Opting In, 

Opting Out 

a Decade Later

Multi-Disciplinary

Research Team

U.S. Department of Housing and Urban Development | Office of Policy Development and Research
Opting In, Opting Out a Decade Later

Multi-Disciplinary Research Team

U.S. Department of Housing and Urban Development

Office of Policy Development and Research

May 08, 2015

Submitted by:

Economic Systems Inc.

Falls Church, VA 

Prepared by:

Anne Ray, Jeongseob Kim, Diep Nguyen, Jongwon Choi

Shimberg Center for Housing Studies

University of Florida

Gainesville, FL 
About MDRT

PD&R developed the Multidisciplinary Research Team (MDRT) vehicle to manage a team of 

qualified researchers. Researchers are selected for their expertise to produce an array of high 

quality, short-turnaround research. MDRT researchers use a variety of HUD and external data 

sources to answer research questions relating to HUD’s priority policies and strategic goals. 

Disclaimer

The contents of this report are the views of the contractor and do not necessarily reflect the views or policies of the 

U.S. Department of Housing and Urban Development or the U.S. government. 

ii
Contents

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

Introduction ..................................................................................................................................... 1

I. Classifying Properties by Program Status ................................................................................... 4

II. Opt-in/Opt-Out Analysis: Descriptive Cross-Tabulations ....................................................... 10

Property and Owner Characteristics ......................................................................................... 12

Financing Characteristics .......................................................................................................... 15

Location Characteristics ............................................................................................................ 16

Tenant Characteristics ............................................................................................................... 19

III. Multivariate Analysis of Property Characteristics and Opt-in/Opt-out Status ....................... 21

IV. Section 202 Direct Loan Program .......................................................................................... 29

Section 202 Developments Compared to Other Section 8 Developments ............................... 29

Opt-Outs in the Section 202 Portfolio ...................................................................................... 34

Section 202 Refinancing and Preservation ............................................................................... 35

V. HUD Rental Assistance Demonstration (RAD) Program ........................................................ 37

Conclusion and Areas for Further Research ................................................................................. 45

References ..................................................................................................................................... 49

Appendix 1. Data Sources and Business Rules for Property Database ........................................ 51

Datasets for Determining Property Outcomes .......................................................................... 51

Step 1. Creating the 2005 Baseline Property Dataset ........................................................... 51

Step 2. Classifying Properties by 2014 Status ...................................................................... 52

Step 3. Determining Reasons for Termination of Assistance ............................................... 52

Step 4. Assigning Outcome Categories ................................................................................ 52

Identifying Section 202 and RAD-Eligible Developments ...................................................... 53

Appendix 2. Multivariate Analysis by Market Phase ................................................................... 55

Appendix 3. Small Area FMR Exploratory Analysis ................................................................... 56

List of Tables

Table 1. HUD Multifamily Assistance Programs ........................................................................... 1

Table 2. Properties by 2005 Baseline HUD Assistance Type ........................................................ 5

Table 3. Property Inventory Changes by Subsidy Type, 2005-2014 .............................................. 5

Table 4. Properties and Units by Outcome, 2005-2014 .................................................................. 7

Table 5. Properties by Summary Outcome Categories, 2005-2014 ................................................ 8

iii
Table 6. Renewal Terms for Section 8 Contracts in 2005-2014 Opt-In Properties ........................ 9

Table 7. Data Sources ................................................................................................................... 11

Table 8. Property Characteristics and Owner Type, 2005 Baseline ............................................. 12

Table 9. Financing Characteristics, 2005 Baseline ....................................................................... 16

Table 10. Locational Characteristics, 2005 Baseline .................................................................... 17

Table 11. Tenant Characteristics, 2005 Baseline .......................................................................... 20

Table 12. Variables for Regression Model ................................................................................... 22

Table 13. Odds Ratios for Opt-Out Decision Model, 2005-2014 ................................................. 24

Table 14. Properties with Section 202 and Section 8 Assistance by Section 8 Outcome 

Categories, 2005-2014 .................................................................................................................. 29

Table 15. Property Characteristics at 2005 Baseline, Section 202 Developments by Outcome 

Status ............................................................................................................................................. 30

Table 16. Location Characteristics at 2005 Baseline, Section 202 Developments by Outcome 

Status ............................................................................................................................................. 31

Table 17. Tenant Characteristics at 2005 Baseline, Section 202 Developments by Outcome 

Status ............................................................................................................................................. 33

Table 18. Section 202 Properties Exiting the Section 8 Inventory through Opt-Out and 

Foreclosure/Abatement by State and Population Served, 2005-2014 .......................................... 35

Table 19. Renewal Terms for Section 8 Contracts in Developments with Section 202 Direct 

Loans, 2014 ................................................................................................................................... 36

Table 20. Property Characteristics at 2005 Baseline, Developments by RAD Eligibility and 

Participation .................................................................................................................................. 38

Table 21. Locational Characteristics at 2005 Baseline, Developments by RAD Eligibility and 

Participation .................................................................................................................................. 40

Table 22. Tenant Characteristics, Developments by RAD Eligibility and Participation .............. 41

Table 23. RAD-Eligible Developments and RAD Participants by State ...................................... 42

Table 24. Net Change in Assisted Units from 2005 Baseline to Post-RAD Conversion, RAD 

Participants .................................................................................................................................... 44

iv
Executive Summary

In 2006, HUD published Multifamily Properties: Opting In, Opting Out and Remaining 

Affordable, prepared by Econometrica and Abt Associates. The study assessed the risk of loss of 

affordable housing from HUD’s Section 8 and Section 236/221(d)(3) multifamily portfolio 

between 1998 and 2004. The authors used descriptive cross-tabulations and multivariate analysis 

to identify property characteristics associated with loss of affordable housing due to owners’ 

decisions to opt out of subsidy programs and HUD enforcement actions. The study found that 

properties with low rents compared to the surrounding Fair Market Rent, that serve a family 

population, and that are owned by for-profit corporations are particularly at risk for opt-outs. 

This study updates the 2006 analysis by replicating the cross-tabulation and multivariate analyses 

for HUD’s multifamily portfolio across the years 2005 through 2014. Both studies show a 

continuing transition from HUD’s older mortgage programs toward greater reliance on Section 8 

rental assistance to provide affordable units. The updated analysis shows that more owners made 

active decisions to opt in to Section 8 assistance in the latter period, alongside a shift away from 

enforcement and foreclosure actions by HUD. 

For the updated study, HUD provided point-in-time property-level datasets for 2005 and 2014 

listing all developments with active project-based rental assistance contracts, mortgages from the 

Section 236 and Section 221(d)(3) Below Market Interest Rate programs (referred to in the 

report as “Section 236/BMIR”), or both. The 2005 baseline dataset comprised over 18,000 

developments with nearly 1.5 million housing units nationwide. 

By comparing the 2005 baseline dataset to the 2014 snapshot, properties were placed in four 

classifications: 

• Opt-ins, where the owner made an active decision to renew Section 8 assistance; 

• Opt-outs, where the owner made an active decision to stop renewal of Section 8 

assistance or prepay a subsidized mortgage; 

• Foreclosure/abatement, where HUD took action to foreclose on an insured mortgage or 

terminate Section 8 assistance; 

• “Other” status, largely mixed opt-out/prepayment decisions and continued assistance to 

properties without an active contract renewal between 2005 and 2014. 

This report recreates the cross-tabulation and multivariate analyses of these outcomes across 

property, neighborhood, and tenant characteristics, with additional variables representing 

regional and local housing market characteristics. The report also describes characteristics and

outcomes for two subsets of the HUD-assisted multifamily stock: developments funded by the 

Section 202 Direct Loan program and developments eligible for conversion from older types of 

rent subsidies to project-based Section 8 assistance under the Rental Assistance Demonstration 

(RAD) program. 

The main findings are as follows: 

Opt-in/opt-out analysis. The updated study finds a high degree of stability in the HUD Section 8 

portfolio over the 2005-2014 study period. In 71 percent of cases, owners actively opted in to 

Section 8 assistance by renewing a contract, typically for either a five-year or 20-year term. 

Many of the other developments also continued to receive Section 8 assistance without needing a 

contract renewal during the study period. 

In contrast, the Section 236/BMIR mortgage programs largely wound down during the study 

period. By 2014, most of these loans had terminated due to prepayment or maturity. Most 

v 
properties with both types of assistance in 2005 continued as Section 8 developments in 2014 

even following prepayment or maturity of a subsidized mortgage. 

The multivariate opt-in/opt-out analysis for 2005-2014 echoes many of the findings in the 

original study, but property characteristics explain less of the variation in owners’ decisions. 

Opt-outs continued to be more likely among properties that had low rent-to-Fair Market Rent 

ratios, were owned by for-profits, and were designated for family occupancy. The rent-to-FMR 

and ownership type variables were much less influential in the current analysis than in the 

original report. Other factors associated with increased risk of opt-outs included partial rather 

than full coverage of units in the development by rental assistance contracts; lower physical 

inspection scores, implying that owners may opt out of Section 8 contracts when properties have 

extensive repair needs or are at risk of contract abatement by HUD; strong neighborhood rental 

housing markets; and strong regional home sales markets. 

Section 202 program. 3,580 properties combined Section 8 rental assistance with loans from the 

Section 202 program, which provides housing for elderly residents and persons with disabilities. 

This segment of the affordable housing inventory proved to be especially stable. Owners of 96 

percent of properties actively renewed Section 8 contracts during the study period. In 

approximately half of the properties where owners opted in to Section 8 assistance, the Section 

202 loan itself was terminated during the study period. This suggests that a large portion of the 

Section 202 inventory is undergoing a transition to new forms of financing and recapitalization. 

The handful of Section 8 opt-outs and abatements were largely confined to a distinct subset of 

Section 202 properties: small, 1980s-era developments serving individuals with chronic mental 

illness or developmental disabilities. 

RAD conversions. Most contracts from early HUD rental assistance programs, the Rent 

Supplement (Rent Supp) and Rental Assistance Program (RAP), cannot be renewed once they 

expire. RAD’s “Component 2” allows owners of developments with expiring Rent Supp and 

RAP contracts to convert assisted units to project-based Section 8 vouchers to preserve 

affordability. The study dataset included 321 properties eligible for conversion under RAD. Of 

these, 63 were converted as of 2014. 

Compared to other properties in the dataset, the RAD-eligible developments were older, larger, 

more likely to be located in suburbs, and less likely to be located outside of metropolitan areas. 

The properties were heavily concentrated in a few Northeastern and Midwestern states (New 

York, New Jersey, Massachusetts, Michigan and Illinois). The converted properties and those 

that are eligible for RAD but have not converted share similar characteristics. The main 

difference between the two is target occupancy. Most RAD-converted properties were designated 

for elderly residents, while most eligible, non-participating developments were designated for 

families. The analysis also showed that compared to the overall inventory, RAD-eligible 

developments were much more likely to be partially assisted at the 2005 baseline; that is, they 

had rental assistance for only some of their units. However, many of the RAD participants added 

assisted units in the conversion process, resulting in a net gain in deeply affordable units. 

vi 
Introduction

This study updates the HUD report Multifamily Properties: Opting In, Opting Out and 

Remaining Affordable, prepared by Econometrica and Abt Associates in 2006. The original 

report examined the loss of affordable housing units associated with HUD’s Section 8 projectbased rental assistance and Section 236 and 221(d)(3) subsidized mortgage programs. The 

authors analyzed property-level data to identify the physical, financial, location, ownership and 

tenant characteristics associated with opt-outs from rental assistance programs, prepayment of 

subsidized mortgages, and enforcement actions by HUD. 

The original study used cross-tabulation and multivariate regression analysis to identify 

characteristics associated with losses to the HUD-assisted inventory between 1998 and 2004. As 

requested, this update replicates the quantitative analyses over a 2005-2014 study period to 

address these research questions:

• Do basic characteristics such as location, ownership, physical attributes and 

neighborhood characteristics explain differences in the prevalence of optouts/prepayments and opt-ins?

• How have the patterns and trends in opt-outs and prepayments changed in recent years?

To answer these questions, we assessed the status of over 18,000 properties in HUD’s 

multifamily portfolio. Together, these developments provide nearly 1.5 million housing units. 

We compared a baseline dataset of these properties from 2005 to the current 2014 inventory to 

determine whether properties have continued to operate as affordable housing. For properties 

that left the affordable inventory, we determined whether they exited the inventory through an 

owner’s choice not to renew Section 8 contracts (“opt out”), prepayment or maturing of 

subsidized mortgages, HUD foreclosure and contract abatement actions, or a combination of 

these. For properties that continue to operate in the assisted inventory, we determined whether 

the owner made an active choice during the study period to renew assistance. 

The HUD programs under analysis mirror those in the original study. Table 1 below is adapted 

from pages 1-3 of the original study, which provided detailed descriptions of the relevant HUD

programs and classified them as “Older” or “Newer” assistance. 

Table 1. HUD Multifamily Assistance Programs

Rental Assistance Mortgage

Older Rent Supplement (“Rent Supp”), Rental Assistance Payment 

(RAP), and Loan Management Set-Aside (LMSA)

Section 221(d)(3), Section 236

Newer Section 8 New Construction/Substantial Rehabilitation 

(NC/SR), Section 8 Moderate Rehabilitation (“Mod Rehab”)1

- 

1 The original study noted that “(t)he Office of Public and Indian Housing manages most projects assisted with 

Section 8 moderate rehabilitation. These projects are not included in the Real Estate Management System (REMS) 

or a comparable database. The REMS database only includes the subset of such projects that are also associated with 

the Section 8 Property Disposition program. Consequently, our analysis of the Section 8 Moderate Rehabilitation 

projects in this study is limited to this part of the stock.” (Finkel et al., 3). The same constraint applies to this report.

1 

 
As in the original report, we constructed a series of variables that might explain differences in 

property outcomes. The variables are based on HUD administrative records of the physical, 

financial, owner, and tenant characteristics of the properties and U.S. Census data describing 

conditions in the surrounding neighborhoods. Because the updated 2005-2014 study takes place 

against a backdrop of extraordinary volatility in the U.S. housing market, we added a series of 

neighborhood and regional housing market characteristics as potential explanatory variables and 

experimented with segmenting the analysis into time periods representing the rise, fall and 

recovery of the housing market. 

The updated study adds two new analyses: an examination of opt-outs and renewals in the 

housing supply for elderly residents and persons with disabilities funded through the Section 202 

Direct Loan program, and a first look at conversion of older HUD-assisted developments to 

long-term Section 8 assistance under the Rental Assistance Demonstration (RAD) program.

The report is organized into five sections. The first section describes the HUD-assisted inventory 

in 2005 and the classification of properties into opt-in, opt-out/prepay, and foreclosure/abatement 

outcome categories based on their status in 2014. The second section contains the crosstabulations of outcomes by property, owner, financing, and tenant characteristics. The third 

section contains the updated multivariate analysis determining the effect of selected 

characteristics on the owners’ opt-in/opt-out decisions. The last two sections provide crosstabulations of these same characteristics for the subsets of properties with Section 202 Direct 

Loans and that are eligible for conversion under the RAD program from expiring Rent Supp and 

RAP assistance to project-based Section 8 vouchers. 

As noted in the original report (Finkel et al., 16), multivariate analysis adds depth to the initial 

findings of the descriptive statistics. The multivariate analysis isolates the influence of each 

variable on the opt-in/opt-out decision. The apparent relationships between some variables and 

property outcomes in the descriptive cross-tabulations may drop out when the other factors are 

controlled. The odds ratios produced in the multivariate analysis show the relative role each 

variable plays in predicting the likelihood of opt-outs, which can help policymakers target the 

properties with the most influential risk factors for loss of affordability. 

The HUD task order request listed specific tables from the original study to be recreated in this 

study. We present a crosswalk between the tables in the original study and those in our study as 

follows: 

2 
Tables in Original Study Tables in Our Study

2.1: Data Elements and Sources for 

the Quantitative Analysis

Table 7: Data Sources

2.2 Table 4: Properties and Units by Outcome, 2005-2014

2.3 Table 4: Properties and Units by Outcome, 2005-2014

3.1 Table 8: Property Characteristics and Owner Type, 2005 Baseline

3.2 Table 8: Property Characteristics and Owner Type, 2005 Baseline

3.3 Table included in footnote 6 

3.4 Table 9: Financing Characteristics, 2005 Baseline

3.6 Table 10: Locational Characteristics, 2005 Baseline

3.7 Table 11: Tenant Characteristics, 2005 Baseline

3.8 Table 8: Property Characteristics and Owner Type, 2005 Baseline

3.1 Table 13: Odds Ratios for Opt-Out Decision Model, 2005-2014

3.10 (to include descriptions of data used in 

the current multivariate analysis and any 

differences from the data and model used in 

the 2006 multivariate analysis) 

Table 12: Variables for Regression Model

3 
I. Classifying Properties by Program Status

This section describes the process by which properties were classified by their program status in 

2014 compared to 2005. For each property in the 2005 inventory, we determined which housing 

programs were initially in place, whether the property continued to operate under those programs 

in 2014; and, if the property exited the inventory, whether it did so because of an owner opt-out 

decision or a HUD decision to terminate assistance. For a full description of the HUD datasets 

and methodology used to construct the 2005/2014 property database, see Appendix 1. 

The 2005 HUD multifamily inventory served as the baseline universe of properties. Properties 

with active subsidies from Section 8 and similar rental assistance programs in 2005, the Section 

236 and 221(d)(3) BMIR programs, or both were included. For brevity, these sets of programs 

are referred to as “Section 8” and “236/BMIR” throughout the report. 

The study universe excluded properties with other types of HUD funding unless they also had 

these specific types of rental assistance and mortgages. Most notably, HUD’s Project Assistance 

Contract (PAC) and Project Rental Assistance Contract (PRAC) programs, associated with the 

Section 202 and 811 Capital Advance programs, were excluded. Similarly, the analysis of 

subsidized mortgages is limited to the 236/BMIR loans and does not include market-rate FHA 

insured loans. Properties with financing from other subsidized loan programs such as Section 

202 Direct Loans or USDA Rural Development mortgages were included only if they also 

receive Section 8 assistance. 

In all, 18,887 developments had active funding from Section 8, 236/BMIR or both in 2005. Of 

these, sufficient data were available for 18,107 properties (96 percent) to allow them to be 

included in the study.2

 These developments provided 1.49 million housing units. This compares 

to a universe of 22,471 properties with 1.57 million units in the dataset for the original study. 

