Planning & Transportation Commission Staff Report (ID # 11400)

Report Type: Study Session Meeting Date: 7/8/2020

City of Palo Alto Planning & Development Services 250 Hamilton Avenue Palo Alto, CA 94301 (650) 329-2442

Summary Title: Study Session on Plan Bay Area 2050 & the State 6th Cycle Regional Housing Needs Allocation Process

Title: Study Session on Plan Bay Area 2050 and the State 6th Cycle Regional Housing Needs Allocation (RHNA) Process

From: Jonathan Lait

Recommendation Staff recommends the Planning and Transportation Commission (PTC) receive the staff report and hold a study session on the Plan Bay Area 2050 process and the State 6th Cycle Regional Housing Needs Allocation (RHNA) process.

Report Summary This report provides an overview of two planning processes: (1) the regional Plan Bay Area 2050 process and (2) the 6th Cycle Regional Housing Needs Allocation (RHNA) process. Plan Bay Area 2050 and the RHNA process are two distinct, yet related processes. Together, these processes seek to help Palo Alto and the Bay Area plan and prepare for regional changes. The report and study session prepare the Planning and Transportation Commission (PTC) and Palo Alto community for the upcoming release of the Plan Bay Area 2050 Draft Blueprint and the release of the Draft Regional Housing Needs Allocation methodology (Draft RHNA methodology). Likewise, this report and study session allow the PTC and members of the public to provide feedback and ask questions regarding these processes. The report explains both planning efforts and their associated components. Palo Alto staff will endeavor to answer questions and relay additional questions and feedback to the Association of Bay Area Governments (ABAG) and Metropolitan Transportation Commission (MTC). Staff hope through discussion, to gain an understanding of PTC and community perspectives on the information available to date.

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Lastly, the study session and report can help prepare the PTC and Palo Alto community members to participate in the public outreach activities hosted by ABAG/MTC regarding these regional planning processes. This discussion is timely, as the Plan Bay Area 2050 Draft Blueprint and the Draft RHNA methodology to allocate housing needs to local jurisdictions are anticipated to be released during summer 2020 and fall 2020, respectively. The report adds to an informational report transmitted to City Council on June 22, 2020.1

Background A. Plan Bay Area 2050 Plan Bay Area 2050 is the San Francisco Bay Area’s update to its “sustainable communities strategy” and its “regional transportation plan.” Senate Bill 375 (2008)2 requires each metropolitan planning organization in California adopt a “sustainable communities strategy” in conjunction with a regional transportation plan in order to achieve greenhouse gas emission reduction targets established by the California Air Resources Board. ABAG serves as the San Francisco Bay Area’s metropolitan planning organization and MTC serves as the regional transportation agency. 3 Plan Bay Area 2040 serves as the current regional transportation plan and sustainable communities strategy, but the plans require periodic updates. The update, currently underway, is led by ABAG/MTC.4 Plan Bay Area 2050 will outline the strategies for regional growth and investment through the year 2050. Plan Bay Area 2050 focuses on four key issues: the economy, the environment, housing and transportation. It proposes to identify pathways to promote equity for residents and greater regional resiliency. Plan Bay Area 2050 will pinpoint policies and infrastructure investments necessary to advance the identified goals of a more affordable, connected, diverse, healthy and vibrant Bay Area. Plan Bay Area 2050 does not change local policies; cities and counties retain all local land use authority. MTC/ABAG staff requested direction and received approval in February 2020 to explore 25 strategies outlined in the Draft Blueprint and see how close the strategies could bring the region toward meeting critical regional goals for transportation, environment, economy, and housing.5,6 The 25 strategies were organized into nine (9) major objectives with an equity lens:

1 June 22, 2020 Informational Report to City Council: https://www.cityofpaloalto.org/civicax/filebank/documents/77349 2 Institute for Local Government SB 375 Resource Center website: https://www.ca-ilg.org/sb-375-resource-center 3 California Transportation Commission Regional Transportation Plan (RTP) Guidelines: https://catc.ca.gov/programs/transportation-planning 4 Plan Bay Area 2050 website: https://www.planbayarea.org/ 5 February 14, 2020 Joint MTC Planning Committee with the ABAG Administrative Committee Agenda Item 5b Plan Bay Area 2050: Draft Blueprint – Strategies staff memo and staff presentation: https://www.planbayarea.org/sites/default/files/pdfs_referenced/Strategies_for_Plan_Bay_Area_2050_Blueprint-Feb_2020_Memo_and_Attachement_B.pdf;

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1. Maintain and Optimize Existing Infrastructure 2. Create Healthy and Safe Streets 3. Enhance Regional and Local Transit 4. Reduce Risks from Hazards 5. Reduce Our Impact on the Environment 6. Spur Housing Production and Create Inclusive Communities 7. Protect, Preserve, and Produce More Affordable Housing 8. Improve Economic Mobility 9. Shift the Location of Jobs. As part of the Draft Blueprint study and preparation, MTC/ABAG staff are working to outline three fiscally constrained versions of the Blueprint and include different portions of the strategies within them. Any of the three versions below might move forward in Fall 2020: (1) Blueprint Basic: Includes available revenues, but does not include new revenues from future regional measures. (2) Blueprint Plus Crossing: Includes available revenues as well as new revenues for transportation, housing, economic development, and environmental resilience, with a key focus of new transportation monies being for a new trans-bay rail crossing. (3) Blueprint Plus Fix It First: Includes available revenues as well as new revenues for transportation, housing, economic development, and environmental resilience, with a key focus of new transportation monies being for system maintenance. MTC/ABAG staff anticipate releasing the Draft Blueprint for public comment in Summer 2020, currently scheduled for July 2020 and running through August 2020. Workshops and other public engagement opportunities will be provided in a manner consistent with COVID-19 State and County Orders. Significant digital and online components are planned. COVID-19 Pandemic and Long-Range Planning On May 8, 2020, MTC/ABAG staff presented a report to ABAG and MTC on the potential regional impacts from the COVID-19 pandemic and the 2020 recession.7 MTC/ABAG staff noted anticipated impacts would primarily affect the early years of Plan Bay Area 2050 due to a rapid change in baseline economic conditions. Additionally, the ripple effects of some anticipated impacts, such as growth in telecommuting, could persist in the years and decades ahead.

https://www.planbayarea.org/sites/default/files/pdfs_referenced/Strategies_for_Plan_Bay_Area_2050_Blueprint-Feb_2020_MTC_Commission_Presentation.pdf 6 Plan Bay Area 2050: Draft Blueprint Fact Sheet (February 2020): https://www.planbayarea.org/sites/default/files/pdfs_referenced/PBA50_DraftBlueprint_FAQ_Booklet.pdf 7 May 8, 2020 Joint MTC Planning Committee with the ABAG Administrative Committee Meeting Agenda and Video Recording: https://mtc.ca.gov/whats-happening/meetings/meetings-archive/joint-mtc-planning-committee-abag-administrative-43

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MTC/ABAG staff noted that the recently completed Horizon Initiative explored strategies for an uncertain future. MTC/ABAG staff reported four key takeaways that aid the region in planning for the future in light of the COVID-19 pandemic. These can be read in the June 5, 2020 newsletter. 8 Though COVID-19 inserts uncertainty into the future of the Bay Area, the initiative to develop and adopt a sustainable communities strategy continues. B. Regional Housing Needs Allocation (RHNA) Process Since 1969, the State of California has required local governments to use their Housing Elements to plan to adequately meet their community’s housing needs. Housing Elements are required by law to be updated on a set schedule. The RHNA process is part of the state housing element law used to determine the number of new homes, and the affordability levels of those homes, local governments must plan and zone for in their Housing Elements. The State Department of Housing and Community Development (HCD) first determines each region’s housing need by income level for an 8-year projection period; this is called the “regional housing need determination.” This projection period is the “RHNA Cycle.” The State of California is entering its 6th RHNA Cycle, which covers years 2022-2030. HCD released the Bay Area’s regional housing need determination to ABAG on June 9, 2020 (Attachment A). HCD indicated a minimum regional housing needs determination of 441,176 total units among four income categories (Table 1). As of the release of this staff report, the ABAG Executive Board has declined to appeal the regional housing needs determination. Appeals must be received by HCD by July 10, 2020.

Table 1: HCD Regional Housing Need Determination-ABAG: 6-30-2022 to 12-21-2030

Income Category Percent Housing Unit Need

Very-Low* 25.9% 114,442

Low 14.9% 65,892

Moderate 16.5.% 72,712

Above-Moderate 42.6% 188,130

Total 100% 441,176

*Extremely-Low 15.5% Included in Very-Low Category Notes: Income Distribution: Income categories are prescribed by California Health and Safety Code (Section 50093, et seq.). Percents are derived based on Census/ACS reported household income brackets and county median income, then adjusted on the percent of cost-burdened households in the region compared with the percent of cost burdened households nationally.

ABAG is responsible for allocating the regional housing need determination amongst San Francisco Bay Area cities and counties. Toward this goal, ABAG convened the current Housing

8 June 5, 2020 Plan Bay Area 2050 newsletter: https://content.govdelivery.com/accounts/CAMTC/bulletins/28f234a

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Methodology Committee (HMC) in October 2019.9 The Housing Methodology Committee is currently evaluating potential RHNA methodologies, any of which could be used for the allocation of the total number of housing units to each local jurisdiction. The Housing Methodology Committee started discussing methodology options using ten factors. The factors are exploratory and help the Committee, jurisdictions, and the public understand how weighting the different factors could distribute the housing need across the region. The ten factors are described in Attachment B and are listed below:10 1. Access to High Opportunity Areas – Percentage of a jurisdiction’s households living in High

Resource Census tracts or Highest Resource on the California Tax Credit Allocation Committee (TCAC)/Housing and Community Development (HCD)Opportunity Map.

2. Divergence Index – Percentage of a jurisdiction’s households living in census tracts where racial demographics differ greatly from the region and where there is a high proportion of high-income households compared to the region.

3. Job Proximity – Auto - Share of region’s total jobs that can be accessed from a jurisdiction by a 30-minute auto commute.

4. Job Proximity – Transit - Share of region’s total jobs that can be accessed from a jurisdiction by a 45-minute transit commute.

5. Vehicle Miles Travelled (VMT) – Total vehicle miles traveled per worker in a jurisdiction, estimated for 2020 using Plan Bay Area 2040 data.

