Small Area Fair Market Rent 

Demonstration Evaluation

Interim Report

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

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, All PHAs ....38

5. Early Impacts of Small 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 FMRbased 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 lowrent 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 loweropportunity areas.

Actual Locations of HCV Holders Following Implementation of SAFMRs

Although SAFMRs are hypothesized to improve HCV holders’ access to units in highopportunity 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 onefourth 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 higheropportunity 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 
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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.
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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 FMRbased 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. 
2

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.
3

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.
4

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

PHA Name (Cluster)

HCV 

Units

TwoBedroom

FMR 2012 

($)

Average 

HCV 

Income

(Percent

of Local 

Median)

Minority/

Hispanic

(%)

62 Years 

Old Plus 

(%)

Tract 

Minority

(%)

Tract 

SingleFamily 

Owner

(%)

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
5

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.
6

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 HighOpportunity 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 nonmetropolitanarea 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 twobedroom 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.
7

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.
8

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

SAFMRs are expected to change the permissible range for 

payment standards across ZIP Codes within a metropolitan 

area.

9 This change is the primary mechanism through which 

SAFMRs might alter HCV holders’ access to and location in 

high-opportunity neighborhoods. However, the extent of this 

change depends on the variation in rents across a metropolitan 

area and how a PHA establishes payment standards—for both 

metropolitan area FMRs and SAFMRs. Our first hypothesis is 

that using SAFMRs instead of metropolitan area FMRs to 

determine payment standards will substantively increase HCV 

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

neighborhoods.

The following two factors affect whether HCV holders can 

successfully use vouchers in a particular neighborhood. First, is a 

good selection of rental units in the neighborhood at the SAFMR 

payment standard? Second, are neighborhood landlords willing 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.

Research Question 1a

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 

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?
9

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 = Small 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 
10

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.
11

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 

higher-opportunity neighborhoods. Chapter 4 examines the 

following specific hypotheses.

• The introduction of SAFMRs will increase existing 

HCV holders’ likelihood of moving both to take 

advantage of higher subsidies in higher-opportunity 

areas and to leave units where subsidies are 

decreased as a result of the lower payment standards.

• Due to the introduction of SAFMRs, HCV holders 

who move will be more likely to move to higheropportunity 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 higheropportunity 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.

Research Question 1b

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?
12

• 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 

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

able to complete unit approval and rental activities promptly 

and effectively. HCV holders’ experience with the program 

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

holder’s experience with the program and the subsequent 

perceptions will be affected by a combination of personal 

circumstances and priorities, the ability of the PHA to 

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

largely address hypotheses discussed in this section, the 

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.

Research Question 1c

How did the change to SAFMRs 

affect HCV holders’ experience with 

the HCV program? What effects did 

it have on tenants’ rent burdens? 

Did they understand how the change 

affected their housing options? 

What were their perceptions of the 

change? Did the change to SAFMRs 

influence HCV holders’ success in 

using vouchers to rent units?
13

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 
14

costs will depend on how successful SAFMRs are in 

deconcentrating HCV holders from high-poverty, low-rent 

neighborhoods.

SAFMRs will also alter some administrative responsibilities of 

PHAs. Some changes are one-time costs associated with 

implementing SAFMRs, such as adapting the PHAs’ systems of 

record (the set of automated tools that maintain HCV program 

and participant information) to take multiple payment standards 

into account. Other changes may be recurring, such as 

answering HCV holder questions about applicable rents in 

multiple locations. The net effects are unclear, although we 

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

example, determining payment standards that reflect ZIP Codelevel 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 

setting 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 onetime 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. 

Research Question 2

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?
15

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 
16

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 highopportunity 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). 
17

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 highopportunity 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.
18

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.
19

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-onone 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.
20

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. 
21

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.
22

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.
23

Exhibit 3-1: Opportunity Indicators

Opportunity Indicator

(Data Source) Description

Overall opportunity index

(Composite of the other opportunity 

indicators)

The overall opportunity index was created specifically for this evaluation. It is the 

percentile rank by renters in the metropolitan area of the simple average of the 

percentile rank indexes for the share of nonpoor, public school quality, employment 

access, and environmental hazards.

Percent nonpoor

(American Community Survey [ACS] 5-

year estimate, 2010–2014)

ACS 5-year estimates provide the percent nonpoor for each census tract. The 

percent nonpoor is the ratio of the population above the poverty level to the total 

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 Proficiency Index, 2011–2012)

School-level data on state examinations for 4th grade students approximate the 

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

(Jobs Proximity Index, 2010)

This index measures the access a neighborhood has to employment opportunities 

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

(Environmental Health Hazard Index, 

2005)

The Environmental Health Hazard Index is a tract-level index of potential exposure 

to toxins based on National Air Toxic Assessment data from the U.S. Environmental 

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 
24

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.
25

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. 
26

4. Changes in Housing Choice Voucher Holders’ Potential Access to HighOpportunity 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.
27

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 twobedroom 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 Dallas 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.
28

Exhibit 4-1: Rental Units and ZIP Codes by Rent Ratio 

n

Rent Ratio

< 0.9 0.9–1.1 > 1.1

All SAFMR PHAs

Units 1,290,864 380,598 (29%) 588,330 (46%) 321,936 (25%)

ZIP Codes 411 87 (21%) 186 (45%) 138 (34%)

Laredo

Units 25,544 6,582 (26%) 15,228 (60%) 3,734 (15%)

ZIP Codes 5 1 (20%) 3 (60%) 1 (20%)

Mamaroneck

Units 143,226 51,090 (36%) 64,066 (45%) 28,069 (20%)

ZIP Codes 67 9 (13%) 32 (48%) 26 (39%)

Chattanooga

Units 53,390 8,638 (16%) 36,152 (68%) 8,600 (16%)

ZIP Codes 30 6 (20%) 21 (70%) 3 (10%)

Cook County

Units 291,302 96,374 (33%) 130,023 (45%) 64,904 (22%)

ZIP Codes 127 37 (29%) 53 (42%) 37 (29%)

Long Beach

Units 107,946 60,531 (56%) 35,990 (33%) 11,425 (11%)

ZIP Codes 13 5 (38%) 5 (38%) 3 (23%)

Dallas

Units 668,981 157,382 (24%) 306,396 (46%) 205,23 (31%)

ZIP Codes 168 29 (17%) 71 (42%) 68 (40%)

Plano

Units 236,040 21,549 (9%) 111,166 (47%) 103,324 (44%)

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.
29

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

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Note: Analysis data set includes all ZIP Codes in public housing agencyservice 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.

