Small Area Fair Market Rent

Demonstration Evaluation

Interim Report

U.S. Department of Housing and Urban Development | Office of Policy Development and Research

Small Area Fair Market Rent

Demonstration Evaluation

Interim Report

Prepared for

U.S. Department of Housing and Urban Development

Prepared by

Meryl Finkel

Samuel Dastrup

Kimberly Burnett

Thyria Alvarez

Carissa Climaco

Tanya de Sousa

August 2017

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Disclaimer

The contents of this report represent the views of the authors and do not necessarily reflect the

views or policies of the U.S. Department of Housing and Urban Development or the U.S.

government.

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Contents

Executive Summary .......................................................................................................................v

1.  About the Small Area Fair Market Rent Demonstration and Evaluation .........................1

Motivation for the Demonstration .............................................................................................1

Design and Implementation of the Demonstration ....................................................................2

Evaluation of the Demonstration ...............................................................................................3

Research Questions ..............................................................................................................4

Structure of This Report and Introduction to Key Methodological Approaches .................5

2.   Hypothesized Impacts on Housing Choice Voucher Holders and Public Housing

Agencies ....................................................................................................................................8

Hypothesized Impacts of SAFMRs on HCV Holders ...............................................................8

Potential for SAFMRs To Affect Location and Relocation of HCV Holders .....................8

Observed HCV Holders’ Locations and Relocations ........................................................11

HCV Holders’ Experience With the Program ...................................................................12

Hypothesized Impacts of SAFMRs on PHAs ..........................................................................13

Implications for Subsidy Costs ..........................................................................................14

Operational Impacts on PHAs ...........................................................................................15

3.   Evaluation Data and Methodology .......................................................................................19

Description of Primary and Secondary Data Sources ..............................................................19

Primary Data ......................................................................................................................19

Secondary Data ..................................................................................................................20

Comparison PHAs ...................................................................................................................25

4.   Changes in Housing Choice Voucher Holders’ Potential Access to High-Opportunity

Neighborhoods........................................................................................................................26

SAFMRs Increase Access to High-Rent ZIP Codes Across Sites ...........................................29

Access to High-Rent ZIP Codes Under SAFMRs Varies Across Sites .............................30

SAFMRs Reduce the Overall Number of Units With Rents Below the FMR .........................32

Declines in Units With Rents Below SAFMR ...................................................................33

Declines in Units With Rents Below SAFMR Are a Consequence of the Concentration

and Distribution of Rental Units Across ZIP Code Types ...........................................36

SAFMRs Increase Access to ZIP Codes With Higher Opportunity Measures ........................37

Opportunity Measures by Rent Ratio ................................................................................37

HCV Holders’ Potential Access to ZIP Codes Across Measures of Opportunity, Al  PHAs ....38

5.   Early Impacts of Smal  Area Fair Market Rents on Housing Choice Voucher Holders ...45

Participant Locations in SAFMR PHAs by Rent Ratio ...........................................................45

Opportunity Measures in ZIP Codes Where SAFMR Holders Reside ....................................52

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Additional Neighborhood Characteristics in ZIP Codes Where HCV Holders Reside ...........57

Summary ..................................................................................................................................61

6.   Perceptions of Tenants’ Experience and Administrative Impacts of Small Area Fair

Market Rents on Public Housing Agencies .........................................................................62

PHA Perceptions of HCV Holders’ Experience With SAFMRs .............................................62

Factors Affecting PHAs’ SAFMR Implementation .................................................................64

PHA Reasons for Using SAFMRs .....................................................................................64

External Factors That Affected Implementation ................................................................65

SAFMR Administrative Impacts on PHAs ..............................................................................68

Modifications to PHA Plans and Administrative Plans .....................................................69

Modifications to Automated Systems ................................................................................70

Analyzing and Setting Payment Standards ........................................................................70

Rent Reasonableness ..........................................................................................................72

Contract Rent Adjustments ................................................................................................72

Inspection System and Process Changes ...........................................................................73

Procedures and Staff Training ...........................................................................................74

Education and Support for Tenants....................................................................................74

Education and Support for Landlords ................................................................................76

Quality Assurance ..............................................................................................................77

Voucher Success Rates ......................................................................................................78

Adequacy of Supplemental Administrative Fees ...............................................................78

Estimate of Financial Impacts of SAFMR Impacts on PHAs ..................................................79

Impacts on Landlords ...............................................................................................................85

7.   Fiscal Effects of Small Area Fair Market Rents on Public Housing Agencies, Housing

Choice Voucher Holders, and Landlords ............................................................................87

Average Per-Unit Payment Standards .....................................................................................87

Rent-Related Effects of SAFMRs ............................................................................................90

PHA Effects: HAP Payments to Landlords .......................................................................90

Landlord Effects: Rent to Landlords ..................................................................................92

HCV Holder Effects: HCV Holder Contributions to Rent ................................................94

8.   Plan for Phase 2 Data Collection ..........................................................................................97

Appendix A. Small Area Fair Market Rent Demonstration Site Selection ............................99

Appendix B. Administrative and Financial Impacts of Small Area Fair Market Rents

on Public Housing Agencies: Consolidated Exhibits ........................................................102

Appendix C. Site Visit Phase 1 Protocol ..................................................................................106

Appendix D. Phase 2 Site Visit Draft Data Collection Instruments ......................................119

Appendix E. Chapters 4, 5, and 7 Cluster Analyses ...............................................................145

References ...................................................................................................................................159

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Executive Summary

Study Background

Key Policy Issues Motivating Study

Housing choice vouchers (HCVs) allow residents to select housing units of their choice, so long

as the units meet certain rent and quality parameters. Accordingly, HCVs theoretically offer

HCV holders the chance to locate in neighborhoods with high-performing schools, low rates of

poverty, and other characteristics associated with opportunity for neighborhood residents. In

practice, however, HCV holders are frequently concentrated in high-poverty neighborhoods with

limited access to the amenities associated with resident opportunity.

This study examines whether and to what extent changes in how subsidy levels are determined

for the HCV program affect HCV holders’ access to opportunity. Currently, the subsidy

available to HCV holders is based on a single rent standard—the Fair Market Rent (FMR)—set

for each metropolitan area (or nonmetropolitan county). The FMR is set by the U.S. Department

of Housing and Urban Development (HUD) generally at the 40th percentile of rents of all units

in that metropolitan area (or nonmetropolitan county) occupied by renter households that moved

to the unit in the past 15 months. FMRs vary by unit size (number of bedrooms), but housing

agencies generally have only a limited ability to adjust the maximum subsidy level to reflect

differences in rent levels between neighborhoods within their jurisdiction.

Rents tend to be higher in certain neighborhoods than others, and neighborhoods with higher

rents tend to have better access to amenities that provide opportunity. For this reason, using a

single metropolitanwide standard as the basis for setting the maximum subsidy available to HCV

holders makes it difficult for them to access housing in areas of opportunity. Under an FMR-

based system, it is much easier for HCV holders to use vouchers in lower-rent areas that

generally also have fewer opportunities.

Consequently, one of the central questions that HUD faces in administering the HCV program is

how to create a more effective means for HCV holders to move to higher-opportunity areas

without significantly raising overall subsidy costs. This question is the primary motivation for

the Small Area Fair Market Rent (SAFMR) demonstration. As the name implies, SAFMRs are

FMRs set using geographic areas that are much smaller—specifically, by ZIP Codes.

HUD’s SAFMR Demonstration

The SAFMR demonstration enables local agencies to increase HCV subsidies in ZIP Codes

where rents are higher than the metropolitan-areawide average and decrease HCV subsidies in

ZIP Codes where rents are lower. The evaluation examines whether and to what extent this shift

from FMRs to SAFMRs helps HCV holders to better access areas of opportunity. The evaluation

also examines how this alternative approach affects HCV holders and landlords, as well as HCV

subsidy and administrative costs.

To test how SAFMRs may potentially affect a range of public housing agency (PHA) types,

HUD selected five PHAs for the demonstration whose conditions differed across various

characteristics. The five demonstration PHAs are the Housing Authority of the City of Laredo

(Texas), the Town of Mamaroneck Housing Authority (New York), the Chattanooga Housing

Authority (Tennessee), the Housing Authority of Cook County (Illinois), and the City of Long

Beach Housing Authority (California).

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In addition to these five PHAs that agreed to participate in the SAFMR demonstration, this

evaluation study also includes 2 (of 12) PHAs in the Dallas, Texas metropolitan area. All

metropolitan Dallas PHAs have been using SAFMRs since 2011 as a result of a legal settlement.

The 2 PHAs included in the study are the Housing Authority of the City of Dallas, the largest

PHA operating in the Dallas metropolitan area, and the Housing Authority of Plano, which

serves a smaller number of HCV holders and whose HCV holders are less likely than those of

the Dallas PHA to be a racial or ethnic minority.

Evaluating the SAFMR Demonstration

The evaluation study looks at the effects of SAFMRs on the following.

•  Potential Access to Opportunity. The extent to which SAFMRs change the number of

units with rents at levels affordable to HCV holders and the number and share of such

units in higher-opportunity areas.

•  Actual Access to Opportunity. The extent to which HCV holders in SAFMR PHAs are

more likely to locate in or move to higher-opportunity areas after implementation of

SAFMRs than before.

•  Costs and Rents. The extent to which subsidy expenditures, administrative expenses,

total rent levels, and tenant contributions to rents change after implementation of SAFMRs.

The study also analyzes qualitative information on how the transition from FMRs to SAFMRs

may have affected residents and PHAs participating in the evaluation.

SAFMRs were implemented in the Dallas metropolitan area in 2011 and in the five demonstration

sites beginning in the fall of 2012. To understand how the introduction of SAFMRs may have

affected outcomes, this interim report examines changes over time between 2010 (predemonstration)

and 2015. To control for trends that may be unrelated to SAFMRs, the report broadly examines

changes over the same time period for a group of comparison PHAs that did not implement

SAFMRs. 1 As described below, a second round of data collection and analysis is planned to inform a final report expected in 2018.

Data collection for this interim report included both primary and secondary or administrative

sources. To collect primary data, we conducted site visits to each of the seven SAFMR PHAs

in May and June 2016. The site visits included one-on-one and group interviews with

executive directors and multiple key staff at each location involved in implementing the

transition to and administration of SAFMRs. The goal of these visits was to learn about the

PHA experience with one-time, transitional, and ongoing administrative efforts and costs

related to SAFMRs and to learn about PHA perceptions of the impacts of SAFMRs on HCV

holders and landlords.

Secondary and administrative data used in the analysis include metropolitan area FMRs,

SAFMRs, ZIP Code tabulations of rent distributions, and administrative Public and Indian

Housing Information Center data maintained by HUD. Our analysis also includes two types of

neighborhood-level indicators—opportunity measures and neighborhood characteristics.

1  The final report will have additional comparisons at the aggregate level and for subgroupings of the comparison PHAs, as well as the individual SAFMR PHAs.

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Neighborhood-level indicators were drawn from the U.S. Census Bureau’s American

Community Survey (ACS), the National Center for Education Statistics, local jurisdictions (in

the case of crime indicators), and the U.S. Environmental Protection Agency.

Key Findings

The following are the principal findings to date.

Potential Access to Opportunity Following Introduction of SAFMRs

•  As expected, SAFMRs increase the pool of units potentially available to HCV holders

that rent below the applicable FMR in high-rent ZIP Codes, and they reduce the pool in

low-rent ZIP Codes. Under ordinary FMRs, nearly three-fourths of units in low-rent ZIP

Codes have gross rents (rent plus utilities) below the FMR, as do slightly more than a

one-fourth of units in high-rent ZIP Codes. Under SAFMRs, the availability of units is

much more evenly distributed across different types of neighborhoods, leading to

increased availability in high-rent ZIP Codes and reduced availability in low-rent ZIP

Codes. Roughly one-half of units have rents below the SAFMR in each neighborhood

type (high rent, low rent, and moderate rent). As expected, changes in the share of units

with rents below the applicable FMR in moderate-rent ZIP Codes are fairly modest. 2

•  However, for the SAFMR PHAs as a whole, the gain in units with rents below the

applicable FMR in high-rent ZIP Codes does not offset the decrease in the number of

units in the low-rent and moderate-rent ZIP Codes, resulting in a net loss of units

potentially available to HCV holders overall. The net effect across all SAFMR PHAs is a

loss of over 22,000 units (3.4 percent) that might otherwise be affordable to HCV

holders. The net loss of units with rents below the applicable FMR exceeded 10 percent

in one site (Long Beach) and was about 4 percent in another (Dallas). Two sites (Cook

County and Laredo) experienced a net loss of units of only 1 to 2 percent, and a third

(Mamaroneck) experienced no net change. In two sites, more units rented for below

SAFMRs than for below the FMR. The net gain in units in Chattanooga was slightly

more than 3 percent; and the Plano site, with geography in the higher-rent section of the

Dallas HUD Metro FMR area, saw an increase of 26 percent. The size of the net change

in units below the applicable FMR in a given geography depends on how rental units are

distributed across low-, moderate-, and high-rent ZIP Codes. In general, if fewer rental

units (that is, a higher rate of homeownership) are in high-rent ZIP Codes than in low-

rent ZIP Codes (that is, a lower rate of homeownership), then there will be fewer units

with rent below SAFMR than with rent below the metropolitan area FMR.

•  The effects of the shift to SAFMRs on the share of units renting below the applicable

FMR varied.   In three PHAs, the number of units in ZIP Codes that experienced increases

in the applicable FMR was the same or nearly the same as the number of units in ZIP

Codes that experienced decreases. In these PHAs, small reductions in the number of units

below the SAFMR in moderate-rent ZIP Codes drove the net decline in units with rents

2  This report defines a moderate-rent ZIP Code as one in which the median rent falls between 90 and 110 percent of the median rent for the metropolitan area as a whole. The report defines ZIP Codes with lower median rents as low rent, and those with higher median rents as high rent.

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below the applicable FMR. That was not the case in Chattanooga, Dallas, Long Beach,

and Plano. In Long Beach, a large majority of rental units were in ZIP Codes where

payment standards declined. 3 This, combined with the sharp drop (13.5 percent) in the

total number of units with rents below the SAFMR, appears to have contributed to a

sense of dissatisfaction among landlords, HCV holders, and PHA staff; Long Beach

similarly reported the greatest challenges to implementation.  In other markets, FMRs

declined in some places but increased in many others, resulting in opportunities for HCV

holders living in neighborhoods with declining payment standards to find qualified units

in neighborhoods with greater access to opportunity. Chattanooga includes a relatively

low number of ZIP Codes (and units within ZIP Codes) where SAFMRs are less than

FMRs and a relatively high number of ZIP Codes and units with SAFMRs that are about

equal to or are greater than the FMR. As a result, Chattanooga saw an increase in units

renting below the applicable FMR with a change to SAFMRs.

•  The high-rent ZIP Codes to which HCV holders are given access via SAFMRs offer

higher opportunity to residents on all measures used, which include lower poverty, higher

school proficiency, higher job proximity, higher environmental quality, and lower rates of

both property and violent crime. As expected, high-rent ZIP Codes offer more

opportunities than do low-rent ZIP Codes. Because SAFMRs increase access to high-rent

ZIP Codes and reduce access to low-rent ZIP Codes, we found not unexpectedly that the

transition to SAFMRs led to an increase in units potentially available to HCV holders in

higher-opportunity areas under SAFMRs compared with FMRs and fewer units in lower-

opportunity areas.

Actual Locations of HCV Holders Following Implementation of SAFMRs

Although SAFMRs are hypothesized to improve HCV holders’ access to units in high-

opportunity areas, the actual experience could be influenced by a number of factors. These

factors include the PHAs’ ability to execute the demonstration and the responses of both

landlords and HCV holders to the changes in payment standards. HCV holders’ current

circumstances may also play a role, as moving to a higher-opportunity area may or may not fit

with a HCV holder’s situation at a given point in time. We summarize key findings based on

early evidence in the following list.

•  Following the implementation of SAFMRs, HCV holders in the demonstration sites are

slightly more likely to live in high-rent ZIP Codes than they were prior to the

demonstration (20 percent compared with 17 percent). In the comparison PHAs, the

percent of households that lived in high-rent ZIP Codes did not change—14 percent of

HCV holders resided in high-rent ZIP Codes in both periods. The slight changes in rents

among the SAFMR PHAs also translate into slight changes in opportunity. Following the

implementation of SAFMRs, 13 percent of HCV holders in the SAFMR PHAs lived in

high-opportunity areas compared with 11 percent prior to implementation; 9 percent of

HCV holders in the comparison PHAs lived in high-opportunity areas in both periods.

3  The  payment standard  is used to calculate the amount of the subsidy per voucher. The allowable range of this percentage is set by HUD at between 90 and 110 percent of the FMR, with some opportunity to request

exceptions from HUD.

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•  The share of new HCV holders across all the SAFMR sites who moved into high-rent

ZIP Codes increased from 14 percent in 2010 to 17 percent in 2015. The size and

direction of the change varied across SAFMR sites, however. The share of new

households that moved into high-rent ZIP Codes quadrupled in Laredo (from 5 percent to

22 percent), doubled in Chattanooga (from 5 to 10 percent), and increased by nearly one-

fourth in Cook County (from 17 to 21 percent). In contrast, in Mamaroneck, the

percentage of new households that moved into high-rent ZIP Codes decreased from 83

percent in 2010 to 59 percent in 2015, a drop of almost one-third. The percent of new

HCV households that moved into high-rent ZIP Codes in the comparison PHAs did not

change—11 percent of new HCV holders resided in high-rent ZIP Codes prior to the

implementation of SAFMRs and 12 percent after. The changes in rents are also reflected

in changes in access to opportunity. Following the implementation of SAFMRs, 11

percent of new HCV holders in the SAFMR PHAs lived in high-opportunity areas

compared with 9 percent prior to implementation; 8 percent of new HCV holders in the

comparison PHAs lived in high-opportunity areas in both periods.

•  Overall about 15 percent of HCV holders moved to new ZIP Codes in a 2-year period

prior to the implementation of SAFMRs; 18 percent moved in a 2-year period after

implementation. Among existing households that moved to new ZIP Codes, the share

moving to high-rent ZIP Codes increased from 18 percent in 2010 to 28 percent in

2015.  No similar trend was observed in the comparison PHAs. Across all evaluation

PHAs, PHA staff reported that declining payment standards in lower-opportunity areas

encouraged HCV households to move from lower-opportunity areas to higher-

opportunity areas. This observation is borne out by the analysis of HCV administrative

data, which shows that existing households in SAFMR PHAs that moved to different ZIP

Codes moved to high-rent areas at a higher rate in 2015 than in 2010. Important

differences exist across SAFMR sites. All SAFMR sites saw increases except Long

Beach, where almost no movers relocated to high-rent ZIP Codes in either time period

(1 percent in 2010 and 2 percent in 2015). In contrast, in Laredo, the share of ZIP Code

movers who moved into high-rent ZIP Codes increased from 6 percent in 2010 to 31

percent in 2015. Plano experienced an increase from 39 percent to 52 percent and

Dallas from 21 percent to 32 percent. The other sites experienced increases of about 5 to

6 percentage points. The changes in rents are also reflected in changes in access to

opportunity. Following the implementation of SAFMRs, 15 percent of mover HCV

holders in the SAFMR PHAs lived in high-opportunity areas compared with 9 percent

prior to implementation; 7 percent of mover HCV holders in the comparison PHAs lived

in high-opportunity areas in both periods.

•  After the implementation of SAFMRs, households moving to different ZIP Codes are

more likely to locate in neighborhoods with lower shares of minorities and higher shares

of household heads with college degrees. These changes were not observed in the

comparison PHAs.

Program Costs and Rents

•  Average per-unit payment standards at SAFMR PHAs declined between 2010 and

2015 in inflation-adjusted (real) terms.  Overall, between 2010 and 2015, the average

payment standard for HCV holders decreased in real terms (in 2015 dollars) by about 11

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percent in the SAFMR PHAs. In contrast, payment standards decreased by 2 percent across

all rent categories in the comparison PHAs. The overall decrease in per-unit payment

standards in SAFMR PHAs indicates that increases in payment standards for households in

high-rent neighborhoods were more than offset by lower payment standards for households

in low-rent neighborhoods. Overall, the average payment standard decreased by 17 percent

in low-rent ZIP Codes, with the largest declines in Dallas, Long Beach, Mamaroneck, and

Plano. At the same time, the average payment standard increased by 12 percent in high-rent

ZIP Codes, with the largest increases in Cook County and Laredo. Plano and Mamaroneck

saw minimal changes in average payment standards in this rent category during this time

period, despite the change to SAFMRs.

•  Housing Assistance Payment (HAP) costs declined in real terms per unit between

2010 and 2015 in SAFMR PHAs.   HAP costs are the subsidy costs that PHAs incur on

behalf of HCV holders for rent and utilities. They decreased an average of 13 percent

between 2010 and 2015 for SAFMR PHAs compared with a decline of about 5 percent for

comparison PHAs. The changes in HAP costs generally followed the same pattern as the

changes in payment standards. Average per-unit HAP payments in low-rent ZIP Codes

decreased by nearly 30 percent, with the largest decreases in Dallas, Long Beach, and

Plano. Average per-unit HAP payments in high-rent ZIP Codes increased by 3 percent,

with the largest increases in Cook County and Laredo. In Dallas and Plano, the average

HAP in the high-rent ZIP Codes actually decreased, reflecting that the payment standard in

high-rent Dallas ZIP Codes increased only modestly and did not increase at all in Plano.

•  Rents paid to landlords remained nearly flat in real terms between 2010 and 2015

but varied by rent category.  Rents to landlords are the sum of the rent paid by the HCV

holder and the payments to landlords made by the PHA on behalf of the HCV holder.

Rents to landlords decreased in low-rent ZIP Codes (by 7 percent on average) and

increased in high-rent ZIP Codes (by 6 percent on average). Rents to landlords did not

decrease by as much as the decrease in payment standards in these areas—perhaps

because rents were adjusted only downward on the second annual recertification of

income after implementation of SAFMRs in ZIP Codes with reduced payment standards.

This is a standard HUD policy designed to provide a transition period to enable tenants

and landlords to adjust to lower payment standards.

•  Average tenant contributions to rent in the SAFMR PHAs increased by 16 percent

between 2010 and 2015.  Increases were larger in low-rent ZIP Codes (about 22 percent)

than in high-rent ZIP Codes (11 percent). By comparison, tenant contributions in the

comparison PHAs rose by about 9 percent during this period, with similar increases

across all rent ranges. The increase in tenant contributions to rent in the low-rent SAFMR

ZIP Codes suggests that some HCV holders did not move when payment standards fell,

and they faced higher tenant rent contributions as a result. The largely similar increases in

tenant contributions to rent in high-rent ZIP Codes in SAFMR and comparison sites

suggests that SAFMRs may not have led to increases for residents of these

neighborhoods.

PHA Impacts

•  The largest expenditures related to SAFMR implementation for most PHAs were

payments to vendors or information technology consultants to modify or adopt automated

x

systems capable of handling ZIP Code-level payment standards. SAFMR implementation

also required intensive staff efforts in several areas including analyzing and setting ZIP

Code-level payment standards and training staff on how to explain and apply the new

payment standards. Also, some impacts were related to contract rent adjustments and

changes to the communications strategy for landlords and tenants. Other impacts on

PHAs were minimal—for example, at most PHAs, SAFMRs required only minor

modifications to PHA administrative processes, such as housing quality standard

inspections, PHA plans and administrative plans, procedures, and quality assurance. That

said, local circumstances and procedures meant it was more difficult for some PHAs than

others to implement SAFMRs.

Plan for Phase 2 Data Collection

Now that the first phase of the project is complete, the second phase of data collection will begin,

which runs through the end of 2017. In Phase 2, we will conduct a second round of site visits to

the same seven PHAs to update the information on administrative processes and costs and to

interview tenants and landlords. The interviews will build on Phase 1 findings and be designed to

complement and enhance the quantitative analyses. In Phase 2, we will also update the analysis

of secondary data with 2016 and 2017 data and provide further analyses with this data.

We will combine and synthesize Phase 1 and Phase 2 findings in the final report to be submitted

in mid-2018.

xi

1. About the Small Area Fair Market Rent Demonstration and Evaluation

Motivation for the Demonstration

Housing Choice Vouchers (HCVs) enable residents to select housing units of their choice as long

as the units meet certain rent and quality parameters. Accordingly, HCVs theoretically offer

HCV holders the chance to locate in neighborhoods with high-performing schools, low poverty

rates, and other characteristics associated with opportunity for neighborhood residents. In

practice, however, HCV holders are frequently concentrated in high-poverty neighborhoods with

limited access to the amenities associated with resident opportunity.

This is particularly the case for HCV holders from racial and ethnic minority groups (Devine et

al., 2003; Galvez, 2010; McClure, 2008, 2011; Owens, 2012; Pendall, 2000).

The use of a single rent standard as the basis for determining the HCV subsidy level throughout a

metropolitan area is among other factors contributing to this concentration. That standard is the

Fair Market Rent (FMR), set annually by the U.S. Department of Housing and Urban

Development (HUD) for each metropolitan area (and nonmetropolitan county) generally at the

40th percentile of rents of all units in that area occupied by renter households that moved to the

unit in the past 15 months. 4

Rents tend to be higher in certain neighborhoods than others, and neighborhoods with higher

rents tend to have better access to amenities that provide opportunity. For this reason, using a

single metropolitanwide standard as the basis for setting the maximum subsidy available to HCV

holders makes it difficult for them to access housing in areas of opportunity. Under an FMR-

based system, it is much easier for HCV holders to use vouchers in lower-rent areas that

generally also have fewer opportunities. (DeLuca, Garboden, and Rosenblatt, 2013; Devine et

al., 2003; Pendall, 2000).

Currently, the subsidy available to HCV holders is generally based on the FMR for each rental

unit size (number of bedrooms) for each metropolitan area. HUD publishes the metropolitan area

FMRs and provides public housing agencies (PHAs) with discretion to set the local  payment

standard, which is used to calculate the subsidy between 90 and 110 percent of the FMR (unless

HUD approves an exception). Policymakers have long been concerned that payment standards

that are set too low could impede HCV holders’ access to quality housing and neighborhoods;

even at 110 percent of the FMR, HCV holders could have limited access to higher-cost areas

where  gross rents (rent plus utilities) are above the payment standard. At the same time, payment

standards that are set too high could drive up subsidy costs and reduce the number of families

served.

Consequently, one of the central questions that HUD faces in administering the HCV program is

how to create a more effective means for HCV holders to move to higher-opportunity areas,

without significantly raising overall subsidy costs. Answering that question is a primary

motivation for HUD’s Small Area Fair Market Rent (SAFMR) demonstration.

4  HUD created a program in 2001 designed to reduce concentration of HCV tenants. The program enables FMRs in some areas to be set at the 50th percentile of rents.

1

Design and Implementation of the Demonstration

HUD’s SAFMR demonstration tests an alternative approach to setting FMRs. In this alternative

approach, the metropolitan area FMRs are adjusted to account for differences in market rents

using a geographic area that is much smaller than the metropolitan area—the ZIP Code level.

To set SAFMRs, HUD multiplies the metropolitan area FMR by the ratio of the ZIP Code

median rent to the metropolitan area median rent. Therefore, if the median rent in a ZIP Code is

25 percent higher (or lower) than the median in that metropolitan area, the SAFMR for that ZIP

Code will be 25 percent higher (or lower) than the metropolitan area FMR. The more localized

SAFMRs enable payment standards (which can now be set between 90 and 110 percent of the

SAFMR rather than the metropolitan area FMR) to vary within a metropolitan area outside of the

range currently permitted. HUD’s hypothesis is that because SAFMRs more accurately reflect

the cost of rental housing in a given neighborhood, this alternative approach would increase the

pool of neighborhoods that HCV holders can access using vouchers relative to the pool defined

by FMRs. SAFMRs are set by HUD once per year and are effective at the beginning of the fiscal

year, October 1st.

SAFMRs could lead to a greater share of HCV holders locating in racially integrated

neighborhoods in support of HUD’s goal of affirmatively furthering fair housing. A move to

SAFMRs could also affect landlords’ interest in and awareness of the HCV program. Higher

payment standard in high-cost ZIP Codes could attract landlords’ interest, whereas lower

payment standards in low-cost ZIP Codes could discourage engagement with the HCV program.

A move to SAFMRs would also affect the local PHAs that administer the HCV program. It

ultimately could alter the average amount PHAs pay landlords for the units they administer.

Households may respond to SAFMRs by more frequently moving to or selecting higher-cost

areas. However, without a corresponding reduction in costs associated with households renting in

lower-cost areas or associated gains in HCV holders’ incomes resulting from PHAs paying a

greater share of rent, a PHA ultimately may not be able to fund as many vouchers as before. (Or,

alternatively, may require additional funding from HUD to continue serving its baseline number

of HCV holders.)

In addition, a switch from voucher payment standards derived from FMRs to SAFMRs would

likely require PHAs to change their administrative processes and systems, particularly initially. 5

To test how SAFMRs may potentially affect a range of PHA types, HUD randomly selected five

PHAs for the demonstration that differed across various characteristics.

•  Chattanooga Housing Authority, Tennessee (CHA).

•  Housing Authority of Cook County, Illinois (HACC).

•  Housing Authority of the City of Laredo, Texas (LHA).

•  Housing Authority of the City of Long Beach, California (HACLB).

•  Town of Mamaroneck Housing Authority, New York.

5  We note that in designing the SAFMR demonstration, HUD provided participating PHAs with additional funding intended to cover initial costs.

2

These PHAs were from distinct clusters of eligible PHAs that met a size threshold and

demonstrated administrative capacity to carry out the SAFMR program. The five PHAs in the

demonstration were recruited at random from clusters of eligible PHAs and accepted the

invitation to participate in the demonstration. The demonstration for the five PHA participants

started at the end of 2012 and ended on September 30, 2016. More information about the design

of the selection of PHAs for the demonstration is available in appendix A.

Evaluation of the Demonstration

In 2015, HUD contracted to conduct an evaluation of the SAFMR demonstration—examining

whether and to what extent providing higher subsidies in ZIP Codes where rents are higher and

lower subsidies in ZIP Codes where rents are lower—helps HCV holders to better access areas

of opportunity. HUD requested that the evaluation also examine how the switch to SAFMRs

affects HCV holders and landlords, as well as HCV subsidy and administrative costs.

In addition to the five PHAs that agreed to participate in the SAFMR demonstration, this study

includes 2 (of 12) PHAs from the Dallas, Texas FMR metropolitan area that have used SAFMRs

since 2011 as a result of a legal settlement. The 2 additional PHAs are—

•  Housing Authority of the City of Dallas.

•  Housing Authority of Plano.

Dallas is the largest of the PHAs in the Dallas metropolitan area. In contrast, the Plano PHA

serves a smaller number of HCV holders and a smaller share of Plano’s HCV holders are

members of a minority group. Appendix A presents the process by which HUD selected the

PHAs and the implication for interpreting our eventual findings.

In this report, we refer to this group of seven PHAs collectively as the SAFMR PHAs. Exhibit 1-1

describes this group. For each, the exhibit also presents selected PHA-level characteristics of its

residents and the neighborhoods in which they live, drawn from the 2011 Picture of Subsidized

Housing data. As the exhibit reflects, the study includes a group of PHAs with a range of

demographic and housing market characteristics.

3

Exhibit 1-1: Small Area Fair Market Rent Demonstration PHAs and Selected Characteristics

Average

HCV

Tract

Two-

Income

Single-

Bedroom

(Percent

Minority/  62 Years

Tract

Family

HCV

FMR 2012

of Local

Hispanic

Old Plus

Minority

Owner

PHA Name (Cluster)

Units

($)

Median)

(%)

(%)

(%)

(%)

Chattanooga Housing

Authority (5)

3,183

628

21

82/2

15

54

48

Housing Authority of Cook

County (6)

12,622

958

21

83/3

18

58

52

Housing Authority of the City

of Laredo (2)

1,368

696

25

100/99

20

95

46

City of Long Beach Housing

Authority (7)

6,556

1,447

20

88/11

23

83

17

Town of Mamaroneck Housing

Authority (4)

647

1,580

21

54/22

32

39

32

Housing Authority of the City

of Dallas (5/6)

18,525

868

21

94/5

17

67

44

Housing Authority of Plano

908

868

25

65/3

29

39

50

FMR = Fair Market Rent. HCV = housing choice voucher. PHA = public housing agency.

Notes: Average HCV income percent of local median, minority and Hispanic percent, and percent 62 years old plus are average characteristics of HCV holders. Tract minority percent is defined as minorities as a percentage of total population in the census tract where HUD-assisted families reside. Tract single-family owner percent is defined as the percentage of households that are owner and occupants of single-family detached homes in the census tract where HUD-assisted families live.

Sources: Clusters determined by HUD for Small Area Fair Market Rent demonstration, except Dallas and Plano, which are assigned to a cluster based on their characteristics; descriptive data from the 2011 Picture of Subsidized Housing PHA-level data Research Questions

We organize the key policy questions prompting this study into two categories: (1) the impacts

of SAFMRs on HCV holders, and (2) the fiscal and administrative impacts of SAFMRs on

PHAs.

The effect of SAFMRs on HCV holders and administering PHAs is the primary focus of this

evaluation. Examining landlord awareness and perceptions of the change (Research Question 1d)

is important, however, because it likely affects both HCV holders and PHAs. Landlords’

willingness to accept HCVs affects HCV holders’ ability to use vouchers. PHAs also have

significant interactions with landlords in setting and negotiating rental contracts that may be

affected by changing to SAFMRs. We will interview landlords and residents in Phase 2 of our

research.

What are the impacts of SAFMRs on HCV holders?

a.   What is the  potential  of SAFMRs to increase access to opportunity and integrated

neighborhoods for HCV holders? That is, how does the number of units with rents below

SAFMRs in high-rent neighborhoods compare with the number below metropolitan area

4

FMRs? How do differences in the number of units with rents below SAFMR and with

rents below FMR relate to measures of neighborhood opportunity and other

neighborhood characteristics? How does this potential vary across metropolitan areas

with different housing markets?

b.   Did changing to SAFMRs increase existing HCV holders’ likelihood of moving to

higher-opportunity or more integrated neighborhoods? Were new HCV holders more

likely to locate in higher-opportunity neighborhoods under SAFMRs than under

metropolitan area FMRs? How did the characteristics of HCV holders’ neighborhoods

change after adoption of SAFMRs?

c.   How did the change to SAFMRs affect HCV holders’ experience with the HCV

program? What effects did the change have on tenants’ rent burdens? Did they

understand how the change affected their housing options? What was tenants’ perception

of the change? Did the change to SAFMRs influence HCV holders’ success in using

vouchers to rent units?

d.    To what extent were landlords, both those participating in the HCV program and

potential new participants, aware of the change in the HCV program? How did landlords

perceive the change?

What are the fiscal and administrative impacts of SAFMRs on public housing authorities?

a.   What are the implications for subsidy costs and ongoing administrative expenses?

b.   What were the financial and human costs of the one-time transition from metropolitan

area FMRs to SAFMRs? How did the change affect PHA interaction with HCV holders

and potential holders? How did the change affect PHA interaction with landlords?

This interim report documents initial responses to these questions. Responses will be updated in

the study’s final report, which will be available in 2018.

Structure of This Report and Introduction to Key Methodological Approaches

The following is an overview of the structure of this report along with introductory notes about

some of the principal methodological approaches used.

Chapter 2. Hypothesized Impacts on Housing Choice Voucher Holders and PHAs. This

chapter presents a discussion of the hypothesized impacts of the move to SAFMRs.

Chapter 3. Evaluation Data and Methodology. This chapter describes the data and

methodology used in this evaluation. The data collection and analysis for this study is to occur in

two phases. Phase 1, the focus of this report, is based on an initial analysis of secondary data for

the years 2008 through 2015, as well as information collected during one site visit to each of the

seven SAFMR PHAs in May and June 2016 to gather data for the analysis of implementation

and administrative costs.

In Phase 2, we will conduct a second round of site visits to the seven SAFMR PHAs. During the

visits, we will interview a sample of 70 HCV holders and 35 landlords in SAFMR areas, and we

will update information on administrative processes and costs. The interviews will build on the

findings in Phase 1 and will complement and enhance the quantitative analyses. In Phase 2, we

will also update the analysis of secondary data with 2016 and 2017 data. We will combine and

synthesize Phase 1 and Phase 2 findings in the final report in 2018.

5

An important consideration in shifting to SAFMRs is how HCV holders and landlords perceive

the shifts. In particular, we want to know whether both existing and new HCV holders

understood how the change affected their housing options and whether it led movers to search

new neighborhoods or affected the rate of moving of existing HCV holders. Similarly, we want

to know whether landlords were aware of the change in the HCV program and whether this

change affected their willingness to rent to HCV holders and the level at which they set rents.

The study’s final report will address these questions more fully to allow for sufficient time to

pass so that HCV holders and landlords have sufficient experience with the SAFMRs to have an

informed view.

To inform a determination of the extent to which neighborhoods provide opportunity, we have

separately included measures of poverty rates, school quality, access to jobs, and environmental

quality. We have created an overall composite index of opportunity using these neighborhood

characteristics. 6 We have relied on a combination of census and administrative data, and where possible, on the nationally available metrics that HUD includes in its Affirmatively Furthering

Fair Housing Assessment Tool.

Chapter 4. Changes in Housing Choice Voucher Holders’ Potential Access to High-

Opportunity Neighborhoods. Chapter 4 assesses the  potential of SAFMRs to increase access to opportunity by comparing the share of all units renting below SAFMRs with the share renting

below FMRs across characteristics of neighborhoods in the service areas of the SAFMR PHAs

and a set of comparison PHAs. In this analysis, we examine the extent to which shifting to

SAFMRs will increase the share of rental units available to HCV holders in high-rent areas—

finding that they do in fact have the potential to achieve this outcome.

To classify neighborhoods, we use the ZIP Code  rent ratio—the ratio of the ZIP Code median

rent to the metropolitan area median rent. HUD uses this ratio in calculating SAFMRs, which are

each ZIP Code’s rent ratio multiplied by the metropolitan area FMR (subject to a maximum cap

of 150 percent of the metropolitan area FMR and a minimum floor of the state nonmetropolitan-

area minimum). 7 For clarity of presentation, we group ZIP Codes into those with rent ratios below 0.9, between 0.9 and 1.1, and greater than 1.1. 8 The first category corresponds to neighborhoods with lower rents, and the third category corresponds to neighborhoods with

higher rents. We then show that the rent ratio is strongly correlated with measures of

neighborhood opportunity—poverty rate, school quality, access to jobs, environmental quality,

and a composite overall index of opportunity combining the four measures.

We complete our analysis of the potential of SAFMRs to increase access to opportunity by

examining the extent to which SAFMRs lead to increases in the number of units affordable to

6  We also gathered data on property and violent crime rates for the analysis. As anticipated, crime data are not available for all the SAFMR PHAs or across all the PHA service areas. In reviewing this draft interim report, we determined that that pattern of available data was confounding results for the crime measures. As such, we are revisiting the analysis of crime data for possible inclusion in the final report.

7  In our data analysis, we do not repeat HUD’s analysis of the special tabulations of ACS data needed to calculate the ratio of the ZIP Code median rent to the metropolitan area median rent. Rather, we calculate rent ratios by inverting the formula used to calculate SAFMRs. That is, we determine a ZIP Code’s rent ratio as the two-bedroom SAFMR divided by the two-bedroom metropolitan area FMR.

8  In Phase 2, we will consider adding rent ratio groupings of below 0.8, between 0.8 and 0.9, between 1.1 and 1.2, and greater than 1.2 to determine if these five categories can provide better insight to moving to areas of opportunity.

6

HCV holders in high-opportunity areas. To measure whether SAFMRs increase the number of

units affordable to HCV holders in high-opportunity areas, we compare the share of units renting

below SAFMRs with the share renting below FMRs for neighborhoods with lower and higher

levels of the neighborhood opportunity measures.

Chapter 5. Early Impacts of Small Area Fair Market Rents on Housing Choice Voucher

Holders. Chapter 5 provides a preliminary assessment of the  observed  effect of adoption of SAFMRs on location and relocation outcomes of both new and existing HCV holders. In this

section, we follow the same structure of analysis used for the potential effect of adoption of

SAFMRs. Now, instead of reporting the share of all rental units with rents below SAFMRs and

FMRs, we report the  actual shares of HCV holders using vouchers across neighborhoods,

classified by the neighborhood rent ratio, neighborhood opportunity measures and neighborhood

characteristics.

Chapter 6. Perceptions of Tenants’ Experience and Administrative Impacts of Small Area

Fair Market Rents on Public Housing Agencies.  Chapter 6 discusses how the shift to

SAFMRs affected PHAs—in particular, the time and expenses associated with implementing the

demonstration and effects on workloads and processes. The chapter also discusses the impacts on

PHAs’ administrative costs, including one-time transition costs and any ongoing costs.

Chapter 7. Fiscal Effects of Small Area Fair Market Rents on Public Housing Agencies,

Housing Choice Voucher Holders, and Landlords. In Chapter 7, we summarize the observed

changes in rents following introduction of SAFMRs, including Housing Assistance Payment

(HAP) costs, rents to landlords, and tenant contributions to rents. To answer the questions

relating to costs, we use both existing data and data collected specifically for the study. We

compare the change in HAP costs over time for SAFMR sites with the change for comparison

sites, using HUD’s Public and Indian Housing Information Center (PIC) data. We also use

qualitative data collected during site visits to provide context for changes in HAP costs,

including events other than the introduction of SAFMRs that could have affected HAP costs. In

addition, we collected data on site at the SAFMR PHAs to learn about transition costs, the nature

and amount of any higher ongoing administrative costs, and the activities PHAs have undertaken

to support mobility for HCV holders.

Chapter 8. Plan for Phase 2 Data Collection. Chapter 8 provides a preview of remaining

research to be conducted for Phase 2 and the final report.

7

2.  Hypothesized Impacts on Housing Choice Voucher Holders and Public

Housing Agencies

This section presents hypothesized impacts on HCV holders of moving to SAFMRs. This is

followed by a discussion of potential impacts on PHAs.

Hypothesized Impacts of SAFMRs on HCV Holders

This section presents hypothesized impacts on HCV holders of using SAFMRs, organized by the

research questions introduced in the section titled Evaluation of the Demonstration. First, we

review the potential for SAFMRs to affect where HCV holders live, looking at the difference in

affordability of units across neighborhoods with differing levels of opportunity under SAFMRs

versus metropolitan area FMRs. Then we discuss how SAFMRs may affect where HCV holders

actually live. We consider how this response might directly affect rents and access to

opportunity. Finally, we discuss how HCV holders may perceive and experience the shift to

SAFMRs. This includes a discussion of the role of landlords in the HCV program, and it

considers how their possible reactions to the shift to SAFMRs might affect HCV holders.

Potential for SAFMRs To Affect Location and Relocation of

HCV Holders

Research Question 1a

SAFMRs are expected to change the permissible range for

payment standards across ZIP Codes within a metropolitan

What is the potential of SAFMRs to

area. 9 This change is the primary mechanism through which

increase access to opportunity and

integrated neighborhoods for HCV

SAFMRs might alter HCV holders’ access to and location in

holders? That is, how does the

high-opportunity neighborhoods. However, the extent of this

number of units with rents below

change depends on the variation in rents across a metropolitan

SAFMRs in high-rent

area and how a PHA establishes payment standards—for both

neighborhoods compare with the

metropolitan area FMRs and SAFMRs. Our first hypothesis is

number below metropolitan area

FMRs? How do differences in the

that using SAFMRs instead of metropolitan area FMRs to

number of units with rents below

determine payment standards will substantively increase HCV

SAFMR and with rents below FMR

holders’ ability to afford to rent units in high-opportunity

relate to measures of neighborhood

neighborhoods.

opportunity and other neighborhood

characteristics? How does this

The following two factors affect whether HCV holders can

potential vary across metropolitan

successful y use vouchers in a particular neighborhood. First, is a

areas with different housing

good selection of rental units in the neighborhood at the SAFMR

markets?

payment standard? Second, are neighborhood landlords wil ing to

participate in the HCV program? We address the first factor here

and the second factor in the following discussion on landlords.