6. Jobs-Housing Balance – Ratio of jobs in a jurisdiction to housing units in the jurisdiction. 7. Jobs-Housing Fit – Ratio of low-wage jobs (less than $3,333/month) within a jurisdiction to

the number of low-cost rental units (less than $1,500/month) in the jurisdiction. 8. Future Jobs – Jurisdiction’s share of forecasted regional jobs based on Plan Bay Area 2050. 9. Transit Connectivity – Jurisdiction’s percentage of the region’s total acres in Transit Priority

Areas (TPAs). 10. Natural Hazards – Percentage of acres within a jurisdiction’s urbanized area with low risk

from natural hazards according to the Modified MTC/ABAG Multi-Hazard Index. In addition to considering these factors, the Housing Methodology Committee is also considering two potential “income allocation” approaches. Income allocation methodologies are used to allocate the number of units at each level of housing affordability across the jurisdictions. As shown in Table 1, the State recognizes 4 income levels. The Discussion section of this report elaborates on the income allocation approaches. The Housing Methodology Committee will have an opportunity to continue refining the RHNA methodology prior to making a recommendation. No decisions or recommendations have been

9 ABAG Regional Housing Needs Allocation (RHNA) Housing Methodology Committee Roster: https://abag.ca.gov/sites/default/files/hmc_roster_january_2020_0.pdf 10 Explanation of Potential Methodology Factors website: https://rhna-factors.mtcanalytics.org/data/RHNA_tool_factors_overview.pdf

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made to date. Though ABAG staff reported initial consensus on the following topics and anticipates that these topics will guide future Housing Methodology Committee discussions: 1. More housing should go to jurisdictions with more jobs than housing and to communities

exhibiting racial and economic exclusion. 2. The RHNA methodology should focus on:

a. Equity, as represented by High Opportunity Areas b. Relationship between housing and jobs

3. Equity factors need to be part of the total allocation, not just the income allocation part of the RHNA methodology

4. Do not limit 6th Cycle RHNA allocations based upon the past RHNA allocations 5. Housing in high natural hazard areas is a concern, but RHNA may not be the best tool to

address this.11 Staff anticipates that the Housing Methodology Committee and ABAG will propose a draft RHNA methodology in Fall 2020. The RHNA methodology would then be sent to HCD in Winter 2021 for consideration relative to the RHNA statutory objectives (Attachment C).12 Cities and counties can then anticipate the release of the draft RHNA numbers for local jurisdictions in Spring 2021; City and public appeals of the draft RHNA numbers in Summer 2021; and final RHNA numbers in Winter 2021. The Housing Methodology Committee may also utilize Plan Bay Area 2050, although the Committee indicated a preference to see the results of the Draft Blueprint before deciding how or if at all to use the Plan as an input for the methodology. At the forthcoming July 9, 2020 meeting, the Housing Methodology Committee is scheduled to discuss how to achieve consistency between RHNA and Plan Bay Area 2050. This will include any potential use of the Plan Bay Area Draft and Final Blueprint as the baseline input for the RHNA methodology and/or utilization of further modified RHNA methodology factors and weights. C. Plan Bay Area 2050 and Regional Housing Needs Allocation (RHNA) The RHNA process is distinct from Plan Bay Area 2050. While Plan Bay Area focuses on growth and development over a 30-year time frame, RHNA focuses on an 8-year projection period. The two planning processes are occurring simultaneously. Additionally, by statute, Plan Bay Area 2050 must be consistent with RHNA. For example, the allocated housing units in RHNA cannot exceed the projected population growth in Plan Bay Area. Table 2 outlines of key milestones for Plan Bay Area 2050, RHNA, and forthcoming Housing Element update processes.

11 Reported during the June 25, 2020 ABAG General Assembly Special Meeting: http://baha.granicus.com/MediaPlayer.php?view_id=1&clip_id=7275 12 Statutory Objectives for RHNA summary website: https://rhna-factors.mtcanalytics.org/data/RHNA_Statutory_Objectives.pdf

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Table 2: ABAG 2023-2031 RHNA and Plan Bay Area 2050 Key Milestones:13

ABAG 2023 RHNA and Plan Bay Area 2050 Key Milestones Proposed Deadline

Housing Methodology Committee Kick-Off October 2019

Subregions Form February 2020

Plan Bay Area 2050 Regional Growth Forecast April 2020

HCD Regional Housing Need Determination Summer 2020 (June 9, 2020)

Plan Bay Area 2050 Draft Blueprint July 2020

ABAG & Housing Methodology Committee Proposed RHNA Methodology, Draft Subregion Shares

Fall 2020

Plan Bay Area 2050 Final Blueprint December 2020

Final Subregion Shares December 2020

Draft RHNA Methodology to HCD for Review Winter 2021

Final RHNA Methodology, Draft Allocation Spring 2021

RHNA Appeals Summer 2021

Final Plan Bay Area 2050 September 2021

Final RHNA Allocation Winter 2021

Housing Element Due Date January 2023 Dates are tentative and subject to change

There may be some interplay between Plan Bay Area 2050 and RHNA. Some potential overlaps include the following:

• A City’s RHNA cannot be higher than the growth projected in Plan Bay Area 2050, although this cap is unlikely to come into effect because Plan Bay Area is a 30-year horizon document and Housing Elements are eight-year horizon documents.

• The Housing Methodology Committee might decide to use the Plan Bay Area 2050 growth geographies as a basis for its suggested methodology for allocating the HCD RHNA determination. The committee might also use the growth geographies in combination with other factors. The RHNA methodology must be “consistent” with Plan Bay Area.

• Plan Bay Area 2050 will affect the region’s RHNA, however, the extent of the influence will be determined in coming months.

Discussion A. Plan Bay Area 2050 While Plan Bay Area 2050 remains under development, City staff have discussed the plan and identified several specific areas where further refinement, clarity, and/or reconsideration of definitions might lead to improved long-range planning. These areas are elaborated below. Staff plan to draft a letter to the ABAG/MTC requesting several updates and providing feedback

13 April 27, 2020 Revised RHNA Timeline: https://abag.ca.gov/sites/default/files/abag_rhna_timelineapril.pdf

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for the development of the Plan. Staff welcome additional feedback from the PTC, as well as questions, that can be put forward to ABAG/MTC regarding Plan Bay Area 2050. Plan Bay Area 2050 Growth Geographies Plan Bay Area 2050 will utilize “growth geographies.” Growth geographies are the areas within the region that MTC/ABAG staff will overlay the strategies in the Plan. The Plan identifies different types of growth geographies with distinct characteristics. These types include:

• Priority Development Areas (PDAs) – Locally nominated, urban areas within ½ mile of high-quality transit that are planned or will be planned for housing and/or job growth.

• Transit Rich Areas (TRAs) – Areas within ½ mile of a rail station, ferry terminal, or bus stop with peak headways of 15 minutes or less.

• High Resource Areas (HRAs) – Areas that offer opportunities for economic advancement, high educational attainment, and good physical and mental. Area are identified in the TCAC/HCD Opportunity Map.14

• Priority Production Areas (PPAs) – locally nominated industrially zoned areas or areas with a high concentration of production, distribution, and repair activities.

As Attachment D shows, the growth geographies cover portions of Palo Alto. While many of the objectives of Plan Bay Area 2050 align with Palo Alto values and even the Palo Alto Comprehensive Plan, the growth geographies may not always align with practical means to realize these objectives. Consequently, Palo Alto staff plan to raise several topics in our correspondence with ABAG/MTC. First, the map shows a ½ mile radius around transit stations and bus stops as proposed growth geographies. While this is meant to indicate an ability to walk or bike to high-quality transit, the reality is that this might not always be the case. These transit-oriented growth geographies may not accurately represent the accessibility of current transit in Palo Alto. As examples:

• Transit service to some bus stops might have changed due to the COVID-19 pandemic. Palo Alto staff will reconfirm that MTC/ABAG staff are using accurate transit headway information for pre-COVID-19 and post-COVID-19 routes and service levels.

• The train tracks sometimes create a physical barrier preventing easy access to the station; while as the crow flies, Caltrain may be ½ mile away, the true walking, biking, or driving distance could be greater. During discussions Palo Alto staff, MTC/ABAG staff noted that other communities face similar challenges and suggested that Palo Alto plan for infrastructure improvements to make such transit accessible via walking and biking.

Second, staff questions that all areas identified as growth geographies are well suited to accommodate the strategies in Plan Bay Area 2050. Staff was somewhat encouraged to hear from MTC/ABAG staff that the strategies will consider both single-family home areas, as well as historic districts and open space areas. MTC/ABAG staff recognize that redevelopment of such

14 TCAC/HCD 2020 Opportunity Maps (June 2020) and Mapping Methodology: https://belonging.berkeley.edu/tcac-opportunity-map-2020; https://belonging.berkeley.edu/tcac-2020-preview.

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single-family home areas into higher density housing requires site assembly that is unpredictable and unlikely. Likewise, they recognize the limitations of redevelopment in historic districts and open space. Although already provided in 2019, Palo Alto staff will resend and reconfirm that MTC/ABAG staff will incorporate historic districts, open space, single-family residential zoning districts, and other relevant zoning into the growth geographies. Third, Palo Alto staff take issue with the process by which ABAG communicated the connection between growth geographies and PDAs. The City of Palo Alto responded to MTC/ABAG’s first call to update the growth geographies on January 13, 2020.15 In February 2020, ABAG and MTC approved pursuit of a second call for more PDA geographies from Bay Area jurisdictions by May 31, 2020.16 Consequently, staff spoke to MTC/ABAG staff explicitly in several phone conversations and email correspondence to understand the reasons for and the pros/cons for responding to the second call for PDAs. At no time did MTC/ABAG staff express that communities that designate up to 50% of their eligible growth geography area as Priority Development Areas may have the rest of the eligible growth geography removed from consideration in Plan Bay Area 2050. Only after the May 31, 2020 deadline for designating new or expanding existing PDAs had passed and after staff inquired why a neighboring city’s transit station area was not indicated to be a growth geography did MTC/ABAG explicitly explained this connection. Such difficulty in obtaining information impairs a jurisdiction’s ability to make fully informed decisions. Nevertheless, the PTC and City Council did consider designating additional areas of the City as PDAs during the first call for PDAs. The PTC was supportive at its November 13, 2019 meeting for designating both Downtown/University Avenue and a narrow area along either side of El Camino Real.17 The City Council designated Downtown/University Avenue, but declined to designate the El Camino Real corridor as a PDA at its January 13, 2020 meeting. Even had Council designated the narrow area along El Camino Real as a PDA, this would not have brought Palo Alto to designation of 50% of PDA eligible areas. Finally, while the COVID-19 pandemic has not eliminated the housing crisis in the State or region, the impacts of COVID-19 on population growth and job growth remain to be seen. While working to address the housing crisis is absolutely necessary, conducting long-range planning processes for a thirty-year cycle may be unwise given the unknown impact of COVID-

15 January 13, 2020 Council Staff Report on PDAs and PCAs: https://www.cityofpaloalto.org/civicax/filebank/documents/74728; Palo Alto City Council adopted two new Priority Conservation Areas (PCAs) – the Baylands PCA and the Foothills PCA – and the new Downtown/University Avenue Priority Development Area (PDA). Palo Alto previously adopted the California Avenue PDA. Both PDAs are viewable on the growth geographies interactive website: https://mtc.maps.arcgis.com/apps/webappviewer/index.html?id=9cf8663fabf4478788312de1bcc2977c. Other cities and counties responded to MTC/ABAG’s second call for local adoption of PDAs, the deadline for which was May 31, 2020. Palo Alto did not participate in this second call, as the City had already participated in and responded to the first call as described above. 16 ABAG Final Call for PDAs website: https://abag.ca.gov/news/final-call-pdas-open-through-may-31-2020 17 November 13, 2019 Planning & Transportation Commission Staff Report: https://www.cityofpaloalto.org/civicax/filebank/documents/74018