73

49

26

50 46 47

54 49

0

10

20

30

40

50

60

70

80

< 0.9 0.9–1.1 > 1.1 Total

Share of all rental units

ZIP Code rent ratio categories

FMRs

SAFMRs
30

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.
31

Exhibit 4-3: Share of Rental Units Below SAFMR and Metropolitan Area FMR by Site

Lower-Rent Ratio ZIP Codes (Rent Ratio < 0.9)

Moderate-Rent Ratio ZIP Codes (0.9 < Rent Ratio < 1.1)

Higher-Rent Ratio ZIP Codes (Rent Ratio >1.1)

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) 

73 80 79

64 76 70 70 63

46 58 54 45 53 46 38 32

0

20

40

60

80

Share of all rental units

FMR

SAFMR

49 57

59 42 51 45 47

40

47 55

65 46

50

52

42 38

0

20

40

60

80

Share of all rental units

FMR

SAFMR

26 14

47

21 24

37

23 21

54 53

77

32

57 69

50 51

0

20

40

60

80

Share of all rental units

FMR

SAFMR
32

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 

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent. 

Note: Analysis data set includes all ZIP Codes in public housing agencyservice 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 

278 289

82

649

174

278

174

626

0

100

200

300

400

500

600

700

< 0.9 0.9–1.1 > 1.1 Total

Units with rent below SAFMR and FMR 

(thousands) 

Rent ratio

FMRs SAFMRs
33

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, 

All ZIP Code Rent Ratios

Difference

Percentage 

Change, SAFMR 

SAFMR FMR 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 Chattanooga and Laredo, but had the 

third largest decrease in units available. In Laredo and Mamaroneck, shifting to the SAFMR resulted in 

virtually no change in the overall 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 Chattanooga and Cook County were also fairly small. 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.
34

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. 
35

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

FMR = Fair Market Rent. SAFMR = Small Area Fair Market Rent.

Note: Analysis data set includes all ZIP Codes in public housing agencyservice 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 

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) 

-10%

-14%

-7%

-14%

-23%

-17%

-8%

-2%

5%

6%

-1%

4%

-6%

-4%

10% 9%

4%

14%

6%

19%

38%

-1% 0%

3%

-2%

-13%

-4%

26%

-30%

-20%

-10%

0%

10%

20%

30%

40%

Percent difference

< 0.9

0.9–1.1

 > 1.1

Total

36%

27%

44%

30% 24%

17%

67%

51% 47%

33% 37%

21% 17%

7%

60%

59%

41%

46%

68%

72%

26%

34% 43%

42%

48%

44% 56%

42%

4%

14% 14%

24%

8% 12% 7%

15% 10%

24%

16%

35%

27%

52%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FMR

SAFMR

FMR

SAFMR

FMR

SAFMR

FMR

SAFMR

FMR

SAFMR

FMR

SAFMR

FMR

SAFMR

Laredo Mamaroneck Chattanooga Long Beach Cook County Dallas Plano

> 1.1

.09–1.1

< 0.9
36

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 lowrent 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

PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 SAFMRs; 2012 American Community Survey 5-year estimates

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

18,000

20,000

22,000

Rental units per ZIP Code

 < 0.9

0.9–1.1

> 1.1
37

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

Component Indexes

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); 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)

29

54

78

0

10

20

30

40

50

60

70

80

Percentile rank in the

 < 0.9 0.9–1.1 > 1.1

metropolitan area

Rent ratio

0

10

20

30

40

50

60

70

80

Percent

nonpoor

School

proficiency

Job

proximity

Environmental

quality

Percentile rank in the 

metropolitan area

 < 0.9 0.9–1.1 > 1.1
38

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).
39

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

All SAFMR PHAs

Units 1,290,864 303,588 607,183 380,092

ZIP Codes 411 45 177 189

Laredo

Units 25,544 8,598 13,212 3,734

ZIP Codes 5 2 2 1

Mamaroneck

Units 143,226 0 67,404 75,821

ZIP Codes 67 0 12 55

Chattanooga

Units 53,390 18,592 16,479 18,319

ZIP Codes 30 7 14 9

Cook County

Units 291,302 28,353 138,119 124,829

ZIP Codes 127 5 59 63

Long Beach

Units 107,946 60,531 34,030 13,385

ZIP Codes 13 5 6 2

Dallas

Units 668,981 187,514 337,940 143,528

ZIP Codes 168 26 84 58

Plano

Units 236,040 33,474 120,048 82,518

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. 
40

Exhibit 4-11: Access to Neighborhood Opportunity—Share of Units Below Applicable FMR by 

Composite Overall Opportunity Index Categories

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 

68

52

34

46 48 52

0

20

40

60

80

< 25 25–75 > 75

Share of all rental units

Overall Opportunity Index

FMR

SAFMR
41

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

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); 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. 

0

10

20

30

40

50

60

70

FMR SAFMR FMR SAFMR FMR SAFMR FMR SAFMR

Percent nonpoor School proficiency Job proximity Environmental

quality

Share of All Rental Units

Component Opportunity Index

< 25

25–75

> 75
42

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

PHA = public housing agency.

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

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. 

24

34

0

35

10

56

28

14

47

52

47

31

47

32

51

51

29

15

53

34

43

12

21

35

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Share of All Rental Units

> 75

25–75

< 25
43

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).
44

Exhibit 4-14: Share of Units Below FMR and SAFMR in ZIP Codes by Overall Opportunity Index

Overall Opportunity Index < 25 

Overall Opportunity Index 25–75

Overall Opportunity Index > 75

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) 

0

20

40

60

80

Share of all rental units

FMR

SAFMR

0

20

40

60

80

Share of all rental units

FMR

SAFMR

0

20

40

60

80

Share of all rental units

FMR

SAFMR
45

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 higheropportunity 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 highopportunity 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 highopportunity 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
46

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

HCV Holder Households 

by Rent Category

Rent Ratio Category

< 0.9 0.9–1.1 > 1.1 Total

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 = 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)

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.
47

Exhibit 5-2: Rent Ratios of ZIP Codes Where HCV Holders Live Before and After SAFMRs

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.

39% 38%

26% 25%

44% 42% 61% 61%

17% 20% 14% 14%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015

SAFMR PHAs Comparison PHAs

Share of HCV holders

> 1.1

0.9–1.1

< 0.9
48

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

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

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 

42% 40%

25% 28%

44% 42%

64% 61%

14% 17% 11% 12%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015

SAFMR PHAs Comparison PHAs

Share of HCV holders

> 1.1

0.9–1.1

< 0.9

36% 31% 24% 25%

46%

41% 62% 62%

18%

28%

14% 14%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015

SAFMR PHAs Comparison PHAs

Share of HCV holders

> 1.1

0.9–1.1

< 0.9
49

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

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)

33%

20% 15% 15%

29% 30% 32% 34%

81% 80%

35% 31%

5% 7%

63%

68%

22% 22%

64% 62%

46% 43%

19% 19%

46%

44%

53% 44%

4%

12%

64% 63%

6% 8%

22% 23%

1% 1%

20% 24%

42%

49%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Laredo Mamaroneck Chattanooga Cook County Long Beach Dallas Plano

Share of HCV holders

> 1.1

0.9–1.1

< 0.9
50

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

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

Note: The table reports the number of ZIP Code movers in the 2 years ending in the year listed in each site by year.