9  Both Collinson and Ganong (2015) and Geyer (2017) include a helpful, more formal analysis of the potential for SAFMRs to change tenant location outcomes. Collison and Ganong’s indepth analysis includes a finding that

“tilting” the rent ceiling in a geography, by such means as introducing SAFMRs, may be effective in increasing the quality of neighborhoods in which HCV holders live. Geyer estimates an empirical housing demand model

and simulates that a shift to tract-level FMRs would substantially increase the quality of neighborhoods chosen by HCV holders.

8

In general, the basic rule from which PHAs set payment standards is the same under

metropolitan area FMRs and under SAFMRs—payment standards must fall between 90 and 110

percent of them,  except that the starting FMR is at the metropolitan area geography under FMRs

and at the ZIP Code under SAFMRs. Exhibit 2-1 depicts the hypothesis that under SAFMRs,

fewer units should be available to HCV holders in low-rent neighborhoods, and more units

should be available in high-rent neighborhoods than with metropolitan area FMRs. Exhibit 2-1 is

a graph of the expected relationship between the share of units in a ZIP Code that are available

with an HCV (that is, rent at or below the FMR) and the median rent in the ZIP Code under both

SAFMRs and a metropolitan area FMR. The line showing the relationship between the share of

units available and ZIP Code rents is steeper under a metropolitan area FMR and flatter under

SAFMRs (because SAFMRs vary based on the ZIP Code median rent). A greater share of units

within a ZIP Code are available in ZIP Codes with lower rents under metropolitan area FMRs

than under SAFMRs, and a smaller share of units within a ZIP Code are available in ZIP Codes

with higher rents under metropolitan area FMRs.

Exhibit 2-1: Hypothesized Neighborhood Share of Units Available Under SAFMRs and

Metropolitan Area FMRs

FMR = Fair Market Rent. HCV = housing choice voucher. SAFMR = Smal  Area Fair Market Rent.

The lines cross where the rent ratio equals 1, and SAFMR equals metropolitan area FMR. This

flattening of the share of units that are available under SAFMRs should influence where HCV

holders can live in multiple ways. It changes the composition of units that are available to new

HCV holders or existing holders who move. Also, some units (in areas where SAFMRs are

lower than metropolitan area FMRs) will no longer be available to existing HCV holders after

9

the full phase in of SAFMRs. Some units where HCV holders already live could be among those

that are no longer available. The strong tails in the SAFMR curve reflect the fact that HUD caps

SAFMRs at both ends of the rent continuum. 10

The exhibit also highlights that the expected effect of SAFMRs within a given neighborhood will

depend on the ZIP Code rent level. Where the ZIP Code median rent is lower than the

metropolitan area median rent, the rent ratio will be less than one, and SAFMRs will reduce the

share of units available to HCV holders. Where the ZIP Code median rent is similar to the

metropolitan area median rent, SAFMRs do not change the number of units that are available.

Where the ZIP Code median rent is higher than the metropolitan area median rent, a larger share

of units will be available under SAFMRs.

Another factor that will determine whether a good selection of rental units is available in

neighborhoods under SAFMRs is the relative number of rental units in ZIP Codes with median

rents below and above the metropolitan area median rent. If fewer rental units are in higher-rent

ratio ZIP Codes than in lower-rent ratio ZIP Codes, the total number of units with rents falling

below the SAFMR-based payment standards will be lower than the total number falling below

the FMR-based standards. The correlation between opportunity and neighborhood rent levels

determines the extent to which this change in the share of units available to HCV holders

increases the potential for HCV holders to access high-opportunity neighborhoods. The

assumption that areas with higher rents also have greater access to opportunity is the basis of the

SAFMR demonstration and HUD’s interest in moving to SAFMRs.

HUD defined opportunity areas as places with greater access to employment and transportation and

better educational opportunities (Federal Register, 2010). This evaluation uses the following

quantitative measures of neighborhood opportunity: poverty rate, school proficiency, employment

access, and environmental quality. We explored the possibility of including access to transportation

in our neighborhood opportunity index. Data on this indicator are not available across all the

SAFMR PHAs. Where data are available, they are highly correlated with employment access.

We, therefore, omitted this indicator from our analysis. Chapter 3 details the included measures.

In light of the factors previously noted, some PHAs (more than others) may experience a greater

potential impact of a shift to SAFMRs on the ability of HCV holders to access high-rent and

high-opportunity areas. The change in the share of units available to HCV holders will depend on

the existing rent dispersion across ZIP Codes. In general, areas with greater dispersion in rents

across neighborhoods within a PHA will have a greater potential for SAFMRs to affect access to

high-opportunity neighborhoods. By contrast, if most ZIP Codes have a median rent that is close

to the metropolitan area median rent, rent ratios will be close to 1, so SAFMRs (calculated as

rent ratio multiplied by FMR) will be close to metropolitan area FMRs. In this case, the number

of units available to HCV holders will be similar under SAFMRs and metropolitan area FMRs.

Additionally, how PHAs exercise the latitude they have to set payment standards within 90 and

110 percent of the FMR and the extent to which HUD grants them exceptions under FMRs for

payment standards outside the basic range may also create differences in the impacts of moving

10  SAFMRs are set to at least the state nonmetropolitan minimum. Therefore, all units in the lowest-rent ZIP Codes (where rents are below this floor) would rent for less than the SAFMR. SAFMRs are capped at 150 percent of the HUD Metro Area FMR. ZIP Codes with very high rents could exist where no units rented below the SAFMR.

10

to SAFMRs. Our analysis of the potential impact of a shift to SAFMRs will document variation

in how SAFMRs affect the share of units affordable across all PHAs in our analysis sample. 11

Observed HCV Holders’ Locations and Relocations

Our next hypotheses are about how SAFMRs will actually affect HCV holders’ decisions about

where to live. We anticipate that some HCV holders, including both new HCV holders and

existing holders, will respond to the option of higher payment standards in higher-opportunity,

higher-cost neighborhoods and lower payment standards in

areas with lower median rents by opting to use voucher in

Research Question 1b

higher-opportunity neighborhoods. Chapter 4 examines the

following specific hypotheses.

Did changing to SAFMRs increase

existing HCV holders’ likelihood of

•  The introduction of SAFMRs will increase existing

moving to higher-opportunity or

HCV holders’ likelihood of moving both to take

more integrated neighborhoods?

advantage of higher subsidies in higher-opportunity

Were new HCV holders more likely

to locate in higher-opportunity

areas and to leave units where subsidies are

neighborhoods under SAFMRs than

decreased as a result of the lower payment standards.   under metropolitan area FMRs?

How did the characteristics of HCV

•  Due to the introduction of SAFMRs, HCV holders

holders’ neighborhoods change after

who move will be more likely to move to higher-

adoption of SAFMRs?

opportunity neighborhoods.

•  Due to the introduction of SAFMRs, new HCV holders will be more likely to initially

locate in higher-opportunity neighborhoods.

•  Fewer HCV holders will live in areas with lower opportunity measures as a result of

SAFMRs.

Ultimately, we also anticipate that living in higher-opportunity neighborhoods will result in

longer-term improvements in outcomes for HCV holders and particularly for their young

children. We note these potential longer-term effects here, although these longer-term effects are

beyond the data and timing limits of this evaluation.

•  Adult HCV holders may see improvements in education outcomes, employment, and

earnings from exposure to neighborhoods with better access to jobs and higher levels of

education. (Although we anticipate these improvements would come over a number of

years, this evaluation will examine short-term employment and income gains only.)

•  Recent research determined that young children in Moving to Opportunity treatment

families had significantly improved college attendance rates and earnings in young

adulthood (Chetty, Hendren, and Katz, 2016). Children in families who move to higher-

opportunity neighborhoods could experience similar benefits because of SAFMRs. (This

evaluation will not examine these outcomes.)

11  Each of the five demonstration PHAs is from a different cluster, which were determined prior to selection based on the number of vouchers, FMR levels, and share of working-age heads of household among HCV holders. As

such, we do not anticipate having variation across SAFMR metro areas in preexisting rental patterns to

empirically test how differences in potential impact across SAFMR metro areas are related to observed HCV

holders’ location and relocation decisions.

11

•  Long-term health outcomes improved for households that moved to lower-poverty

neighborhood in the Moving to Opportunity experiment (Ludwig et al., 2011). HCV

holders induced to live in higher-opportunity neighborhoods through SAFMRs may also

experience these benefits. (This evaluation will not examine these outcomes.)

Other factors exist beyond the financial incentives of varying payment standards that influence

the extent to which these hypotheses will hold. For example, HCV holders’ preferences for

neighborhoods may or may not align with neighborhoods that have higher levels of opportunity

as defined by HUD and this study. In addition, some key determinants of household location

choices might work against locating in high-opportunity neighborhoods. These include

landlords’ willingness to participate in the HCV program and accept vouchers and HCV holders’

ties to extended family and community in neighborhoods where they currently reside.

HCV Holders’ Experience With the Program

A successful move to a high-opportunity area requires a

motivated HCV holder who is able to find a qualifying unit

Research Question 1c

with affordable rent, a willing landlord, and a PHA that is

How did the change to SAFMRs

able to complete unit approval and rental activities promptly   affect HCV holders’ experience with

and effectively. HCV holders’ experience with the program

the HCV program? What effects did

also wil  depend on landlords’ responses. Therefore, an HCV

it have on tenants’ rent burdens?

holder’s experience with the program and the subsequent

Did they understand how the change

perceptions will be affected by a combination of personal

affected their housing options?

What were their perceptions of the

circumstances and priorities, the ability of the PHA to

change? Did the change to SAFMRs

execute, and the landlord’s behavior. Phase 2 research will

influence HCV holders’ success in

largely address hypotheses discussed in this section, the

using vouchers to rent units?

results of which will be included in the evaluation’s final

report.

Personal Circumstances and Priorities

The goal of SAFMRs is to make more units in high-opportunity areas available to HCV holders.

Access to units in neighborhoods that were previously out of reach may be attractive to HCV

holders who are dissatisfied with their current neighborhoods or units but were unable to find

alternative affordable housing.

At the same time, the goal of moving to a high-opportunity area may or may not be in sync with

an HCV holder’s situation at a given point in time. Although affordability is a primary concern,

tenant behavior is often driven by other social, emotional, or logistical factors, including

hesitation to switch their children’s schools (DeLuca and Rosenblatt, 2010), fear of the unknown

(for example, not knowing the area or fearing rejection) (Charles, 2006), or lack of information

about the benefits of opportunity neighborhoods (Darrah and DeLuca, 2014).

Even for HCV holders who are eager to move, issues such as the cost of moving, including

security deposits and first and last month’s rent, can be significant barriers. Many low-income

families also understand the bang-for-the-buck trade-off they face when searching for housing.

They can find cheaper units with more bedrooms or more amenities in higher-poverty areas,

which may accommodate their families’ needs for space and amenities (Rosenblatt and DeLuca,

2012; Wood, 2014). To assess the impact of SAFMRs, it is important to understand these

additional influences on HCV holders’ decisionmaking.

12

PHA Execution

PHA actions can have large impact on an HCV holder’s overall success with the program and

particularly with mobility initiatives. The concept of payment standards is difficult for some

HCV holders to grasp, even when only one payment standard exists per bedroom size. The

PHA’s success in explaining the opportunities presented by SAFMR, the impact that multiple

payment standard areas have on the HCV holder’s options, and how much a HCV holder will

pay could have a significant influence on outcomes.

The PHA’s decisions regarding payment standards also could factor into HCV holder success

rates. If PHAs are concerned about having sufficient funding and use flexibility to keep payment

standards lower than the market, HCV holders may not be able to access some high-opportunity

areas. In addition, SAFMRs may attract landlords in high-opportunity areas who have higher

expectations for the PHA’s customer service and timeliness in inspecting and approving units or

negotiating rent based on the PHA’s exercise of the rent reasonableness standard. The PHA’s

ability to respond will be an important factor in HCV holders’ success.

Landlords’ Response

The market response likely will be based, in part, on the extent to which the SAFMRs and the

resulting payment standards actually provide sufficient funding that will make it possible for

HCV holders to rent units in high-opportunity areas. Although landlords in lower-cost

neighborhoods may consider lowering (or not increasing) rent to retain a good tenant, it is less

likely that landlords in high-opportunity areas will make rent concessions.

However, landlords are not required to participate in the HCV program in most jurisdictions, so

the success of SAFMRs in enabling HCV holders to access high-opportunity areas will depend

on landlords’ willingness to participate in the program. Discrimination, actual and perceived,

will also affect whether HCV holders seek, find units, and remain in high-opportunity areas,

particularly because landlord discrimination is common in high-rent neighborhoods (Galiani,

Murphy, and Pantano, 2012). Also evidence shows that some landlords more aggressively

market units to HCV holders when the units are in higher-poverty neighborhoods (Rosen, 2014).

The combination of these forces—HCV holders’ personal circumstances and priorities, PHA’s

ability to execute, and landlords’ responses—will interact to determine the impacts on HCV

holders. For this interim report, we describe some aspects of HCV holder experiences with the

program. This report focuses on impacts that are measurable using administrative data. The

study’s final report will include additional qualitative measures of impact that will be estimated

based on interviews with HCV holders and landlords and additional analysis exploring the

quantitative findings.

Hypothesized Impacts of SAFMRs on PHAs

The switch to SAFMRs will affect both HAP contract costs (the level of rent subsidy) and

administrative costs.

SAFMRs are likely to affect PHAs in several ways. First, they will likely cause impacts on the

subsidy costs per voucher. PHAs have fixed budgets for vouchers during any particular year, so

increases in subsidy costs will reduce the number of households that can be served (or require

additional funding from HUD to serve the same number of households). Decreases will increase

the number of households (or require less funding). We hypothesize that per-unit subsidy costs

will decline in some neighborhoods and increase in others. The net effect on per-unit subsidy

13

costs will depend on how successful SAFMRs are in

Research Question 2

deconcentrating HCV holders from high-poverty, low-rent

What are the fiscal and

neighborhoods.

administrative impacts of SAFMRs

on public housing authorities?

SAFMRs will also alter some administrative responsibilities of

PHAs. Some changes are one-time costs associated with

a. What are the implications for

subsidy costs and ongoing

implementing SAFMRs, such as adapting the PHAs’  systems of

administrative expenses?

record (the set of automated tools that maintain HCV program

and participant information) to take multiple payment standards

b.   What were the financial and

human costs of the one-time

into account. Other changes may be recurring, such as

transition from metropolitan area

answering HCV holder questions about applicable rents in

FMRs to SAFMRs? How did the

multiple locations. The net effects are unclear, although we

change affect PHA interaction with

anticipate that at least short-term costs will increase. For

HCV holders and potential holders?

example, determining payment standards that reflect ZIP Code-

How did the change affect PHA

interaction with landlords?

level SAFMRs may be a more complex process than

determining payment standards using a single metropolitan area

FMR. The demands on PHAs of this added complexity might

be partially offset for a PHA that previously designated multiple payment standard areas through

exception requests to HUD, as the flexibility of SAFMRs reduces the administrative complexity of

set ing payment standards.

In this section, we first discuss the hypothesized impacts of SAFMRs on subsidy costs. Then we

consider the potential impacts on PHAs in terms of ongoing administrative expenses and the one-

time transition from metropolitan area FMRs to SAFMRs.

Implications for Subsidy Costs

In some ZIP Codes, SAFMRs are lower than metropolitan area FMRs. In others, SAFMRs are

higher. The formula for SAFMRs adjusts FMRs to the ZIP Code level, based on how the median

rent in each ZIP Code compares with the metropolitan area median rent. The motivation for the

SAFMR demonstration is that currently a disproportionate share of HCV holders live in ZIP

Codes where median rents are below the metropolitanwide median. With a decline in payment

standards in these ZIP Codes that is of similar magnitude to increases in ZIP Codes with median

rents greater than the metropolitanwide medians, subsidy costs will be lower under SAFMRs if

HCV holders remain in the same units or ZIP Codes. With enough moves to higher median rent

ZIP Codes, however, per-voucher subsidy costs will increase, unless the moves are directly

associated with offsetting increases in HCV holder income (and thus, increases in an HCV

holder’s rent contribution of 30 percent of income).

One analysis of 2014 HUD voucher administrative data and 2015 FMRs and SAFMRs indicates

that voucher costs would drop by about 6 percent if SAFMRs were implemented nationally and

HCV holders continued to live in the same neighborhoods (Sard and Rice, 2015). In practice, as

discussed previously, this result is unlikely, because the change in policy will prompt a

behavioral response (Collinson and Ganong, 2015).

Ultimately, the change in HAP costs will depend on how many HCV holders choose high-rent

neighborhoods. If few HCV holders move to high-rent neighborhoods, we expect per-unit voucher

HAP costs to decrease on average, because the HAP cost in low-rent neighborhoods decrease

without any offsetting increase by moves to high rent neighborhoods. If enough HCV holders

move to high-rent neighborhoods, we expect per-unit voucher HAP costs to increase on average.

14

The net effect could vary over time, as existing HCV holders and landlords may not fully adjust

to SAFMRs for many years. During the transition phase in which families in lower-cost areas are

held harmless for 2 years, there could be increases in subsidy costs as some families move to

higher-cost areas, but others stay in lower-cost areas at higher subsidy levels (due to the

transition period), leading to increases in subsidy costs. After this transition period, lower

payment standards in some neighborhoods may eventually offset initial subsidy increases for

units in other neighborhoods.

Also, landlords in higher-cost neighborhoods may initially be unwilling to accept vouchers,

limiting the number of HCV holders who locate in higher-cost neighborhoods. Over time, with

increased familiarity with the program, more landlords in higher-cost neighborhoods may decide

to participate. This increased participation could lead to gradually increasing subsidy costs.

Chapter 6 details our approach to measuring changes in subsidy costs.

Operational Impacts on PHAs

Recognizing that PHAs participating in the demonstration would incur costs to transition from

metropolitan area FMRs to SAFMRs, HUD provided supplemental administrative fees to

upgrade the computer software to administer the Section 8 voucher program and other necessary

expenses. The increase varied by the number of vouchers the PHA administered to a maximum

of $300,000 (Kahn and Newton, 2013). These expenses include additional outreach and briefings

for families and landlords on the SAFMRs, assistance with relocation issues resulting from the

use of SAFMRs, changes to rent reasonableness determinations, and additional training and

hiring of staff.

Like HUD, we expect that implementation of SAFMR will alter some PHA administrative

responsibilities. It may also increase the volume of transactions the PHA must process or the

level of effort required to complete certain activities. In the short term, as PHAs change policies

and procedures and staff, HCV holders, and landlords adjust to them, the net effect is likely to be

an increase in administrative costs. It is unclear how long the adjustment may take and what the

net effect will be on costs and PHA level of effort over time. This section describes the potential

impacts on PHAs that should be considered and, to the extent possible, measured during the study.

The hypothetical operational impacts on PHAs fall into three categories.

•  One-time impacts are the actions and costs required initially to establish SAFMRs. We

hypothesize these initial actions and costs will be completed within the first year of

SAFMR implementation and, once done, are not repeated.

•  Transitional impacts are activities and costs that represent a learning curve for the PHA,

HCV holders, and landlords. We hypothesize that these activities will generally occur

during the first year of SAFMR implementation.

•  Continuing impacts are program activities or costs that have fundamentally changed as a

result of SAFMR, and we hypothesize that they will continue for the life of the program.

Potential One-Time Impacts

Modifications to PHA Policies and Plans.   We expect modifications to both administrative

plans and PHA plans to implement SAFMRs. Administrative and PHA plans typically contain

language related to establishing payment standards, determining rent reasonableness and HCV

15

holder payments, and encouraging the participation of landlords outside poverty or minority

concentration areas. Some PHAs include extensive procedural documentation in administrative

plans, which may require more expansive modifications.

Modification to PHA Systems.  PHAs’ systems of record that maintain participant information

and other automated tools may require modifications. Systems of record generally contain

payment standards and utility allowances. Systems of records may require changes to

accommodate the necessary number of payment standards, as well as changes to business logic

to permit the selection of different payment standards for the same unit size (number of

bedrooms) in each payment standard area. (HCV units will use new SAFMR payment standards.

Families already in areas where payment standards go down under SAFMRs will maintain HAP

levels regardless of the new payment standard for the first 2 years; however, families who move

into this area will have HAPs capped by the new payment standard immediately.)

Modifications similar to those required for the system of record may also be needed on the rent

reasonableness tool a PHA uses, depending on its functionality. In addition, most PHAs have

some kind of tool to help HCV holders calculate whether units in which they are interested are

affordable. That tool also likely will need adjustments. In addition, the transition from

metropolitan area FMRs to SAFMRs will have human or intangible impacts. For example,

during the short term, staff may experience some confusion during the transition and additional

stress as they learn new procedures and skills. It is also possible that this additional stress will

lead to some staff turnover.

Potential Transitional Impacts

Transitional impacts are activities and costs that begin with the initial implementation of

SAFMR but are likely to continue for multiple years to make the necessary adjustments for

SAFMRs. This category includes changes in activity volume and the level of effort required to

complete activities.

Establishing Payment Standard Areas and Amounts.  PHAs go through the process of

establishing payment standards whenever FMRs change, but the process becomes more complex

with SAFMRs. With a single FMR and payment standard, PHAs have to balance the need to set

payment standards so that total payments are within their available funds, at the same time

ensuring full utilization of funds. They must also balance the competing goals of providing fewer

tenants with more subsidy assistance or helping more tenants with smaller subsidies. Some PHAs

have also used sub-area payment standard areas to make it easier for families to find housing

without the PHA inflating rents in lower-rent areas. Other PHAs, however, may not have

experience setting multiple payment standard areas.

The task of setting payment standards is more challenging with an increased number of FMRs to

consider and the greater emphasis on using payment standards to encourage moves to high-

opportunity areas. One question is simply to define the number of payment standard areas.

Should a separate payment standard exist for every ZIP Code? Is it better to combine multiple

ZIP Codes into a more limited number of payment standard areas? In addition, setting payment

standards in a rational way under SAFMRs may require more knowledge of market conditions in

sub-areas of a PHA’s jurisdiction (although to a significant extent, the SAFMR analysis already

provides the PHA with key information on varying rents by area).

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The first year will be the most challenging, but learning how best to administer multiple FMRs and

to assess their impact on access to high-opportunity neighborhoods likely will be a multiyear effort.

Rent Reasonableness Data and Protocols.   In theory, the rent reasonableness determination for a particular unit is independent from the FMR for the same area. By definition, any FMR area

(large or small) will contain units that should rent for amounts both higher and lower than the

FMR based on the rents of other comparable units in that area. One possibility is that the

determination of rent reasonableness under SAFMRs may need less comparative data, as local

area baseline rents will largely be embedded in the SAFMR. Alternatively, staff may not be as

familiar with the housing stock in high-opportunity areas where few, if any, HCV holders

currently live. Landlords may negotiate more aggressively—for example, by objecting to (or

advocating for) using a comparable unit rent that is in another payment standard area.

Contract Rent Adjustments.  SAFMR payment standards may cause changes in the behaviors

of some current landlords and, thereby, increase the number of requests for contract rent

adjustments or extend contract rent negotiations. In areas where payment standards have

increased, landlords who initially made rent concessions to accommodate specific HCV holders

may be motivated to request contract rent adjustments if they understand that adjustments will

not affect the HCV holder. Similarly, landlords in those areas that have not requested contract

rent adjustments on a regular basis (because they knew any additional increase would be the

burden of the HCV holder) may be more likely to request rent adjustments under SAFMRs.

Inspections.   If SAFMRs are successful in making accessible previously unavailable neighborhoods, HCV holders will become dispersed over larger areas. This dispersal may affect the productivity of

inspectors, increasing time per inspection and also travel costs to inspection sites.

In some locations, inspectors are part of the rent reasonableness process. Such inspectors may

need to become familiar with housing stock and rents in additional areas.

Communication and Outreach Strategy and Materials.   PHAs have multiple audiences that

need different messages about SAFMR. Current program participants (landlords and HCV

holders) will have somewhat different concerns than will applicants on waiting lists and

landlords not yet participating. This variety of concerns will require revisions to written and

electronic materials such as landlord brochures, briefing packets, reexamination packets, web

pages, and briefing videos. Although the biggest impact of this material revision process will be

in the first year of the program, it is likely that PHAs will learn from initial efforts and will

continue to make modifications to outreach approaches and educational materials over time.

Procedures Changes and Staff Training. Detailed procedures and policies will need to be

developed and will require staff training and retraining at all levels. Experience with the program

will both enable and require PHAs to make improvements in procedures and policies over time.

Support for Tenants. Unless a PHA has already administered a mobility program, the extra

effort associated with encouraging and assisting households to consider moving to high-

opportunity areas is likely to increase workloads. HCV holders currently living in areas where

payment standards decrease will need additional attention in order to understand both the timing

and impact of changes on individual situations, as well as housing search assistance and

counseling on the benefits of moving to potentially unfamiliar neighborhoods. As of now, some

PHAs do not bring in current recipient movers for briefings. With SAFMR, they likely will have

to schedule such briefings.

17

Support for Landlords.   Working with new landlords in high-opportunity areas initially may require higher levels of effort for PHA staff until landlords become familiar with the program.

This increased effort may include more or specialized briefings. Housing Assistance Payment

contract and rent negotiations may be more protracted as well. Outreach and education will be

required for current landlords to help with understanding the impact on current HCV holders.

Landlord behaviors and questions will differ for those where payment standards decrease versus

those where payment standards increase.

Potential Continuing Impacts

SAFMRs may create fundamental and permanent changes in program operations and costs after

the transition period.

Inspections.  If SAFMR accomplishes its purpose and HCV holders become more dispersed over

larger areas, this dispersal may have long-lasting effects on inspector productivity and travel

costs.

Support for HCV Holders and Landlords.  Permanently increased levels of support for HCV

holders and landlords may be needed because of the additional complexities and continued

changes of multiple payment standards and the challenges associated with mobility moves.

Quality Assurance.  HUD-50058 errors, stemming from selection of the incorrect payment

standard, are not unusual even with metropolitan area FMRs. Staff may confuse the voucher size

for which a family qualifies with the family-selected unit size when selecting a payment

standard. The need for staff to select among multiple payment standard schedules will increase

dramatically under SAFMR, and the magnitude of this challenge depends on the set up of a

PHA’s system of record. The increased risk of input error may require new and vigilant quality

assurance processes.

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3.  Evaluation Data and Methodology

Description of Primary and Secondary Data Sources

The following describes the combination of primary and secondary data we used to estimate the

impacts of SAFMRs on PHAs, HCV holders, and landlords.

Primary Data

To collect primary data, we made site visits to each of the seven SAFMR PHAs in May—June

2016. Senior project staff conducted site visits and lasted 1 to 2 days. Site visits included one-on-

one and group interviews with executive directors and each location’s multiple key staff

members that were involved in implementing the transition to and administration of SAFMRs.

Site visits focused on PHA experience with one-time, transitional, and ongoing administrative

efforts and the costs related to SAFMRs in 11 different PHA work areas.

One-time (expected to be completed within the first year of transitioning to SAFMRs)—

•  Modifications to PHA policies and plans.

•  Modifications to PHA systems.

Transitional (expected to extend beyond the first year of the transition)—

•  Contract rent adjustments.

•  Communication and outreach strategy and materials for tenants and landlords.

•  Procedures changes and staff training.

•  Support for tenants, including additional tenant briefings and search assistance.

•  Support for landlords.

Ongoing—

•  HCV budgeting and planning, including setting payment standards.

•  Rent reasonableness determinations.

•  Inspections.

•  Quality assurance.

Site visitors also requested other information including documentation of costs, voucher success

rates over time, examples of outreach and training materials, and tenant and landlord briefing

materials. Some PHAs were unable to provide complete documentation of the costs related to

SAFMRs, therefore the discussion in chapter 5 is based primarily on the recollections and

descriptions of direct expenditures and staff hours spent implementing and administering

SAFMRs.

Site visitors also asked PHA executive directors and staff to provide context for SAFMR

implementation. As discussed in chapter 5, local and national factors such as federal budget

sequestration, local market conditions, and local litigation were important in how SAFMRs were

implemented by each PHA.

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The site visits were also used to gather information about whether and how PHAs track tenant

experiences (such as number of units visited and location of units visited during the housing

search) that can supplement our understanding of the household-level impacts of SAFMRs on

HCV households.

Site visitors also asked PHAs for their perspectives on the experience of HCV holders and

landlords with SAFMRs. Our analysis of household-level impacts described in chapter 5

includes this PHA staff insight into tenant experiences. The data collection protocol for Phase 1

site visits is in appendix C.

During the summer of 2017, the second round of site visits will focus on the impacts of the

SAFMR demonstration on HCV holders and landlords and will include interviews and focus

group discussions with members. Summer 2017 site visits will also follow up with PHAs on

administrative efforts and costs related to the ongoing administration of SAFMRs.

Secondary Data

Secondary and administrative data used in the analysis include metropolitan area FMRs,

SAFMRs, ZIP Code tabulations of rent distributions, and administrative PIC data maintained by

HUD. 12 Our analysis also includes two types of neighborhood-level indicators—opportunity measures and neighborhood characteristics. Neighborhood-level indicators drew from the U.S.

Census Bureau’s American Community Survey (ACS), the National Center for Education

Statistics, local jurisdictions (in the case of crime indicators), and the U.S. Environmental

Protection Agency.

FMR, SAFMR, and rent distributions. To improve the usability of SAFMRs for HCV holders and

PHAs, HUD chose to define SAFMRs by ZIP Codes rather than by alternative small geography

concepts such as a census tract. ZIP Codes are commonly known and available to HCV holders

considering a particular unit, whereas census geographies are not. To facilitate our analysis, HUD

provided special ZIP Code-level tabulations of the ACS 2008–2012 and ACS 2009–2013 data files

that include counts of units by number of bedrooms and rent range for ZIP Codes. The ranges start

small, in increments of $50. 13 These fine increments for ZIP Code-level rent distributions enabled us to analyze the effects of SAFMRs on the potential access to high-opportunity neighborhoods at

the ZIP Code corresponding to the geography at which SAFMRs are defined.

The Census Bureau provides HUD special tabulations of ACS data on which FMRs are updated

annually. Currently, FMRs for the HCV program are determined for metropolitan areas,

nonmetropolitan counties, and areas known as HUD Metro FMR Areas. SAFMRs are based on

ZIP Codes, approximated by ZIP Code Tabulation Area. The most recent SAFMRs available are

for fiscal year 2017.

12  Data from the U.S. Census Bureau were at the level of ZIP Code Tabulation Area (ZCTA), which are the Census Bureau’s representation of ZIP Code service areas defined by the U.S. Postal Service. Although USPS ZIP Code service area definitions may change, ZIP Code service areas and ZCTAs are nearly always identical. Our analysis required the standard translation of ZCTAs to postal ZIP Codes and reverse that is necessary to link census measures to a set of addresses.

13  The 5-year ACS reports the number of units in 21 categories, where rent is less than $100, then with cutoffs increasing by $50 up to $799, then $800–$899, $900–$999, $1,000–$1,249, $1,250–$1,499, $1,500–$1,999, and

$2,000 and up.

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PIC Data.  PIC is HUD’s administrative data on the Housing Choice Voucher and public

housing programs. PIC has individual-level detail on household characteristics and income and

rent information that determines both the amount a HCV holder pays and the amount of the

voucher subsidy. For this interim report, we requested HCV program PIC data from HUD for

each of the seven SAFMR public housing agencies and PIC data for an additional 138 PHAs as a

comparison group in the study. From the same HUD-defined clusters as the five PHAs in the

SAFMR demonstration, the additional 138 PHAs were eligible to participate in the SAFMR

demonstration but were not invited or did not accept the invitation. We use the additional 138

PHAs to provide a comparative analysis for the business-as-usual HCV program under Fair

Market Rents.

We requested PIC data at the HCV household level for our primary outcomes of interest and

analysis variables.

•  ZIP Code and census tract where a voucher is used.

•  Whether a household moves.

•  Tenant income and rent payments.

•  Total HAP.

•  Household demographics.

These measures were collapsed to an annual basis (using the ZIP Code and census tract

combination where a household spent most of the year), beginning in 2009 and extending

through 2015. For greater presentation clarity, we focused on 3 years of data in our analysis.

•  2010—before SAFMRs were implemented.

•  2013—when SAFMRs were first implemented, but new SAFMR payment standards were

not binding for HCV holders previously under lease.

•  2015—after all HCV holders in SAFMR PHAs were subject to SAFMR-based payment

standards.

Initial analyses showed that comparisons of 2010 versus 2015 provided the most relevant and

clear information on the effects of SAFMRs, therefore this comparison is the focus of much of

the presented analysis.

To limit the amount of personally identifiable information in the data transferred from HUD to

the research team, we used household identifiers unique to the study and reported household

location at the ZIP Code and census tract level, rather than individual addresses. This protection

of personal data affected the extent to which we were able to observe households moving within

a neighborhood. We used the limited characteristics available in the data extract about the unit in

which a household resided to construct a proxy for whether a household moved without changing

census tract or ZIP Code. These characteristics are the construction year of the unit, the number

of bedrooms in the unit, and the property type (for example, high-rise apartment versus

townhome). If these characteristics changed from one year to the next, but the census tract and

ZIP Code did not, we inferred that a household had moved within its census tract and ZIP Code.

The caveat is that this proxy will not capture a move between two units in the same ZIP Code

and census tract that commonly share all three characteristics.

21

PHA Service Areas.  For analysis of the potential for Small Area Fair Market Rents to affect

neighborhood access, we determined the set of ZIP Codes within a PHA’s service area to know

where HCV holders could use a voucher under SAFMR payment standards. Because SAFMRs

are defined for ZIP Codes, we identified a list of ZIP Codes for each PHA in our analysis that

constituted the PHA’s service area. 14 For the SAFMR PHAs, we reviewed the PHA

administrative plans for the descriptions of each PHA’s service area, then used geographic

crosswalks and geographic information system, or GIS, analysis to determine ZIP Codes

defining that PHA service area. 15 For the relevant analyses of the 138 comparison PHAs, we relied on PIC data to identify ZIP Codes where HCV holders actually reside. To capture ZIP

Codes that might be more affordable under SAFMRs where HCV holders could live, we used

GIS analysis to identify all ZIP Codes within the same county that were geographically adjacent

to residences of some HCV holders.

Share of Units Renting Under Metropolitan Area FMRS and SAFMRs by ZIP Code.  As a

measure of how SAFMRs might change an HCV holder’s ability to use a voucher, we calculated

the percentage of housing units in each ZIP Code that rent below the metropolitan area FMR and

below the SAFMR using the special tabulations of ACS data. 16 To calculate the share of units in each ZIP Code accessible to an HCV holder for each time period (before and after introduction

of SAFMRs), we divided the total number of units that rented below the FMR or SAFMR by the

total number of units in the ZIP Code (from ACS data). 17

Opportunity Measures.  Exhibit 3-1 presents the opportunity measures used for this evaluation.

The opportunity measures designed for this study are all initially derived from census tract-level

measures. To facilitate analysis corresponding to the ZIP Code-based SAFMRs, we used

population-weighted crosswalks to translate the indexes to the ZIP Code level.

14  No known data source currently exists that provides definitions of PHA service areas.

15  The Mamaroneck HCV program is one of 17 distinct HCV programs serving parts of Westchester County, NY, a county of nearly one million people. Although the PHA’s original service area was Mamaroneck village, the

Town of Mamaroneck, and Larchmont village, the PHA merged with another PHA in 2008 and now has voucher

holders throughout Westchester County. Although the PHA has a relatively small HCV program of between 600

and 700 units, after reviewing the PHA’s payment standard schedules and conferring with the housing authority staff on what they consider to be the service area, our analysis of the Mamaroneck PHA’s rental market was expanded to include all Westchester County.

16  This measure does not account for rent reasonableness determinations or how PHAs may set payment standard or request exceptions. As such, the share of units renting under FMR or SAFMR may overstate actual affordability, in neighborhoods where payment standards are both relatively higher and relatively lower under SAFMRs as

compared with metropolitan area FMRs. A PHA may determine the rent for a particular unit is not reasonable even if it falls below the payment standard for the area. Our primary data collection includes discussions with PHAs about whether the adoption of SAFMRs affects rent reasonableness determinations. The measure takes into account rent data and unit counts and FMRs and SAFMRs for all different unit sizes (number of bedrooms). The rent data were in bands, so we had unit counts for different levels of rent for each unit size. We compared those rents with the FMR and SAFMR for the unit size to determine the number of units that rented below the FMR and SAFMR.

17  The process by which PHAs set payment standards is often complex and has many inputs and contributing

factors. As such, we did not develop an empirical model of this process to predict payment standards for the comparison analysis PHAs in this evaluation. Rather, we used the difference between SAFMRs and metropolitan area FMRs as a proxy for the difference between payment standards set based on SAFMRs and payment

standards set based on metropolitan area FMRs.

22

Exhibit 3-1: Opportunity Indicators

Opportunity Indicator

(Data Source)

Description

Overall opportunity index

The overall opportunity index was created specifically for this evaluation. It is the

(Composite of the other opportunity

percentile rank by renters in the metropolitan area of the simple average of the

indicators)

percentile rank indexes for the share of nonpoor, public school quality, employment

access, and environmental hazards.

Percent nonpoor

ACS 5-year estimates provide the percent nonpoor for each census tract. The

(American Community Survey [ACS] 5-

percent nonpoor is the ratio of the population above the poverty level to the total

year estimate, 2010–2014)

population for whom we determined poverty status. Note that we use the rate

nonpoor (1 minus poverty rate) rather than the more traditional poverty rate, so that

the index can be consistent with the other indexes and combined into a single

composite measure.

Public school quality

School-level data on state examinations for 4th grade students approximate the

(School Proficiency Index, 2011–2012)    quality of local public schools. We base the measure on the public school(s) nearest to each block group and school zone from the School Attendance Boundary

Information System. We weighted block group data by numbers of households to

create census tract-level data. The higher the score, the higher the school system

quality is in a neighborhood.

Employment access

This index measures the access a neighborhood has to employment opportunities

(Jobs Proximity Index, 2010)

as measured by the distance between block groups and job locations weighted by

employment size. We weighted block group data by numbers of households to

create census tract-level data. The higher the index value, the better the access to

employment opportunities for residents in a neighborhood.

Environmental hazards

The Environmental Health Hazard Index is a tract-level index of potential exposure

(Environmental Health Hazard Index,

to toxins based on National Air Toxic Assessment data from the U.S. Environmental

2005)

Protection Agency. The higher the value, the better the environmental quality of a

neighborhood.

To facilitate comparisons across the opportunity measures and across geographies, we

normalized the percent nonpoor, public school quality, employment access, environmental

hazard, and crime   indexes to be the percentile of the raw index within the population of renters in the metropolitan area. For example, a public school quality percentile score of 50 for a ZIP Code

indicates that one-half of the renters in a metropolitan area live in ZIP Codes with a school

quality measure lower than that of the ZIP Code, and one-half live in ZIP Codes with a school

quality measure higher than that of the ZIP Code. To create the overall opportunity index, we

average these component index percentile scores and calculate the percentile of the average score

within the metropolitan area.

For clarity of presentation, we categorized ZIP Codes using both the rent ratio measure

(neighborhood rent levels relative to metropolitan area rent levels) and the overall opportunity

23

index. For the rent ratio, we created three groups using cutoffs of 0.9 and 1.1 to identify lower,

middle, and higher-rent ratio neighborhoods. 18

•  Below 0.9—the median rent in a ZIP Code is less than 90 percent of the metropolitan

area median rent.

•  Between 0.9 and 1.1—the ZIP Code median rent is within 10 percent of the metropolitan

area median rent.

•  Above 1.1—the ZIP Code median rent is more than 110 percent of the metropolitan area

median rent.

For each individual opportunity measure (and for the composite index of opportunity measures),

we use cutoffs of the 25th and 75th percentiles within the applicable metropolitan area to again

create three groups.

•  ZIP Codes with opportunity values below 25 contain the one-fourth of renters in the

metropolitan area with the lowest score for that opportunity measure or for the index as a

whole.

•  ZIP Codes with opportunity values above 75 contain the one-fourth of renters in the

metropolitan area with the highest score for that opportunity measure or for the index as a

whole.

•  ZIP Codes with opportunity values between 25 and 75 contain the one-half of renters in

the metropolitan areas with intermediate opportunity scores.

Neighborhood Characteristics.  In addition to opportunity measures, we conducted an

additional analysis of how SAFMRs change where HCV holders reside in terms of key

socioeconomic and demographic characteristics of the community. The most prominent was the

ZIP Code rent ratio. The other measures we included are racial and ethnic composition, the share

of families with children, and percent of adults with college degrees. 19 These characteristics are not included in our composite measure of opportunity, but they are important for understanding

the effects of SAFMRs on neighborhood composition where HCV holders reside.

ACS data are collected on a rolling basis monthly during a calendar year. This report uses

neighborhood estimates from data collected during a time period spanning 5 years. As such,

ACS waves are not suited to measuring year-to-year differences, as each year’s data includes

multiple years. It is also not anticipated that neighborhood characteristics will change as a

result of implementing SAFMRs, particularly during the time frame studied in the evaluation.

Therefore, for this evaluation, we focus our analysis on comparing SAFMRs with metropolitan

area FMRs based on a single snapshot of neighborhood characteristics at around the time the

SAFMR demonstration began. We used 5-year ACS estimates for 2010–2014 for these

characteristics.

18  The final report will use up to five groups to look at rents outside the payment standard discretion of PHAs.

19  The final report will include other characteristics such as share of households headed by seniors.

24

Comparison PHAs

In addition to analyzing the seven SAFMR PHAs, we used a set of comparison PHAs that

continued to use metropolitan-area based FMRs (that is, these PHAs did not use SAFMRs). The

comparison PHAs included all PHAs in the clusters from which the five SAFMR demonstration

PHAs were drawn. As described in appendix A, all PHAs deemed eligible to participate in the

demonstration were assigned to the clusters based on size and the percentage of the working age

population.

Our comparison group includes all PHAs in clusters that include demonstration PHAs: clusters 2,

4, 5, 6, and 7. This comparison group represents 138 PHAs and slightly more than 550,000

voucher households (in 2015). 20 This number of households provides a broad base that enables comparisons for different types of neighborhoods—notably those for which SAFMRs would

increase and decrease payment standards. Because the two Dallas-area PHAs were not selected

into the SAFMR demonstration, they were not part of any cluster. However, Dallas would have

fallen on the border of clusters 5 and 6, and Plano would have been classified in cluster 2.

Therefore, the comparison PHAs in clusters 2, 5, and 6, nonetheless, provide a viable

comparison for the two PHAs.

Appendix E includes each of the analyses in chapters 4, 5, and 7 broken out for each cluster.

20  Note that the 138 comparison PHAs included the four PHAs within three of these clusters that declined the invitation to participate in the SAFMR demonstration. We are not aware of any evidence that these PHAs differ from demonstration PHAs in a way that would make them unsuitable for inclusion in the set of analysis PHAs.

25

4.  Changes in Housing Choice Voucher Holders’ Potential Access to High-

Opportunity Neighborhoods

This chapter presents preliminary findings on the  potential impacts of the switch to SAFMRs on HCV holders’ ability to  access low-, moderate-, and high-rent areas. Key findings of this

analysis follow, focusing on SAFMR PHAs as a group. The chapter documents individual

variation from PHA to PHA, which was considerable.

•  SAFMRs increase the number of units with rents below the applicable FMR in high-rent ZIP

Codes and reduce the number in low-rent ZIP Codes.

•  However, the gain in units with rents below the applicable FMR in high-rent ZIP Codes does

not offset the loss in the number of units below the FMR in low-rent ZIP Codes, resulting in

a net loss of units with rents below the FMR.

•  The high-rent ZIP Codes to which HCV holders gain access via SAFMRs offer higher

opportunity to residents on all measures: lower poverty, higher school proficiency, higher job

proximity, higher environmental quality, and lower rates of both property and violent crime.

As described in the previous section titled Hypothesized Impacts of SAFMRs on HCV Holders,

SAFMRs are expected to change the permissible range for payment standards across ZIP Codes

within a metropolitan area. In general, with the implementation of SAFMRs, it is expected that

payment standards increase in high-rent ZIP Codes and decrease in low-rent ZIP Codes,

particularly for ZIP Codes with median rent much greater than or less than the metropolitan area

median. In such ZIP Codes, the range of 90 to 110 percent of SAFMR within which PHAs can

set a SAFMR-based payment standard may be outside the 90 to 110 percent range of the

metropolitan area FMRs.