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19 on critical variables. A temporary extension of the timeline may provide sufficient time to gather data, for circumstances to change so that the Plan is more useful to the region and to jurisdictions. Plan Bay Area 2050 Draft and Final Blueprints Once the Draft and Final Blueprint for Plan Bay Area 2050 are released, staff will review the documents and provide both PTC and City Council with the analysis. Part of the analysis will be confirming the accuracy of the growth geographies for Palo Alto, as discussed above. Other aspects of the analysis will involve understanding how the Draft and Final Blueprint support, coincide, or conflict with the Palo Alto Comprehensive Plan, especially regarding housing density, jobs concentrations, community character, and transit investments. It’s important to note that Plan Bay Area 2050 does not impact a jurisdiction’s local land use authority. Palo Alto still retains authority to adopt local land use regulations, including zoning. B. Regional Housing Needs Allocation (RHNA) ABAG, through the work of the Housing Methodology Committee, will allocate the regional housing need determination among the region’s jurisdictions. To assist the members of the Housing Methodology Committee in its work, as well as board members, planners, and the general public, ABAG created a tool that allows users to visualize how the regional housing need determination would look if mapped over the Bay Area. ABAG then updated the visualization tool to incorporate the HCD regional housing need determination received on June 9, 2020. Palo Alto has a hypothetical baseline allocation in the visualization tool of 4,475 housing units out of the RHND of 441,176 new housing units for the region (Table 1). This “hypothetical baseline allocation” represents what Palo Alto’s RHNA could be if the allocation was based entirely on Palo Alto’s existing share of the region’s households in 2019 and if each jurisdiction in the region experienced the same hypothetical 16% growth rate. Factors and Weighting The Housing Methodology Committee developed their top three RHNA methodology options during Winter 2019 into Spring 2020.18 Figure 1 shows a summary of the factors and weighting in these top three options. For definitions of the phrases in the Figure, please see attachments B and E. Figure 2 shows how the top three options could translate into hypothetical allocations by Bay Area County in comparison with the final RHNA methodology for the previous 5th Cycle and with the household growth predicted in Plan Bay Area 2040. Table 3 shows the top three RHNA methodology options relative to the hypothetical baseline allocation for Palo Alto. The

18 April 27, 2020 Housing Methodology Committee discussion summary, including a summary of the Committee-developed initial RHNA methodology options: https://abag.ca.gov/sites/default/files/hmc_rhna_methodology_update_april2020.pdf

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equity factor “Access to High Opportunity Areas” plays a large role in each of the options, as shown in Figure 1.

Figure 1: Summary of Factors and Weights for Top Three Methodology Options (March 2020 Options)

Given the interplay between the factors, and the ways placing homes near jobs and transit in a high opportunity community advances regional objectives, Palo Alto could end up with RHNA higher than the “hypothetical baseline allocation.”

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Figure 2: Summary of Allocations by Bay Area County for Top Three Methodology Options

Table 3: Influence of Top Three RHNA Methodology Options on Hypothetical Palo Alto RHNA Housing Units

Hypothetical Growth Rate (% Increase over Housing

Units in 2019) Hypothetical

Housing Units

+/- Housing Units from Hypothetical

Baseline

Palo Alto Hypothetical Baseline Allocation 16% 4,475 -

Top Three RHNA Methodology Options (Using HMC Identified Factors & Weights):

Housing/Jobs Crescent 21% 5,819 +1,344 units

Code Red to Address Housing Need 22% 6,087 +1612 units

Balanced Equity-Jobs-Transportation 24% 6,532 +2,057 units

City staff explored further how the 10 factors identified by the Housing Methodology Committee independently influence hypothetical growth rates in Palo Alto. Table 4 shows the influence of each factor compared to the hypothetical baseline allocation for Palo Alto. Staff ran the visualization tool separately for each factor with weighting of only that factor at 100%. Half of the factors reduced the growth rate below the hypothetical 16% growth rate, while the other half resulted in an increase. The equity factor “Access to High Opportunity Areas” has the highest influence for Palo Alto. Factors “Future Jobs,” “Job Proximity to Transit,” “Transit Connectivity” result in lower growth rates and a lower number of housing units when compared with the hypothetical Palo Alto baseline allocation. There appeared to be less interest in utilizing the “Natural Hazards” factor during discussion at the June 19, 2020 meeting.

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Table 4: RHNA Methodology Factor Influence on Hypothetical Palo Alto RHNA Housing Units

Hypothetical Growth Rate (% Increase over Housing

Units in 2019) Hypothetical

Housing Units +/- Housing Units from Hypothetical Baseline

Palo Alto Hypothetical Baseline Allocation 16% 4,475 -

RHNA Methodology Factors:

Access to High Opportunity Areas 26% 7,226 +2,751 units

Jobs-Housing Balance 24% 6,678 +2,203 units

Job Proximity - Auto 23% 6,482 +2,007 units

Vehicle Miles Travelled 19% 5,119 +644 units

Jobs-Housing Fit 19% 5,118 +643 units

Natural Hazards 15% 4,161 -314 units

Divergence Index 15% 4,064 -411 units

Future Jobs 13% 3,691 -784 units

Job Proximity - Transit 13% 3,459 -1,016 units

Transit Connectivity 11% 3,113 -1,362 units

Income Allocation and Affordability Levels

Four income groups are used to establish affordability levels for housing distributed within the RHNA methodology. The RHNA must be allocated in accordance with statutory requirements, including (1) increase affordability in an equitable manner; (2) improve the balance between low-wage jobs and housing affordable to low-wage workers (jobs-housing fit); (3) allocate less RHNA in an income category when a jurisdiction already has a disproportionately high share of households in that income category; (4) affirmatively further fair housing.

Income Groups Household Income in Reference to Area Median Income

Very Low Income Households earning less than 50 percent of AMI

Low Income Households earning 50 - 80 percent of AMI

Moderate Income Households earning 80 - 120 percent of AMI

Above Moderate Income Households earning 120 percent or more of AMI

The Housing Methodology Committee is considering 2 approaches to allocating the income groups across the region: Income Shift or Bottom Up. The income shift approach applies a selected income allocation to the total housing allocation. The bottom-up approach uses the income allocation to build the total allocation.

Figure 3 illustrates an income shift approach. It provides a hypothetical example of how a jurisdiction’s total housing unit allocation could be distributed between the affordability levels using different income shift percentages. At a 0% income shift, a “high opportunity area”

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jurisdiction, such as Palo Alto, could receive an income allocation that reflects the jurisdiction’s current income distribution. The RHNA assigned to that jurisdiction would then require most units be above moderate income. A 100% income shift means the jurisdiction’s income allocation reflects the average income allocation across the entire region. In discussions, Housing Methodology Committee members have expressed support for a 125% or 150% income shift.

The bottom up approach breaks out the region’s allocation into the affordable housing units (very-low income to moderate income) and the market-rate housing units (above moderate income). It then further allocates these units based on either two or three factors. The two-factor bottom-up approach allocates half of the affordable units to high opportunity areas and half based on jobs-housing fit. The market-rate units are allocated half to jobs-proximity auto and the remaining half based on jobs-housing balance. Figure 4 shows the allocation for a three-factor bottom up approach. Using this approach, the income allocation is based on characteristics of areas across the region (e.g.: high opportunity, jobs-housing balance) instead of simply allocating the income groups across jurisdictions.

Figure 3: Illustration of Income Shift Method for Income Allocation RHNA Methodology

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At their May and June 2020 meetings, the Housing Methodology Committee indicated that they would like this 6th Cycle to take a more tailored bottom-up approach to the income allocation portion of the RHNA methodology, as utilization of a simplified percentage income shift methodology shown in Figure 3 could result in localized housing displacement impacts and other considerations for some cities. Figure 4 shows an example of the bottom-up approach.

Past Progress and Upcoming Work

For context, Palo Alto received 1,988 housing units during the 5th Cycle RHNA process. At the end of 2019, Palo Alto had issued building permits for 554 housing units. Palo Alto is not on pace to meet its RHNA for the 5th Cycle. Many jurisdictions face the same challenge (Table 5).

Table 5: Bay Area Regional Housing Needs Allocation Progress: 1999-201819

RHNA Permits Percent of RHNA Permitted

Cycle Total Need

Permits Issued

All Very Low Income

Low Income

Moderate Income

Above Moderate

Income

1999-2006 230,743 213,024 92% 44% 79% 38% 153%

2007-2014 214,500 123,098 57% 29% 26% 28% 99%

2015-2023* 187,994 121,973 65% 15% 15% 25% 126%

2023-2031** 441,176 TBD TBD TBD TBD TBD TBD *Only includes building permits issued in 1025-2018**Recently issued by HCD

Staff intends to correspond with the Housing Methodology Committee. Staff will share

19 Reported during the June 25, 2020 ABAG General Assembly Special Meeting: http://baha.granicus.com/MediaPlayer.php?view_id=1&clip_id=7275

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Figure 4: Bottom-Up Three-Factor Concept

City of Palo Alto Planning & Development Services Department Page 16

feedback on the methodology, including questions and suggestions for improving the methodology. Staff welcome the PTC’s feedback and questions, so those can be included in the correspondence. Should PTC and members of the public like additional background information, recent Housing Methodology Committee staff reports are included as Attachment E. These staff reports include detailed information on potential income allocationmethodologies.

Ultimately, once finalized, the RHNA for Palo Alto will form the basis for the update of the City’s Housing Element. The Housing Element, which must be adopted by January 2023 and certified by HCD, must include zoning that accommodates the RHNA housing units within the City of Palo Alto.

Environmental Review Discussion of Plan Bay Area 2050 or RHNA in a study session does not constitute a project under the California Environmental Quality Act (CEQA). There will be no motion or recommendation by the PTC resulting from this study session. Plan Bay Area 2050 does require environmental review under CEQA and an Environmental Impact Report (EIR) will be prepared by the lead agencies.

Public Notification, Outreach & Comments Notification of this study session was sent to the Palo Alto Daily Post on June 26, 2020. City Council has received some public comments on Plan Bay Area 2050 and RHNA. Topics mentioned in the comments include requests for public study session(s) in Palo Alto specifically and for Palo Alto to weigh in early on the RHNA methodology being discussed by the Housing Methodology Committee. Comments also advocated for MTC/ABAG to address the jobs-housing imbalances in the region, ensure that the Plan Bay Area 2050 modeling is realistic, make adjustments to the pace of the Plan Bay Area 2050 and RHNA processes due to COVID-19 circumstances, and MTC/ABAG to hold effective public forums.

Next Steps City staff will continue to follow the preparation of Plan Bay Area 2050, the 6th Cycle RHNA process, and keep City Council, PTC, and the Palo Alto community abreast of these regional planning initiatives. Staff anticipates scheduling a discussion before City Council in August.

Alternative Actions None.