Laredo Mamaroneck Chattanooga

Cook 

County

Long 

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)

25%

13% 0%

25% 29% 30%

39% 38%

78% 87%

44%

34%

6%

16%

71%

64%

17%

16%

66% 61% 45% 41%

20% 20%

37%

46%

52%

41%

5%

22%

83%

59%

5% 10% 17% 21%

1% 1%

19% 20%

42% 42%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Laredo Mamaroneck Chattanooga Cook County Long Beach Dallas Plano

Share of new HCV holders

> 1.1

0.9–1.1

< 0.9

32%

12%

32%

24% 27% 32% 33% 31%

78% 79%

30% 24%

11% 13%

62%

57% 23% 32%

67% 57%

43%

38%

21% 21%

49%

44%

51%

35%

6%

31%

45% 44%

6% 11%

24% 30%

1% 2%

21%

32% 39%

52%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Laredo Mamaroneck Chattanooga Cook County Long Beach Dallas Plano

Share of ZIP Code movers

> 1.1

0.9–1.1

< 0.9
51

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

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.

Originating ZIP Code Type

Total

HCV Holders 

Moving Within 

Neighborhood (Not 

in Chart)

Total HCV Holders 

in Analysis 

(Including 

Nonmovers) < 0.9 0.9–1.1 > 1.1

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.

54%

47%

25%

25%

12%

15%

36%

37%

55%

46%

46%

37%

10%

16%

19%

28%

42%

48%

0% 20% 40% 60% 80% 100%

2010

2015

2010

2015

2010

2015

< 0.9 0.9 - 1.1 > 1.1

Share of movers by rent ratio category of destination ZIP Code

Rent ration category of 

originating ZIP Code

< .9

0.9–1.1

> 1.1
52

Exhibit 5-7: Rent Ratios in ZIP Code Movers’ Destination ZIP Codes by Rent Ratio in Origination 

ZIP Code—Comparison Public Housing Agencies

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 

Moving Within 

Neighborhood (Not 

in Chart)

Total HCV Holders 

in Analysis 

(Including 

Nonmovers) < 0.9 0.9–1.1 > 1.1

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. 

47%

48%

16%

17%

14%

15%

44%

43%

73%

72%

46%

47%

9%

9%

11%

10%

40%

38%

0% 20% 40% 60% 80% 100%

2010

2015

2010

2015

2010

2015

< 0.9 0.9 - 1.1 > 1.1

Share of movers by rent ratio category of destination ZIP Code

Rent ratio category of 

originating ZIP Code

< .9

0.9–1.1

> 1.1
53

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

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

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. 

44% 43% 46% 46%

45% 44% 45% 46%

11% 13% 9% 9%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015

SAFMR PHAs Comparison PHAs

Share of all HCV holders

> 75

25–75

< 25
54

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

ZIP Code Movers

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

50% 49% 46% 46%

40% 40% 46% 46%

9% 11% 8% 8%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015

SAFMR PHAs Comparison PHAs

Share of new households

> 75

25–75

< 25

43% 37%

50% 49%

48%

47%

43% 44%

9% 15%

7% 7%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015

SAFMR PHAs Comparison PHAs

Share of recent movers

> 75

25–75

< 25
55

Exhibit 5-10 Distribution of Opportunity Index for All HCV Holders—by SAFMR PHA

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.

41% 43%

0% 0%

62% 65%

15% 19%

81% 80%

51%

46%

6% 10%

56%

45%

17% 18%

25% 21%

65%

63%

19% 19%

44%

45%

56% 48%

4%

12%

83% 82%

13% 14%

20% 18%

0% 0%

5% 9%

38% 42%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Laredo Mamaroneck Chattanooga Cook County Long Beach Dallas Plano

Share of all HCV holders

> 75

25–75

< 25
56

Exhibit 5-11: Distribution of Opportunity Index by SAFMR PHAs—New and ZIP Code Mover 

Households

New Households

ZIP Code Movers

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

47%

36%

0% 0%

61% 63%

17%

22%

78%

87%

53% 50%

8%

26%

48%

42%

6%

33%

27% 23%

59%

61%

21%

13%

44%

43%

60%

49%

5%

22%

94%

67%

12% 14%

24%

17%

0% 1% 3% 7%

33%

25%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Laredo Mamaroneck Chattanooga Cook County Long Beach Dallas Plano

Share of new HCV holders

> 75

25–75

< 25

44%

36%

0% 0%

67% 64%

15%

22%

78% 79%

49%

38%

10%

18%

49%

33%

34%

26%

22%

20%

69%

63%

21% 21%

45%

47%

56% 39%

6%

31%

66%

74%

12% 16% 16% 15%

1% 0%

5%

15%

33%

44%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Laredo Mamaroneck Chattanooga Cook County Long Beach Dallas Plano

Share of ZIP Code movers

> 75

25–75

< 25
57

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 onehalf 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.
58

Exhibit 5-12: Share of All HCV Holders by Racial and Ethnic Composition of Neighborhood—

SAFMR PHAs

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)

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. 

54% 54%

8% 8%

16% 19%

40% 39%

28% 29%

59% 58%

7% 7%

64% 64%

25% 24%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015 2010 2015

Percent White (nonHispanic)Percent Black Percent Hispanic

Share of HCV holders

> 75

25–75

< 25
59

Exhibit 5-13: Share of All HCV Holders by Neighborhood Racial and Ethnic Composition—New 

HCV Holders and ZIP Code Movers

New HCV Holders

ZIP Code Movers

HCV = housing choice voucher. 

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)

52% 56%

7% 7%

16% 18%

41% 37%

32% 30%

56% 55%

6% 7%

61% 63%

27% 27%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015 2010 2015

Percent White (nonHispanic)Percent Black Percent Hispanic

Share of new HCV holders

> 75

25–75

< 25

56% 53%

6% 7%

17% 23%

39% 39%

27% 31%

58%

55%

5% 8%

67% 62%

25% 22%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015 2010 2015

Percent White (nonHispanic)Percent Black Percent Hispanic

Share of ZIP Code movers

> 75

25–75

< 25
60

Exhibit 5-14: Share of All HCV Holders by Neighborhood Percentage of Households With 

Children and With a College Degree—SAFMR PHAs

All HCV Holders

New HCV Holders

ZIP Code Movers

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)

13% 12%

49% 48%

51% 51%

41% 42%

36% 37%

10% 10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015

With children (percent) College degree (percent)

Share of HCV holders

> 75

25–75

< 25

23% 13%

41% 50%

46%

50%

47% 40%

31% 38%

12% 10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015

With children (percent) College degree (percent)

Share of new HCV holders

> 75

25–75

< 25

11% 9%

51% 46%

52% 54%

42% 43%

37% 38%

8% 10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015

With children (percent) College degree (percent)

Share of ZIP Code movers

> 75

25–75

< 25
61

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.
62

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 
63

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 highopportunity 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.
64

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 higherquality 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.
65

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 = Small 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 
66

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 eventually 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.
67

Exhibit 6-2: Change in (Rolling Average) Vacancy Rates, 2011–2014

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, 

4.0%

5.0%

6.0%

7.0%

8.0%

9.0%

10.0%

11.0%

12.0%

2007–2011 2008–2012 2009–2013 2010–2014

 Cook County PHA

 Long Beach PHA

 Mamaroneck PHA

 Chattanooga PHA

 Dallas PHA

 Laredo PHA

 Plano PHA
68

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 higheropportunity 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.
69

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

Type of 

Impact Hypothesized Impacts Observed Impacts

Magnitude of 

Observed Impacts 

(Preliminary)

One time. Revisions to PHA plan and administrative plan 

required to establish payment standards, rent 

reasonableness, and tenant rents.