Thus, compared with metropolitan area FMRs, a larger share of units in high-rent ZIP Codes should

be affordable to HCV holders in a SAFMR PHA, and a smaller share of units should be affordable

to HCV holders in low-rent neighborhoods. This change is the primary mechanism through which

SAFMRs might alter HCV holders’ access to and location in high-rent ZIP Codes. To the extent

that rents correlate with access to opportunity, changes in the share of units affordable with

vouchers in high- and low-rent neighborhoods will shift HCV holders’ access to opportunity.

To identify low-, moderate-, and high-rent ZIP Codes, we use the  rent ratio,  which is

comparable across metropolitan areas and ZIP Codes. The rent ratio is the ratio of the median

rent in each ZIP Code to the median rent for the metropolitan area. HUD uses rent ratio to

calculate SAFMRs (Federal Register, 2010).

Definition:

Rent Ratio =

Median Gross Rent for ZIP Code

Median Gross Rent for the CBSA (metropolitan area)

The two-bedroom SAFMR for a ZIP Code equals the two-bedroom FMR multiplied by the rent

ratio. HUD calculated the rent ratios for each ZIP Code using special tabulations of ACS data. 21

21  SAFMRs are also bounded below by the state nonmetropolitan minimum FMR and above by 150 percent of the 40th percentile rent metropolitan area FMR.

26

Using rent ratios already calculated for SAFMRs by HUD, we were able to identify rent ratios

for each ZIP Code as the ratio of the two-bedroom SAFMR (or hypothetical SAFMR) to the two-

bedroom metropolitan area FMR, avoiding the need to repeat HUD’s analysis of the special

tabulations of rent ratios:

Calculation for analysis:

Rent Ratio =  SAFMR (2 bed)

FMR (2 bed)

Using this ratio—

•  If the rent ratio > 1, it means that the median rent in the ZIP Code is greater than the median

rent of its metropolitan area. For example, a rent ratio of 1.25 means that the ZIP Code

median rent is 25 percent  higher than the metropolitan area median rent.

•  A rent ratio of 1 means that the median rent in the ZIP Code is  equal to  the median rent of its metropolitan area.

•  If the rent ratio < 1, it means that the median rent in the ZIP Code is lower than the

median rent of its metropolitan area. For example, a rent ratio of 0.75 means that the ZIP

Code median rent is 25 percent  lower than the metropolitan area median rent.

All ZIP Codes with published SAFMRs and available counts of rental units (from ACS data),

one-half of rental units are in ZIP Codes with a rent ratio between 0.9 and 1.08, the 25th and 75th

percentile, respectively, of the ZIP Code rent ratio across all rental units. Based on this

distribution, we define neighborhoods as follows.

•  Low-rent neighborhoods are ZIP Codes with a rent ratio below 0.9.

•  Moderate-rent neighborhoods are ZIP Codes with a rent ratio from 0.9 to 1.1.

•  High-rent neighborhoods are ZIP Codes with a rent ratio above 1.1.

Exhibit 4-1 summarizes the SAFMR PHAs as a group and individually, reporting the number of

rental units and ZIP Codes in the jurisdiction and in each rent ratio category. 22 As shown, the PHAs are a diverse group in terms of size and rental unit distribution. Laredo is by far the

smallest, with only five ZIP Codes and fewer than 26,000 rental units. Dallas is by far the largest

with 168 ZIP Codes and more than 650,000 rental units. Note that the numbers of units and ZIP

Codes in each PHA’s jurisdiction do not sum to the All SAFMR PHAs values, because all but

one of the Plano PHA’s ZIP Codes is also in the Dallas PHA jurisdiction.

22  To determine PHA jurisdictions for the chapter 4 analysis, we reviewed PHA administrative plans, annual plans, and websites. We also verified jurisdictions with PHAs during site visits. For Mamaroneck, we include all of Westchester County, because the PHA actively seeks to locate HCV holders throughout the county. For Dallas, we include ZIP Codes that are subject to SAFMRs (approximately 2 percent of HCV holders in Dallas live

outside the Dal as HMFA). For Plano, we include ZIP Codes in Collin County plus additional ZIP Codes close to the county border in Dallas and Denton counties based on the number of Plano HCV holders that live in those ZIP

Codes.

27

Exhibit 4-1: Rental Units and ZIP Codes by Rent Ratio

Rent Ratio

n

< 0.9

0.9–1.1

> 1.1

Units

1,290,864

380,598

(29%)

588,330

(46%)

321,936

(25%)

All SAFMR PHAs

ZIP Codes

411

87

(21%)

186

(45%)

138

(34%)

Units

25,544

6,582

(26%)

15,228

(60%)

3,734

(15%)

Laredo

ZIP Codes

5

1

(20%)

3

(60%)

1

(20%)

Units

143,226

51,090

(36%)

64,066

(45%)

28,069

(20%)

Mamaroneck

ZIP Codes

67

9

(13%)

32

(48%)

26

(39%)

Units

53,390

8,638

(16%)

36,152

(68%)

8,600

(16%)

Chattanooga

ZIP Codes

30

6

(20%)

21

(70%)

3

(10%)

Units

291,302

96,374

(33%)

130,023

(45%)

64,904

(22%)

Cook County

ZIP Codes

127

37

(29%)

53

(42%)

37

(29%)

Units

107,946

60,531

(56%)

35,990

(33%)

11,425

(11%)

Long Beach

ZIP Codes

13

5

(38%)

5

(38%)

3

(23%)

Units

668,981

157,382

(24%)

306,396

(46%)

205,23

(31%)

Dallas

ZIP Codes

168

29

(17%)

71

(42%)

68

(40%)

Units

236,040

21,549

(9%)

111,166

(47%)

103,324

(44%)

Plano

ZIP Codes

52

1

(2%)

21

(40%)

30

(58%)

SAFMR = Small Area Fair Market Rent. PHA = public housing agency.

Note: Analysis data set includes all ZIP Codes in PHA service areas where SAFMRs are implemented. Percentage of total counts for each row in parentheses.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 SAFMRs; 2012 American Community Survey 5-year estimates (total rental units) As shown in Exhibit 4-1, the share of rental units in high-rent and low-rent ZIP Codes varies

considerably by PHA. This variation has to do, in part, with how rent levels within a PHA’s

jurisdiction differ from rent levels within the broader metropolitan area on which its metropolitan

area FMR is based. Rent ratios in Long Beach are calculated using the median rent for the much

larger Los Angeles-Long Beach HUD Metro FMR Area. Although 3 of the 13 ZIP Codes in

Long Beach’s jurisdiction have median rents that are more than 110 percent of the Metro FMR

Area median, these ZIP Codes have few rental units (for example, high owner-occupancy rates).

As such, more than nine times as many rental units have rent ratios below 0.9 in the five ZIP

Codes as in the three high-rent ratio ZIP Codes. These examples illustrate that, particularly for

PHAs that operate in a small portion of a metropolitan area, SAFMRs may be mostly above or

mostly below metropolitan area FMRs.

This variation is consistent with the motivation for SAFMRs. Under traditional FMRs, a small

PHA in a large metropolitan area faces FMRs that may be too generous or too stingy for its

particular jurisdiction, because the FMRs are based on rents in the broader metropolitan area.

28

Keep in mind when interpreting the analyses presented in this chapter that some SAFMR PHAs

had SAFMRs that were mostly higher than the metropolitan area FMR in their area, when others

had SAFMRs that were mostly lower. We categorize rent ratios, opportunity measures, and

neighborhood characteristics based on how ZIP Codes compare with the metropolitan area in

which they are located. This comparison is useful in interpreting results in light of opportunities

available across a ZIP Code’s larger community (metropolitan area). It is less useful for

observing possible effects within a smaller geography that is mostly different (for example, with

a consistently higher or lower rent ratio) from the encompassing larger geography.

SAFMRs Increase Access to High-Rent ZIP Codes Across Sites

As expected, SAFMRs increase the share of rental units with rents below the FMR in high-rent

neighborhoods and reduce the share in low-rent neighborhoods. Exhibit 4-2 shows the share of

rental units with rents below the FMR and SAFMR by rent ratio across the study ZIP Codes. 23

Under metropolitan area FMRs, nearly three-fourths of units in low-rent ZIP Codes are below

the FMR, as are slightly more than one-fourth of units in high-rent ZIP Codes.

Exhibit 4-2: Share of Rental Units Below SAFMR and Metropolitan Area FMR

80

70

ts 60

al uni 50

nt 40

73

al re

FMRs

30

of

SAFMRs

re

49

54

50

46

47

49

20

Sha

26

10

0

< 0.9

0.9–1.1

> 1.1

Total

ZIP Code rent ratio categories

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Note: Analysis data set includes all ZIP Codes in public housing agency service areas where SAFMRs are implemented.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units)

23  We calculated the share of units with rents below the applicable FMR using a file from HUD that had rents by unit size in range bands for each ZIP Code. To calculate the share of units with rents below the applicable FMR, we assumed within the rent range band that included the applicable FMR that one-half the units had rents below the applicable FMR. For each unit size, we determined the number of units renting below the respective unit size FMR in the respective unit count by unit size rent range. We then summed these units across unit sizes and divided by the total number of rental units across all unit sizes to determine the share of all rental units below the applicable FMR.

29

By design and in practice, under SAFMRs, affordability is much more evenly distributed across

types of neighborhoods. Roughly one-half of units have rents below the FMR in each

neighborhood type. Also as expected, changes in the share of units with rents below the FMR in

moderate-rent ZIP Codes (“moderate” meaning between 0.9 and 1.1) are fairly modest.

Access to High-Rent ZIP Codes Under SAFMRs Varies Across Sites

The pattern of rental units with rents below FMRs is generally similar to the pattern for the

SAFMR PHAs as a group, but important differences exist across PHAs. Exhibit 4-3 shows the

share of units with rents below the applicable FMR by rent ratio for each of the SAFMR sites.

Across all sites, a consistent pattern holds—the share of units with rents below SAFMR, and thus

potentially available to HCV holders in high-rent ZIP Codes, increases. It decreases in low-rent

ZIP Codes. 24 The largest changes in the share of units below the FMR in low-rent ZIP Codes were in Dallas and Plano, where the share of rental units with rents below SAFMRs dropped by

slightly more than 30 percentage points compared with metropolitan area FMRs. The smallest

change was in Chattanooga with a drop of 19 percentage points. In high-rent ZIP Codes, the

largest change was in Laredo, where the share of units with rents below SAFMR increased by 39

percentage points. The smallest change was again in Chattanooga, with an increase of only 11

percentage points compared with metropolitan area FMRs.

In moderate-rent ZIP Codes, changes in the share of units with rents under SAFMRs are

mostly modest, falling at or below 5 percentage points in five of the seven PHAs. The share

drops in comparison to metropolitan area FMR for four of the seven. The difference ranges

from 1 percentage point in Cook County and Laredo to 5 percentage points in Dallas. For three

PHAs, Chattanooga, Long Beach, and Mamaroneck, the share of units in moderate-rent ZIP

Codes with rents under SAFMR is higher than under metropolitan area FMR. In Chattanooga,

this difference is small (4 percentage points), but in Long Beach, the difference is 7 percentage

points higher than under metropolitan area FMRs. In Mamaroneck, the difference is 6 percentage

points.

24  Throughout this chapter we use the phrase  applicable FMR to refer to side-by-side comparisons of SAFMR and FMR.

30

Exhibit 4-3: Share of Rental Units Below SAFMR and Metropolitan Area FMR by Site

Lower-Rent Ratio ZIP Codes (Rent Ratio < 0.9)

ts 80

60

al unint 40 73 80 79

76

64

70

70

63

al re 20

46

58

54

45

53

46

38

FMR

of

32

re

SAFMR

0

Sha

Moderate-Rent Ratio ZIP Codes (0.9 < Rent Ratio < 1.1)

ts 80

60

al unint 40 49 57 65 46

52

40

al re 20

47

55

59

42

51 50 45

4742

38

FMR

ofre

SAFMR

0

Sha

Higher-Rent Ratio ZIP Codes (Rent Ratio >1.1)

80

ts

60

al unint 40

77

69

54

53

al re

47

57

50

51

20

FMR

of

26

37

32

14

21

24

23

21

re

SAFMR

0

Sha

FMR = Fair Market Rent. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Note: Analysis data set includes all ZIP Codes in PHA service areas where SAFMRs are implemented.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units)

31

SAFMRs Reduce the Overall Number of Units With Rents Below the FMR

Importantly, for the SAFMR PHAs as a whole, and in five of the seven individual SAFMR

PHAs, fewer units have rents below the SAFMR than the metropolitan area FMR (Exhibit 4-4).

The exhibit shows that as intended, SAFMRs increase potential access to units in high-rent

neighborhoods, more than doubling the number of units with rents below the SAFMR. By the

same token, SAFMRs decrease the number of units with rents below the SAFMR in low-rent

neighborhoods, reducing the number of units available to HCV holders by more than one-third.

SAFMRs also slightly reduce units below the FMR in moderate-rent neighborhoods.

Exhibit 4-4: Count of Rental Units (1,000s) Below Metropolitan Area FMR and SAFMR

700

R

FM

600

and R 500

FM )

400

SA ands

low

649 626

300

bent (thous

200

ith re

278

289 278

ts w

100

174

174

niU

82

0

< 0.9

0.9–1.1

> 1.1

Total

Rent ratio

FMRs

SAFMRs

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Note: Analysis data set includes all ZIP Codes in public housing agency service areas where SAFMRs are implemented.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units)

The net effect is that across all site ZIP Codes, a total of slightly more than 648,500 units have

rents below the metropolitan area FMR, compared with slightly less than 626,500 units with

rents below the SAFMR. This difference is a loss of more than 22,000 units that have rents that

indicate they might otherwise be available to HCV holders. With a total of more than 1,290,000

rental units in these ZIP Codes, this difference represents a potential loss of 1.7 percent of the

total rental stock with rents that are accessible by HCV holders, or 3.4 percent of the stock that

would have rents under metropolitan area FMRs.

The effective impact of the loss of these units to HCV holders could be similar to a decrease in

the rental vacancy rate of the same magnitude in terms of the difficulty of finding units. HCV

32

holders’ challenge to finding units under SAFMRs could potentially be further compounded by

other challenges, such as a lack of familiarity with many of the neighborhoods where a larger

number of units become available. 25

Note that this discussion assumes that PHAs set payment standards equal to FMRs, which was

not always the case. Because this discussion focuses on the  potential availability of units to

voucher holders under SAFMRs, and because PHAs in the future may make decisions about

payment standards different from the SAFMR PHAs, we believe that a focus on 100 percent of

the FMR and SAFMR was the most appropriate approach for this part of the analysis. 26

Declines in Units With Rents Below SAFMR

The decline in units with rents below the applicable FMR due to the introduction of SAFMRs is

not evenly distributed across the study sites. As shown in Exhibit 4-5, most of the drop in units

with rents below the SAFMR is for the Dallas and Long Beach PHAs. Dallas is not surprising,

because it is the largest PHA in terms of total rental units.

Exhibit 4-5: Comparison of Total Units With Rents Below the SAFMR and Metropolitan Area FMR

Total Units With Rents Below FMR,

Percentage

All ZIP Code Rent Ratios

Change, SAFMR

SAFMR

FMR

Difference

Versus FMR

All SAFMR PHAs

626,483

648,607

– 22,125

– 3.4

Laredo

14,163

14,317

– 208

– 1.4

Mamaroneck

90,665

90,955

– 290

– 0.3

Chattanooga

23,395

22,673

721

3.2

Cook County

152,749

155,401

– 2,652

– 1.7

Long Beach

54,140

62,575

– 8,435

– 13.5

Dallas

291,066

302,246

– 11,180

– 3.7

Plano

101,009

80,163

20,846

26.0

FMR = Fair Market Rent. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Note: Analysis data set includes all ZIP Codes in PHA service areas where SAFMRs are implemented.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units)

Long Beach, however, is the fifth largest PHA, ahead of only Chat anooga and Laredo, but had the

third largest decrease in units available. In Laredo and Mamaroneck, shifting to the SAFMR resulted in

virtual y no change in the overal  number of units below the applicable FMR. These PHAs are unique

among the SAFMR PHAs in that their jurisdictions are the same as the geographies for which their

respective FMRs are calculated. Impacts in Chat anooga and Cook County were also fairly smal . In

percentage terms, Long Beach had by far the largest drop in units with rents below the applicable FMR.

25  We will examine potential challenges tenants face in responding to SAFMRs in the second phase of this study.

26  In the final report we will review the payment standards for these seven PHAs and compare them with the payment standards for the comparison PHAs.

33

The size of the net change in units below the applicable FMR in a given geography depends on

how rental units are distributed across low-, moderate-, and high-rent ZIP Codes. In general, if

fewer rental units (that is, a higher rate of homeownership) are in high-rent ZIP Codes than in

low-rent ZIP Codes (that is, a lower rate of homeownership), then the shift to SAFMRs will

mean fewer units with rents below the SAFMR than with rents below the metropolitan area

FMR. The cap on SAFMRs of 150 percent of the 40th percentile metropolitan area FMR may

also contribute to this difference. If a given PHA has many very high rent ZIP Codes (rents

greater than 150 percent of the FMR) relative to the overall metropolitan area, the cap on

SAFMRs may result in few rental units renting below the SAFMR. Of course, these units would

not rent below the FMR either. The final report will include additional analysis of the importance

of the 150 percent cap in determining the share of units that are affordable under SAFMRs.

Exhibit 4-6 shows the difference in units with rents below SAFMR and FMR by rent ratio for

each PHA as a share of units with rents below FMR. No consistently predictable pattern

emerges. In some PHAs, including Dallas and Cook County, the loss of units renting below the

applicable FMR in low-rent neighborhoods roughly offsets the gain in units in high-rent

neighborhoods. In these PHAs’ jurisdictions, the drop in units with rents below SAFMRs is

driven by the decline in units with rents below SAFMR in moderate-rent neighborhoods. In

Chattanooga and Laredo, the net change in units in each neighborhood type is very small,

therefore additions and subtractions essentially cancel each other out. In Mamaroneck, units in

low-rent neighborhoods declined significantly, but these changes roughly equal the combined

increase in affordable units in moderate-rent and high-rent neighborhoods. In Long Beach, large

losses in low-rent neighborhoods more than offset small gains in high-rent and moderate-rent

neighborhoods, leading to a substantial net decline. In Plano, a substantial (nearly 40 percent)

increase in the high-rent neighborhoods occurred, but the combined decrease in the low- and

moderate-rent neighborhoods is only 12 percent. Additionally, as shown in Exhibit 4-7, the share

of units under the applicable FMR in each PHA’s jurisdiction in each ZIP Code rent ratio

category varies widely across the SAFMR sites.

Exhibit 4-7 shows the share of all rental units with rents below the applicable FMR in each

PHA’s jurisdiction within each rent ratio category. Exhibit 4-7 depicts how SAFMRs result in a

different distribution of the types of ZIP Codes where units are likely to be affordable to HCV

holders. In Long Beach, for example, SAFMRs have the biggest effect on the availability of units

in low-rent ZIP Codes. As shown in Exhibit 4-7, nearly 70 percent of all units with rents under

metropolitan area FMRs in Long Beach are in low-rent ZIP Codes, whereas only about one-half

of the units renting under SAFMRs in Long Beach fell within low-rent ZIP Codes. In Long

Beach, the loss in the share of units with rent below the applicable FMR in low-rent ZIP Codes

was offset by increases split between the shares in moderate- and high-rent ZIP Codes. In

Laredo, by contrast, the share of available units in moderate-rent ZIP Codes changed relatively

little, so that declines in the share of units in low-rent ZIP Codes were offset mostly by gains in

high-rent ZIP Codes.

34

Exhibit 4-6: Difference in Units With Rents Below SAFMR and Metropolitan Area FMR as a

Percentage of Units With Rents Below FMR by Rent Ratio

38%

40%

26%

30%

19%

20%

ce

14%

en

10%

9%

10%

6%

6%

iffer

5%

4% 3%

4%

< 0.9

t d

0%

cen

0.9–1.1

-2% -1%

0%

-1% -2%

-4%

-4%

Per -10%

> 1.1

-7%

-6%

-10%

-8%

Total

-14%

-14%

-13%

-20%

-17%

-23%

-30%

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Note: Analysis data set includes all ZIP Codes in public housing agency service areas where SAFMRs are implemented.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units)

Exhibit 4-7: Distribution of Units With Rents Below Applicable FMR by ZIP Code Rent Ratio

100%

4%

8%

14%

14%

12%

7%

15%

10%

16%

90%

24%

24%

27%

80%

26%

35%

52%

70%

60%

34%

43%

41%

60%

48%

59%

68%

46%

72%

42%

50%

> 1.1

44%

56%

40%

.09–1.1

67%

30%

51%

42%

< 0.9

44%

47%

20%

36%

33%

37%

27%

30%

24%

10%

17%

21%

17%

7%

0%

R

R

R

R

R

R

R

FM

MR

FM

MR

FM

MR

FM

MR

FM

MR

FM

MR

FM

MR

SAF

SAF

SAF

SAF

SAF

SAF

SAF

Laredo

Mamaroneck Chattanooga

Long Beach

Cook County

Dallas

Plano

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Note: Analysis data set includes all ZIP Codes in public housing agency service areas where SAFMRs are implemented.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units)

35

Declines in Units With Rents Below SAFMR Are a Consequence of the Concentration and

Distribution of Rental Units Across ZIP Code Types

A partial explanation for the loss of more than 22,000 units potentially available to voucher

holders is shown in Exhibit 4-8, which summarizes the average number of rental units in low,

moderate, and high-rent ratio ZIP Codes overall and in each PHA’s jurisdiction. As shown,

across the SAFMR ZIP Codes, fewer rental units of any kind—whether affordable to voucher

holders or not—are in high-rent ZIP Codes than in low-rent ZIP Codes. This difference is also

true in six of the seven PHAs’ jurisdictions and dramatically so in Long Beach and Plano, with

an average of more than 12,000 rental units per low-rent ZIP Code but only about 4,000 per

high-rent ZIP Code. The one exception is Chattanooga, which has more rental units per high-rent

ZIP Code than per low-rent ZIP Code. This distribution of rental units is to be expected, as low-

rent ZIP Codes tend to be densely populated urban neighborhoods, and high-rent ZIP Codes tend

to be lower-density suburban neighborhoods. Nevertheless, with declining FMRs in low-rent ZIP

Codes and increasing FMRs in high-rent ZIP Codes, the net effect was an overall loss in units.

Exhibit 4-8: Average Number of Rental Units per ZIP Code by Rent Ratio Category

22,000

20,000

18,000

ode 16,000

P C 14,000

r ZI 12,000

ts pe 10,000

< 0.9

8,000

al uni

0.9–1.1

6,000

ent

> 1.1

R 4,000

2,000

0

PHA = public housing agency. SAFMR = Smal  Area Fair Market Rent.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 SAFMRs; 2012 American Community Survey 5-year estimates

36

SAFMRs Increase Access to ZIP Codes With Higher Opportunity Measures

We now turn to examining the extent to which the shift to SAFMRs increases the potential access of

HCV holders to areas of opportunity. As noted in chapter 3, our measures of access to opportunity

are poverty, school proficiency, job proximity, environmental quality, and incidence of crime.

Opportunity Measures by Rent Ratio

The series of panels in Exhibit 4-9 show the opportunity measures by rent ratio, confirming the

hypothesis that opportunities are greater in high-rent ZIP Codes. In low-rent ZIP Codes, the

average value (over all rental units) of our index of overall opportunity is 29, compared with 54

in moderate-rent ZIP Codes, and 78 in high-rent ZIP Codes. Therefore, on average, rental units

in low-rent ratio ZIP Codes have a lower value of the overall opportunity index than 71 percent

of all rental units in the metropolitan area, whereas rental units in the high-rent ratio ZIP Codes

have an index value that is greater than 78 percent of all rental units in the metropolitan area.

Exhibit 4-9: Opportunity Measures by Rent Ratio

Overall Index

80

70

60

the a 50

in  are 40

78

nk itan  30

54

20

ile ra

29

10

ent etropolm 0

Perc

< 0.9

0.9–1.1

> 1.1

Rent ratio

Component Indexes

80

70

60

50

a 40

in the  are 30

k

20

ran itan  10

tile

0

Percent

School

Job

Environmental

cen etropol

nonpoor

proficiency

proximity

quality

m

Per

< 0.9

0.9–1.1

> 1.1

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Smal  Area Fair Market Rents; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units); 2014 ACS 5-year estimates (poverty rate/percent nonpoor); School Proficiency Index, 2011–2012 (HUD Open Data); Job Proximity Index, 2010 (HUD Open Data); Environmental Health Hazard Index, 2005 (HUD Open Data)

37

Findings observed for the component indexes (plus crime rates, where data are available) are as

follows.

•  A higher rate of nonpoverty indicates higher opportunity, and the exhibit shows higher

rates of nonpoor in high-rent ratio ZIP Codes.

•  Higher rates of school proficiency indicate higher-quality schools, and the exhibit shows

that high-rent ZIP Codes have higher school proficiency indexes.

•  A higher job proximity index indicates better access to jobs. The exhibit shows slightly

higher measures of job access in high-rent ZIP Codes.

•  A higher environmental quality index indicates better quality, and the exhibit shows

higher index values for high-rent ZIP Codes.

Although we observe differences across rent ratio categories for all the component indexes,

percent nonpoor and school proficiency vary the most across categories, followed by

environmental quality. Job proximity has less variation, reflecting the fact that commuting

distance is less positively correlated with rents.

All indexes are normalized to be percentiles of the index value divided by all rental units in the

metropolitan area. Therefore, an index value that is 10 points higher than another indicates an

additional 10 percent of all units in the metropolitan area that have a lower value of the

underlying opportunity measure. This normalization improves comparability of average across

metropolitan areas and enables the component measures to be combined into the overall

opportunity index on a common scale.

HCV Holders’ Potential Access to ZIP Codes Across Measures of Opportunity, All PHAs

The previous exhibits show that, as expected, high-rent ratio ZIP Codes tend to have higher

measures of neighborhood opportunity than do low-rent ratio ZIP Codes. We now turn to the

potential for SAFMRs to change HCV holders’ access to opportunity measures. To do so, we

mirror the approach used for rent ratios above—measuring the share of units with rents below

SAFMRs and metropolitan area FMRs across three categories of ZIP Codes. Now, we categorize

ZIP Codes based on relative values of the opportunity index within the respective metropolitan

area. This categorization provides a measure of the relative access to opportunity provided by

living in a housing unit in the ZIP Code versus other ZIP Codes in the metropolitan area.

For each individual opportunity index and for the composite index as a whole, we created three

categories of ZIP Codes based on the index score.

•  Low Opportunity.  ZIP Codes with index values below 25 (containing the 25 percent of

units in the metropolitan area with the lowest values of the index).

•  Moderate Opportunity.  ZIP Codes with index values in the 25–75 range (containing the

50 percent of all units with index values that are both below at least 25 percent of the

units with the highest index values and above the 25 percent of all units in the

metropolitan area with the lowest index values).

•  High Opportunity.  ZIP Codes with index values greater than 75 (containing the 25

percent of the units in the metropolitan area with the highest index values).

38

The following exhibits report the share of units renting below SAFMR and metropolitan area

Fair Market Rent in each of these three categories to examine the hypothesis that SAFMR will

increase the potential for housing choice voucher holders to access higher-opportunity

neighborhoods. We note that the set of ZIP Codes in each category can be different from one

index to the next. To provide context for the estimates presented in the following exhibits, we

first describe the analysis sample for the overall opportunity index (Exhibit 4-10). Because the

numbers of ZIP Codes and units they contain in each category are similar across the different

opportunity indexes, we report this detail for the overall opportunity index only.

Exhibit 4-10: Analysis Sample for Determining Share of Units With Rents Below SAFMR and

Metropolitan Area FMR by Overall Index Category

Overall Index Category

n

< 25

25–75

> 75

Units

1,290,864

303,588

607,183

380,092

All SAFMR PHAs

ZIP Codes

411

45

177

189

Units

25,544

8,598

13,212

3,734

Laredo

ZIP Codes

5

2

2

1

Units

143,226

0

67,404

75,821

Mamaroneck

ZIP Codes

67

0

12

55

Units

53,390

18,592

16,479

18,319

Chattanooga

ZIP Codes

30

7

14

9

Units

291,302

28,353

138,119

124,829

Cook County

ZIP Codes

127

5

59

63

Units

107,946

60,531

34,030

13,385

Long Beach

ZIP Codes

13

5

6

2

Units

668,981

187,514

337,940

143,528

Dallas

ZIP Codes

168

26

84

58

Units

236,040

33,474

120,048

82,518

Plano

ZIP Codes

52

2

23

27

FMR = Fair Market Rent. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units); 2014 ACS 5-year estimates (poverty rate/percent nonpoor); School Proficiency Index, 2011–2012 (HUD Open Data); Job Proximity Index, 2010 (HUD Open Data); Environmental Health Hazard Index, 2005 (HUD

Open Data)

Exhibit 4-11 shows the findings for the composite overall opportunity index. The exhibit shows

that slightly more than two-thirds (68 percent) of units in the ZIP Codes in the lowest quartile of

opportunity neighborhoods had rents below the metropolitan area FMR. In contrast, less than

one-half (46 percent) of units in these ZIP Codes had rents below the SAFMR. At the higher end

of the opportunity scale, slightly more than one-half (52 percent) of units had rents below the

SAFMR, compared with about one-third (34 percent) that had rents below the metropolitan area

FMR.

39

Exhibit 4-11: Access to Neighborhood Opportunity—Share of Units Below Applicable FMR by

Composite Overall Opportunity Index Categories

80

ts 60

al unint 40 al re

FMR

68

of

SAFMR

re

52

46

48

52

20

Sha

34

0

< 25

25–75

> 75

Overal  Opportunity Index

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Note: All ZIP Codes in public housing agency service areas where SAFMRs are implemented.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units)

This finding shows that Small Area Fair Market Rents are doing what they are intended to do. A

larger share of units are potentially available to HCV holders in high-opportunity areas under

SAFMRs compared with metropolitan area FMRs, and fewer units are potentially available to

HCV holders in low-opportunity areas.

Exhibit 4-12 presents the distribution of units for each of the opportunity indexes separately,

which shows the same pattern. For each individual index, more units are potentially available to

HCV holders in high-opportunity areas under SAFMRs compared with metropolitan area FMRs,

and fewer units are available in low-opportunity areas.

•  Seventy percent of units in ZIP Codes in which the percent of nonpoor households is low

(that is, with the highest concentration of poverty) had rents below the metropolitan area

FMR, compared with 33 percent of units in ZIP Codes where the percent of nonpoor

households is high (that is, with the lowest concentration of poverty). In contrast, fewer

than one-half of units in ZIP Codes in which the percent of nonpoor households is low

had rents below the SAFMR, although more than one-half of units in ZIP Codes where

the percent of nonpoor households is low had rents below the SAFMR. In other words,

many more units in low-poverty areas could potentially be affordable to HCV holders

under SAFMRs compared with metropolitan area FMRs.

•  Similarly, 70 percent of units in ZIP Codes with low school proficiency had rents below

the metropolitan area FMR, but only nearly 35 percent of units in ZIP Codes with high

40

school proficiency had rents below the metropolitan area FMR. In contrast, slightly more

than one-half of units in both low- and high-proficiency school ZIP Codes had rents

below the SAFMR. This difference means that access to ZIP Codes with more proficient

schools is greater under SAFMRs compared with metropolitan area FMRs.

•  A similar, but smaller, difference can be seen for environmental quality. Nearly one-half

of all units in each environmental quality area have rents below the metropolitan area

FMR. In contrast, about 40 percent of units in low-quality ZIP Codes have rents below

the SAFMR, compared with more than 60 percent of units in higher environmental

quality ZIP Codes.

•  Improvements in job proximity are less pronounced than for other measures.

Exhibit 4-12: Access to Neighborhood Opportunity, Share of Units Below Applicable FMR by

Component Opportunity Indexes

70

60

nits 50

l U

nta 40

l Re 30

< 25

20

25–75

> 75

Share of Al 10

0

FMR

SAFMR

FMR

SAFMR

FMR

SAFMR

FMR

SAFMR

Percent nonpoor School proficiency

Job proximity

Environmental

quality

Component Opportunity Index

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Note: Al  ZIP Codes in public housing agency service areas where SAFMRs are implemented.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units); 2014 ACS 5-year estimates (poverty rate/percent nonpoor); School Proficiency Index, 2011–2012 (HUD Open Data); Job Proximity Index, 2010 (HUD Open Data); Environmental Health Hazard Index, 2005 (HUD

Open Data)

The prior exhibits indicate that a larger share of the units in high-opportunity neighborhoods is

affordable to HCV holders under SAFMRs compared with under metropolitan area FMRs.

However, we showed in the SAFMRs Reduce the Overall Number of Units With Rents Below the

FMR section that SAFMRs tend to reduce the overall number of units with rents below the FMR.

Therefore, although more of the units in ZIP Codes with higher opportunity measures may be

available, we must also consider the distribution of rental units across opportunity categories.

41

To do so, Exhibit 4-13 summarizes the share of all rental units that fall in each overall

opportunity index category for SAFMR public housing agencies as a group and individually. As

reflected in the exhibit, units in high-opportunity ZIP Codes are not equally available across the

PHAs. For example, in Mamaroneck, no ZIP Codes, and therefore no rental units, are in the

lowest-opportunity category. HCV holders cannot use vouchers from Mamaroneck in a ZIP

Code that is in the metropolitan area’s bottom quartile (based on our overall opportunity index)

without porting vouchers to another PHA jurisdiction. Correspondingly, nearly 53 percent of

units are in ZIP Codes in the highest opportunity index category.

Exhibit 4-13: Share of All Rental Units Across Overall Opportunity Index Categories, by PHA

100%

15

12

90%

21

29

34

35

80%

43

nits

53

32

70%

l U

nta 60%

52

51

50%

l Re

47

31

> 75

40%

51

47

25–75

30%

56

47

< 25

20%

Share of Al

34

35

10%

24

28

10

14

0%

0

PHA = public housing agency.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Smal  Area Fair Market Rents; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units); overal  opportunity index At the other extreme, the shares in Long Beach almost perfectly reverse those in Mamaroneck:

56 percent of all rental units are in the lowest opportunity index category, and only 12 percent

are in the top quartile of the overall opportunity index. Cook County follows Mamaroneck as

among PHAs with the lowest number of rental units in the lowest overall opportunity index

neighborhoods. Only 10 percent of all Cook County rental units are in ZIP Codes in this

category, and 43 percent are in the highest opportunity index ZIP Codes.

The exhibit highlights some limits to the impacts that SAFMR can have on HCV holders’

location in high-opportunity neighborhoods. In Long Beach, a small portion of rental units—

whether below FMR or not—are in high-opportunity neighborhoods (in the Los Angeles

metropolitan area). Therefore, far less potential exists for SAFMRs to improve access to these

neighborhoods than in Cook County, Mamaroneck, or even Chattanooga. Regardless, as shown

in Exhibit 4-13, SAFMRs do improve HCV holders’ access to high opportunity index ZIP Codes

in every PHA jurisdiction. The exhibit compares the shares of units in each overall composite

index category for each of the SAFMR sites.

42

Exhibit 4-14 shows the same pattern as previous exhibits. Across the SAFMR sites, SAFMRs

reduce access to low-opportunity ZIP Codes and increase access to high-opportunity ZIP Codes.

The differences in access to opportunity between FMRs and SAFMRs are much larger in some

PHAs than in others, however. In Chattanooga and Laredo, shifting from the FMR to the

SAFMR leads to only a modest reduction in the share of units in ZIP Codes in low-opportunity

areas renting below the applicable FMR—about 6 to 9 percentage points. The differences in

Dallas, Long Beach, and Plano, in contrast, are quite large. SAFMRs appear to have a much

larger impact in those places than in the other PHAs. Again in Mamaroneck, no ZIP Codes have

an overall opportunity index in the bottom quartile.

In the middle, one-half of the overall opportunity index category, the differences are smaller. In

general, the share of units in ZIP Codes with median rents below the SAFMR is smaller than the

share below the FMR in this index level. However, in Long Beach, the share of units is slightly

larger under SAFMRs than under FMRs.

The largest differences are generally in the top quartile of the opportunity index, which is in the

bottom panel of Exhibit 4-14. In Laredo, the share of units renting below the SAFMR in ZIP

Codes in the top quartile of the overall opportunity index is more than triple the share renting

below FMRs, from 14 percent of units to more than one-half (53 percent). Note, however, that

because this analysis is conducted at the ZIP Code level, and Laredo has only five ZIP Codes, it

is mathematically prone to very large swings.

Likewise, in Plano and Dallas, potentially accessible units in the highest opportunity quartile are

more than double under SAFMRs, and they are nearly double in Long Beach (although few

rental units are in such ZIP Codes in Long Beach).

43

Exhibit 4-14: Share of Units Below FMR and SAFMR in ZIP Codes by Overall Opportunity Index

Overal  Opportunity Index < 25

80

ts

60

al unint 40

al re 20

FMR

of

SAFMR

re 0

Sha

Overal  Opportunity Index 25–75

80

ts

60

al unint 40

al re 20

FMR

of

SAFMR

re 0

Sha

Overal  Opportunity Index > 75

80

ts

60

al unint 40

al re 20

FMR

of

SAFMR

re 0

Sha

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Note: Al  ZIP Codes in public housing agency service areas where SAFMRs are implemented.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units)

44

5.  Early Impacts of Small Area Fair Market Rents on Housing Choice

Voucher Holders

In contrast to the prior chapter, which focused on the potential impact of SAFMRs on HCV

holders’ ability to access areas of opportunity, this chapter focuses on the  actual effects. In brief, we found—

•  Based on the early evidence, it appears that following the implementation of SAFMRs,

HCV holders in the SAFMR PHAs are slightly more likely to live in higher-opportunity

ZIP Codes than before implementation of SAFMRs. The improved access to higher-

opportunity ZIP Codes in SAFMR PHAs is especially evident when looking at existing

HCV holders moving to new ZIP Codes and the locations of new HCV holders. These

changes were not observed in the comparison PHAs.

•  Households moving to a different ZIP Code are more likely to locate in neighborhoods

that have a lower share of minorities and a higher share of household heads with a college

degree after the introduction of SAFMRs. These changes were not observed in the

comparison PHAs.

Although SAFMRs are hypothesized to improve HCV holders’ access to units in high-

opportunity areas, their actual experience could be influenced by a number of factors. These

factors include the PHAs’ ability to execute the demonstration, landlords’ response to the

changes in payment standards, and voucher holders’ current circumstances. Moving to a high-

opportunity area may or may not be possible given an HCV holder’s situation at the time.

Although affordability is a primary concern, tenant behavior is often driven by other social,

emotional, or logistical factors, including hesitation to switch their children’s schools (DeLuca

and Rosenblatt, 2010), fear of the unknown (for example, not knowing the area or fearing

rejection) (Charles, 2006), or lack of information about the benefits of opportunity

neighborhoods (Darrah and DeLuca, 2014).

Even for HCV holders who are eager to move, issues such as the cost of moving (including

security deposits and first and last month’s rent) can be a significant barrier. Many low-income

families also understand the bang-for-the-buck trade-off they face when searching for housing.

They can find cheaper units with more amenities in higher-poverty areas, which may

accommodate their families’ needs for space and amenities (Wood, 2014; Rosenblatt and

DeLuca, 2012).

Participant Locations in SAFMR PHAs by Rent Ratio

We now turn to where voucher holders  actually live following the implementation of SAFMRs.

Throughout this section, we compare HCV holder location outcomes in 2010, before SAFMRs

went into effect, with outcomes in 2015, after SAFMR-based payment standards were in effect

for all HCV holders. We first look at where HCV holders live in terms of ZIP Code rent ratios,

before turning to an analysis of where HCV holders live relative to opportunity measures.

Other factors that could influence HCV holders’ location outcomes—notably a recovering

economy and housing market—were changing over this period. Therefore, we also make

comparisons over the same time period versus the average location outcomes of a large group of

PHAs where SAFMRs were not in effect (the comparison group PHAs). This comparison

45

provides a baseline scenario for what otherwise could have been expected in the SAFMR PHAs.

Further, we note that we use static measures of neighborhood characteristics, typically for a

period coinciding with the beginning of the SAFMR demonstration. For example, measures

(including the rent ratio), based on ACS data, use the 2008–2012 year ACS vintage. See chapter

3 for more details on how we measure neighborhood characteristics. 27

The comparisons in this section are calculated using the ZIP Codes where HCV holders live as

recorded in the last quarter of administrative data in which they are observed in the respective

year. Exhibit 5-1 reports the sample size for the analyses that follow. Our analysis is of the

households in SAFMR PHAs (44,084 in 2010 and 48,453 in 2015) and the relatively large

number of households we observe in the 138 comparison PHAs (Not SAFMR PHAs), over

550,000 in each year.

Exhibit 5-1: Number of HCV Holder Households in Analysis Sample by ZIP Code Rent Ratio

Rent Ratio Category

HCV Holder Households

< 0.9

0.9–1.1

> 1.1

Total

by Rent Category

2010

2015

2010

2015

2010

2015

2010

2015

SAFMR PHAs

17,354

18,425

19,186

20,457

7,544

9,571

44,084

48,453

Laredo

514

315

980

1,084

56

193

1,550

1,592

Mamaroneck

90

83

131

117

387

340

608

540

Chattanooga

945

1,232

2,081

2,524

207

308

3,233

4,064

Cook County

4,051

4,903

5,866

6,175

2,758

3,285

12,675

14,363

Long Beach

5,487

5,709

1,264

1,321

57

74

6,808

7,104

Dallas

6,198

6,099

8,156

8,730

3,514

4,798

17,868

19,627

Plano

69

84

708

506

565

573

1,342

1,163

Comparison PHAs (138)

144,295

138,452

340,106

338,726  77,290  75,378  561,691

552,556

HCV = housing choice voucher. PHA = public housing agency. SAFMR = Smal  Area Fair Market Rent.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 SAFMRs (rent ratio calculation); HUD Public and Indian Housing Information Center administrative data extract (counts)

Exhibit 5-2 shows where HCV holders live in terms of the rent ratios of their units’ ZIP Codes in

2010 and 2015 for the seven SAFMR PHAs and the comparison set of Not SAFMR PHAs. The

exhibit provides indications that SAFMRs may have changed where some HCV holders reside.

Across all SAFMR PHAs, 17 percent of HCV households lived in high-rent ratio ZIP Codes in

2010 prior to the implementation of SAFMRs. This percentage increased to 20 percent in 2015.

HCV holders moved to these high-rent ratio ZIP Codes from both the moderate-rent ratio ZIP

Codes, which decreased their share by 2 percentage points, and the low-rent ratio ZIP Codes

decreased by one percentage point. In contrast, in the comparison PHAs, the percentage living in

high-rent ratio ZIP Codes stayed constant at 14 percent.

27  It is possible that changes in HCV payment standard will ultimately indirectly influence neighborhood

characteristics, including some opportunity measures. However, it is unlikely that these changes will be evident for multiple years, and examining such changes is beyond the scope of this study.

46

Exhibit 5-2: Rent Ratios of ZIP Codes Where HCV Holders Live Before and After SAFMRs

100%

90%

17%

20%

14%

14%

80%

rsde 70%

60%

44%

42%

V hol

61%

61%

C 50%

> 1.1

H 40%

of

0.9–1.1

re 30%

< 0.9

Sha 20%

39%

38%

26%

25%

10%

0%

2010

2015

2010

2015

SAFMR PHAs

Comparison PHAs

HCV = housing choice voucher. SAFMR = Small Area Fair Market Rent.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 SAFMRs (rent ratio calculation); HUD Public and Indian Housing Information Center administrative data extract (counts)

Overall, a larger share of HCV holders in the SAFMR PHAs live in ZIP Codes with rent ratios

below 0.9 (about 39 percent) than in the comparison PHAs (26 percent). In other words, the

SAFMR PHAs tend to have lower rents relative to their metropolitan areas compared with other

PHAs in the selection universe. This difference may be part of the motivation for joining the

demonstration. It also may be due to the fact that because Dallas was not part of the

demonstration per se, it was not part of a selection cluster, and thus, none of the PHAs in the

comparison group necessarily resemble Dallas. Therefore, it is more instructive to look at the

change in the percent of households in each rent ratio prior to and after the implementation of the

demonstration. Although the changes in the number of HCV holders living in moderate and low

rent ratio ZIP Codes in SAFMR PHAs represent a relatively small share of HCV holders living

in these neighborhoods, the 3 percentage point change in the share living in higher-rent ratio

neighborhoods is large considering the initial starting share.