Report Author & Contact Information PTC20 Liaison & Contact Information Rebecca Atkinson, Planner Rachael Tanner, Assistant Director

(650) 329-2596 (650) 329-2441rebecca.atkinson@cityofpaloalto.org rachael.tanner@cityofpaloalto.org

20 Emails may be sent directly to the PTC using the following address: planning.commission@cityofpaloalto.org

3

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City of Palo Alto Planning & Development Services Department Page 17

Attachments:

• Attachment A: HCD Regional Housing Needs Determination Letter (June 9, 2020) (PDF)

• Attachment B: Potential RHNA Factors Overview (PDF)

• Attachment C: RHNA Statutory Objectives (PDF)

• Attachment D: Draft Growth Geographies (February 2020) (PDF)

• Attachment E: Housing Methodology Committee Staff Reports (PDF)

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ATTACHMENT 1

HCD REGIONAL HOUSING NEED DETERMINATION ABAG: June 30, 2022 through December 31, 2030

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ATTACHMENT 2

HCD REGIONAL HOUSING NEED DETERMINATION: ABAG June 30, 2021 through December 31, 2030

Methodology ABAG: PROJECTION PERIOD (8.5 years)

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Fair Housing and Equity Factors 1. Access to High Opportunity Areas Impact More housing units allocated to jurisdictions with the most access to

opportunity. Definition The percentage of a jurisdiction’s households living in census tracts labelled High

Resource or Highest Resource based on opportunity index scores. Data source HCD/TCAC 2020 Opportunity Maps1 2. Divergence Index Impact More housing allocated to jurisdictions that are more segregated compared to

the rest of the region. Definition The divergence index score for a jurisdiction, which is a calculation of how

different a jurisdiction’s demographics are from the region. Data source U.S. Census Bureau, American Community Survey 2014-2018, Tables B03002 and

B19013 Jobs and Jobs-Housing Fit Factors

3a. Job Proximity - Auto Impact More housing allocated to jurisdictions with easy access to region’s job centers. Definition Share of region’s total jobs that can be accessed from a jurisdiction by a 30-

minute auto commute. Data source MTC, Travel Model One 3b. Job Proximity - Transit Impact More housing allocated to jurisdictions with easy access to region’s job centers. Definition Share of region’s total jobs that can be accessed from a jurisdiction by a 45-

minute transit commute. Data source MTC, Travel Model One 4. Vehicle Miles Travelled (VMT) Impact More housing allocated to jurisdictions with a high number of vehicle miles

travelled per worker. Definition Total modeled vehicle miles traveled per worker in 2020 from Plan Bay Area 2040.2 Data source MTC 5. Jobs-Housing Balance Impact More housing allocated to jurisdictions with a high number of jobs relative to the

amount of housing. Definition Ratio of jobs within a jurisdiction to housing units in the jurisdiction. Data source MTC; U.S. Census Bureau, ACS 2014-2018; Census LEHD LODES for 2015-2017

1 For more information on the Opportunity Map, see pages 10-13 of this document from the March 2020 HMC meeting’s agenda packet. 2 Data from Plan Bay Area 2050 would be used once it is available.

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HMC Meeting #5 | March 12, 2020 | Page 2

6. Jobs-Housing Fit Impact More housing allocated to jurisdictions with a high number of low-wage jobs

relative to the number of low-cost rental units. Definition Ratio of low-wage jobs (less than $3,333/month) within a jurisdiction to the

number of low-cost rental units (less than $1,500/month) in the jurisdiction. Data source MTC; U.S. Census Bureau, ACS 2014-2018; Census LEHD LODES for 2015-2017 7. Future Jobs3 Impact More housing allocated to jurisdictions with a higher share of projected jobs. Definition Jurisdiction’s share of the region’s forecasted jobs based on Plan Bay Area 2050. Data source MTC

Transportation Factor 8. Transit Connectivity Impact More housing allocated to jurisdictions with existing and planned transit

infrastructure. Definition Jurisdiction’s percentage of the region’s total acres within Transit Priority Areas. Data source MTC

Other Factors of Importance 9. Natural Hazards Impact More housing is allocated to areas with low natural hazard risk. Definition Percentage of acres within a jurisdiction’s urbanized area in locations with low risk

from natural hazards according to the Modified MTC/ABAG Multi-Hazard Index.4 Data source MTC; USGS liquefaction susceptibility; CAL FIRE FRAP LRA/SRA data; FEMA (flood

zones); Alquist-Priolo Fault Zones (California Geological Survey)

3 Although ABAG would likely use data for year 2031 once Plan Bay Area 2050 data is available, this factor is currently based on data for year 2050 from the Clean and Green future due to greater reliability for using year 2050 for this data source. 4 For more information on the Modified MTC/ABAG Multi-Hazard Index, see pages 14-15 of this document from the March 2020 HMC meeting’s agenda packet.

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Statutory Objectives for RHNA California Government Code Section 65584(d)

1) Increasing the housing supply and the mix of housing types, tenure, and

affordability in all cities and counties within the region in an equitable

manner, which shall result in each jurisdiction receiving an allocation of

units for low- and very low-income households.

2) Promoting infill development and socioeconomic equity, the protection of

environmental and agricultural resources, the encouragement of efficient

development patterns, and the achievement of the region’s greenhouse

gas reductions targets provided by the State Air Resources Board pursuant

to Section 65080.

3) Promoting an improved intraregional relationship between jobs and

housing, including an improved balance between the number of low-wage

jobs and the number of housing units affordable to low-wage workers in

each jurisdiction.

4) Allocating a lower proportion of housing need to an income category when

a jurisdiction already has a disproportionately high share of households in

that income category, as compared to the countywide distribution of

households in that category from the most recent American Community

Survey.

5) Affirmatively furthering fair housing, which means taking meaningful

actions, in addition to combating discrimination, that overcome patterns of

segregation and foster inclusive communities free from barriers that restrict

access to opportunity based on protected characteristics. Specifically,

affirmatively furthering fair housing means taking meaningful actions that,

taken together, address significant disparities in housing needs and in

access to opportunity, replacing segregated living patterns with truly

integrated and balanced living patterns, transforming racially and ethnically

concentrated areas of poverty into areas of opportunity, and fostering and

maintaining compliance with civil rights and fair housing laws.

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6) Pending state legislation: Reducing development pressure within very high

fire risk areas.

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Attachment D: Plan Bay Area 2050: DRAFT Blueprint Growth Geographies for Study

(Adopted by ABAG Executive Board and MTC Commission, February 2020, for Study in Draft Plan Bay Area 2050) (https://mtc.maps.arcgis.com/apps/webappviewer/index.html?id=9cf8663fabf4478788312de1bcc2977c)

(enlarged map on next page)

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(enlarged legend on previous page)

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HMC Interim Meeting Materials | April 2020 | Page 1

TO: Housing Methodology Committee DATE: April 27, 2020 FR: Deputy Executive Director, Policy RE: Initial RHNA Methodology Options

Overview The Housing Methodology Committee’s (HMC) objective is to recommend an allocation methodology for dividing up the Bay Area’s Regional Housing Need Determination among the region’s jurisdictions. This Regional Housing Needs Allocation (RHNA) methodology is a formula that calculates the number of housing units assigned to each city and county, and the formula also distributes each jurisdiction’s housing unit allocation among four affordability levels. At the last several meetings, the HMC has identified and prioritized potential factors to include in the methodology for determining a jurisdiction’s total housing need. The HMC will have an opportunity to consider factors for the income allocation at its meeting in May. Initial Methodology Options At the January HMC meeting, ABAG staff presented maps showing the regional distribution among jurisdictions for potential factor topics (e.g., jobs-housing fit, transit proximity, etc.).1 For the March HMC meeting, staff translated the priority factor topics identified at the January meeting into allocation factors and made adjustments to the factors based on HMC feedback. The revised set of factors was incorporated into an online visualization tool2 that allowed HMC members, working in small groups, to continue to prioritize factors and to explore sample RHNA methodologies by applying a weight to each factor used. Each group used the tool to create several methodology options, chose a name for the methodology it favored, and presented it to the rest of the committee. HMC members and audience members then voted for the methodologies they liked best. Figure 1 shows the results of the voting. Figure 1: Results of Dot Voting for Methodology Options3

  

1 The maps from the January HMC meeting can be viewed at https://abag.ca.gov/rhna-maps 2 The visualization tool is available at: https://rhna-factors.mtcanalytics.org/ 3 Maps for each group’s methodology are available at: https://abag.ca.gov/our-work/housing/rhna-regional-housing-needs-allocation/housing-methodology-committee

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Figure 2 compares the factors and weights for the three methodology options that received the most votes.4 The choices made by the HMC members in developing these options demonstrate that equity is a top priority for most participants. The methodology options also emphasize the importance of linking housing and jobs. Some of the methodologies recognized the importance of encouraging growth near transit and considering natural hazards, but these received less emphasis than equity and jobs-housing relationships. Figure 2: Summary of Factors and Weights for Top Three Methodology Options

 Housing/Jobs Crescent Code Red to Address

Housing Need Balanced Equity-Jobs-

Transportation Figure 3 compares the share of units allocated to the jurisdictions in each county for the three methodology options that received the most votes. The chart indicates that there were minimal differences in how units were distributed at the county level among the three methodology options. Figure 3 also shows each county’s share of housing unit growth from ABAG’s 5th Cycle RHNA methodology and Plan Bay Area 2040 as points of reference. In general, the three methodology options would direct more units to jurisdictions in the North Bay and San Mateo

 4 For more information on the factors included in the methodology visualization tool, see pages 5-9 of this memo from the March 2020 HMC agenda packet.

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County and fewer units to jurisdictions in Alameda and Santa Clara counties relative to ABAG’s 5th Cycle RHNA and Plan Bay Area 2040. Figure 3: Allocations by County for Top Three Methodology Options

Output by Jurisdiction Geography ABAG staff also analyzed the output of the top three methodologies by jurisdiction geography using a framework developed as part of prior Plan Bay Area processes, simply to understand the general distribution across different typologies of places. This framework assigns each jurisdiction to one of four geographies that reflect its role and spatial location within the region. The four categories are: Big Three; Bayside; Inland, Delta and Coastal; and Unincorporated.5 Figure 4 shows the share of units that would be allocated to each of these four areas from the

 5 The following is a list of cities and towns by geographical area: Big Three: San Jose, San Francisco, Oakland; Bayside: Alameda, Albany, Atherton, Belmont, Belvedere, Berkeley, Brisbane, Burlingame, Campbell, Colma, Corte Madera, Cupertino, Daly City, East Palo Alto, El Cerrito, Emeryville, Fairfax, Foster City, Fremont, Hayward, Hercules, Hillsborough, Larkspur, Los Altos, Los Altos Hills, Los Gatos, Menlo Park, Mill Valley, Millbrae, Milpitas, Monte Sereno, Mountain View, Newark, Pacifica, Palo Alto, Piedmont, Pinole, Portola Valley, Redwood City, Richmond, Ross, San Anselmo, San Bruno, San Carlos, San Leandro, San Mateo, San Pablo, San Rafael, Santa Clara, Saratoga, Sausalito, South San Francisco, Sunnyvale, Tiburon, Union City, Vallejo, Woodside; Inland, Delta and Coastal: American Canyon, Antioch, Benicia, Brentwood, Calistoga, Clayton, Cloverdale, Concord, Cotati, Danville, Dixon, Dublin, Fairfield, Gilroy, Half Moon Bay, Healdsburg, Lafayette, Livermore, Martinez, Moraga, Morgan Hill, Napa, Novato, Oakley, Orinda, Petaluma, Pittsburg, Pleasant Hill, Pleasanton, Rio Vista, Rohnert Park, San Ramon, Santa Rosa, Sebastopol, Sonoma, St. Helena, Suisun City, Vacaville, Walnut Creek, Windsor, Yountville; Unincorporated: all unincorporated areas

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three methodology options. Figure 4 also shows each county’s share of household growth from ABAG’s 5th Cycle RHNA methodology and Plan Bay Area 2040 as points of reference. Compared to ABAG’s 5th Cycle RHNA methodology and Plan Bay Area 2040, the three methodology options would direct more housing growth to jurisdictions in the Bayside and Unincorporated areas, less household growth to the Big Three cities, and similar amounts of housing growth to jurisdictions in the Inland, Delta, and Coastal area. Figure 4: Allocations by Jurisdiction Type for Top Three Methodology Options

Next Steps Now that the HMC has identified several options for determining a jurisdiction’s total allocation, for the May HMC meeting, ABAG staff will introduce ideas for how to determine the income distribution of those units. Staff will also revisit the discussion around potential criteria for evaluating the methodology outputs to ensure that the RHNA meets statutory objectives. An understanding of these topics among HMC members will set the stage for a discussion in Summer 2020 about how to achieve consistency between RHNA and Plan Bay Area 2050, including potential use of the Plan’s Blueprint as the baseline input for the RHNA methodology and/or modification of the RHNA methodology factors and weights.