In both studies, Section 8 only housing made up the vast majority of properties and units. In the 

2006 study, these properties made up 75 percent of the dataset, rising to 84 percent for the 

current study (see Table 2). Properties with both Section 8 and 236/BMIR were the next largest 

group. Only eight percent of properties in the original study had 236/BMIR and no Section 8, 

with that proportion dropping to just four percent for the current study.

2 The dataset excluded 780 properties that had active Section 8 or 236/BMIR subsidies in 2005. Most were Section 8 

developments that were excluded because their status in 2014 was ambiguous. They did not appear in HUD’s 

database of active Section 8 contracts but also were not listed as opt-outs or abated contracts. Spot-checks of state 

and local preservation databases indicate that most of these likely continue as active properties, possibly that have 

merged with other properties or undergone financing or ownership changes. An additional 104 properties had Rent 

Supp or RAP contracts that expired during the study period. These owners did not have an opt-in choice to renew 

assistance other than participation in the RAD program. In other cases, properties were excluded from the dataset 

because property records contained contradictory information or because their 236/BMIR status was unclear.

4 

 
Table 2. Properties by 2005 Baseline HUD Assistance Type

Subsidy Type Properties % of Properties Units % of Units

% of Properties 

in Original Study 

(1998 Baseline)

Section 8 only 15,280 84% 1,147,165 77% 75%

236/BMIR only 522 3% 53,542 4% 8%

Section 8 & 

236/BMIR 2,305 13% 286,309 19% 17%

Total 18,107 100% 1,487,016 100% 100%

Source: HUD, 2005 Active Properties and Active Financing Files, iREMS.

We compared the 2005 baseline property dataset to property data from 2014 to determine 

whether each property continued as subsidized housing (“stayers”) or left the subsidized 

inventory between 2005 and 2014 (“leavers”). This analysis is summarized in Table 3. It shows 

considerably more stability in the inventory between 2005 and 2014 compared to the original 

1998-2004 study period. In total, only eight percent of properties (1,452 properties with 125,629 

units) were lost during the 2005 through 2014 period, compared to 19 percent of properties in the 

original study. This reflects a greater predominance of Section 8 only properties, the most stable 

set of properties in both studies. It also reflects greater stability among properties with both 

Section 8 and 236/BMIR assistance. Of the Section 8/236/BMIR inventory in 2005, 88 percent 

remained in the affordable inventory in 2014, although the next step in the analysis will show 

that most of these had terminated 236/BMIR and continued with only Section 8 assistance. 

Table 3. Property Inventory Changes by Subsidy Type, 2005-2014

Subsidy Type

Remained in 2014 Inventory

(Stayers)

Left Inventory by 2014

(Leavers)

Leavers As 

% of Subsidy 

Type, 2006 

Study # 

% of Subsidy 

Type # % of Subsidy Type

Section 8 only 14,543 95% 737 5% 9%

236/BMIR only 79 15% 443 85% 82%

Section 8 & 236/BMIR 2,033 88% 272 12% 32%

Total 16,655 92% 1,452 8% 19%

Source: HUD, 2005 and 2014 Active Properties and Active Financing Files, iREMS.

The comparison also reflects the loss of the 236/BMIR only inventory during both periods. Most 

of the small inventory of these properties left the affordable stock during the original study 

period, and most of the remaining properties exited the program between 2005 and 2014. 

Finally, we categorized each property based the reason for its retention or removal from the 

subsidized inventory between 2005 and 2014. The categories are based on the subsidy type, 

whether the property stayed in or left the inventory, and the nature of the owner’s or HUD’s 

decision to terminate or continue each type of subsidy. 

5 
For the Section 8 properties, removal from the inventory might come through an owner’s 

decision to opt out of an expiring contract (“Opt-out”) or HUD’s abatement of an ongoing 

contract due to poor physical or financial condition of the property (“Contract Abatement”). 

Properties that stayed in the inventory because an owner actively renewed a Section 8 contract 

between 2005 and 2014 were characterized as “Opt-in,” while properties where all Section 8 

contracts continued from 2005 through 2014 with no need for the owner to renew a contract were 

characterized as “No Choice.” 

For the 236/BMIR properties, reasons for leaving the inventory included an owner’s decision to 

prepay a mortgage (“Prepayment”) or HUD’s decision to foreclose on the mortgage 

(“Foreclosure”). The new analysis adds “Maturity” as an outcome category for 236/BMIR 

mortgages. Very few 236/BMIR mortgages were scheduled to mature during the original 1998-

2004 study period, but most were scheduled to mature during the 2005-2014 period. Some 

properties did continue to show active 236/BMIR mortgages in 2014 (“Active”), although most 

of these were scheduled to mature by the end of 2017. 

Table 4 shows the breakdown of properties and units by subsidy type and outcome. The 

“Outcome, Detail” column shows the outcomes specific to each type of assistance. Properties 

with both types of Section 8 and 236/BMIR in 2005 are categorized by the combination of 

outcomes. 

The “Outcome, Summary” column shows four larger outcome categories that are used in the 

descriptive cross-tabulations: 

• “Opt-in” refers to properties with a Section 8 opt-in and either no 236/BMIR mortgage or 

a 236/BMIR mortgage that is still active or has matured.

• “Opt-out” refers to properties with a Section 8 opt-out, 236/BMIR prepayment or both. 

Properties with both types of assistance were included if the owner actively terminated 

both subsidies or if the property had a Section 8 opt-out and a maturing mortgage.

• “Foreclosure/abatement” refers to properties where HUD abated a Section 8 contract due 

to property conditions, foreclosed upon a 236/BMIR mortgage, or both. A small number 

of these properties were “stayers” in 2014 but were undergoing the contract abatement 

process. 

• The “Other” category covers a number of situations without a clear opt-in, opt-out or 

foreclosure and abatement action. These include properties where the owner has not had 

to make a Section 8 renewal choice and a small number of properties where 236/BMIR 

mortgages were terminated for reasons other than prepayment, maturity or foreclosure. It 

also includes properties where the owner made a mixed decision to continue one type of 

assistance but not the other. Most notably, hundreds of properties have prepaid 

236/BMIR mortgages but continue to have active Section 8 assistance.

The rightmost three columns of Table 4 show the breakdown of properties by outcome found in 

Table 2.2 in the original 2006 study (Table 2.2, page 14) for comparison purposes. The “Original 

Study Status Category” refers to letter/number combinations used to code properties by outcome 

in Table 2.2 of the 2006 study.

6 
Table 4. Properties and Units by Outcome, 2005-2014

2005 

Subsidy 

Type Outcome, Detail

Outcome, 

Summary Properties

% of 

Properties Units

% of 

Units

Original 

Study

Status 

Category

Original 

Study # of 

properties

Original

Study % of 

Properties

Section 8 

only

Opt in Opt-ins 11,677 64% 840,686 57% 1A1 9,268 41%

No choice Other 2,866 16% 254,912 17% 1A2 6,056 27%

Opt out Opt-Outs/ Prepays 514 3% 35,544 2% 1B 894 4%

Contract Abatement Foreclosure/ 

Abatement 223 1% 16,023 1% 1C 582 3%

236/ 

BMIR 

only

Active Other 79 0.44% 8,724 0.59% 2A 348 2%

Prepaid Opt-Outs/Prepays 122 0.67% 13,156 0.88% 2B 429 2%

Foreclosure Foreclosure/ 

Abatement 11 0.06% 1,623 0.11% 2C1 1,018 4%

Maturity Other 199 1% 19,109 1% N/A N/A N/A

Other Termination Other 111 0.61% 10,930 0.74% 2C2 93 0.41%

Section 8 

& 236/ 

BMIR

Section 8 opt in; 236/BMIR active Opt-ins 450 2% 63,284 4% 3A1 1,858 8%

Section 8 no choice; 236/BMIR active Other 208 1% 28,746 2% 3A2 49 0.22%

Section 8 opt out; 236/BMIR active Other 2 0.01% 134 0.01% 3D 77 0.34%

Section 8 opt in; 236/BMIR prepaid Other 667 4% 79,809 5% 3C1 588 3%

Section 8 opt out; 236/BMIR prepaid Opt-Outs/Prepays 61 0.34% 4,391 0.30% 3B 392 2%

Section 8 no choice; 236/BMIR prepaid Other 13 0.07% 1,534 0.10% 3C2 3 0.01%

Contract Abatement and Foreclosure Foreclosure/ 

Abatement 59 0.33% 6,043 0.26% 3E1 785 3%

Section 8 opt in; 236/BMIR matured Opt-ins 463 3% 50,202 3% N/A N/A N/A

Section 8 no choice; 236/BMIR matured Other 5 0.03% 456 0.03% N/A N/A N/A

Section 8 opt out; 236/BMIR matured Opt-Outs/Prepays 51 0.28% 5,828 0.39% N/A N/A N/A

Section 8 opt in; 236/BMIR termination 

(unknown/other) Opt-ins 196 1% 29,041 1.95% N/A N/A N/A

Section 8 no choice; 236/BMIR 

termination (unknown/other) Other 29 0.16% 3,859 0.26% N/A N/A N/A

Other Termination Other 101 0.56% 12,982 0.87% 3E2 31 0.14%

Total 18,107 1,487,016 22,741

Source: HUD, 2005 and 2014 Active Properties, Active Financing and Active Contracts Files, iREMS; Terminated Contracts Database; Terminated Multifamily 

Mortgages Database

7 
Table 5 shows the distribution of properties by the four summary outcome categories for the 

current study. 

Table 5. Properties by Summary Outcome Categories, 2005-2014

 Opt-ins

Opt-outs/ 

Prepays

Foreclosure/

Abatement Other Total

Number of properties 12,786 748 293 4,280 18,107

Percent of properties 71% 4% 2% 24% 100%

Source: HUD, 2005 and 2014 Active Properties, Active Financing and Active Contracts Files, iREMS; Terminated 

Contracts Database; Terminated Multifamily Mortgages Database

In the original report, the most common outcome (68 percent of cases) was continued 

affordability in a Section 8 only property, either through an opt-in contract renewal or a “no 

choice” continuation of an existing contract. This pattern was even stronger in the new analysis, 

with 80 percent of properties in the Section 8 only opt-in and no choice categories. The balance 

shifted more heavily toward opt-ins in the current analysis, as there were fewer properties that 

had not reached their first opt-in decision point than in the earlier study period. Less than four 

percent of the Section 8 only properties left the inventory during the study period. Most did so 

through owner opt-outs, with a smaller number of HUD-abated contracts.

For properties with both types of assistance, the most common outcome was a Section 8 opt-in 

combined with an end to the 236/BMIR mortgage through prepayment, maturity, or other 

termination reasons. Most of the other Section 8/236/BMIR properties had a Section 8 opt-in and 

an active 236/BMIR mortgage, although most of these mortgages will also be maturing in the 

next 2-3 years. Conversely, Section 8 opt-outs upon mortgage termination did not appear to be a 

threat to the inventory. Owners of properties with prepaid or maturing mortgages opted out of 

Section 8 assistance in only 112 cases.

Within the small stock of 236/BMIR properties without Section 8 that remained in 2005, most 

left the subsidized inventory through mortgage maturity, prepayment, or another termination 

reason by 2014. 

A key difference in results between the original and current studies is the role of foreclosure and 

enforcement actions. In the original study, 582 Section 8 only properties, 1,018 236/BMIR only 

properties, and 785 Section 8/236/BMIR properties were lost to HUD enforcement actions. In 

contrast, in the current analysis, only 293 properties were lost to the inventory through 

foreclosure and abatement actions, mostly through abatement of contracts in Section 8 only 

properties.3 It may be that failing properties were cleared from the inventory in the earlier study 

period, leaving a stronger inventory to persist throughout the 2005-2014 period. In particular, it 

is unlikely that a mortgage would be foreclosed upon only a few years before maturity compared 

to earlier in the life of the loan. 

3 Some of this may reflect differences in definition. This category in the original report includes properties that were 

“referred to [HUD’s] Enforcement Center” (p. 10), while the category in the current analysis only includes 

properties listed as having Section 8 contracts terminated due to troubled status (default, demolished, failed Housing 

Quality Standards, foreclosure, and fraud) and a small number of properties whose termination type in HUD’s 

Terminated Mortgage database is “Assigned.”

8 

 
Overall, the 2005-2014 analysis reflects two trends: the continuation of most Section 8 assistance 

and the winding down of the 236/BMIR program. First, of properties with Section 8 assistance in 

2005, 94 percent still had Section 8 assistance in 2014. Seventy-seven percent of these involved 

an active opt-in decision at least once during the study period. As Table 6 shows, the contracts 

renewed between 2005 and 2014 range from short to long term, with five-year and 20-year 

renewals the most common. 

Table 6. Renewal Terms for Section 8 Contracts in 2005-2014 Opt-In Properties4

Contract Term in Years Percentage of Contracts

1 7%

5 37%

10 3%

20 43%

All other (less than 5 years) 7%

All other (more than 20 years) 2%

Source: HUD, 2014 Active Contracts File, iREMS

Second, only 26 percent of properties with active 236/BMIR mortgages in 2005 still had active 

mortgages in 2014. Thirty-one percent of the 236/BMIR mortgages were prepaid during the 

study period, 25 percent matured, and the rest were terminated due to foreclosure or other 

reasons. 

Note that mortgage prepayment does not necessarily signal an opt-out decision, as most 

properties with prepaid mortgages also were Section 8 opt-ins. The distinction between 

prepayment and maturity may not have been as meaningful during the 2005-2014 period as in 

the earlier study, since most mortgages were due to mature by 2014. Of prepaid mortgages in the 

dataset, 57 percent were prepaid five years or less before their maturity date, including 24 

percent that prepaid within two years of the maturity date. In fact, HUD and advocates have 

encouraged owners to prepay maturing 236/BMIR mortgages in properties where some or all 

units do not have Section 8 assistance so that unassisted tenants can receive Section 8 tenant 

protection vouchers (HUD, 2012a). 

Finally, subsets of the 2005 baseline dataset were classified as participants in the Section 202 

Direct Loan program and RAD-eligible properties. Properties were included in the analysis only 

if they were part of the original Section 8 and 236/BMIR dataset; for example, developments 

with Section 202 Direct Loans but no rental assistance in any units would not be included. In 

total, 3,580 developments in the study dataset had active Section 202 Direct Loans in 2005, 

amounting to 20 percent of the total study dataset. For RAD, 321 developments in the study 

dataset had Rent Supp or RAP assistance and were designated as eligible for conversion by 

HUD. These properties are referred to as “RAD-eligible” in the report. Of the RAD-eligible 

developments, 63 underwent conversion to project-based Section 8 vouchers between 2012 and 

2014 (referred to as “RAD participants”).

4 Includes Section 8 Housing Assistance Payments (HAP) contract terms for 2014 active properties with contracts 

renewed between 2005 and 2014. Does not include “no choice” properties where the same contracts continued 

throughout the entire period without renewal. Does not include other Section 8 contract types, such as PAC or 

PRAC. Note that some properties have more than one Section 8 contract, including some that may be of different 

lengths or renewed on different schedules. Values do not add up to 100% due to rounding.

9 

 
II. Opt-in/Opt-Out Analysis: Descriptive Cross-Tabulations

The original Opting In, Opting Out study provided a series of cross-tabulations to describe the 

property, owner, tenant, and location characteristics associated with heightened risk of loss of 

properties through opt-outs and foreclosure. The report emphasized heightened risk to for-profit 

owned, family developments where rents were lower than the Fair Market Rent for the 

surrounding region. In this chapter, we provide updates of the cross-tabulations covering the 

2005-2014 study period. 

As the previous chapter showed, the balance of property outcomes shifted between the two study 

periods. Fewer properties left the inventory; more properties reached Section 8 decision dates 

and owners actively opted in to assistance; and the Section 236/BMIR programs largely wound 

down and had less effect on the balance of opt-outs and HUD foreclosure actions. Nevertheless, 

where there were property losses, the updated cross-tabulations show similar patterns of property 

characteristics as in the earlier study.

Unless otherwise noted, properties are placed in the descriptive categories that fit their 2005 

baseline characteristics rather than their current 2014 conditions. This is consistent with the 

approach taken in the original report, which used 1998 as a baseline. For example, a property 

owned by a for-profit corporation in 2005 but sold to a non-profit by 2014 would still be 

classified as having for-profit ownership. Using baseline characteristics provides several 

advantages. It focuses the analysis on the question of how property conditions at a moment in 

time affect future opt-in/opt-out decisions. It is also necessary in order to provide a consistent 

comparison across the entire study dataset, as no 2014 characteristics would be available for 

properties that left the assisted inventory during the study period. Moreover, many characteristics 

are likely to remain largely the same during the nine-year period, particularly physical 

characteristics (e.g., unit mix and building type) and tenant characteristics (e.g., average 

household size and income). 

The characteristics most likely to change during the study period are related to ownership and 

financing. In particular, some properties underwent preservation transactions that kept them in 

the inventory, where without the transaction the owner might have chosen to opt out of a Section 

8 contract. Those transactions often involve a change of ownership from a for-profit to a nonprofit entity, changes in financing programs and contract rents, and improved conditions leading 

to higher physical inspection scores. Therefore, classifying these properties by their baseline 

conditions may underestimate the opt-out risk that would have been present had the same owner 

been in place throughout the entire 2005-2014 study period. We note the study of properties’ 

year-over-year histories and the effects of preservation initiatives on opt-in/opt-out as areas for 

further research in the conclusion. 

Table 7 shows the sources of data for HUD property outcomes and the characteristics for the 

cross-tabulation analyses (analogous to Table 2.1 in the original report). 

10
Table 7. Data Sources

Variables Data Source

Section 8 and 236/BMIR program status 2005 and 2014 Active Properties; 2005 and 2014 

Active Financing; 2005 and 2014 Active Contracts 

(datasets generated from HUD iREMS) 

236/BMIR termination date and reason Terminated Mortgages database

Section 8 termination date and reason HUD Terminated Contracts database

Section 8 contract renewal start dates and terms 2014 Active Contracts database

Property geocoding (Census Tract, MSA, Census 

Division, metropolitan location)

Generated from HUD iREMS

Property characteristics (size, occupancy type, 

building type, program type, % assisted, building age 

based on occupancy date, REAC score)

2005 Active Properties, Multifamily Building Type 

(generated from HUD iREMS)

Units by number of bedrooms 2005 Active Properties

Ownership type 2005 Active Participants database (generated from 

HUD iREMS)

Neighborhood characteristics (median household 

income, median gross rent, median value of owneroccupied housing, homeownership rate, poverty rate, 

homeowner and renter vacancy rate, racial/ethnic 

composition)

U.S. Census Bureau, 2005-2009 American 

Community Survey. Summary files constructed by 

Minnesota Population Center. National Historical 

Geographic Information System: Version 2.0. 