Some PHAs have administrative plans with extensive 

procedural documentation, which may require more 

substantial modifications.

With one exception, 

only minor 

modifications (if any) 

were needed.

Minor.

PHA = public housing agency. SAFMR = Small Area Fair Market Rent.
70

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 

Type of 

Impact Hypothesized Impacts Observed Impacts

Magnitude of 

Observed Impacts 

(Preliminary)

One time. May require systems of record adaptations to 

accommodate additional payment standards and to 

permit selection of different payment standards for 

same unit size.

Additional tools that may require adaptations include 

rent reasonableness data system and tool for 

determining unit affordability.

Four PHAs needed 

modifications to 

systems of record; 

effort required varied 

widely.

Five of seven PHAs had 

additional automated 

tools that typically 

required minor 

modifications.

Varies: minor to 

significant.

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.
71

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

Type of 

Impact Hypothesized Impacts Observed Impacts

Magnitude of 

Observed Impacts 

(Preliminary)

Transitional, 

ongoing.

Although the first year will be the most challenging, 

expanding the PHAs’ analysis from a single set of 

FMRs to multiple will require ongoing analysis that is 

more complex than before SAFMR.

Focusing on using FMRs as a mobility tool, rather than 

strictly a funds management tool, will be new to some 

PHAs.

Setting SAFMR 

payment standards is 

much more time 

consuming, because 

many more market 

areas exist.

Significant for most 

PHAs.

PHA = public housing agency. SAFMR = Small 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.
72

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 

Type of 

Impact Hypothesized Impacts Observed Impacts

Magnitude of 

Observed Impacts 

(Preliminary)

Transitional. May simplify rent reasonableness determinations,

because local area rents are embedded in the SAFMR.

Alternatively, PHA staff may have to be more 

knowledgeable about sub-areas and may not be 

familiar with the housing stock and market in highopportunity areas.

With one exception, 

only minor 

modifications were 

needed.

Minor.

PHA = public housing agency. SAFMR = Small 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.
73

Exhibit 6-7: Preliminary Assessment of SAFMR Demonstration Impacts: Contract Rent 

Adjustments 

Type of 

Impact Hypothesized Impacts Observed Impacts

Magnitude of 

Observed Impacts 

(Preliminary)

Transitional. SAFMR payment standards may cause a change in 

behavior of current landlords and increase the number 

of requests for rent adjustments or protract the setting 

process. 

Landlords who have not regularly requested rent 

adjustments, because they knew any additional 

increase would be borne by the tenant, may request in 

greater numbers in areas where the payment standard 

goes up.

SAFMRs increased 

requests for rent 

adjustments for most 

PHAs, especially in 

areas where payment 

standards increased.

Moderate.

SAFMR = Small 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

Type of 

Impact Hypothesized Impacts Observed Impacts

Magnitude of 

Observed Impacts 

(Preliminary)

Transitional, 

ongoing.

If housing choice voucher holders become more 

dispersed, this increase in areas where voucher 

holders live may have an effect on inspector 

productivity and increase travel costs.

In some locations, inspectors are part of the rent 

reasonableness process. Inspectors will need to 

become familiar with housing stock and rents in 

additional areas.

No necessary changes to 

process.

More need for inspections as 

the number of moves 

increased; more 

geographically dispersed 

units also meant longer 

travel times for inspectors.

Moderate.

SAFMR = Small Area Fair Market Rent.
74

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

Type of 

Impact Hypothesized Impacts Observed Impacts

Magnitude of 

Observed Impacts 

(Preliminary)

Transitional. Will require development of detailed procedures and

policies, as well as training and retraining of staff at 

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.

Some retraining 

needed.

Moderate.

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.
75

Exhibit 6-10: Preliminary Assessment of SAFMR Demonstration Impacts: Education and Support 

for Tenants

Type of 

Impact Hypothesized Impacts Observed Impacts

Magnitude of 

Observed Impacts

(Preliminary)

Transitional, 

ongoing.

Unless a PHA has already administered a mobility 

program, the associated level of effort encouraging

and assisting households to consider moving to highopportunity areas is likely to increase. 

Current program participants living in areas where 

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.

With two exceptions, 

PHAs have not increased 

efforts to encourage or 

assist households to 

move.

Minor.

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 higheropportunity 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.
76

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 longstanding 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.
77

Exhibit 6-11: Preliminary Assessment of SAFMR Demonstration Impacts: Education and Support 

for Landlords

Type of 

Impact Hypothesized Impacts Observed Impacts

Magnitude of 

Observed Impacts 

(Preliminary)

Transitional, 

ongoing.

Landlords currently participating in the Housing 

Choice Voucher program will require more 

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. 

Minor landlord 

education.

Minor.

SAFMR = Small 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

Type of 

Impact Hypothesized Impacts Observed Impacts

Magnitude of 

Observed Impacts 

(Preliminary)

Ongoing. HUD-50058 errors based on selection of the wrong 

payment standard are not unusual even with areawide FMRs. When selecting the payment standard, 

staff may confuse the voucher size for which the 

family qualifies and the unit size the family selects. 

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.

Five of seven public 

housing agencies 

reported short-term 

increases in quality 

assurance efforts.

Minor.

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 
78

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. Chattanooga 

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 substantially. Laredo, Long Beach, and Plano

could not isolate the impact of SAFMRs from other factors they saw as being more important, such 

as overall changes in the rental housing market including vacancy rates and gentrification. Likewise, 

Dallas reported that overall market conditions determined the success rate, not SAFMRs. 

Chattanooga 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 
79

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.
80

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, 

and CEO—80 

hours PHA 

administrative 

plan, 80 hours for 

PHA plan.

Minimal—time 

not recorded.

Administrator—

5-10 hours; 

Administrator—2 

hours.

Finance 

director—24 

hours 

administrative 

plan, 2 hours for 

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.
81

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 

County

Dallas Long Beach Mamaroneck Plano

IT specialist—

40 hours/$900.

No change. Software vendor 

+ IT director + 

CFO—250 

hours/$35,000 to 

software vendor.

GoSection8 system 

changes for rent 

reasonableness.

$400 to software 

vendor + 6 ED; 40 

hours for 

automated 

spreadsheet.

Office supplies, 

postage, software 

modifications 

$10,705; finance 

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. 
82

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 

hours to date.

Created or updated 

ZIP Code and 

payment standards

spreadsheets—100 

hours to date.

VP-HCV, CFO, COO, 

president/CEO, 120 

hours.