Exhibit 5-3 shows how the ZIP Code rent ratio location changes over time for households that

first use HCVs (new HCV holders) and for existing HCV holders that move from one ZIP Code

to another. Because it can take multiple years for a household to contemplate and follow through

with a move, it is helpful to separately analyze the subgroups that are new or actually move. We

focus specifically on movers to new ZIP Codes in order to examine the potential of SAFMRs to

facilitate moves to higher opportunity areas.

To increase the number of new and moving HCV holders in our sample, we observe new

voucher use and moves over a 2-year window. We count households that were new or moved in

2009 or 2010 in the 2010 bars, and households that were new or that moved in 2014 and 2015 in

the 2015 bars. Exhibit 5-3 shows the rent ratio of the ZIP Code in which the new household first

uses a voucher and the new (destination) ZIP Code for the moving household.

47

Exhibit 5-3: Rent Ratios of ZIP Codes Where New HCV Holders and Recent ZIP Code Movers Live

Before and After SAFMRs

New HCV Holders

100%

14%

90%

17%

11%

12%

80%

rsde 70%

44%

60%

42%

V hol

64%

61%

C 50%

> 1.1

H 40%

of

0.9–1.1

re 30%

< 0.9

Sha 20%

42%

40%

25%

28%

10%

0%

2010

2015

2010

2015

SAFMR PHAs

Comparison PHAs

Notes: There were 2,541 new HCV holders in 2010 and 5,332 in 2015 in SAFMR PHAs. There were 71,739 new HCV holders in 2010 and 64,160

in 2015 in Not SAFMR PHAs.

Movers to New ZIP Codes

100%

90%

18%

14%

14%

28%

80%

rsde 70%

60%

46%

V holC 50%

41%

62%

62%

> 1.1

H 40%

of

0.9–1.1

re 30%

< 0.9

Sha 20%

36%

31%

24%

25%

10%

0%

2010

2015

2010

2015

SAFMR PHAs

Comparison PHAs

HCV = housing choice voucher. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Notes: There were 8,704 mover households in 2010 and 7,649 in 2015 in the SAFMR PHAs. There were 119,474 in 2010 and 95,246 in 2015 in Not SAFMR PHAs.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 SAFMRs (rent ratio calculation); HUD Public and Indian Housing Information Center administrative data extract (counts)

The same general pattern observed for all HCV holders holds for new HCV holders and is even

starker for those who move to new ZIP Codes. At the comparison PHAs the share of new HCV

holders and movers to new ZIP Codes who live in high-rent ratio ZIP Codes remained constant

48

at about 11 and 14 percent, respectively. In the SAFMR PHAs, the share of new households that

rented units in high-rent ratio ZIP Codes rose from 14 percent in 2010 to 17 percent in 2015. The

change for ZIP Code mover households was even greater, rising from 18 percent in 2010 to 28

percent in 2015. In other words, in 2015, more than a one-fourth of voucher households in the

SAFMR PHAs that moved to a new ZIP Code moved into high-rent ratio ZIP Codes. For new

HCV holders, the increased share in higher-rent ratio ZIP Codes relative to 2010 is drawn from

both moderate- and lower-rent ratio ZIP Codes by between 1 and 2 percentage points each. Still,

40 percent of new voucher holders initially use vouchers in neighborhoods with median rents

below 90 percent of the metropolitan area median in 2015. For existing HCV holders that move

to new ZIP Codes, the 10 percentage point increase in 2015 relative to 2010 in the share of

recent movers that locate in high-rent ratio neighborhoods represents a 5 percentage point

reduction in both moderate- and lower-rent ratio neighborhoods.

Exhibit 5-4 presents the share of HCV holders living in ZIP Codes within each rent ratio

category by site for all HCV holders. The exhibit shows that overall increases in the percentages

of voucher households in high-rent ratio ZIP Codes were concentrated in three sites—Dallas,

Laredo, and Plano. In Laredo, the share increased from 4 to 12 percent, in Dallas, from 20 to 24

percent, and in Plano, from 42 to 49 percent.

Exhibit 5-5 breaks out HCV holders into new and existing holders that move to a new ZIP Code.

The share of new households that rented in high-rent ratio ZIP Codes increased in Chattanooga,

Cook County, Dallas, and Laredo, and all sites except Mamaroneck experienced increases in the

share of ZIP Code movers who moved to high-rent ratio ZIP Codes.

Exhibit 5-4: Share of HCV Holders by Rent Ratios in ZIP Codes Where They Live Before and After

SAFMRs

100%

4%

1%

1%

6%

12%

8%

90%

22%

19%

19%

23%

20%

24%

80%

42%

49%

70%

rs

64%

63%

63%

de

64%

62%

60%

46%

V hol

68%

46%

43%

44%

C 50%

> 1.1

40%

81%

80%

0.9–1.1

re of H

< 0.9

Sha

53%

30%

44%

22%

22%

20%

33%

35%

29%

30%

32%

34%

31%

10%

20%

15%

15%

5%

7%

0%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Laredo

Mamaroneck Chattanooga Cook County Long Beach

Dallas

Plano

HCV = housing choice voucher. SAFMR = Small Area Fair Market Rent.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 SAFMRs (rent ratio calculation); HUD Public and Indian Housing Information Center administrative data extract (counts)

49

Exhibit 5-5: Distribution of Rent Ratios by Site for All SAFMR Public Housing Agencies—New

HCV Holders and ZIP Code Movers

New HCV Holders

100%

5%

5%

1%

1%

10%

90%

22%

17%

21%

20%

20%

19%

20%

rs 80%

42%

42%

de 70%

59%

V hol 60%

71%

66%

61%

45%

37%

83%

41%

C

46%

H 50%

w

64%

> 1.1

40%

78%

87%

41%

0.9–1.1

16%

52%

30%

re of ne

< 0.9

20%

39%

38%

44%

Sha

34%

25%

25%

29%

30%

10%

13%

0

1%

7%

16%

6%

0%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Laredo

Mamaroneck Chattanooga Cook County

Long Beach

Dallas

Plano

Note: The table reports the number of new households (in the 2 years ending in the year listed) in each site by year.

Laredo

Mamaroneck

Chattanooga

Cook County

Long Beach

Dallas

Plano

2010

170

18

458

363

411

989

132

2015

253

61

722

1,223

774

2,202

97

ZIP Code Movers

1%

2%

100%

6%

6%

11%

90%

24%

21%

21%

21%

31%

30%

32%

ers 80%

45%

44%

39%

52%

70%

mov

62%

60%

67%

57%

ode

43%

49%

> 1.1

50%

38%

P C

44%

40%

57%

23%

32%

78%

79%

0.9–1.1

51%

30%

35%

re of ZI

< 0.9

20%

Sha

32%

32%

24%

27%

32%

33%

31%

30%

24%

10%

12%

11%

13%

0%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Laredo

Mamaroneck Chattanooga Cook County Long Beach

Dallas

Plano

Note: The table reports the number of ZIP Code movers in the 2 years ending in the year listed in each site by year.

Cook

Long

Laredo

Mamaroneck

Chattanooga

County

Beach

Dallas

Plano

2010

287

62

667

2,596

1,068

3,793

231

2015

194

34

563

2,386

631

3,665

176

HCV = housing choice voucher. SAFMR = Small Area Fair Market Rent.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Small Area Fair Market Rents (rent ratio calculation); HUD Public and Indian Housing Information Center administrative data extract (shares)

50

One notable exception to these observations exists. In Mamaroneck, among new HCV holders,

the share that rented in high-rent ratio ZIP Codes decreased from 83 percent to 59 percent

following the implementation of SAFMRs. However, only 18 new voucher holders were in

Mamaroneck in 2009 and 2010, so these numbers may reflect the idiosyncratic circumstances of

a small number of households.

Exhibits 5-6 and 5-7 provide greater detail on the HCV holders who moved ZIP Codes. These

exhibits break out the share of movers who moved to ZIP Codes in each rent ratio category based

on the rent ratio category of the HCV holder’s originating ZIP Code. Exhibit 5-6 shows movers

in SAFMR PHAs. As reflected in the exhibit, increases in the share of movers who relocate to

ZIP Codes with rent ratios greater than 1.1 originated from HCV holders moving from all types

of rent ratio neighborhoods. For example, the share of mover households that move from a ZIP

Code with a rent ratio less than 0.9 into a different ZIP Code with a rent ratio less than 0.9 falls

from 54 percent in 2010 to 47 percent in 2015, corresponding to a 6 percentage point increase in

the share of households that move into ZIP Codes with a rent ratio greater than 1.1.

Exhibit 5-6: Rent Ratios in ZIP Code Movers’ Destination ZIP Codes by Rent Ratio in Origination

ZIP Code—SAFMR PHAs

2010

54%

36%

10%

9

< 0. 2015

47%

37%

16%

of ode

1 2010

P C

25%

55%

19%

category ZI

- 1.9

< .9

ting

0. 2015

25%

46%

28%

tion

0.9–1.1

ra nagi

> 1.1

ent ori

2010

12%

46%

42%

R

1

> 1. 2015

15%

37%

48%

0%

20%

40%

60%

80%

100%

Share of movers by rent ratio category of destination ZIP Code

Notes: The following table reports the number of households (in the 2 years ending in the year listed) that moved from an originating ZIP Code in each rent ratio category. The number of households that moved but stayed in the same ZIP Code and the total number of households in the move analysis (including those that did not move) are included for context.

HCV Holders

Total HCV Holders

Originating ZIP Code Type

Moving Within

in Analysis

Neighborhood (Not

(Including

< 0.9

0.9–1.1

> 1.1

Total

in Chart)

Nonmovers)

2010

2,459

2,754

1,008

6,221

2,729

40,636

2015

2,642

3,426

1,457

7,525

2,442

42,471

HCV = housing choice voucher.

51

Exhibit 5-7: Rent Ratios in ZIP Code Movers’ Destination ZIP Codes by Rent Ratio in Origination

ZIP Code—Comparison Public Housing Agencies

2010

47%

44%

9%

9

< 0.

of

2015

48%

43%

9%

ode

P CZI

1 2010

16%

73%

11%

category

- 1.

< .9

tio  ting

9

na

0. 2015

ra

17%

72%

10%

0.9–1.1

gi

entR ori

> 1.1

2010

14%

46%

40%

1

> 1. 2015

15%

47%

38%

0%

20%

40%

60%

80%

100%

Share of movers by rent ratio category of destination ZIP Code

Notes: The following table reports the number of households (in the 2 years ending in the year listed) that moved from an origination ZIP Code in each rent ratio category. The number of households that moved but stayed in the same ZIP Code and the total number of households in the move analysis (including those that did not move) are included for context.

Originating ZIP Code Type

Total

HCV Holders

Total HCV Holders

Moving Within

in Analysis

Neighborhood (Not

(Including

< 0.9

0.9–1.1

> 1.1

in Chart)

Nonmovers)

2010

20,668

49,282

9,856

79,806

34,862

496,728

2015

22,815

58,524

12,098

93,437

30,662

503,139

HCV = housing choice voucher. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Exhibit 5-7 shows that these patterns of moves do not appear in the comparison PHAs. Rather,

the share of moving households locating to ZIP Codes in particular rent ratio categories is

consistent from 2010 to 2015 across all origination ZIP Code rent ratio categories. As shown on

the table at the bottom of Exhibit 5-6, the share of movers who moved ZIP Codes increased from

70 percent in 2010 to 76 percent in 2015, although the share of all HCV holders who moved over

a 2-year period stayed roughly the same at 22 percent in 2010 and 23.5 percent in 2015.

However, as reflected in the table at the bottom of Exhibit 5-7, similar increases are seen in the

comparison PHAs, suggesting the change observed in SAFMR PHAs may be due to factors

besides the introduction of SAFMRs.

Opportunity Measures in ZIP Codes Where SAFMR Holders Reside

We next turn to a comparison of the opportunity measures of locations where voucher holders

live before and after introduction of SAFMRs.

52

Exhibit 5-8 shows that the share of HCV holders living in higher-opportunity areas changed

slightly following the implementation of SAFMRs. Prior to implementation of SAFMRs, 11

percent of voucher holders in SAFMR PHAs lived in ZIP Codes with the highest opportunity

levels, whereas about 13 percent following implementation. As was the case with rent ratios,

effectively no change occurred in the comparison PHAs.

Exhibit 5-8: Share of All HCV Holders by Opportunity Categories Before and After SAFMRs

100%

11%

13%

9%

9%

90%

80%

rsde 70%

45%

44%

45%

46%

60%

V holC 50%

> 75

al H 40%

25–75

ofre

< 25

30%

Sha

44%

43%

46%

46%

20%

10%

0%

2010

2015

2010

2015

SAFMR PHAs

Comparison PHAs

HCV = housing choice voucher. PHA = public housing agency. SAFMR = Smal  Area Fair Market Rent.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units); HUD Public and Indian Housing Information Center administrative data extract (counts); overal  opportunity index

Exhibit 5-9 shows that similar patterns hold for new HCV holders (top panel) and for recent

movers (bottom panel). Relative to all households, new households are in ZIP Codes with lower

overall opportunity measures. As with all households, the share moving into ZIP Codes in the

top quartile of the overall opportunity measure increased by only 2 percentage points between

2010 and 2015. As with all HCV holders, the share of new households in Not SAFMR PHAs in

ZIP Codes in each overall opportunity category remains nearly the same in 2015 and 2010.

The bottom panel of Exhibit 5-9 shows that the share of recent movers to new ZIP Codes living

in higher-opportunity neighborhoods increased by 6 percentage points, whereas the share living

in the ZIP Codes with bottom quartile overall opportunity scores similarly declined.

Exhibit 5-10 shows how the changes in the share of households living in each category of overall

opportunity index ZIP Code varied across the SAFMR PHAs. The overall change in opportunity

measures is driven nearly entirely by the same three PHAs as were the rent changes shown

above—Dallas, Laredo, and Plano. The share of all HCV holders living in higher-opportunity

ZIP Codes does not change much between 2010 and 2015 in the other four SAFMR PHAs.

53

Exhibit 5-9: Share of All Housing Choice Voucher Holders by Opportunity Categories Before and

After SAFMRs—New Households and ZIP Code Movers

New Housing Choice Voucher Holders

100%

9%

11%

8%

8%

90%

80%

ds 70%

40%

40%

46%

46%

ehol 60%

hous 50%

w

> 75

ne 40%

25–75

of

< 25

re 30%

50%

49%

46%

46%

Sha 20%

10%

0%

2010

2015

2010

2015

SAFMR PHAs

Comparison PHAs

ZIP Code Movers

100%

9%

7%

7%

15%

90%

80%

ers 70%

43%

44%

ov

48%

m 60%

47%

cent 50%

> 75

re 40%

25–75

ofre

< 25

30%

Sha

50%

49%

20%

43%

37%

10%

0%

2010

2015

2010

2015

SAFMR PHAs

Comparison PHAs

PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units); HUD Public and Indian Housing Information Center administrative data extract (counts); overall opportunity index

54

Exhibit 5-10 Distribution of Opportunity Index for All HCV Holders—by SAFMR PHA

0%

0%

100%

4%

5%

12%

9%

13%

14%

90%

20%

18%

19%

19%

38%

80%

42%

25%

21%

44%

70%

56%

45%

45%

s

60%

83%

82%

er

old h 50%

63%

> 75

65%

25–75

HCV

40%

81%

80%

< 25

f all

56%

48%

e o

62%

65%

30%

51%

Shar

46%

20%

41%

43%

10%

17%

18%

15%

19%

10%

0%

0%

6%

0%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Laredo

Mamaroneck Chattanooga Cook County Long Beach

Dallas

Plano

HCV = housing choice voucher. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Small Area Fair Market Rents; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units); HUD Public and Indian Housing Information Center administrative data extract (counts); overall opportunity index

Exhibit 5-11 shows the variation by PHA among new HCV holders and ZIP Code movers. The

trends for changes in access to higher opportunity areas are mostly similar to the trends for

access to high-rent ZIP Codes. One notable difference between the two measures, however, is in

Cook County, where gains in access to high-rent ZIP Codes do not translate into gains in access

to higher-opportunity ZIP Codes.

55

Exhibit 5-11: Distribution of Opportunity Index by SAFMR PHAs—New and ZIP Code Mover

Households

New Households

0%

1%

100%

5%

3%

7%

12%

14%

13%

17%

90%

22%

24%

21%

25%

33%

80%

27%

23%

44%

rs

48%

43%

70%

de

67%

60%

V hol

42%

C

94%

61%

H 50%

49%

> 75

w

59%

87%

25–75

40%

78%

60%

re of ne

< 25

30%

61%

63%

Sha

53%

47%

50%

20%

36%

33%

22%

26%

10%

17%

6%

8%

0%

0%

0%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Laredo

Mamaroneck Chattanooga Cook County Long Beach

Dallas

Plano

ZIP Code Movers

1%

0%

100%

6%

5%

12%

16%

16%

15%

15%

90%

21%

21%

31%

33%

80%

22%

44%

20%

45%

70%

49%

66%

74%

47%

60%

ers

33%

63%

50%

> 75

69%

mov

25–75

ode

40%

78%

79%

56%

39%

P C

< 25

67%

64%

30%

49%

re of ZI

44%

20%

36%

38%

34%

Sha

26%

10%

22%

15%

18%

10%

0%

0%

0%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Laredo

Mamaroneck Chattanooga Cook County Long Beach

Dallas

Plano

HCV = housing choice voucher. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Smal  Area Fair Market Rents; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units); HUD Public and Indian Housing Information Center administrative data extract (counts); overal  opportunity index

56

Additional Neighborhood Characteristics in ZIP Codes Where HCV Holders

Reside

In addition to affecting the opportunity profile of the neighborhoods where HCV holders locate,

implementing SAFMRs may result in HCV holders living in neighborhoods with a different

demographic makeup. We examine a few examples of neighborhood demographic characteristics

in this section—racial and ethnic composition of the neighborhood, share of households with

children, and share of households where the head of household has a college degree. We stress

that these characteristics are not measures of opportunity provided by the neighborhood. Rather,

they provide insight into whether SAFMRs affect whether HCV holders live in more racially and

ethnically integrated neighborhoods and in neighborhoods with different household demographic

profiles.

We examine these neighborhood characteristics using a methodology similar to the one we used

to examine the opportunity measures. To enable us to draw comparisons for all SAFMR PHAs

together, we normalize each characteristic as a percentile over all renters in the metropolitan

area. We then report the share of HCV holders living in ZIP Codes in the bottom quartile, middle

one-half, and top quartile of the metropolitan area for each characteristic. This common

categorization also enables us to present the information on different characteristics together in a

common exhibit. However, we calculate the percentile normalization for each characteristic

independently.

Exhibit 5-12 shows that more than one-half of HCV holders in SAFMR PHAs live in

neighborhoods that are in the bottom quartile of the share of households that are White, and more

than one-half live in neighborhoods that are in the top quartile of the share of households that are

Black. Slightly more than one-half of HCV holders live in ZIP Codes that are in the middle one-

half of the metropolitan area distribution for Hispanic share. About a one-fourth of HCV

households live in the top quartile of the share that are Hispanic, although slightly less than a

one-fourth live in the bottom quartile.

In terms of the effects of SAFMRs, the exhibit shows essentially no change across all SAFMR

PHAs combined between 2010 and 2015 in the share of housing choice voucher holders who live

in neighborhoods in ZIP Codes in each percentile category of the share of households that are

White, non-Hispanic, and Black. The share of households residing in neighborhoods in the

bottom quartile of percent Hispanic increases from 16 percent to 19 percent. This increase is

likely related to our findings of larger effects in the Texas PHAs. Similarly, no change exists for

the Non SAFMR comparison PHAs (not shown) and no remarkable changes emerge across

individual SAFMR PHAs (tabulations not shown).

However, a closer look at households that newly receive a voucher and households that have

recently moved to a new ZIP Code show that SAFMRs may be beginning to alter HCV holder

location outcomes in terms of the neighborhoods’ racial and ethnic composition in which they

live. Exhibit 5-13 shows that HCV holders new to receiving vouchers less frequently locate in

ZIP Codes in the middle one-half of the metropolitan area distribution of percentage White in

2015 compared with in 2010, with the share living in these ZIP Codes falling from 41 to 37

percent. By contrast, in 2015, new HCV holders are more frequently locating in ZIP Codes that

are in the top quartile of percent Black residents. The trend for percent Hispanic for new

households is similar to that observed for all HCV holders, with the share residing in the ZIP

Codes with the lowest share Hispanic increasing from 16 to 18 percent.

57

Exhibit 5-12: Share of All HCV Holders by Racial and Ethnic Composition of Neighborhood—

SAFMR PHAs

100%

7%

7%

90%

25%

24%

80%

rs

40%

39%

70%

de

64%

64%

60%

V holC 50%

H

59%

58%

> 75

of 40%

25–75

re 30%

54%

54%

< 25

Sha

28%

29%

20%

10%

16%

19%

8%

8%

0%

2010

2015

2010

2015

2010

2015

Percent White (non-

Percent Black

Percent Hispanic

Hispanic)

HCV = housing choice voucher. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Smal  Area Fair Market Rents; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units); HUD Public and Indian Housing Information Center administrative data extract (counts)

In contrast, HCV holders who move to new ZIP Codes are more often moving to neighborhoods

in the top quartile of percentage White, at 8 percent of new movers in 2015 compared with 5

percent of new movers in 2010. The percentage moving into neighborhoods in the top quartile of

percentage Black is still the highest among all categories, at 62 percent in 2015, but this result is

a decrease from the 67 percent observed in 2010. Also, a sizable gain occurred in the share

locating in neighborhoods with comparatively low Hispanic shares. We note that no such trends

are present in comparison PHAs (tabulations not shown), as the percentages of new voucher

holders and movers in each category for each race or ethnicity essentially stay the same.

Exhibit 5-14 shows that the patterns observed for race and ethnicity—no changes for all HCV

holders taken together, but some changes for new HCV holders and ZIP Code movers—are

mirrored in the ZIP Code share of households with children and share of adults with a college

degree. Essentially no changes are observed over all HCV holders. However, smaller shares of

both new HCV holders and those moving to new ZIP Codes locate in ZIP Codes in the bottom

quartile of the metropolitan area in terms of households with children. This phenomenon is

particularly the case for new HCV holders, where the share locating in this bottom quartile falls

from 23 percent to 12 percent.

58

Exhibit 5-13: Share of All HCV Holders by Neighborhood Racial and Ethnic Composition—New

HCV Holders and ZIP Code Movers

New HCV Holders

100%

6%

7%

90%

27%

27%

rs 80%

de

41%

37%

70%

61%

63%

V hol 60%

C H 50%

w

> 75

56%

55%

ne 40%

25–75

of 30%

re

< 25

52%

56%

32%

30%

20%

Sha

10%

16%

18%

7%

7%

0%

2010

2015

2010

2015

2010

2015

Percent White (non-

Percent Black

Percent Hispanic

Hispanic)

ZIP Code Movers

100%

5%

8%

90%

25%

22%

80%

ers

39%

39%

ov 70%

67%

62%

m 60%

ode 50%

55%

P C

58%

> 75

ZI 40%

25–75

of 30%

re

56%

< 25

53%

20%

Sha

27%

31%

10%

17%

23%

6%

7%

0%

2010

2015

2010

2015

2010

2015

Percent White (non-

Percent Black

Percent Hispanic

Hispanic)

HCV = housing choice voucher.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Smal  Area Fair Market Rents; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units); HUD Public and Indian Housing Information Center administrative data extract (counts)

59

Exhibit 5-14: Share of All HCV Holders by Neighborhood Percentage of Households With

Children and With a College Degree—SAFMR PHAs

Al  HCV Holders

100%

10%

10%

rs 90%

de 80%

36%

37%

70%

41%

42%

60%

V holC 50%

> 75

H 40%

51%

51%

of

25–75

30%

re

49%

48%

20%

< 25

Sha 10%

13%

12%

0%

2010

2015

2010

2015

With children (percent)

College degree (percent)

New HCV Holders

100%

rs

12%

10%

90%

de

31%

80%

38%

70%

47%

40%

V hol 60%

C 50%

H

> 75

46%

w 40%

50%

25–75

ne 30%

of 20%

41%

50%

< 25

re 10%

23%

13%

0%

Sha

2010

2015

2010

2015

With children (percent)

College degree (percent)

ZIP Code Movers

100%

8%

10%

90%

ers 80%

37%

38%

ov 70%

42%

m

43%

60%

ode 50%

> 75

40%

P C

52%

54%

25–75

30%

ZI

51%

46%

20%

< 25

ofre 10%

11%

9%

0%

Sha

2010

2015

2010

2015

With children (percent)

College degree (percent)

HCV = housing choice voucher. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Smal  Area Fair Market Rents; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units); HUD Public and Indian Housing Information Center administrative data extract (counts)

60

For the share of adults with a college degree, we see opposite patterns for new HCV holders and

those moving across ZIP Codes. Between 2010 and 2015, the share of new voucher holders

locating in ZIP Codes in the bottom quartile in the metropolitan area for this characteristic

increases 9 percentage points to 50 percent, whereas for ZIP Code movers, it falls by 5 percentage

points to 46 percent. Again, these trends are not observed for the comparison public housing

agency sample (tabulations not shown).

Summary

In summary, it appears that Small Area Fair Market Rents are working as intended—increasing

access to units in higher-opportunity areas and decreasing access in lower-opportunity areas.

This finding that SAFMRs are working as intended is borne out when looking at rents and at

opportunity measures. In practice it also appears that as HCV holders move to new units

(whether through new entry into the HCV program or existing voucher recipients moving to a

new unit in a different ZIP Code), they are more likely to move to higher-opportunity areas

compared with pre-SAFMR moves. No similar changes are observed in a broad group of

comparison PHAs where SAFMRs were not implemented.

The areas where new and moving HCV holders locate after the introduction of SAFMRs change

in terms of other neighborhood characteristics, as well. In particular, households moving to a

different ZIP Code after the introduction of SAFMRs are more likely to locate in neighborhoods

that have a lower share of minority residents and a higher share of adults with a college degree.

Phase 2 of the evaluation will revisit all these measures. If the trends observed in the current

report continue, we expect that as more HCV holders move, we will see more HCV holders

living in areas of higher opportunity.

One (likely) unexpected consequence of the demonstration is that across the SAFMR PHAs,

fewer units overall are available at rents below the prevailing Fair Market Rent. This

consequence is because the increase in units below the SAFMR in the high-rent ZIP Codes is

more than offset by the decrease in units in the low-rent ZIP Codes. This problem is greater in

some areas (such as Long Beach) than others, suggesting that a shift to SAFMRs may make it

more difficult for participants to use vouchers. The data also show that some PHA jurisdictions

(again, Long Beach) have a relatively small number of rental units in areas of opportunity (when

opportunity is measured in relation to the entire metropolitan area), which limits the potential of

SAFMRs to enhance access to high-opportunity areas.

61

6.  Perceptions of Tenants’ Experience and Administrative Impacts of Small

Area Fair Market Rents on Public Housing Agencies

We begin by reporting findings from our conversations with PHA staff at the SAFMR PHAs

about their perceptions of tenants’ experience. Then, in the following three sections, we report on

data collected during site visits. We first describe the steps PHAs took to implement SAFMRs,

including changes to systems and processes needed to administer SAFMRs. We then quantify

these impacts (to the extent data are available) in terms of staff effort and expenditures for items

such as additional staff hires and software updates to accommodate SAFMRs, which is followed

by a section reporting on the financial impacts of SAFMRs on PHAs. The final section

documents PHA perspectives on the ways SAFMRs affected landlords and how landlords

responded.

PHA Perceptions of HCV Holders’ Experience With SAFMRs

The focus of the second round of site visits in 2017 will be participants’ experience with

SAFMRs. Here, we report on how  PHAs perceive  these additional influences on tenant

decisionmaking. We also gathered information from PHAs about HCV holders’ success in

finding units and the reasons leading to that success. The following is a summary of what we

learned about HCV holders’ experiences shifting to SAFMRs.

The initial challenge was for HCV holders to understand changes in units they could afford

under SAFMR. After explanation from the PHA and some experience with the demonstration,

HCV holders (according to PHAs) now understand the program and what they can afford. In

general, PHAs believe that they needed fairly limited additional efforts to educate HCV holders

about SAFMRs. PHAs created some new briefing materials, such as a chart of payment

standards by unit size and ZIP Code and a brief explanatory letter. These materials were either

mailed to HCV holders as part of the reexamination packet or provided to tenants during annual

reexamination briefings. None of the PHAs reported holding additional briefings to explain

SAFMRs, however Plano reported lengthening the usual briefing to explain SAFMRs. Long

Beach and Mamaroneck noted that attendance at briefings was higher than usual, because more

households requested moves. Some tenants reportedly learned about the higher payment

standards in high-opportunity areas through fellow HCV holders.

PHAs reported that new families generally found it easier to understand SAFMRs than did

existing HCV holders, because new families did not have prior experience with a different

payment standard process. Existing families were sometimes initially confused by the change in

payment standards, but all PHAs reported that they now understand SAFMRs. Plano noted that

port-ins had difficulty understanding SAFMRs, because it was a change from the program to

which they were accustomed. Chattanooga noted that an additional change in utility allowance

was implemented at the same time as the shift to SAFMRs, and that this utility allowance change

was more difficult to explain (and for tenants to understand) than SAFMRs.

Many HCV holders searched for units in new areas, although the extent of searching in new

areas tended to vary by the type of participant. PHAs generally provided limited assistance to

HCV holders interested in moving to high-opportunity neighborhoods. Most PHAs reported that

HCV holders often searched in high-opportunity areas, but some ultimately stayed in the same

areas because of established networks. In some PHAs, high-opportunity areas tended to have

62

fewer transportation options, which also led some households to stay in current neighborhoods.

Mamaroneck reported that other costs in addition to transportation, such as higher childcare

costs, were a barrier to families moving to higher-opportunity neighborhoods.

PHAs perceived that existing elderly or voucher holders with disabilities were less likely to

search for new units in high-opportunity neighborhoods, because their current units were in close

proximity to medical providers and other resources. As a result, when payment standards

declined in low-rent ZIP Codes, those tenants sometimes were required to pay higher rents. At

least two PHAs, Dallas and Long Beach, reported that some elderly or HCV holders with

disabilities left the program because of the increased tenant shares of rent for units. It may be that

in some sense, what constitutes a higher-opportunity neighborhood is in the eye of the beholder.

That is, elderly or tenants with disabilities may view the amenities available in current

neighborhoods as more valuable than those offered by neighborhoods with higher rents and

opportunities. It may also simply be that moving to different neighborhoods is more difficult for

elderly people and people with disabilities.

Long Beach reported that HCV holders had great difficulty searching for units in high-

opportunity areas. These neighborhoods had few rental units, and even with SAFMRs, the

market rents were not affordable. Landlords preferred to rent to nonvoucher holders and at rents

above those allowed for by SAFMR payment standards.

PHAs reported mixed experiences with HCV holders’ search times. Long Beach said that

voucher holders had more difficulty finding units, because SAFMRs significantly lagged the

market due to gentrification in key areas. SAFMRs are published only annually and payment

standards were too low to cover the rents in these areas. Long Beach reported that discrimination

based on the source of income also played a factor. Long Beach now gives a standard of 120-day

search time, with extensions up to 180 days and longer. The PHA applied for 120-percent

exception rents in several ZIP Codes. 28 At the time, the PHA probably was not aware that in addition to previously described factors, SAFMRs reduced the number of units with rents below

SAFMRs compared with FMRs by 12 percent, nearly double the decline of any other PHA in the

demonstration.

Mamaroneck and Plano said that the search time was sometimes longer, because tenants were

unfamiliar with new, higher-opportunity neighborhoods. Other PHAs, including Laredo and

Chattanooga, believed thatno changes in either the level of difficulty finding units or search

times ocurred. Cook County said it was now easier for voucher holders to find units because of

the higher payment standards. (Chattanooga reported that zero-income renters had a harder time

finding units than other renters, although this difficulty was unrelated to SAFMRs.)

Factors other than rents affected where tenants chose to live. Chattanooga reported that HCV

holders initially searched in high-rent areas. However, because those areas lack public

transportation, ultimately HCV holders without transportation chose to live in midrange

neighborhoods that had good access to bus transportation. The same PHA reported that many

households preferred to stay in familiar areas, or comfort zones.

28  The exception had not been granted at the time of the first site visit. We will confirm whether the exception is approved during the second site visit.

63

Factors Affecting PHAs’ SAFMR Implementation

As expected, we found that SAFMRs implementation altered some PHA administrative

responsibilities and, at times, increased the volume of transactions the PHA must process and the

level of effort required to complete certain activities. As discussed in detail in the remainder of this

section, the initial adjustment took roughly a year, during which PHAs set payment standards at the

ZIP Code level for the first time, created materials explaining and promoting SAFMRs to landlords

and tenants, trained staff on the new payment standards, made modifications to software systems to

accommodate many more payment standards, and sent tenants letters of impact.

In the short term, as PHAs changed policies and procedures and as staff, tenants, and landlords

adjusted, the net effect was always an increase in administrative costs.

After initial adjustments, the extent of impacts on PHAs declined, but nearly all PHAs also

reported permanently higher ongoing administrative costs. This section first recaps motivations

of the PHAs for participating in the SAFMR demonstration and then describes the approach

PHAs took to implement the demonstration. The section titled Opportunity Measures in ZIP

Codes Where SAFMR Holders Live provides a preliminary analysis of the magnitude of these

administrative impacts, and the section titled Additional Neighborhood Characteristics in ZIP

Codes Where HCV Holders Live discusses related expenditures and staff effort.

PHA Reasons for Using SAFMRs

The five PHAs in the SAFMR Demonstration (Chattanooga, Cook County, Laredo, Long Beach,

and Mamaroneck) were randomly selected and agreed to participate. Four of these five PHAs

reported that a key reason for implementing SAFMRs was to make units available to HCV

holders in higher-opportunity neighborhoods. 29 One PHA specifically noted that SAFMRs could help to deconcentrate HCV tenants. The fifth PHA reported joining reluctantly, after what it

perceived to be encouragement from HUD and also a sense that eventually SAFMRs were to be

implemented more broadly, with the demonstration as a way to get ahead of schedule. Secondary

reasons for participation in the SAFMR demonstration included the increased administrative fees

that accompanied participation in the demonstration and PHA desires to attract new landlords to

the program, reduce Housing Assistance Payment costs, and enable tenants to move to higher-

quality units.

The mixed motivations for implementing SAFMRs may not necessarily dictate the results. That

is, some PHAs encouraged or required to join reported that the benefits experienced by HCV

holders, as a result of the demonstration, may outweigh the costs and inconvenience to the PHA.

Alternatively, given the specific market context, PHAs that joined voluntarily may find that

SAFMRs have not enabled enough tenant moves to higher-opportunity areas—or have

introduced enough other challenges—to warrant the extra cost and effort of implementation. The

study’s final report due in 2018 will shed further light on the importance of market context.

A court settlement required that the two Dallas area PHAs in this study, Dallas and Plano, use

SAFMRs. Therefore, beginning in 2011, Dallas and Plano, along with every other PHA in the

seven-county Dallas, TX area, were required to use SAFMRs. Because Dallas and Plano are not

part of the SAFMR Demonstration, their experiences and motivations are different.

29  Note that none was a Moving To Work PHA.

64

External Factors That Affected Implementation

Factors external to SAFMRs and the demonstration also affected implementation. These factors

varied across PHAs and include federal budget sequestration, source of income legislation,

housing desegregation litigation, and market conditions.

Federal Budget Sequestration

Five PHAs—Cook County, Dallas, Long Beach, Mamaroneck, and Plano—cited sequestration in

2013 as a factor that slowed implementation of SAFMRs and probably dampened its effects.

Sequestration refers to automatic, across-the-board budget cuts to discretionary-funded

programs, passed into law in 2011, that were to take effect if Congress could not agree on a plan

to reduce the national budget deficit by a specific amount. These cuts started in March 2013,

resulting in reduced payments for both operating and capital funds for PHAs.

The timing almost certainly affected the SAFMR demonstration. Although Dallas and Plano

implemented SAFMRs in 2011, the remaining five PHAs had not signed agreements to operate

under SAFMRs until October 2012. By early 2013, several PHAs were still transitioning to

SAFMRs and interrupted by sequestration. As a result, most PHAs temporarily stopped issuing

new vouchers, reducing the number of households enrolled in the HCV program. One PHA was

forced to recall vouchers already issued, resulting in fewer households joining the HCV program

during the early stages of SAFMR implementation. Reportedly, sequestration also affected

staffing levels in PHAs, including Cook County, Dallas, and Long Beach. In general, this staff

reduction increased workloads for PHA staff members. The Long Beach PHA also reported that

relationships with landlords suffered as a consequence of staff reduction, further exacerbating

challenges to HCV holders in finding rental units, which we will discuss further in this chapter.

At the end of 2013, federal policymakers agreed on a budget that reduced the effects of

sequestration for 2014 and 2015. Although this agreement technically ended the PHA budget

crunch brought on by sequestration, PHAs report that it took significant time—as long as a year

in some cases—to return to normal funding and staffing levels. Exhibit 6-1 summarizes this

timing of the implementation of SAFMRs and subsequent sequestration.

Exhibit 6-1: Timeline of Sequestration

Spring 2011

SAFMRs effective for Dallas and Plano PHAs.

October 2012

SAFMRS effective for the five PHAs participating in the demonstration.

March 2013

Sequestration takes effect.

December 2013

Agreement to end sequestration.

PHA = public housing agency. SAFMR = Smal  Area Fair Market Rent.

Source of Income Discrimination Legislation

Other external events affected SAFMR implementation and, to some extent, made each site a

unique experiment. In Cook County and Mamaroneck, legislation protecting against discrimination

by landlords on the basis of the source of income occurred at about the same time as the SAFMR

65

implementation (2013 and 2012, respectively). Both locations reported that this coincidence

helped smooth the transition by increasing the number of landlords participating and the units

available to HCV holders, and thus, voucher success rates at least temporarily. 30,31

Housing Market Conditions

Market conditions generally varied over the course of the demonstration, and these conditions

had important effects in many areas on HCV holders’ ability to move to higher-opportunity

neighborhoods. PHA staff in Chattanooga, Dallas, Long Beach, Mamaroneck, and Plano all

expressed a belief that declining vacancy rates affected HCV holders in their jurisdictions during

the time period of this evaluation.

We examined vacancy rates independently and found that, in five of the seven SAFMR PHA

jurisdictions, rolling average rental vacancy rates fell from 2011 to 2014—sharply in

Chattanooga and Dallas, somewhat less so in Cook County and Plano, and only slightly in Long

Beach (Exhibit 6-2). PHA staff in these places believed that, at the beginning of the

demonstration, the relatively loose rental market made HCV holders attractive tenants, which

increased the options available to the voucher holders. Over the course of the demonstration,

however, several PHAs reported that the improving housing market may have had the opposite

effect. All five PHAs reported that a looser rental market at the beginning of SAFMR

implementation meant that HCV holders had more opportunities to move. These opportunities

have declined since then because of the tightening market.

In addition to citing a generally improving economy, PHA staff we spoke with in Dallas and

Plano believe this tightening rental market was related to the relocation of large, new employers

to the area, which increased competition for housing units. In Long Beach, vacancy rates

declined only slightly and probably do not fully account for the much greater difficulty in finding

rental units reported by PHA staff. HCV holders’ experiences in Long Beach may be more

closely related to the number of units affordable to them declining as a result of SAFMRs, as

discussed in chapter 4. As with declining vacancy rates, the likely effect of a decline in units

renting below the applicable FMR would be to make it harder for HCV holders to find units.

In the Mamaroneck PHA, with HCV holders throughout Westchester County, New York, our

data does not demonstrate the perception that overall vacancy rates declined for the county as a

whole. However, it is possible that the rental market tightened in the portions of the county

where its HCV holders primarily searched. ACS data show that rolling average rental vacancy

rates in Westchester County increased slightly from 2011 to 2014. Vacancy rates were relatively

flat in the Laredo PHA jurisdiction. Compared with areas with tightening rental markets, in

Laredo and Mamaroneck, HCV tenants may have encountered less competition and, therefore,

less difficulty in moving to rental units in higher-opportunity areas to take advantage of higher

payment standards available under SAFMR.

30 Cook County cited high voucher success rates and attributed these higher rates of success to the legislation.

Mamaroneck cited specific apartment buildings that became available to HCV tenants because of the legislation.

31 Both PHAs expected that some landlords would eventual y use credit quality discrimination, which is legal, to exclude voucher holders to the extent possible. However, in the short term, and importantly for these PHAs during the transition to SAFMRs, legislation prohibiting discrimination on the basis of source of income helped voucher holders.

66

Exhibit 6-2: Change in (Rolling Average) Vacancy Rates, 2011–2014

12.0%

11.0%

10.0%

Cook County PHA

9.0%

Long Beach PHA

Mamaroneck PHA

8.0%

Chattanooga PHA

7.0%

Dal as PHA

Laredo PHA

6.0%

Plano PHA

5.0%

4.0%

2007–2011

2008–2012

2009–2013

2010–2014

PHA = public housing agency.

Source: American Community Survey

The tightening of rental housing markets in most SAFMR jurisdictions may have made it more

difficult for HCV holders to find housing in high-opportunity areas of the jurisdictions, leading

to lower effects of SAFMRs relative to what might have occurred in looser rental markets where

HCV holders have more selection and less difficulty finding new rental units.

Other market effects were also in play in Long Beach where PHA staff believe that large

numbers of foreclosures from the housing market downturn led to increased competition for

rental units. At the same time, that PHA reported that gentrification of the city’s downtown

raised rents and reduced the number of units available to HCV holders. The PHA specifically

suggested that HCV holders not relocate, if possible, to avoid losing their current units and

risking not being able to find new rentals.

Related to the rapidly changing housing market conditions experienced in some evaluation sites,

PHA staff at Long Beach, Mamaroneck, and other locations believe that SAFMRs, which are set

by HUD once per year and often not implemented by PHAs until the next January, sometimes

lagged the market. This lag meant that even the higher SAFMRs available in higher-cost

neighborhoods were reported to be too low to cover market rents by the time they went into

effect. HCV holders are generally at a disadvantage relative to other renters (hence the need for

protection from discrimination on the basis of source of income), and this lag may have further

disadvantaged HCV holders.

PHA Policy Changes

Other policy changes made by PHAs also affected HCV holders and their likelihood of moving.

Although these changes did not directly affect the SAFMR implementation, they may have

affected HCV holders’ location decisions and their success in using vouchers. For example,

67

Dallas reported that voucher success rates dropped sharply in 2014, but that this decrease was

related to a change in the occupancy standard that reduced the number of bedrooms for which

tenants qualified. 32 Some households were over-housed as a result of the change and moved to units with lower tenant rent burdens. All this change in occupancy standard was exacerbated by

reductions in staffing that resulted from sequestration.

SAFMR Administrative Impacts on PHAs

In general, the primary impacts of the transition to SAFMRs on PHA program administration

were due to changes in effort and procedures related to analyzing and setting payment standards,

training staff on how to explain and apply the new payment standards, and the need to either

modify or adopt automated systems capable of handling ZIP Code-level payment standards.

There were also some impacts related to contract rent adjustments, changes to the

communications strategy for landlords and tenants, and increased effort for Housing Quality

Standards inspections because of the reduced geographic concentration of HCV holders’ housing

search areas.

Other impacts on PHAs were minimal, meaning that not much time or money was spent on these

tasks.

•  Although most PHAs changed outreach materials for tenants to include multiple payment

standards, they did not substantially change other kinds of tenant outreach or support

such as briefings.

•  For most PHAs, PHA administrative processes required only minor modifications, such

as rent reasonableness evaluations, PHA plans and administrative plans, procedures, and

quality assurance.

Some of the administrative changes related to SAFMRs were unavoidable. For example, all

PHAs had to modify automated systems designed to administer vouchers with one metropolitan

area FMR to accommodate ZIP Code-level payment standards.