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HMC Meeting #6 | May 14, 2020 | Page 1

Item 5, Attachment A

TO: Housing Methodology Committee DATE: May 14, 2020 FR: Deputy Executive Director, Policy RE: Options for the Income Distribution Component of the RHNA Methodology

Overview The Association of Bay Area Governments (ABAG), with guidance from the Housing Methodology Committee (HMC), must allocate the Regional Housing Needs Determination (RHND) to the cities and counties in the nine-county Bay Area. The RHND is the total number of housing units assigned to a region by the California Department of Housing and Community Development (HCD). HCD also divides a region’s RHND across four levels of housing affordability that correspond to different income categories. Ultimately, the HMC will need to recommend a Regional Housing Needs Allocation (RHNA) methodology that both assigns a total number of housing units to each Bay Area jurisdiction and distributes each jurisdiction’s allocation among the four affordability levels. Jurisdictions in turn must update their housing elements to show how they will accommodate their share of housing needs for each income group. RHNA Income Categories A healthy and inclusive housing market is characterized by housing options for a range of workers, family types, and incomes. Both the number of units available is important and the cost at which these units are provided are critically important. For the Bay Area, one of the most expensive housing markets in the country, the urgency of providing a range of housing opportunities is even more pronounced. Pursuant to state housing element law (Government Code section 65584, et seq.), HCD is charged with determining the regional housing needs for the Bay Area for the period from 2023 to 2031. HCD divides the region’s housing need among four separate income groups:

• Very Low Income: households earning less than 50 percent of Area Median Income (AMI) • Low Income: households earning 50 - 80 percent of AMI • Moderate Income: households earning 80 - 120 percent of AMI • Above Moderate Income: households earning 120 percent or more of AMI

ABAG has not yet received the RHND from HCD; this is anticipated to occur in the next one to two months. In lieu of the RHND, Table 1 shows the distribution of Bay Area households by income from the most recent Census Bureau data for reference purposes. Table 1 Bay Area Households, By Major Income Group Income Group Income Limit Households Percent Very Low Income 0 - $47,350 678,673 25.3% Low Income $47,351 - $75,760 411,670 15.3% Moderate Income $75,760 - $113,640 459,169 17.1% Above Moderate Income $113,640 + 1,136,896 42.3%

Source: U.S. Census Bureau, American Community Survey PUMS data, 2018 5-year release

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Considerations for the Income Allocation The Bay Area is a large and complex region: close to 8 million people reside in 109 jurisdictions across a 7,000 square mile geography with a number of distinctive subregions and economies. The region contains a range of community types and economic situations, with some communities encompassing a range of income groups, while others skew to either the low-income or high-income side of the spectrum. Housing Element Law includes the objective that RHNA “[a]llocat[e] 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,”1 meaning the RHNA methodology will in part be assessed by HCD in terms of how the allocation works to counter-balance existing concentrations of wealth or poverty. As noted in previous HMC meetings, meeting this objective will require that the RHNA methodology direct market-rate units to jurisdictions that currently have a higher concentration of lower-income households, which could exacerbate the potential for displacement of existing residents. The RHNA methodology must also improve coordination between the locations of low-wage jobs and housing affordable to low-wage workers (jobs-housing fit) and affirmatively further fair housing, which will require allocating more lower income units to communities that historically have not provided affordable housing. Examples of Income Allocation Methodologies from Other Regions At the December 2019 HMC meeting, ABAG staff presented a summary of the methodologies created by other regions for the current RHNA cycle, as well as ABAG’s methodology for the previous RHNA cycle (2015-2023).2 Although these RHNA methodologies differ substantially, they have primarily used one of two approaches for the income allocation: an income shift or an income shift modified by equity-focused factors. These two approaches are described below. Income Shift – used by the San Diego region3 this cycle and by ABAG last cycle4 In this approach, a jurisdiction’s distribution of households by income is compared to the distribution for the region or county the jurisdiction is in. The jurisdiction’s allocation of units by income category is then adjusted so the jurisdiction will move toward the region’s income distribution over time. Thus, jurisdictions that have a higher percentage of existing households in a given income category compared to the region receive a smaller share of units in that income category. In some cases, the income shift multiplier applied to a jurisdiction varies based on how much the jurisdiction’s household income distribution differs from the region or county. In the simplest example, ABAG’s 2015-2023 RHNA methodology moved each jurisdiction’s income distribution 175 percent toward the region’s income distribution. A 100 percent shift means a jurisdiction’s allocation of units by income category mirrors the region’s existing income distribution. The 175 percent shift would close the gap between a jurisdiction’s income distribution and the region’s distribution more quickly. The first step in this calculation is to 1 See California Government Code Section 65584(d). 2 See this document from the December 2019 HMC meeting agenda packet. 3 See page 6 of the San Diego Association of Governments RHNA methodology document. 4 See pages 11-12 of ABAG’s Final Regional Housing Need Plan for the San Francisco Bay Area: 2015–2023.

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compare a jurisdiction’s share of households in each income category to the region’s share of households in that income category. The difference between the region and the jurisdiction is then multiplied by 175 percent to create an adjustment factor. The adjustment factor is added to the jurisdiction's existing proportion of households in the income category to determine the total share of the jurisdiction's housing unit allocation for that income category. Figure 1 shows a visual representation of the income shift from ABAG’s last RHNA methodology. This process is repeated for each of the four income categories. The result is that a jurisdiction with a higher proportion of households in an income category compared to the region receives a smaller allocation of housing units in that same category, and vice versa. Figure 1 Income Shift from ABAG 5th Cycle RHNA Methodology Income Shift Plus Equity-Focused Factors – used by the Los Angeles and Sacramento regions This approach uses an income shift approach conceptually similar to the one described above paired with other factors related to affirmatively furthering fair housing and improving jobs-housing fit. After the jurisdiction is compared to the region or county, the factors included in the methodology are used to increase or decrease the amount that the jurisdiction’s income distribution is adjusted. The factors used by the Sacramento region’s income methodology are the share of housing units in high opportunity areas, as defined by the State’s Opportunity Map, and a jurisdiction’s jobs-housing fit ratio.5 Jurisdictions receive more very low- and low-income units if they have a higher share of housing units in high opportunity areas or a higher ratio of low-wage workers to housing units affordable to those workers. In the Los Angeles region’s income methodology,6 a larger income shift multiplier is applied to a jurisdiction where more than 70 percent of the population lives in “high segregation and poverty”/”low resource” or “highest resource” census tracts as defined by the State’s Opportunity Map.7 Notably, the potential methodologies developed by the HMC in March 2020 include equity-focused factors related to high opportunity areas and jobs-housing fit in the determination of a jurisdiction’s total allocation, while other regions use these equity-focused factors solely in the income allocation.

5 See pages 29-34 of the Sacramento Area Council of Governments RHNA methodology document. 6 See pages 13-17 of the Southern California Association of Governments RHNA methodology document. 7 For more information on the Opportunity Map, see pages 10-13 of this document from the March 2020 HMC meeting’s agenda packet.

Existing Regional

Proportion in Income Category

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Shift Multiplier

Adjustment Factor

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Share of Jurisdiction

RHNA in Income

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Potential Approaches to the Income Allocation ABAG staff has developed three relatively distinct methodological approaches to the income distribution component of RHNA, described in more detail below. The first two—the income shift and factor-based approach—are aligned with the methodologies used by other regions. Both approaches are proposed to be applied as a second step in the allocation process, after the use of a factor-based methodology to determine a jurisdiction’s total allocation. The third approach would take an entirely different tack and use different weights and/or factors for different income categories, with the sum of the results for the four income categories determining a jurisdiction’s total allocation. Approaches A and B: Income Methodologies that are Applied to the Total Allocation At the March HMC meeting, committee members used an online visualization tool to experiment with different factors-based methodologies for allocating a total number of housing units to jurisdictions based on a hypothetical RHND. Figure 2 shows the three methodology options developed during the small group discussions that received the most votes from HMC members and members of the audience.8 As noted above, these potential methodologies developed by the HMC include equity-focused factors in the determination of a jurisdiction’s total allocation, while other regions’ methodologies for the current RHNA cycle do not use equity-focused factors for this purpose. The other regions relied on either the long-range regional plan or factors related to jobs and transit to determine a jurisdiction’s total allocation, while using equity-focused factors related to affirmatively furthering fair housing and jobs-housing fit solely in the income allocation. Figure 2 Comparison of Top Three Methodology Options from March 2020 HMC Meeting

Housing/Jobs Crescent Code Red to Address

Housing Need Balanced Equity-Jobs-

Transportation

8 See the summary of the initial methodology options from the March HMC meeting.

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Approach A: Income Shift Applied to Total Allocation This approach resembles the income allocation method from ABAG’s 2015-2023 RHNA, using an income shift approach where the local and regional income distributions are compared. For this approach, the income allocation shifts the local distribution closer to or beyond the regional distribution, depending on the income shift multiplier. In the last cycle, the income shift multiplier used by ABAG was 175 percent (see Figure 1 for more information on how the income shift multiplier impacts the income allocation). In theory, setting the income shift multiplier above 100 percent could close the gap between a jurisdiction’s income distribution and the region’s distribution in a shorter period of time, but this more aggressive shift could also increase the potential for displacement by directing more market-rate units to jurisdictions with higher proportions of existing lower-income households. To illustrate the shift approach on cities with different income profiles, Figure 3 shows the effect of using an income shift approach with a 175 percent multiplier. City A is a relatively high-income city with good access to jobs. City B has a lower income profile, with less job access. City C is somewhere in between, falling close to the regional income distribution. We will use these same sample cities to illustrate how they fare with each income allocation approach. Figure 3 Hypothetical Example of Income Shift Approach, Using 175 Percent Multiplier

This approach directly addresses the state objective of “[a]llocating 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.”9 A smaller shift than 175 percent is also possible and may be appropriate given HMC members’ previously stated concerns about assigning large numbers of above moderate-income housing in lower income jurisdictions at risk of gentrification. 9 See California Government Code Section 65584(d)(4).