Community 

program 

specialist—15 to 

16 hours for 

affordability 

spreadsheet.

Director—40 hours 

initially; 10 hours 

to update annually.

All 

staff—

1 hour.

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 
83

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 

support and

overtime.

No change. 7 hours per week. No change.

PHA = public housing agency. SAFMR = Small Area Fair Market Rent.
84

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. 

Chattanooga, Cook County, Laredo, Long Beach, Mamaroneck, and Plano all reported increases in 

the number of requests for rent adjustments they attributed to the implementation of SAFMRs. 

Chattanooga 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 Chattanooga 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 

hours annually to 

respond to 

increase in 

requests.

No change. Additional 

overtime, landlord

services.

No change. None named. Slight increase 

during change in 

standards.

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.
85

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 

and tenant 

briefings present 

an increase.

None reported. New client and 

landlord guide.

No change. Minimal additions 

to the website and 

forms.

Finance 

director—10 

hours.

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 
86

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 landlordtenant 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.
87

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. 
88

Exhibit 7-1: Payment Standard by Rent Ratio SAFMR and Comparison PHAs

PHA = public housing agency. SAFMR = Small 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

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.

1,116

1,008

1,098

1,120

955

958

1,249

1,004

 $800

 $900

 $1,000

 $1,100

 $1,200

 $1,300

< 0.9 0.9–1.1 > 1.1 All

Payment standard

Rent ratio category

SAFMR PHAs

2010

2015

994960

1,117

990

979

939

1,090

969

 $800

 $900

 $1,000

 $1,100

 $1,200

 $1,300

< 0.9 0.9–1.1 > 1.1 All

Rent ratio category

Comparison PHAs

2010

2015
89

Exhibit 7-2: Payment Standards by Rent Ratio by Site

Low-Rent ZIP Codes (Rent Ratio < 0.9)

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

High-Rent ZIP Codes (Rent Ratio > 1.1)

PHA = public housing agency. SAFMR = Small 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 1,116 700 1,389 1,719 1,105 734 955 669 1,060 996 1,0641,377 1,037 660 700

713

 $600

 $800

 $1,000

 $1,200

 $1,400

 $1,600

 $1,800

Payment standard

2010 2015

1,008

747

1,3791,6591,134

743

958

1,102

988

808

1,174

1,523

1,130

724

886

830

 $600

 $800

 $1,000

 $1,200

 $1,400

 $1,600

 $1,800

Payment standard

2010 2015

1,098

755

1,4321,6681,251

755

1,1861,108

1,249

1,209

1,557

1,690

1,455

837

1,220

1,108

 $600

 $800

 $1,000

 $1,200

 $1,400

 $1,600

 $1,800

Payment standard

2010 2015
90

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

HAP = Housing Assistance Payment. PHA = public housing agency. SAFMR = Small 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. 711 680 779

709

554

584

805

617

 $400

 $500

 $600

 $700

 $800

 $900

< 0.9 0.9–1.1 > 1.1 All

HAP costs

Rent ratio category

SAFMR PHAs

2010 2015

612

596

709

615

596

566

670

587

 $400

 $500

 $600

 $700

 $800

 $900

< 0.9 0.9–1.1 > 1.1 All

Rent ratio category

Comparison PHAs

2010 2015
91

Exhibit 7-4: HAP Costs by Site

Low-Rent ZIP Codes (Rent Ratio < 0.9)

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

High-Rent ZIP Codes (Rent Ratio > 1.1)

HAP = Housing Assistance Payment. PHA = public housing agency. SAFMR = Small Area Fair Market Rent.

Notes: All 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

711

402

907947722

447

590613

554

367

675

882

657

393

397382

 $300

 $400

 $500

 $600

 $700

 $800

 $900

 $1,000

HAP costs

2010 2015

680

454

902934733

465

690592

584

454

780

928

714

431

522

472

 $300

 $400

 $500

 $600

 $700

 $800

 $900

 $1,000

HAP costs

2010 2015

779

548

998950815460770661

805

803

1,052

1,028

898

515

761

646

 $400

 $500

 $600

 $700

 $800

 $900

 $1,000

 $1,100

HAP costs

2010 2015
92

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

PHA = public housing agency. SAFMR = Small 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. 939

888

1,014

882

871

1,085

 $700

 $800

 $900

 $1,000

 $1,100

< 0.9 0.9–1.1 > 1.1

Rent to landlord

Rent ratio category

SAFMR PHAs

2010 2015

809796954

828

800

957

 $700

 $800

 $900

 $1,000

 $1,100

< 0.9 0.9–1.1 > 1.1

Rent ratio category

Comparison PHAs

2010 2015
93

Exhibit 7-6: Average Rent to Landlords by Site

Low-Rent ZIP Codes (Rent Ratio < 0.9)

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

High-Rent ZIP Codes (Rent Ratio > 1.1)

PHA = public housing agency. SAFMR = Small Area Fair Market Rents.

Notes: All 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 939 590 1,233 1,359 950 577 749870882 601 1,1161,287 917 584 706

671

 $500

 $750

 $1,000

 $1,250

 $1,500

Rent to landlord

2010 2015

888

648

1,2571,345

977

605

860845

871

672

1,194

1,377

975

621

839

820

 $500

 $750

 $1,000

 $1,250

 $1,500

Rent to landlord

2010 2015

1,014

699

1,3431,3481,072

635

962963

1,085

1,001

1,403

1,442

1,149

687

1,052

978

 $500

 $750

 $1,000

 $1,250

 $1,500

Rent to landlord

2010 2015
94

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 

16 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 highrent 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

HCV = housing choice voucher. PHA = public housing agency. SAFMR = Small 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

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. 354 359 398 363 431 423444 430

$0

$100

$200

$300

$400

$500

< 0.9 0.9–1.1 > 1.1 All

HCV holder contribution

Rent ratio category

SAFMR PHAs

2010 2015

387370408383

443

396 412 419

$0

$100

$200

$300

$400

$500

< 0.9 0.9–1.1 > 1.1 All

Rent ratio category

Comparison PHAs

2010 2015
95

Exhibit 7-8: Average HCV Holder Contribution to Rent by Site

Low-Rent ZIP Codes (Rent Ratio < 0.9)

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

High-Rent ZIP Codes (Rent Ratio > 1.1)

HCV = housing choice voucher. PHA = public housing agency. SAFMR = Small Area Fair Market Rent. 