Minneapolis, MN: University of Minnesota, 2011.

Home sales market (change in Housing Price Index) Federal Housing Finance Agency Housing Price Index

Rental market (change in HUD Fair Market Rent) HUD annual FMR datasets, 2005-2014

Tenant characteristics (length of residence, household 

size, percent minority-headed, percent elderly, percent 

with children, household income as percentage of 

AMI)

HUD Picture of Subsidized Households, 2005

11
Property and Owner Characteristics

Table 8 below includes the property and owner characteristics similar to those found in Tables 

3.1, 3.2 and 3.8 in the original report.5

Table 8. Property Characteristics and Owner Type, 2005 Baseline

Property Characteristics Opt-ins

Opt-outs/ 

Prepays

Foreclosure/ 

Abatement All Other Total

Number of Properties 12,786 748 293 4,280 18,107 

Percent of Properties 71% 4% 2% 24% 100%

Development Size

1-49 44% 46% 51% 28% 40%

50-99 27% 23% 22% 32% 28%

100-199 23% 22% 18% 30% 25%

200 or more 6% 8% 9% 10% 7%

Average number of units 77 79 81 98 82

Unit Size

0-bedroom units 7% 5% 4% 5% 7%

1-bedroom units 55% 37% 29% 46% 52%

2-bedroom units 25% 43% 43% 31% 27%

3-bedroom units 11% 14% 20% 15% 12%

4+-bedroom units 2% 1% 3% 2% 2%

Average number of bedrooms 1.6 1.8 2 1.7 1.6

Occupancy Type

Elderly/Disabled 59% 25% 30% 38% 52%

Family 41% 75% 70% 62% 48%

Building Type

Rowhouse 10% 6% 8% 8% 9%

Townhouse 3% 5% 2% 4% 3%

Semi-Detached 5% 3% 3% 4% 5%

Detached 5% 4% 7% 1% 4%

Walk-up/Garden 36% 56% 55% 37% 37%

Mid-Rise 3% 1% 2% 3% 3%

Mixed 12% 13% 15% 18% 14%

High-Rise/Elevator 28% 11% 9% 25% 26%

HUD Program Type

Newer 80% 57% 55% 58% 73%

Older 20% 43% 45% 42% 27%

5 Table 8 includes owner type and REAC inspection scores, which were placed in Tables 3.2 and 3.8 separate from 

other property characteristics in the original report. Table 3.2 in the original report included management review 

scores for owners, which were unavailable for this report. Table 3.8 included detailed financial information for the 

properties that was also unavailable in the datasets provided for this report.

12

 
Table 8. Property Characteristics and Owner Type, 2005 Baseline (continued)

Property Characteristics Opt-ins

Opt-outs/ 

Prepays

Foreclosure/ 

Abatement All Other Total

Detailed HUD Program Type 

Sec. 8 NC/SR 25% 25% 16% 25% 25%

Sec. 202 29% 4% 15% 3% 21%

Sec. 8/LMSA 19% 25% 41% 25% 21%

Sec. 8/515 10% 7% 8% 2% 8%

Sec. 8/HFDA 12% 11% 7% 27% 15%

Sec. 8/Preservation 2% 3% 1% 2% 2%

Sec. 8/PD 3% 10% 9% 1% 3%

Rent Supp/RAP 1% 0% 0% 6% 2%

No Rental Subsidy 0% 16% 4% 9% 3%

Average Percentage of 

Assisted Units 94% 60% 82% 83% 90%

Categories of Rent-to-FMR Ratio

Below 80% FMR 11% 28% 21% 16% 13%

Between 80% & 100% 24% 37% 41% 30% 26%

Between 101% & 120% 27% 20% 22% 30% 27%

Between 121% & 130% 11% 6% 5% 8% 10%

Between 131% & 140% 8% 3% 5% 6% 7%

Between 141% & 160% 10% 4% 2% 6% 9%

Over 160% FMR 9% 3% 3% 4% 7%

Building Age Categories 

Before 1975 17% 38% 33% 30% 21%

1975 - 1979 20% 19% 21% 30% 22%

1980 - 1985 46% 35% 31% 37% 43%

After 1985 17% 8% 15% 3% 13%

Ownership Type

Non-Profit 49% 25% 36% 26% 43%

For-profit 33% 40% 32% 39% 35%

Limited Dividend 15% 27% 28% 30% 19%

Other 2% 8% 4% 4% 3%

Missing data 14% 16% 12% 21% 15%

REAC Physical Inspection Score (1-100)

Median 91 88 79 89 90

1-59 2% 5% 24% 4% 3%

60-69 6% 9% 12% 6% 6%

70-89 36% 42% 36% 41% 37%

90-100 56% 44% 28% 50% 54%

Source: HUD, 2005 and 2014 Active Properties, Active Financing, Active Contracts and Multifamily Building Type

Files, iREMS; Terminated Contracts Database; Terminated Multifamily Mortgages Database 

13
The original report emphasized the loss of affordability in properties with family occupancy 

type, low rent-to-Fair Market Rent (FMR) ratios, financing through HUD’s older assistance 

programs, and for-profit ownership. These patterns are also present in the current crosstabulations, although the associations are not as strong as in the original report. 

First, family properties continued to make up 75 percent of opt-outs and 70 percent of 

foreclosure/abatements, even though more than half of the total inventory in 2005 consisted of 

properties for the elderly and persons with disabilities. In the original report, approximately 90 

percent of opt-outs and foreclosures took place in family properties. The new cross-tabulation 

also shifts more properties with elderly or disabled occupants to the opt-in category from the “all 

other” category, indicating that more of these properties required an active renewal choice during 

the 2005-2014 study period. 

Second, properties with low rent-FMR ratios continued to be overrepresented among opt-outs. 

Only 13 percent of all properties in the study had rent-FMR below 80 percent, but they 

represented 28 percent of all opt-outs. Properties with rents at 80-100 percent of FMR made up 

26 percent of the total inventory but 37 percent of opt-outs. Properties with lower rents also were 

more heavily represented among the foreclosure/abatement properties. Again, this pattern was 

similar to the original report but of weaker magnitude. In the original report, 59 percent of optouts and 49 percent of foreclosure/abatements took place in properties with rents below 80 

percent of FMR, and most of the remaining losses were among properties with rents at 80-100 

percent of FMR.

Third, the original study emphasized property losses among the Older Assisted HUD properties, 

which made up 37 percent of the total inventory but 70 percent of opt-outs and 89 percent of 

foreclosure/abatement properties. By the second study period, more of the Older Assisted 

properties had already left the inventory, particularly those originally funded by the Section 236 

and 221(d)(3) BMIR programs. In the current dataset, 27 percent of properties were in the Older 

Assisted category. These properties were still overrepresented in the property loss categories in 

the second period, with 43 percent of opt-outs and 42 percent of foreclosure/abatements, but 

clearly more of the property loss problem had shifted to the Newer Assisted inventory.

Finally, properties owned by profit-motivated companies, including for-profit and limited 

dividend corporations, continued to be at higher risk of opt-out and foreclosure/abatement. 

Nevertheless, the current analysis shows some increase in opt-outs by non-profits. While the 

share of non-profit properties in the overall inventory was the same in the two study periods, 

non-profits made up 25 percent of opt-outs in the later study, compared to just nine percent in the

earlier study. The prevalence of non-profit-owned properties in the opt-in category also 

increased, from 36 percent of opt-ins in the original study to 49 percent in the current study. The 

combined opt-in/opt-out prevalence indicates that more non-profits faced a renewal choice 

during the second study period than the first.6

 

6 To delve deeper into the lack of opt-outs among non-profit owned properties, the original study included a 

breakdown of opt-in/opt-out outcomes by the Older/Newer HUD assistance categories for non-profit owned 

properties only (Table 3.3, page 25). The comparison is less relevant in the current study as non-profit ownership 

became more prevalent among opt-out properties. Nevertheless, the analogous results for the 2005-2014 analysis are 

presented here for comparison purposes.

Opt-ins

Opt-outs/ 

Prepays

Foreclosure/ 

Abatement All Other Total

Newer Assisted, Non-Profit Owned 89% 2% 1% 8% 100%

Older Assisted, Non-Profit Owned 64% 4% 2% 30% 100%

14

 
Other findings from the current property characteristics cross-tabulations are as follows:

• Property and unit size. Foreclosure/abatement was more common among developments 

with fewer than 50 units than in larger properties. It was also more common among 

properties with higher shares of two and three bedroom units. This is likely associated 

with these properties’ family occupancy type. 

• Building type. Opt-outs and foreclosure/abatement were more common among low-rise 

multifamily building types (walkups, garden apartments) and less common among highrises. This pattern held during both study periods. Again, this appears to be associated 

with occupancy type. High-rises are predominantly occupied by elderly households, 

while walkups are largely occupied by families.

• Detailed HUD programs. In the original study, properties with no rental subsidies (i.e., 

236/BMIR) made up substantial shares of opt-outs and foreclosures (25 percent and 48 

percent, respectively). During the current study period, however, the remaining properties 

with 236/BMIR mortgages either approached or reached their mortgage maturity dates; 

therefore, opt-outs and particularly foreclosures were less common. The original study 

also showed a disproportionate rate of opt-out and foreclosure among properties with 

Section 8 LMSA funding. In the current study, these properties showed lower opt-out 

rates but higher foreclosure rates. Finally, Section 202 properties made up less than two 

percent of abatements in the original study but 15 percent in the current study, despite 

similar prevalence in the overall study dataset. 

• Percent assisted units. In the original study, opt-out properties had a lower percentage of 

units with rental assistance than other properties (72 percent of units assisted on average 

for opt-outs versus 85 percent for the overall study dataset). This tendency was even 

stronger during the current study period, with an average of 60 percent assisted units in 

opt-out properties versus 90 percent in the overall dataset. This reflects the prevalence of 

236/BMIR prepayment in properties that either had no Section 8 assistance or were only 

partially assisted.

• Building age. In both datasets, opt-outs and foreclosure/abatement were more common in 

properties built or occupied before 1975. 

• REAC scores. HUD’s Real Estate Assessment Center (REAC) conducts physical 

inspections of assisted units. Properties are scored on a scale of 0-100, with 60 as a 

passing score. The median REAC score for properties in the current dataset was 91, much 

higher than the median score of 73 in the original study. As would be expected, in both 

studies the median score for properties facing foreclosure/abatement were lower (79 in 

the current dataset, 69 in the earlier study). 

Financing Characteristics

Table 9 provides more detail about federal and state financing involved in the properties, similar 

to Table 3.4 in the original report.

15
Table 9. Financing Characteristics, 2005 Baseline

Financing Characteristics Opt-ins

Opt-outs/ 

Prepays

Foreclosure/ 

Abatement All Other Total

Number of Properties 12,786 748 293 4,280 18,107 

Percent of Properties 71% 4% 2% 24% 100%

Primary Form of Financing

FHA Insured 27% 39% 23% 51% 33%

Section 202/811 27% 4% 12% 2% 20%

Section 515 10% 7% 8% 2% 8%

All Other 36% 51% 57% 45% 39%

Total 100% 100% 100% 100% 100%

HFA Related Properties

Number of HFA related 1,553 82 21 1,140 2,796 

Percent of HFA related 56% 3% 1% 41% 100%

FHA-insured 24% 21% 19% 24% 24%

Non-insured 76% 79% 81% 76% 76%

Total 100% 100% 100% 100% 100%

Source: HUD, 2005 and 2014 Active Properties, Active Financing, Active Contracts and Multifamily Building Type

Files, iREMS; Terminated Contracts Database; Terminated Multifamily Mortgages Database 

Table 9 shows the decline in the presence of FHA-insured properties (with either 236/BMIR or 

market-rate loans) in the assisted inventory. These properties made up over half of properties in 

the original dataset but just a third in the current dataset. As in the earlier study, opt-outs were 

rare among Section 202/811 developments. However, opt-outs by properties with financing from 

USDA’s Section 515 program increased since the last study period. In the original study, 

properties with USDA Section 515 mortgages made up a large proportion of opt-ins and a very 

small proportion of opt-outs compared to their presence in the overall inventory. In this dataset, 

their share of opt-ins (10 percent) and opt-outs (7 percent) closely mirrors their eight percent 

share of the overall inventory. 

Table 9 also shows a shift in the status of properties financed by mortgage revenue bonds from 

state housing finance agencies (“HFA Related”). In both studies, few of these properties were 

lost to the inventory through opt-out or foreclosure during the study period, although that small 

rate of opt-out did increase (from 1.4 percent in the original study to 3 percent of HFA properties 

in the current dataset). More importantly, in the first study, many of the HFA-financed properties 

had not yet reached their first Section 8 contract renewal date, with 75 percent of the properties 

concentrated in the “All Other” category and only 23 percent classified as opt-ins. In the current 

study period, the majority of these properties met their contract renewal dates. Only 41 percent 

were classified in the “All Other” category. Table 9 shows that the owners of most of these 

properties chose to renew rental assistance, with 56 percent of HFA-related properties now 

classified as opt-ins and only three percent classified as opt-outs.

Location Characteristics

Table 10 shows the cross-tabulation of property outcomes by location, corresponding to Table 

3.6 in the original report. It tabulates properties by region of the country (“Census Division”), 

placement in a city, suburb, or non-metropolitan area (“Metropolitan Location”), and the average 

housing market and demographic characteristics of the Census tracts surrounding the properties. 

For the current report, we included additional characteristics to round out the picture of the 

16
neighborhood housing market: median value of owner-occupied housing, owner-occupied 

housing vacancy rate, and rental vacancy rate. We also added Hispanic households to the 

race/ethnicity neighborhood analysis. 

In addition to neighborhood market characteristics, the new analysis includes regional home sale 

and rental market indicators with average conditions in the Metropolitan Statistical Area (MSA). 

For properties outside of an MSA, we use the statewide non-MSA value. For sales, we show the 

average change in the FHFA Housing Price Index (HPI) in regions surrounding the properties 

over the 2005-2014 study period. For additional information about volatility in the regional 

housing markets, we also provide the change over three housing market phases: “strong” (2005-

2007), “weak” (2007-2012) and “recovering” (2012-2014). All changes are measured from the 

first quarter of the first year to the first quarter of the last year in the period. For rents, we use the 

average change in HUD Fair Market Rent (FMR) for a two-bedroom apartment in the regions 

surrounding the properties. Because average FMRs continued to rise throughout 2005-2014, we 

do not separate the study period into shorter phases as we do with sale prices.

Table 10. Locational Characteristics, 2005 Baseline

Locational Characteristics Opt-ins

Opt-outs/ 

Prepays

Foreclosure/ 

Abatement All Other Total

Number of properties 12,770 742 293 4,264 18,0697

Percent of properties 71% 4% 2% 24% 100%

Census Division

New England 10% 4% 3% 14% 10%

Mid Atlantic 13% 10% 9% 15% 13%

East North Central 18% 12% 20% 20% 18%

West North Central 11% 19% 16% 11% 11%

South Atlantic 16% 16% 22% 14% 15%

East South Central 7% 6% 8% 9% 8%

West South Central 7% 10% 15% 6% 7%

Mountain 5% 7% 3% 5% 5%

Pacific 13% 16% 4% 8% 12%

Metropolitan Location

Suburb 31% 28% 22% 33% 31%

Principal city 51% 60% 61% 53% 52%

Non-metropolitan 18% 12% 18% 15% 17%

Neighborhood Characteristics

Median household income $39,831 $41,937 $35,371 $38,363 $39,498

Median gross rent $693 $741 $652 $675 $690

Median value of owner 

i d h i

$197,022 $200,939 $146,169 $191,037 $194,958

Homeownership rate 52% 51% 51% 50% 52%

Poverty rate 22% 20% 26% 23% 22%

Homeowner vacancy rate 3% 3% 4% 4% 3%

Renter vacancy rate 7% 8% 10% 8% 8%

7 Four properties in the Northern Mariana Islands, 12 properties in the U.S. Virgin Islands, and 22 properties with 

insufficient geocoding information are excluded.

17

 
Table 10. Locational Characteristics, 2005 Baseline (continued) 

Locational Characteristics Opt-ins

Opt-outs/ 

Prepays

Foreclosure/ 

Abatement All Other Total

Racial/ethnic composition 

White 60% 59% 48% 58% 59%

African American 19% 19% 36% 22% 20%

Hispanic 15% 15% 11% 14% 15%

Asian 3% 3% 2% 3% 3%

Other 3% 3% 3% 3% 3%

Minority 40% 41% 52% 42% 41%

Regional Housing Market 

Home sales market

Average percent change in 

Housing Price Index (HPI), 

full study period (2005 Q1-

2014 Q1)

3% 6% 5% 1% 3%

Average percent change in

HPI, strong market period 

(2005 Q1 -2007 Q1)

14% 16% 14% 12% 14%

Average percent change in

HPI, weak market period 

(2007 Q1-2012 Q1)

-13% -14% -11% -13% -13%

Average percent change in

HPI, recovering market 

period (2012 Q1 -2014 Q1)

6% 8% 4% 6% 6%

Rental market

Average percent change in

FMR, 2005-2014 27% 27% 29% 26% 27% 

Source: HUD, 2005 and 2014 Active Properties, Active Financing, and Active Contracts Files, iREMS; Terminated 

Contracts Database; Terminated Multifamily Mortgages Database; 2005 and 2014 Fair Market Rents; U.S. Census 

Bureau, 2005-2009 American Community Survey; Federal Housing Finance Agency Housing Price Index 

The analysis shows some similarities and some changes in the geographic locations of opt-outs. 

The greatest change was the status of the West North Central region, ranging from the Dakotas, 

Minnesota and Iowa down through Missouri, Kansas and Nebraska. In the original study, this 

area had a relatively low share of opt-outs. In the current study it made up 19 percent of opt-outs, 

even though it only represented 11 percent of total properties. This is largely due to a high optout rate for properties in North Dakota, which has experienced a boom in oil production in recent 

years. Nearly 17 percent of North Dakota’s 133 properties underwent an opt-out, compared to an 

opt-out rate of just four percent nationwide. 

