
units to these jurisdictions would provide more housing options to residents in those areas.
However, the liMC was opposed to adjusting allocations based on high housing cost burdens
because there was concern that, as noted above, including a factor based on existing
conditions in a jurisdiction would ultimately lead to the over-concentration of low-income
households in an area. In addition, committee members were committed to the idea that the
need for affordable housing is a regional problem that each local government should have an
equal share in addressing.
3. Spheres of Influence
Every city in the Bay Area has a "sphere of influence (SOl)". A city's SOl can be either
contiguous with or beyond the city's boundaries. It is the areas that the city is responsible for
planning, as it is the probable future boundary of the city, including areas that may eventually
be annexed by the city. The SOl is designated by the county Local Area Formation
Commission (LAFCO). The LAFCO influences how government responsibilities are divided
among jurisdictions and service districts within a county. If there is planned household or
employment growth within the unincorporated portion of an SOl during the RHNA period,
the allocation methodology must include a rule for allocating housing needs to the affected
city or county.
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Draft Regional Housing Needs Allocation, 4th Revision
Therefore, the HMC recommends that each local jurisdiction with the land-use permitting
authority in a SOl should plan for the housing needed to accommodate housing growth,
existing employment and employment growth in such areas. A 100 percent allocation of the
housing need to the jurisdiction that has land use control over the area would ensure that the
jurisdiction that plans for accommodating the housing units also receives credit for any built
units during the RHNA period.
There are differences in whether a city or county has jurisdiction over land use and
development within unincorporated SOls. In response to these variations, the HMC
recommends the following SOl rules:
1. In Napa, Santa Clara, Solano, and Sonoma Counties, the allocation of housing need
generated by the unincorporated SOl will be assigned to the cities.
2. In Alameda, Contra Costa, and Marin Counties, the allocation of housing need generated
by the unincorporated SOl will be assigned to the county. )
The HMC recognizes that, although these guidelines reflect the general approaches to SOls in
each county, adjustments may be needed to better reflect local conditions. Requests for SOl
allocation adjustments may arise during the RHNA comment or revision period. Therefore,
the HMC recommends that the methodology include the following criteria for handling such
requests:
1. Adjustments to SOl allocations shall be consistent with any pre-existing written
agreement between the city and county that allocates such units, or
2. In the absence of a written agreement, the requested adjustment would allocate the units
to the jurisdiction that has permitting authority over future development in the SOL
4. Transfer of Units
After the initial allocation, each local jurisdiction may request that it be allowed to transfer
units with willing partner(s), in a way that maintains total need allocation amongst all transfer
parties, maintains income distribution of both retained and transferred units, and includes a
package of incentives to facilitate production of housing units. This transfer rule would allow
the transfer of allocated housing need between willing jurisdictions in conjunction with
financial and non-financial resources, while maintaining the integrity of the state's RHNA
objectives by preventing any jurisdiction from abdicating its responsibility to plan for
housing across all income categories. Transfers done in this manner may facilitate increased
housing production in the region.
The HMC recommends the following criteria for responding to requests for revisions that
transfer units among local jurisdictions:
1. Transfer requests must have at least two willing partners and the total number of units
within the group requesting the transfer cannot be reduced.
2. Transfers must include units at all income levels in the same proportion as initially
allocated.
I The County of San Mateo (formed a RHNA subregion) and the City and County of San Francisco (irrelevant) have
been omitted.
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Draft Regional Housing Needs Allocation, 4th Revision
3. All members of the transfer group must retain some allocation of very low and low
income units.
4. The proposed transfer must include a specifically defined package of incentives and/or
resources that will enable the jurisdiction(s) receiving an increased allocation to provide
more housing choices than would otherwise occur absent the transfer and the
accompanying incentives or resources.
5. If the transfer results in a greater concentration of very low or low income units in the
receiving jurisdiction, the effect must be offset by findings by the members of the transfer
group that address the RHNA objectives. For example, the findings might include (a)
there is such an urgent need for more housing choices in those income categories that the
opportunity to effect more housing choices in these categories offsets the impacts of
over-concentration, or (b) the package of incentives and/or resources are for mixed
income projects, or (c) the package of incentives and/or resources are for "transitional"
housing for very low or low income households being relocat~d for rehabilitation of
existing very low or low income units, or (d) the package of incentives and/or resources
are for additional units that avoid displacement or "gentrification" of existing
communities.
6. For the transfer of very low and low income units, there are restrictions that ensure the
long-term affordability of the transferred units.
7. Transfers must comply with all other statutory constraints and be consistent with the
RHNA objectives.
In addition to guaranteeing that transfers meet the RHNA statutory objectives, these criteria
'promote regional policies to increase housing supply and provide more housing choices. The
criteria state that the transfer must include the resources necessary to improve housing
choices and, specifically, in a way that would not otherwise be possible without the transfer.
The long-term affordability restrictions on very low and low income transferred units ensure
that these units will contribute to a fundamental increase in affordable housing choices.
The criteria also emphasize development of affordable units and are therefore consistent with
the state RHNA objective that every jurisdiction does its "fair share" to provide affordable
housing. The requirement that jurisdictions must retain some very low and low income units
and the stipulation that transfers must maintain the same income distribution as is initially
allocated ensure that a jurisdiction cannot abdicate its responsibility to provide affordable
units. The criteria also ensure that the benefits created by the transfer outweigh any possible
negative effects of an over-concentration oflower income households.
5. Subregions
The County of San Mateo, in partnership with all twenty cities in the county, has formed a
subregion, as allowed by state statute. The subregion has designated the City/County
Association of Governments (C/CAG) as the entity responsible for coordinating and
implementing the subregional RHNA process.
As required by statute, ABAG will assign a share of the regional need to the San Mateo
subregion "in a proportion consistent with the distribution of households" in Projections
2007. The subregion is responsible for completing its own RHNA process that is parallel to,
but separate from, the regional RHNA process. The subregion will create its own
methodology, issue draft allocations, handle the revision and appeal processes, and then issue
final allocations to members of the subregion.
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Draft Regional Housing Needs Allocation, 4th Revision
Although the subregion is working independently of the regional RHNA process, ABAG is
ultimately responsible for ensuring that all of the region's housing need is allocated. Thus, if
the subregion fails at any point in its attempt to develop a final RHNA allocation for the
subregion, ABAG must complete the allocation process for the members of the subr~gion.
In the event that the San Mateo subregion fails to complete the RHNA process, the HMC
recommends the methodology include the following guidelines for handling the allocation of
units to jurisdictions within the subregion:
1. If the members of the subregion adopts a "default allocation," ABAG will allocate using
the default allocation. A "default allocation" is the allocation which a member of the San
Mateo RHNA subregion receives if it "opts out" of the subregion.
2. If the subregion fails before ABAG has made any allocation, ABAG combines the
subregional share with the rest of the regional need and allocates the total regional need
to the entire region using ABAG's RHNA methodology.
3. If the subregion fails after ABAG has made its initial allocation, ABAG separately
allocates the subregional share among only the members of the subregion. ABAG uses its
RHNA methodology to do so.
This approach is recommended by the HMC because it minImiZeS the extent of any
reallocations that could occur as a result of subregional failure and preserves the integrity of
the respective efforts of ABAG and C/CAG. Keeping San Mateo separated once ABAG has
completed its initial allocation also provides the most certainty to all jurisdictions about what
their allocation will be.
VI. Regional Projections
Every two years, ABAG produces a long-run regional forecast called Projections. The
Projections forecast provides specific information for population, households, employment and
other related variables. In Projections 2007, values are reported for year 2000, and then for each
five year increment to 2035.
Several related models are used to perform the forecast. The economic model balances demand
for the production of goods and services with the supply of productive capacity. The demographic
model uses birth rates, death rates and migration data to forecast future population using a
cohort-survival model. A great deal of data is required by the models, including information on
economic relationships and trends, population-related information like births, deaths and
migration, as well as land use and land use policy data.