On the other hand, some of the PHAs’ administrative changes in response to the demonstration

were related to local market conditions or the pattern of changes in FMRs in the PHAs’

jurisdictions. For example, although PHAs were not required to recruit new landlords to

participate in the HCV program in ZIP Codes newly accessible to HCV holders, local market

conditions led some PHAs to make more extensive landlord recruitment efforts than others. The

Long Beach PHA, for example, made more intensive efforts to recruit landlords than did other

PHAs, because HCV holders had an unusual amount of difficulty finding units in higher-

opportunity ZIP Codes. In addition, some PHAs’ existing practices made it easier to integrate

SAFMRs than others’ practices. For example, PHAs with automated access to rental market

information found changes to the rent reasonableness evaluation process to be minimal. The

PHA that accessed rental market information to evaluate rent reasonableness manually

experienced more difficulty integrating SAFMRs.

32  It was not uncommon for PHAs to change occupancy standards in response to sequestration, but Dallas was the only one among the demonstration PHAs that did so.

68

In the following sections, we present the hypothesized list of impacts on PHAs and the

preliminary assessment of the magnitude of actual impacts based on the first round of site visits

(which will be further investigated during the second round of site visits in Phase 2). One-time

impacts are adjustments that PHAs made once to implement SAFMRs and include changes to

PHA plans and administrative plans and modifications to automated systems. Transitional

impacts are those that occurred over a period of adjustment (generally about a year) and include

new procedures and staff training. Finally, ongoing impacts are permanent increases in the effort

required to administer HCVs and include increased quality assurance efforts.

We categorize these observed impacts as minor, moderate, or significant and reflect average

impacts across all the PHAs. Specific PHAs may have had a different experience from the

general experience. We highlight these idiosyncratic experiences where relevant.

In addition to presenting and categorizing the impacts, the following sections also include

narrative overviews of some of the more salient impacts of SAFMRs on the PHAs, including

PHA views on the adequacy of the supplemental administrative fees HUD provides to cover the

costs of implementing SAFMRs.

Modifications to PHA Plans and Administrative Plans

Dallas is the only PHA that made significant modifications to its PHA plan and administrative

plan to accommodate SAFMRs. Five of the seven PHAs made either no or minimal changes

(spending one hour of staff time) to update plans for SAFMRs. Plano made moderate changes to

its administrative plan but essentially no changes to its PHA plan. Mamaroneck made significant

changes to both plans at the time the SAFMR demonstration started, but this modification was

motivated by the merger of two PHAs and not by the demonstration. Exhibit 6-3 presents our

assessment of the impact of SAFMRs on modifications to PHA plans and administrative plan.

Exhibit 6-3: Preliminary Assessment of SAFMR Demonstration Impacts: PHA Plan and

Administrative Plan Changes

Magnitude of

Type of

Observed Impacts

Impact

Hypothesized Impacts

Observed Impacts

(Preliminary)

One time.

Revisions to PHA plan and administrative plan

With one exception,

Minor.

required to establish payment standards, rent

only minor

reasonableness, and tenant rents.

modifications (if any)

Some PHAs have administrative plans with extensive

were needed.

procedural documentation, which may require more

substantial modifications.

PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

69

Modifications to Automated Systems

Four PHAs (Chattanooga, Dallas, Mamaroneck, and Plano) found it necessary to modify the

automated systems that accommodate additional payment standards. 33 Modifications typically involved contracting with software providers, such as Visual HOMES, to customize the system

of record software PHAs used to perform functions to maintain participant household data and

calculate landlord payments. Two of the four PHAs (Dallas and Plano) required significant

efforts to modify automated systems, whereas the upgrade was minor for Chattanooga and

Mamaroneck. Other automated tools required updates, such as a property listings database (for

Mamaroneck), automated affordability calculators (Dallas and Long Beach), and tools for

assessing rent reasonableness (Mamaroneck and Plano). Modifications to other automated tools

generally required minor effort, except for Mamaroneck’s rent reasonableness database, which

(as described below) requires significant ongoing effort. Exhibit 6-4 presents our assessment of

the impact of SAFMRs on modifications to PHA’s automated systems.

Exhibit 6-4: Preliminary Assessment of SAMFR Demonstration Impacts: Modifications to

Automated Systems

Magnitude of

Type of

Observed Impacts

Impact

Hypothesized Impacts

Observed Impacts

(Preliminary)

One time.

May require systems of record adaptations to

Four PHAs needed

Varies: minor to

accommodate additional payment standards and to

modifications to

significant.

permit selection of different payment standards for

systems of record;

same unit size.

effort required varied

Additional tools that may require adaptations include

widely.

rent reasonableness data system and tool for

Five of seven PHAs had

determining unit affordability.

additional automated

tools that typically

required minor

modifications.

PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Analyzing and Setting Payment Standards

All PHAs had to decide how to set payment standards in response to SAFMRs. The number of

FMRs in each PHA’s jurisdiction multiplied dramatically. Even the smallest PHA (Laredo)

increased the number from one FMR to five SAFMRs per unit size. The largest PHA (Dallas)

now has 343 FMRs per unit size in its jurisdiction. HUD’s policy, requiring that new payment

standards not be applied to existing HCV holders until the second annual reexamination

following the effective date of the payment standard reduction, was an added complication for

PHAs in setting payment standards. The intent of the policy was to smooth the transition in the

case of a decline in payment standards. This 2-year  hold harmless period meant that both

33  Laredo PHA staff had almost entirely turned over since implementing SAFMRs. Therefore, current staff did not know whether changes either to the system of record or to any other automated tools were required to

accommodate the demonstration.

70

metropolitan area FMRs and SAFMRs were effective in some ZIP Codes at the same time—one

that would apply to HCV holders moving into the ZIP Code and one based on old metropolitan

area FMRs for existing tenants. Further, PHAs had to decide at what level, between 90 and 110

percent of FMR, to set payment standards for each ZIP Code or other sub-area within the

jurisdiction. Exhibit 6-5 presents our assessment of the impact of SAFMRs on PHAs analyzing

and setting payment standards.

Exhibit 6-5: Preliminary Assessment of SAFMR Demonstration Impacts: Analyzing and Setting

Payment Standards

Magnitude of

Type of

Observed Impacts

Impact

Hypothesized Impacts

Observed Impacts

(Preliminary)

Transitional,

Although the first year will be the most challenging,

Setting SAFMR

Significant for most

ongoing.

expanding the PHAs’ analysis from a single set of

payment standards is

PHAs.

FMRs to multiple will require ongoing analysis that is

much more time

more complex than before SAFMR.

consuming, because

Focusing on using FMRs as a mobility tool, rather than  many more market

strictly a funds management tool, will be new to some  areas exist.

PHAs.

PHA = public housing agency. SAFMR = Smal  Area Fair Market Rent.

Most PHAs included several factors in determining appropriate payment standards—the number

of households affected by payment standard increases and decreases, the impact on HAP costs,

and the PHAs’ administrative burden. The process of determining appropriate payment standards

is a time-consuming process that requires analysis of each ZIP Code. PHAs reported that because

ZIP Code payment standards were new, more effort was generally involved in setting payment

standards during the first year than in subsequent years, although much of the process had to be

repeated each year.

Chattanooga, Long Beach, and Plano initially set all payment standards at 100 percent of the

SAFMR. For Plano, a desire to avoid a lawsuit that might stem from any appearance of

differential treatment motivated this decision. After the hold harmless period ended, both

Chattanooga and Long Beach increased payment standards to 110 percent of SAFMR for some

of or all their jurisdiction. In April 2015, Long Beach implemented an across-the-board increase

in response to the decreases in payment standards in many ZIP Codes under SAFMRs and the

sharp drop in units available to HCV holders in the PHA’s jurisdiction (Exhibit 4-5 in chapter 4)

and as an incentive for new landlords in high-opportunity areas. Chattanooga restricted the

increase to 110 percent of the SAFMR to areas where SAFMRs required declines in the payment

standard, to reduce the administrative burden of handling decreases in payment standards, as

well as the risk of errors in selecting the correct payment standard.

Two PHAs tried to make the process of analyzing and setting payment standards more

manageable by grouping ZIP Codes so that fewer FMRs than ZIP Codes exist, with mixed

success. Cook County uses this approach and currently has 21 payment standards per unit size

rather than one for each of its 170 or so ZIP Codes. Dallas tried to create groups of ZIP Codes

with the same payment standard for the first 2 years of SAFMR implementation and then

decided the ZIP Code-level approach worked better.

71

Rent Reasonableness

PHAs generally did not find approaches to evaluating rent reasonableness any more difficult

using SAFMRs than metropolitan area FMRs, with one notable exception. Five of the seven

PHAs reported using an automated rent reasonableness system or some automated source of

information for rent reasonableness such as GoSection8, a property rental listings database for

PHAs populated with rental unit information by landlords. Landlords list units on this website,

families search available units on it, and PHAs use it to select units comparable with those being

evaluated. One PHA said the only change to this reported approach is that staff now select

comparable units for the rent reasonableness determination only within the same ZIP Code.

Exhibit 6-6 presents our assessment of the impact of SAFMRs on rent reasonableness

determinations.

Exhibit 6-6: Preliminary Assessment of SAFMR Demonstration Impacts: Rent Reasonableness

Magnitude of

Type of

Observed Impacts

Impact

Hypothesized Impacts

Observed Impacts

(Preliminary)

Transitional.

May simplify rent reasonableness determinations,

With one exception,

Minor.

because local area rents are embedded in the SAFMR.  only minor

Alternatively, PHA staff may have to be more

modifications were

knowledgeable about sub-areas and may not be

needed.

familiar with the housing stock and market in high-

opportunity areas.

PHA = public housing agency. SAFMR = Smal  Area Fair Market Rent.

Mamaroneck’s PHA, which does not use an automated source of property information for rent

reasonableness determinations, is the exception. It constructs a property information database

from scratch using market listings, generally from the multiple listing service. Three property

listings for every unit size for each market area (for example, ZIP Code) are entered into its rent

reasonableness database. Creating the database was a time-consuming process, and because it

must be updated regularly, the PHA hired an intern to work about 5 hours a week on an ongoing

basis to update the database. This database also helps set payment standards by serving as a

check on how close market rents are to SAFMRs, among other things. Prior to the

demonstration, this manual approach to rent reasonableness was relatively simple, because PHA

staff viewed their jurisdiction as a single-market area.

Contract Rent Adjustments

SAFMRs increased requests from landlords for contract rent adjustments for five of the seven

PHAs for units in areas where payment standards went up. However, higher payment standards

did not always result in the PHA approving the contract rent adjustment, because rent

reasonableness determinations did not always support the higher rent requested. Two PHAs

reported that landlords were often sensitive to whether rent increases fell on tenants or the PHA

and withdrew requests for adjustments when becoming aware that tenants would pay higher

rents. Exhibit 6-7 presents our assessment of the impact of SAFMRs on contract rent

adjustments.

72

Exhibit 6-7: Preliminary Assessment of SAFMR Demonstration Impacts: Contract Rent

Adjustments

Magnitude of

Type of

Observed Impacts

Impact

Hypothesized Impacts

Observed Impacts

(Preliminary)

Transitional.

SAFMR payment standards may cause a change in

SAFMRs increased

Moderate.

behavior of current landlords and increase the number  requests for rent

of requests for rent adjustments or protract the setting  adjustments for most

process.

PHAs, especially in

Landlords who have not regularly requested rent

areas where payment

adjustments, because they knew any additional

standards increased.

increase would be borne by the tenant, may request in

greater numbers in areas where the payment standard

goes up.

SAFMR = Smal  Area Fair Market Rent.

Inspection System and Process Changes

SAFMR impacts on unit inspections were moderate. The inspection process required no changes;

however, units in neighborhoods that had previously been difficult for HCV holders to access

became more geographically dispersed in most PHA jurisdictions, which required inspectors to

travel longer distances. In addition, as HCV holders responded to higher payment standards and

searched for units in higher-cost neighborhoods, the number of inspections increased. Dallas, the

largest PHA, hired two new inspectors to compensate for longer travel times. Chattanooga hired

a part-time inspector to work on a contract basis for the same reason. Exhibit 6-8 presents our

assessment of the impact of SAFMRs on inspection systems and processes.

Exhibit 6-8: Preliminary Assessment of SAFMR Demonstration Impacts: Inspection System and

Process Changes

Magnitude of

Type of

Observed Impacts

Impact

Hypothesized Impacts

Observed Impacts

(Preliminary)

Transitional,

If housing choice voucher holders become more

No necessary changes to

Moderate.

ongoing.

dispersed, this increase in areas where voucher

process.

holders live may have an effect on inspector

More need for inspections as

productivity and increase travel costs.

the number of moves

In some locations, inspectors are part of the rent

increased; more

reasonableness process. Inspectors will need to

geographically dispersed

become familiar with housing stock and rents in

units also meant longer

additional areas.

travel times for inspectors.

SAFMR = Smal  Area Fair Market Rent.

73

Procedures and Staff Training

Most PHAs conducted formal or informal staff training about the demonstration, impact letters

sent to tenants, and new payment standards. Some PHAs reported that training was considerable.

For others, it was minimal, taking place during regular team meetings or on the job. Exhibit 6-9

presents our assessment of the impact of SAFMRs on PHA procedures and staff training.

Exhibit 6-9: Preliminary Assessment of SAFMR Demonstration Impacts: Procedures and Staff

Training

Magnitude of

Type of

Observed Impacts

Impact

Hypothesized Impacts

Observed Impacts

(Preliminary)

Transitional.

Will require development of detailed procedures and

Some retraining

Moderate.

policies, as well as training and retraining of staff at

needed.

all levels.

Experience with the program will both enable and

require the PHA to make improvements in procedures

and training over time. The end of the 2-year hold

harmless period will eventually reduce the need for

manual selection of payment standards.

PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Education and Support for Tenants

With two exceptions, PHAs did not increase the search assistance provided to HCV holders in

response to the demonstration. As part of normal course of operations, all the SAFMR PHAs

provide at least limited search assistance, such as maintaining lists of available units on their

websites or referring HCV holders to online rental listing services. Four of the seven PHAs

provided more extensive search assistance or mobility counseling to HCV holders than the basic

required level of support, even before adoption of SAFMRs. PHAs with no additional search

assistance or mobility counseling report that this lack of additional assistance is because their

operating budgets are not able to fund it. PHAs that do offer additional search assistance

sometimes obtain funding from external sources. Exhibit 6-10 presents our assessment of the

impact of SAFMRs on education and support for tenants.

74

Exhibit 6-10: Preliminary Assessment of SAFMR Demonstration Impacts: Education and Support

for Tenants

Magnitude of

Type of

Observed Impacts

Impact

Hypothesized Impacts

Observed Impacts

(Preliminary)

Transitional,

Unless a PHA has already administered a mobility

With two exceptions,

Minor.

ongoing.

program, the associated level of effort encouraging

PHAs have not increased

and assisting households to consider moving to high-

efforts to encourage or

opportunity areas is likely to increase.

assist households to

Current program participants living in areas where

move.

payment standards decrease will need additional staff

attention in understanding both the timing and the

impact of payment standard reductions on individual

situations.

Some PHAs do not hold in-person briefings for current

housing choice voucher participants who move

(movers). With SAFMR, PHAs likely will have to

schedule additional briefings.

PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Cook County was one of the two PHAs that expanded search assistance in response to the

demonstration. Cook County staff reported perceiving SAFMRs as key to the PHA’s efforts to

provide successful mobility counseling. Although Cook County has had some form of a mobility

program for a number of years, staff reported believing the success of the program was limited

by low payment standards under metropolitan area FMRs relative to market rents in higher-

opportunity neighborhoods. Staff describe previous mobility counseling efforts as reactive to

tenants interested in moving to higher-opportunity neighborhoods. Its expanded mobility

program will have the capability to proactively encourage tenants to move.

Directly related to the introduction of SAFMRs and the ability to more closely approximate

market rents in higher-cost neighborhoods, Cook County began providing mobility counseling

targeting five different groups, adapting its approach to the needs and interests of each group—

serial movers (those who moved in each of the past 3 years, to focus on family stability), those

living in high-poverty areas, Family Self Sufficiency families, families on the waiting list, and

people issued vouchers to move but stayed in place. Cook County aims to assist 140 households

with moves to higher-opportunity neighborhoods over the next year (2016–2017). The PHA

funds its mobility counseling using HCV administrative fees.

A second PHA, Long Beach, also added mobility counseling services to help HCV holders

search in higher-opportunity areas. The primary source of information for HCV holders

searching for units had traditionally been the GoSection8 website, but Long Beach added

mobility counseling in 2016 in response to declining numbers of landlords listing properties on

the website. PHA staff believe this decline is because the PHA’s payment standards are low

relative to the market. Long Beach offers small-group mobility counseling to HCV holders when

their voucher search time nears expiration or has been extended. The counseling is an added

responsibility for the deputy executive director, with no new staff hired to perform this function.

75

Chattanooga and Dallas are other PHAs where tenants receive more than basic search assistance

(both before and after adoption of SAFMRs). Chattanooga has a  housing navigator who

establishes relationships with landlords (some of whom have waived application fees and

security deposits to make it easier for families to lease up), meets with tenants, and negotiates

rent on behalf of housing choice voucher holders when needed. In part, a grant from the

Maclellan Foundation funded the position, beginning in 2015, to address the needs of homeless

and HCV holders with disabilities who were unable to find suitable housing without assistance.

The housing navigator also lists units available to HCV holders on Chattanooga’s website. The

position was not created in response to SAFMRs but may be useful to HCV holders interested in

moving to higher-opportunity neighborhoods.

The Dallas PHA reported that it does not provide housing search and related assistance for HCV

holders moving to higher-opportunity areas, because outside housing counseling agencies

provide this service. The three remaining PHAs provide only limited search assistance to HCV

holders, and the level of assistance did not change either with SAFMR implementation or in

response to declining payment standards in some areas. For example, Laredo provides HCV

holders with lists of landlords. Mamaroneck conducts new tenant briefings, during which they

provide lists of landlords. Plano lists rental units on its website.

PHAs generally did not provide financial assistance to tenants as part of the SAFMR

demonstration. In Cook County, however, households moving to high-opportunity areas received

up to $500 in assistance to cover security deposits. This amount increased to $1,000 in July 2016

as part of Cook County’s new mobility counseling program. No other PHA provided financial

assistance to voucher holders as part of the SAFMR demonstration. Mamaroneck has a long-

standing policy of providing HCV holders financial assistance with security deposits through the

Westchester County Department of Social Services. This policy did not change with the

demonstration.

Education and Support for Landlords

PHAs made efforts to recruit new landlords and educate current landlords, especially among

PHAs where HCV holders struggle to find units. PHA efforts to support landlords include letters

or outreach to inform them about the demonstration and updated information on PHA websites.

One PHA created a property owners lease-up guide to explain the overall HCV program and

provide SAFMR demonstration information. Several PHAs said they hold regular forums,

meetings, or fairs with landlords where they discuss the SAFMR demonstration. Other PHAs

held meetings specifically to explain the demonstration or to orient new landlords. Two PHAs

held meetings in high-opportunity areas in an effort to recruit new landlords. One PHA met with

rental agents to explain the demonstration. Another PHA temporarily hired a real estate agent at

the beginning of the demonstration to recruit new landlords. One PHA lost funding for one of its

two landlord recruiter positions as a result of sequestration and has not been able to refund the

position. Exhibit 6-11 presents our assessment of the impact of SAFMRs on PHAs education and

support for landlords.

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Exhibit 6-11: Preliminary Assessment of SAFMR Demonstration Impacts: Education and Support

for Landlords

Magnitude of

Type of

Observed Impacts

Impact

Hypothesized Impacts

Observed Impacts

(Preliminary)

Transitional,

Landlords currently participating in the Housing

Minor landlord

Minor.

ongoing.

Choice Voucher program will require more

education.

education to understand impact on current tenants.

Landlord behaviors and questions will be different

depending on whether payment standards

decrease or increase.

New landlords in high-opportunity areas initially

may require higher levels of attention until they

become familiar with the SAFMR program.

SAFMR = Smal  Area Fair Market Rent.

In Long Beach, where the demonstration caused a decline in the number of units affordable to

HCV holders, the PHA made extensive efforts to educate existing landlords and recruit new

landlords. They sent letters to landlords and held monthly landlord meetings, as well as held

events for landlords in high-opportunity areas, created an infomercial for its website, advertised

with real estate and apartment association magazines, and held new owner orientations.

Quality Assurance

Most PHAs reported that, initially, on adoption of SAFMRs, the incidence of errors made by

staff related to selecting the correct payment standards increased. The reported frequency of

errors declined over time as staff learned new systems and processes. Exhibit 6-12 presents our

assessment of the impact of SAFMRs on quality assurance.

Exhibit 6-12: Preliminary Assessment of SAFMR Demonstration Impacts: Quality Assurance

Magnitude of

Type of

Observed Impacts

Impact

Hypothesized Impacts

Observed Impacts

(Preliminary)

Ongoing.

HUD-50058 errors based on selection of the wrong

Five of seven public

Minor.

payment standard are not unusual even with area-

housing agencies

wide FMRs. When selecting the payment standard,

reported short-term

staff may confuse the voucher size for which the

increases in quality

family qualifies and the unit size the family selects.

assurance efforts.

Staff may also need to select among multiple

payment standard schedules when processing a

dated or retroactive transaction. SAFMRs increase

this risk and will require vigilant quality assurance

processes.

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

With many more payment standards and a 2-year adjustment period for tenants in ZIP Codes

where payment standards declined, five PHAs reported that the number of errors initially

77

increased. Mamaroneck reported no increase in errors, although an initial effort was made to

increase data quality checks. One PHA did not have information about error rates.

As a result of increases in errors, most PHAs increased their quality assurance efforts (for

example, by doubling the number of files reviewed for accuracy), although the increase in the

overall level of effort was modest. The largest reported effort was about 5 hours per month.

Voucher Success Rates

Changes in the share of HCV holders who successfully use HCVs to lease a unit (success rates)

can affect both the wellbeing of HCV holders and the administrative workload of PHAs, who

need to issue more vouchers to utilize the same amount of funds if success rates decline. Most

PHAs could not provide data on voucher success rates during Phase 1 site visits, but to the extent

possible, we plan to probe this issue further, as well as the related issue of whether SAFMRs

seem to have affected the ability of PHAs to fully use all available HCV units and funds (the

HCV utilization rates for units and funds) in Phase 2 of this research. Because the number of

units under lease affects the amount of administrative fees earned by a PHA, the HCV unit

utilization rate also has implications for both low-income families and PHAs.

As noted previously, both the hypothesized and actual effects of SAFMR on voucher success rates

are ambiguous. If landlords in high-opportunity ZIP Codes are reluctant to rent to HCV holders,

SAFMRs could reduce success rates. On the other hand, if SAFMRs successfully increase access

to high-opportunity ZIP Codes to HCV holders, voucher success rates could increase.

As expected, PHA-reported impacts of SAFMRs on voucher success rates are mixed. Chat anooga

reported that SAFMRs affected voucher success rates and utilization positively because of improved

access to high-rent ZIP Codes. Most PHAs believed that SAFMRs were a factor in voucher success

rates, but other factors were far more important. For example, Cook County and Mamaroneck did not

see an impact from SAFMRs but believed that legislation banning discrimination on the basis of

source of income improved voucher success rates substantial y. Laredo, Long Beach, and Plano

could not isolate the impact of SAFMRs from other factors they saw as being more important, such

as overal  changes in the rental housing market including vacancy rates and gentrification. Likewise,

Dallas reported that overal  market conditions determined the success rate, not SAFMRs.

Chat anooga reported that SAFMRs affected voucher success rates and utilization positively because

of the improved access to high-rent ZIP Codes.

We will examine utilization rates further in Phase 2 and further examine success rates to the

extent data are available.

Adequacy of Supplemental Administrative Fees

Recognizing that PHAs implementing SAFMRs incur costs to transition from metropolitan area

FMRs to SAFMRs, HUD provided supplemental administrative fees based on the fees incurred

during the implementation of SAFMRs in Dallas. The increase varied by the number of HCVs

administered by the public housing agency, up to a maximum of $300,000 for the five demonstration

PHAs (Kahn and Newton, 2013). The additional administrative fees were anticipated to cover

additional expenses such as upgrading computer software used to administer the HCV program,

additional outreach and briefings for families and landlords on the Small Area Fair Market

78

Rents, assistance with relocation issues resulting from SAFMRs, changes to rent reasonableness

determinations, additional training and hiring of staff, and other necessary expenses.

We hypothesized that SAFMR implementation would have two potential financial impacts on

PHAs—changes in HAP costs could affect the number of vouchers PHAs can issue (discussed in

chapter 6) and implementation could have direct costs. PHAs identified a third cost. If HAP costs

increase, the corresponding decline in the number of vouchers a PHA can issue could affect the

PHA’s administrative fee earnings. That is, HUD pays administrative fees based on the number

of units leased in a given month, therefore overall administrative fees could decline if changes in

HAP expenditures resulted in fewer families being assisted.

PHAs were fairly evenly split in reporting whether the additional administrative fees HUD

provided were sufficient to cover the expense involved in implementing and administering

SAFMRs. Although some PHAs reported that the additional administrative fee provided did

cover the up-front costs, ongoing costs may exist that will not be covered once the demonstration

terminates. For example, one PHA hired a contract inspector to handle the overflow work arising

when units are more geographically dispersed, resulting in longer travel times. This expense is

expected to be ongoing, but the additional administrative fee is not.

Estimate of Financial Impacts of SAFMR Impacts on PHAs

The previous section described the steps PHAs took to implement SAFMRs and the types of

impacts experienced. This section estimates the magnitude of these impacts in terms of staff

hours or out-of-pocket expenses. During site visits, we attempted to collect data to estimate the

financial impacts on the SAFMR PHAs. Each PHA collected and maintained information in

widely varying ways, therefore we were not able to systematically sum and compare financial

impacts across sites. The information presented below provides the range of financial impacts.

In some cases, no financial expenditures existed, because staff worked on implementation either

in small increments over time or in place of other responsibilities. These nonfinancial impacts

are also described. The information available was not generally detailed, so these findings should

be considered rough guides. Because of staff turnover, Laredo was unable to provide any

information about financial impacts, therefore we exclude that PHA here.

We summarize the main findings in the following bullets, followed by more detailed descriptions

and charts comparing costs in terms of financial expenditures and staff effort to implement

SAFMRs across the PHAs.

Despite changes in the administration of the program, few PHAs reported updating

administrative plans as a result of switching to SAFMRs. Every PHA is required to maintain an

administrative plan for the HCV program. These plans typically contain language related

establishing payment standards, determining rent reasonableness and tenant payments, and

encouraging participation by owners outside of poverty or minority concentration areas. Some

PHA administrative plans also include extensive procedural documentation. Only three PHAs

reported updating their administrative plans. Dallas’s updates included adding substantial

guidance on how to conduct briefings and conduct reexaminations. Plano also updated its

administrative plan to reflect the payment standards in effect. The remaining demonstration site

PHAs reported no other substantial changes. Exhibit 6-13 describes the administrative resources

that PHAs used to modify PHA plans and administrative plans to implement SAFMRs.

79

Exhibit 6-13: Administrative Impacts of SAFMRs on PHAs on PHA Plan and Administrative Plan

Changes

PHA

Chattanooga

Cook County

Dallas

Long Beach

Mamaroneck

Plano

Director—1 hour.   No change.

VP, President,

Minimal—time

Administrator—

Finance

and CEO—80

not recorded.

5-10 hours;

director—24

hours PHA

Administrator—2  hours

administrative

hours.

administrative

plan, 80 hours for

plan, 2 hours for

PHA plan.

PHA plan.

CEO = chief executive officer. PHA = public housing agency. SAFMR = Small Area Fair Market Rent. VP = vice president.

Modifications to automated systems were the largest one-time cost incurred by most PHAs.

Payment standards are generally embedded in a PHA’s system of record, and thus, the switch to

SAFMRs often required updates to the PHA’s software. The extent of the cost depended on the

PHA’s automated system and whether or not significant changes were required to accommodate

multiple payment standards for the same unit size. Estimated expenditures to update systems of

record ranged from $0 to $35,000 per PHA, generally paid to information technology consultants

or software vendors, in addition to required staff effort.

Dallas and Plano incurred the highest cost and staff effort to update their automated systems.

Dallas required updates to two automated systems——the system of record and an automated

tool to calculate affordability based on ZIP Code. Dallas estimated that software updates required

250 hours of staff time from the software vendor and the PHA’s information technology director

and CFO in addition to payment to the software vendor for changes. 34 Plano similarly reported spending about 120 hours of the finance director’s time to update the system of record to

accommodate 160 payment standards (as of 2016) rather than the one payment standard used

under metropolitan area Fair Market Rents. Plano’s finance director also spent about 3 to 4 hours

updating the rent reasonableness software and about 10 hours updating the website.

Chattanooga reported 40 hours of staff time from an information technology software specialist

who updated the software that pulls the proper payment standards, plus $900 in expenditures.

Although the PHA went from 1 to 35 payment standards in the first year of SAFMR

implementation, its rent reasonableness system required only minor updates to change the search

criteria to pull comparable units within the same ZIP Code.

Long Beach and Mamaroneck also reported that their systems of record needed modifications

to add additional payment standards under SAFMRs, but these costs tended to be less than in

Chattanooga, Dallas, and Plano. Because Long Beach’s software already allowed for multiple

FMRs, system of record software updates incurred no significant costs. Long Beach, however,

incurred a cost of an unknown amount to update GoSection8, its rent reasonableness tool, as

well as a property listings database, requiring the upload of all payment standards, which

increased from 1 to 12 under SAFMRs, to facilitate rent reasonableness and affordability

34  PHA staff documented changes to the software, tested the changes to the software, and did data entry to reflect the increased number of jurisdictions.

80

determinations. Long Beach also needed to update its affordability worksheet the staff uses.

This update was a one-time cost of about 16 hours of staff time by the community program

specialist.

Mamaroneck also required updates to the system of record software to accommodate multiple

payment standards. This cost was estimated at about $400 in vendor charges and required 6

hours of staff time to communicate needs to the software vendor. Mamaroneck hired an intern to

conduct market research and update the rent reasonableness database, which is a manual process.

This ongoing effort sums to about 7 hours a week (5 hours for an intern, 2 hours for the

executive director). Cook County was the only PHA not reporting an automated system update

cost, because it uses a large spreadsheet instead.  Exhibit 6-14 describes the administrative

resources that PHAs used to modify automated systems to implement SAFMRs.

Exhibit 6-14: Administrative Impacts of SAMFRs on PHAs on Modifications to Automated

Systems

PHA

Chattanooga

Cook

Dallas

Long Beach

Mamaroneck

Plano

County

IT specialist—

No change.

Software vendor

GoSection8 system

$400 to software

Office supplies,

40 hours/$900.

+ IT director +

changes for rent

vendor + 6 ED; 40

postage, software

CFO—250

reasonableness.

hours for

modifications

hours/$35,000 to

automated

$10,705; finance

software vendor.

spreadsheet.

director—124 hours.

CFO = chief financial officer. ED = executive director. IT = information technology. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

The cost of setting payment standards under SAFMRs varied depending on the amount of time

PHAs spent adjusting HUD-issued FMRs. PHAs use payment standards to determine maximum

amounts to pay HCV-participating landlords for a housing units depending on size and location.

HUD issues FMRs in August of each year. PHAs have discretion to set their payment standards

between 90 and 110 percent of the FMR without HUD approval. PHAs may choose to vary

payment standards from the FMR to decrease the rent burden on HCV tenants, increase the

subsidy amount to increase the ability of HCV holders to access high-opportunity areas, or make

it easier for HCV holders to successfully use vouchers. Many of the demonstration PHAs used

this discretion to vary payment standards within 90 and 110 percent of the SAFMR when

determining payment standards, often to reduce the potential negative impacts of the

demonstration on landlords and tenants and on the PHA.

Plano set its payment standards at 100 percent of HUD’s published SAFMRs. As a result, some

costs arose in updating materials to accommodate the new and more numerous payment

standards. The amount of time and effort required to set payment standards was minimal.

Likewise, Long Beach initially kept all payment standards at 100 percent of SAFMR based on

the assumption that ZIP Code-level SAFMRs closely approximated the market. In April 2015,

Long Beach increased all payment standards to 110 percent of the SAFMR. The decision to

increase payment standards was the result of the PHA’s analysis of rent burden and rent

reasonableness for tenants in both high-opportunity and low-opportunity areas, indicating that

payments standards set at 100 percent of SAFMR were below market, and therefore, requiring a

change.

81

Chattanooga, Cook County, Dallas, and Mamaroneck conducted additional analyses before

setting payment standards under the demonstration. Each reported this process to be more time

consuming than the process used before the demonstration. Each year, Chattanooga analyzes

HUD-provided FMRs to determine which areas experienced increases and decreases to payment

standards to calculate the number of households affected if payment standards were set at 100

percent of SAFMR. To date, this analysis is an ongoing cost of about 40 hours of the director’s

time.

Dallas spent a significant amount of time considering new payment standards during the initial

implementation of SAFMRs. This significant amount of time is due to Dallas having a large

number of ZIP Codes in its jurisdiction and SAFMRs being imposed on Dallas, creating an

additional burden to a PHA that had altered its operation of the HCV program to meet

obligations under a 20-year racial discrimination suit ( Walker v.  HUD).   Ultimately, Dallas decided to use each ZIP Code’s SAFMR as its payment standard, rather than consolidate ZIP

Codes into groups. The analysis conducted by Dallas required about 120 staff hours from the

HCV program vice president, the chief financial officer, the chief operating officer, and the

president and CEO of the PHA.

Initially, Mamaroneck did not set payment standards for all ZIP Codes within its jurisdiction,

rather focused only on setting payment standards in areas where HCV tenants lived. As tenants

moved into new areas, staff added payment standards for the new ZIP Codes. Mamaroneck also

analyzed the HUD-provided SAFMRs by ZIP Code to determine whether payment standards

should be set at 90 percent, 100 percent, or 110 percent. Prior to SAFMRs, Mamaroneck set most

rents at 110 percent of FMR. Exhibit 6-15 describes the administrative resources that PHAs used

to analyze and set payment standards to implement SAFMRs.

Exhibit 6-15: Administrative Impacts of SAFMRs on PHAs on Analyzing and Setting Payment

Standards

PHA

Chattanooga

Cook County

Dallas

Long Beach

Mamaroneck

Plano

Director—40

Created or updated

VP-HCV, CFO, COO,  Community

Director—40 hours  All

hours to date.

ZIP Code and

president/CEO, 120  program

initially; 10 hours

staff—

payment standards

hours.

specialist—15 to

to update annually.  1 hour.

spreadsheets—100

16 hours for

hours to date.

affordability

spreadsheet.

CEO = chief executive officer. CFO = chief financial officer. COO = chief operating officer. PHA = public housing agency. SAFMR = Small Area Fair Market Rent. VP-HCV = vice president of Housing Choice Voucher program.

Costs to determine rent reasonableness varied across PHAs depending on whether PHA’s rent

reasonableness systems required staff labor. Regardless of FMR or payment standards, it is the

responsibility of each PHA to verify that rents are reasonable relative to comparable units.

Individual PHAs take different approaches in finding comparable units. Most use databases or

websites to find comparable units. Some PHAs manually populate databases, and others are

more automated. PHAs that manually populated rent reasonableness databases had higher

ongoing costs when the number of payment standards increased. PHAs that were strict in

verifying rent reasonableness prior to the SAFMR demonstration minimized the impacts of

82

SAFMRs in some ways. That is, fewer units were likely to face reduced rents in areas with

declining payment standards if the PHA had already established that it was paying the reasonable

rent and the approved rent was below the new SAFMR payment standard.

Chattanooga and Plano use fully automated systems to determine rent reasonableness, therefore

no ongoing costs are associated with rent reasonableness determinations in these PHAs outside

of any one-time costs to update the software or search criteria. Cook County has not changed its

process to determine rent reasonableness as a result of SAFMRs, so no additional costs have

incurred. It continues to use a housing listing website that collects data that landlords input.

Long Beach reported only a one-time cost to determine rent reasonableness under SAFMRs.

Regulation required Long Beach to complete new rent reasonableness determinations, because

SAFMRs led to a decrease in the applicable payment standard of more than 5 percent. This new

determination in rent reasonableness, in turn, resulted in Long Beach informing HCV landlords

to decrease rents. Some landlords accepted lower rents, but many did not. The SAFMR decrease

and the resulting rent renegotiations required the PHA to nearly double the number of rent

reasonableness determinations from 1,886 units to 3,349 units.

Dallas reported that additional costs related to extra office support and overtime incurred since

the implementation of SAFMRs. Despite these costs, staff reported that rent reasonableness

determinations are easier now under SAFMRs, because it is easier to find comparable properties

in high-opportunity areas.

Mamaroneck incurred one-time and ongoing costs associated with doing market research and

creating a rent reasonableness database. Initially, staff populated the rent reasonableness

database with property listings for every unit size and ZIP Code, which previously was needed

only for the one payment standard. Mamaroneck populates its rent reasonableness database with

data it gathers from surveys it administers to landlords participating in HCV, as well as

nonparticipating landlords. As a result, constant updates to the database are needed. Additionally,

the PHA does market research in an area every time a participant searches for a new apartment.

As noted previously, database updates require an ongoing effort of about 7 hours a week.

Mamaroneck did not have the capacity to take on this additional work, so it hired an intern to

help with updates. Mamaroneck created the database to store comparable rents after the

demonstration began; however, the switch to SAFMRs did not directly require it. Previously,

Mamaroneck collected comparable units from rental advertisements in the newspaper and stored

them in tenants’ files each year. The creation of a database represents a technological

advancement for the PHA more than a direct cost incurred because of the demonstration. Exhibit

6-16 describes the administrative resources that PHAs used to determine rent reasonableness to

implement SAFMRs.

Exhibit 6-16: Administrative Impacts of SAFMRs on PHAs on Rent Reasonableness

PHA

Chattanooga

Cook County

Dallas

Long Beach

Mamaroneck

Plano

No change.

No change.

Additional office

No change.

7 hours per week.

No change.

support and

overtime.

PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

83

The number of requests for contract rent adjustments increased as a result of SAFMRs.

Responding to these requests is an ongoing cost for most PHAs. Contract rents represent the

combined amount of rent that HCV tenants pay and the amount of rent the PHA pays by

voucher. HCV landlords may seek a contract rent adjustment for a number of reasons, one being

an increase in payment standards. In areas where payment standards have gone up, landlords

who initially made rent concessions to accommodate specific tenants may be motivated to

request contract rent adjustments if they believe the PHA will absorb increased costs. 35

Similarly, landlords in areas that have not requested rent adjustments on a regular basis (because

they knew additional increases would be borne by tenants) may be more likely to request rent

adjustments if costs will not be borne by tenants.

Under the demonstration, many landlords requested contract rent adjustments when payment

standards increased under SAFMRs, because allowable assistance payments were higher.

Chat anooga, Cook County, Laredo, Long Beach, Mamaroneck, and Plano al  reported increases in

the number of requests for rent adjustments they attributed to the implementation of SAFMRs.

Chat anooga reported the greatest impact, stating that it now requires an estimated 192 hours of the

director’s time annually to respond to increased requests. Most PHAs report that the increase in

requests for contract rent adjustments was temporary. We will examine whether this increase was

also temporary for Chat anooga during the Phase 2 site visit. Exhibit 6-17 describes the

administrative resources that PHAs used to make contract rent adjustments to implement SAFMRs.

Exhibit 6-17: Administrative Impacts of SAFMRs on PHAs on Contract Rent Adjustments

PHA

Chattanooga

Cook County

Dallas

Long Beach

Mamaroneck

Plano

Director—192

No change.

Additional

No change.

None named.

Slight increase

hours annually to

overtime, landlord

during change in

respond to

services.

standards.

increase in

requests.

PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

The communication strategy adopted by PHAs for landlord and tenant education involved

moderate, one-time, ongoing costs. PHAs regularly conduct briefings to inform HCV tenants and

landlords about the HCV program. All PHAs had to update materials they provide landlords and

tenants, as well as content on PHAs’ websites to reflect SAFMRs. Many PHAs found that these

updates required only limited effort. In Long Beach, these updates to materials and PHA’s

website were the only changes to the PHA’s communication strategy and reflected about 16

hours of the community program specialist’s time. Plano similarly updated its communication

and outreach strategy to include updated materials and reported a one-time estimated cost of

$750 for this effort. Cook County and Mamaroneck similarly did not report any other changes—

other than website updates for the latter—but neither could provide cost estimates for updating

briefing materials.

35  Note that landlords should only request contract rent adjustments if they believe their units are worth more on the market, but other factors probably play a role in contract rent adjustment requests.

84

In addition to updating briefing materials, Chattanooga began holding monthly meetings for

landlords. They reported monthly meetings as an ongoing annual cost of approximately $500. In

response to the increasing numbers of families requesting to relocate, Dallas increased the

number of relocation briefings held each month for tenants. Dallas reported an initial estimated

cost of $55,000 and an ongoing estimated cost of $10,000 a year for these activities. Exhibit 6-18

describes the administrative resources that PHAs used to modify communication strategies to

implement SAFMRs.

Exhibit 6-18: Administrative Impacts of SAFMRs on PHAs on Communication Strategy

PHA

Chattanooga

Cook County

Dallas

Long Beach

Mamaroneck

Plano

Monthly landlord

None reported.

New client and

No change.

Minimal additions

Finance

and tenant

landlord guide.

to the website and

director—10

briefings present

forms.

hours.

an increase.

PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Impacts on Landlords

We would expect to find that landlords’ interest in and awareness of the HCV program to be

affected by the implementation of SAFMRs. We would similarly expect that higher payment

standards in high-cost ZIP Codes to attract landlord interest, whereas lower payment standards in

low-cost ZIP Codes could discourage engagement with the program. We have not yet spoken

with landlords, but we did speak with PHAs that reported this outcome was typically the case.

These results are based on secondhand reports, and therefore, are considered preliminary. During

the second round of site visits (Phase 2), we will interview landlords directly to learn whether

they are aware of the change in the HCV program and whether this change affects both their

willingness to rent to HCV holders and the level at which they set rents.

Some PHAs also reported that the competition with HCV holders for units in high-rent areas

differed. In low-rent neighborhoods, early indications are that many applicants for units were

HCV holders or low-income households without vouchers, so that HCV holders are attractive to

landlords as tenants. In higher-opportunity neighborhoods, PHAs report that HCV holders are

competing with market-rate households with higher incomes, and that landlords prefer to rent to

the higher-income households.

Again, the main findings are summarized in the bullets below, followed by more detailed

descriptions.

With one exception, PHAs conducted only limited landlord outreach with the

implementation of SAFMRs.  After introduction of the SAFMRs, PHAs generally did not make

extensive additional efforts to recruit new landlords in high-opportunity areas, with the exception

of Long Beach. As noted previously, Long Beach did a number of things to recruit new

landlords, including sending letters, holding monthly landlord meetings and specific events for

landlords in high-opportunity areas, creating an infomercial, and advertising with real estate and

apartment association magazines. Several PHAs hold regular landlords briefings during the

course of regular operations, and one PHA reported specifically locating these briefings in

higher-opportunity neighborhoods in an effort to attract those landlords. Other PHAs reported

85

that their landlord briefings were generally not heavily advertised to new landlords, nor were

they primarily designed to help recruit new landlords to the HCV program. They were primarily

intended to help landlords already in the HCV program understand program changes.

Some communication with landlords was, of course, reactive in nature. For example, some

landlords have units in multiple ZIP Codes, and they approached PHAs with questions about the

new differences across neighborhoods when FMRs were previously more consistent across

neighborhoods. PHAs reported regularly responding to questions from landlords when SAFMRs

were initially rolled out. Questions about SAFMRs are far less frequent as landlords gain

experience with the new approach to setting payment standards.

Not surprisingly, landlords’ responses depended on whether SAFMRs increased or decreased

their units’ rental value. As noted previously, some landlords were willing to accept lower rents

in ZIP Codes where payment standards declined, either to assist tenants they wanted to retain or

to avoid vacancies, or both. Landlords were more likely to accept rent reductions if landlord-

tenant relationships were good. Sometimes, landlords who requested contract rent adjustments

dropped the requests after realizing that rent increases were to be paid by tenants. Some PHAs

reported that landlords seemed more likely to be willing to drop rent adjustment requests for

elderly and tenants with disabilities (a practice not necessarily unique to SAFMRs). Mamaroneck

reported that this occurrence was sometimes the case, as did Long Beach. PHAs reported that

landlords were also more willing to accept lower rents if they believed the situation was

temporary and that payment standards would rise in the future.