The Pacific region, in contrast, was overrepresented among opt-outs in both periods. In the 

original study, it had 13 percent of total properties but 21 percent of opt-outs; in the latter period, 

it had 16 percent of opt-outs but still just 12 percent of total properties. The Mountain and West 

South Central regions also had relatively high proportions of opt-outs in the original study and, 

to a lesser extent, in the current study. 

The analysis also shows some shifts in the metropolitan location of property losses. In both 

studies, central city locations had a high share of opt-outs. In the earlier study, suburban 

locations also showed a relatively high share of opt-outs, but in the current analysis, suburban 

locations were underrepresented among opt-outs. Foreclosure/abatements were concentrated in 

18
city locations and were underrepresented in suburban locations in both studies. However, in the 

second study, foreclosures/abatements also became more prevalent in non-metropolitan 

locations. 

As in the previous study, properties in neighborhoods with stronger markets were more prone to 

opt-outs, while those in weaker markets were more prone to foreclosure/abatement. Among optout properties, median household income, median rent, and home values were higher, while the 

poverty rate was lower. For the foreclosure/abatement properties, these patterns were reversed, 

with lower than average incomes, rents and prices, and higher poverty and vacancy. In the 

neighborhoods surrounding the opt-in properties, poverty and vacancy rates were similar to optout neighborhoods, but average rents, incomes, and sales prices were somewhat lower. The 

neighborhoods surrounding the opt-in and opt-out properties also had similar racial and ethnic 

composition, while the areas around the foreclosure/abatement properties had a higher average 

minority population. 

While all of the property categories showed rising regional home prices in the 2005-2007 period, 

falling prices in the 2007-2012 years, and new growth in prices in the 2012-2014 period, there 

were modest differences between the categories. The properties lost to the inventory through 

both opt-outs and foreclosure/abatement tended to be located in regions with stronger home sales 

price growth over the full 2005-2014 period. For opt-out properties, average sales price growth 

was particularly strong during the 2005-2007 housing boom and 2012-2014 recovery. The 

foreclosure/abatement properties were located in regions where growth was strong during the 

2005-2007 period and where price falls during the 2007-2012 were not quite as steep. 

As to rental markets, increases in the average two-bedroom FMR were very similar across the 

property outcome categories, with somewhat higher average increases for foreclosure/abatement 

properties (close to 29 percent compared to 27 percent for other types of properties). The 

comparatively strong growth in both regional home prices and FMRs for the 

foreclosure/abatement properties shows that while failing properties may be located in weaker 

market neighborhoods, these neighborhoods are often situated within regions with strong 

housing markets. 

Tenant Characteristics

Table 11 shows the average values for a variety of characteristics of tenant households living in 

the subsidized properties during the 2005 baseline year. It corresponds to Table 3.7 in the 

original report. Data come from HUD’s 2005 Picture of Subsidized Households dataset. Two

variables are specified in slightly different forms than in the original report due to the format of 

the Picture dataset: 1) we provide percentages of all persons with a disability in the units rather 

than just household heads, and 2) households with children are divided into those with two adults 

present and those with one adult. 

19
Table 11. Tenant Characteristics, 2005 Baseline

Average Tenant Characteristics Opt-ins

Opt-outs/ 

Prepays

Foreclosure/ 

Abatement

All 

Other Total

Length of residence (years) 5.9 5.9 5.2 5.8 5.9

Household size 1.6 1.9 2.1 1.9 1.7

Percent minority-headed 40% 46% 66% 45% 41%

Percent of all persons with disability 22% 17% 17% 16% 20%

Percent elderly-headed households 49% 30% 20% 39% 46%

Percent households with 2+ adults and 

children 5% 9% 8% 8% 6%

Percent households with 1 adult and 

children 20% 30% 41% 28% 22%

Household income as a percentage of 

area median income (AMI) 23% 22% 17% 22% 22%

Source: HUD, 2005 and 2014 Active Properties, Active Financing, and Active Contracts Files, iREMS; 

Terminated Contracts Database; Terminated Multifamily Mortgages Database; 2005 Picture of Subsidized 

Households

The tenant characteristics cross-tabulations from the two periods show many similarities. Both 

show higher levels of stability in the housing stock for elderly households, with a higher 

percentage of elderly households in the opt-in category and proportionately low percentages of 

elderly in the opt-out and foreclosure/abatement categories. Single-parent households with 

children were disproportionately present in the opt-out category and, in particular, 

foreclosure/abatement properties, as are minority-headed households. One difference between 

the two studies is the shorter average length of tenure in the current study for residents of the 

foreclosure/abatement properties. Poor property conditions may encourage tenants to move 

sooner; conversely, vacancies caused by tenant moves would contribute to further financial 

distress at the properties. The shorter tenure may also be an effect of having fewer elderly 

tenants, who tend to move less frequently than younger households. 

Both studies show similar average incomes across the opt-in, opt-out, and “all other” properties, 

with expected lower average incomes in the failing foreclosure/abatement properties. However, 

average incomes in the overall stock fell substantially between the two study periods, with an 

average of 28 percent of area median income (AMI) in the original study period and 22 percent 

of AMI in the current study. At least in part, this reflects the lower proportion of Section

236/BMIR only properties in the second dataset. These properties do not have associated deep 

rental subsidies and tend to serve a more moderate income population. 

20
III. Multivariate Analysis of Property Characteristics and Opt-in/Opt-out 

Status

The second type of quantitative analysis in the original report was a multivariate analysis to 

isolate the effects of property, financing, and location characteristics on owners’ decisions to opt 

into or out of Section 8 assistance. The original analysis included the Section 8 only properties 

where the owner had to make an explicit decision to renew or opt out of a contract during the 

1998-2004 study period. Properties that also had a 236/BMIR mortgage and those where the 

owner did not have to make a Section 8 renewal choice during the study period were excluded 

from the dataset.

In this report, we provide three versions of the updated multivariate analysis covering the Section 

8 opt-in/opt-out decision during the 2005-2014 study period. All of the variables from the 

original analysis are included in the update, although some are expressed in slightly different 

categories. The first version replicates the model in the original study: an analysis of Section 8 

only developments, using the same set of variables. The second version uses a larger study 

dataset that adds the Section 8 + 236/BMIR properties in addition to the Section 8 only

properties. This model includes additional variables: REAC physical scores of the properties and 

the percentage minority and homeownership rate in the surrounding Census tract. We also added 

the presence of a maturing 236/BMIR mortgage as a potential opt-out risk factor, a variable that 

would not have been relevant during the previous study period. A third version of the model adds 

neighborhood housing market variables comparing median rent and median home values for the 

Census tracts surrounding the properties to county-level values, and regional housing market 

variables comparing MSA-level changes in the Housing Price Index and Fair Market Rents 

during the weaker market years in the middle of the study period. 

Table 12 below shows the variables included in the updated analysis, comparable to Table 3.10 

in the original report. The rightmost column describes the direction we anticipate for each 

variable. For variables repeated in both study periods, this is a description of the results from the 

previous analysis. For new variables, we suggest an expected direction. 

21
Table 12. Variables for Regression Model

Variable Type Variable Categories

In Original 

Report? Original Results or Expected Direction

Property Property size 

(units)

Less than 50 units (ref.) 

50-99 units 

100-199 units 

200+ units 

Yes Original study: reduced odds of opt-out 

as project size increases.

Density % of 3+ bedroom units Yes Original study: reduced odds of opt-out 

as number of larger units increase.

Occupancy 

type 

Family

Elderly/Disabled (ref.)

Yes Original study: increased odds of opt-out 

in family properties

Building type Detached /Semidetached 

Other types (ref.) 

Yes Original study: slightly increased odds of 

opt-out in detached/semi-detached. 

Ownership type Nonprofit

For-profit/Limited 

Dividend (ref.)

Yes Original study: increased odds of opt-out 

for non-profit owned properties.

REAC Physical REAC score (1 

point increase)

No Expected direction: Unknown. Higher 

REAC score may increase odds of optout because properties in good physical 

condition are more marketable to 

market-rate tenants. However, owners of 

properties in poor condition could opt 

out of subsidy programs to preempt a 

HUD decision to abate subsidies. 

Ratio of rentto-FMR Less than 80% 

80-99.9%

100-119.9% (ref.)

120-129.9%

130-139.9%

140-159.9%

160% or more 

Yes Original study: strongly increased odds 

of opt-out for properties with lower rentto-FMR, particularly less than 80 

percent.

Financing Older Assisted 

HUD programs 

Older Assisted = 1 

Newer Assisted=0 

Yes Original study: increased odds of opt-out 

for Older Assisted properties.

100 percent 

assisted8 

Projects with 100 

percent of units with 

rental assistance =1 

Other = 0 

Yes Original study: strongly increased odds 

of opt-out for 100 percent assisted 

properties.

FHA insured FHA insured =1

Other =0

No Expected direction: Lower odds of optouts for properties with insured 

mortgages since some FHA mortgages 

impose affordability restrictions.

HFA related HFA related =1

Other =0

Yes9 Original results: Lower odds of opt-out 

for HFA-financed properties.

Mortgage 

matured

Matured mortgage of 

Section 236/BMIR 

properties = 1

Other =0

No Expected direction: Higher odds of optout for properties with maturing 

mortgages, since restrictions associated 

with the mortgage are ending.

8 Developments were classified as “100 percent assisted” if total units exceeded assisted units by no more than two. 

The two-unit difference was allowed to account for developments where 1-2 units are used as offices for 

management, maintenance, security, and so forth.

9 Expressed as "Not federally financed mortgage" as proxy for HFA-financed property. Current study uses direct 

measure of HFA funding.

22

 
Table 12. Variables for Regression Model (continued) 

Variable Type Variable Categories

In Original 

Report? Original Results or Expected Direction

Neighborhood Poverty rate % persons below 

poverty level in a 

Census tract 

Yes Original results: Lower odds of opt-out 

for properties in neighborhoods with 

higher poverty rate.

Minority rate % of minority (All races 

except non-Hispanic 

White) in a Census tract

No Expected direction: Lower odds of optout because of difficulty marketing 

developments in racially segregated 

areas.

Home

ownership rate

% of owner occupied 

housing in a Census 

tract

No Expected direction: Unknown. Higher 

homeownership rate may signal an 

overall stronger neighborhood housing 

market, but also may imply a smaller 

surrounding renter population, making it 

more difficult to attract tenants.

Location Metropolitan 

location 

Suburbs (ref.)

Principal cities 

Non-metropolitan 

Yes Original study: increased odds of opt-out 

for central city and non-metropolitan 

locations

Census division New England 

Mid Atlantic 

East North Central 

West North Central 

South Atlantic(ref.)

East South Central 

West South Central 

Mountain 

Pacific 

Yes Original study: increased odds of opt-out 

for Mid Atlantic, East North Central, 

West North Central, West South Central, 

Mountain and Pacific regions; decreased 

odds for New England and East South 

Central. 

Housing 

Market

Neighborhood 

rent level

Ratio of median gross 

rent between a Census 

tract and a county

No Expected direction: increased odds of 

opt-out for properties in neighborhoods 

with a high tract rent-county rent ratio.

Neighborhood 

housing value

Ratio of median value 

of owner occupied 

housing between a 

Census tract and a 

county

No Expected direction: increased odds of 

opt-out for properties in neighborhoods 

with a high tract value-county value 

ratio.

Regional sale 

market

Percent change in 

Housing Price Index 

2007 Q1-2012 Q1

25% or more

0-24.99%

-25%~ 0.01% (ref.)

-50%~ -25.01%

Lower than -50% 

No Expected direction: increased odds of 

opt-out for properties in neighborhoods 

with positive change in HPI.

Regional rental 

market

Percent change in FMR 

during the recession 

2007 Q1- 2012 Q1

20% or more

10%-19.99%

0%-9.99%

Less than 0% (ref.)

No Expected direction: increased odds of 

opt-out for properties in neighborhoods 

with higher positive change in FMR.

23
Table 13 shows the results of the new regression analyses, corresponding to Table 3.11 in the 

original report. The leftmost set of odds ratios are the results for the Section 8 only model with 

the variables from the original report. The middle set shows the new model that includes Section 

8 + 236/BMIR properties and some of the new variables, but excludes the four new 

neighborhood and regional housing market variables. The rightmost set of odds ratio includes the 

market variables. 

Overall, the findings across the three models were very consistent. The direction and statistical 

significance of the common variables was similar, with slight changes in the magnitude of the 

odds ratios. The discussion following the table uses the results from the rightmost “Model with 

Housing Market Variables” columns unless otherwise noted. 

Table 13. Odds Ratios for Opt-Out Decision Model, 2005-2014

Replication of 

Original Model

(Section 8 Only)

Model without 

Housing Market 

Variables (Section 8 

Only and Section 8 + 

236/BMIR)

Model with 

Housing Market 

Variables (Section 

8 Only and Section 

8 + 236/BMIR)

Variable Odds 

Ratio

PvalueOdds 

Ratio P-value Odds Ratio

Pvalue

Property size

(ref. less than 50)

Property size 50-99 0.383 *** 0.380 *** 0.388 ***

Property size 100-199 0.389 *** 0.324 *** 0.339 ***

Property size 200+ 0.291 *** 0.231 *** 0.232 ***

Density % of units with 3 or more 

bedrooms 0.239 *** 0.352 *** 0.332 ***

Occupancy types Family

(ref.elderly/disabled) 2.413 *** 2.249 *** 2.207 ***

Building types Detached or semi-detached

(ref. other) 1.009 1.064 1.044

Ownership types Non-profit

(ref. for-profit/limited 

dividend)

0.498 *** 0.482 *** 0.472 ***

REAC physical 

score 1 percentage point increase N/A 0.983 *** 0.982 ***

Program Older programs 0.471 *** 0.540 *** 0.552 ***

100% assisted units 0.145 *** 0.127 *** 0.132 ***

FHA insured N/A 0.437 * 0.432 *

HFA related 1.584 * 2.069 *** 1.999 **

Matured Section 236/BMIR N/A 1.085 1.016

Neighborhood Poverty rate 0.245 ** 0.169 *** 0.356

Minority rate N/A 0.656 0.869

Homeownership rate N/A 0.427 ** 0.359 **

Rent-to-FMR

(ref. 100-119.9%)

Rent-to-FMR less than 80% 2.952 *** 2.627 *** 2.990 ***

Rent-to-FMR 80-99.9% 1.763 *** 1.733 *** 1.885 ***

Rent-to-FMR120-129.9% 0.792 0.717 0.730

Rent-to-FMR130-139.9% 0.459 ** 0.430 ** 0.455 **

Rent-to-FMR140-159.9% 0.642 0.585 0.564 *

Rent-to-FMR 160% or more 0.573 0.539 0.512 *

Metropolitan

Location 

(Ref. suburbs)

Principal cities 1.540 *** 1.356 ** 1.213

Non-metropolitan 1.078 1.164 1.088

24
Table 13. Odds Ratios for Opt-Out Decision Model, 2005-2014 (continued) 

Replication of 

Original Model

(Section 8 Only)

Model without 

Housing Market 

Variables (Section 8 

Only and Section 8 + 

236/BMIR)

Model with 

Housing Market 

Variables (Section 

8 Only and Section 

8 + 236/BMIR)

Variable Odds 

Ratio

PvalueOdds 

Ratio P-value Odds Ratio

Pvalue

Census Division

Ref. South Atlantic

New England 0.426 ** 0.361 *** 0.363 ***

Mid Atlantic 1.296 0.932 0.827

East North Central 0.858 0.659 * 0.612 **

West North Central 1.841 *** 1.433 * 1.233

East South Central 0.664 0.886 0.790

West South Central 1.267 1.219 0.780

Mountain 1.751 ** 1.653 ** 1.879 **

Pacific 1.006 0.798 0.853

Neighborhood

market

Neighborhood rent N/A N/A 4.036 ***

Neighborhood housing value N/A N/A 1.365 **

Regional sale

market during 

recession (2007-

2012)

(ref. moderate 

decline; -25% to 

0% change )

Severe decline: change of 

-50% or more N/A N/A 0.929

Decline: -25% to -50 % N/A N/A 0.942

Growing: 0% to 25% N/A N/A 1.634 ***

Strongly growing: 25% or 

more

N/A N/A 1.483

Regional rental

Market (2007-

2012) 

(ref. FMR 

declining)

Stable: 0-10% N/A N/A 1.385

Growing: 10-20% N/A N/A 1.197

Strongly growing: 20% or 

more

N/A N/A 1.218

Total number of properties 9,085 10,120 10,023

Opt-outs 278 364 361

Pseudo R2 0.1811 0.1969 0.2101

Asterisks in the p-value columns denote level of significance. *** indicates p<0.01; ** indicates p<0.05; * indicates 

p<0.1. A blank cell in the p-value column indicates result was not statistically significant.

Source: HUD, 2005 and 2014 Active Properties, Active Financing, Active Contracts and Multifamily Building Type

Files, iREMS; Terminated Contracts Database; Terminated Multifamily Mortgages Database; 2005 Picture of 

Subsidized Households; 2005 and 2014 Fair Market Rents; U.S. Census Bureau, 2005-2009 American Community 

Survey; Federal Housing Finance Agency Housing Price Index 

As with the cross-tabulation analyses, the new analysis contains many findings similar in 

direction to the original analysis but with a different magnitude. The original report emphasized 

rent-to-FMR ratio and owner type as key explanatory variables. In the original model, properties 

with rent-to-FMR below 80 percent were almost 12 times more likely to opt out than those at 

100-120 percent of FMR, the reference case. Properties sponsored by for-profits were more than 

six times more likely than those owned by non-profits to opt out. 

In the new study, the direction of findings for rent-to-FMR and owner type was the same, but the 

effects were less strong. Properties with rent-to-FMR below 80 percent were 2.7 times more 

likely to opt out than the 100-120 percent of FMR reference case, while non-profit owned 

25
properties were half as likely to opt out as those owned by for-profit or limited dividend 

corporations. The results were similar across the replication of the original model and both of the 

new models. These results suggest that the opportunity for higher rents in the private market 

continued to play a role in pushing for-profit owners of properties with below-market rents to opt 

out of subsidies, but that this factor was considerably less influential in 2005-2014 than during 

the original study period. HUD researchers have suggested that the new zip code-level Small 

Area FMRs could be used to measure the gap between neighborhood rents and property rents 

more accurately. We include an exploration of the effects of Small Area FMRs on the rent-FMR 

analysis as Appendix 3. 