Since Projections 2003, ABAG has assumed the "Network of Neighborhoods" land use pattern,
as developed through the Smart Growth Strategy/Regional Livability Footprint Project. This
pattern expects higher levels of housing production. It also assumes that an increasing proportion
of regional growth occurs near transit and in existing urban areas. In the Projections 2007
forecast, additional housing production and a shift in the pattern of development primarily occurs
in the later part of the forecast. Earlier in the forecast, population growth is generally consistent
with the California Department of Finance (DOF) forecast. The distribution of growth is
generally consistent with local general plans.
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San Francisco Bay Area
Draft Regional Housing Needs Allocation, 4'h Revision
ABAG has continually collected information on local land use as part of its modeling efforts. The
forecast is produced for nearly 1400 census tracts in the region and shows the existing land use
and the capacity of each tract to support additional population or economic activities.
Because the forecast is based on local land use information, forecasted growth occurs in locations
that are consistent with local plans. However, even with 1400 census tracts, only so much detailed
information can be included. We may know that moderate growth can occur in an area without
specifically understanding that a portion of that area is a nature preserve. We may know that
growth should not occur in an area, but it may not be clear whether it is due to a physical
limitation, or a general plan policy.
November 2006, Page 16
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Draft Regional Housing Needs Allocation: Example Calculation
u
Committee PrevIous
Proposal RHNA
11/6/06 12:21 PM
Regional Need 2Jq,~'!4:: , 230 743 ,
ALAMEDA 2,182 2,162
ALBANY 260 277
BERKELEY 2,983 1,269
DUBLIN 3,326 5,436
EMERYVILLE 1,786 777
FREMONT 5,693 6,708
HAYWARD 3,537 2,835
LIVERMORE 3,564 5,107
NEWARK 928 1,250
OAKLAND 19,698 7,733
PIEDMONT 37 49
PLEASANTON 3,402 5,059
SAN LEANDRO 2,423 870
UNION CITY 2,135 1,951
UNINCORPORATED 1,938 5,310
ALAMEDA COUNTY 43,335 46,793
ANTIOCH 2,334 4,459
BRENTWOOD 2,895 4,073
CLAYTON 162 446
CONCORD 3,474 2,319
DANVILLE 550 1,110
EL CERRITO 626 185
HERCULES 427 792
LAFAYETTE 378 194
MARTINEZ 1,066 1,341
MORAGA 221 214
OAKLEY 749 1,208
ORINDA 233 221
PINOLE 304 288
PITTSBURG 2,237 2,513
PLEASANT HILL 588 714
RICHMOND 2,828 2,603
SAN PABLO 280 494
SAN RAMON 3,263 4,447
WALNUT CREEK 2,660 1,653
UNINCORPORATED 3,209 5,436
CONTRA COSTA CNTY 28,295 34,710
BELVEDERE 24 10
CORTE MADERA 227 179
FAIRFAX 65 64
LARKSPUR 701 303
MILL VALLEY 261 225
NOVATO 1,459 2,582
ROSS 25 21
SAN ANSELMO 137 149
SAN RAFAEL 1,571 2,090
SAUSALlTO 181 207
TIBURON 124 164
UNINCORPORATED 1,001 521
MARIN COUNTY 5,869 6,515
e~ rt--
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Draft Regional Housing Needs Allocation: Example Calculation
l/
Committee Previous
Proposal RHNA
11/6/06 12:21' PM
Regional Need 230,74:3 I 230,743 I
AMERICAN CANYON 685 1,323
CALlSTOGA 89 173
NAPA 1,901 3,369
ST HELENA 115 142
YOUNTVILLE 83 87
UNINCORPORATED 621 1,969
NAPA COUNTY 3,494 7,063
SAN FRANCISCO 43,434 20,372
SAN MATEO COUNTY 18,177 16,305
CAMPBELL 735 777
CUPERTINO 1,104 2,720
GILROY 1,634 3,746
LOS ALTOS 300 261
LOS ALTOS HILLS 77 83
LOS GATOS 530 402
MILPITAS 2,804 4,348
MONTE SERENO 39 76
MORGAN HILL 1,366 2,484
MOUNTAIN VIEW 2,967 3,423
PALO ALTO 3,836 1,397
SAN JOSE 33,748 26,114
SANTA CLARA 6,484 6,339
SARATOGA 275 539
SUNNYVALE 4,696 3,836
UNINCORPORATED 179 1,446
SANTA CLARA COUNTY 60,775 57,991
BENICIA 581 413
DIXON 686 1,464
FAIRFIELD 3,660 3,812
RIO VISTA 1,149 1,391
SUISUN CITY 657 1,004
VACAVILLE 2,734 4,636
VALLEJO 3,236 3,242
UNINCORPORATED 94 2,719
SOLANO COUNTY 12,796 18,681
CLOVERDALE 528 423
COTATI 438 567
HEALDSBURG 433 573
PETALUMA 2,085 1,144
ROHNERT PARK 1,835 2,124
SANTA ROSA 6,715 7,654
SEBASTOPOL 167 274
SONOMA 334 684
WINDSOR 721 2,071
UNINCORPORATED 1,314 6,799
SONOMA COUNTY 14,569 22,313
REGION 230,743 230,743
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v
ASSOCIATION OF BAY AREA GOVERNMENTS
EXECUTIVE BOARD
RESOLUTION NO. 13-06
RESOLUTION AUTHORIZING RELEASE OF A PROPOSED RHNA
METHODOLOGY FOR PUBLIC REVIEW AND COMMENT
WHEREAS, the Association of Bay Area Governments (ABAG) is a joint
powers agency formed pursuant to the agreement of its members and California
Government Code 99 6500, et seq., and is the council of governments (COG) for the
San Francisco Bay Area; and
WHEREAS, pursuant to the Housing Element Law ("Act") at California
Government Code 99 65580, et seq., each COG and the California Department of
Housing and Community Development (HCD) are required to determine the
existing and projected housing needs in the COG's region; and
WHEREAS, under the Act, ABAG determines each city's and county's share
of the regional housing needs through the regional housing need allocation process
(RHN.A); and
WHEREAS, the Executive Board authorized formation of the Housing
Methodology Committee (HMC) and charged it, in part, with the responsibility of
advising staff on the methodology for allocating the regional housing need among
local jurisdictions (RHNA Methodology) ; and
WHEREAS, the HMC advised staff to prepare the Draft RHNA Methodology
described in the staff memorandum dated October 26, 2006 for consideration by the
Executive Board; and
WHEREAS, the Act requires that ABAG release the Draft RHNA
Methodology for at least a sixty (60) day public review and comment period and
conduct a public hearing to receive written and oral comments; and
WHEREAS, staff recommends release of the Draft RHNA Methodology for
public review and comment.
NOW, THEREFORE, BE IT RESOLVED: the Executive Board of the
Association of Bay Area Governments hereby:
1. Authorizes staff to release the Draft RHNA Methodology for public review
and comment and to provide notification of such release to the general
public and to all cities, counties and any city and county in the region; and
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3lOO4LP
fJ ,-
ASSOCIATION OF BAY AREA GOVERNMENTS
RESOLUTION NO. 13-06
2. Authorizes staff to conduct a public hearing on the Draft RHNA
Methodology at the January 18,2007 Executive Board meeting; and
3. Directs staff to coordinate receipt and review of all comments for
presentation to the Executive Board at its January 18, 2007 meeting.
The foregoing adopted by the Executive Board this 16th day of November, 2006.
David Cortese
President
Certification of Executive Board Approval
I, the undersigned, the appointed and qualified Secretary-Treasurer of the
Association of Bay Area Governments (Association), do hereby certify that the
foregoing resolution was adopted by the Executive Board of the Association at a
duly called meeting held on the 16th day of November, 2006.