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Approach B: Using Factors Applied to Total Allocation Similar to Approach A, this option is also applied after determining a jurisdiction’s total allocation using a factor-based methodology. In this income allocation approach, factors are used to assign units for the lower two income groups (very low- and low-income units). As an initial example, staff used the Jobs-Housing Fit and High Opportunity Areas factors. The Jobs-Housing Fit factor specifically relates to the relationship between lower-wage workers and housing units affordable to those workers and the High Opportunity Areas factor affirmatively furthers fair housing by assigning more lower-income units to high opportunity areas, both objectives call for in Housing Element law.10 As noted earlier, other regions often paired the factor-based approach with the income shift. However, these are approaches are not dependent on one another, and ABAG is presenting them independently to make them easier to understand. In this approach, each jurisdiction starts with the same income distribution, as determined by HCD for the RHND. A jurisdiction’s share of units in the lower income categories is then adjusted up or down based on whether a city has relatively high or low scores compared to the region for the Jobs-Housing Fit and High Opportunity Areas factors. ABAG staff capped a jurisdiction’s adjustment from the RHND income distribution at 30 percent (15 percent for each of the two factors). Once the total share of lower income units is determined, the remainder of a jurisdiction’s units (as determined by the total allocation methodology) are assigned to the higher income categories (moderate- and above moderate-income units). Once these totals are set, the allocation is disaggregated into the four income categories using shares from the regional income distribution. Figure 4 shows the effect of this factor-based income approach for three hypothetical cities with different income profiles. Both City A (higher income) and City C (average income) received the same income distribution, which demonstrates the impact of the cap that limits the extent to which the distribution can deviate from the regional distribution. Setting this cap at a different level would potentially result in different outcomes.

10 See California Government Code Section 65584(d)(3) and (5).

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Figure 4 Hypothetical Example of Factor-Based Income Allocation Approach

Approach C: Using Bottom-Up Income Allocation to Build the Total Allocation In contrast to Approaches A and B, this income allocation approach does not start with a total allocation assigned with a factor-based methodology. Instead, this approach uses factors to determine allocations for the four income categories, and the sum of these income group allocations represents a jurisdiction’s total allocation. Factors and weights could be modified, as appropriate, by the HMC. As an initial example, ABAG staff used the Jobs-Housing Fit and High Opportunity Areas factors to determine the allocation of lower income units (very low- and low-income) and the Jobs-Housing Balance and Job Proximity-Auto factors to determine the allocation of higher income units (moderate- and above-moderate income).11 A jurisdiction’s income distribution is determined based on how the jurisdiction scores relative to the rest of the region on the selected factors. The jurisdiction’s total allocation is calculated by summing the results for each income category. As noted above for Approach B, the Jobs-Housing Fit factor specifically relates to the relationship between lower-wage workers and housing units affordable to those workers and the High Opportunity Areas factor supports affirmatively further fair housing by assigning more lower-income units to high opportunity areas. The Jobs-Housing Balance and Job Proximity-Auto are included because of their emphasis on the relationships between housing and jobs for moderate- and higher-income households. While many other combinations of factors are possible, staff selected these factors to make this approach conceptually similar to Approach B for a more meaningful comparison.

11 These factors used the same definitions and methodology as those used in the total income allocation.

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Figure 5 Hypothetical Example of Bottom-Up Income Allocation Approach

Similarities and Differences of the Potential Income Methodology Approaches The approaches represent different ways to distribute a jurisdiction’s RHNA across the four income categories. Approaches A and B both start with a total allocation and then divide it into income groups. Approach A uses an income shift multiplier to bring a jurisdiction’s income distribution toward the regional income distribution. Approach B, however, relies on how a jurisdiction scores relative to the region on two factors (high opportunity areas and jobs-housing fit), which impacts the allocation of lower income units. Approach A may be the simpler and more mechanical approach: it does not use factors and focuses solely on rebalancing income distributions in jurisdictions. Approach B, on the other hand, uses factors to move the income distribution rather than just shifting it towards the regional distribution. Unlike the first two options, Approach C does not start with a total allocation created by a factor-based methodology. While it uses the same factor-based data as the other approaches, Approach C could become more complex since the HMC needs to select factors and weights for each of the four income groups. Consequently, Approach A may be preferable for having a more standardized method for assigning the total allocations to jurisdictions. However, Approach C may offer more control over the allocations to individual income groups within jurisdictions. Approach B represents somewhat of a hybrid of the other two: this approach builds off a factor-based methodology for total allocation like Approach A, but offers more flexibility than Approach A’s straightforward income shift.

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Table 2 Summary of Benefits and Drawbacks for Income Allocation Approaches Income Allocation Approach Benefits Drawbacks Approach A: Income Shift

• Builds on work HMC has already done on total allocation

• Allows narrative focus to be on factors for total allocation

• Simpler concept, easier to explain • Directly related to statutory objective • Multiplier can be adjusted to

complement underlying total allocation methodology

• Does not include ability to finetune income allocations based on factors

Approach B: Factor-Based

• Builds on work HMC has already done on total allocation

• Retains the two-step methodology approach of total income first, then income allocation, which may be more familiar from other RHNA methodologies

• Allows opportunity to finetune results for a particular income category

• Using factors also included in the total allocation methodology may result in overweighting those factors

• Additional complexity compared to Income Shift Approach may not be warranted, given that equity-related factors already included in total allocation

Approach C: Bottom-Up

• Allows more fine-grained control over allocations for a particular income category

• Could be simpler than Approach B, depending on number of factors used

• New approach that departs from work HMC has done to date

• Could be more complex, depending on number of factors used

Next Steps At the May HMC meeting, committee members will have an opportunity to use the online visualization tool to apply the income shift approach to hypothetical total allocation methodologies and explore the impact of selecting different income shift multipliers (Approach A). Staff will also seek feedback from the committee about pursuing the other approaches presented here.

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TO: Housing Methodology Committee DATE: May 14, 2020 FR: Deputy Executive Director, Policy RE: Potential Metrics for Evaluating the RHNA Methodology

Overview The Housing Methodology Committee’s (HMC) objective is to recommend an allocation methodology for dividing up the Bay Area’s Regional Housing Need Determination among the region’s jurisdictions. This Regional Housing Needs Allocation (RHNA) methodology is a formula that calculates the number of housing units assigned to each city and county, and the formula also distributes each jurisdiction’s housing unit allocation among four affordability levels. ABAG will submit the methodology to the Department of Housing and Community Development (HCD) for approval, and HCD will determine whether the methodology furthers the five objectives identified in Housing Element Law. Developing the methodology is a complex process; therefore, staff proposes to identify metrics that can be used to evaluate different methodology options developed by the HMC. These metrics can help ensure that any proposed methodology will meet the statutory RHNA objectives and further regional planning goals. The five RHNA statutory objectives embody many different policy goals, some of which are not always aligned with each other. One purpose of these metrics is to inform the HMC’s decisions about how to effectively balance these goals while developing a methodology that meets the required objectives. Importantly, any evaluation metrics the HMC chooses need to reflect the narrow scope of RHNA. The primary role of the RHNA methodology is to encourage a regional pattern of housing growth for the Bay Area, and RHNA does not play a role in identifying specific locations within a jurisdiction that will be zoned for housing. Accordingly, this memo presents options for evaluation metrics that can assess whether a methodology furthers the statutory objectives and other high priority regional policy goals directly related to RHNA. Staff seeks the HMC’s feedback on what measures might be the most relevant or helpful for evaluating potential RHNA methodologies. Potential Evaluation Framework for the RHNA Methodology Staff has developed a set of potential metrics for evaluating RHNA methodology options suggested by the HMC (Tables 1 and 2). In the tables below, each statutory objective has been reframed as a question to help the HMC assess how well a methodology option achieves state requirements and regional planning goals. The wording of the question reflects the language the statute uses to define the objectives.1 Each statutory objective is accompanied by potential quantitative metrics for evaluating the allocation produced by a methodology. This question-oriented evaluation framework can assist the HMC with developing a cohesive narrative for 1 See California Government Code Section 65584(d).

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explaining how a methodology produces optimal outcomes for the region and achieves the objectives required by law. Metrics Identified by HCD At the January 2020 HMC meeting, staff presented an overview of the analysis conducted by HCD in evaluating the RHNA methodologies completed by other regions in California. Staff reviewed the approval letters HCD provided to the Sacramento Area Council of Governments (SACOG), San Diego Association of Governments (SANDAG), and Southern California Association of Governments (SCAG).2 In these letters, HCD describes how the RHNA methodologies further each of the five statutory objectives. While the letters do not provide specific measures for evaluating the methodologies, these documents give a sense of the criteria HCD will use to determine whether the draft methodology selected by ABAG sufficiently achieves the statutory objectives.3 The metrics in Table 1 come directly from statements HCD made in the letters to SACOG, SANDAG, and SCAG explaining why their methodologies achieve the statutory objectives. HCD’s explanations vary across the letters and mention some metrics more consistently than others. Table 1 notes which metrics appear in all three letters sent by HCD. In addition to considering the metrics identified in HCD’s letters, the HMC may wish to incorporate additional measures for evaluating proposed RHNA methodologies. Table 2 presents evaluation metrics developed by staff related to Objective 24, Objective 55, and a possible new sixth objective (pending state legislation, more details provided below). In its letters to other regions, HCD discussed how RHNA methodologies achieved Objective 2 by either aligning with the existing locations of jobs and transit or by being based on long-range regional plans, similar to Plan Bay Area 2050. ABAG staff wanted to provide the HMC with more specific quantitative measures for assessing whether a methodology achieves this objective, which are listed in Table 2. The paragraphs below provide more context for the metrics in Table 2 related to Objective 5 and the pending sixth objective. Additional Metrics for Fair Housing and Racial Equity One of the statutory objectives for RHNA is that the methodology must affirmatively further fair housing. Housing Element Law defines affirmatively furthering fair housing as:

“taking meaningful actions, in addition to combating discrimination, that overcome patterns of segregation and foster inclusive communities free from barriers that restrict access to

2 For copies of letters HCD sent to other regions, see this document from the January 2020 HMC meeting agenda packet. 3 For a summary of the evaluation metrics alluded to in the HCD letters, see this document from the January 2020 HMC meeting agenda packet. 4 Objective 2 is “Promoting infill development and socioeconomic equity, the protection of environmental and agricultural resources, the encouragement of efficient development patterns, and the achievement of the region’s greenhouse gas reductions targets provided by the State Air Resources Board.” See California Government Code Section 65584(d)(2) for more information. 5 Objective 5 is “Affirmatively furthering fair housing.” See California Government Code Section 65584(d)(5) for more information.