One variable where results were the same in both study periods is occupancy type. The original 

study found that that properties with a family population were more likely to opt out than those 

with elderly or disabled occupancy (odds ratio 2.3). All of the current models also showed a 2.2-

2.4 odds ratio for this variable. 

For another key variable, our results differed from the regression analysis in the original study. 

The cross-tabulation analysis in the original study showed that opt-out properties tended to have 

lower percentages of assisted units, but when regression analysis was used to control for other 

factors, the original study showed that properties with rental assistance in all units were nearly 14 

times more likely to opt out than those with partial Section 8 assistance. In the current regression 

analyses, however, properties with all units assisted were 7-8 times less likely to opt out than 

partially assisted properties, consistent with the findings in the cross-tabulation analyses in both 

reports. 

Results for physical property characteristics were similar to those in the original study. Opt-out 

risk was higher for properties with fewer than 50 units and with lower concentrations of large 

units. As in the original report, the effect of detached/semi-detached buildings versus other 

building types was not significant.10 

A new finding in this study was the effect of REAC physical condition scores on opt-outs. 

Higher REAC scores were associated with slightly lower odds of opt out, a counterintuitive 

finding that is nevertheless consistent with cross-tabulation analyses in both the previous and 

current reports. A one-point increase in REAC score resulted in a decrease in odds of opt-out of 

approximately 1.6 percent. As noted earlier, it may be that owners chose to opt out of assistance 

for properties with lower REAC scores to preempt a potential HUD abatement of subsidies.

Also, owners who were planning to opt out of subsidies and sell or convert properties to marketrate may have delayed investing in physical improvements until after the opt-out.

Characteristics related to financing programs also had significant effects. Properties with older 

HUD assistance were only half as likely to opt out as those funded under newer HUD programs 

(odds ratio .552). State HFA-related properties were more likely to opt out than other properties

(odds ratio 1.999). Both of these results differed from the original study, which showed that 

Older Assisted properties were more likely opt out and showed no statistically significant effect 

from HFA financing. (Finkel et al., 37) A possible explanation for the current results is that that 

the Newer Assisted properties and the state-financed properties were more likely to be reaching 

their first opt-out decision during the study period than the Older Assisted or non-state-financed 

10 We tested different model specifications for multifamily building types, such as mid- and high-rise. The results 

showed that low-density building types were at higher risk for opt out. Compared to the high-rise reference category, 

properties with detached/semi-detached units were more likely to opt out, followed closely by properties with a mix 

of building types. Mid-rise properties were also more likely to opt out than high-rises. 

26

 
properties. We speculate that owners of properties are more likely to take their first opportunity 

to opt out rather than to renew a contract initially and subsequently opt out. 

Surprisingly, owners of properties with 236/BMIR mortgages maturing by 2014 were less likely 

to opt out of Section 8 assistance. While high levels of contract renewals among properties with 

expiring mortgage-related affordability restrictions seems counterintuitive, this result is 

consistent with the property outcomes summarized in Table 4. The table shows that owners 

renewed rental assistance contracts in over 90 percent of Section 8 properties that also had 

maturing 236/BMIR mortgages. 

The analysis shows that metropolitan and neighborhood locations affect opt-out decisions. As in 

the original report, properties located in central cities were more likely to opt out than those in 

suburban locations. Properties in higher poverty neighborhoods were also less likely to opt out. 

The poverty effects were linked to the weak housing markets in high poverty neighborhoods; as 

the side-by-side models in Table 13 show, when neighborhood housing market variables were 

controlled, the effect of the poverty rate became both weaker and less statistically significant. 

Properties in neighborhoods with higher homeownership rates were also less likely to opt out. It

may be that the lack of an active rental market in neighborhoods with high homeownership rates 

discourages market-rate conversion of properties. The percentage of minority residents was not a 

statistically significant variable.

The model with housing market variables shows that, as expected, properties in neighborhoods 

with higher median rents compared to their surrounding counties were more likely to opt out 

(odds ratio of 4.0 corresponding to a 100 percent increase in the ratio of tract to county rent). For 

rents, neighborhoods had more of an effect than regions. The regional rental market trends, 

measured as percentage change in the Fair Market Rent for the MSA or non-metropolitan area, 

did not show statistically significant effects. On the other hand, regional trends in the prices of 

for-sale homes did appear to have an effect. Properties in regions with moderate growth in the 

Housing Price Index (0-24.9 percent growth from 2007 to 2012) were more than 1.6 times more 

likely to opt out than in areas where home prices declined. The properties in regions with 

strongest HPI growth (25 percent or more growth from 2007 to 2012) also showed an increased 

tendency toward opt-out (1.5 odds ratio), but the result was not statistically significant. 

To account for the volatility in the overall housing market during the study period, we also tested 

a regression model that segmented properties by the year of opt-in/opt-out decision into three 

market periods: strong (2005-2007), weak (2008-2011), and recovering (2012-2014). For opt-ins, 

the decision year refers to the last year in which the owner renewed a Section 8 contract. For optouts, the decision year refers to the year the contract was terminated according to HUD’s 

Terminated Contracts database.11 A table showing the odds ratio results in the segmented model 

is included as Appendix 2. Surprisingly, the odds ratio results did not vary greatly across the 

three market phases. Opt-outs were somewhat less likely among larger properties during the 

2012-2014 recovering market phase, a volatile time when owners may have been reluctant to risk

the large investment needed to bring a 100+ unit development to market. During the weak 

market period, the effect of family occupancy on the likelihood of opt-out was more pronounced. 

In general, however, the directions of increased or decreased likelihood of opt-out by property 

and neighborhood characteristics were consistent across the housing market time periods. 

11 Note that a limitation of this model is that it only counts the last opt-in decision by the owner. Because only 2005 

and 2014 snapshots of the Section 8 contract databases were available, we do not know whether a contract was 

renewed more than once. This will minimize the count of opt-ins and exaggerate the impact of opt-ins in the earlier 

phases, particularly the 2005-2007 phase. This may result in an overestimate of the odds ratios during those periods.

27

 
In sum, the updated regression analysis shows that throughout the 2005-2014 period, many of the 

same factors identified as significant in the original report have an effect on owners’ opt-in/optout decisions. In the current study period, however, those factors explain less of the variation in 

outcomes than in the original analysis. We discuss possible reasons for this finding in the 

conclusion. 

28
IV. Section 202 Direct Loan Program

HUD’s Section 202 program provides financing for affordable housing developments for elderly 

residents and persons with disabilities. The 202 program developed in two phases. The first 

phase, the Section 202 Direct Loan program, was established by the Housing Act of 1959. The 

program provided 40-50 year low-interest loans to non-profit organizations for construction, 

rehabilitation and acquisition costs for housing serving elderly or disabled residents. The loans 

can be prepaid or refinanced, although this does not extinguish affordability restrictions. 

HUD launched the second phase of the program in 1990. Instead of loans, HUD provides capital 

advances for construction, acquisition and rehabilitation costs through the Section 202 program 

for elderly housing and the parallel Section 811 program serving persons with disabilities. The 

capital advances do not carry interest and do not need to be repaid as long as the property 

continues to serve low-income elderly or disabled residents for a 40-year term. Because the 

Section 202/811 Capital Advance program does not offer a meaningful opt-out choice to owners, 

the program is excluded from this analysis. 

This section of the report focuses on the property characteristics and opt-in/opt-out decisions 

associated with the subset of Section 8 developments with Section 202 Direct Loan assistance. 

These properties made up a substantial portion of the 2005-2014 study dataset. In 2005, 3,580 

properties combined Section 8 assistance with a Section 202 loan, amounting to 20 percent of the 

properties in the overall study dataset. As Table 14 shows, these properties were among the most 

stable in the Section 8 inventory. Of the Section 202 developments in the Section 8 inventory in 

2005, fully 98 percent continued to receive Section 8 assistance in 2014. Nearly all of these 

involved an active opt-in by the property owner. 

Table 14. Properties with Section 202 and Section 8 Assistance by Section 8 Outcome Categories, 2005-

2014

 Opt-ins

Opt-outs/ 

Prepays

Foreclosure/ 

Abatement Other Total

Number of properties 3,419 27 36 98 3,580

Percent of properties 95.5% 0.8% 1.0% 2.7% 100%

Number of units 192,036 502 830 5,720 199,088

Percent of units 96.5% 0.3% 0.4% 2.9% 100%

Source: HUD, 2005 and 2014 Active Properties, Active Financing and Active Contracts Files, iREMS; Terminated 

Contracts Database

In the following section, we examine the differences in property, location, and tenant 

characteristics between the Section 202 developments and the rest of the study dataset. Next, we 

look at the extent of Section 8 opt-outs, foreclosure/abatements, and loan termination within the 

Section 202 portfolio. Because the sample of Section 202 developments undergoing opt-outs was 

so small and opt-out does not end affordability restrictions, we do not include a multivariate 

regression model.

Section 202 Developments Compared to Other Section 8 Developments

Tables 15-17 compare the subset of Section 202 developments to developments without 202 

loans and show the breakdown of the Section 202 developments by property characteristics and 

rental assistance opt-in/opt-out status. Again, the characteristics come from the 2005 baseline 

29
dataset unless otherwise noted. The property characteristics table excludes the owner type and 

financing categories; by definition, the 202 developments share the same financing program and 

are owned by non-profits.

Table 15. Property Characteristics at 2005 Baseline, Section 202 Developments by Outcome Status

Non202 Section 

202 

Opt-ins

Opt-outs/ 

Prepays

Foreclosure/ 

Abatement

All 

Other

Section 202 Developments Only

Number of Properties 14,500 3,580 3,419 27 36 98

Percent of Properties 100% 100% 96% 0.8% 1% 3% 

Development Size 

 1-49 36% 57% 56% 93% 89% 59%

50-99 29% 24% 25% 7% 8% 19%

100-199 26% 17% 18% 0% 3% 18%

200 or more 8% 2% 2% 0% 0% 5%

Average number of units 89 56 56 19 23 58

Unit Size 

 0-bedroom units 5% 16% 17% 1% 24% 9%

 1-bedroom units 48% 81% 81% 95% 63% 88%

 2-bedroom units 31% 3% 3% 4% 13% 3%

 3-bedroom units 14% 0% 0.10% 0% 0% 0%

4+-bedroom units 2% 0% 0% 0% 0% 0%

Average number of bedrooms 1.6 1 1 1 1.2 1 

Occupancy Type

Family 59% 0% 0% 0% 0% 0%

Elderly 40% 67% 68% 11% 17% 62%

Disabled 3% 33% 32% 89% 83% 38%

Building Type 

Row house 8% 12% 12% 4% 0% 12%

Townhouse 4% 0% 1% 0% 0% 0%

Semi-Detached 4% 5% 5% 0% 0% 8%

Detached 3% 9% 9% 40% 29% 11%

Walk-up/Garden 41% 23% 23% 40% 49% 26%

Mid-Rise 3% 3% 3% 0% 0% 2%

Mixed 16% 6% 6% 12% 11% 4%

High-Rise/ Elevator 22% 41% 42% 4% 11% 36%

Average Percentage of Assisted Units 88% 96% 96% 89% 90% 91%

30
Table 15. Property Characteristics at 2005 Baseline, Section 202 Developments by Outcome Status

(continued)

 

Non202 Section 

202 

Opt-ins

Opt-outs/ 

Prepays

Foreclosure/ 

Abatement

All 

Other

Section 202 Developments Only

Categories of Rent-to-FMR ratio 

Below 80% FMR 15% 6% 5% 23% 6% 11%

Between 80% & 100% 29% 14% 14% 27% 19% 13%

Between 101% & 120% 29% 22% 22% 4% 25% 20%

Between 121% & 130% 10% 11% 12% 8% 8% 9%

Between 131% & 140% 7% 11% 11% 8% 22% 11%

Between 141% & 160% 7% 17% 17% 15% 3% 14%

Over 160% FMR 4% 20% 20% 15% 17% 21%

Building Age Categories 

Before 1975 26% 4% 4% 7% 6% 8%

1975 - 1979 27% 5% 5% 0% 3% 4%

1980 - 1985 43% 44% 43% 74% 39% 84%

After 1985 5% 47% 49% 19% 53% 4%

REAC Physical Inspection Score (1-

100) 

Median 90 93 93 93 93 94

 1-59 4% 1% 0.50% 0% 8% 1%

60-69 7% 4% 4% 0% 3% 4%

70-89 39% 28% 29% 19% 14% 23%

90-100 50% 67% 67% 81% 75% 72%

Source: HUD, 2005 and 2014 Active Properties, Active Financing, Active Contracts and Multifamily Building Type

Files, iREMS; Terminated Contracts Database 

Table 16. Location Characteristics at 2005 Baseline, Section 202 Developments by Outcome Status

 

Non-202 

Section 

202 

Opt-ins

Optouts/ 

Prepays

Foreclosure/ 

Abatement

All 

Other

Section 202 Developments Only

Number of Properties 14,492 3,577 3,416 27 36 98

Percent of Properties 100% 100% 95% 1.00% 1% 3%

Census Division 

New England 11% 8% 8% 0% 0% 33%

Mid Atlantic 12% 15% 16% 15% 0% 5%

East North Central 18% 18% 18% 11% 33% 11%

West North Central 12% 10% 10% 30% 6% 9%

South Atlantic 15% 15% 15% 11% 33% 16%

East South Central 7% 9% 9% 4% 14% 0%

West South Central 7% 8% 8% 4% 3% 13%

Mountain 5% 5% 5% 15% 6% 9%

Pacific 0.12 0.11 0.12 0.11 0.06 0.03

31
Table 16. Location Characteristics at 2005 Baseline, Section 202 Developments by Outcome Status 

(continued)

 

Non-202 

Section 

202 

Opt-ins

Optouts/ 

Prepays

Foreclosure/ 

Abatement

All 

Other

Section 202 Developments Only

Metropolitan Location 

Suburb 31% 33% 33% 33% 19% 36%

Principal city 51% 54% 54% 56% 67% 50%

Non-metropolitan 18% 13% 13% 11% 14% 14%

Neighborhood Characteristics 

Median household income $38,781 $42,395 $42,282 $40,340 $41,771 $47,150

Median gross rent $680 $727 $728 $656 $655 $749

Median value of owner occupied 

housing $192,346 $204,116 $203,723 $133,770 $162,397 $252,526

Homeownership rate 51% 53% $53% 59% 58% 53%

Poverty rate 23% 20% 20% 17% 20% 20%

Homeowner vacancy rate 3% 3% 3% 2% 3% 3%

Renter vacancy rate 8% 7% 7% 9% 10% 6%

Racial/ethnic composition 

White 59% 61% 61% 68% 62% 69%

African American 21% 18% 18% 11% 24% 13%

Hispanic 15% 14% 14% 16% 6% 12%

Asian 3% 3% 3% 2% 2% 4%

Other 3% 3% 3% 3% 6% 3%

Minority 41% 39% 39% 32% 38% 31%

Regional Housing Market 

Average percent change in 

Housing Price Index (HPI), full 

study period (2005 Q1-2014 Q1)

3% 2% 2% 7% 5% 1%

Average percent change in HPI, 

strong market period (2005 Q1 -

2007 Q1)

13% 14% 14% 17% 14% 12%

Average percent change in HPI, 

weak market period (2007 Q1-

2012 Q1)

-13% -14% -14% -11% -11% -13%

Average percent change in HPI, 

recovering market period (2012 

Q1 -2014 Q1)

6% 6% 6% 4% 4% 4%

Average percent change in FMR, 

2005-2014 27% 26% 27% 27% 29% 26%

Source: HUD, 2005 and 2014 Active Properties, Active Financing, and Active Contracts Files, iREMS; Terminated 

Contracts Database; 2005 and 2014 Fair Market Rents; U.S. Census Bureau, 2005-2009 American Community 

Survey; Federal Housing Finance Agency Housing Price Index 

32
Table 17. Tenant Characteristics at 2005 Baseline, Section 202 Developments by Outcome Status

Non-202 Section 202 

Opt-ins

Opt-outs/ 

Prepays

Foreclosure/ 

Abatement

All 

Other

Section 202 Developments Only

Length of residence (years) 6 6 6 9 4.1 7.2

Household size 1.8 1.1 1.1 1 1.2 1.1

Percent minority-headed 44% 30% 30% 19% 33% 26%

Percent of all persons with disability 17% 35% 34% 82% 54% 36%

Percent elderly-headed households 40% 70% 70% 22% 39% 69%

Percent households with 2+ adults 

and children 7% 0.10% 0.10% 0% 1.20% 0%

Percent households with 1 adult and 

children 28% 0.30% 0.30% 0% 3.70% 0.20%

Household income as a percentage of 

area median income (AMI) 22% 25% 25% 24% 22% 25%

Source: HUD, 2005 and 2014 Active Properties, Active Financing, and Active Contracts Files, iREMS; Terminated 

Contracts Database; 2005 Picture of Subsidized Households

As Table 15 shows, the Section 202 developments had some distinctive physical characteristics. 

They tended to be smaller than other Section 8 developments. More than half had fewer than 50 

units, and three-quarters of Section 202 units were studios or one-bedroom apartments. However, 

the 202 developments also were more likely to be configured in high-rise buildings (41 percent) 

compared to the non-202 developments (22 percent). The 202 developments also tended to be 

newer, with 47 percent built after 1985, compared to just 5 percent of the non-202 developments. 

REAC physical inspection scores tended to be higher for the 202 developments, with two-thirds 

of properties scoring in the 90-100 range, compared to just over half for the overall inventory. 

Tables 15 and 17 show that the Section 202 developments also served a distinct tenant 

population. By definition, these were projects for occupancy by elderly residents (two-thirds of 

the developments) and persons with disabilities (one-third of the 202 developments). In 

comparison, 40 percent of non-202 developments were senior developments and just three

percent were reserved for persons with disabilities. The actual tenant characteristics also reflect 

these differences. In the 202 developments, 70 percent of households were elderly and 35 percent 

of units included at least one person with a disability, compared to 40 and 17 percent 

respectively in the non-202 developments. The households in 202 developments were less likely 

to be minority-headed (30 percent in 202 developments compared to 44 percent in the other 

developments). However, average household income was similar in both groups (25 percent of 

AMI in 202 developments compared to 22 percent in the non-202 developments).