Henry L. Gardner
Secretary-Treasurer
Approval as To Legal Form
Kenneth K. Moy
Legal Counsel
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32 oc>'-\lP
ASSOCIATION OF BAY AREA GOVERNMENTS
o
"
Representing City and County Governments of the San Francisco Bay Area
A~AG
MEMO
To:
From:
Date:
Subject:
Housing Methodology Committee (HMC)
ABAG Staff
October 11, 2006
Scenarios for Allocating Units by Income
Background
There are two primary goals at the heart of the Regional Housing Need Allocation (RHNA}Proce1Ss..Tbe
first is to increase the supply of housing in California by allocating a share of the sta~-wide Musif\g need
to each city and county. The second is to ensure that local governments consider the bou$iUg rtee4Sof
persons at all income levels as they prepare their Housing Elements.
To achieve these two goals, the allocation of the regional housing need to local governments occurs in
two stages. The first is to allocate housing units to each city and county in the region. Tbe second is to
split each jurisdiction's total allocation into the four income categories established by the State. Tbe four
income categories defined by the State are:
.
Very Low: households with income up to 50 percent of the county's area median income (AMI)
Low: households with income between 50 and 80 percent of the county's AMI
Moderate: households with income between 80 and 120 percent of the county's. AMI
Above-Moderate: households with income above 120 percent of the county's AMI
.
'-
.
.
The goals and requirements of the allocation of units by income are specifically addressed in the RHNA
objectives. The first is that all cities and counties are responsible for doing their "fair share'; and planning
for at least some of the region's need for very-Iow- and low-income units. I The second is that the
allocation methodology must avoid or mitigate the over-concentration of income groups inajurisdiction.2
The RHNA allocation methodology must assign the regional need to each jurisdiction Ina way that fully
allocates the units in each income category and complies with the two objectives listed above, The HMC
requested that ABAG staff generate several possible scenarios for allocating units by income. This memo
shows the effects of different strategies for allocating units in each income category.:;
Allocation Scenarios
When allocatinl! units bv income. oarticularlv affordable units. there is an underlvins:rtension ~tween
trying to ensure~that all ~ommuni'ties do thei; ;'fair share" and ~esponding to existing-needs for housing.
For example, allocating more low-income units to a jurisdiction that has a higher proportion of low-
income residents would help to meet the community's existing needs. However, this strategy would
I Government Code Section 65584(d)(l).
Government Code Section 65584(d)(4).
The allocation of units by income occurs after jurisdictions receive their share of the regional housing need. Since
the methodology for this base allocation has not yet been determined, the scenarios show the percent of units in
each income category that a jurisdiction would rec~ive, rather than a number of housing units.
Mailing Address: P.O. Box 2050 Oakland, California 94604-2050 (510) 464-7900 Fax: [510J 464-7970 info@abag.ca.gov
Joseph P. Bort MetroCenter 101 Eighth Street Oakland, California 94607-4756
Attachment 2
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Scenarios for Allocating Units by Income
10/11/06
Page 2
u
promote the further concentration of low-income households in that community and would not move the
region toward a more equitable distribution of income.
The examples developed by ABAG staff are based on the following possible scenarios (Attached):
.
Scenario I: Moving every jurisdiction 50 percent toward the county average income distribution
Scenario 2: Moving every jurisdiction 50 percent toward the regional average income distribution
Scenario 3: Allocating units to each jurisdiction based on the county's average income
distribution
Scenario 4: Including a factor to address high housing cost burdens
.
.
.
Moving Toward a County or Regionallncome Distribution
Scenarios 1 and 2 both attempt to balance the existing need for housing with the goal of creating a more
equitable income distribution. Both start with a jurisdiction's existing income distribution. In Scenario I,
this existing distribution is moved 50 percent toward the county average income distribution. In
Scenario 2, the existing distribution is moved 50 percent toward the regional average.
Comparing a jurisdiction to the county takes local differences in income into account. In contrast, using
the regional income distribution attempts to overcome county-level differences in income to create a more
equitable distribution region-wide.
These scenarios start with ajurisdiction's existing income distribution and then move part ofthe way to
either the county or regional household income average. As a result, these methodologies try to assign
units to where they are currently needed while also creating a more fair income distribution. However, the
fact that the method starts with the existing conditions means that jurisdictions with more households in
affordable categories (relative to the regional average) must still plan for disproportionately more
affordable housing, and those with less than the regional average must plan for less.
It can be argued that this approach balances meeting the existing need in a specific jurisdiction with the
goal of having all jurisdictions do their "fair share" to meet the region's housing needs. At the same time,
these approaches can be described as perpetuating the over-concentration of the region's lower income
populations in certain communities.
Using the County lncome Distribution
In contrast to the first two scenarios, Scenario 3 does not take a jurisdiction's existing income distribution
into account. In this case, each jurisdiction is assigned the same distribution as the county-wide
distribution. In effect, this "equal share" approach applies the county-wide income distribution to each
jurisdiction within the county.
A primary benefit of this approach is that it is consistent with the idea that every jurisdiction should do its
"fair share" to provide affordable housing. It also promotes a more equitable income distribution by
moving every jurisdiction in a county to the same standardized income distribution. This method also
avoids over-concentrating an income group in a jurisdiftion. However, one potential drawback of this
strategy is that by excluding existing conditions, it doe~ not do enough to address the existing needs for
affordable housing.
34 Db4LQ ~. ~
Scenarios for Allocating Units by Income
10/11/06
Page 3
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High Housing Cost Burdens
As part of its discussions, the HMC felt that high housing cost burdens would be mostappropriately
considered as part of the discussion of housing affordability categories. We typically look at hol.ls~hQld
income to assess affordability. .,
Summary
This memo outlines several possible methods for accomplishing the allocation of units ~yinc9m~ fot
RHNA. It also describes some of the advantages and drawbacks of the different strategies.
In selecting an allocation methodology, the HMC must consider the extent to which it distribl!ltes housing
units in a way that:
· Provides for the housing needs of persons at all income levels .
· Ensures that every jurisdiction does its "fair share" to provide affordable housing
· Encourages an equitable distribution of incomes throughout the region
· A voids over-concentrating an income group in a jurisdiction.
Existing Income Allocation 50% Toward Region Average bounty Aver~ge
Housing
Very Very Very Cost
Low Low Mod Above Low Low Mod Above LQW Low Mod Above Burden
<50% <80% <120% Mod <50% <80% <120% Mod <500;0 <80% <120% MOd >30%
ALAMEDA 22% 17% 20% 42% 23% 17% 19% 42% 24% 16% 18%. 42% 31%
ALBANY 22% 18% 20% 40% 23% 17% 19% 41% 24% 16%. 18% 42% 33%
BERKELEY 35% 15% 15% 35% 26% 16% 18% 40% 24% 16% 18% 42% 38%
DUBLIN 9% 12% 19% 60% 20% 15% 19% 46% 24% 160/.; 18% 42% 32%
EMERYVILLE 31% 18% 19% 31% 25% 17% 19% 39% 24% 16% 18% 42% 42%
FREMONT 12% 11% 18% 58% 20% 15% 19% 46% 24% 16% 18% 42% 31%
HAYWARD 23% 19% 23% 35% 23% 17% 20% 40% 24% 16% 18% 42% 35%
LIVERMORE 12% 12% 18% 57% 20% 15% 19% 46% 24%. 16% 18% 42% 31%
NEWAFIK 13% 14% 21% 52% "4 21% 16% 19% 44% 24"!" 10% 18% 42% 31%
OAKLAND 36% 19% 16% 29% a$%' 26% 17% 18% 38% 24% 16% 18% 42% 37%
PIEDMONT 9% 5% 9% 78% ..6()~' 19% 13% 16% 51% 24% 16% 18% 42% 28%
.... ". < : 0')
PLEAStiNTON 9% 9% 16% 66% 54%, 20% 14% 18% 48% 24% 16% 18% 42% 29%
SAN LEANDRO 23% 20% 22% 35% 18%' 38% 23% 17% 20% 40% 24% 16% 18% 42% 30%
UNION CITY 15% 12% 19% 55% 1"\0/0", 4S"!il 21% 15% 19% 45% 24% 16% 18% 42% 32%
UNINCORPORA TED 20% 17% 20% 42% 11% 42% 22% 17% 19% 42% 24% 16% 18% 42% 32%
Alameda County 24% 16% 18% 42%. 4~% 23% 16% 19% 42% 24% 16% 18% 42% 34%
',.