PHAs reported that some landlords were unwilling to accept lower rents, therefore tenant shares

of rent increased to make up for declines in payment standards. Some landlords were angered by

reductions in FMRs, having made previous investment decisions based on higher rents. Some

landlords responded by leaving the HCV program, although this response varied by PHA, and in

particular, by the extent of decreases in payment standards. Chattanooga, which had not had

payment standard decreases until 2016, reported that landlords were not leaving the program

because of SAFMRs, as did Mamaroneck. Other PHAs, including Dallas, Laredo, Long Beach,

and Plano reported that some landlords left the program. Phase 2 follow up with PHAs will

include additional data requests on or PHA estimates of the number of landlords changing

program participation after the introduction of SAFMRs.

All PHAs reported that new landlords in higher-opportunity neighborhoods joined the HCV

program as a result of SAFMRs. As noted previously, new legislation in Cook County and

Mamaroneck prohibiting discrimination on the basis of source of income was helpful to

households in those jurisdictions. In general, several PHAs made efforts to recruit new landlords.

Note that these landlord responses are secondhand reports by PHA staff members. During Phase

2 of the evaluation, we will interview landlords directly to get a better sense of their experiences

with SAFMRs.

86

7.  Fiscal Effects of Small Area Fair Market Rents on Public Housing

Agencies, Housing Choice Voucher Holders, and Landlords

At least three groups are financially affected by the changes in HCV payment standards resulting

from SAFMRs.

•  PHAs—by changes in per-unit and total Housing Assistance Payment contract costs.

•  Landlords—by new payment standards affecting program HAP contract payments they

receive.

•  HCV holders—by changes in the HCV holder contributions to rents resulting from new

payment standards.

This chapter describes the effects of SAFMRs on payment standards and rents. This chapter

starts by examining changes in average payment standards overall and separately for each PHA.

This chapter then describes rent-related effects of SAFMRs on PHAs, landlords, and HCV

holders.

Average Per-Unit Payment Standards

Following the change from metropolitan area FMRs to SAFMRs, each PHA must determine the

level at which to set its payment standards. PHAs may simply use the SAFMR as the payment

standard or may vary payment standards within 90 to 110 percent of the SAFMR in any

particular ZIP Code. PHAs may then choose to keep separate payment standard zones for each

ZIP Code or combine multiple ZIP Codes into a single zone in order to reduce the total number

of zones with separate payment standards.

In this section, we examine the change in the average per-household payment standard between

2010 and 2015. These changes stem both from the switch from metropolitan area FMRs to

SAFMRs and from discretionary decisions that PHAs make in setting payment standards under

the new SAFMR regime. The average payment standard is important, because it affects both the

maximum rents that HCV holders can afford and the amount of HAP subsidies that PHAs need

to expend.

In general, the impact on average payment standards results from—

•  Increases in payment standards applicable to households that live in high-rent ZIP Codes

offset by decreases in payment standards applicable to households that live in low-rent

ZIP Codes.

Changes in payment standards in each location and the number of households that reside in each

type of location determine the net effect. In other words, average payment standards are strongly

affected by SAFMRs’ success in deconcentrating households from lower-cost neighborhoods.

Exhibit 7-1 compares changes over time in payment standards for both SAFMR and the 138

comparison PHAs in order to provide an apples-to-apples comparison of changes in payment

standards. The exhibit shows overall changes in the average payment standard and changes in the

average payment standard for units renting in particular rent categories.

87

Exhibit 7-1: Payment Standard by Rent Ratio SAFMR and Comparison PHAs

SAFMR PHAs

Comparison PHAs

$1,300

$1,300

$1,200

$1,200

rd  $1,100

nda

$1,100

ta

nt s  $1,000

2010

1,090

2010

$1,000

2015

2015

Payme

1,120

$900

979

969

1,249

$900

1,008

1,004

939

1,116 955

1,098

958

994

960

1,117

990

$800

$800

< 0.9

0.9–1.1

> 1.1

All

< 0.9 0.9–1.1 > 1.1

All

Rent ratio category

Rent ratio category

PHA = public housing agency. SAFMR = Smal  Area Fair Market Rent.

Note: Al  values expressed in 2015 dollars.

Sources: FY2015 Fair Market Rents; HUD FY2015 SAFMRs; HUD Public and Indian Housing Information Center administrative data As Exhibit 7-1 shows, between 2010 and 2015, the average payment standard decreased in real

terms (in 2015 dollars) by 11 percent across all rent categories combined in the SAFMR PHAs. 36

In contrast, the average payment standard decreased by about 2 percent in the comparison PHAs.

A combination of a 17-percent decrease in the average payment standard of units in low-rent ZIP

Codes, a 5-percent decrease in the average payment standard of units in moderate-rent ZIP

Codes, and an increase of about 12 percent in the average payment standard of units in high-rent

ZIP Codes drove the overall decrease in payment standards in SAFMR PHAs. The overall

decrease indicates that increases in payment standards for households in high-rent neighborhoods

were more than offset by lower payment standards for households in low-rent neighborhoods.

Exhibit 7-2 presents the information separately for each of the SAFMR PHAs by rent ratio,

showing a similar pattern to the overall measure in Exhibit 7-1, although the magnitude of the

changes varied across sites. The largest decreases in payment standards from 2010 to 2015 in

low-rent ZIP Codes were in Dallas, Long Beach, Mamaroneck, and Plano. The largest increases

in payment standards in high-rent ZIP Codes were in Cook County and Laredo. Mamaroneck

and Plano saw minimal changes in payment standards in this high-rent ratio category during this

time period, despite the switch to SAFMRs.

36  All values in this chapter are expressed in 2015 dollars so that reported changes over time are changes in real terms.

88

Exhibit 7-2: Payment Standards by Rent Ratio by Site

Low-Rent ZIP Codes (Rent Ratio < 0.9)

$1,800

d  $1,600

dar  $1,400

an

t st  $1,200

377

en  $1,000

1,

m  $800

116

389

060

719

105

037

734

064

713

Pay

1,

955

700

669

1,

1,

1,

1,

1,

660

996

700

1,

$600

2010

2015

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

$1,800

d  $1,600

dar  $1,400

an

523

t st  $1,200

1,

en  $1,000

m

747 808

174

130

724

886

830

$800

008

379

1,

659

134

743

1,

102

Pay

1,

958

1,

1,

1,

1,

988

$600

2010

2015

High-Rent ZIP Codes (Rent Ratio > 1.1)

$1,800

d  $1,600

dar

690

$1,400

an

557

1,

t st  $1,200

1,

4551,

en  $1,000

249

m

1,

209

2201,

$800

098

755

1,

432

668

251

755

186

108

108

Pay

1,

1,

1,

1,

1,

1,

1,

837

$600

2010

2015

PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Note: Al  values expressed in 2015 dollars.

Sources: FY2015 Fair Market Rents; HUD FY2015 SAFMRs; HUD Public and Indian Housing Information Center administrative data

89

Rent-Related Effects of SAFMRs

PHA Effects: HAP Payments to Landlords

SAFMRs have a major impact on PHAs’ HAP payments to landlords. Exhibit 7-3 shows HAP costs

incurred by PHAs. In 2010, HAP costs across SAFMR PHAs averaged $709 per unit (in 2015 dollars)

and $617 in 2015 for an average decrease of 13 percent. In contrast, the average HAP decreased from

$615 to $587, a decline of about 5 percent, in the comparison PHAs. Note that HAP costs can change

for reasons other than changes in rents and payment standards. For example, increases in HCV holder

incomes and decreases in the applicable utility allowance can lead to increases in HCV holder

contributions to rent that result in decreases in HAP payments by PHAs to landlords.

Exhibit 7-3: HAP Costs for SAFMR and Comparison PHAs by Rent Ratio

SAFMR PHAs

Comparison PHAs

$900

$900

s

$800

$800

st

$700

779

805

$700

P co

711

HA

$600

680

709

709

$600

670

617

$500

612

$500

596

615

596

587

554

584

566

$400

$400

< 0.9

0.9–1.1

> 1.1

All

< 0.9

0.9–1.1

> 1.1

All

Rent ratio category

Rent ratio category

2010

2015

2010

2015

HAP = Housing Assistance Payment. PHA = public housing agency. SAFMR = Smal  Area Fair Market Rent.

Note: All values in 2015 dollars.

Sources: FY2015 Fair Market Rents; HUD FY2015 SAFMRs; HUD Public and Indian Housing Information Center administrative data In the SAFMR PHAs, average per-unit HAP costs decreased by nearly 30 percent in low-rent

ZIP Codes and increased by about 3 percent in high-rent ZIP Codes. In the comparison PHAs,

the changes in HAP payments were similar across rent categories, declining by 3 percent in the

low-rent ZIP Codes and by 6 percent in the high-rent ZIP Codes.

Exhibit 7-4 shows changes across sites. As expected, the change in HAP costs follows the same

pattern as the change in payment standards. Average per-unit HAP payments in lower-rent ZIP

Codes saw the largest decreases in Dallas, Long Beach, and Plano. Average per-unit HAP

payments in high-rent ZIP Codes saw the largest increases in Laredo and Cook County. In Dallas

and Plano, the average HAP in the high-rent ZIP Codes actually decreased, reflecting the fact

that the payment standard in these ZIP Codes in Dallas increased only modestly and in Plano, did

not increase at all between 2010 and 2015.

Changes in HAP are not linearly related to changes in payment standards. Among SAFMR

PHAs, HAP increases only 3 percent, although average per-unit payment standards increased by

13 percent in higher-cost ZIP Codes. In other words, among SAFMR PHAs, increases in HAP in

high-rent ZIP Codes are small relative to increases in payment standards. This disparity may be

because once payment standards increase, it takes time for landlords to request rent increases

(and for PHAs to approve the requests) or because some rents are already reasonable below both

the old and new payment standard.

90

Exhibit 7-4: HAP Costs by Site

Low-Rent ZIP Codes (Rent Ratio < 0.9)

$1,000

$900

s  $800

st

882

$700

$600

AP co

675

H  $500

657

447

402

393

397

382

$400

711

554

367

907

947

722

590

613

$300

2010

2015

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

$1,000

$900

s  $800

928

st  $700

780

$600

714

AP coH  $500

584

$400

680

522

454

902

934

733

454

465

690

592

431

472

$300

2010

2015

High-Rent ZIP Codes (Rent Ratio > 1.1)

$1,100

$1,000

$900

052

s

1,

0281,

$800

st

898

$700

805

803

761

AP co

$600

H

$500

779

646

548

998

950

815

460

770

661

515

$400

2010

2015

HAP = Housing Assistance Payment. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Notes: Al  values expressed in 2015 dollars. Mamaroneck had only nine housing choice voucher holders in low-rent ZIP Codes, therefore regard information with caution.

Sources: FY2015 Fair Market Rents; HUD FY2015 Small Area Fair Market Rents; HUD Public and Indian Housing Information Center administrative data

91

Landlord Effects: Rent to Landlords

The payment standard level affects the rent payment to the landlord, but payments to landlords

can vary for several reasons. For example, the rent to landlords can exceed the payment standard

if the HCV holder is willing to pay rent above the payment standard. As another example,

specific units may not qualify to rent at levels equal to the full payment standard if market

analysis indicates that reasonable rent values are below payment standards. Further, PHAs can

approve rent exceptions for specific units should a situation warrant it; for example, to prevent an

elderly person from moving.

Rents to landlords comprise rents paid by HCV holders and PHA payments to landlords on

behalf of HCV holders. Average rents to landlords stayed more or less flat in real terms in both

SAFMR and comparison PHAs from 2010 to 2015, decreasing by 1 percent in the former and

increasing by 1 percent in the latter. However, Exhibit 7-5 shows that, in the comparison PHAs,

rents to landlords stayed constant across all ZIP Code types, whereas in the SAFMRs, the pattern

varied by rent ratio. As expected, rents to landlords decreased in low-rent ZIP Codes (by about

7 percent) and increased in high-rent ZIP Codes (by about 6 percent). This change confirms that

HCV holders in low-rent ZIP Codes were able to find lower cost units, either through existing

landlords accepting lower rents or by finding new units.

Exhibit 7-5: Rent to Landlords for SAFMR and Comparison PHAs by Rent Ratio

SAFMR PHAs

Comparison PHAs

$1,100

$1,100

d

$1,000

$1,000

1,085

or

andl  $900

$900

957

ent to l

882

$800

871

R

$800

939

888

1,014

828

$700

800

809

796

954

< 0.9

0.9–1.1

> 1.1

$700

Rent ratio category

< 0.9

0.9–1.1

> 1.1

Rent ratio category

2010

2015

2010

2015

PHA = public housing agency. SAFMR = Smal  Area Fair Market Rent.

Note: All values expressed in 2015 dollars.

Sources: FY2015 Fair Market Rents; HUD FY2015 SAFMRs; HUD Public and Indian Housing Information Center administrative data Exhibit 7-6 shows the rents to landlords by site. In low-rent ZIP Codes, average rents to

landlords decreased by 10 percent in Long Beach, 6 percent in Mamaroneck, and 30 percent in

Plano. In high-rent ZIP Codes, average rents increased in all sites, except Long Beach and Plano,

by 7 percent or more. Rents increased in high-rent ZIP Codes by about 9 percent in Dallas and

30 percent in Laredo. Changes in rents to landlords in low- and high-rent ZIP Codes were

modest in the other PHAs. Average rents in moderate-rent ZIP Codes changed only modestly, if

at all, in most PHAs.

92

Exhibit 7-6: Average Rent to Landlords by Site

Low-Rent ZIP Codes (Rent Ratio < 0.9)

$1,500

$1,250

287

$1,000

1,

1161,

$750

ent to landlord

882

233

359

917

R

939

590

601

1,

1,

950

577

584

749

870

706

671

$500

2010

2015

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

$1,500

$1,250

3771,

$1,000

1941,

$750

975

ent to landlordR

871

257

345

605 621

888

839

820

648

1,

1,

977

860

845

672

$500

2010

2015

High-Rent ZIP Codes (Rent Ratio > 1.1)

$1,500

$1,250

403

442

1,

1,

$1,000

085

149

1,

1,

001

0521,

978

ent to landlord  $750

R

014

1,

343

348

072

1,

699

1,

1,

1,

635

962

963

687

$500

2010

2015

PHA = public housing agency. SAFMR = Small Area Fair Market Rents.

Notes: Al  values expressed in 2015 dollars. Mamaroneck had only nine housing choice voucher holders in low-rent ZIP Codes, therefore regard information with caution.

Sources: FY2015 Fair Market Rents; HUD FY2015 SAFMRs; HUD Public and Indian Housing Information Center administrative data

93

HCV Holder Effects: HCV Holder Contributions to Rent

Finally, Exhibits 7-7 and 7-8 look at the average monthly HCV holder contributions to rent.

Exhibit 7-7 shows that in the SAFMR PHAs, HCV holder contributions increased an average of

18 percent (in real terms) between 2010 and 2015. HCV holder contributions to rent increased

the most in low-rent ZIP Codes, increasing from $354 to $431, or a 22-percent increase—

perhaps due to increases in rent contributions by HCV holders who did not wish to move despite

drops in payment standards that lowered HAP payments to landlords on their behalf. HCV

holder contributions to rent also rose substantially in moderate-rent ZIP Codes, rising from about

$359 to $423, an 18-percent increase. The increase in HCV holder contributions to rent in high-

rent ZIP Codes was smaller, rising from about $400 to $444, or an 11-percent increase. A

different pattern applied in the comparison PHAs, where HCV holder contributions rose by about

9 percent during this period, with roughly similar increases across all rent ranges. Taken

together, the data for the two sets of PHAs suggests SAFMRs led to substantial increases in

HCV holder contributions to rent in low-rent ZIP and moderate-rent ZIP Codes but only slight

increases in HCV holder contributions to rent in high-rent ZIP Codes.

Exhibit 7-7: Average HCV Holder Contribution to Rent by Rent Ratio

SAFMR PHAs

Comparison PHAs

$500

$500

on $400

$400

443

ibuti

431

423

444

430

396

412

419

$300

ontr

$300

r cde $200

$200

V hol $100

$100

CH

354

359

398

363

387

370

408

383

$0

$0

< 0.9

0.9–1.1

> 1.1

All

< 0.9

0.9–1.1

> 1.1

All

Rent ratio category

Rent ratio category

2010

2015

2010

2015

HCV = housing choice voucher. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Note: Al  values expressed in 2015 dollars.

Sources: FY2015 Fair Market Rents; HUD FY2015 SAFMRs; HUD Public and Indian Housing Information Center administrative data Note that changes in HCV holder contributions to rent can result from changes in payment standards

(particularly when payment standards decrease without corresponding decreases in unit rents),

changes in the actual rents of selected units, changes in HCV holder incomes, and changes in

utility allowances. Although voucher holders may not spend more than 40 percent of income at the

time of initial lease up, they are able to contribute more of their income after the first year of a lease.

Exhibit 7-8 shows HCV holder contributions to rent by site by rent category. In low-rent ZIP

Codes, most PHAs generally follow a similar pattern of substantial increases in HCV

contributions to rent—24 percent in Chattanooga, 8 percent in Cook County, 38 percent in

Dallas, 21 percent in Long Beach, and 13 percent in Plano. In Laredo and Mamaroneck, average

tenant contributions to rent actually declined; however, as noted, the sample size in Mamaroneck

was very small, so regard this finding with caution.

94

Exhibit 7-8: Average HCV Holder Contribution to Rent by Site

Low-Rent ZIP Codes (Rent Ratio < 0.9)

$600

$500

$400

518

ontribution

$300

431

439

407

426

353

384

352

$200

older c

$100

354

364

428

454

356

284

295

379

V h

$0

CH

2010

2015

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

$600

$500

$400

487

478

ontribution $300

423

453

491

395

352

360

$200

older c $100

359

385

456

448

372

303

343

367

V h

$0

CH

2010

2015

High-Rent ZIP Codes (Rent Ratio > 1.1)

$600

$500

$400

514

444

445

454

463

ontribution $300

418

359

$200

321

older c $100

398

360

459

448

407

333

381

455

V h

$0

CH

2010

2015

HCV = housing choice voucher. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Notes: Al  values expressed in 2015 dollars. Mamaroneck had only nine HCV holders in low-rent ZIP Codes, therefore regard information with caution.

Sources: FY2015 FMRs; HUD FY2015 SAFMRs; HUD Public and Indian Housing Information Center administrative data

95

Tenant contributions increased in similar proportions in moderate-rent ZIP Codes for all PHAs

except Laredo, where tenant contributions decreased.

In the high-rent ZIP Codes, tenant contributions remained the same at the five demonstration

sites and increased slightly in Dallas and Plano. We will explore this disparity in the changes by

site as part of the Phase 2 analysis.

96

8.  Plan for Phase 2 Data Collection

In Phase 2, during the second 18 months of the study, we will conduct a second round of site

visits to the same seven PHAs to update the information on administrative processes and costs

and to interview tenants and landlords. The interviews will build on the findings in Phase 1 and

are designed to complement and enhance the quantitative analyses. In Phase 2, we will also

update the analysis of secondary data with 2016 and 2017 data. We will combine and synthesize

Phase 1 and Phase 2 findings in the final report.

Our hypothesis in developing the study approach and budget is that most of the costs of

implementing SAFMRs are one time and upfront. As such, our primary data collection relating

to costs and our primary implementation analysis will come from the Phase 1 site visits.

However, as part of the Phase 2 site visits, we will confirm and refine our estimates of ongoing

administrative costs. We will also update any information regarding PHA experiences.

The primary purpose of the Phase 2 site visits will be to interview housing choice voucher

holders and landlords in person to learn qualitatively whether tenants and landlords are aware of

changes in payment standards and how changes affected (or could affect) HCV holders’

decisions about housing and neighborhood choice and landlord participation in the HCV

program. 37, 38 We will conduct the interviews in Phase 2, rather than Phase 1, to form our interview guides from initial empirical findings.

We plan to interview a total of 70 tenants and 35 landlords across the seven SAFMR evaluation

sites, 10 tenants and 5 landlords at each of the seven SAFMR PHAs. 39 We propose conducting a modest number of interviews, in keeping with budget constraints. Regardless, we think it is

sufficient number of interviews to identify any major issues regarding awareness and

understanding of SAFMRs that will be important for interpreting the study’s quantitative

findings and for future policy development. 40

Ideally, we will interview a range of HCV holders, including new HCV holders, movers, and

people who both do and do not lease in high-opportunity area. In addition, we will interview a

range of landlords, including those who experienced decreases in payment standards and those

owning units in high-opportunity areas with rents within reach of HCV holders who either do or

do not rent to HCV holders. We will work with the PHAs to obtain listings of HCV holders and

landlords from which to sample and will use public data sources, such as craigslist.com, to

identify landlords not currently renting through the HCV program. We will determine the exact

mix of interviewees in the Phase 2 research design and refine our determination based on the

Phase 1 empirical findings.

37  We will conduct telephone interviews in place of those we cannot complete in person.

38  We do not expect tenants or landlords to be familiar with the term small area FMRs fair market rents, so we will use language that is more meaningful to them, such as “payment standards established by the public housing authority.”

39  During the design phase, we will discuss whether it makes sense to cluster landlord interviews in a subset of sites, such as sites where the shift to SAFMRs does not appear to have substantially changed locational outcomes.

40  We opted to conduct individual interviews rather than focus groups because of the potential sensitivity of the subject matter for landlords and the need to probe on individual experiences and understanding for tenants.

97

Appendix D provides drafts of the discussion guides that we will use in the Phase 2 interviews

with landlords, tenants, and public housing agencies. Interview instruments from Dr. DeLuca’s

ongoing research informed these discussion guides (DeLuca and Edin, 2015). Topics for HCV

holder interviews include tenant awareness and understanding of changing payment standards,

decisions around recent moves (for movers), and factors contributing to decisions in selecting

current units. Topics for landlord interviews include landlord awareness and understanding of

HCV program and payment standards, appropriateness of payment standards to market, and

experience with and attitudes toward the HCV program. The discussion guides that we will use

for Phase 2 interviews with PHAs are similar to those used in Phase 1, which we will use to

obtain updated perspectives on PHA experiences on the shift to Small Area Fair Market Rents.

Two-person teams will conduct Phase 2 site visits, with junior visitors creating the interview

notes and lead interviewers reviewing and approving final versions. The site visitors will submit

comprehensive notes from tenant and landlord interviews, with information organized by

interview protocol question. Following the completion of the site visits, all site visitors will meet

to compare the findings across sites and identify themes from the interviews. We will also

systematically review the interview notes for themes using NVivo. 41 The interview notes will serve as primary qualitative data sources from site visits, and we will use the notes in

conjunction with the study’s other data sources to conduct the final analysis and complete the

final report.

41  NVivo™ is a software package designed for the management and analysis of qualitative data. We will upload and code data into the software to facilitate efficient, systematic, reliable, and replicable analyses. NVivo can identify prevalent themes from the interviews and help us to understand patterns regarding PHA and respondent

characteristics relating to the themes.

98

Appendix A. Small Area Fair Market Rent Demonstration Site Selection

HUD’s process for selecting public housing agencies (PHAs) for the Small Area Fair Market

Rent (SAFMR) demonstration had three stages: (1) selecting a pool of PHAs that met initial

criteria; (2) organizing the PHAs into selection clusters; and (3) inviting PHAs to participate in

the demonstration seque1ntially in random order. Invitation rounds proceeded until a sufficient

number of PHAs had agreed to participate. A discussion in more detail on each stage follows.

Screening Criteria

HUD began selection by developing a pool of 247 local PHAs and 20 state PHAs, each of which

met the following set of initial criteria (Kahn and Newton, 2013).

1.  Had at least 500 vouchers in use as of September 30, 2011.

2.  Had at least 10 housing choice voucher (HCV) tenants living in ZIP Codes where the

SAFMR exceeded the metropolitan area Fair Market Rent (FMR) by more than 10

percent in fiscal year 2012.

3.  Had at least 10 HCV tenants living in ZIP Codes where the SAFMR was more than

10 percent less than the metropolitan area FMR.

4.  Had attained at least 95 percent HCV family reporting in Public and Indian Housing

Information Center.

5.  Was not troubled, as determined by the Section 8 Management Assessment Program.

6.  Had the administrative capacity to carry out the SAFMR program.

7.  Had not been involved in litigation that would seriously impede its ability to administer

the HCV program.

Clustering

HUD organized the 247 eligible local PHAs into eight selection clusters using key HCV program

and housing market conditions that are not expected to change as a result of implementing

SAFMRs. HUD created two tiers of clusters, with the first four groups based on the number of

vouchers (small, large) and the two-bedroom metropolitan area FMR (low, high). Then these

four groups were split in two based on the number of working-age heads of household (percent

high, percent low). Exhibit A-1 presents the cluster definitions. Exhibit A-2 depicts the formation

of the first-tier grouping based on metropolitan area FMR and PHA size.

Exhibit A-1: PHA Selection Cluster Definitions

Small Number of Vouchers

Large Number of Vouchers

Low

High % working age

High % working age

metropolitan

70 PHAs (Cluster 1)

59 PHAs (Cluster 5)

area FMR

Low % working age

Low % working age

38 PHAs (Cluster 2)

21 PHAs (Cluster 6)

High

High % working age

High % working age

metropolitan

25 PHAs (Cluster 3)

7 PHAs (Cluster 7)

area FMR

Low % working age

Low % working age

18 PHAs (Cluster 4)

9 PHAs (Cluster 8)

FMR = Fair Market Rent. PHA = public housing agency.

99

Exhibit A-2: PHA Selection Cluster Size and FMR Values

FMR = Fair Market Rent. HACLA = Housing Authority of the City of Los Angeles. MFMR = metropolitan area FMR. NYC HPD = New York City Department of Housing Preservation and Development. NYCHA = New York City Housing Authority. PHA = public housing agency.

Source: HUD Office of Policy Development and Research analysis during cluster formation process

Random Ordering

HUD randomly ordered PHAs within each cluster. Next, it invited the PHA at the top of each

cluster list to participate in the evaluation. When an invited PHA declined to participate, HUD

reached out to the next PHA in that cluster. After a number of rounds, five PHAs from five of the

eight clusters had agreed to participate in the SAFMR demonstration, and HUD determined this

mix was adequate for both the demonstration and its evaluation.

How Selection Informs the Evaluation

HUD’s selection process informs both our evaluation design and the interpretation of our

findings. Because PHAs were randomized before invitation to participate in the demonstration,

each of the demonstration PHAs and the other PHAs within its cluster do not differ in any

systematic way. This randomization adds confidence to the impact interpretation of the

evaluation findings, that SAFMRs cause changes in our outcomes of interest.

However, because available resources allowed for the inclusion of only one PHA per cluster in

the demonstration, not enough statistical power exists to support analysis as a  cluster random

controlled trial  design. Rather, we report average changes over time for the SAFMR PHAs

100

combined, and for each PHA individually, comparing these average changes with averages for

other PHAs that were eligible but not included in the demonstration (the 138 comparison PHAs)

and averages for all PHAs in each cluster individually.

The initial selection criteria affect the interpretation of the results of this evaluation to varying

degrees. Criteria 2 and 3 ensure that the transition to SAFMRs will affect some existing HCV

holders in that the payment standards applying to their vouchers will likely change. As discussed

previously, this likely change is a key factor in determining whether SAFMRs will affect HCV

holders. Findings of impact in the evaluation may not hold for PHAs with less rent dispersion.

Similarly, a lack of findings in the evaluation, with a sample that already contained some

households accessing high-rent neighborhoods (criteria 2), may not hold for areas where at least

10 HCV holders were not accessing neighborhoods with high-rent levels under metropolitan area

FMRs but would be able to with SAFMRs.

Criteria 4 through 6 affect the interpretation of our findings on the impact of SAFMRs on PHAs,

because they limit only the types of PHAs in the demonstration to those that are high performing

(that is, reporting to Public and Indian Housing Information Center, not troubled, deemed able to

carry out the demonstration, and not involved in litigation). Thus, we cannot be confident that

findings from the demonstration would apply to lower-performing agencies.

101

Appendix B. Administrative and Financial Impacts of Small Area Fair Market Rents on Public Housing Agencies: Consolidated Exhibits

This appendix consolidates and reproduces the exhibits in the sections entitled Factors Affecting PHAs’ SAFMR Implementation and SAMFR Administrative Impacts on PHAs.

Exhibit B-1. Administrative Impacts of SAFMRs on PHAs

Impact

PHA

Chattanooga

Cook County

Dallas

Long Beach

Mamaroneck

Plano

PHA plan and

Director—1 hour.

No change.

VP, president, and

Minimal—time not

Administrator—5–10  Finance director—24

administrative plan

CEO—80 hours PHA

recorded.

hours;

hours administrative plan,

changes

administrative plan, 80

Administrator—2

2 hours for PHA plan.

hours for PHA plan.

hours.

Modifications to

IT specialist—40

No change.

Software vendor + IT

GoSection8 system

$400 to software

Office supplies, postage,

automated systems  hours/$900.

director + CFO—250

changes for rent

vendor + 6 ED; 40

software modifications

hours/$35,000 to

reasonableness.

hours for automated

$10,705; finance

software vendor.

spreadsheet.

director—124 hours.

Analyzing and

Director—40 hours to  Created and updated

VP-HCV, CFO, COO,

Community program

Director—40 hours

All staff—1 hour.

setting payment

date.

spreadsheet of ZIP Codes  president/CEO, 120

specialist—15 to 16

initially; 10 hours to

standards

and payment standards—

hours.

hours for affordability  update annually.

100 hours to date.

spreadsheet.

Rent

No change.

No change.

Additional office

No change.

7 hours per week.

No change.

reasonableness

support + overtime.

Contract rent

Director—192 hours

No change.

Additional overtime,

No change.

None named.

Slight increase during

adjustments

annually to respond to

landlord services.

change in standards.

increase in requests.

Inspection system

New hire: contract

No change.

Two new inspectors

Increased number of

Increase in travel

No change.

and process

inspector—$15,000

hired.

inspections.

distance for

changes

per year for overflow

inspections.

inspections.

102

Impact

PHA

Chattanooga

Cook County

Dallas

Long Beach

Mamaroneck

Plano

Communication

Monthly landlord

None reported.

New client and

No change.

Minimal additions to

Finance director—10

strategy

briefing, tenant

landlord guide.

the website and

hours.

briefings present an

forms.

increase.

Procedures and

Staff training for

Training held as part

$40,000 in staff

Phone calls with

Increase in trainings.

Staff trainings.

staff training

SAFMR: director,

of regular staff

training.

HUD, increase in staff

housing specialist,

meetings.

trainings.

inspector.

Education and

None reported.

Created new mobility

New relocation and

Tenants given more

Tenants given more

Longer briefings with

support for tenants

counseling program in  briefing packets.

information during

information during

tenants.

conjunction with

normal briefings.

normal briefings.

SAFMRs.

Education and

Monthly landlord

None reported.

Additional landlord

Recruiting events

Landlords given more   None reported.

support for

briefings.

training.

held; landlord mailing;  information during

landlords

advertising; created

normal briefings.

new owner

orientation;

newsletter;

attendance at

apartment

association meetings

and trade shows.

Quality assurance   Director—65 hours

Increase in HUD-

More frequent quality  Increase in file

No change.

Increase in errors.

per year.

50058 errors.

control.

review.

CEO = chief executive officer. CFO = chief financial officer. COO = chief operating officer. ED = executive director. IT = information technology. PHA = public housing agency. SAFMR = Small Area Fair Market Rent. VP = vice president. VP-HCV = vice president of Housing Choice Voucher program.

103

Exhibit B-2: Administrative Impacts of SAFMRs on PHAs

PHA

Impact

Chattanooga

Cook County

Dallas

Long Beach

Mamaroneck

Plano

PHA Plan and

Director—1 hour.

No change.

VP, president, and

Minimal—time not

Administrator—5–10

Finance director—

Administrative

CEO—80 hours PHA

recorded.

hours; Administrator—

24 hours

Plan changes

Administrative Plan, 80

2 hours

Administrative

hours for PHA Plan

Plan, 2 hours PHA

Plan

Modifications to

IT specialist—40

No change.

Software vendor + IT

GoSection8 system

$400 to software

Office supplies,

automated systems  hours/$900.

director + CFO—250

changes for rent

vendor + 6 ED; 40 hours  postage, software

hours at $35,000 to

reasonableness.

for automated

modifications

software vendor.

spreadsheet.

$10,705; finance

director—124

hours.

Analyzing and

Director—40 hours to

Created and updated

VP-HCV, CFO, COO,

Community program

Director—40 hours

All staff—1 hour.

setting payment

date.

spreadsheet of ZIP

president and CEO, 120  specialist—15 to 16

initially; 10 hours to

standards

Codes and payment

hours.

hours for affordability

update annually

standards—100

spreadsheet.

hours to date.

Rent

No change.

No change.

Additional office

No change.

7 hours per week.

No change.

reasonableness

support + overtime.

Contract rent

Director—192 hours

No change.

Additional overtime,

No change.

None named.

Slight increase

adjustments

annually to respond to

landlord services.

during change in

increase in requests.

standards.

Inspection system

New hire: contract

No change.

Two new inspectors

Increased number of

Increase in travel

No change.

and process

inspector—$15,000

hired.

inspections.

distance for

changes

per year for overflow

inspections.

inspections.

Communication

Monthly landlord

None reported.

New client and

No change.

Minimal additions to

Finance director—

strategy

briefing, tenant briefings

landlord guide.

the website and forms.  10 hours.

present an increase.

104

PHA

Impact

Chattanooga

Cook County

Dallas

Long Beach

Mamaroneck

Plano

Procedures and

Staff training for

Training held as part

$40,000 in staff

Phone calls with HUD,

Increase in trainings.

Staff trainings.

staff training

SAFMR: director,

of regular staff

training.

increase in staff

housing specialist,

meetings.

trainings.

inspector.

Education and

None reported.

Created new mobility  New relocation and

Tenants given more

Tenants given more

Longer briefings

support for tenants

counseling program

briefing packets.

information during

information during

with tenants.

in conjunction with

normal briefings.

normal briefings.

SAFMRs.

Education and

Monthly landlord

None reported.

Additional landlord

Recruiting events held;  Landlords given more

None reported.

support for

briefings.

training.

landlord mailing;

information during

landlords

advertising; created

normal briefings.

new owner orientation;

newsletter; attendance

at apartment

association meetings

and trade shows.

Quality assurance   Director—65 hours per  Increase in HUD-

More frequent quality

Increase in file review.   No change.

Increase in errors.

year.

50058 errors.

control.

CEO = chief executive officer. CFO = chief financial officer. COO = chief operating officer. ED = executive director. IT = information technology. PHA = public housing agency. SAFMR = Small Area Fair Market Rent. VP = vice president. VP-HCV = vice president of Housing Choice Voucher program

105

Appendix C. Site Visit 1 Protocol

GUIDANCE TO SITE VISITORS:

•  Keep probing PHA staff on a question until you get an answer that makes sense. You may need to

repeat their answer back to them to make sure you’ve understood them properly.

•  You may need to rephrase some questions to make sure staff understood the question well enough

to confidently answer or say “no, we don’t do that.”

C.1   Phase 1: Background and Experience with SAFMR Transition

C.1.1  Experience with Transition to SAFMRs

I’d like to start by asking about you and your experience with the SAFMR demonstration.

1.   What is your background and experience with the organization?

2.   What was the motivation of the PHA for joining the demonstration?

3.   Generally speaking, how has the transition proceeded? What steps were required to make the initial

transition to SAFMRs?

4.   Were there any unexpected implementation issues?

5.   Are there any other changes outside of the PHA that may have influenced how SAFMRs were

implemented or accepted? For example, did sequestration affect the timing of the roll out or any

administrative procedures? Did source of income protection affect landlord attitudes? Were there any

other changes?

C.1.2  Background Information

6.   I’d like to review some basic background information on your PHA to make sure I have a good

understanding before we go into more details.  (Find the available information for this table in

_County_Data.xls file in the PHA folder; ask the PHA to fill in any gaps and/or confirm or correct

the information.)

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PHA Background Information

Data item

Explanation

Cities/counties served by the HCV program:

(List cities or counties)

Jurisdiction in square miles:

(Miles)

Pre-SAFMR payment standard (FMR schedule)

(Complete payment standard schedule for all

bedroom sizes and all sub-areas for the year

before the transition to SAFMR)

Current payment standard by ZIP Code

(Complete payment standard schedule for all

bedroom sizes and all ZIP Codes for the current

year)

Fiscal year (FY) end date:

(Date)

Number of tenant-based vouchers under lease at end of last FY by

(Number of regular, VASH, FUP, enhanced

type

vouchers, etc.)

Current number of tenant-based vouchers under lease:

(Number)

Budget utilization rates:**

(Percent = HAP dollars spent / HAP budget

allocated)

At end of FY 2015 (if available):

At end of FY 2014:

At end of FY 2013:

At end of FY 2012:

At end of FY 2011:

At end of FY 2010:

At end of FY 2009:

Current budget utilization rate:

Unit utilization rates:**

(Percent = units under lease / units allocated)

At end of FY 2015 (if available):

At end of FY 2014:

At end of FY 2013:

At end of FY 2012:

At end of FY 2011:

At end of FY 2010:

At end of FY 2009:

Current unit utilization rate:

Software used to submit PIC data:

(Name of software)

*Special vouchers include: homeownership vouchers, VASH, FUP, Mainstream 1, Mainstream 5, non-elderly

disabled, tenant protection, disaster voucher.

**Source: VMS (Voucher Management System)

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7.   Is your PHA a stand-alone agency, a unit of government, i.e., part of the city, county, or state government, a nonprofit contractor, or a for-profit contractor? ( If part of government, identify which agency and which level of government.)

8.   What is the current rental market vacancy rate, either overall or within subdivisions of your

jurisdiction? What sources do you use for your estimate? What is the vacancy rate in the portion of

the market affordable to voucher holders? What sources do you use for your estimate?

9.   [Refer to table, PHA Background Information] Has your budget utilization rate changed as a result of the switch to SAFMRs? If so, how and why? If not, why not?

10.   [Refer to table, PHA Background Information]  Have unit utilization rates changed as a result of SAFMRs? In what way? If not, why not? If so, does this differ for ZIP Codes with higher versus

lower payment standards? Do you expect impacts to dissipate over time? If not, why not?

C.1.3  Participant experience (from the PHA perspective)

11.  Do you feel that families who have been awarded a voucher understand what rent they can afford in

different neighborhoods under the SAFMR policy? Was it difficult to explain the policy to existing

residents? Has it been difficult to explain the policy to new voucher recipients? If not, why not?

12.  How have SAFMRs affected where new voucher recipients search? Are they more likely to search in

areas of opportunity? Where they lease up? Are they more likely to lease up in areas of opportunity?

If not, why not?

13.  Does the PHA provide search assistance in areas where payment standards increased? Does the PHA

provide assistance to households that live in areas where the payment standard decreased?

14.  Have you changed your policies regarding the amount of time voucher recipients have for their initial search since the switch to SAFMRs? What, if any, changes have you made in your policies related to

granting extensions? Has this had an impact on the PHA’s administrative duties or costs? If so, what

has the impact been?

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15.  Do participants have a hard time finding units to rent that meet program requirements? If so, why? If not, why not? How has this changed since implementation of SAFMRs? Does this vary by

neighborhood?

15a.   To what extent, if at all, is the security deposit on units in high-opportunity neighborhoods a

barrier to relocating (e.g., because security deposits tend to be higher on higher-cost units)?

16 .  [Refer to table Voucher Success Rates] What is your estimate of voucher success rates from 2012-2015? Do you think SAFMRs have affected voucher success rates? If so, how? What other factors

have affected voucher success rates since implementing SAFMRs (e.g., sequestration, change in

market tightness)? How important is SAFMR relative to other factors? Has the impact of SAFMR

been different for new voucher recipients and existing voucher holders? If so, what are the

differences?

Voucher Success Rates*

PHA Fiscal Year

PHA estimate of success rate

Year prior to implementation (list

year:______)

Year during implementation (list year: _____)

Year following implementation (list year:

_____)

*The success rate is defined as the percentage of new voucher holders that successfully lease a qualifying unit in the program.

17.  Has the switch to SAFMRs had an effect on the rate at which existing HCV participants move? If so,

what effect is that? If not, why not?

18.  Has the switch to SAFMRs had an effect on where existing HCV participants who choose to move

end up leasing up? If so, what effect is that? If not, why not?

19.  How have current tenants in areas where rents decreased responded? Have they left the program?

Have their landlords accepted lower rents? Have their out-of-pocket costs increased – if so, for what

reasons?

20.  Does the PHA track information on involuntary moves (e.g., tracked in PIC, termination notice, unit

no longer meets rent reasonableness standard, landlord foreclosure, landlord sale)? Can we get a

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sample of this data? Has there been a change in the number of involuntary moves as a result of SAFMRs? If so, how has it changed and what are the reasons for this?

21.   When was the last time you opened your waiting list? Did you get more or fewer applicants than the last time you opened your list? Do you a sense of whether the switch to SAFMRs had any effect on

the number of applicants on the waiting list? Why or why not?

22.  Overall, do you feel that residents are pleased with the change to SAFMRs?

23.  What information does the PHA keep on household search or other indicators of tenants’ experience

with SAFMRs (e.g., requests for extensions of time, use of resource rooms, use of housing search and

assistance programs)? Is there any information on neighborhoods targeted by families, or any

additional information beyond what is in PIC? Can we get samples of this information?

24.  Does the PHA have any other thoughts on impacts of the demonstration on residents?

C1.4  Landlord experience (from the PHA perspective)

25.  Since implementation of SAFMRs, has the PHA reached out to landlords in opportunity areas? What

type of outreach? If not, why not?

26.  Has the policy been difficult to explain to landlords? Do you get a lot of complaints from landlords?

Has this taken a lot of PHA staff time to address? Have complaints gone down over time? Why or

why not?

27.  What has been the reaction of these landlords?

28.  Have a significant number of new landlords in areas where payment standards have increased begun

to participate in the program since the switch to SAFMRs? Why do you think that is?

29.  How many new landlords in areas with higher payment standards have joined the program?

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30.  Overall, how many landlords were renting to HCV holders before the transition to SAFMRs? How many landlords are renting to HCV holders today? What do you think are the reasons for any

changes?

31.  What has been the reaction of existing landlords to SAFMRs? Have landlords in areas experiencing a

sizable decrease in payment standards renewed their leases at lower rents? Have they ended their

relation with the program? Why or why not?

31a.  Have you noticed any changes in neighborhood development patterns that might be related to

SAFMRs? For example, more development in higher-opportunity areas and less development

in lower-opportunity areas? Or changes in the types of development? If so, what changes have

you noticed? How are SAFMRs related to these changes? For example, do SAFMRs affect

development of RAD, tax credit, and other properties? If so, how? Have you observed that

SAFMRs are affecting private development? If so, how? What is the magnitude of these

effects?

C.2   Phase 1: PHA Costs of SAFMR Transition

C.2.1  Housing Assistance Payment Costs

32.  How did you expect per-unit HAP costs to change as a result of SAFMRs? (e.g., how are payment

changes being applied? What happens if the payment standard goes up? stays the same? goes down?

Does the tenant or landlord pay the difference?) How did your expectation affect how you set HAP,

and how you communicated SAFMRs to tenants and landlords?

33.  How has the transition actually affected per-unit HAP costs? (Do you actively track changes?) Have

changes been a result of changes in contract rents for voucher units, payment standards, or both?

Please explain.

C.2.2  HCV Program Staffing/Labor Costs

34 .  PHA will be asked in advance to provide a list staff members/titles (columns A and B).  The following is the list of employees/titles. For each person, could you indicate whether there has been a change in

role/responsibilities as a result of SAFMR? If yes, please also indicate whether there has been a) a

change in base salary; b) a change in overtime pay; and c) whether the person is newly hired. As

appropriate, please indicate the corresponding cost of each of these role/responsibility changes.

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Staffing changes related to SAFMR

If YES in column C (change in role/responsibilities), then:

Change in role/

Change in salary  Change in overtime as

responsibilities as

as a result of

a result of SAFMR? (If

New hire as a result of

Employee

a result of

SAFMR? (If yes,

yes, provide annual

SAFMR? (If yes, provide

name or

SAFMR? (If yes,

provide annual $

change in overtime

annual total

initials

Title

describe)

change)

pay)

compensation)

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35.  Can you tell me who performs each of the following functions? For each activity/task, have there been changes resulting from SAFMR? Are these changes one-time or ongoing?