Notes: All 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 354 364 428 454 356 284

295379

431

353

518

439

384

352

407

426

$0

$100

$200

$300

$400

$500

$600

HCV holder contribution

2010 2015

359385456448372303343367

423

352

487

478

395

360

453

491

$0

$100

$200

$300

$400

$500

$600

HCV holder contribution

2010 2015

398360459448407333381455

444

359

445

454

418

321

463

514

$0

$100

$200

$300

$400

$500

$600

HCV holder contribution

2010 2015
96

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.
97

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.
98

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 NVivoTM 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.
99

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 

metropolitan 

area FMR

High % working age

70 PHAs (Cluster 1)

Low % working age

38 PHAs (Cluster 2)

High % working age

59 PHAs (Cluster 5)

Low % working age

21 PHAs (Cluster 6)

High 

metropolitan 

area FMR

High % working age

25 PHAs (Cluster 3)

Low % working age

18 PHAs (Cluster 4)

High % working age

7 PHAs (Cluster 7)

Low % working age

9 PHAs (Cluster 8)

FMR = Fair Market Rent. PHA = public housing agency.
100

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 
101

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. 
102

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 

administrative plan 

changes

Director—1 hour. No change. VP, president, and 

CEO—80 hours PHA 

administrative plan, 80 

hours for PHA plan.

Minimal—time not 

recorded.

Administrator—5–10 

hours; 

Administrator—2 

hours.

Finance director—24 

hours administrative plan, 

2 hours for PHA plan.

Modifications to 

automated systems

IT specialist—40 

hours/$900.

No change. Software vendor + IT 

director + CFO—250 

hours/$35,000 to 

software vendor.

GoSection8 system 

changes for rent 

reasonableness.

$400 to software 

vendor + 6 ED; 40 

hours for automated 

spreadsheet.

Office supplies, postage, 

software modifications 

$10,705; finance 

director—124 hours.

Analyzing and 

setting payment 

standards

Director—40 hours to 

date.

Created and updated 

spreadsheet of ZIP Codes 

and payment standards—

100 hours to date.

VP-HCV, CFO, COO, 

president/CEO, 120 

hours.

Community program 

specialist—15 to 16 

hours for affordability 

spreadsheet.

Director—40 hours 

initially; 10 hours to 

update annually.

All staff—1 hour.

Rent 

reasonableness 

No change. No change. Additional office 

support + overtime.

No change. 7 hours per week. No change.

Contract rent 

adjustments

Director—192 hours 

annually to respond to 

increase in requests.

No change. Additional overtime, 

landlord services.

No change. None named. Slight increase during 

change in standards.

Inspection system

and process 

changes

New hire: contract 

inspector—$15,000

per year for overflow 

inspections.

No change. Two new inspectors 

hired.

Increased number of 

inspections.

Increase in travel 

distance for 

inspections.

No change.
103

Impact PHA

Chattanooga Cook County Dallas Long Beach Mamaroneck Plano

Communication 

strategy

Monthly landlord 

briefing, tenant 

briefings present an 

increase.

None reported. New client and 

landlord guide.

No change. Minimal additions to 

the website and 

forms.

Finance director—10 

hours.

Procedures and 

staff training

Staff training for

SAFMR: director, 

housing specialist, 

inspector.

Training held as part 

of regular staff 

meetings.

$40,000 in staff 

training.

Phone calls with 

HUD, increase in staff 

trainings.

Increase in trainings. Staff trainings.

Education and 

support for tenants

None reported. Created new mobility 

counseling program in 

conjunction with 

SAFMRs.

New relocation and 

briefing packets.

Tenants given more 

information during 

normal briefings.

Tenants given more 

information during 

normal briefings.

Longer briefings with 

tenants.

Education and 

support for 

landlords

Monthly landlord 

briefings.

None reported. Additional landlord 

training.

Recruiting events 

held; landlord mailing; 

advertising; created 

new owner 

orientation; 

newsletter; 

attendance at 

apartment 

association meetings 

and trade shows.

Landlords given more 

information during 

normal briefings.

None reported.

Quality assurance Director—65 hours 

per year.

Increase in HUD50058 errors.More frequent quality 

control.

Increase in file 

review.

No change. Increase in errors.

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.
104

Exhibit B-2: Administrative Impacts of SAFMRs on PHAs

PHA

Impact Chattanooga Cook County Dallas Long Beach Mamaroneck Plano

PHA Plan and 

Administrative 

Plan changes

Director—1 hour. No change. VP, president, and 

CEO—80 hours PHA 

Administrative Plan, 80 

hours for PHA Plan

Minimal—time not 

recorded.

Administrator—5–10 

hours; Administrator—

2 hours

Finance director—

24 hours 

Administrative 

Plan, 2 hours PHA 

Plan

Modifications to 

automated systems

IT specialist—40 

hours/$900.

No change. Software vendor + IT 

director + CFO—250 

hours at $35,000 to 

software vendor.

GoSection8 system 

changes for rent 

reasonableness.

$400 to software 

vendor + 6 ED; 40 hours 

for automated 

spreadsheet.

Office supplies, 

postage, software 

modifications 

$10,705; finance 

director—124 

hours.

Analyzing and 

setting payment 

standards

Director—40 hours to 

date.

Created and updated 

spreadsheet of ZIP 

Codes and payment 

standards—100 

hours to date.

VP-HCV, CFO, COO, 

president and CEO, 120 

hours.

Community program 

specialist—15 to 16 

hours for affordability 

spreadsheet.

Director—40 hours 

initially; 10 hours to 

update annually

All staff—1 hour.

Rent 

reasonableness 

No change. No change. Additional office 

support + overtime.

No change. 7 hours per week. No change.

Contract rent 

adjustments

Director—192 hours 

annually to respond to 

increase in requests.

No change. Additional overtime, 

landlord services.

No change. None named. Slight increase 

during change in 

standards.

Inspection system

and process 

changes

New hire: contract 

inspector—$15,000

per year for overflow 

inspections.

No change. Two new inspectors 

hired.

Increased number of 

inspections.

Increase in travel 

distance for 

inspections.

No change.

Communication 

strategy

Monthly landlord

briefing, tenant briefings 

present an increase.

None reported. New client and 

landlord guide.

No change. Minimal additions to 

the website and forms.

Finance director—

10 hours.
105

PHA

Impact Chattanooga Cook County Dallas Long Beach Mamaroneck Plano

Procedures and 

staff training

Staff training for

SAFMR: director, 

housing specialist, 

inspector.

Training held as part 

of regular staff 

meetings.

$40,000 in staff 

training.

Phone calls with HUD, 

increase in staff 

trainings.

Increase in trainings. Staff trainings.

Education and 

support for tenants

None reported. Created new mobility 

counseling program 

in conjunction with 

SAFMRs.

New relocation and 

briefing packets.

Tenants given more 

information during 

normal briefings.

Tenants given more 

information during 

normal briefings.

Longer briefings 

with tenants.

Education and 

support for 

landlords

Monthly landlord 

briefings.

None reported. Additional landlord 

training.

Recruiting events held; 

landlord mailing; 

advertising; created 

new owner orientation; 

newsletter; attendance 

at apartment 

association meetings 

and trade shows.

Landlords given more 

information during 

normal briefings.

None reported.

Quality assurance Director—65 hours per 

year.

Increase in HUD50058 errors.More frequent quality 

control.

Increase in file review. No change. Increase in errors.