;:.... :g~~~ 18$,;; 22% .,:J~$%~
ANTIOCH 23% 19% 23% 36% 22% 17% 20% 40% 22% 17% 20% 41% 34%
BRENTWOOD 18% 17% 21% 44% :mo/c;, Zr~:i:.;,',(~~: 21% 17% 20% 42% 22% 17% 20% 41% 30%
CLAYTON 10% 8% 16% 67% 116%: 19% 15% 19% 48% 22% 17% 20% 41% 25%
"';>
CONCORD 25% 20% 23% 32% ....... 36%, 23% 17% 20% 39% 22% 17% 20% 41% 33%
DANVILLE 8% 9% 12% 72% ,.. 56% 19% 15% 18% 49% 22% 17% 20% 41% 32%
EL CERRITO 24% 20% 20% 35% 230/.;' 2~';; "'a~?~ 23% 18% 20% 40% 22% 17% 20% 41% 26%
HERCULES 11% 17% 24% 49% 1(Wo 22~'. 45~" 19% 17% 21% 43% 22% 17% 20% 41% 32%
LAFAYETTE 11% 10% 16% 63% t7~i$ ',1~~o 526k, 20% 15% 19% 47% 22% 17% 20% 41% 26%
MARTII'IEZ 20% 17% 23% 40% 21% 21o/~:.. 40o/c)" 22% 17% 20% 41% 22% 17% 20% 41% 29%
MORAGA 11% 11% 16% 62% ,1:7"4 l"'~~%' 20% 15% 19% 46% 22% 17% 20% 41% 26%
OAKLEY 15% 18% 30% 37% 1:9% 21% 17% 22% 40% 22% 17% 20% 41% 36%
ORINDA 10% 9% 12% 69% . l' 19% 15% 18% 48% 22% 17% 20% 41% 26%
PlNOLE 19% 18% 25% 38% .~ 21% 17% 21% 40% 22()/ci 17% 20% 41% 27%
PITTSBURG 29% 22% 23% 27% 24% 18% 20% 38% 22% 17% 20% 41% 34% ().)
PLEASI\NT HILL 19% 16% 23% 42% 22% 17% 20% 42% 22% 17% 20% 41% 31 % \.7{
RICHMOND 35% 22% 20% 23% 25% 18% 20% 37% 22% 17% 20% 41% 36%
SAN PABLO 43% 21% 18% 17% 28% 18% 19% 35% 22% 17% 20% 41% 40o/~
SAN RAMON 8% 11% 17% 65% 19% 15% 19% 47% 22% 17% 20% 41% 29%
WALNUT CREEK 22% 18% 20% 41% 22% 17% 20% 41% 22% 17% 20% 41% 29% -t:
UNINCORPORATED 22% 16% 19% 43% 22% 16% 19% 42% 22% 17% 20% 41% 32% S
Contru Costa County 22% 17% 20% 41% 22% 17% 20% 41% 22% 11% 20% 41% 32%
C
Existing Income Allocation
Very
Low Low Mod
<50% <80% <120%
BELVEDERE 15% 8% 12%
CORTE MADERA 16% 17% 20%
FAIRFAX 24% 23% 22%
LARKSPUR 22% 19% 19%
MILL VALLEY 19% 14% 14%
NOVATO 23% 20% 21%
ROSS 14% 9% 18%
SAN ANSELMO 23% 16% 20%
SAN RAFAEL 28% 19% 18%
SA USA LITO 17% 14% 18%
TIBURON 12% 14% 15%
UNINCORPORATED 21% 15% 16%
Marin County 22% 17% 18%
AMERICAN CANYON 22% 17% 19%
CALlSTOGA 29% 23% 19%
NAPA 22% 19% 20%
ST HELENA 20% 15% 17%
YOUNTVILLE 28% 15% 21%
UNINCORPORATED 16% 15% 18%
Napa County 21% 18% 19%
San Francisco 25% 15% 16%
ATHERTON 4% 6% 9%
BELMONT 17% 17% 18%
BRISBANE 23% 21% 17%
BURLINGAME 22% 19% 19%
COlMA 27% 20% 28%
DALY CITY 25% 19% 24%
EAST PALO ALTO 40% 21% 19%
FOSTER CITY 11% 14% 18%
HALF MOON SAY 21% 13% 20%
HILLSBOROUGH 8% 6% 9%
MENLO PARK 20% 14% 17%
MILLBRAE 24% 17% 20%
PACIFICA 17% 18% 25%
~. ;'" .
. ','.: ',.
....:t.... I;.
-:,',:" '<;:;
64% :":r.~~i
53 Yo. 21 ~, . ,t$Xo. . . 1-$% .',: 48~,
3 01 .. ":;'3'01 .' .'1'9" ill.' :..; '2"';';;';": '3"g'bi.
5/0 ..'.... '" lO..... : :/0:' ..... ,voYo":"''-: /9'
~~~:i';~';'l~'e'!::~Jf~t~,
35Yo,. '..25%;' .:.18%..: ,18.~' :,,39~.
~~ }iil;;Jllt,!!ltfl
42%..:::,~%'.' ;.'r%,.:,:,., ~'~%':/':~~lW
. E~:':'irII:~I~1
42 Yoc ; 2H/o '.::1 a ?{;;, ..::/1~Al;.,-;t':"A.2%
:,: ~)~;:: ~~;~~~::':",<.,; :~~;~ \\~~'::";1.;:~'::.'. ~ ~ t. f:~'::;;:.~'2~~:: ~'l:\ l~;~./'~ ~'~".~"':;
44% : ,<2Sil;{, ':.15% ~.;~; 1'6o/ii.....:~"~%
:~ f~;~:ji;~~;~i;~;}~{;~~~i~
40%'"
25%
32%
20%
57% ,\",.c'''''.f.i>~
E~ilgf~~(i~R~l~;*~::fg~~,~
50% Toward Region Average ", County Average .',
Housing
Very . Very Cost
Low Low Mod Above LOw ,Low, Mod Above Burden
<50% <80% <120% Mod <50% ~80%.. <:i 20% Mod >30%
21% 15% 17% 47% 22% 17%, '18%, ,42% 35%
21% 17% 19% 43% 22%. ,.17% 18%' 42% 31%
23% 18% 20% 39% 22% 17% 18%. 42% 41%
22% 17% 19% 42% 22% 11% 18% 42% 34%
22% 16% 18% 45% 22%. .17% ' 18% 420/0 33%
23% 18% 19% 40% 22% 11% ' 18% 42% 38%
21% 15% 19% 46% ,220/0,' 17o/~ 18% 42% 43%
23% 16% 19% 42% 22%. 170/0 18% 42% 37%
24% 17% 19% 40% 22% 17% 18% 42% 37%
21% 16% 19% 44% 22%. .17% 18% 42% 35%
20% 16% 18% 46% ,22%, " 17~/o " 18% 42% 31%
22% 16% 18% 43% 22%,. 17% , , 18% 42% 33%
23% 17% 19% 42% 22% 17% 18% 42% 35%
. -' .' .
22% 17% 19% 42% .21% '. :18% :19%', ,'42% 31%
24% 19% 19% 38% '2~%,'> 18% 19% 42% 30%
22% 17% 19% 41% 210/0 180/0' 19% ,42% 32%
22% 16% 19% 43% 21%. ' 180ici 19% ,'42% 32%
24% 16% 20% 40% ' 21o/~ '18% '.19% 42% 35%
21% 16% 19% 44% 21ro "18% 19% 42% 26%
22% 17% 19% 42% 21%. ' 18o/c " , ,19%' 42% 31%
" : ..,'0,
..