The 202 developments also tended to have higher rents than other properties. Almost half of 202 

developments had rent-FMR ratios of 130 percent or greater, compared to less than one-fifth of 

the other developments. This is likely related to the higher level and cost of supportive services 

associated with housing for elderly residents or persons with disabilities.

Location characteristics were very similar across the 202 and non-202 developments. The 

regional distributions throughout the U.S. were nearly identical. The 202 developments were 

somewhat more likely to be located in metropolitan areas, either in cities or suburbs. Median 

incomes, rents and home prices tended to be higher in the neighborhoods surrounding the 202 

developments. Racial and ethnic composition of neighborhoods and regional housing market 

trends look very similar for the two sets of developments. 

33
Opt-Outs in the Section 202 Portfolio

The small number of properties that did undergo a Section 8 opt-out or abatement action formed 

a distinct subset of the Section 202 inventory. As Table 15 shows, these developments were 

overwhelmingly small, composed of one-bedroom units, and targeted toward persons with 

disabilities. Although much of the 202 inventory is made up of high-rises, most of the opt-outs 

were made up of single family homes or multifamily walkups. For the opt-outs, average length 

of residency was particularly long (nine years) and the percentage of minority-headed 

households was particularly small (19 percent). 

Using more detailed occupancy categories, it becomes clear that most of the 202 properties with 

an opt-out or foreclosure/abatement action fit a specific profile. These were small properties with 

20 units or less in single family homes or walkup apartments developed in the 1980s, largely 

serving persons with developmental disabilities or chronic mental illness. As Table 18 shows, 

most of the losses were concentrated in a few states that lost four or more of the properties. 

Nearly one-fifth of the properties were located in Wisconsin, including four 

foreclosure/abatement properties with similar names that appear to have been owned by the same 

organization. 

34
Table 18. Section 202 Properties Exiting the Section 8 Inventory through Opt-Out and 

Foreclosure/Abatement by State and Population Served, 2005-2014

Population Served

State

Chronically Mentally 

Ill, Developmentally 

Disabled Elderly

Physically 

Disabled Total

AR 1 0 0 1 

CA 0 1 0 1 

FL 2 1 1 4 

GA 2 1 0 3 

HI 1 0 0 1 

IA 3 1 0 4 

IL 0 0 1 1 

IN 1 0 0 1 

KS 1 0 0 1 

KY 0 1 0 1 

MI 1 0 0 1 

MN 1 0 0 1 

MO 2 0 0 2 

NC 6 0 0 6 

NE 0 1 0 1 

NM 5 0 0 5 

NY 4 0 0 4 

OR 1 0 1 2 

SC 1 0 0 1 

SD 0 1 0 1 

TN 5 0 0 5 

TX 0 1 0 1 

WA 0 1 0 1 

WI 8 0 4 12

WV 1 0 0 1 

WY 1 0 0 1 

Total 47 9 7 63

Source: HUD, 2005 and 2014 Active Properties, Active Financing and Active Contracts Files, 

iREMS; Terminated Contracts Database

Section 202 Refinancing and Preservation 

While owners of properties with Section 202 loans overwhelmingly renewed Section 8 assistance 

between 2005 and 2014, many of the 202 mortgages themselves were terminated during this 

period. Of the 3,419 developments with a Section 8 opt-in, half (1,715) had Section 202 loans 

that were terminated by 2014. This was not due to a wave of maturing mortgages. Only 24 of 

these properties had Section 202 loans scheduled to mature by the end of 2014, with most of the 

others scheduled to mature between 2020 and 2030. Instead, most of the properties underwent 

Section 202 prepayment and refinancing. HUD provides the option of prepaying and refinancing 

these loans to enable owners to reduce interest rate and debt service and to make capital 

35
improvements (HUD, 2013c). Most of the opt-in properties (1,238) used HUD financing options 

including FHA-insured financing and Green Retrofit grants. 12 Other options for refinancing 202 

properties include private-market loans, Low-Income Housing Tax Credits, and tax-exempt bond 

financing (Dunaway and Morgan, 2014). Owners prepaying a Section 202 loan are required to 

extend affordability requirements for at least 20 years following the loan’s original maturity date. 

Unlike Section 236 prepayments, therefore, prepayment of the Section 202 loan reflects a 

decision to preserve the development as affordable housing. 

The length of Section 8 contracts shows that termination of Section 202 loans clearly has a 

stabilizing effect on combined Section 202/Section 8 properties. Among developments with 

terminated 202 loans and continuing Section 8 assistance, most (58 percent) currently have rental 

assistance contracts with a 20-year term, with about one-third (31 percent) renewing for just five 

years. These figures are reversed for the properties with continuing 202 loans: half are operating 

under five-year contracts and one-third under 20-year contracts. 

Table 19. Renewal Terms for Section 8 Contracts in Developments with Section 202 Direct Loans, 2014

Section 8 Contract Term in Years 202 Loan Active in 2005 and 

2014 202 Loan Inactive in 2014

1 4% 2%

2-4 2% 1%

5 52% 31%

10 5% 3%

15 1% 1%

16-19 1% 1%

20 34% 58% 

Greater than 20 1% 2%

Source: HUD, 2005 Active Properties and Inactive Section 202 Loan Files, iREMS

In addition, eight developments from the study dataset with partial Section 8 assistance will be 

participating in HUD’s Senior Preservation Rental Assistance Contracts (SPRAC) program

(HUD, 2014b). The SPRAC program provides 20-year rental assistance contracts to Section 202 

Direct Loan developments where some or all units did not previously have Section 8 assistance. 

The new contracts prevent the unassisted tenants from being displaced when the owner 

refinances or recapitalizes the property.13

In sum, housing developed under the Section 202 Direct Loan program remains a stable and 

reliable source of affordable housing for elderly residents and persons with disabilities—even as 

a large portion of the inventory transitions out of the Section 202 program itself. 

12 HUD programs used to refinance Section 202 developments include 221(d)3 and (d)4 mortgages, 223(a)(7) 

mortgages, and 542(c) risk sharing with state and local housing finance agencies. 

13 Four other properties also have been approved for SPRAC participation but were not part of the 2005 baseline 

dataset, presumably because they had no active Section 8 assistance in 2005.

36

 
V. HUD Rental Assistance Demonstration (RAD) Program

The Rental Assistance Demonstration (RAD) program enables the conversion of a variety of 

types of HUD-funded housing to long-term project-based Section 8 assistance. The first 

component of RAD allows public housing authorities to convert public housing and Section 8 

Moderate Rehabilitation (Mod Rehab) developments to Section 8 project-based rental assistance. 

This section of the report focuses on the second component of RAD, which allows owners to 

convert developments from the Rent Supplement (Rent Supp) and Rental Assistance Payment 

(RAP) programs to the Section 8 program (HUD, 2013a). 14

Rent Supp and RAP are 1960s-70s era precursors to the Section 8 program. Contracts from these 

programs are undergoing a wave of expirations. Unlike a Section 8 contract, Rent Supp and RAP 

contracts in FHA-insured developments cannot be renewed.15 Tenants in developments with 

expiring contracts receive tenant protection vouchers (TPVs) that allow them to continue to pay 

rents based on their income level, either onsite or in another rental development. The RAD 

second component allows owners to preserve affordability at insured developments with expiring 

Rent Supp and RAP contracts by converting the tenant-based vouchers to 15-year project-based 

vouchers (PBVs), which stay with the specific housing development for current and future 

tenants. Thus, RAD conversion offers the opportunity to preserve Rent Supp and RAP 

developments as long-term affordable housing. It also offers owners the opportunity for owners 

to refinance and recapitalize aging properties, as the long-term project-based vouchers provide a 

steady stream of revenue to support debt service or leverage low-income housing tax credits

(GAO, 2014). 

In the study dataset, 321 developments were eligible for RAD Component 2 conversion. 

Between 2012 and 2014, 70 developments underwent RAD conversions, 63 of which appear in 

the study dataset.16 In this section of the report, we compare the property, location and tenant 

characteristics from the 2005 baseline dataset for the RAD-eligible portion of the dataset to the 

overall inventory. Within the RAD-eligible inventory, we compare properties with completed

RAD conversions (“RAD participants”) with the remainder of the RAD-eligible developments 

(“Non-participants”). 

The analysis relies on descriptive cross-tabulations and does not include multivariate analysis. A 

test of a multivariate model showed few statistically significant differences between the two 

groups. Moreover, the decision to participate in RAD is not as clear-cut as an owner’s decision to 

14 Section 8 Mod Rehab developments may also be eligible for RAD Component 2. They are excluded from this 

analysis because Mod Rehab contracts are administered by public housing authorities and are not included in HUD’s 

multifamily datasets.

15 Rent Supp and RAP contracts in developments without FHA-insured mortgages could be amended and converted 

to project-based Section 8 contracts before the RAD program was launched. HUD’s records indicate that 33 older 

contracts have been converted to project-based Section 8 outside of RAD in recent years.

16 HUD published a list of active Rent Supp and RAP contracts in March 2012 

(http://portal.hud.gov/hudportal/documents/huddoc?id=RS_RAP_Units_03092012.xlsx). The list included 316 

developments that were eligible to apply for RAD conversion. HUD also provided us with datasets listing 70 

developments that underwent RAD conversion from 2012 to 2014, including 63 from the study dataset. Ten of the 

converted properties had not been included in the March 2012 eligible properties list. Therefore, to identify the set 

of RAD-eligible properties, we combined 53 properties from the study dataset that were on both the conversioneligible and conversion-complete lists, the 10 properties that were on the conversion-complete list but not on the 

conversion-eligible list, and 258 properties that were on the conversion-eligible list but had not undergone 

conversion, for a total of 321 properties. 

37

 
opt into or out of a Section 8 contract renewal. In addition to the owner’s choice, HUD must 

approve the conversion. Also, some “non-participants” may be developments where the owner 

has expressed interest in or applied for RAD conversion but the process is not complete.

Tables 20-22 show the property, location and tenant characteristics from the 2005 baseline data 

for RAD-converted developments, non-participants that are RAD-eligible, and the balance of the 

study datasets. The HUD financing programs categories are not included because by definition, 

the RAD-eligible properties were funded by the Rent Supp or RAP older HUD assistance 

programs. 

Table 20. Property Characteristics at 2005 Baseline, Developments by RAD Eligibility and Participation

Property Characteristics Not RADEligible

RADEligibleRAD 

Participant

RAD NonParticipant

Number of Properties 17,825 321 63 258

Percent of Properties 100% 100% 20% 80%

Development Size

1-49 41% 19% 21% 18%

50-99 28% 19% 13% 20%

100-199 24% 38% 38% 38%

200 or more 7% 25% 29% 24%

Average number of units 81 161 156 162

Unit Size 

 0-bedroom units 7% 10% 12% 10%

 1-bedroom units 52% 47% 70% 45%

 2-bedroom units 27% 27% 13% 29%

 3-bedroom units 12% 12% 3% 13%

4+-bedroom units 2% 3% 2% 3%

Average number of bedrooms 1.6 1.7 1.3 1.7

Occupancy Type[1] 

Elderly/Disabled 53% 45% 61% 42%

Family 47% 55% 39% 58%

Building Type

Rowhouse 9% 2% 7% 1%

Townhouse 3% 7% 7% 7%

Semi-Detached 5% 0% 0% 0%

Detached 4% 1% 0% 1%

Walk-up/Garden 37% 23% 24% 23%

Mid-Rise 3% 4% 3% 4%

Mixed 14% 20% 12% 22%

High-Rise/ Elevator 26% 44% 47% 43%

Average Percentage of Assisted Units 90% 53% 52% 53%

38
Table 20. Property Characteristics at 2005 Baseline, Developments by RAD Eligibility and Participation

(continued)

Property Characteristics Not RADEligible

RADEligibleRAD 

Participant

RAD NonParticipant

Categories of Rent-to-FMR ratio 

Below 80% FMR 12% 61% 51% 64%

Between 80% & 100% 26% 23% 25% 22%

Between 101% & 120% 28% 9% 13% 8%

Between 121% & 130% 10% 4% 7% 3%

Between 131% & 140% 8% 2% 0% 3%

Between 141% & 160% 9% 0% 0% 0%

Over 160% FMR 8% 2% 5% 1%

Building Age Categories 

Before 1975 21% 50% 73% 44%

1975 - 1979 22% 46% 20% 52%

1980 - 1985 44% 3% 5% 3%

After 1985 13% 1% 2% 1%

Ownership Type

Non-Profit 43% 36% 41% 35%

For-profit 35% 15% 24% 13%

Limited Dividend 19% 45% 35% 48%

Other 3% 3% 0% 4%

Missing data 15% 42% 46% 41%

REAC Physical Inspection Score (1-100) 

Median 90 87 89 87

1-59 3% 6% 2% 7%

60-69 6% 10% 10% 10%

70-89 37% 41% 43% 40%

90-100 54% 43% 46% 42%

Source: HUD, 2005 and 2014 Active Properties, Active Financing, Active Contracts and Multifamily 

Building Type Files, iREMS; Active Rent Supp and RAP Contracts, 2012; Approved RAD Conversions 

2012-2014 

39
Table 21. Locational Characteristics at 2005 Baseline, Developments by RAD Eligibility and 

Participation

Locational Characteristics Not RADeligible

RADeligibleRAD 

Participant

NonParticipant

Number of properties 17,787 321 63 258

Percent of properties 100% 100% 20% 80%

Census Division 

New England 10% 17.4% 12.7% 18.6%

Mid Atlantic 12% 44.5% 49.2% 43.4%

East North Central 18% 18.7% 22.2% 17.8%

West North Central 11% 4.0% 1.6% 4.7%

South Atlantic 15% 8.1% 4.8% 8.9%

East South Central 8% 1.2% 3.2% 0.8%

West South Central 7% 0.3% 0.0% 0.4%

Mountain 5% 0.6% 0.0% 0.8%

Pacific 12% 5.0% 6.3% 4.7%

Metropolitan Location

Suburb 31% 44% 46% 43%

Principal city 52% 51% 44% 53%

Non-metropolitan 17% 5% 10% 4%

Neighborhood Characteristics 

Median household income $39,486 $40,096 $38,953 $40,375 

Median gross rent $689 757 709 768

Median value of owner occupied housing $193,979 $252,431 $237,846 $256,092 

Homeownership rate 52% 39% 44% 38%

Poverty rate 22% 23% 23% 23%

Homeowner vacancy rate 3% 4% 3% 4%

Renter vacancy rate 8% 7% 7% 7%

Racial/ethnic composition 

White 59% 50% 55% 49%

African American 20% 27% 25% 28%

Hispanic 15% 16% 15% 16%

Asian 3% 5% 2% 5%

Other 3% 2% 3% 2%

Minority 41% 50% 45% 51%

40
Table 21. Locational Characteristics at 2005 Baseline, Developments by RAD Eligibility and 

Participation (continued)

Locational Characteristics Not RADeligible

RADeligibleRAD 

Participant

NonParticipant

Regional Housing Market 

Average percent change in Housing Price Index 

(HPI), full study period (2005 Q1-2014 Q1) 3% -3% -2% -3%

Average percent change in HPI, strong market 

period (2005 Q1 -2007 Q1) 14% 11% 11% 11%

Average percent change in HPI, weak market 

period (2007 Q1-2012 Q1) -13% -16% -14% -16%

Average percent change in HPI, recovering 

market period (2012 Q1 -2014 Q1) 6% 4% 3% 5%

Rental market

Average percent change in FMR, 2005-2014 27% 22% 22% 22%

Source: HUD, 2005 and 2014 Active Properties, Active Financing, and Active Contracts, iREMS; Active 

Rent Supp and RAP Contracts, 2012; Approved RAD Conversions 2012-2014; U.S. Census Bureau, 

2005-2009 American Community Survey; Federal Housing Finance Agency Housing Price Index 

Table 22. Tenant Characteristics, Developments by RAD Eligibility and Participation

Tenant Characteristics

Not RADeligibleRADeligibleRAD 

Participant

NonParticipant

Number of Properties 17,825 321 63 258

Percent of Properties 100% 100% 20% 80%

Length of residence (years) 5.9 7.6 6.6 7.8

Household size 1.7 1.7 1.6 1.7

Percent minority-headed 41% 50% 43% 52%

Percent of all persons with disability 20% 11% 13% 11%

Percent elderly-headed households 46% 51% 56% 49%

Percent households with 2+ adults and children 6% 8% 7% 8%

Percent households with 1 adult and children 22% 18% 17% 18%

Household income as a percentage of area median 

income (AMI) 22% 25% 26% 25%

Source: HUD, 2005 and 2014 Active Properties, Active Financing, and Active Contracts Files, iREMS; Terminated 

Contracts Database; Active Rent Supp and RAP Contracts, 2012; Approved RAD Conversions 2012-2014; 2005 

Picture of Subsidized Households

The most striking difference between the RAD-eligible developments and the other 

developments is their regional location. Nearly half of the RAD-eligible developments were 

located in the Mid-Atlantic region, and most of the rest were located in New England or the East 

North Central region (i.e., upper Midwest). As Table 23 below shows, this is because 

developments eligible for RAD conversion were heavily concentrated in just a few states. Threefourths were located in New York, New Jersey, Massachusetts, Michigan, and Illinois. These 

states also accounted for the bulk of developments actually participating in RAD.

41
Table 23. RAD-Eligible Developments and RAD Participants by State

State

RAD-Eligible 

Developments

% of RAD 

Eligible 

Developments

RAD 

Participants

% of RAD 

Participants

NY 83 26% 19 30%

NJ 56 17% 11 17%

MA 50 16% 7 11%

MI 41 13% 9 14%

IL 15 5% 5 8%

MD 11 3% 1 2%

CA 10 3% 2 3%

VA 9 3% 0 0%

MN 8 2% 0 0%

WA 5 2% 2 3%

CT 4 1% 0 0%

PA 4 1% 1 2%

GA 3 1% 1 2%

TN 3 1% 2 3%

FL 2 1% 0 0%

KS 2 1% 1 2%

OH 2 1% 0 0%

SD 2 1% 0 0%

WI 2 1% 0 0%

AK 1 0.3% 0 0%

CO 1 0.3% 0 0%

DC 1 0.3% 1 2%

KY 1 0.3% 0 0%

LA 1 0.3% 0 0%

ME 1 0.3% 0 0%

MT 1 0.3% 0 0%

ND 1 0.3% 0 0%

VT 1 0.3% 1 2%

Total 321 100% 63 100%

Source: HUD, Active Rent Supp and RAP Contracts, 2012; Approved RAD Conversions 2012-2014

Half of the inventory was located in central cities, both for RAD-eligible developments and the 

other developments. With their concentration in densely populated Northeastern and Midwestern 

states, the RAD-eligible developments were more likely to be located in suburbs (44 percent of 

RAD-eligible developments versus 31 percent of the remainder of the inventory) and less likely 

to be located outside of metropolitan areas (five percent of RAD-eligible versus 17 percent of 

other developments). The properties actually participating in RAD were similarly likely to be 

located in suburbs (46 percent); more likely than non-participants to be in non-metropolitan 

locations, including rural counties in New York, Tennessee, Vermont, and Washington; and less 

likely than either non-participants or the remainder of the inventory to be located in central cities.