Staff Performing Activities/Tasks

Activity/task

Who performs it (initials or title)

Any changes resulting from

SAFMR? One-time or ongoing?

Waiting List/Selection

Initial eligibility determinations

Voucher issuance

Rent reasonableness

HQS Inspections

Informal reviews

Annual re-certifications

Move processing

Executing HAP contracts

Processing HAP payments

Data entry

Customer service/complaint resolution

Landlord outreach

FSS program

Voucher homeownership

Case management

Clerical functions

Portability specialist

Management/oversight

Other: ______________________________

Other:_______________________________

C.2.3  Potential Impacts on Systems and Admin Plans from Implementing SAFMRs

36.  Were modifications to the PHA Administrative Plan and PHA Plan required to implement SAFMRs?

If so, describe these modifications and complete the table [Potential one-time impacts]. If there have

been no changes, why not (i.e., what characteristics of your systems or processes made it possible to

adapt without changes)?  (Note that the pre- and post-demonstration PHA Administrative Plan and

PHA Plan will have been requested prior to the site visit.)

37.  Were modifications to the PHA’s system of record required to implement SAFMRs? If so, describe

these modifications and complete the table [Potential one-time impacts]. If there have been no

changes, why not (i.e., what characteristics of your systems or processes made it possible to adapt

without changes)?

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38.  Were modifications to other automated tools required to implement SAFMRs? If so, describe these modifications and complete the table [Potential one-time impacts]. If there have been no changes,

why not (i.e., what characteristics of your systems or processes made it possible to adapt without

changes)?

Potential one-time impacts

SAFMR-related

Total cost

Staff hours needed  Staff performing

Cost of consultants

change

expenditures

for modifications

modifications

or temporary staff

hired to assist with

modifications

PHA Administrative

Plan

PHA Plan

PHA system of

record

Other automated

tools:  list (e.g., rent

reasonableness)

________________

Other automated

tools:  list

________________

Other automated

tools:  list

________________

C.2.4  Potential Transitional Impacts from Implementing SAFMRs

39.  How were payment standards set prior to SAFMRs? Were they the same for the entire PHA?

40.  Describe the steps you took to set payment standards following the implementation of SAFMRs.

Were these steps more time consuming than the process you took to set payment standards before?

How much more time was involved? Have you updated your payment standards since that time?

What was involved in doing this? Complete the table [Potential transitional impacts]. If the steps were

not different, why not?

41.  Has the shift to SAFMRs led to any changes in how you determine if rents are reasonable? What

changes ( get as much detail as possible)? Is determining rent reasonableness now easier, harder, or about the same as it was before the introduction of SAFMRs? If it is different, complete the table

[Potential transitional impacts]. If there have been no changes, why not (i.e., what characteristics of

your systems or processes made it possible to adapt without changes)?

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42.  Have SAFMRs changed the number of requests for contract rent adjustments or extended contract

rent negotiations? If so, describe how and complete the table [Potential transitional impacts]. If there

have been no changes, why not?

43.  Have SAFMRs changed the number of requests from voucher holders for extensions of search time?

If so, how? Does this add costs? If so, complete the table [Potential transitional impacts]. If not, do

you have ideas about why this has not changed?

44.  Have SAFMRs changed the productivity of HQS inspectors in terms of time per inspection and/or

increased costs for travel to inspections (e.g., because units are dispersed over a larger area)? If so,

describe and complete the table [Potential transitional impacts]. If there have been no changes, why

not (i.e., what characteristics of your systems or processes made it possible to adapt without

changes)?

45.  Have any changes to the PHA’s communication and outreach strategy and materials (e.g., landlord

brochures, briefing packets, reexamination packets, web site and briefing videos) been required as a

result of implementing SAFMRs? If so, provide these materials, and describe and complete the table

[Potential transitional impacts]. If there have been no changes, why not (i.e., what characteristics of

your systems or processes made it possible to adapt without changes)?

46.  Prior to the SAFMR demonstration, had you defined opportunity areas, either formally or informally?

If so, how does the PHA define opportunity areas? Is there a direct match between the SAFMRs and

the opportunity and non-opportunity areas? Has this created any difficulties providing information to

families searching? If so, describe and complete the table [Potential transitional impacts].

47.  Has the PHA been administering a mobility program? If so, has the switch to SAFMRs changed the

level of effort associated with providing support for tenants (e.g., encouraging them to move to

opportunity areas, helping tenants understand the implications of reductions in payment standards in

some areas)? If so, describe and complete the table [Potential transitional impacts].

48.  Have additional briefings with tenants been required as a result of SAFMRs? If so, describe and

complete the table [Potential transitional impacts]. If not, why do you think additional briefings with

tenants have not been needed?

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49.  Have SAFMRs changed the level of support for landlords the PHA provides (e.g., more and/or specialized briefings or outreach)? If so, describe and complete the table [Potential transitional

impacts]. If not, why do you think additional support for landlords has not been needed?

50.  Have SAFMRs changed the incidence of HUD-50058 errors (e.g., because of selection of the wrong

payment standard)? Have changes to the quality assurance process been required to deal with this? If

so, describe and complete the table [Potential transitional impacts].

50a.  Have SAFMRs changed the effort or process of preparing data for VMS? If so, how and why?

Have you incurred costs as a result of this additional effort?

51.  What other costs have you incurred as a result of the transition to SAFMRs?

52.  Have any costs decreased as a result of the transition to SAFMRs? (E.g. rent reasonableness costs?) If so, how and why?

Potential transitional impacts

Were costs one-time or

ongoing? If ongoing, are

Time period

Staff hours

Staff

higher costs permanent

over which

needed for

performing

SAFMR-related

or wil  they decline over

Total cost

expenditures

additional

additional

change

time?

expenditures  were incurred

effort

effort

Process of

establishing

payment

standards

Process of

determining rent

reasonableness

data

Requests for CRAs

or extended

contract rent

negotiations

Additional

requests from

voucher holders

for extended

search time

Inspector

productivity

116

Were costs one-time or

ongoing? If ongoing, are

Time period

Staff hours

Staff

higher costs permanent

over which

needed for

performing

SAFMR-related

or wil  they decline over

Total cost

expenditures

additional

additional

change

time?

expenditures  were incurred

effort

effort

PHA

communication

and outreach

strategy and

materials

Support for

tenants

Support for

landlords

Quality assurance

processes

C.2.5  Other Program Costs

53.  Have office building costs charged to HCV changed as a result of implementing SAFMRs? If so,

how? (e.g., need for extra office space that resulted in expanding leased space in building?) If so,

please provide documentation of additional expenditures. Were these costs temporary or ongoing?

54.  Have any of the office expenses charged to HCV shown in the chart below changed as a result of

implementing SAFMRs? If so, how?

HCV Office Expense Changes

Of ice expense

Affected by SAFMR? (Y/N)

Cost of additional expenses

Of ice supplies

Of ice equipment

Communication devices

Postage and mailing costs

Record storage

Banking costs

Costs of shredding sensitive records

Audit costs

Limited English Proficiency, 504

compliance, fair housing laws,

translation of documents, interpretation

services

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55.  Has implementing SAFMRs affected the PHA’s vehicle expenses charged to HCV? If so, how? (e.g., more miles needed to inspect units no longer concentrated in a few tracts?) What is the total

additional vehicle expense?

56.  What additional training / conferences / professional association affiliation; publications and

administrative expenses associated with pertinent training, conferences and membership in affiliated

associations were required to implement SAFMRs? This cost should also include any amounts

associated with travel costs.

C.2.6  Conclusion

57.  Have there been any other changes in PHA processes or procedures as a result of SAFMRs? And any

cost implications?

58.  Has the additional administrative fee provided by HUD for participating in the demonstration been

sufficient to cover the up-front switch to SAFMRs and additional transitional or ongoing costs?

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Appendix D. Phase 2 Site Visit Draft Data Collection Instruments

D.1  Interview Guide for PHAs – Update

In the Phase 2 site visit interviews with PHAs, we will revisit many of the same topics as discussed

during site visit 1 to get updated perceptions of the PHAs’ experience with switching to SAFMRs as well

as their view of participant and landlord experience with the change in payment standards. We will also

conduct interviews with landlords and HCV residents to understand their experiences with switching to

SAFMRs.

The following are the draft interview protocols for the landlord, resident, and PHA interviews planned for Phase 2.

D.1.1  Draft Interview Guide for Landlords

INTRODUCTORY SCRIPT

Note that the protocol will be notated and updated to reflect any site-specific considerations (e.g.

commonly used terms identified in preparatory conversations with PHAs before the site visits)

As I mentioned over the phone, I’m _______________ from Quadel Consulting working on an Abt

Associates research team. We’re conducting a research study for the Department of Housing and Urban

Development on their Small Area Fair Market Rent policy. We’re interested in learning what it’s like to

be a landlord and work with the housing authority. We’ve found that a lot of folks talk to tenants, but the landlord perspective is usually overlooked. So we want to learn about your work. We’re focusing on how

the Small Area Fair Market Rent policy has affected you, but also what it’s like for you to work with the

housing authority and renting in general.

This is more of an informal conversation than a formal interview. We have been hired to provide an

independent research perspective. The [name of PHA] provided us with the contact information for 50

landlords in the [City], and you are one of five that we will be talking to for the study. After we talk to all five of the landlords, we will combine everyone’s perspectives into a research report that we will send to HUD that may become available for the general public to read. We may include short quotes or

summaries of individual comments from our conversations with landlords, but nothing you say will be

attached to your name, your company, or your personal information directly. The [ name of PHA] does not know which landlords we are interviewing. We hope to get your unfiltered on-the-ground perspective, so

your comments will be anonymous and this conversation will be confidential.

I would like to record our conversation because I don’t want to take many notes during the interview.

This way, I can really concentrate on what you have to say. If you want me to turn the recorder off for any reason or at any time, just say so. No one will hear the recording except for the research team and the

assistant who transcribes it. Then, we will erase it. We will take out your name and any other identifying information from the transcript.

Is it okay if I start recording now?  [Get verbal consent].

A.  Background

A1.  How long have you been in the rental property business? How did you get into it?

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A2.  Tell me about the properties you own or manage.

Probes: All properties

How many properties/units do you own/manage?

Where are they?

Tell me all about the buildings (structure, size, quality, condition, number of units)

Tell me all about the locations (neighborhood, neighborhood reputation, proximity to amenities,

demographics of neighborhood)

A3. Tell me about your tenants. How do you usually find tenants?

PROBES: marketing strategy, screening, rejections, applications

A4.  [ Interviewer, ask if not already discussed:] Do you currently or have you in the past rented to people with Housing Choice Vouchers (sometimes known as Section 8 or housing vouchers)?

Interviewer: Determine which term landlord is familiar with and use throughout.

Interview: If Yes (currently or formerly had HCV holder tenant), continue with A5; if No, then skip to

Section B.

A5.  How many units (if any) do you currently rent to Section 8/Housing Choice Voucher holders? About what percentage of all your units does this represent?

A6. Tell me about your thinking when you first decided to accept a Section 8/Housing Choice Voucher holder as a tenant. What went in to that decision?

Probes:

When did that happen (e.g., how long have you been participating in Section 8 / Housing Choice

Voucher program)? Was this a result of PHA outreach? If so, what was effective about this PHA

recruitment/outreach effort? Could this effort have been improved?

If not, what prompted this? Was it a new tenant approaching you? Or an existing tenant asking you to

rent with a voucher?

A7. Do you market specifically to Section 8/Housing Choice Voucher holder tenants? What do you do

differently when marketing to voucher holders?

A8.  What about the physical unit? What types of characteristics make a unit attractive to Section

8/Housing Choice Voucher holders? Do you do certain types of renovations or advertise particular units

or types of units if you’re targeting voucher holders?

A9. How hard is it to get a Section 8/Housing Choice Voucher holder tenant? Do they have a lot of choice in your market?

A10. Tell me about Section 8/Housing Choice Voucher holder tenants. How are they different from

market rate tenants? What are the advantages and disadvantages of renting to voucher holders?

Probes: behavior, upkeep, length of tenure; assurance that you will get the rent on time,

government intervention, tenants, bureaucracy, inspections

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B.  Understanding of SAFMRs

B1. The [fill in PHA name] sets a payment standard every year that helps determine the maximum

amount a housing choice voucher will pay for rent. Before 2011/2013, there was basically one payment

standard for each apartment size (by number of bedrooms) for all of [City], with limited exceptions. In

2011/2013, [fill in PHA Name] started to set payment standards based on ZIP Codes. What’s your

understanding about this change in policy (e.g., that there are now different payment standards for

different areas)?

B2.  If you knew about this change, how did you learn about it? Did the [fill in PHA name] notify you?

How were you notified? Was the notification clear? How did you deal with questions you had about the

new payment standards?

Interviewer: if landlord was aware of change in payment standards ask B3, otherwise skip to B4

B3.  What did [fill in PHA name] explain to you about the purpose of the change in payment standards?

What is your understanding of the intention of the ZIP Code-based payment standards (e.g., to ensure that

voucher holders have access to units in a broader range of neighborhoods, including neighborhoods with

high-performing schools, low crime rates, and other important amenities)?

Interviewer: if landlord is not familiar with payment standards, skip to section C

B4.  How well do the [fill in PHA name] payment standards reflect the market? Are there some types of neighborhoods where the payment standards correctly reflect the market and others where they do not? In

what types of neighborhoods do the payment standards correctly reflect the market? In what types of

neighborhoods do payment standards not reflect the market? Are payment standards too high or too low

in these neighborhoods?

C.  Satisfaction with HCV Program

Now I want to switch gears a little and get your perspective of the Section 8/Housing Choice Voucher

program in general.

C1. PARTICIPATING LANDLORDS: Tell me about your experience with the Section 8/Housing

Choice Voucher program.

Probes: How satisfied are you with the Section 8/Housing Choice Voucher holder program? What are

the major positives and negatives of the program? How have SAFMRs/the change in payment

standards affected your interest in the program? How has your satisfaction/dissatisfaction with the

program changed over the last several years? [SKIP TO D2]

C2.  NON-PARTICIPATING LANDLORDS: Tell me what you know about the Section 8/Housing

Choice Voucher holder program.

Probes: what kind of landlords, tenants, and rental units is the program for? What are the potential

positives and negatives of the program from a landlord’s perspective? What factors would determine

whether a landlord marketed to HCV holders or accepted HCVs? Have you considered marketing to

Section 8/Housing Choice Voucher holder tenants? Tell me more about that.

C3. NON-PARTICIPATING LANDLORDS: Have you considered marketing to tenants with Section

8/Housing Choice vouchers? Tell me more about that.

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Probes: Have you made a decision not to lease to Section 8/Housing Choice Voucher holders or have you simply not been asked to do so? If you have not yet been asked to participate, would you consider

doing so? If not, why not? What changes would be needed to encourage you to do so?

C4.  NON-PARTICIPATING LANDLORDS: Have you had any interactions with [PHA name] or gotten

any outreach from them regarding the Housing Choice Voucher program? Could improvements to the

PHA’s recruitment/outreach efforts change whether you marketed to or agreed to rent to tenants with a

Housing Choice Voucher? If so, what changes would be necessary?

D.  Impact of Changing Payment Standards

D1.  NON-PARTICIPATING LANDLORDS: Under the new policy of the [fill in name of PHA], the

maximum rent subsidy will vary from one part of the city to another. In areas with higher rents, the PHA

will provide a higher rent subsidy and in areas with lower rents, the PHA will provide a lower rent

subsidy. The purpose of this change is to ensure the rent subsidies more closely match local market

conditions. Tell me about how this change might influence whether or not you market or rent to Housing

Choice Voucher tenants?

Interviewer: the rest of this section will only be relevant for participating landlords

D2.  What happened (if anything) when [name of PHA] started using new payment standards? Did you

have units where the payment standard went up? Where the payment standard went down? What share of

units/how many units are in ZIP codes where payment standards went up? Where payment standards went

down?

D3.   For landlords with units in ZIP Codes where payment standards went down: What happened to units you own/manage in ZIP Codes where payment standards went down?

Probes: Did the new payment standard fall below the rent you were charging (or would have charged

if you were planning to raise the rent)? How have you responded to these declines? For units

occupied by HCV holders that have been affected (have had their 2nd annual reexamination); what

action(s) did you take (e.g., maintain or reduce the contract rent, change responsibilities for utilities or other costs, notify the tenant of intent to take units out of HCV program for business reasons)?

D4.   For landlords with units in ZIP Codes where payment standards went up:  What happened to properties you own/manage in ZIP Codes where payment standards went up?

Probes: Was it easier to find renters? Did you add units to the Housing Choice Voucher program in

response to increasing payment standards? Did you change/increase your marketing to Section

8/Housing Choice Voucher holders? Were there any other effects of increasing payment standards on

your business? How did it affect the rents you charge?

D5.  Have you ever made any rent concessions (e.g., charged what you consider below-market rents) for voucher holders? How often? For what reasons?

Probe: Were these concessions different from those offered to other renters, and if so in what way?

D6.  As you probably know, even if the applicable rent falls below the payment standard, the PHA is required to determine if the actual rent being changed is reasonable. These decisions are called “rent

reasonableness determinations.” Do you think the rent reasonableness determinations the PHA has made

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are generally fair and reflective of market conditions? Why or why not? For units where you believe the PHA’s rent reasonableness determination is not reflective of the market, how do you generally respond

(e.g., did this change your decision to rent to a particular household or participate in the program)?

D7.  Do you plan to continue to accept Section 8/Housing Choice Voucher holders as tenants for any

units? If not, why not? If so, which units? All the units you own/manage, or some of them? How do you

determine which units to rent to HCV holders?

E.  Conclusion

E1.  Is there anything else about being a landlord in [City], working with [PHA], or your experience with the Section 8/Housing Choice Voucher program that you think we should know for our research study?

Thank you for your time and participation.

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D.1.2  Draft Interview Guide for HCV Tenants

Introduction

My name is __________ and I am with Quadel Consulting working on a research study for the

Department of Housing and Urban Development. Today we will be talking about your experiences with

the Housing Choice Voucher program and with neighborhoods in the city. We might end up talking about

a few different things, but we will talk mostly about places you’ve lived, houses and apartments you’ve

rented around here, and those you’ve thought about renting or living in.

I have some questions prepared, and you might have some things you want to bring up too. So think of

this as a conversation, rather than an “interview.” You can stop talking at any time. If I raise an issue or ask a question you don’t want to talk about, just say so and we will move on to something else. No big

deal.

I don’t work for [name of PHA]. The [name of PHA] provided us with the contact information for 100

HCV holders in the [City], and you are one of 10 that we will be talking to for the study. After we talk to all of the families, we will combine everyone’s perspectives into a research report that we will send to

HUD that may become available for the general public to read. We may include short quotes or

summaries of individual comments from our conversations with families that receive vouchers in the

report, but nothing you say will be attached to your name or personal information directly. Your

comments will be anonymous. None of your responses will affect your eligibility for assistance.

I would like to record our conversation because I don’t want to take many notes during the interview.

This way, I can really concentrate on what you have to say. If you want me to turn the recorder off for any reason or at any time, just say so. No one will hear the recording except for the research team. Then we

will erase it. We will take out your name and any other identifying information from the written

transcript.

Any questions?

OK, let’s start.

Is it okay if I turn on the recorder now? [ Get verbal consent].

Background/Selection Criteria

Note to interviewer: Prefill this section based on administrative data. Based on this information, assign each household to one of the six household move types in Exhibit 1. Interview questions vary based on

household move type. During the interview, please confirm which category the household belongs to.

Proceed with the interview using the questions appropriate to the household move type.

A.  Voucher receipt year

  Post-SAFMR voucher recipient (2011 or later for Dallas and Plano; 2013 or later for

demonstration PHAs)

a.  Neighborhood type

  Low rent ratio

  High rent ratio

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  Pre-SAFMR voucher recipient (before 2011 for Dallas and Plano; before 2013 for

demonstration PHAs)

b.  Move type and neighborhood type

  Low rent ratio – no move

  Move from a low rent ratio ZIP to another low rent ratio ZIP

  Move from a low rent ratio ZIP to a high rent ratio ZIP

  Multiple neighborhood types and moves (e.g., initial move from a low rent ZIP to a

high rent ZIP then back to a low rent ZIP)

B.  Household type

  Senior (62+) or disabled

  Parent(s) with minor children

Note to interviewer: The goal of the study is to interview households in six categories of Household Move Type (see Exhibit 1). Please check the household type prior to the interview. Interview questions vary according to the Household Move Type.

Exhibit 1: Household Move Type

Interviewer: Enter ‘X’ in

row for correct

household move type

Households who became voucher holders before change to SAFMR

(before 2011 for Dallas and Plano; before 2013 for demonstration PHAs)

Group 1: Same unit – low rent ZIP Code

Group 2: Mover – low to high rent ZIP Code

Group 3: Mover – low to low rent ZIP Code

Group 4: Repeat mover – low to high to low rent ZIP Code

Households who became voucher holders after change to SAFMR (2011

or later for Dal as and Plano; 2013 or later for demonstration PHAs)

Group 5: New HCV holder (since implementation) – low rent ZIP Code

Group 6: New HCV holder (since implementation) – high rent ZIP Code

Note to interviewer: For respondents in Dallas and Plano, use 2011 as the year SAFMRs were adopted.

For all other respondents, use 2013.

A. Housing History

A1. Tell me the story of the place you are living right now.

[Interviewer: if the respondent does not volunteer this information, follow up with ALL of these probes: How did you end up here? What was the main reason you ended up here? Is employment a reason why

you are living here? When did you move here? Who lives with you? Is anyone in your household elderly?

Is anyone in your household disabled?]

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A2. If you had to do it over again, would you have chosen this unit [“house” or “apartment”]? What are some things you like about this [house/apartment]? Tell me what you wish you had known about this

house/unit before you moved in.

A3.  If you had to do it over again, would you have chosen this neighborhood? What are some things you like about this neighborhood? Tell me what you wish you had known about this neighborhood before

moving in.

A4.  Tell me about your housing choice voucher. We understand that you first received a voucher from

[fill in PHA name] in  [YEAR]. Is that correct?

Interviewer: If the respondent has lived at its current address since at least 2010 (for Dallas and Plano) or 2012 (for other PHAs), skip to Question A6. If not, proceed to Question A5.

A5.  I’d like to find out about each place you’ve lived since [interviewer, choose one: 2011 (for

Dallas/Plano) / 2013 (for other PHAs)]. You’ve already told me about your current place. Where did you

live before that?

Probes: How did you end up there? What was the main reason you moved there? When did you

move there? What was the address/ZIP Code/cross streets? Tell me the whole story of how you

left that place. Why did you decide to leave?

Interviewer: Repeat A5 as often as necessary to get all units back to either a) unit prior to first receiving a voucher (may have continued to live there upon receiving voucher) OR b) unit at the time SAFMRs were introduced.

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A6.  I’d like to make sure I have all the details right about the places we’ve talked about.  [Interviewer, please work with the respondent to complete Exhibit 2, Housing History, working backwards from the current unit to previous units. Stop at a) unit prior to their first voucher unit OR b) their unit the year before the time SAFMRs were introduced.]

Location

Description (for

use in identifying

Address/ZIP

Used Housing

unit, e.g., Andrews

Code/cross-

Dates of

Same ZIP Code as  Choice Voucher to  First Unit Rented  First Move Since

St.)

streets

Residence

previous unit?

rent unit?

with a Voucher?  2011/2013 (Y/N)?

Current unit

Prior unit #1

Prior unit #2

Prior unit #3

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B. Understanding of SAFMRs

Interviewer: Questions B1 to B6 are only for voucher holders who initially received their voucher in

2010 or earlier (for Dallas or Plano) or in 2012 or earlier (for other PHAs), before the change to

SAFMRs.

For voucher holders who received their vouchers more recently, after the transition to SAFMRs had

already occurred, SKIP TO B7:

READ TO RESPONDENTS: Before starting the next set of questions, I’m going to describe to you what I understand to be a change in the policies of the  [fill in PHA name] that has occurred since you first got your voucher. You may already know this, and if so, please be patient as I walk through this explanation.

The  [fill in PHA name]  sets policies for the housing choice voucher program that determine the maximum amount a housing choice voucher will pay for rent for a 1 bedroom, 2-bedroom, 3-bedroom or larger

apartment or house. These maximums are called “payment standards.” In the past, the  [name of PHA] set one payment standard for each apartment or house size (number of bedrooms) for all of their vouchers. 1

Starting in [ 2011/2013],  [fill in PHA name]  began setting different payment standards for different ZIP

Codes in the [name of metropolitan area]. So now for example, a 2-bedroom apartment can have different

payment standards depending on the zip code where it is located.

B1.  Are you aware that you can rent more expensive apartments in some parts of the city than in others?

(Probes: How did you find out about this new rent policy? Did the PHA notify you of this change? If so,

how were you notified? What did you understand from the notification? Was the notification clear?)

B2.  Do you understand why the  [fill in PHA name]  set the new rent policy? (Interviewer: probe to get at the respondent’s understanding of the intention of the ZIP Code-based payment standards)?

B3.  Can you tell me what you remember about what the PHA told you about the new rent policy when

you attended your annual recertification in  [2011/2013]?  This was right after the new rent policy had been adopted. (Probes: Was any additional information given to you? What additional information did

you receive? What kind of things did they give you (e.g., handouts, verbal explanation, referral to a

website)? Was information provided to you any other way, such as at a tenant meeting or in a newsletter?)

B4. When you first learned about the new rent policy, did you find it confusing at all? If so, what was confusing? (Probe: Were your questions about the new rent policy answered? When and how were your

questions answered (during recertification, during a phone call, with information on the PHA website,

other)? Are you still confused at all about the new rent policy?)

B5.  Did you get information about the new rent policy from sources other than the PHA, such as other voucher holders? What did you learn from these other sources?

B6.  Could the  [fill in PHA name] have done anything differently to make it easier to understand how the new rent policy works and what it was intended to do? If so, what?

1 The study team will verify that the PHA had not used its authority to have multiple payment standards prior to adopting SAFMRs.

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Interviewer: Questions B7 to B11 are only for voucher holders who initially received their voucher in

2011 or later (for Dallas or Plano) or in 2013 or later (for other PHAs), so after the change to SAFMR.

For voucher holders who received their vouchers more recently, after the transition to SAFMRs had

already occurred, SKIP TO Section C]:

READ TO RESPONDENTS: Before starting the next set of questions, I’m going to describe to you what I understand to be a policy of  [fill in PHA name]. You may already know this, and if so, please be patient as I walk through this explanation. The  [fill in PHA name] sets policies for the housing choice voucher program that determine the maximum amount a housing choice voucher will pay for rent for a 1 bedroom,

2-bedroom, 3-bedroom or larger apartment or house. These maximums are called payment standards. The

[fill in PHA name] sets different payment standards for different ZIP Codes in the [name of metropolitan area], so for example, a 2-bedroom apartment can have different payment standards depending on the ZIP

Code where it is located.

B7. Are you aware that you can rent more expensive apartments in some parts of the city than in others?

(Probe for whether respondent understood that payment standards varied by ZIP Code.) Did you learn

about this from the PHA, from other sources, or both? If from other sources, what were these other

sources?

B8. Do you find this policy confusing in any way? If so, what? (Probes: Did you have any questions

about the policy when you first learned about it? Were your questions about the rent policy answered?

When and how were your questions answered (during re/certification, during a phone call, with

information on the PHA website, other)?)

B9. Did you get information about this rent policy from sources other than the PHA, such as other

voucher holders? What did you learn from these other sources?

B10. Could the [fill in PHA name] have done anything differently to make it easier to understand how the rent policy works and what it is intended to do? If so, what?

C. MOVE EXPERIENCE AND SEARCH PROCESS

Interviewer: Questions C1 to C7 are only for voucher holders who initially received their voucher in

2010 or earlier (for Dallas or Plano) or in 2012 or earlier (for other PHAs), so before the change to

SAFMR, AND who have moved (in Exhibit 1, “Group 2: Mover – low to high rent ZIP Code,” “Group

3: Mover – low to low rent ZIP Code, or “Group 4: Repeat mover – low to high to low rent ZIP Code”).

For voucher holders who received their vouchers more recently (Groups 5 and 6), after the transition

to SAFMRs had already occurred, SKIP TO C8:

I’d like to find out more about your [Interviewer: choose the first permanent move  (e.g., not short-term or temporary) since SAFMR went into effect in their location] decision to move. This would have been in

[date] from  [location 1 to location 2].

C1. Tell me all about the move from [location 1 to location 2, e.g., Stratton Street to Walnut Ave.].

[Interviewer: skip any topics that were already discussed in Section A.]

Probes:

•  Did you move to a different ZIP Code?

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•  Was the rent higher/lower/the same in location 2?

•  Why did you move? Tell me all the things that made you want to/need to move:

−  Required by PHA or landlord

−  Required by personal circumstances (health, education, child care, work, family, disability,

other)

−  Housing unit (quality of housing unit, size of housing unit, other)

−  Rent was going to increase if I stayed

−  Couldn’t afford the rent anymore

−  For a better opportunity (lower crime, better environment, better schools, better access to

jobs, to be closer to work, better access to transportation, better access to health care, better

access to other community amenities (what?), closer to friends, closer to family, other)

•  What would you say was the most important reason why you decided to move? ( Alternate

question, if the most important reason is hard for the respondent to discern: Of all the reasons

you decided to move, which do you think might have been the biggest reason?)

Interviewer: (Check prior to interview)

   Household moved from a low rent ratio area to a high rent ratio area after SAFMR (Group 2) –

ask questions C2-C4 then skip to question C6;

   Household moved from a low rent ratio area to another low rent ratio area (may have stayed in

the same ZIP code) after SAFMR (Group 3) – skip to C5;

   Household moved from a low rent ratio area to a high rent ratio area and back to a low rent

ratio area after SAFMR (Group 4) – skip to question C7.

C2.  Group 2 Households with low rent ratio to high rent ratio move: Did the fact that your voucher would allow you to afford a higher rent unit in your new location affect your decision to move? How

important was this to your decision to move?

C3.  Group 2 Households with low rent ratio to low rent ratio move: Do   you think neighborhoods with higher rents are generally “better” for you and your family? If so, how are they better? Schools? Access to jobs? Safety/crime rate? Environmental quality? Housing quality? Access to other amenities?

Interviewer: If the respondent says no or seems confused about the question, ask the question again,

this time filling in the name of the specific neighborhood in place of “neighborhoods with higher

rents.” Do you think [neighborhood name] is better for you and your family than the neighborhood

you lived in before that?

C4.  Group 2 Households with low rent ratio to high rent ratio move: Are there any negatives of living in higher-rent areas like  [insert neighborhood name] for you and your family? If so, what are those negatives?

C5.  Group 3 Households with low rent ratio to low rent ratio move: Did you consider the fact that your voucher would allow you to afford a higher rent unit in some ZIP Codes when you moved to [ current unit location description, e.g., State Street]? Did that affect your search for a new unit or your decision to move in any way? If so, how?

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C6. Tell me a little about the neighborhood in [ prior unit #1 location description]. What did you like the best about that neighborhood? What did you like the least? How does the neighborhood in [ current unit]

compare to the neighborhood in [ prior unit # location description]? How is/was it better? How is/was it worse? What do you like most about your current neighborhood? What do you like least?

C7. Group 4 Repeat Mover Households with low to high to low rent ratio moves: We have already

talked about your move to [location description of current unit]. Now I would like to ask you to about

your move to [prior unit #1 location description].

•  When you moved in  [move year] to  [prior unit #1 location description, e.g., Washington Street], did you move to a different ZIP Code? If yes, do you know if rents there were generally higher or

lower? Tell me all the things that made you want to move.  If this is the last move (to the current

unit),  ask: Are you happy with your decision to move/the location of your new unit? What do you like/dislike about it? Do you plan to move within the next year?

•  Now I’d like to ask you about your move in to [prior unit #2 location description.] When you

moved in  [move year] to  [prior unit #2 location description, e.g., Appleton Road], did you move to a different ZIP Code? If yes, was it higher or lower rent? Tell me all the things that made you

want to move.  If this is the last move (to the current unit), ask: Are you happy with your decision to move/the location of your new unit? What do you like/dislike about it? Do you plan to move

within the next year?

Interviewer: Questions C8 to C10 are only for voucher holders who initially received their voucher in

2011 or later (for Dallas or Plano) or in 2013 or later (for other PHAs), so after the change to SAFMR

AND who have moved (in Exhibit 1, “Group 5: New HCV holder – low rent ZIP Code,” and “Group 6:

New HCV holder – high rent ZIP Code”).

For voucher holders who initially received their voucher earlier and for voucher holders who have

never moved, skip to Section D.

C8. Can you tell me about when you first got your voucher? Did you decide to move right away, or did you decide to stay where you were and use your voucher to lease in place? How did you make that

decision? What were the main things that went into that decision?

When you (eventually) moved, tell me about that decision.

Probes:

•  Did you move to a different ZIP Code?

•  What were rents like in the place you moved to? Were they higher, lower, or about the same as the

rents in the neighborhood you lived in before receiving the voucher?

•  Tell me all the things that made you want to/need to move:

−  Required by PHA or landlord

−  Required by personal circumstances (health, education, child care, work, family, disability, other)

−  Housing unit (quality of housing unit, size of housing unit, other)

−  Couldn’t afford the rent anymore

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−  Voluntary for a better opportunity (lower crime, better environment, better schools, better access

to jobs, better access to transportation, better access to health care, better access to other

community amenities (what?), closer to friends, closer to family, other)

•  What would you say was the most important reason why you decided to move?  (Alternate question, if

the most important reason is hard for the respondent to discern: Of all the reasons you decided to move, which do you think might have been the biggest reason?)

C9. Did the PHA’s policy of having different payment standards for different neighborhoods affect your decision to move? How important was this? Do you think you would have moved even without the higher

payment standards? Would you have moved to the same apartment?

C10. Tell me a little about the neighborhood in  [prior unit #1 location description]. What did you like the best about that neighborhood? What did you like the least? How does the neighborhood in  [current unit]

compare to the neighborhood in  [prior unit # location description]?  How is/was it better? How is/was it worse? What do you like most about your current neighborhood? What do you like least?

Interviewer: Questions C11 to C13 are only for voucher holders who have moved according to the

Housing History in Section A. These voucher holders can be in any of Groups 2 through 6. To

facilitate the discussion, start by looking at a map of the area with the respondent that shows

neighborhoods.

I’d like to find out more about your search process when you decided to move from  [location 1] to

[location 2] [Interviewer: choose the first permanent move (e.g., not short-term or temporary) since

SAFMR went into effect in their location]. This would have been in  [date]  from [ location 1 to location 2].

C11. Tell me about your search for a new unit.

Probes:

•  How did you go about searching for a house or apartment [at the time you moved to location 2]?

•  Looking at the map, show me the places you looked at during your search. How did you decide where

to look?

•  Looking at the map, were there neighborhoods you wanted to look at but didn’t? What were the

reasons you didn’t look at those neighborhoods? Can you tell me about them? What were all the

things that made you want to look at these places?

•  [ Interviewer: prior to the interview, identify higher-rent neighborhoods in the PHA’s jurisdiction. ]

Did you  consider looking for a unit in [ names of specific areas] with higher rents? Why/why not?

•  Did you  actually look for a unit in [ names of specific areas] with higher rents? Why/why not?

•  Were you familiar with [ names of specific areas] with higher rents? Have you been there before? Did you know how to get there?

C12.  What assistance did the [ fill in PHA name] give you in helping you search for a new unit? (Probe for list of participating landlords, information about neighborhoods, maps, search tips, counseling from PHA

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staff?) Did this assistance include helping to identify or pursue units in ZIP Codes with higher payment

standards? If so, how?

C13. Did the landlord you now rent from know about the HCV program before you moved in? Had

he/she rented to HCV holders in the past? If he/she was unfamiliar with the program, were you able to

explain it? Did the landlord give you any trouble about having a voucher? If so, what kind of trouble?

Before finding your current unit, did you contact any landlords who refused to accept the voucher? If so,

did they tell you why they refused?

D. Experience with SAFMRs

Interviewer: Questions D1 to D2 are only for voucher holders who initially received their voucher in

2010 or earlier (for Dallas or Plano) or in 2012 or earlier (for other PHAs), so before the change to

SAFMR (in Exhibit 1, Groups 1-4).

For voucher holders who received their vouchers more recently, after the transition to SAFMRs had

already occurred (Groups 5 and 6), SKIP TO E1:

D1.  In the last few years when your lease/housing contract has come up for an annual review, do you recall whether the payment standard for the unit you were living in ever went down? (Probe if needed to

clarify: were you told by your landlord that you would need to pay more in rent in order to stay in your

unit because of a change in the PHA’s rent policy?) Y/N If so, by how much? Which unit were you living

in at the time?  [IF YES, continue to D2. IF NO, skip to D6.]

D2.  If the payment standard went down: When was that? It looks like you were living in [ unit location description] (from Housing History) when the payment standard went down. I’m interested in knowing how that change in payment standard might have affected your rent or anything else about your

experience. Could you tell me the whole story about what happened at that time? [ Interviewer: record

whether the resident seemed to understand the questions and have confidence in their answers. ]

Prompts:

•  Did the amount you had to pay for rent change? Did it go up or down?

•  Did other housing-related costs change (e.g., payments for utilities or lawn service)? Did these go up

or down? What was that like for you? What effect did this have for you and your family?

•  Do you know whether the amount of rent the landlord received from the [insert PHA name] change?

−  Tell me about whether you discussed this with the landlord? How did this happen? Did the

landlord ever suggest that you should move or leave?

•  Did you decide to move?

E. STAYERS – people who have NEVER MOVED

Interviewer: Section E is only for voucher holders who have not moved according to the Housing

History in Section A, Group 1 and some members of Groups 5 and 6.

E1. You said you  [leased in place/have not moved]  since  [SAFMRs were implemented/you first received your housing voucher].

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E2. Did you/do you think about moving since you received your voucher? If so, approximately when was this?

IF Yes (considered moving), continue to E3, otherwise skip to E8:

E3.  What made you decide to stay here?

Probes:

•  Found a unit, but it didn’t work out (why? Related to inspection, or rent reasonableness, or other?)

•  Couldn’t find a landlord willing to rent to you

•  Ran out of time

•  Changed mind for other reasons (what?)

E4. Were moving-related expenses such as a security deposit a factor in your decision not to move? How important was this factor?

E5. If there is an elderly and/or disabled member of your household, was this a factor in your decision not to move? If so, how did it factor into your decision?

E6.  If there is a school-age child in your household, was this a factor in your decision not to move? If so, how did it factor into your decision?

E7.   [For respondents who have thought about moving]  Did you take any specific steps to search for a unit? If so, what kinds of things have you done to search for a new unit?

  Look at apartment listings

  Visit the neighborhood

  Visit units

  Talk to friends or family about moving

  Call landlords

  Other (what:_______________________)

  Nothing

Experience with Search Process

If respondent reported making search efforts in E7, continue to E8, otherwise skip to E14:

E8. What made/makes you want to consider a move? Were higher payment standards in some

neighborhoods a factor? (Probes: Schools? Access to jobs? Safety/crime rate? Environmental quality?

Housing quality? Access to other amenities? Other?)

E9.  Has the  [fill in PHA name] given you any assistance in helping you search for a new unit? Did this assistance include helping to identify or pursue units in [ names of specific areas with higher rents]? If so, how?

E10.  Looking at the map, did you search for a new unit in [ names of specific areas with higher rents]?

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IF Yes (searched in higher-rent ZIP Code), continue to E11, otherwise skip to E13:

E11. What was your experience with searching for a unit in [ names of specific areas with higher rents]?

What were your primary sources of information about available units?

E12.  Did the landlords you contacted know about the HCV program? Had any of the landlords rented to HCV holders in the past? If the landlords were unfamiliar with the program, were you able to explain it?

Did the landlords give you any trouble about having a voucher? Did the landlords accept the voucher?

IF No (did not search in higher-rent ZIP Code):

E13. What kept you from searching for a unit in [ names of specific areas with higher rents]? Where did you search instead? Why?

Future move decisions

E14. Are you considering a move in the future? Y/N

E15.   If Yes, considering moving: What are your reasons for considering a move in the future?

Probes:

•  Will be required by PHA or landlord

•  Will be required by personal circumstances (health, education, child care, work, family,

disability, other)

•  Housing unit (quality of housing unit, size of housing unit, other)

•  Purely voluntary for a better opportunity (lower crime, better environment, better schools, better

access to jobs, better access to transportation, better access to health care, better access to other

community amenities (what?), closer to friends, closer to family, other)

•  To move to a better neighborhood with higher payment standards

F. Demographics

I have just a few final questions for you to make sure we have all of our details right.

F1.  How do you identify yourself?

  White

  Black

  Mixed

  Hispanic

  Asian

  Other

F2.  Tell me about how far you got in school.

Probe:

−  High School completion? GED?

−  Training programs?

−  College classes?

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F3.  Are you currently working? Did you work for pay last week? If yes, about how many hours per week do you usually work? How much are you paid by the hour (or if not paid by hour ask for wages for other

time period). If you are not currently working, when would you say was the last time (month/year) when

you worked for pay?

F4.  Who lives in your household with you? (number of adults and children) What are the ages of your children?  [to identify special considerations in the decision to move]

F5. Interviewer: Indicate gender of respondent: ________.

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D.1.3  Draft Interview Guide for PHAs

A.1 Phase 2: PHA Background and Experience with SAFMR Transition

A.1.1  Experience with Transition to SAFMRs

I’d like to start by asking about you and your experience with the SAFMR demonstration.

1.   [IF THE PERSON IS NEW TO THE STUDY] What is your background and experience with the

organization? [IF PREVIOUSLY INTERVIEWED] Have you had any changes in your role with the

agency since we met last year?

2.   [ Begin by summarizing what we understood from the Phase 1 site visit. ] In general, how has your experience with the transition to SAFMRs changed since our last visit [on  date]? What, if anything, has become more difficult? What, if anything, has become easier/more routine?

3. Are there any changes outside of the PHA that may have influenced your HCV program over the last year? For example, changes in state or local housing policy?

A.1.2  Background Information

4.  [Refer to pre-populated table, PHA Background Information]  Has anything changed since our

conversation last year in terms of basic background information on your PHA?

PHA Background Information

Data item

Explanation

Cities/counties served by the HCV program:

(List cities or counties)

Jurisdiction in square miles:

(Miles)

Current payment standard by ZIP code

(Complete payment standard schedule for all

bedroom sizes and all ZIP codes for the current

year)

Number of tenant-based vouchers under lease at end of last FY by

(Number of regular, VASH, FUP, enhanced

type

vouchers, etc.)

Current number of tenant-based vouchers under lease:

(Number)

Budget utilization rates:**

(Percent = HAP dollars spent / HAP budget

allocated)

At end of FY 2016 (if available):

At end of FY 2015:

At end of FY 2014:

At end of FY 2013:

At end of FY 2012:

At end of FY 2011:

At end of FY 2010:

At end of FY 2009:

Current budget utilization rate:

Unit utilization rates:**

(Percent = units under lease / units allocated)

At end of FY 2016 (if available):

At end of FY 2015 (if available):

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Data item

Explanation

At end of FY 2014:

At end of FY 2013:

At end of FY 2012:

At end of FY 2011:

At end of FY 2010:

At end of FY 2009:

Current unit utilization rate:

Software used to submit PIC data:

(Name of software)

*Special vouchers include: homeownership vouchers, VASH, FUP, Mainstream 1, Mainstream 5, non-elderly

disabled, tenant protection, disaster voucher.

**Source: VMS (Voucher Management System)

5. How has the housing market changed since our visit last year (prompts: vacancy rates, rents)? How are these changes affecting voucher holders?

6.  [Refer to table, PHA Background Information]  Based on our information, it looks like your budget utilization rate from 2009-2016 has [stayed the same, gone up, gone down]. Is this right? What do you

think are the primary reasons for this change [for the stability in this rate over time]? [probe for other reasons]

[If the respondent volunteers SAFMRs] How do you think the switch to SAFMRs may have

affected your budget utilization rate? Did SAFMRs have a large effect as compared to other factors?

What factor had the largest effect? Have any impacts related to SAFMRs dissipated over time? If not,

why not?