" ..'2~% -- 15o/~ 33%
24% 16% 18% 43% 1e% 44%
OJ
n
J1
t
'~ .' .
\:
Existing Income Allocation 50% Toward Region Average C6urityAv~rl:1g~
Housing
Very Very VfjJr'j Cost
Low Low Mod Above Low Low Mod Above Low Low Mod Above Burden
<50% <80% <120% Mod <50% <80% <120% Mod <50% . .:zBOOZ(J, . <1200/0 Mod >30%
PORTOlA VALLEY 10% 6% 9% 75%
REDWOOD CITY 24% 18% 20% 38%
SAN BRUNO 24% 20% 24% 32%
SAN CARLOS 17% 15% 17% 52%
SAN MA TEO 24% 19% 20% 37%
SOUTH SAN FRANCISCO 25% 20% 24% 31% .
WOODSIDE 9% 9% 11% 71%
UNINCORPORATED 18% 13% 19% 51%
San Mateo County 21% 17% 20% 41% ",;~1% 21% 1'7% 20% 41% 33%
,'";'
CAMPBELL 23% 20% 21% 35% 21% ,"~~" 23% 18% 20% 40% 22% 17% 20% 41% 33%
CUPERTINO 14% 12% 17% 56% 1;ij,p 19%' 20% 16% 19% 45% 22% 17% 20% 41% 28%
GILROY 27% 20% 23% 30%, 19% 2f'~~ 3po/c;' 23% 18% 20% 39% 22% 17% 20% 41% 34%
LOS ALTOS 12% 10% 12% 66% t4~, ",,1~0& Q3~!~' 20% 15% 18% 48% 22% 17% 20% 41% 26%
LOS ALTOS HILLS 9% 6% 9% 76% 12% '" 14~?" 5~~, 19% 14% 17% 50% 22% 17% 20% 41% 33%
17% 15% 16% 53% 16% " , , 47%,
LOS GATOS 21% 16% 19% 44% 22% 17% 20% 41% 32%
MILPITAS 16% 16% 21% 47% '1~~~, 44% 21% 16% 20% 43% 22% 17% 20% 41% 29%
MONTE SERENO 11% 8% 10% 70%,'" 1304';,: 20% 15% 17% 49% 22% 11% :2.0% 41% 34%
MORGAN HILL 21% 13% 20% 46% 22% 16% 20% 43% 22% 17% 20% 41% 32%
MOUNTAIN VIEW 24% 18% 20% 38% 23% 17% 20% 41% 22% 17% 20% 41% 31%
PALO ALTO 21% 13% 15% 51% 22% 16% 18% 44% 22% 11% 20% 41% 28%
SAN JOSE 23% 18% 21% 38% 23% 17% 20% 40% 22% 17% 20% 41% 33%
SANTA CLARA 24% 18% 22% 36% 23% 17% 20% 40% 22% 17% 20% 41% 29%
SARATOGA 12% 7% 10% 71% 120/0 ",~~,o/O" 20% 14% 17% 49% 22% 17% 20% 41% 27%
SUNNYVALE 21% 17% 22% 41% 1'7.% 41 ii/l) 22% 17% 20% 41% 22% 17% 20% 41% 27%
UNINCORPORATED 25% 17% 17% 41% 1'7% 4.1% 23% 17% 19% 41% 22% 17% 20% 41% 31%
Santa Clara County 22% 17% 20% 41% 17% 'L,:~1~o 22% 17% 20% 41% 22% 17% 20% 41% 31%
BENICIA 16% 14% 18% 52% 1$%,;, 21% 16% 19% 44% 21% 17% 21% 40% 31%
DIXON 17% 20% 23% 40% ?T~~~:, 21% 18% 21% 41% 21% 17% 21% 40% 30%
FAIRFIELD 22% 19% 21% 38% 22% 17% 20% 40% 21% 17% 21% 40% 30% \..).)
RIO VISTA 30% 19% 22% 29% ;i~5.~' 24% 17% 20% 38% 21% 17% 21% 40% 32% :.J
SUI SUN CITY 15% 15% 25% 46%. :;,jQ% 20% 16% 21% 42% 21% 17% 21% 40% 31 "Ik)J
VACAVILLE 18% 17% 21% 43% ""~Q$; 21% 17% 20% 42% 21% 17% 21% 40% 300;'
VALLEJO 25% 18% 21% 36% 23% 17% 20% 40% 21% 17% 21% 40% 33% ...(:.
UNINCORPORATED 19% 16% 20% 45%;. ' 21% 17% 20% 42% 21% 17% 21% 40% 30% b
\< ":i,,,,. '.,:'
Solano County 21% 17% 21% 40%' >t'!1'~Ai', 22% 17% 20% 41% 21% 17% 21% 40% 31%
. ",'j",lt" .
;:
C
CLOVERDALE
COT A TI
HEALDSBURG
PETALUMA
ROHNEFH PARK
SANTA ROSA
SEBASTOPOL
SONOMA
WINDSOR
UNINCORPORATED
Sonoma County
Bay Area
2.000 Ct.-OSI),.>
30%
18%
24%
17%
21%
22%
29%
26%
16%
22%
21%
23%
Low Mod
<80% <120%
19%
20%
19%
14%
19%
18%
17%
17%
14%
17%
17%
17%
19%
22%
23%'
20%
22%
22%
23%
19%
20%
19%
21%
19%
41 Yo".<20,Yo,{~8~ '.,~j%.;,.,.~,41%;
3401 1~~..::L2'-:'3.'''o;';'';, "1' 8"a;',";~'~~dP;":X~:7"'~h:-
10,,'."'1," . '/0' '," r70' ,:,j~ ~lO'\"'V '\7 .~::-:;
i;r~~?l~j~I~~~
32 Yo .,,-j~5*,.. .17*',:",22%:, ":.~6}{j:,
38:0 ::;;:1A1~';,..\::1t:~::>'{\~Q.~n;;j~~::
50 Yo ,-:-.:1-9?k,;,. ..' .16'%, ",,20 J{,.\,.,.,~.~
50% Toward Region Average ... .. '~Glmty Av~rage
. ,. ',.. '.'
... . Housing
Very ',Very Cost
Low Low Mod Above ..Low, Low, Mod Above Burden
<50% <80% <120% Mod ,', .<150%' . .<::80% .:':120%' MQd >30%
..
'.
24% 17% 20% 39% ..~1% :', '17%:' ,21%. 41% 36%
21% 17% 20% 41% ~1~'. ,17%. ~1% "41% 35%
23% 17% 21% 39% ....21%:; 17% 21% .41% 30%
21% 16% 20% 43% ?1% .. 170/0 ': 21% 41CYo 31%
22% 17% 20% 41% 21% 11%'.'. 21%' . 41 0/0: 38%
22% 17% 20% 40% '.~1% : 17% '21% 41% 33%
:
24% 17% 21% 39% 21<>(0- 17% . 21% . 41% 35%
23% 17% 19% 41% 21% 17% 21% : 41% 36%
21% 16% 20% 43% 21% 17%. .21% 41% 34%
22% 17% 20% 41% ,21% 17'Yc' 21% 41% 32%
" .' ..0,
22% 17% 20% 41% '21%. .17% 21% 41% 33%
, .
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c
ASSOCIATION OF BAY AREA GOVERNMENTS
:3q Ob4LD
o
Representing Ci!y and County Governments of the San Francisco Bay Area
ABAG
MEMO
To:
From:
Date:
Subject:
Housing Methodology Committee (HMe)
ABAG Staff
January 4, 2007
Alternative Income Allocation Method
Background
On November 16, 2006, ABAG's Executive Board authorized the release of the Housing Methodology
Committee's draft methodology for the Regional Housing Needs Allocation (RHNA) for 2007-2014. The
release of the methodology opened a 60-day public comment period. The comment period will close on
January 18, 2007. On that date, staff will bring to the Executive Board recommendations for the fi~al
RHNA methodology.