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
106

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.)
107

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 

type

(Number of regular, VASH, FUP, enhanced 

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) 
108

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?
109

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 
110

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?
111

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.
112

Staffing changes related to SAFMR

Employee 

name or 

initials Title

Change in role/ 

responsibilities as 

a result of 

SAFMR? (If yes, 

describe)

If YES in column C (change in role/responsibilities), then:

Change in salary 

as a result of 

SAFMR? (If yes, 

provide annual $ 

change) 

Change in overtime as 

a result of SAFMR? (If 

yes, provide annual 

change in overtime 

pay) 

New hire as a result of 

SAFMR? (If yes, provide 

annual total 

compensation) 
113

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)?
114

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 

change

Total cost 

expenditures 

Staff hours needed 

for modifications

Staff performing 

modifications

Cost of consultants 

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)?
115

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? 
116

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

SAFMR-related 

change

Were costs one-time or 

ongoing? If ongoing, are 

higher costs permanent 

or will they decline over 

time?

Total cost 

expenditures

Time period 

over which 

expenditures 

were incurred

Staff hours 

needed for 

additional 

effort

Staff 

performing 

additional 

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
117

SAFMR-related 

change

Were costs one-time or 

ongoing? If ongoing, are 

higher costs permanent 

or will they decline over 

time?

Total cost 

expenditures

Time period 

over which 

expenditures 

were incurred

Staff hours 

needed for 

additional 

effort

Staff 

performing 

additional 

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

Office expense Affected by SAFMR? (Y/N) Cost of additional expenses

Office supplies

Office 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 Dallas 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?]
126

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.
127

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 

unit, e.g., Andrews 

St.)

Address/ZIP 

Code/crossstreetsDates of 

Residence

Same ZIP Code as 

previous unit?

Used Housing 

Choice Voucher to 

rent unit?

First Unit Rented 

with a Voucher?

First Move Since 

2011/2013 (Y/N)?

Current unit

Prior unit #1 

Prior unit #2

Prior unit #3
128

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.
129

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?
130

• 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?
131

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
132

− 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 
133

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].
134

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]?
135

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?
136

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: ________.
137

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 

type

(Number of regular, VASH, FUP, enhanced 

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):
138

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.] 
139

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?
140

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? 
141

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 

change

Site visit 1 review Additional or corrected information

Total cost 

expenditures 

reported during 

site visit 1

Staff hours 

reported 

during site 

visit 1

Additional/Corre

cted cost 

expenditures

Additional/corre

cted staff hours 

needed

Titles of 

staff 

performing 

work

PHA 

Administrative 

Plan

$_______________

_

Examples of 

expenditures:

$______________

__

Examples of 

expenditures:

PHA Plan $_______________

_

Examples of 

expenditures:

$______________

__

Examples of 

expenditures:

PHA system of 

record

$_______________

_

Examples of 

expenditures:

$______________

__

Examples of 

expenditures:
142

Other automated 

tools: list (e.g., 

rent 

reasonableness) 

______________

__

$_______________

_

Examples of 

expenditures:

$______________

__

Examples of 

expenditures:

Other automated 

tools: list

______________

__

$_______________

_

Examples of 

expenditures:

$______________

__

Examples of 

expenditures:

Other automated 

tools: list

______________

__

$_______________

_

Examples of 

expenditures:

$______________

__

Examples of 

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

SAFMR-related 

change

Total cost 

expenditures 

reported during 

site visit 1

Staff hours 

reported 

during site 

visit 1

Additional or 

corrected cost 

expenditures

Additional/

corrected staff 

hours needed

Titles of staff 

performing 

work

Process of 

establishing 

payment standards

$____________

Examples of 

expenditures:

$____________

Examples of 

expenditures:

Process of 

determining rent 

reasonableness 

data

$____________

Examples of 

expenditures:

$____________

Examples of 

expenditures:

Requests for CRAs 

or extended 

contract rent 

negotiations

$____________

Examples of 

expenditures:

$____________

Examples of 

expenditures:
143

Additional requests 

from voucher 

holders for 

extended search 

time

$____________

Examples of 

expenditures:

$____________

Examples of 

expenditures:

HQS Inspector 

productivity

$____________

Examples of 

expenditures:

$____________

Examples of 

expenditures:

PHA 

communication and 

outreach strategy 

and materials

$____________

Examples of 

expenditures:

$____________

Examples of 

expenditures:

Support for tenants $____________

Examples of 

expenditures:

$____________

Examples of 

expenditures:

Support for 

landlords

$____________

Examples of 

expenditures:

$____________

Examples of 

expenditures:

Quality assurance 

processes

$____________

Examples of 

expenditures:

Other: list

________________

$____________

Examples of 

expenditures:

$____________

Examples of 

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?
144

Per-unit HAP costs, 2 bedroom

PHA Fiscal Year

Abt estimate of per-unit HAP 

costs (2- bedroom units)

Abt estimate of per-unit 

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?
145

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)

n

Rent Ratio

< 0.9 0.9–1.1 > 1.1

Cluster 2

Units (%) 1,469,289 314,731 21% 814,382 55% 340,176 23%

ZIP Codes (%) 691 170 25% 342 49% 179 26%

Cluster 4

Units (%) 1,155,434 251,473 22% 496,709 43% 407,363 35%

ZIP Codes (%) 367 70 19% 153 42% 144 39%

Cluster 5

Units (%) 6,654,848 1,479,591 22% 3,775,731 57% 1,399,525 21%

ZIP Codes (%) 2020 457 23% 997 49% 566 28%

Cluster 6

Units (%) 2,291,603 659,636 29% 1,118,690 49% 513,276 22%

ZIP Codes (%) 673 154 23% 311 46% 208 31%

Cluster 7

Units (%) 680,520 251,976 37% 255,185 37% 173,358 25%

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 Small Area Fair Market Rents; 2012 American Community Survey 5-year estimates (total rental 

units) 
146

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)

Moderate Rent Ratio ZIP Codes (0.9 < Rent Ratio < 1.1)

Higher Rent Ratio ZIP Codes (Rent Ratio >1.1)

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) 

71 78

68 67

83

46 41 41 41 46

0

20

40

60

80

100

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Share of all rental units

FMR

SAFMR

54 56 49 51

67

52 53 47 49 58

0

20

40

60

80

100

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Share of all rental units

FMR

SAFMR

35 37

27 33

57

68 63 55

69 76

0

20

40

60

80

100

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Share of all rental units

FMR

SAFMR
147

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 

(1,000s)

Difference 

(1,000s)

Percentage 

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)

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)

-10%

-15%

-12%

-14%

-19%

-2% -2% -2% -2%

-5%

15%

17%

12%

16%

7%

3%

0%

-2%

-1%

-18% -20%

-15%

-10%

-5%

0%

5%

10%

15%

20%

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Percent difference

< 0.9

0.9–1.1

 > 1.1

Total
148

Exhibit E-6: Distribution of Units With Rents Below Applicable FMR by ZIP Code Rent Ratio, by 

Comparison Cluster (Corresponds to Exhibit 4-7)

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)

Sources: HUD FY2015 Fair Market Rents; HUD FY2015 Small Area Fair Market Rents; 2012 American Community Survey 5-year estimates 