[If the respondent does not volunteer SAFMRs] Do you think the switch to SAFMRs may have

affected your budget utilization rate? Why or why not? [If the respondent agrees they may have had an

effect, ask whether the effect of the switch to SAFMRs was large or small compared to other factors.]

What factor had the largest effect on budget utilization rates over this time? Have any impacts related to SAFMRs dissipated over time? If not, why not?

7.  [Refer to table, PHA Background Information]  Based on our information, it looks like your unit utilization rate from 2009-2016 has [stayed the same, gone up, gone down]. Is this right? What do you

think are the primary reasons for this change over time [for the stability in this rate over time]? [probe for other reasons]

[If the respondent volunteers SAFMRs] How do you think the switch to SAFMRs may have

affected your unit utilization rate? Did SAFMRs have a large effect as compared to other factors? What

factor had the largest effect? Have any impacts related to SAFMRs dissipated over time? If not, why not?

[If the respondent does not volunteer SAFMRs] Do you think the switch to SAFMRs may have

affected your unit utilization rate? Why or why not? [If the respondent agrees they may have had an

effect, ask whether the effect of the switch to SAFMRs was large or small compared to other factors.]

138

What factor had the largest effect on unit utilization rates over this time? Have any impacts related to

SAFMRs dissipated over time? If not, why not?

A.1.3  Participant Experience (from the PHA perspective)

8. Is there any remaining confusion among families who have been awarded a voucher about what rent

they can afford in different neighborhoods under the SAFMR policy? How has voucher holders’

understanding of the policy changed over the last year? Have you changed your approach to explaining

the policy? If so, how?

9.  Have you seen any change since last year in where voucher recipients search? Are they more likely to search in areas of opportunity? Have you seen changes in where they lease up? Are they more likely to

lease up in areas of opportunity? If not, why not?

10.  Have you made any changes since last year in search assistance to voucher recipients in areas where payment standards increased after SAFMRs were implemented? Have you made any changes since last

year in assistance to households that live in areas where the payment standard decreased after SAFMRs

were implemented?

11.  Has your policy regarding the amount of time voucher recipients have for their initial search changed since last year? To what do you attribute any changes in the time needed for voucher recipients’ initial

search (market, SAFMRs, other)?

12.  Has it become more or less difficult in the past year for participants to find units to rent that meet program requirements? If so, why? To what do you attribute changes (market, SAFMRs, other)? If not,

why not? Does this vary by neighborhood?

13.  [Refer to pre-populated table, Voucher Success Rates] According to the information you provided prior to our visit, voucher success rates from 2012-2016 are in the table below. Are these correct? What

factors do you think account for the changes in voucher success rates over this period? [probe for other

reasons, e.g., sequestration, change in market conditions]

[If the respondent volunteers SAMFRs] How do you think the switch to SAFMRs may have

affected voucher success rates? Did SAFMRs have a large effect as compared to other factors? What

factor had the largest effect? Has the impact of SAFMR been different for new voucher recipients and

existing voucher holders? If so, what are the differences? Have any impacts related to SAFMRs dissipated

over time? If not, why not?

[If the respondent does not volunteer SAFMRs] Do you think the switch to SAFMRs may have

affected voucher success rates? Why or why not? [If the respondent agrees they may have had an effect,

as whether the effect of the switch to SAFMRs was large or small compared to other factors.] What factor

had the largest effect on budget utilization rates over this time? Have any impacts related to SAFMRs

dissipated over time? If not, why not?

139

Voucher Success Rates*

PHA Fiscal Year

PHA estimate of success rate

2012

2013

2014

2015

2016

*The success rate is defined as the percentage of new voucher holders that successfully lease a qualifying unit in the program.

14. Has there been a change in the last year in the rate at which existing HCV participants move? If so, what effect is that? Is this different for different types of HCV participants (e.g., disabled, elderly, those with children)? Has the rate of moving increased in particular types of neighborhoods, such as areas

where SAFMRs have gone up or gone down? If not, why not? To what do you attribute this (SAFMRs,

change in administrative procedures, market, other)?

15. Has there been a change in the last year in where existing HCV participants who choose to move end up leasing up? If so, what effect is that? Is this different for different types of HCV participants (e.g., disabled, elderly, those with children)? If not, why not? To what do you attribute this (SAFMRs, change

in administrative procedures, market, other)?

16. Thinking about people who have moved to opportunity areas since the switch to SAFMRs, have

households generally stayed there? If so, why? To what extent do households move to opportunity areas

and then later move back to lower-rent areas?

17.  Thinking about tenants who were in ZIPs where payment standards have decreased due to SAFMRs,

how have they responded? Have they left the program? Have their landlords accepted lower rents? Have

their out-of-pocket costs increased – if so, how and for what reasons? How else have they responded?

What is the frequency of each of these outcomes?

18. Has there been a change over the past year in the number of involuntary moves as a result of

SAFMRs? If so, how has it changed and what are the reasons for this?

19.  Has your experience over the past year led you to draw any new conclusions about how SAFMRs

may have impacted residents? If so, what are those conclusions? What experience led you to draw these

new conclusions?

A.1.4  Landlord Experience (from the PHA perspective)

20.  Over the last year, has the PHA done any additional outreach to landlords in opportunity areas? If so, why? What type of outreach? How has this changed from prior outreach? If not, why not?

21.  Are there remaining challenges to explaining SAFMRs to landlords? Have complaints from landlords related to SAFMRs gone down or up over the last year? Why do you think that is? How much PHA staff

time have landlord complaints related to SAFMRs taken to address over the last year?

22.  Have you added new landlords to the program over the last year in areas where payment standards have increased? In areas where payment standards have decreased? If so, how many? Is this more or less

than you added in other years since introduction of SAFMRs? Why do you think that is?

140

23.  Have you lost landlords over the last year in areas where payment standards decreased? In areas where payment standards have increased? If so, how many? Is this more or less than you lost in other

years since introduction of SAFMRs? Why do you think that is?

24.  What other reactions have landlords had over the last year in areas where payment standards

decreased? Have landlords in areas experiencing a sizable decrease in payment standards renewed their

leases at lower rents? Shifted costs to tenants (if so, what)? Why or why not?

25.  Have you noticed any changes in neighborhood development patterns that might be related to

SAFMRs since they were implemented? For example, more development in higher-opportunity areas and

less development in lower-opportunity areas? Or changes in the types of development? If so, what

changes have you noticed? Are these changes related to SAFMRs in any way? For example, do SAFMRs

affect the development of RAD, tax credit, and other properties? If so, how? Have you observed or heard

that SAFMRs are affecting private development? If so, how? How big are these effects?

A.2 Phase 2: PHA Costs of SAFMR Transition

A.2.3  Potential Impacts on Systems and Admin Plans from Implementing SAFMRs

26.   Interviewer: The table “Potential one-time impacts” should be pre-populated with information from site visit 1.  Column 1 summarizes our understanding of one-time costs from our last site visit. Please review our summary to see if the figures match your general understanding of the one-time costs of

moving to SAFMRs. If you think any of the numbers are incorrect, we’d appreciate it if you would

correct the table so we have as accurate an understanding as possible. Have any additional one-time

expenditures been required since last year because of SAFMRs? If so, describe these modifications and

complete the table [Potential one-time impacts]. [ Note to interviewer: Confirm with the PHA staff member that expenditures and staff hours are directly related to SAFMRs. Leave out expenditures and staff hours not directly related to SAFMRs. ]

Potential one-time impacts

SAFMR-related

Site visit 1 review

Additional or corrected information

change

Total cost

Staff hours

Titles of

expenditures

reported

Additional/Corre Additional/corre

staff

reported during  during site

cted cost

cted staff hours  performing

site visit 1

visit 1

expenditures

needed

work

PHA

$_______________

$______________

Administrative

_

__

Plan

Examples of

Examples of

expenditures:

expenditures:

PHA Plan

$_______________

$______________

_

__

Examples of

Examples of

expenditures:

expenditures:

PHA system of

$_______________

$______________

record

_

__

Examples of

Examples of

expenditures:

expenditures:

141

Other automated  $_______________

$______________

tools:  list (e.g.,

_

__

rent

Examples of

Examples of

reasonableness)  expenditures:

expenditures:

______________

__

Other automated  $_______________

$______________

tools:  list

_

__

______________ Examples of

Examples of

__

expenditures:

expenditures:

Other automated  $_______________

$______________

tools:  list

_

__

______________ Examples of

Examples of

__

expenditures:

expenditures:

For impacts where there has been a change since last year, please describe these.

A.2.4 Potential Transitional Impacts from Implementing SAFMRs

27.  Interviewer: The table “Potential transitional impacts” should be pre-populated with information from site visit 1.  Column 1 summarizes our understanding of transitional costs from our last site visit.

Transitional costs are costs expected to be incurred during the 1-2 year period of transition to SAFMRs.

Please review our summary to see if the figures match your general understanding of the one-time costs

of moving to SAFMRs. If you think any of the numbers are incorrect, we’d appreciate it if you would

correct the table so we have as accurate an understanding as possible. Have any additional transitional

expenditures been required since last year because of SAFMRs? If so, describe these corrections and/or

additional expenditures and complete the table. [ Note to interviewer: Confirm with the PHA staff member that expenditures and staff hours are directly related to SAFMRs. Leave out expenditures and staff hours not directly related to SAFMRs. ]

Potential transitional impacts

Site visit 1 review

Additional or corrected information

Total cost

Staff hours

expenditures

reported

Additional or

Additional/

Titles of staff

SAFMR-related

reported during

during site

corrected cost  corrected staff

performing

change

site visit 1

visit 1

expenditures

hours needed

work

Process of

$____________

$____________

establishing

Examples of

Examples of

payment standards  expenditures:

expenditures:

Process of

$____________

$____________

determining rent

Examples of

Examples of

reasonableness

expenditures:

expenditures:

data

Requests for CRAs  $____________

$____________

or extended

Examples of

Examples of

contract rent

expenditures:

expenditures:

negotiations

142

Additional requests  $____________

$____________

from voucher

Examples of

Examples of

holders for

expenditures:

expenditures:

extended search

time

HQS Inspector

$____________

$____________

productivity

Examples of

Examples of

expenditures:

expenditures:

PHA

$____________

$____________

communication and  Examples of

Examples of

outreach strategy

expenditures:

expenditures:

and materials

Support for tenants  $____________

$____________

Examples of

Examples of

expenditures:

expenditures:

Support for

$____________

$____________

landlords

Examples of

Examples of

expenditures:

expenditures:

Quality assurance

$____________

processes

Examples of

expenditures:

Other:  list

$____________

$____________

________________  Examples of

Examples of

expenditures:

expenditures:

For impacts where there has been a change since last year, please describe these.

28. Have the impacts of SAFMRs discussed during Site Visit 1 dissipated over time? How and why?

29.  For PHAs not known to be administering a mobility program: Have you started a mobility program in the last year? If so, how was this related to the shift to SAFMRs?

30.  For PHAs administering a mobility program: How have SAFMRs affected your mobility program?

Has the switch to SAFMRs changed the level of effort associated with providing support for tenants (e.g.,

encouraging them to move to opportunity areas, helping tenants understand the implications of reductions

in payment standards in some areas)?

31.  What other costs have you incurred as a result of the transition to SAFMRs? Have impacts of

SAFMRs been permanent, or have they dissipated over time? What additional costs, if any, have gone up

on a permanent basis?

32. Have any costs decreased as a result of the transition to SAFMRs? (E.g. rent reasonableness costs?) If so, what costs are these? How and why have they changed?

A.2.5  Other Program Costs

33.  Interviewer: See the pre-populated table, “Per-unit HAP costs.”  Based on our information, it looks like your per-unit HAP costs from 2012-2016 have [stayed the same, gone up, gone down]. Is this right?

How do you think SAFMRs have affected per-unit HAP costs? Have changes been a result of changes in

contract rents for voucher units, changes in payment standards, changes in household income or a

combination of these? Please explain. What other factors have affected per-unit HAP costs over the same

time period? How important are these factors relative to SAFMRs?

143

Per-unit HAP costs, 2 bedroom

Abt estimate of per-unit HAP

Abt estimate of per-unit

PHA Fiscal Year

costs (2- bedroom units)

HAP costs (all units)

2012

2013

2014

2015

2016

34.  Have any new staff been hired specifically to handle increased workload related to SAFMRs? To

perform what function(s)? When was this person(s) hired? Full-time or part time? What is their total

compensation (annual salary plus fringe)?

A.2.6  Conclusion

35. Have there been any other changes in PHA processes or procedures as a result of SAFMRs? If so,

what are the cost implications of these changes?

36.  What advice do you have for other PHAs implementing SAFMRs? Do you think the benefits of

adopting SAFMRs have outweighed the costs? If so, why?

144

Appendix E. Chapters 4, 5, and 7 Cluster Analyses

Exhibit E-1: Comparison Cluster Table

Small Area Fair Market Rent Public Housing

Agency

Comparison Cluster(s)

Laredo

Cluster 2

Mamaroneck

Cluster 4

Chattanooga

Cluster 5

Cook County

Cluster 6

Long Beach

Cluster 7

Dallas

Clusters 5 and 6

Plano

Clusters 5 and 6

Exhibit E-2: Rental Units and ZIP Codes by Rent Ratio, by Comparison Cluster (Corresponds to

Exhibit 4-1)

Rent Ratio

n

< 0.9

0.9–1.1

> 1.1

Units (%)

1,469,289

314,731

21%

814,382

55%

340,176

23%

Cluster 2

ZIP Codes (%)

691

170

25%

342

49%

179

26%

Units (%)

1,155,434

251,473

22%

496,709

43%

407,363

35%

Cluster 4

ZIP Codes (%)

367

70

19%

153

42%

144

39%

Units (%)

6,654,848

1,479,591

22%

3,775,731

57%

1,399,525

21%

Cluster 5

ZIP Codes (%)

2020

457

23%

997

49%

566

28%

Units (%)

2,291,603

659,636

29%

1,118,690

49%

513,276

22%

Cluster 6

ZIP Codes (%)

673

154

23%

311

46%

208

31%

Units (%)

680,520

251,976

37%

255,185

37%

173,358

25%

Cluster 7

ZIP Codes (%)

182

55

30%

69

38%

58

32%

Notes: Analysis data set includes all ZIP Codes in public housing agency service areas where Small Area Fair Market Rents are implemented.

Percentage of total counts for each row in parentheses.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Smal  Area Fair Market Rents; 2012 American Community Survey 5-year estimates (total rental units)

145

Exhibit E-3: Share of Rental Units Below SAFMR and Metropolitan Area FMR, by Comparison Cluster (Corresponds to Exhibit 4-3)

Lower Rent Ratio ZIP Codes (Rent Ratio < 0.9)

100

80

unitstal 60

l ren

FMR

40

83

of al

SAFMR

71

78

68

67

e

46

20

41

41

41

46

Shar

0

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Moderate Rent Ratio ZIP Codes (0.9 < Rent Ratio < 1.1)

100

80

unitstal 60

l ren

FMR

40

of al

SAFMR

67

e

54

56

52

53

49

51

47

49

58

20

Shar

0

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Higher Rent Ratio ZIP Codes (Rent Ratio >1.1)

100

80

unitstal 60

l ren

FMR

40

of al

SAFMR

68

e

63

69

76

55

57

20

Shar

35

37

27

33

0

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units)

146

Exhibit E-4: Comparison of Total Units With Rents Below the SAFMR and Metropolitan Area FMR, by Comparison Cluster (Corresponds to Exhibit 4-5)

Total Units With Rents Below

FMR, All ZIP Code Rent Ratios

Difference

Percentage

(1,000s)

(1,000s)

Change, SAFMR

Versus FMR

SAFMR

FMR

Cluster 2

798

780

– 18

2%

Cluster 4

621

625

4

– 1%

Cluster 5

3,145

3,227

82

– 3%

Cluster 6

1,171

1,186

15

– 1%

Cluster 7

395

481

85

– 18%

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Note: Analysis data set includes all ZIP Codes in public housing agency service areas where SAFMRs are implemented.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units)

Exhibit E-5: Difference in Units With Rents Below SAFMR and Metropolitan Area FMR as a

Percentage of Units With Rents Below FMR by Rent Ratio, by Comparison Cluster (Corresponds

to Exhibit 4-6)

20%

17%

15%

16%

15%

12%

10%

7%

ce

5%

3%

en

< 0.9

0%

iffer

0%

0.9–1.1

t d

-1%

> 1.1

cen

-2%

-2%

-2% -2%

-2%

-5%

Total

Per

-5%

-10%

-10%

-12%

-15%

-15%

-14%

-19%

-18%

-20%

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Note: Analysis data set includes all ZIP Codes in public housing agency service areas where SAFMRs are implemented.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units)

147

Exhibit E-6: Distribution of Units With Rents Below Applicable FMR by ZIP Code Rent Ratio, by Comparison Cluster (Corresponds to Exhibit 4-7)

100%

90%

36%

37%

80%

56%

44%

57%

48%

42%

47%

53%

56%

70%

R 60%

31%

50%

> 1.1

42%

46%

53%

0.9–1.1

40%

54%

55%

38%

32%

applicable FM

< 0.9

28%

32%

30%

20%

44%

32%

37%

10%

29%

30%

18%

16%

31%

19%

23%

0%

FMR SAFMR FMR SAFMR FMR SAFMR FMR SAFMR FMR SAFMR

Share of units below

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Note: Analysis data set includes all ZIP Codes in public housing agency service areas where SAFMRs are implemented.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units)

Exhibit E-7: Average Number of Rental Units per ZIP Code by Rent Ratio Category, by

Comparison Cluster (Corresponds to Exhibit 4-8)

5,000

4,000

ode

P C 3,000

r ZI

< 0.9

ts pe

0.9–1.1

2,000

> 1.1

al uni

entR 1,000

0

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Smal  Area Fair Market Rents; 2012 American Community Survey 5-year estimates 148

Exhibit E-8: Analysis Sample for Determining Share of Units With Rents Below SAFMR and Metropolitan Area FMR by Overall Index Category by Comparison Cluster (Corresponds to

Exhibit 4-10)

Overall Index Category

n

< 25

25–75

> 75

Cluster 2

Units

1,469,140

313,619

736,719

418,803

ZIP Codes

690

90

313

287

Cluster 4

Units

1,155,545

300,386

511,089

344,071

ZIP Codes

367

45

139

183

Cluster 5

Units

6,654,848

2,142,377

3,194,423

1,318,047

ZIP Codes

2,020

450

931

639

Cluster 6

Units

2,291,603

599,027

1,193,344

499,232

ZIP Codes

673

107

287

279

Cluster 7

Units

680,520

191,900

343,974

144,645

ZIP Codes

182

34

76

72

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units); 2014 ACS 5-year estimates (poverty rate/percent nonpoor); School Proficiency Index, 2011–2012 (HUD Open Data); Job Proximity Index, 2010 (HUD Open Data); Environmental Health Hazard Index, 2005 (HUD Open Data)

Exhibit E-9: Share of All Rental Units Across Overall Opportunity Index Categories by

Comparison Cluster (Corresponds to Exhibit 4-13)

100%

90%

20

29

22

30

34

80%

ts 70%

al uni 60%

nt

52

51

> 75

50%

50

44

48

al re

25–75

40%

of

< 25

re 30%

Sha 20%

10%

21

26

32

26

28

0%

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Small Area Fair Market Rents; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units); overall opportunity index

149

Exhibit E-10: Share of Units Below FMR and SAFMR in ZIP Codes by Overall Opportunity Index by Comparison Cluster (Corresponds to Exhibit 4-14)

Overal  Opportunity Index < 25

100

80

unitstal 60

l ren

FMR

86

40

of al

SAFMR

66

69

63

66

e

54

45

51

51

54

20

Shar

0

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Overal  Opportunity Index 25–75

100

80

unitstal 60

l ren

FMR

40

SAFMR

of al

67

e

56 52

52 53

57

47

51

45

48

20

Shar

0

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Overall Opportunity Index > 75

100

80

unitstal 60

l ren

FMR

40

of al

SAFMR

e

58

63

58

58 65

47

20

Shar

38

44

29

35

0

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Note: Al  ZIP Codes in public housing agency service areas where SAFMRs are implemented.

Sources: HUD FY2015 FMRs; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units)

150

Exhibit E-11: Number of HCV Holder Households in Analysis Sample by ZIP Code Rent Ratio by Comparison Cluster (Corresponds to Exhibit 5-1)

HCV Holder

Rent Ratio Category

Households

Total

by Rent

< 0.9

0.9–1.1

> 1.1

Category

2010

2015

2010

2015

2010

2015

2010

2015

Cluster 2

15,199

15,513

27,081

26,959

6,101

6,396

48,381

48,868

Cluster 4

9,580

10,221

17,287

14,890

8,292

6,354

35,159

31,465

Cluster 5

77,483

70,353

221,450

221,495

40,056

38,626

338,989

330,474

Cluster 6

26,783

25,509

55,763

56,539

17,582

18,487

100,128

100,535

Cluster 7

15,250

16,856

18,525

18,843

5,259

5,515

39,034

41,214

HCV = housing choice voucher.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Small Area Fair Market Rents (rent ratio calculation); HUD Public and Indian Housing Information Center administrative data extract (counts)

Exhibit E-12: Share of HCV Holders by Rent Ratios in ZIP Codes Where They Live Before and

After SAFMRs by Comparison Cluster (Corresponds to Exhibit 5-4)

100%

13%

13%

12%

12%

18%

13%

13%

18%

90%

24%

20%

80%

70%

47%

46%

60%

56%

55%

47%

65%

67%

56%

49%

56%

50%

V holders

> 1.1

0.9–1.1

C

40%

< 0.9

30%

Share of H

20%

39%

41%

31%

32%

32%

27%

23%

27%

10%

21%

25%

0%

2010

2015

2010

2015

2010

2015

2010

2015

2010

2015

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

HCV = housing choice voucher. SAFMR = Small Area Fair Market Rent.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 SAFMRs (rent ratio calculation); HUD Public and Indian Housing Information Center administrative data extract (counts)

151

Exhibit E-13: Distribution of Rent Ratios by Site for All SAFMR PHAs—New HCV Holders and ZIP Code Movers by Comparison Cluster (Corresponds to Exhibit 5-5)

New HCV Holders

100%

12%

10%

10%

10%

90%

19%

13%

14%

14%

22%

16%

80%

olders 70%

V h

57%

47%

42%

60%

60%

C

48%

39%

68%

65%

60%

61%

H 50%

> 1.1

40%

f new

0.9–

30%

1.1

20%

40%

42%

< 0.9

Share o

28%

34%

34%

39%

22%

25%

27%

25%

10%

0%

2010

2015

2010

2015

2010

2015

2010

2015

2010

2015

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Note: The following table reports the number of new households (in the 2 years ending in the year listed) in each site by year.

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

2010

5,933

3,348

48,425

10,924

3,109

2015

7,092

3,141

38,260

12,043

3,624

ZIP Code Movers

100%

15%

15%

13%

12%

90%

21%

15%

12%

11%

19%

22%

80%

olders 70%

39%

48%

43%

V h 60%

C

53%

53%

46%

66%

68%

55%

H

53%

50%

> 1.1

40%

0.9–1.1

f new 30%

< 0.9

46%

20%

40%

45%

Share o

31%

32%

33%

10%

22%

20%

26%

25%

0%

2010

2015

2010

2015

2010

2015

2010

2015

2010

2015

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Note: The following table reports the number of ZIP Code movers (in the 2 years ending in the year listed) in each site by year.

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

2010

7,318

4,029

81,447

18,638

8,042

2015

6,209

2,835

63,110

16,165

6,927

HCV = housing choice voucher. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

152

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 SAFMRs (rent ratio calculation); HUD Public and Indian Housing Information Center administrative data extract (shares)

Exhibit E-14: Distribution of Opportunity Index for All HCV Holders—by SAFMR PHA

(Corresponds to Exhibit 5-10)

100%

5%

5%

14%

13%

11%

11%

8%

7%

90%

19%

16%

80%

40%

41%

40%

41%

70%

48%

49%

60%

49%

56%

56%

V holders

52%

C 50%

> 75

25–75

40%

< 25

Share of al H 30%

55%

54%

52%

52%

20%

41%

40%

36%

30%

31%

29%

10%

0%

2010

2015

2010

2015

2010

2015

2010

2015

2010

2015

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

HCV = housing choice voucher. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units); HUD PIC administrative data extract (counts); overall opportunity index

153

Exhibit E-15: Distribution of Opportunity Index by SAFMR PHA—New and ZIP Code Mover

Households by Comparison Cluster (Corresponding to Exhibit 5-11)

New Households

100%

6%

5%

10%

10%

9%

9%

16%

13%

18%

90%

19%

80%

43%

42%

70%

45%

44%

olders

51%

52%

60%

V h

58%

48%

49%

C

58%

H

> 75

50%

25–75

f new 40%

< 25

30%

Share o

51%

52%

47%

47%

20%

34%

38%

37%

33%

25%

29%

10%

0%

2010

2015

2010

2015

2010

2015

2010

2015

2010

2015

ZIP Code Movers

100%

5%

5%

14%

13%

11%

11%

8%

7%

90%

19%

16%

80%

40%

41%

40%

41%

70%

48%

49%

> 75

60%

56%

56%

49%

52%

overs 50%

25–75

40%

30%

55%

54%

52%

52%

20%

41%

< 25

36%

40%

30%

31%

29%

10%

Share of ZIP code m

0%

2010

2015

2010

2015

2010

2015

2010

2015

2010

2015

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

HCV = housing choice voucher. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 SAFMRs; 2012 American Community Survey (ACS) 5-year estimates (special tabulation for HUD of rent and rental units by ZIP Code Tabulation Area); 2012 ACS 5-year estimates (total rental units); HUD Public and Indian Housing Information Center administrative data extract (counts); overall opportunity index

154

Exhibit E-16: Payment Standards by Rent Ratio by Comparison Cluster (Exhibit 7-2)

Low-Rent ZIP Codes (Rent Ratio < 0.9)

$1,800

$1,600

d

dar  $1,400

an

t st  $1,200

2010

en

2015

m  $1,000

1,577

Pay  $800

901

1,205

874

985

871

1,125

857

959

1,531

$600

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

$1,800

$1,600

d

dar  $1,400

an

t st  $1,200

2010

en

2015

m  $1,000

1,588

Pay  $800

1,232

904

941

861 847

1,168

882

934

1,586

$600

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

High-Rent ZIP Codes (Rent Ratio > 1.1)

$1,800

$1,600

d

dar  $1,400

an

t st  $1,200

2010

en

2015

m  $1,000

Pay

983

1,451

981

1,162

1,633

$800

967

1,151

950

1,359

1,627

$600

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Sources: FY2015 Fair Market Rents; HUD FY2015 Smal  Area Fair Market Rents; HUD Public and Indian Housing Information Center administrative data

155

Exhibit E-17: HAP Costs by Comparison Cluster (Corresponds to Exhibit 7-4)

Low-Rent ZIP Codes (Rent Ratio < 0.9)

$1,100

$1,000

$900

sst  $800

2010

$700

AP coH

2015

$600

$500

737

533

600

1,051

523 495

706

509

583

1,004

$400

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

$1,100

$1,000

$900

sst  $800

2010

$700

AP co

1,091

H

2015

$600

752

562

566

$500

495 472

720

530

549

1,068

$400

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

High-Rent ZIP Codes (Rent Ratio > 1.1)

$1,100

$1,000

$900

sst  $800

2010

$700

AP coH

2015

$600

900

624

744

1,078

$500

601

844

584

726

547

1,044

$400

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

HAP = Housing Assistance Payment.

Notes: Al  values expressed in 2015 dollars. Mamaroneck had only nine housing choice voucher holders in low-rent ZIP Codes, therefore regard information with caution.

Sources: FY2015 Fair Market Rents; HUD FY2015 Small Area Fair Market Rents; HUD Public and Indian Housing Information Center administrative data

156

Exhibit E-18: Average Rent to Landlords by Comparison Cluster

(Corresponds to Exhibit 7-6)

Low-Rent ZIP Codes (Rent Ratio < 0.9)

$1,500

$1,350

$1,200

$1,050

$900

ent to landlord

2010

2015

R

1,021

$750

815

1,353

727 731

1,014

694 701

809

1,349

$600

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

$1,500

$1,350

$1,200

$1,050

$900

ent to landlord

2010

2015

R

1,060

$750

806

1,437

702

1,046

736

801

1,413

713

738

$600

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

High-Rent ZIP Codes (Rent Ratio > 1.1)

$1,500

$1,350

$1,200

$1,050

$900

1,232

ent to landlord

2010

2015

R

1,029

1,457

$750

820

833

1,229

826

1,024

1,433

814

$600

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Notes: Al  values expressed in 2015 dollars. Mamaroneck had only nine housing choice voucher holders in low-rent ZIP Codes, therefore regard information with caution.

Sources: FY2015 Fair Market Rents; HUD FY2015 Small Area Fair Market Rents; HUD Public and Indian Housing Information Center administrative data

157

Exhibit E-19: Average HCV Holder Contribution to Rent by Comparison Cluster (Corresponds to Exhibit 7-8)

Low-Rent ZIP Codes (Rent Ratio < 0.9)

$600

$500

$400

ontribution $300

2010

2015

older c $200

354

420

329

489

V hC $100

340

H

327

376

298

327

458

$0

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

$600

$500

$400

ontribution $300

2010

2015

older c $200

367

432

503

V h

353

370

C $100

H

338

381

322

352

469

$0

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

High-Rent ZIP Codes (Rent Ratio > 1.1)

$600

$500

$400

ontribution $300

2010

2015

older c $200

412

413

455

575

V h

471

C $100

H

368

434

372

435

510

$0

Cluster 2

Cluster 4

Cluster 5

Cluster 6

Cluster 7

HCV = housing choice voucher.

Notes: Al  values expressed in 2015 dollars. Mamaroneck had only nine HCV holders in low-rent ZIP Codes, therefore regard information with caution.

Sources: FY2015 Fair Market Rents; HUD FY2015 Smal  Area Fair Market Rents; HUD Public and Indian Housing Information Center administrative data 158

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160

U.S. Department of Housing and Urban Development

Office of Policy Development and Research

Washington, DC 20410-6000

August 2017

Document Outline

- Cover

- Title Page

- Disclaimer

- Contents

- Executive Summary

- Key Policy Issues Motivating Study

- HUD’s SAFMR Demonstration

- Evaluating the SAFMR Demonstration

- Potential Access to Opportunity Following Introduction of SAFMRs

- Actual Locations of HCV Holders Following Implementation of SAFMRs

- Program Costs and Rents

- PHA Impacts

- 1. About the Small Area Fair Market Rent Demonstration and Evaluation

- Motivation for the Demonstration

- Design and Implementation of the Demonstration

- Evaluation of the Demonstration

- Research Questions

- Structure of This Report and Introduction to Key Methodological Approaches

- Chapter 2. Hypothesized Impacts on Housing Choice Voucher Holders and PHAs. This chapter presents a discussion of the hypothesized impacts of the move to SAFMRs.

- Chapter 3. Evaluation Data and Methodology. This chapter describes the data and methodology used in this evaluation. The data collection and analysis for this study is to occur in two phases. Phase 1, the focus of this report, is based on an initial ...

- Chapter 4. Changes in Housing Choice Voucher Holders’ Potential Access to High-Opportunity Neighborhoods. Chapter 4 assesses the potential of SAFMRs to increase access to opportunity by comparing the share of all units renting below SAFMRs with the s...

- Chapter 5. Early Impacts of Small Area Fair Market Rents on Housing Choice Voucher Holders. Chapter 5 provides a preliminary assessment of the observed effect of adoption of SAFMRs on location and relocation outcomes of both new and existing HCV hold...

- Chapter 6. Perceptions of Tenants’ Experience and Administrative Impacts of Small Area Fair Market Rents on Public Housing Agencies. Chapter 6 discusses how the shift to SAFMRs affected PHAs—in particular, the time and expenses associated with implem...

- Chapter 7. Fiscal Effects of Small Area Fair Market Rents on Public Housing Agencies, Housing Choice Voucher Holders, and Landlords. In Chapter 7, we summarize the observed changes in rents following introduction of SAFMRs, including Housing Assistan...

- Chapter 8. Plan for Phase 2 Data Collection. Chapter 8 provides a preview of remaining research to be conducted for Phase 2 and the final report.

- 2. Hypothesized Impacts on Housing Choice Voucher Holders and Public Housing Agencies

- Hypothesized Impacts of SAFMRs on HCV Holders

- Potential for SAFMRs To Affect Location and Relocation of HCV Holders

- Observed HCV Holders’ Locations and Relocations

- HCV Holders’ Experience With the Program

- Personal Circumstances and Priorities

- PHA Execution

- Landlords’ Response

- Hypothesized Impacts of SAFMRs on PHAs

- Implications for Subsidy Costs

- Operational Impacts on PHAs

- Potential One-Time Impacts

- Potential Transitional Impacts

- Potential Continuing Impacts

- 3. Evaluation Data and Methodology

- Description of Primary and Secondary Data Sources

- Primary Data

- Secondary Data

- Comparison PHAs

- 4. Changes in Housing Choice Voucher Holders’ Potential Access to High-Opportunity Neighborhoods

- SAFMRs Increase Access to High-Rent ZIP Codes Across Sites

- Access to High-Rent ZIP Codes Under SAFMRs Varies Across Sites

- SAFMRs Reduce the Overall Number of Units With Rents Below the FMR

- Declines in Units With Rents Below SAFMR

- Declines in Units With Rents Below SAFMR Are a Consequence of the Concentration and Distribution of Rental Units Across ZIP Code Types

- SAFMRs Increase Access to ZIP Codes With Higher Opportunity Measures

- Opportunity Measures by Rent Ratio

- HCV Holders’ Potential Access to ZIP Codes Across Measures of Opportunity, All PHAs

- 5. Early Impacts of Small Area Fair Market Rents on Housing Choice Voucher Holders

- Participant Locations in SAFMR PHAs by Rent Ratio

- Opportunity Measures in ZIP Codes Where SAFMR Holders Reside

- Additional Neighborhood Characteristics in ZIP Codes Where HCV Holders Reside

- Summary

- 6. Perceptions of Tenants’ Experience and Administrative Impacts of Small Area Fair Market Rents on Public Housing Agencies

- PHA Perceptions of HCV Holders’ Experience With SAFMRs

- Factors Affecting PHAs’ SAFMR Implementation

- PHA Reasons for Using SAFMRs

- External Factors That Affected Implementation

- Federal Budget Sequestration

- Source of Income Discrimination Legislation

- Housing Market Conditions

- PHA Policy Changes

- SAFMR Administrative Impacts on PHAs

- Modifications to PHA Plans and Administrative Plans

- Modifications to Automated Systems

- Analyzing and Setting Payment Standards

- Rent Reasonableness

- Contract Rent Adjustments

- Inspection System and Process Changes

- Procedures and Staff Training

- Education and Support for Tenants

- Education and Support for Landlords

- Quality Assurance

- Voucher Success Rates

- Adequacy of Supplemental Administrative Fees

- Estimate of Financial Impacts of SAFMR Impacts on PHAs

- Impacts on Landlords

- 7. Fiscal Effects of Small Area Fair Market Rents on Public Housing Agencies, Housing Choice Voucher Holders, and Landlords

- Average Per-Unit Payment Standards

- Rent-Related Effects of SAFMRs

- PHA Effects: HAP Payments to Landlords

- Landlord Effects: Rent to Landlords

- HCV Holder Effects: HCV Holder Contributions to Rent

- 8. Plan for Phase 2 Data Collection

- Appendix A. Small Area Fair Market Rent Demonstration Site Selection

- Screening Criteria

- Clustering

- Random Ordering

- How Selection Informs the Evaluation

- Appendix B. Administrative and Financial Impacts of Small Area Fair Market Rents on Public Housing Agencies: Consolidated Exhibits

- Appendix C. Site Visit 1 Protocol

- GUIDANCE TO SITE VISITORS:

- PHA Background Information

- Voucher Success Rates*

- Staffing changes related to SAFMR

- Staff Performing Activities/Tasks

- Potential one-time impacts

- Potential transitional impacts

- HCV Office Expense Changes

- Appendix D. Phase 2 Site Visit Draft Data Collection Instruments

- INTRODUCTORY SCRIPT

- A. Background

- B. Understanding of SAFMRs

- C. Satisfaction with HCV Program

- D. Impact of Changing Payment Standards

- E. Conclusion

- Introduction

- Background/Selection Criteria

- A. Housing History

- B. Understanding of SAFMRs

- C. MOVE EXPERIENCE AND SEARCH PROCESS

- D. Experience with SAFMRs

- E. STAYERS – people who have NEVER MOVED

- Experience with Search Process

- Future move decisions

- F. Demographics

- A.1 Phase 2: PHA Background and Experience with SAFMR Transition

- A.1.1 Experience with Transition to SAFMRs

- A.1.2 Background Information

- PHA Background Information

- A.1.3 Participant Experience (from the PHA perspective)

- Voucher Success Rates*

- A.1.4 Landlord Experience (from the PHA perspective)

- A.2 Phase 2: PHA Costs of SAFMR Transition

- A.2.3 Potential Impacts on Systems and Admin Plans from Implementing SAFMRs

- Potential one-time impacts

- A.2.4 Potential Transitional Impacts from Implementing SAFMRs

- Potential transitional impacts

- A.2.5 Other Program Costs

- Per-unit HAP costs, 2 bedroom

- A.2.6 Conclusion

- Appendix E. Chapters 4, 5, and 7 Cluster Analyses

- References

Table of Contents

Cover

Title Page

Disclaimer

Contents

Executive Summary

Key Policy Issues Motivating Study

HUD’s SAFMR Demonstration

Evaluating the SAFMR Demonstration

Potential Access to Opportunity Following Introduction of SAFMRs

Actual Locations of HCV Holders Following Implementation of SAFMRs

Program Costs and Rents

PHA Impacts

1. About the Small Area Fair Market Rent Demonstration and Evaluation

Motivation for the Demonstration

Design and Implementation of the Demonstration

Evaluation of the Demonstration

Research Questions

Structure of This Report and Introduction to Key Methodological Approaches

Chapter 2. Hypothesized Impacts on Housing Choice Voucher Holders and PHAs. This chapter presents a discussion of the hypothesized impacts of the move to SAFMRs.

Chapter 3. Evaluation Data and Methodology. This chapter describes the data and methodology used in this evaluation. The data collection and analysis for this study is to occur in two phases. Phase 1, the focus of this report, is based on an initial ...

Chapter 4. Changes in Housing Choice Voucher Holders’ Potential Access to High-Opportunity Neighborhoods. Chapter 4 assesses the potential of SAFMRs to increase access to opportunity by comparing the share of all units renting below SAFMRs with the s...

Chapter 5. Early Impacts of Small Area Fair Market Rents on Housing Choice Voucher Holders. Chapter 5 provides a preliminary assessment of the observed effect of adoption of SAFMRs on location and relocation outcomes of both new and existing HCV hold...

Chapter 6. Perceptions of Tenants’ Experience and Administrative Impacts of Small Area Fair Market Rents on Public Housing Agencies. Chapter 6 discusses how the shift to SAFMRs affected PHAs—in particular, the time and expenses associated with implem...

Chapter 7. Fiscal Effects of Small Area Fair Market Rents on Public Housing Agencies, Housing Choice Voucher Holders, and Landlords. In Chapter 7, we summarize the observed changes in rents following introduction of SAFMRs, including Housing Assistan...

Chapter 8. Plan for Phase 2 Data Collection. Chapter 8 provides a preview of remaining research to be conducted for Phase 2 and the final report.

2. Hypothesized Impacts on Housing Choice Voucher Holders and Public Housing Agencies

Hypothesized Impacts of SAFMRs on HCV Holders

Potential for SAFMRs To Affect Location and Relocation of HCV Holders

Observed HCV Holders’ Locations and Relocations

HCV Holders’ Experience With the Program

Personal Circumstances and Priorities

PHA Execution

Landlords’ Response

Hypothesized Impacts of SAFMRs on PHAs

Implications for Subsidy Costs

Operational Impacts on PHAs

Potential One-Time Impacts

Potential Transitional Impacts

Potential Continuing Impacts

3. Evaluation Data and Methodology

Description of Primary and Secondary Data Sources

Primary Data

Secondary Data

Comparison PHAs

4. Changes in Housing Choice Voucher Holders’ Potential Access to High-Opportunity Neighborhoods

SAFMRs Increase Access to High-Rent ZIP Codes Across Sites

Access to High-Rent ZIP Codes Under SAFMRs Varies Across Sites

SAFMRs Reduce the Overall Number of Units With Rents Below the FMR

Declines in Units With Rents Below SAFMR

Declines in Units With Rents Below SAFMR Are a Consequence of the Concentration and Distribution of Rental Units Across ZIP Code Types

SAFMRs Increase Access to ZIP Codes With Higher Opportunity Measures

Opportunity Measures by Rent Ratio

HCV Holders’ Potential Access to ZIP Codes Across Measures of Opportunity, All PHAs

5. Early Impacts of Small Area Fair Market Rents on Housing Choice Voucher Holders

Participant Locations in SAFMR PHAs by Rent Ratio

Opportunity Measures in ZIP Codes Where SAFMR Holders Reside

Additional Neighborhood Characteristics in ZIP Codes Where HCV Holders Reside

Summary

6. Perceptions of Tenants’ Experience and Administrative Impacts of Small Area Fair Market Rents on Public Housing Agencies

PHA Perceptions of HCV Holders’ Experience With SAFMRs

Factors Affecting PHAs’ SAFMR Implementation

PHA Reasons for Using SAFMRs

External Factors That Affected Implementation

Federal Budget Sequestration

Source of Income Discrimination Legislation

Housing Market Conditions

PHA Policy Changes

SAFMR Administrative Impacts on PHAs

Modifications to PHA Plans and Administrative Plans

Modifications to Automated Systems

Analyzing and Setting Payment Standards

Rent Reasonableness

Contract Rent Adjustments

Inspection System and Process Changes

Procedures and Staff Training

Education and Support for Tenants

Education and Support for Landlords

Quality Assurance

Voucher Success Rates

Adequacy of Supplemental Administrative Fees

Estimate of Financial Impacts of SAFMR Impacts on PHAs

Impacts on Landlords

7. Fiscal Effects of Small Area Fair Market Rents on Public Housing Agencies, Housing Choice Voucher Holders, and Landlords

Average Per-Unit Payment Standards

Rent-Related Effects of SAFMRs

PHA Effects: HAP Payments to Landlords

Landlord Effects: Rent to Landlords

HCV Holder Effects: HCV Holder Contributions to Rent

8. Plan for Phase 2 Data Collection

Appendix A. Small Area Fair Market Rent Demonstration Site Selection

Screening Criteria

Clustering

Random Ordering

How Selection Informs the Evaluation

Appendix B. Administrative and Financial Impacts of Small Area Fair Market Rents on Public Housing Agencies: Consolidated Exhibits

Appendix C. Site Visit 1 Protocol

GUIDANCE TO SITE VISITORS:

PHA Background Information

Voucher Success Rates*

Staffing changes related to SAFMR

Staff Performing Activities/Tasks

Potential one-time impacts

Potential transitional impacts

HCV Office Expense Changes

Appendix D. Phase 2 Site Visit Draft Data Collection Instruments

INTRODUCTORY SCRIPT

A. Background

B. Understanding of SAFMRs

C. Satisfaction with HCV Program

D. Impact of Changing Payment Standards

E. Conclusion

Introduction

Background/Selection Criteria

A. Housing History

B. Understanding of SAFMRs

C. MOVE EXPERIENCE AND SEARCH PROCESS

D. Experience with SAFMRs

E. STAYERS – people who have NEVER MOVED

Experience with Search Process

Future move decisions

F. Demographics

A.1 Phase 2: PHA Background and Experience with SAFMR Transition

A.1.1 Experience with Transition to SAFMRs

A.1.2 Background Information

PHA Background Information

A.1.3 Participant Experience (from the PHA perspective)

Voucher Success Rates*

A.1.4 Landlord Experience (from the PHA perspective)

A.2 Phase 2: PHA Costs of SAFMR Transition

A.2.3 Potential Impacts on Systems and Admin Plans from Implementing SAFMRs

Potential one-time impacts

A.2.4 Potential Transitional Impacts from Implementing SAFMRs

Potential transitional impacts

A.2.5 Other Program Costs

Per-unit HAP costs, 2 bedroom

A.2.6 Conclusion

Appendix E. Chapters 4, 5, and 7 Cluster Analyses

References