42
Also in keeping with their location in older, densely populated states, the RAD-eligible 

developments tended to be located in neighborhoods with higher median rents and home values, 

lower homeownership rates, and higher minority populations than the other developments (Table 

21). This was particularly true for the RAD non-participants. Other neighborhood characteristics 

were substantially the same across the inventory of RAD participants, RAD non-participants, and 

other developments.

The RAD-eligible developments also differed from the other developments in their physical 

characteristics. Because they were funded by early HUD programs, the RAD-eligible 

developments were considerably older than the overall study dataset. Table 20 shows that half of 

RAD-eligible developments were built before 1975, and nearly all of the others were built 

between 1975 and 1979. RAD-eligible developments also tended to be larger, with twice as 

many units on average compared to other developments, and were much more likely to be 

located in high-rises. These qualities were similar across the RAD participants and nonparticipants alike, with the RAD participants particularly concentrated in the pre-1975 stock (73 

percent of RAD participants) and buildings with 200 or more units (29 percent). 

Not surprisingly given the age of the stock, REAC physical inspection scores were slightly lower 

for the RAD-eligible developments. The vast majority still passed their inspections by scoring 60 

or higher, but 51 percent of RAD-eligible developments fell into the lower passing levels (score 

of 60-89) compared to 43 percent of the other developments. Note that 98 percent of RAD 

participants had passing REAC scores, a threshold requirement for participation in the program. 

In the overall dataset, slightly more than half of developments (53 percent) were designated for 

occupancy by elderly residents or persons with disabilities. In the RAD-eligible properties, the 

majority of developments served families (55 percent). Here, however, the RAD participants and 

non-participants diverge. Most developments participating in RAD were designated for elderly 

occupancy (61 percent), while most non-participants were designated for family occupancy (58 

percent). 

Table 22 shows that actual tenant characteristics were very similar across the development types. 

Tenants in RAD-eligible developments tended to stay in their units longer, particularly in the 

RAD non-participant properties. RAD participants had a somewhat higher average number of 

elderly residents compared to both the non-participants and the remainder of the study properties. 

The RAD-eligible developments had a higher percentage of minority residents than the noneligible properties, but the actual RAD participants did not. This is likely linked to the lower 

percentage of minority households in elderly and disabled occupancy developments, which 

predominate among RAD participants.

Rents for assisted units tended to be lower in the RAD-eligible inventory compared to other 

developments. Sixty-one percent of RAD-eligible developments had average rent-to-FMR ratios 

below 80 percent in 2005, compared to just 12 percent of other developments. Most of the rest of 

the RAD-eligible developments had rent-to-FMR ratios of 80-100 percent. The RAD participants 

did have slightly higher rent-to-FMR ratios than the non-participants; 51 percent of participants 

had a rent-to-FMR ratio below 80 percent, compared to 64 percent of non-participants. To this 

extent, the decision to participate in RAD appears different from the decision to opt in to a 

Section 8 contract renewal, for which a low rent-to-FMR ratio signals elevated opt-out risk. 

Finally, RAD-eligible developments were much more likely to be partially assisted than the other 

developments at the 2005 baseline. On average, half of units in the RAD-eligible developments 

were assisted. This holds for both the RAD participants and non-participants. However, this 

characteristic is subject to change because of the RAD program itself. Under RAD Component 2, 

43
the number of assisted units in a converted property can change in either direction. The number 

may go down if there have been substantial numbers of vacant assisted units preceding the 

conversion, which would reduce the number of tenant protection vouchers eligible for 

conversion to project-based assistance. On the other hand, it may increase if the conversion also 

involves a mortgage prepayment that otherwise would have resulted in the issuing of tenant 

protection vouchers to residents in unassisted units. Table 24 tracks the changes in the number of 

assisted units in the 63 developments from the original study dataset participating in RAD.

Table 24. Net Change in Assisted Units from 2005 Baseline to Post-RAD Conversion, RAD Participants

Properties by change in assisted units

Decrease >= 5 Net change in assisted units

units

Change w/in 5 

units

Increase >=5 

units

10 17 36 3,025

Source: HUD, 2005 Active Properties, iREMS; Approved RAD Conversions 2012-2014

As Table 24 shows, more than half of developments increased their total of assisted units by at 

least five, and most of the rest maintained similar levels of assistance (net change of five or 

fewer assisted units). Because the developments increasing their levels of assistance include a 

number of large properties, the RAD conversions result in a 3,025-unit increase in assisted units 

from the 2005 baseline for the 63 properties.

In sum, the small inventory of RAD-eligible developments represents a distinct niche in HUD’s 

multifamily portfolio—older, larger developments clustered in a small group of Northeastern and 

Midwestern states. For the most part, the RAD-converted properties were similar to those that 

were eligible but had not yet undergone conversion. The main difference between the two was 

occupancy type: majority elderly occupancy type for RAD participants versus a majority of 

family developments for non-participants. The analysis also illustrates the potential of RAD 

Component 2 to preserve and increase the number of deeply affordable housing units in the 

project-based supply, as most converted developments increased their number of units with 

rental assistance above the original 2005 baseline. 

44
Conclusion and Areas for Further Research

The updated quantitative analysis describes shifts in the HUD multifamily portfolio between the 

original 1998-2004 study period and the current 2005-2014 study period. Even as Section 

236/BMIR assistance was largely phasing out, the Section 8 inventory showed more continuity. 

Fewer properties underwent opt-out, and far fewer were subject to foreclosure and contract 

abatement. At the same time, more owners actively opted to continue participation in the Section 

8 program. These opt-ins took a variety of forms: traditional renewal of expiring Section 8 

contracts over contract terms ranging from short-term (1-5 years) to long-term (typically 20 

years); refinancing Section 202 loans with a continued commitment to the Section 8 program; 

and conversion of older HUD assistance to long-term project-based Section 8 through the RAD 

program.

The updated analysis shows that to the extent Section 8 opt-outs continued to occur, many 

properties were subject to similar risk factors to those identified in the original study, including 

family occupancy, for-profit ownership, low rent-FMR ratios, and location in less distressed 

neighborhoods. While these factors were present in the second study phase, most were less 

influential. The quantitative analysis showed more variability in these characteristics among the 

properties lost to the affordable inventory, and the regression analysis showed that together these 

characteristics explained less variation in the opt-in/opt-out decision than before. For example, of 

the 748 opt-outs, over one-third (271, or 36 percent) were owned by non-profit organizations, 

served elderly or disabled tenants, or both. While it will be important for preservation advocates 

to continue to focus on properties with traditional risk factors, it will also be important not to 

assume that other properties are immune from opt-out risk.

It may be that many of the properties most at risk of loss to the inventory, either weak properties 

in financial and physical distress or strong properties with potential to attract market-rate tenants, 

already left the assisted inventory during the earlier wave of opt-outs documented by the 1998-

2004 analysis. The middle-of-the-road inventory that remained in the 2005-2014 study sample 

was more stable, particularly as an increasing number of owners made one or more active 

decisions to renew subsidy contracts. The study period also coincided with the volatility in the 

U.S. housing markets and economy. Once the heated housing markets of 2005-2007 that might 

have incentivized condominium conversion and other types of land redevelopment had passed, 

owners may have seen advantages in avoiding risky property status changes in an unsettled time.

Another reason that the 2005-2014 period may have generated a smaller, more variable field of 

opt-outs is the maturing of the affordable housing preservation infrastructure during this time. A 

wide variety of federal, state, local, and extra-governmental initiatives were put in place to 

preserve at-risk properties (HUD, 2013b):

• HUD Mark-to-Market. The “full restructuring” option in HUD’s Mark-to-Market 

program provides restructured, favorable mortgage terms to owners of Section 8 

developments in exchange for reducing rents to market values and extending affordability 

restrictions. The majority of Mark-to-Market activity took place during the study period 

for the original report (closings at 1,239 properties from 2000 to 2004). However, the 

program continued to operate during the 2005-2014 study period, with closings on full 

restructuring of an additional 597 properties during this time. (HUD, 2015).

• State allocations of Low Income Housing Tax Credits (LIHTC) for preservation. 

According to the National Housing Trust, 45 states provide incentives for preservation 

through allocation of competitive (9%) tax credits, including 16 states with explicit set45
asides of competitive tax credits for preservation. Many states also devote bonds and 4% 

credits to preservation projects. (National Housing Trust, n.d.) The use of LIHTC to 

finance preservation of at-risk properties accelerated during the study period. From 2005 

to 2013, LIHTC assistance was put into place for 994 Section 8 projects with 110,103 

units, compared to 591 properties with 73,044 units during the earlier 1998-2004 study 

period.17

• Section 202 refinancing. As described earlier, the portfolio of developments with older 

Section 202 loans is undergoing a wave of refinancing resulting in physical 

improvements and preservation and extension of Section 8 assistance.

• Preservation databases, including risk targeting data. A number of states and cities have 

launched property databases or improved existing data tools to flag properties at risk of 

loss to the affordable inventory.18 The databases track properties’ subsidy expiration 

dates as a measure of opt-out risk. Many also track other risk variables such as owners’ 

expressed intent to opt out, for-profit vs. nonprofit ownership, REAC scores or other 

measures of physical condition, and neighborhood market conditions. 

• Additional state, local, and nongovernmental initiatives. Since 2001, the John D. and 

Catherine T. MacArthur Foundation’s Window of Opportunity initiative has underwritten 

tremendous growth in the rental preservation infrastructure, including support for 

capacity building among national and local non-profit developers, building sources of 

private capital for preservation, local and state interagency preservation councils, legal 

assistance and organizing support for tenants, and policy advocacy and research. 

(MacArthur Foundation, 2009).

These initiatives targeted precisely the types of properties where risk factors indicated potential 

losses through failure or market-rate conversion. To the extent that preservation efforts have 

been successful, we would expect to see reduced property losses in general and among high-risk 

properties in particular.

We recommend several areas for further research to help us understand the new environment for 

preservation and risk to the assisted housing inventory. First, we recommend tracking the effects 

of the preservation initiatives described above on increasing opt-ins and stability within the 

assisted multifamily inventory. How many opt-in properties have benefited from preservation 

programs, and to what extent have the preserved properties been those with risk factors for 

failure or market-rate conversion? The first step would be to create a property-level preservation 

flag tracking refinancing of subsidized properties, ownership changes, and participation in 

formal federal, state, and local preservation initiatives. This will require data collection from 

state and local housing agencies and intermediaries with active preservation programs. A first 

17 Public and Affordable Housing Research Corporation and National Low Income Housing Coalition, extract from 

National Housing Preservation Database, http://www.preservationdatabase.org. Includes developments with active 

Section 8 and LIHTC assistance, and where LIHTC start date is 1994 or later. Excludes developments where Section 

8 assistance is coded as PAC, PRAC, or RAD public housing conversion.

18 See, for example, the Shimberg Center’s Florida Assisted Housing Inventory at 

http://flhousingdata.shimberg.ufl.edu/AHI_introduction.html; CEDAC’s database for Massachusetts at 

http://cwc.cedac.org/Uploads/Files/CEDACExpUseReportDecember2014.pdf; and the NYU Furman Center’s 

Subsidized Housing Inventory Project at http://datasearch.furmancenter.org. The National Housing Preservation 

Database website includes a page of links to additional state and local preservation databases, located at 

http://www.preservationdatabase.org/localpartners.php.

46

 
step could be to use HUD’s mortgage databases and the recently launched national preservation 

database (http://www.preservationdatabase.org) to identify 1970s-80s era HUD properties that 

were refinanced via FHA-insured mortgages, underwent a Section 202 loan prepayment with 

extension of affordability requirements, or took on additional subsidies from LIHTC and HOME 

in recent years.

Second, we recommend detailed analysis of contract renewal histories to determine whether the 

proliferation of short-term renewals signals future risk to the inventory. While most property 

owners did make an active choice to opt in to assistance during the study period, half of the 

contracts were renewed for terms of five years or less. Tracking the year-over-year renewal 

histories allows us to test whether owners who renew a contract for 1-5 years tend to renew these 

contracts again upon expiration, or whether opt-outs are often preceded by a short-term contract 

renewal. Constructing full opt-in histories will require annual Section 8 contract datasets. The 

two point-in-time datasets available for this study provided a partial picture of renewals, but 

information was not available about short-term renewals in the intervening years between 2005 

and 2014.

Third, given the weakened influence of the traditional opt-out risk factors, we recommend 

further examination of opt-outs in developments without these risk factors. These include 

developments serving elderly residents or persons with disabilities, non-profit-owned properties, 

and developments that do not appear to be especially vulnerable to market-rate conversion (e.g.,

those in distressed neighborhoods or whose contract rents are in line with or higher than the 

surrounding market rate). Case studies of these properties could help us understand the factors 

that lead to non-traditional opt-outs, such as changes in non-profits’ interest and ability to 

maintain aging subsidized properties. This is particularly true for older developments whose 

owners have not taken advantage of preservation options such as Section 202 and RAD 

conversions. This understanding, in turn, could help practitioners prevent unnecessary opt-outs 

and facilitate preservation transactions through refinancing and ownership changes when needed.

Fourth, we recommend learning more about the neighborhoods surrounding the ongoing, opt-in 

properties by matching their locations to newly available datasets from HUD. The Location 

Affordability Index (http://www.locationaffordability.info/lai.aspx) can be used to assess transit 

accessibility and travel costs for the subsidized developments. Datasets developed for the Fair 

Housing and Equity Assessment (FHEA) and the Affirmatively Furthering Fair Housing tool can 

be used to assess properties’ neighborhood locations in terms of racial and ethnic concentrations 

of poverty, school proficiency, jobs proximity, labor market engagement, transit proximity, and 

environmental health hazards exposure (HUD, n.d. and 2014a).

Fifth, we recommend building evaluation of property and neighborhood characteristics into the 

growing Section 8(bb) initiative, which enables HUD to transfer Section 8 budget authority from 

one contract to another. While transfer of Section 8 authority across properties has taken place on 

a case-by-case basis in the past, the issuing of a recent notice formalizing the process will 

accelerate these transfers. (HUD, 2014c). The notice calls for receiving properties to be located 

in neighborhoods with low or declining poverty or in Choice Neighborhood or otherwise 

revitalizing areas, and to pass REAC inspection requirements or have a plan in place for repairs. 

When a sufficient history of Section 8(bb) transfers is in place, we recommend a study 

comparing the characteristics of the sending and receiving properties. This will help us 

understand the extent to which transfers allow HUD to preserve deep affordability while 

improving property and neighborhood conditions within the assisted inventory.

47
Sixth, we recommend a parallel data effort to catalog the risk to the Department of Agriculture’s 

Rural Development (RD) multifamily inventory from mortgage expirations in the coming years. 

The National Housing Law Project estimates that three-quarters of the current 440,000 rural and 

farmworker units will exit the assisted inventory in the next ten years as 40-50 year mortgages 

made in the 1960-70s begin to mature.(Anders, 2015). However, there is no publicly available 

dataset that provides mortgage maturity dates at the property level for the RD inventory. 

Obtaining and compiling information will be essential to preserving this stock of affordable 

rental units.

Finally, we recommend public release of the property-level dataset developed for this report with 

opt-in/opt-out status, property and tenant characteristics, and neighborhood conditions. A single, 

national source of this linked information for the active Section 8 portfolio would be very useful 

for preservation practitioners, advocates and researchers. Currently, the preservation community 

pulls together this information piecemeal at the state and local level. We recommend posting the 

integrated dataset to HUD’s multifamily data web page and updating it annually with extracts 

from the active properties, active financing, terminated mortgages and FHA opt-out files. The 

catalogue of properties that have undergone opt-outs and foreclosure/abatement would be 

particularly helpful, as preservation actors do not currently have access to a single integrated list 

of developments lost to the affordable housing inventory.

48
References

Anders, G. (2015). RD Mortgage Maturities Threaten Hundreds of Thousands of Residents. 

National Housing Law Project Bulletin. 45:1. http://nhlp.org/article/rd-mortgage-maturitiesthreaten-hundreds-thousands-residents. 

Department of Housing and Urban Development. (n.d.) Description of FHEA Mapping Tool.

Retrieved from http://egis.hud.gov/ArcGIS/rest/services/oshc/Fhea/MapServer. 

Department of Housing and Urban Development. (2014a). Fair Housing Assessment Tool. 

Retrieved from http://www.huduser.org/publications/pdf/AFFH-Assessment-Tool-2014.pdf. 

Department of Housing and Urban Development. (2012a). Guidance on Eligibility for Tenant 

Protection Vouchers Following Certain Housing Conversion Actions (HUD Notice H-2012-

3). Retrieved from https://portal.hud.gov/hudportal/documents/huddoc?id=12-03hsgn.pdf

Department of Housing and Urban Development. (2012b). List of Active Rent Supp and RAP 

Contracts [Data file]. Retrieved from 

http://portal.hud.gov/hudportal/documents/huddoc?id=RS_RAP_Units_03092012.xlsx. 

Department of Housing and Urban Development. (2015). M2M Transactions Report. Retrieved 

from http://portal.hud.gov/hudportal/documents/huddoc?id=m2mstran.xls. 

Department of Housing and Urban Development. (2013a). Rental Assistance Demonstration –

Final Implementation, Revision 1 (HUD Notice PIH-2012-32 (HA), REV-1). Retrieved 

from http://portal.hud.gov/hudportal/documents/huddoc?id=pih2012-32rev1.pdf. 

Department of Housing and Urban Development. (2014b). Senior Preservation Rental 

Assistance Contract (SPRAC) Program. Retrieved from 

http://portal.hud.gov/hudportal/documents/huddoc?id=sprac_faq_2014.pdf. 