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opportunity based on protected characteristics. Specifically, affirmatively furthering fair housing means taking meaningful actions that, taken together, address significant disparities in housing needs and in access to opportunity, replacing segregated living patterns with truly integrated and balanced living patterns, transforming racially and ethnically concentrated areas of poverty into areas of opportunity, and fostering and maintaining compliance with civil rights and fair housing laws.”6

HCD’s discussion of affirmatively furthering fair housing in its letters to SACOG, SANDAG, and SCAG centers solely on data from the Opportunity Map produced by the California Tax Credit Allocation Committee (TCAC) and HCD. HCD’s evaluation of whether other regions’ methodologies further this objective focused on whether a methodology directs lower income RHNA to jurisdictions with a high percentage of households living in census tracts labelled High Resource or Highest Resource on the Opportunity Map.7 However, the HMC could use other evaluation metrics—in addition to the Opportunity Map scores—to ensure the RHNA methodology has a maximum impact on overcoming patterns of segregation and fostering inclusive communities. For example, some HMC members and community stakeholders have expressed interest in evaluation metrics that consider racial segregation more explicitly and specifically focus on areas with housing markets characterized by socioeconomic and racial exclusion. The metrics in Table 2 accompanying Objective 5 reflect this input from stakeholders as well as staff’s interpretation of statutory language related to affirmatively furthering fair housing. Pending Addition of Sixth Statutory Objective Senate Bill 182 (Jackson) would add a new RHNA objective to Housing Element Law and add wildfire risk to the list of factors that must be considered for the RHNA methodology. Indications are that this bill will be passed this year and apply to this RHNA cycle for ABAG. Although the bill includes specifics about addressing fire risks, nothing in the bill prohibits ABAG from considering wildfire risk in addition to other hazards. Additionally, throughout the methodology development process, the HMC has expressed an interest in minimizing the number of households who face high risk from natural hazards. Hazard risk reduction is also a priority within ABAG/MTC’s long-range planning efforts. Table 2 proposes a metric related to this potential sixth objective that uses the revised ABAG/MTC Multi-Hazard Index presented to the HMC at its March 2020 meeting.8

6 See California Government Code Section 65584(d). 7 For more information on the Opportunity Map, see pages 10-13 of this document from the March 2020 HMC meeting’s agenda packet. 8 For more information on the revised ABAG/MTC Multi-Hazard Index, see pages 14-15 of this document from the March 2020 HMC meeting’s agenda packet.

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Table 1. Metrics Based on HCD’s Evaluation of Other Region’s Methodologies

*Metrics highlighted in bold with asterisks (**) appear in all three letters sent by HCD to other regions.

Statutory Objective Possible Metric Data Source

Objective 1: Does the allocation 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?

1a. Higher percentage of RHNA as lower income units for jurisdictions with the highest housing costs**

Census ACS for 2014-2018

1b. Higher percentage of RHNA as lower income units for jurisdictions with highest percent of single-family homes

Census ACS for 2014-2018

Objective 2: Does the allocation promote infill development and socioeconomic equity, the protection of environmental and agricultural resources, the encouragement of efficient development patterns, and the achievement of the region’s greenhouse gas reductions targets?

2a. Higher percentage of RHNA total unit allocations to jurisdictions with highest percentage of the region’s jobs

MTC, Census LEHD for 2017

Objective 3: Does the allocation promote an improved intraregional relationship between jobs and housing, including an improved balance between the number of low-wage jobs and the number of housing units affordable to low wage workers in each jurisdiction?

3a. Higher percentage of RHNA as lower income units for jurisdictions with the highest ratio of low-wage jobs to housing units affordable to low-wage workers

MTC, Census ACS for 2014-2018, Census LEHD for 2017

Objective 4: Does the allocation direct 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?

4a. Lower percentage of RHNA as lower income units for jurisdictions with a higher share of lower-income households9

Census ACS for 2014-2018

4b. Higher percentage of RHNA as lower income units for jurisdictions with a higher share of higher-income households10

Census ACS for 2014-2018

Objective 5: Does the allocation affirmatively further fair housing?

5a. Higher percentage of RHNA as lower income units for jurisdictions with the most households in High Resource/Highest Resource tracts**

HCD/TCAC 2020 Opportunity Maps

9 Lower-income households includes households in the very low- and low-income groups (<80% of Area Median Income). 10 Higher-income households includes households in the moderate- and above moderate-income groups (>=80% of Area Median Income).

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Table 2. Additional Evaluation Metrics Proposed by ABAG Staff

Statutory Objective Possible Metric Data Source

Objective 2: Does the allocation promote infill development and socioeconomic equity, the protection of environmental and agricultural resources, the encouragement of efficient development patterns, and the achievement of the region’s greenhouse gas reductions targets?

2b. Higher RHNA total unit allocations for jurisdictions with the highest percent of the region’s total Transit Priority Area acres

MTC

2c. Percentage of jurisdictions whose RHNA housing growth through 2031 is less than or equal to housing growth projected in Plan Bay Area 2050 through 2050

MTC

Objective 5: Does the allocation affirmatively further fair housing?

5b. Higher percentage of RHNA total unit allocations compared to the jurisdiction percentage of regional households, calculated for jurisdictions with a higher share of higher-income households with highest divergence scores

Census ACS for 2014-2018

5c. Higher percentage of RHNA as lower income units for jurisdictions with a higher share of higher-income households with highest divergence scores

Census ACS for 2014-2018

Objective 6 (pending state legislation): Does the allocation promote resilient communities, including reducing development pressure within very high fire risk areas?

6a. Lower total units allocated per household for jurisdictions with highest percent of urbanized area at high risk from natural hazards11

MTC; Census ACS for 2014-2018; USGS liquefaction susceptibility; CAL FIRE FRAP LRA/SRA data; FEMA (flood zones), Alquist-Priolo Fault Zones (California Geological Survey)

Next Steps ABAG staff has added many of the proposed evaluation metrics to the online visualization tool (https://rhna-factors.mtcanalytics.org) to enable users to evaluate different methodology options. HMC members will have an opportunity at the May meeting to assess the three methodology options created in March as a starting place for exploring the use of these metrics. Staff will be seeking feedback about the metrics prior to their use at future meetings.

11 For more information ABAG/MTC Multi-Hazard index used to assess hazard risk, see pages see pages 14-15 of this document from the March 2020 HMC meeting’s agenda packet.

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Item 5, Attachment A

TO: Housing Methodology Committee DATE: June 19, 2020 FR: Deputy Executive Director, Policy RE: Options for the Income Distribution Component of the RHNA Methodology

Overview The Regional Housing Needs Allocation (RHNA) methodology must assign a total number of housing units to each Bay Area jurisdiction and distribute each jurisdiction’s allocation among four income categories that include households at all income levels. In a letter dated June 9, 2020, the California Department of Housing and Community Development (HCD) provided ABAG with the Regional Housing Needs Determination (RHND) for the Bay Area (Table 1). Table 1: ABAG Regional Housing Needs Determination from HCD Income Category Percent Housing Unit Need Very Low 25.9% 114,442 Low 14.9% 65,892 Moderate 16.5% 72,712 Above Moderate 42.6% 188,130 Total 100% 441,176

The RHNA methodology’s income allocation component is crucial for creating a methodology that successfully achieves the statutory objectives of RHNA. This memo delves deeper into the income allocation methodology approaches that received the most support from Housing Methodology Committee (HMC) members and the audience at the May HMC meeting. For the purpose of the memo and analysis, we have updated the numbers to reflect the RHND from HCD. Refresher on Statutory Requirements Housing Element Law includes the objective that RHNA “[a]llocat[e] 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”1 meaning the RHNA methodology will in part be assessed by HCD in terms of how the allocation works to counter-balance existing concentrations of wealth or poverty. State law also requires the RHNA methodology to improve coordination between the locations of low-wage jobs and housing affordable to low-wage workers (jobs-housing fit). The RHNA methodology must also affirmatively further fair housing, which will require allocating more lower income units to communities that historically have not provided affordable housing. Potential Income Allocation Methodologies Presented at May HMC Meeting At the May HMC meeting, staff presented several possible methodologies for allocating units by income that are aligned with the statutory objectives of RHNA. The options presented represent two fundamentally different processes for determining units by income:

1 See California Government Code Section 65584(d).

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• Income Shift. In this approach, the total number of units allocated to a jurisdiction is identified first, and the income allocation methodology is used to distribute that total among the four income categories.2 Two variants of this approach can be seen in other regions’ RHNA methodologies: Income Shift (used by the San Diego region and ABAG last RHNA cycle) and Income Shift Plus Equity-Focused Factors (used by the Los Angeles and Sacramento regions).

• Bottoms-Up. In this approach, the income allocation methodology is used to identify the number of units for each income category, and the sum of units in the four income categories equals a jurisdiction’s total allocation. This approach was developed based on feedback provided by HMC members.

After presenting these options, staff asked HMC members and members of the audience for feedback about which income allocation approach they preferred and which multiplier they liked best for the Income Shift approach. Voting results are displayed in Figure 1 and Figure 2. The comment received by email is in Appendix A. Figure 1 shows that the Bottom-Up and Income Shift approaches received the most support. There was only minimal support for the Income Shift Plus Equity-Focused Factors approach, which indicates this approach is not as complementary to the total allocation methodologies the HMC is considering. Notably, the regions that used the Income Shift Plus Equity-Focused Factors approach used equity-related factors solely in the income allocation methodology. The HMC, however, has expressed support for using equity-related factors in the total allocation methodology, which makes the addition of equity-related factors in the income allocation less imperative. Figure 1: Feedback About Income Allocation Methodology Approaches Based on today’s presentation and your experience using the online visualization tool, do you feel that using the income shift approach in ABAG’s RHNA methodology will successfully achieve the statutory objectives?

2 State law defines the following RHNA income categories:

• Very Low Income: households earning less than 50 percent of Area Median Income (AMI) • Low Income: households earning 50 - 80 percent of AMI • Moderate Income: households earning 80 - 120 percent of AMI • Above Moderate Income: households earning 120 percent or more of AMI

0 5 10 15 20

No, I’ll email comments

No, please explore bottom up approach

No, please explore factor-based adjustment oflower-income units applied to total allocation

Yes

HMC Public

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Figure 2 shows there is strong support for an income shift multiplier between 100% and 150%, if the Income Shift approach is selected to move forward. Figure 2: Feedback About Income Shift Multiplier What level of income shift combined with the HMC’s total allocation methodologies from March seems to most effectively accomplish the statutory objectives and further regional planning goals?

Income Shift In the Income Shift approach, a jurisdiction’s distribution of households by income is compared to the distribution for the region. The Income Shift moves the local income distributions closer to or beyond the regional distribution, depending on the income shift multiplier. A jurisdiction that has a higher percentage of existing households in a given income category compared to the region receives a smaller share of units in that income category, and vice versa. This approach directly addresses the state objective of “[a]llocating 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.”3 Figure 3 shows the steps in the Income Shift process. This process is repeated for each of the four income categories. Figure 3: Income Shift Methodology An income shift multiplier of 100% results in every jurisdiction’s RHNA mirroring the region’s existing income distribution. In theory, setting the income shift multiplier above 100 percent could close the gap between a jurisdiction’s income distribution and the region’s distribution in a shorter period of time. However, this more aggressive shift could also increase the potential for displacement by directing more market-rate units to jurisdictions with higher proportions of existing lower-income households.