29% 18%

32%

16% 31% 19%

37%

23%

44%

30%

54%

28%

42%

32%

55%

32%

46%

38%

31%

53%

56%

53%

44%

42%

57%

56%

48%

47%

36% 37%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FMR SAFMR FMR SAFMR FMR SAFMR FMR SAFMR FMR SAFMR

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Share of units below applicable FMR

> 1.1

0.9–1.1

< 0.9

0

1,000

2,000

3,000

4,000

5,000

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Rental units per ZIP Code

 < 0.9

0.9–1.1

> 1.1
149

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)

n

Overall Index Category

< 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) 

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

21 26 32 26 28

50 44 48

52 51

29 30 34

20 22

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Share of all rental units

> 75

25–75

< 25
150

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)

Overall Opportunity Index < 25 

Overall Opportunity Index 25–75

Overall Opportunity Index > 75

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) 

66 69 63 66

86

54 45 51 51 54

0

20

40

60

80

100

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Share of all rental units

FMR

SAFMR

56 52 47 51

67

52 53 45 48

57

0

20

40

60

80

100

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Share of all rental units

FMR

SAFMR

38 44

29 35

58 58 63

47

58 65

0

20

40

60

80

100

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Share of all rental units

FMR

SAFMR
151

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 

Households 

by Rent 

Category

Rent Ratio Category Total

< 0.9 0.9–1.1 > 1.1

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) 

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)

31% 32% 27%

32%

23% 21%

27% 25%

39% 41%

56% 55%

49%

47% 65% 67% 56% 56%

47% 46%

13% 13%

24% 20%

12% 12%

18% 18%

13% 13%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Share of HCV holders

> 1.1

0.9–1.1

< 0.9
152

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

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

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.

28% 34% 34% 39%

22% 25% 27% 25%

40% 42%

60%

57%

48% 39% 68% 65% 60% 61%

47% 42%

12% 10%

19% 22%

10% 10% 13% 14% 14% 16%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Share of new HCV holders

> 1.1

0.9–

1.1

< 0.9

31% 32% 33%

46%

22% 20% 26% 25%

40% 45%

53% 53% 46%

39%

66% 68% 55% 53%

48% 43%

15% 15% 21% 15% 12% 11%

19% 22%

13% 12%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Share of new HCV holders

> 1.1

0.9–1.1

< 0.9
153

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)

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

30% 31% 29%

36%

55% 54%

41% 40%

52% 52%

56% 56%

52%

49%

40% 41%

48% 49%

40% 41%

14% 13%

19% 16%

5% 5%

11% 11% 8% 7%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Share of all HCV holders

> 75

25–75

< 25
154

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

ZIP Code Movers

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

25% 29% 34% 33%

51% 52%

38% 37%

47% 47%

58%

58% 48% 49%

43% 42%

51% 52%

45% 44%

16% 13% 18% 19%

6% 5% 10% 10% 9% 9%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Share of new HCV holders

> 75

25–75

< 25

30% 31% 29%

36%

55% 54%

41% 40%

52% 52%

56% 56%

52%

49%

40% 41%

48% 49%

40% 41%

14% 13%

19% 16%

5% 5%

11% 11% 8% 7%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2010 2015 2010 2015 2010 2015 2010 2015 2010 2015

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Share of ZIP code movers

> 75

25–75

< 25
155

Exhibit E-16: Payment Standards by Rent Ratio by Comparison Cluster (Exhibit 7-2)

Low-Rent ZIP Codes (Rent Ratio < 0.9)

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

High-Rent ZIP Codes (Rent Ratio > 1.1)

Sources: FY2015 Fair Market Rents; HUD FY2015 Small Area Fair Market Rents; HUD Public and Indian Housing Information Center administrative data

901 1,205 874 985

1,577

871 1,125 857 959 1,531

 $600

 $800

 $1,000

 $1,200

 $1,400

 $1,600

 $1,800

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Payment standard

2010

2015

861

1,232 904 941

847 1,168 882 934 1,586

1,588

 $600

 $800

 $1,000

 $1,200

 $1,400

 $1,600

 $1,800

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Payment standard

2010

2015

983 1,451 981 1,162 1,633

950 1,359

967 1,151

1,627 $600

 $800

 $1,000

 $1,200

 $1,400

 $1,600

 $1,800

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Payment standard

2010

2015
156

Exhibit E-17: HAP Costs by Comparison Cluster (Corresponds to Exhibit 7-4)

Low-Rent ZIP Codes (Rent Ratio < 0.9)

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

High-Rent ZIP Codes (Rent Ratio > 1.1)

HAP = Housing Assistance Payment.

Notes: All 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

523

737 533 600 1,051

495 706 509 583 1,004

 $400

 $500

 $600

 $700

 $800

 $900

 $1,000

 $1,100

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

HAP costs

2010

2015

495

752 562 566

1,091

472 720 530 549 1,068

 $400

 $500

 $600

 $700

 $800

 $900

 $1,000

 $1,100

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

HAP costs

2010

2015

601

900 624 744 1,078

547

844 584 726

1,044 $400

 $500

 $600

 $700

 $800

 $900

 $1,000

 $1,100

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

HAP costs

2010

2015
157

Exhibit E-18: Average Rent to Landlords by Comparison Cluster 

(Corresponds to Exhibit 7-6)

Low-Rent ZIP Codes (Rent Ratio < 0.9)

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

High-Rent ZIP Codes (Rent Ratio > 1.1)

Notes: All 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

727 731 1,014 694 809 1,349

1,021

701

815 1,353

 $600

 $750

 $900

 $1,050

 $1,200

 $1,350

 $1,500

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Rent to landlord

2010

2015

702 1,046 736 801 1,413 713

1,060

738

806 1,437

 $600

 $750

 $900

 $1,050

 $1,200

 $1,350

 $1,500

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Rent to landlord

2010

2015

820

1,229 826 1,024 1,433 814

1,232

833

1,029 1,457

 $600

 $750

 $900

 $1,050

 $1,200

 $1,350

 $1,500

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

Rent to landlord

2010

2015
158

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)

Moderate-Rent ZIP Codes (0.9 < Rent Ratio < 1.1)

High-Rent ZIP Codes (Rent Ratio > 1.1)

HCV = housing choice voucher. 

Notes: All 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 Small Area Fair Market Rents; HUD Public and Indian Housing Information Center administrative data

327 376 298 327 458

354 420 329

340

489

$0

$100

$200

$300

$400

$500

$600

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

HCV holder contribution

2010

2015

338 381 322 352 469

367 432

353 370

503

$0

$100

$200

$300

$400

$500

$600

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

HCV holder contribution

2010

2015

368 434 372 435 510

412

471

413 455 575

$0

$100

$200

$300

$400

$500

$600

Cluster 2 Cluster 4 Cluster 5 Cluster 6 Cluster 7

HCV holder contribution

2010

2015
159

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U.S. Department of Housing and Urban Development

Office of Policy Development and Research

Washington, DC 20410-6000

August 2017