Several comments received on the draft RHNA method pertain to the income allocation component of the
methodology. Some local jurisdictions believe the proposed income allocation methodology does not do
enough to alleviate existing concentrations of poverty. There is concern that, because the draft
recommendation assigns an "equal share" to each jurisdiction and does not take a jurisdiction's existing
income distribution into account, it unfairly burdens jurisdictions with existing high concentrations of
poverty. As a result, the draft method is perceived to perpetuate regional social and economic inequities.
Staff has developed three alternative income allocation scenarios for consideration by the HMC and the
ABAG Executive Board at its meeting on January 18th. In contrast to the draft methodology, these
alternative scenarios take into account existing income distributions within individual communities and
attempt to address existing concentrations of poverty. This staff report describes these alternative income
allocations.
HMC Recommended Income Allocation
In the recommendation to the ABAG Executive Board, the HMC and ABAG staff proposed that each
local jurisdiction plan for income-based housing units in the same ratio as the regional average income
distribution. This is deemed an "equal share" approach because each jurisdiction would receive the same
proportion of housing units in each affordability category (very-low, low, moderate, and above moderate).
Although cOlisidered an equitable approach, this income allocation method does not consider existing
concentrations of poverty in a community. Based on 2000 Census figures, the regional income
distribution is:
.
Very Low, 23 Percent
Households with income up to 50 percent of the county's area median income (AMI)
Low, 16 Percent
Households with income between 50 and 80 percent ofthe county's AMI
Moderate, 19 Percent
Households with income between 80 and 120 percent of the county's AMI
Above-Moderate, 42 Percent
Households with income above 120 percent of the county's AMI
.
.
.
Mailing Address: P.O. Box 2050 Oakland, California 94604-2050 (510) 464-7900 Fax: (510) 464-7970 info@abag.ca.gov
Joseph P. Bort MetroCenter 101 Eighth Street Oakland, California 94607-4756
Attachment 2
l-\O O"b 4\J2
RHNA Income Allocation 1/04/'07
Page 2
Percent Adjustment Toward Regional Average
By allocating each jurisdiction an equal share based on the regional income distribution, the draft
allocation scenario moves each jurisdiction 100 percent toward the regional income distribution. It is
focused on promoting an equitable regional distribution for future housing production, but does not
consider existing concentrations of poverty in a community or take steps to reduce them.
In contrast, the first two alternative income allocation scenarios give each jurisdiction either 150 or 175
percent of the difference between their 2000 household income distribution and the 2000 regional
household income distribution.
The first step in this process is to determine the difference between the regional proportion of households
in an income category and the jurisdiction's proportion for that category. This difference is then
multiplied by either 150 or 175 percent to determine an "adjustment factor." Finally, this adjustment
factor is added to the jurisdiction's initial proportion of households in the income category, which results
in the total share of the jurisdiction's housing unit allocation that will be in that income category.
Using the 175 percent factor and the City of Oakland's very low income category as an example,
36 percent of households in Oakland were in this category, while the regional total was 23 percent.
Oakland
-23
Total
Share
13
-13
Multiplier
175%
Adjustment
Factor
City
Jurisdiction
Proportion
36
Regional
Proportion
23
Difference
The difference between 23 and 36 is -13. This is multiplied by 175 percent for a result of -22.75 (rounded
to 23). This is then added to the city's original distribution of36 percent, for a total share of 13 percent. A
similar calculation for Piedmont, which has a relatively low proportion of households in the "very low"
income category yields the following results:
Piedmont
24
Total
Share
33
City
Jurisdiction
Proportion
9
Regional
Proportion
23
Difference
14
Multiplier
175%
Adjustment
Factor
As shown above, those jurisdictions that have a larger proportion of households in an income category
will receive a smaller allocation of housing units in that category. Conversely, those jurisdictions that
have a relatively low proportion of households in a category would receive a higher allocation of housing
units in that category.
The effect of these allocation scenarios is to change the income distribution in each jurisdiction to more
closely match the regional distribution by taking both a jurisdiction's existing conditions and future
development into account. By addressing existing concentrations of poverty, these scenarios more
aggressively promote an equitable regional income distribution. The multiplier determines how
aggressively the scenario functions; the higher the multiplier, the more aggressive.
Tiered Adjustment Based on Concentration of Poverty
The third alternative scenario is similar to the first two alternatives in that it uses existing conditions to
move each jurisdiction closer to the regional income distribution. The key difference in this scenario is
that jurisdictions are first separated into three groups based on the jurisdiction's proportion of low- and
very low-income households compared to the proportion for the region. The three groups correspond to
L.\ \ Db~
RHNA Income Allocation 1/04/07
Page 3
three different multipliers (like the 175 percent example used above) that determine how far a jurisdiction
must move toward the regional income distribution.
The fIrst step in this process is to add together the percentages of very low and low income households in
a jurisdiction. Each jurisdiction's result is then compared to the regional proportion. Based on this
comparison, jurisdictions are put into one of three categories:
.
Low concentration: where less than 25 percent of total households have very low or low incomes.
Moderate concentration: where less than 45 percent of total households have very low or low
Incomes.
.
.
High concentration: where more than 45 percent of total households have very low or low
incomes (San Pablo is the highest in the region at 65 percent).
Jurisdictions in the low concentration category, such as Livermore, Pleas anton, Clayton, Danville, and
Los Altos Hills move the furthest (185 percent) toward the regional average. Those in the moderate
concentration category, such as Albany, Walnut Creek, Napa, San Francisco, and San Jose, move 180
percent and those in the high concentration category, which includes Berkeley, Oakland, Richmond, San
Rafael, Gilroy, and Sebastopol, move 175 percent.
Once the multiplier for the jurisdiction has been determined, the steps for determining the jurisdiction's
share of housing units in each income category is the same as the one for the fIrst alternative methodology
described above.
Taking the City of Piedmont example used above, this scenario would result in a higher share of very
low-income units for the city because the city falls into the low concentration category and has a
multiplier of 185 percent. Here, the share is 35 percent compared to 33 percent in the example above.
14
Multiplier
185%
Adjustment
Factor
Total
Share
City
Piedmont
Jurisdiction
Proportion
9
Regional
Proportion
23
Difference
26
35
The result of this allocation scenario is that jurisdictions with a low concentration of low and very low
income households get higher allocations of very low- and low-income housing units. Those jurisdictions
that already have a high concentration of very low- and low-income households are allocated fewer units
in these categories.
As in the fIrst alternative scenario, the effect of this allocation scenario is to change the income
distribution in each jurisdiction to more closely match the regional distribution by taking both a
jurisdiction's existing conditions and future development into account. This third alternative scenario
specifIcally looks at the proportion of very low- and low-income households in a jurisdiction as the factor
for determining how far the jurisdiction must move toward the regional average income distribution.
Summary
The alternative allocation scenarios described above have been designed to promote a more equitable
regional income distribution by addressing existing concentrations of poverty in individual jurisdictions.
The scenarios demonstrate different possible approaches and outcomes for moving jurisdictions toward
the region's income distribution. Staff recommends that the HMC consider these alternative income
allocations and come to a consensus on a recommendation to the ABAG Executive Board.