3 See California Government Code Section 65584(d)(4).

0 5 10 15 20 25

150% - 200%

100% - 150%

50% - 100%

0% - 50%

HMC Public

Existing Regional

Proportion in Income Category

Existing Jurisdiction Proportion in Income Category

X% Income

Shift Multiplier

Adjustment Factor

Existing Jurisdiction Proportion in Income Category

Share of Jurisdiction

RHNA in Income

Category

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Based on the feedback from the May meeting, staff has developed charts to demonstrate the impacts of applying the income shift multipliers of 100 percent, 125 percent, and 150 percent. Figure 4 shows the results for cities with different income profiles.4 City A’s residents are largely higher-income households and the city has good access to jobs. City B has a lower income profile, with less job access. City C is somewhere in between, falling close to the regional income distribution. Figure 4: Hypothetical Comparison of Effects of Different Income Shift Multipliers

Bottom-Up Income Allocation to Build the Total Allocation In contrast to the Income Shift, the Bottom-Up income allocation approach does not start with a total allocation assigned with a factor-based methodology. Instead, this approach uses factors to determine allocations for the four income categories, and the sum of these income group allocations represents a jurisdiction’s total allocation. Staff has developed two concepts for the Bottom-Up approach, using some of the same factors that have received the most attention and support from the HMC for use in the total allocation (see Table 2). Staff also chose factors where there was more variation in the scores that jurisdictions received, since greater variation increases the factor’s impact in creating distinctions between the allocations jurisdictions receive. A jurisdiction’s allocation within each income category is determined based on how the jurisdiction scores relative to the rest of the region on the selected factors. The jurisdiction’s total allocation is calculated by summing the results for each income category.

4 Figure 4 shows the results from applying the three Income Shift multipliers to the Balanced Equity-Jobs-Transportation methodology developed by HMC members at the March meeting. The results from the three sample methodology options from March were very similar, so staff is only presenting one set of results for the sake of simplicity. The use of the Balanced Equity-Jobs-Transportation option is not an endorsement of this option. View a summary of the sample methodology options from the March meeting for more information.

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Table 2: Factors and Weights for Bottom-Up Income Allocation Variations

Bottom-Up 2-Factor Concept Affordable: Very Low and Low • Access to High Opportunity Areas 50% • Jobs-Housing Fit 50%

Bottom-Up 3-Factor Concept Affordable: Very Low and Low • Access to High Opportunity Areas 40% • Jobs-Housing Fit 40% • Job Proximity – Transit 20%

Market-Rate: Moderate and Above Moderate • Job Proximity – Auto 50% • Jobs-Housing Balance 50%

Market-Rate: Moderate and Above Moderate • Job Proximity – Auto 50% • Job Proximity – Transit 30% • Jobs-Housing Balance 20%

The Bottom-Up 2-Factor Concept uses two factors, weighted equally at 50 percent, for each combined income group. 5 It includes the Jobs-Housing Fit and High Opportunity Areas factors to determine the allocation of affordable units (very low- and low-income). The Jobs-Housing Fit factor specifically relates to the relationship between lower-wage workers and housing units affordable to those workers and the High Opportunity Areas factor supports affirmatively further fair housing by assigning more lower-income units to high opportunity areas. The two factors used to determine the allocation of market-rate units (moderate- and above-moderate income) are the Jobs-Housing Balance and Job Proximity-Auto factors. The Jobs-Housing Balance and Job Proximity-Auto factors are included in the methodology for higher-income units because of their emphasis on the relationships between housing and jobs. Locating market-rate housing close to jobs can provide more options for these households to live near their work, which aligns with the statutory objectives and the HMC’s policy priorities. The Bottom-Up 3-Factor Concept uses three factors to determine the allocation for each income category. It includes the High Opportunity Areas (40 percent weight), Jobs-Housing Fit (40 percent weight), and Job Proximity – Transit (20 percent weight) factors for allocating affordable units. The market-rate units are allocated using the Job Proximity – Auto (50 percent weight), Job Proximity – Transit (30 percent weight), and Jobs-Housing Balance (20 percent weight) factors. This concept includes the same factors as the Bottom-Up 2-Factor Concept, but with different weights. It also adds Job Proximity – Transit as the third factor to encourage more housing near transit, in alignment with the goal of reducing greenhouse gas emissions. Figure 5 shows the pattern for how very low-income units are allocated throughout the Bay Area for several of the Income Shift options and the Bottom-Up options. Jurisdictions shown in dark red have a higher share of very low-income units as a portion of their allocation. Figure 6 shows the same information for above moderate-income units.

5 These factors used the same definitions and methodology as those used in the total income allocation.

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Figure 5: Comparison of Shares of Very Low-Income Units for Income Allocation Options

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Figure 6: Comparison of Shares of Above Moderate-Income Units for Income Allocation Options

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Figure 7 compares the results, including the total allocation and share of units in each income category, for the three multipliers for the Income Shift approach and the two concepts for the Bottom-Up approach. One issue HMC members have raised about the Income Shift is that a higher multiplier is desirable for allocating affordable housing units to communities with more higher-income households but a higher multiplier also directs more market-rate housing to communities with more lower-income households, raising concerns about possible displacement. One benefit of the Bottom-Up approach is that it allows for the allocations for affordable and market-rate units to be set independently, so directing more affordable units to communities with more higher-income households would not necessarily result in more market-rate units going to communities with more lower-income households. For City A (the disproportionately higher-income hypothetical jurisdiction), the two Bottom-Up concepts result in shares of very low- and low-income units that are consistent with the 125 percent Income Shift. For City B (the disproportionately lower-income hypothetical jurisdiction), the share of Above Moderate-Income units is slightly above the 100 percent Income Shift. Although the share of Above Moderate-Income units for City B is smaller in the Bottom-Up concepts, City B still receives a higher share of Above Moderate-Income units than City A or City C. The Bottom-Up concepts seem to provide balance between directing affordable units to communities with more higher-income households while also directing a smaller share of market-rate housing to communities with more lower-income households. The Income Shift approach has only minimal effects on hypothetical City C, since its share of households in each income category is similar to the shares for the region as a whole. The income shift multiplier is applied to the difference between the region and the jurisdiction, and it has only a minimal impact when this difference is small. The Bottom-Up concepts both result in higher shares of affordable units for City C compared to the Income Shift options. One feature of the Bottom-Up approach is that there is less predictability about what the total allocation will be. For City A, one variation resulted in a similar number of total units as the Income Shift, while the second variation resulted in a smaller total allocation. There is a similar pattern in the results for City C. For City B, both Bottom-Up concepts resulted in higher total allocations.

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Figure 7: Hypothetical Comparison of Total Allocations by Income

16% 26% 28% 31% 29% 29%9%15% 16% 18% 17% 17%13%16% 17% 18% 15% 15%

61%43% 38% 33% 39% 39%

0

1,000

2,000

3,000

4,000

5,000

ExistingDistribution

100% Shift 125% Shift 150% Shift Bottom-Up2-Factor

Bottom-Up3-Factor

City A (disproportionately higher-income today)

Very Low Low Moderate Above Moderate

47%26% 28% 31% 29% 29%

26%

15% 16% 18%17% 17%

16%

16% 17% 18%15% 15%

10%43% 50% 58%

46% 46%

0200400600800

1,0001,2001,400

ExistingDistribution

100% Shift 125% Shift 150% Shift Bottom-Up2-Factor

Bottom-Up3-Factor

City B (disproportionately lower-income today)

Very Low Low Moderate Above Moderate

25% 26% 26% 26% 31% 32%

17% 15% 15% 14% 18% 18%16% 16% 17% 17% 14% 14%

42% 43% 43% 43% 36%36%

01,0002,0003,0004,0005,0006,0007,000

ExistingDistribution

100% Shift 125% Shift 150% Shift Bottom-Up2-Factor

Bottom-Up3-Factor

City C (similar to region's current income profile)

Very Low Low Moderate Above Moderate

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Table 3: Pros/Cons for Income Shift and Bottom-Up Income Allocation Approaches Income Shift Bottom-Up Pros

• Allows greater control over total unit allocations

• Directly addresses statutory objective to balance disproportionate concentrations in each income category

Pros • Allows more fine-grained control for

income allocation: allocations for affordable units and market-rate units can be set independently

Cons • Increasing the share of affordable units in

higher-income jurisdictions means more market-rate units must be directed to other jurisdictions

• No ability to finetune income allocations using factors

Cons • Less predictability for the total unit

allocations to jurisdictions

Next Steps At the June meeting, HMC members will have an opportunity to provide feedback about the different income allocation options. The discussion will focus on the following questions:

• Based on the RHND, 41 percent of the units that must be allocated by the RHNA methodology are affordable (very low- and low-income units). What is the right balance for allocating affordable housing?

o Should jurisdictions that are mostly high-income households receive a larger percentage of their RHNA (above 41%) as affordable housing?

o Should jurisdictions with significant populations of low-income households receive a larger percentage of their RHNA (above 41%) as affordable housing?

• Based on the RHND, 59 percent of the units that must be allocated by the RHNA methodology are market-rate (moderate- and above moderate-income units). What is the right balance for allocating market-rate housing?

o Due to concerns about displacement in low-income communities, should jurisdictions that are mostly high-income households receive a larger percentage of their RHNA (above 59%) as market-rate housing?

o Should communities with more low-income residents receive a larger percentage of their RHNA (above 59%) as market-rate units so that jurisdictions that are mostly high-income households are allocated more affordable housing?

• Feedback to staff about refining options: o If ABAG uses an income shift methodology, what income shift multiplier would

you feel most comfortable with? o If ABAG uses a bottom-up methodology, do you like the factors staff selected for

allocating affordable units? o If ABAG uses a bottom-up methodology, do you like the factors staff selected for

allocating market-rate units? o Do you prefer the income shift approach or the bottom up approach?

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Appendix A: Comments Emailed to Staff about Income Allocation Approaches Only one HMC member submitted written comments related to the survey. Response from Pat Eklund:

1. What level of income shift combined with the HMC's total allocation methodologies from March seems to most effectively accomplish the statutory objectives and further regional planning goals?

b. 50% - 100%

2. Based on today's presentation and your experience using the online visualization tool, do

you feel that using the income shift approach in ABAG's RHNA methodology will successfully achieve the statutory objectives?

d. No, and I’ll email comments to rhna@thecivicedge.com -- We need to re-do today. Due to COVID-19, we need to reduce what we think we can get done in these meetings. Limit them to 2 hours and focus on 1 issue. Maybe do preparation ahead of time if there is a tool that needs to be used. I feel as though my comments have not been captured since I was not able to participate even as a member. This is my 3rd RHNA cycle I have participated in .. and, probably one of the more frustrating ones. We are trying to accomplish too much and what is being sacrificed is our input. There is NO time for input .. My suggestion – limit each meeting to 1 issue .. if we are still on a time crunch .. then meet twice a month. These 3-4 hour meetings are NOT appropriate or good .. again what gets sacrificed is the quality of our input and getting input from all of us. There are some that already have made up their minds and their input is being characterized for the group. By the way, my abstention on these items was NOT noted by Brad Paul. I did not vote or really participate because it took me almost the whole time to figure out how to get in to the break out session by phone. That technological glitch was forgotten when this was set up. I want to thank Paisley for trying to help me .. she did a great job given the challenges .. but, bottom line – we are trying to do too much too fast .. SLOW DOWN! The quality of the input is being sacrificed.

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