Income Catego. .Iternatives
Existing % into 3 Groups
Existing Percentages Plus Existing Percentages Plus Higher Existing Concentration Gets
Average Regional Percentage 150% Regional Average Minus Exist 175% Regional Average Minus Exist Lower Allocation of Affordable
Draft Allocation Proposal II 150% Toward Regional Average U 175% Toward Regional Average I Tiered Adjustment
Very Very Very Very
T~tli!' . Low Low Mod Above Low Low Mod Above Low Low Mod Above Low Low Mod Above
Ne~!l. <50% <80% <120% Mod <50% <80% <120% Mod <50% <80% <120% Mod <50% <80% <120% Mod
ALAMEDA 2.,O7~. 469 343 399 864 483 339 395 858 485 338 394 858 488 337 396 862
ALBANY 59 43 50 109 61 42 49 111 61 41 49 112 61 41 49 112
BERKELEY 614 449 521 1,130 448 468 579 1,218 361 479 608 1,266 362 479 612 1,272
DUBLIN 778 569 661 1,432 1,013 648 666 1,115 1,121 690 669 960 1,171 706 674 901
EMERYVILLE 348 254 295 640 283 243 293 716 249 239 293 757- 250 239 295 761
FREMONT 1,092 799 927 2,009 1,357 923 947 1,604 1,476 988 957 1,406 1,533 1,012 968 1,332
HAYWARD 757 554 643 1,394 758 506 585 1,499 751 484 556 1,557 754 479 554 1,576
LIVERMORE 774 567 657 1,425 958 636 674 1,157 1,040 673 682 1,028 1,080 686 690 979
NEWARK 203 149 172 374 246 161 164 327 265 168 160 304 270 169 160 301
OAKLAND 3,867 2,831 3,284 7,117 2,766 2,595 3,521 8,208 2,189 2,487 3,641 8,782 2,197 2,486 3,666 8,825
PIEDMONT 8 6 7 16 11 8 9 9 12 10 10 5 13 10 11 4
PLEASANTON 834 610 708 1,535 1,087 753 772 1,078 1,204 827 804 853 1,258 855 822 766
SAN LEANDRO 424 310 360 780 423 280 330 841 419 267 315 874 420 263 314 885
UNION CITY 455 333 386 837 539 382 389 702 576 408 390 637 587 413 393 626
UNINCORPORATED 507 371 430 932 539 360 416 925 549 356 409 925 554 355 411 929
ALAMEDA COUNTY 11,189 8,190 9,502 20,593 10,972 8,344 9,788 20,368 10,711 8,361 9,831 20,571 11,000 8,531 10,013 20,133
ANTIOCH 521 381 442 958 521 357 400 1,023 516 347 380 1,059 518 344 378 1,071
BRENTWOOD 635 465 539 1,168 708 458 509 1,132 738 457 493 1,118 748 456 494 1,120
CLAYTON 33 24 28 60 42 30 30 42 47 33 31 33 49 35 32 30
CONCORD 706 517 599 1,299 672 466 536 1,445 649 443 505 1,524 648 438 502 1,546
DANVILLE 125 92 106 230 166 113 127 147 185 124 138 106 194 129 143 90
EL CERRITO 118 86 100 217 114 77 97 233 112 73 96 242 112 72 96 245
HERCULES 97 71 83 179 124 71 73 163 136 71 69 156 139 71 68 155
LAFAYETTE 81 59 69 149 102 71 75 110 112 77 78 91 116 80 80 83
MARTINEZ 236 173 201 435 251 168 183 444 255 166 174 449 258 166 174 452
MORAGA 50 37 43 93 63 42 47 70 69 45 49 59 72 46 50 55
OAKLEY 169 124 144 312 198 119 104 328 210 118 83 338 214 117 80 341
ORINOA 50 37 42 92 64 45 51 61 70 49 55 46 73 51 57 40
PINOLE 69 51 59 128 76 48 49 133 78 47 45 136 79 47 44 137
PITTS BURG 457 335 388 842 397 283 352 989 363 259 334 1,066 365 258 336 1,072
PLEASANT HILL 134 98 114 247 145 100 104 244 150 101 99 243 151 101 98 244
RICHMOND 624 457 530 1,149 462 376 525 1,396 376 337 523 1,524 378 337 526 1,532
SAN PABLO 64 47 54 118 35 40 56 152 20 37 57 169 20 37 57 170
SAN RAMON 744 545 632 1,370 994 637 676 987 1,110 686 698 798 1,163 704 711 726
WALNUT CREEK 499 365 424 919 510 351 420 927 510 346 417 935 513 344 420 940
UNINCORPORATED 828 606 703 1,524 844 622 708 1,489 844 632 711 1,476 848 633 716 1,480 ..r:
CONTRA COSTA COUNTY 6,242 4,569 5,301 11,489 6,489 4,476 5,122 11,516 6,391 4,455 5,091 11,665 6,656 4,466 5,063 11,530 rJ
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Income Catego. .Iternatives
Existing % into 3 Groups
Existing Percentages Plus Existing Percentages Plus Higher Existing Concentration Gets
Average Regional Percentage 150% Regional Average Minus Exist 175% Regional Average Minus Exist Lower Allocation of Affordable
.0""'.'.,1 Draft Allocation Proposal II 150% Toward Regional Average II 175% Toward Regional Average U Tiered Adjustment
Very Very Very Very
Low Low Mod Above Low Low Mod Above Low Low Mod Above Low Low Mod Above
<50% <80% <120% Mod <50% <80% <120% Mod <50% <80% <120% Mod <50% <80% <120% Mod
BELVEDERE 6 4 5 10 7 5 6 7 7 6 6 6 7 6 6 6
CORTE MADERA 52 38 44 96 61 37 44 90 64 37 43 88 65 37 43 88
FAIRFAX 16 12 14 30 16 10 13 34 15 8 12 36 15 8 12 36
LARKSPUR 138 101 118 255 142 93 119 258 143 90 119 260 144 89 120 262
MILL VALLEY 63 46 53 116 68 50 60 100 70 52 64 92 70 52 65 91
NOVATO 324 237 275 596 320 212 259 640 315 200 252 664 315 198 252 672
ROSS 6 4 5 11 7 5 5 8 7 6 5 7 8 6 5 7
SAN ANSELMO 24 18 21 45 24 18 20 45 24 18 20 45 24 18 20 45
SAN RAFAEL 353 258 299 649 312 241 311 695 288 233 317 721 289 233 319 724
SAUSALlTO 40 29 34 74 46 32 35 65 48 33 36 61 49 33 36 60
TIBURON 28 20 24 51 34 22 26 41 37 23 28 35 38 23 28 34
UNINCORPORATED 155 113 131 284 160 119 142 263 161 122 148 253 162 123 150 252
MARIN COUNTY 1,204 882 1,023 2,217 1,196 843 1,041 2,246 1,210 861 1,072 2,182 1,187 825 1,058 2,277
AMERICAN CANYON 157 115 133 288 160 112 134 287 159 112 135 287 160 111 136 288
CALISTOGA 20 15 17 37 17 12 17 43 16 10 17 46 16 10 18 46
NAPA 433 317 368 798 439 294 361 823 438 283 357 839 440 280 359 846
ST HELENA 26 19 22 48 28 20 23 45 28 20 24 43 29 21 24 43
YOUNlVlLLE 19 14 16 35 17 14 15 37 15 15 15 38 15 15 15 39
UNINCORPORATED 141 104 120 260 163 109 123 231 173 112 124 217 175 113 125 215
NAPA COUNTY 797 583 677 1,467 824 561 673 1,465 833 550 672 1,469 835 550 676 1,476
SAN FRANCISCO COUNTY 9,158 6,703 7,778 16,855 8,759 7,061 8,387 16,285 8,477 7,268 8,695 16,055 8,464 7,301 8,816 16,080
SAN MATEO COUNTY 4,132 3,024 3,509 7,605 4,132 3,024 3,509 7,605 4,292 2,930 3,382 7,667 4,302 2,923 3,374 7,671
CAMPBELL 167 122 142 308 166 109 134 331 163 102 130 344 164 101 130 . 348
CUPERTINO 252 184 214 463 300 208 224 381 322 220 229 342 328 222 231 335
GILROY 358 262 304 660 328 231 277 749 310 216 263 796 311 216 265 800
LOS ALTOS 68 50 58 126 85 59 69 89 92 64 74 71 96 66 77 64
LOS ALTOS HILLS 17 13 15 32 23 17 19 19 25 19 21 12 26 19 22 10
LOS GA TOS 121 88 102 222 138 93 111 192 145 96 115 177 147 96 117 175
MILPITAS 593 434 503 1,091 683 442 474 1,022 721 449 460 991 733 449 460 989
MONTE SERENO 9 7 8 16 11 8 9 11 12 9 10 8 13 9 11 7
MORGAN HILL 300 220 255 553 315 242 249 522 319 255 247 509 321 257 248 508
MOUNTAIN VIEW 623 456 529 1,146 611 429 518 1,195 600 417 513 1,224 601 414 515 1,235
PALO ALTO 840 615 714 1,547 879 679 789 1,370 889 714 827 1,286 896 720 840 1,275 ~
SAN JOSE 7,522 5,506 6,388 13,844 7,462 5,265 6,046 14,485 7,361 5,166 5,877 14,855 7,378 5,140 5,883 14,995
SANTA CLARA 1,351 989 1,147 2,487 1,328 955 1,055 2,636 1,304 942 1,009 2,719 1,306 938 1,006 2,748 l}l
SARATOGA 63 46 53 115 78 58 66 75 85 65 73 55 88 67 76 47
SUNNYVALE 1,037 759 881 1,908 1,087 746 824 1,927 1,102 743 795 1,943 1,111 742 795 1,955 J1
UNINCORPORATED 36 26 31 67 35 26 32 67 34 26 33 67 34 26 33 68
SANTA CLARA COUNTY 13,357 9,777 11,344 24,584 .13,528 9,569 10,895 25,071 13,668 9,642 10,940 24,813 13,553 9,484 10,709 25,560 .L
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Income Catego. .Iternatives
Existing % into 3 Groups
Existing Percentages Plus Existing Percentages Plus Higher Existing Concentration Gets
Average Regional Percentage 150% Regional Average Minus Exist 175% Regional Average Minus Exist Lower Allocation of Affordable
Draft Allocation Proposal II 150% Toward Regional Average II 175% Toward Regional Average n Tiered Adjustment
Very Very Very Very
Low Low Mod Above Low Low Mod Above Low Low Mod Above Low Low Mod Above
<50% <80% <120% Mod <50% <80% <120% Mod <50% <80% <120% Mod <50% <80% <120% Mod
BENICIA 114 84 97 210 132 91 100 183 139 95 102 170 141 96 103 168
DIXON 157 115 133 288 177 101 121 293 186 95 116 296 188 93 115 298
FAIRFIELD 829 607 704 1,526 839 568 665 1,594 836 550 645 1,634 840 546 646 1,649
RIO VISTA 262 192 223 482 222 177 204 556 200 170 194 594 201 170 196 597
SUISUN CITY 135 99 115 248 158 104 99 235 168 108 91 230 171 108 90 230
VAGAVILLE 624 456 530 1,148 686 451 499 1,121 711 451 484 1,111 720 450 485 1,114
VALLEJO 700 512 594 1,288 669 485 572 1,367 648 474 561 1,411 647 471 563 1,426
UNlNGORPORA TED 21 16 18 39 23 16 17 37 24 16 17 37 24 16 17 37
SOLANO COUNTY 2,841 2,080 2,413 5,229 2,907 1,993 2,277 5,386 2,990 1,998 2,236 5,338 2,932 1,950 2,214 5,519
CLOVERDALE 114 84 97 210 95 78 97 235 85 75 97 248 85 75 97 250
COTATI 85 63 73 157 94 57 68 159 98 54 66 160 99 53 65 161
HEALDSBURG 90 66 76 165 87 61 69 181 84 58 65 189 84 58 65 192
PETALUMA 466 341 395 857 524 364 390 781 549 376 388 745 556 379 390 742
ROHNERT PARK 429 314 364 790 445 294 341 816 449 286 329 832 453 284 329 839
SANTA ROSA 1,509 1,105 1,282 2,778 1,539 1,053 1,183 2,899 1,540 1,031 1,134 2,969 1,548 1,025 1,131 2,996
SEBASTOPOL 38 28 32 70 33 27 29 78 31 27 28 83 31 27 28 83
SONOMA 76 56 64 140 71 54 65 145 68 53 65 148 68 53 66 150
WINDSOR 158 116 134 291 181 123 133 262 191 128 132 248 194 128 133 247
UNINGORPORA TED 299 219 254 549 306 214 253 547 306 213 253 548 308 212 254 551
SONOMA COUNTY 3,263 2,389 2,772 6,006 3,377 2,324 2,627 6,103 3,395 2,319 2,612 6,104 3,242 2,166 2,396 5,799
REGION 52,183 38,197 44,319 96,044 52,183 38,197 44,319 96,044 52,183 38,197 44,319 96,044 52,166 38,151 44,327 96,098
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CITY OF DUBLIN
100 Civic Plaza, Dublin, California 94568
Website: http://www.ci.dublin.ca.us
January 17, 2007
VIA FACSIMILE AND MAIL
(510) 464-7970
Association of Bay Area Governments
Attn: Paul Fassinger, Research Director
P.O. Box 2050
Oakland, CA 94604-2050
Subject: Alternative RHNA Allocation Methodology, Income Allocation
Dear Mr. Fassinger:
Thank you for providing us with an opportunity to review and comment on the draft Regional
Housing Needs Allocation methodology which was authorized for release by the ABAG
Executive Board on November 16, 2006.
The City of Dublin supports the proposed methodology for the allocation of the region's housing
need which is based on 40% household growth, 20% existing employment, 20% employment
growth, 10% household growth near transit and 10% employment growth near transit. Dublin
also supports the methodology for the allocation of housing units by income level which is based
on an "equal share" approach where every jurisdiction would provide 23% very low income
units, 16% low income units, 19% moderate income units and 42% above moderate income
units.
At the January 4, 2007 Housing Methodology Committee (HMC) meting, ABAG presented 3
alternative methodologies for the allocation of housing units by income level in response to
comments received by some local jurisdictions. The alternative methodology recommended by
the HMC uses a 175% multiplier to calculate the allocation of housing units by income level in
an effort to address existing concentrations of lower income housing within the region.
Dublin supports both ABAG and the HMC in their efforts to reduce existing concentrations of
lower income housing by considering alternative methodologies for the allocation of housing
units based on income level however, we feel that the 175% multiplier is too aggressive for the
7-year planning period. The City respectfully requests that ABAG staff and the Executive Board
consider a less aggressive multiplier of 100% or 125%. It is our belief that a less aggressive
multiplier still works towards the goal of reducing existing concentrations of lower income
Area Code (925) . City Manager 833-6650 . City Council 833-6650 . Personnel 833-6605 . Economic Development 833-6650
Finance 833-6640 . Public Works/Engineering 833-6630 . Parks & Community Services 833-6645 . Police 833-6670
Planning/Code Enforcement 833-6610 . Building Inspection 833-6620' Fire Prevention Bureau 833-6606
Attachment 3
4 ta OC> ~\.C>
housing while producing income allocations that are more realistic and achievable for local
jurisdictions.
In addition to concerns over the aggressiveness of the multiplier, the City is also concerned about
the lack of services available to serve lower income households in the Tri-Valley area. Again,
while Dublin supports the efforts to reduce existing concentrations of lower income housing, we
feel that approaching the issue incrementally will not only set more realistic goals for our
community and the entire Bay Area, but will also give the support service providers, needed by
lower income households, the opportunity to redirect resources to outlying areas as the service
population grows.
The City of Dublin has made great strides during the last RHNA cycle to remove constraints and
zone for lower income housing. Over the last RNHA cycle, the City of Dublin has constructed
3,585 housing units offering a range of housing opportunities for all income levels and will
continue to work towards providing housing that is affordable to all income levels. We
recognize the need to address existing concentrations of lower income housing and believe that
taking smaller steps towards achieving this goal will yield better results than setting unrealistic
goals that will not be achievable.
Best Regards,
Janet Lockhart
Mayor
CC: Richard Ambrose, Dublin City Manager
Henry Gardner, ABAG Executive Director
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