Department of Housing and Urban Development. (2014c). Transferring Budget Authority of a 

Project-Based Section 8 Housing Assistance Payments Contract under Section 8(bb)(1) of 

the United States Housing Act of 1937. (HUD Notice H-2014-14). Retrieved from 

http://portal.hud.gov/hudportal/documents/huddoc?id=14-14hsgn.pdf. 

Department of Housing and Urban Development. (2013b). Preserving Affordable Rental 

Housing: A Snapshot of Growing Need, Current Threats, and Innovative Solutions. 

Evidence Matters Newsletter. Retrieved from 

http://www.huduser.org/portal/periodicals/em/EM_Newsletter_Summer_2013_FNL.pdf. 

Department of Housing and Urban Development. (2011). Small Area FMRs [Data file]. 

Retrieved from http://www.huduser.org/portal/datasets/fmr/smallarea/index.html.

Department of Housing and Urban Development. (2013c). Updated Requirements for 

Prepayment and Refinance of Section 202 Direct Loans. (HUD Notice H-2013-17). 

Retrieved from http://portal.hud.gov/hudportal/documents/huddoc?id=13-17hsgn.pdf.

Dunaway, G. & Morgan, G. (2014). Preservation Strategies for 202 Properties [Powerpoint 

Slides] Retrieved from Leading Age Web site: 

https://my.leadingage.org/conference/GetAttachment.ashx?sessionID=a1Wd0000000bb0kE

AA&attachmentID=00Pd000000EJ8qCEAT. 

49
Finkel, M., Hanson, C., Hilton, R., Lam, K, and Vandawalker, M. (2006). Multifamily 

Properties: Opting In, Opting Out and Remaining Affordable. Retrieved from 

http://www.huduser.org/Publications/pdf/opting_in.pdf.

Government Accountability Office. (2014) HUD Rental Assistance Demonstration: Information 

on Initial Conversions to Project-Based Vouchers (GAO Report 14-402). Retrieved from 

http://www.gao.gov/assets/670/662736.pdf. 

John D. and Catherine T. MacArthur Foundation. (2009). Window of Opportunity: Preserving 

Affordable Rental Housing Investment Summary. Evidence Matters. Retrieved from 

http://www.macfound.org/press/publications/window-opportunity-investment-summary. 

National Housing Trust. (n.d.). State and Local Preservation Resources. Retrieved from 

http://www.nhtinc.org/state_and_local_preservation_resources.php#data. 

50
Appendix 1. Data Sources and Business Rules for Property Database

The basis for this analysis is the property-level dataset with information about programs and 

financing for HUD-assisted multifamily portfolio at two points in time: 2005 and 2014. Each 

record in the dataset is a property with active subsidy in 2005 from at least one of these 

programs: 1) a rental assistance contract under the Section 8, Rent Supplement or RAP programs 

(referred to generally as “Section 8” in the report, or 2) an insured mortgage under the Section 

221(d)(3)BMIR or 236 programs (referred to as “236/BMIR” in the report. Properties that 

participated in these programs but exited before 2005 were not included. Property outcomes were 

determined based on which of these program types were active in 2005 and whether the 

properties still had active assistance in 2014. Properties that left the inventory between 2005 and 

2014 were classified as opt-outs or foreclosure/abatement depending on their reasons for leaving. 

Properties that stayed in the inventory were classified based on whether the owner had a choice 

to opt in to assistance through contract renewal, or whether the assistance simply continued from 

2005 or earlier without a decision.

Datasets for Determining Property Outcomes

HUD provided several output files from its Integrated Real Estate Management System 

(iREMS), organized as follows:

1) Active Properties, 2005 and 2014

These files provided general property-level data and program information for each property in 

HUD’s multifamily portfolio, including location, target population, and financing programs. The 

two files are snapshots of the portfolio in 2005 and in 2014.

2) Active Financing, 2005 and 2014

These files provided more detail on financing for each multifamily property at the two points in 

time, including detailed program types, loan amounts, balances, and start and end dates.

3) Active Rental Assistance Contracts, 2005 and 2014

These files provided information about active rental assistance contracts at the two points in 

time, including contract start and expiration dates, specific rental assistance program, rent-toFMR ratio, and number of units by the number of bedrooms. 

3) Contract Terminations, 2005-2014

This file lists Section 8 contracts that were terminated from January 1, 2005 to the present. It 

includes reasons for the termination such as opt-out or various statuses for failing properties 

(default, demolished, etc.).

4) Terminated Mortgages, 2014

This file lists HUD-insured multifamily mortgages that have been terminated and the reason for 

their termination, including maturity, assignment (foreclosure), or prepayment.

Step 1. Creating the 2005 Baseline Property Dataset

First, properties were classified by 2005 program type. Properties were identified as having 

active Section 8 assistance in 2005 if the Active Properties 2005 record for the property showed 

a “yes” in the “is_sec8_ind,” “ is_rent_supplement_ind” or “ is_section_236_rap_ind” column. 

Properties were identified as having Section 236/221BMIR assistance if either program was 

active in 2005. The program was identified as active if the Active Properties 2005 record for the 

property showed a “yes” in the “is_236_ind” or “is_bmir_ind” column.

51
Based on these two steps, properties were classified as “Section 8 only,” “Section 8 + 

236/221BMIR,” or “236/221BMIR” for their baseline 2005 status. Only properties with at least 

one of these types of active assistance in 2005 were retained in the study dataset.

Step 2. Classifying Properties by 2014 Status

Next, we identified the 2014 status of these same types of assistance for the properties in the 

2005 baseline dataset. We used the same rules as above but with the Active Properties file from 

2014 to assign Section 8 and Section 236/221BMIR status. We classified properties as “leavers” 

if they no longer appeared in the 2014 Active Properties dataset or if they appeared but did not 

have either Section 8 or Section 236/221BMIR assistance in place. We classified properties as 

“stayers” if they had at least one of these programs in place.

For properties with Section 8 in both years, we used the 2005 and 2014 Active Rental Assistance 

Contracts files to determine whether Section 8 assistance had been actively renewed or whether a 

contract from 2005 or earlier simply continued throughout the study period, with no owner 

choice to renew or opt out. Section 8 was considered to be renewed if a contract with the same 

contract number appeared in both the 2005 and 2014 files with a later TRACS expiration date for 

the 2014 version of the contract, or at least one new contract (with new contract number) 

appeared in the 2014 database that had not been listed for the property in 2005. 

Step 3. Determining Reasons for Termination of Assistance

For properties with Section 8 in 2005 but no Section 8 in 2014, we classified the type of 

termination using the “Termination_Reason” column from the Contract Terminations file. 

Contracts with a Termination_Reason of “Opted out” were classified as owner opt-outs; 

properties with a Termination_Reason of “Default,” “Demolished,” “Failed HQS” (Housing 

Quality Standards inspection), “Foreclosure,” or “Fraud” were considered to be HUD-abated 

contracts. 

For properties with Section 236/BMIR mortgages in 2005 but not in 2014, we used HUD’s 

Terminated Mortgages database to find the reason for termination. The field “TERM_TYPE” 

indicates if the mortgage ended due to maturity, prepayment, or assignment (foreclosure). As 

described below, we considered prepayment to be a form of owner opt-out, assignment to be a 

decision by HUD to cancel assistance to a troubled property, and mortgage maturity to be a 

neutral status that does not imply a decision by HUD or by the owner.

Step 4. Assigning Outcome Categories

We used the information above to assign each property in the 2005 baseline dataset to three 

definitive statuses (Opt-Ins, Opt-Outs/Prepays, and Foreclosure/Abatement) and a miscellaneous

“All Other” category. Note that the All Other category includes all Section 8 + Section 

236/BMIR properties where outcomes for the two types of assistance point in different 

directions. For example, a property where the owner prepaid the Section 236 mortgage (an “optout” decision) but renewed a Section 8 contract (an “opt-in” decision) would be placed in the All 

Other category. The exception is the combination of a mortgage that has matured—a neutral 

status—with a Section 8 contract where there has been a definitive opt-in, opt-out, or HUD 

abatement. In those cases, we use the Section 8 contract decision only to determine the outcome 

category.

Opt-ins: Properties were classified as “Opt-Ins” if they were stayers where the owner renewed a 

Section 8 contract between 2005 and 2014, plus either: 1) they were Section 8 only properties 

without active Section 236/BMIR in 2005, which made up a large majority of cases, or 2) they 

had an active 236/BMIR mortgage or one that matured between 2005 and 2014. 

52
Opt-Outs/Prepays: Properties were classified as “Opt-Outs/Prepays” if they were leavers with 

Section 8 only in 2005 and a terminated Section 8 contract identified as an owner opt-out under 

Step 3, or if they were leavers with Section 236/BMIR only in 2005 and their mortgage was 

identified as prepaid under Step 3. Properties with Section 8 + Section 236/BMIR in 2005 were 

placed in this category if they had a Section 8 contract identified as an owner opt-out and the 

236/BMIR mortgage either was prepaid (active decision by the owner) or matured (no active 

decision by owner or HUD). 

Foreclosure/Abatement: This status refers to leavers where HUD took action to end subsidies to 

a troubled property. They include Section 8 only properties where the contract termination 

reason was default, fraud, or other reasons included in the definition of HUD-abated contracts 

from Step 3. They also include Section 236/BMIR properties where the termination reason for 

the mortgage was “assigned.” While this status category would also include Section 8 + Section 

236/BMIR properties with both a HUD-abated Section 8 contract and either a HUD-assigned 

mortgage (active decision by HUD) or matured mortgage (no active decision needed), in practice 

no properties had these combinations of characteristics.

All Other: This category includes a number of types of properties, both stayers and leavers. The 

largest subset is Section 8 properties with the same contract in 2005 and 2014, with no renewal 

decision needed during the study period. The All Other category also includes a number of 

Section 8 + Section 236/BMIR properties where the owner, HUD or both made mixed decisions 

regarding maintaining or terminating assistance. The most common combination was a renewed 

Section 8 contract (owner opt-in) plus a prepaid Section 236/BMIR mortgage (owner opt-out). 

The study dataset excludes 780 properties that had Section 8 or 236/BMIR assistance in 2005 

where the 2014 status of the programs is unknown or ambiguous. The largest subset of these 

were properties that appeared to have ended Section 8 assistance because they have dropped out 

of the 2014 Active Contracts file, but were not listed in the Contract Terminations (“FHA Optouts”) file. Our review of state and local preservation datasets and HUD’s review of its Section 8 

database indicates that many of these contracts are in fact associated with active Section 8 

projects. Others are RAP or Rent Supp properties where the contract expired with no option to 

renew. While these properties were not included in the overall opt-in/opt-out analysis, they were 

included in the analysis of RAD-eligible properties where appropriate.

Identifying Section 202 and RAD-Eligible Developments

Properties were identified as part of the Section 202 Direct Loan subset if they appeared in the 

original study dataset, the Active Properties 2005 file had a “yes” in either the “is_sec_202” or 

“is_202_811_ind” column, and the Active Financing 2005 file indicated a Section 202 Direct 

Loan in the Section of the Act column (“soa_description_text”). To determine whether the 

Section 202 Direct Loan itself was still active in 2014, the same rules were applied to the Active 

Properties and Active Financing 2014 files. The overall outcome status (Opt-in, Opt-Out, 

Foreclosure/Abatement, Other) was retained from the previous analysis and generally refers to 

the status of the Section 8 contract at the development over the 2005-2014 period, rather than the 

status of the 202 loan.

The RAD-eligible developments were identified using additional datasets from HUD. In March 

2012, HUD released a dataset of properties with active Rent Supp and RAP contracts 

(http://portal.hud.gov/hudportal/documents/huddoc?id=RS_RAP_Units_03092012.xlsx) that 

were presumably eligible to apply for RAD conversion. HUD also provided lists of 70 

developments where conversion of Rent Supp and RAP contracts to project-based vouchers was 

complete. Matching these datasets to the 2005 baseline dataset identified 321 developments that 

53
were either listed in the 2012 active Rent Supp/RAP contracts database, included in the list of 

completed conversions, or both. These are identified as “RAD-eligible” in the report. Sixty-three 

of these developments were identified as “RAD participants” based on their inclusion in the 

completed conversion dataset. The remaining seven completed RAD conversions do not appear 

in the Active Properties 2005 file and thus are not included in the analysis. 

54
Appendix 2. Multivariate Analysis by Market Phase

Table A2.1. Odds Ratio Results Segmented by Housing Market Strength, 2005-2014

Housing Market

Strong

2005-2007

Weak

2008-2011

Recovering

2012-2014

Variable Odds 

Ratio

PvalueOdds 

Ratio

PvalueOdds 

Ratio

Pvalue

Property size

(ref.: less than 50)

Property size 50-99 0.136 *** 0.460 *** 0.448 ***

Property size 100-199 0.218 *** 0.344 *** 0.249 ***

Property size 200+ 0.127 *** 0.166 *** 0.216 ***

Density % of units with 3 or more 

bedrooms

0.280 * 0.206 *** 0.659

Occupancy types Family (ref. elderly/disabled) 2.956 *** 3.278 *** 1.232

Building types Detached or semi-detached

(ref. other)

1.122 0.918 1.391

Ownership types Non-profit (ref. forprofit/limited dividend)0.571 0.588 ** 0.489 **

REAC physical score 1 percentage point increase 0.992 0.984 ** 0.999

Program Older programs 0.593 0.409 *** 0.905

100% assisted units 0.087 *** 0.103 *** 0.147 ***

FHA insured 0.324 0.415 0.294

HFA related 1.770 2.236 * 2.011

Matured Section 236/BMIR 

mortgage

0.301 0.810 2.872 ***

Neighborhood Poverty rate 0.020 *** 0.653 0.106

Minority rate 0.698 0.492 * 0.809

Homeownership rate 0.201 0.503 0.618

Rent-to-FMR (ref. 

100-119.9%)

Rent-to-FMR less than 80% 3.628 *** 3.276 *** 1.518

Rent-to-FMR 80-99.9% 1.973 * 2.135 *** 1.166

Rent-to-FMR120-129.9% 0.398 0.914 0.630

Rent-to-FMR130-139.9% 0.160 * 0.691 0.379

Rent-to-FMR 140-159.9% 0.375 0.550 0.867

Rent-to-FMR 160% or more 0.199 * 0.735 0.435

Metropolitan

Location (ref. 

suburbs)

Principal cities 1.788 * 1.301 1.159

Non-metropolitan 1.229 1.230 1.063

Census Division

(ref. South Atlantic)

New England 0.561 0.303 ** 0.292

Mid Atlantic 0.483 0.921 1.264

East North Central 0.789 0.691 0.344 *

West North Central 1.271 1.075 2.079 *

East South Central 0.440 0.766 1.591

West South Central 0.712 1.771 0.587

Mountain 0.837 1.638 1.356

Pacific 0.600 0.835 1.022

N 643 4684 4543

Opt-outs 114 162 88

Pseudo R2 0.3385 0.2209 0.1875

Asterisks in the p-value columns denote level of significance. *** indicates p<0.01; ** indicates p<0.05; * indicates 

p<0.1. A blank cell in the p-value column indicates result was not statistically significant.

Source: HUD, 2005 and 2014 Active Properties, Active Financing, Active Contracts and Multifamily Building Type

Files, iREMS; Terminated Contracts Database; Terminated Multifamily Mortgages Database; 2005 Picture of 

Subsidized Households; 2005 and 2014 Fair Market Rents; U.S. Census Bureau, 2005-2009 American Community 

Survey; Federal Housing Finance Agency Housing Price Index 

55
Appendix 3. Small Area FMR Exploratory Analysis

HUD’s hypothetical Small Area Fair Market Rents (SAFMRs) provide zip code-level estimates 

of market rents for metropolitan areas. To the extent that the SAFMRs provide a more accurate 

estimate of the market-rate rents in a particular neighborhood than the metropolitan FMRs, a low 

rent-to-SAFMR value may be a better predictor of an owner’s decisions to opt out of subsidies 

than a low rent-to-FMR ratio. 

To test the potential use of SAFMRs in the opt-in/opt-out analysis, we created an adjustment 

factor to estimate property-level rent-to-SAFMR ratios from the original rent-to-FMR ratios 

provided by HUD. The adjustment factor is the ratio of the 2011 metropolitan two-bedroom 

FMR to the small area two-bedroom FMR.19

Estimated rent-to-SAFMR = rent-to-FMR * (2BR FMR/2BR SAFMR)

In this way, we calculated estimated rent-to-SAFMR for 7,907 properties with either Section 8 

alone or Section 8 + 236/BMIR in 2005 and where the owner made an explicit opt-in or opt-out 

decision by 2014. The study set only includes properties in metropolitan areas, as small area 

FMRs are not calculated for non-metropolitan locations. The properties were placed in the same 

categories for rent-to-SAFMR as they were for regular rent-to-FMR in the regression analysis: 

less than 80 percent, 80-100 percent, 100-120 percent, 120-130 percent, 130-140 percent, 140-

160 percent, and 160 percent or more. Table A3.1 shows the breakdown of the 7,907 properties 

by rent-to-SAFMR category. 

Table A3.1. Properties by Rent-to-Small Area FMR Categories

Rent-to-Small Area FMR Ratio % of Properties

Below 80% FMR 12%

Between 80% & 100% 27%

Between 101% & 120% 28%

Between 121% & 130% 10%

Between 131% & 140% 7%

Between 141% & 160% 9%

Over 160% FMR 7%

Using small area FMRs rather than the traditional MSA-level FMRs will change the analysis 

only if rents in the neighborhoods surrounding the subsidized properties are different from the 

overall metro-wide rents. If subsidized properties tend to be located in neighborhoods with 

relatively low rents compared to the surrounding region, the rent-to-SAFMR ratio should be 

higher than rent-to-FMR. Conversely, properties located in neighborhoods with stronger markets 

19 HUD published FY2011 Hypothetical Small Area FMRs at 

http://www.huduser.org/portal/datasets/fmr/smallarea/index.html. One limitation is the use of 2011 data to create the 

adjustment factor rather than 2005 baseline characteristics, as was used throughout the rest of the report. Small Area 

FMRs were not calculated before 2011.

56

 
than in the surrounding area should have lower rent-to-SAFMR ratios compared to the regular 

rent-to-FMR calculations.

In fact, most properties stay in the same category regardless of whether metropolitan or small 

area FMRs are used. Within the study set, 59 percent of properties remained in the same rent-toSAFMR category as their original rent-to-FMR category. Nearly all others moved by one 

category. In 23 percent of cases, the property moved into a category one level higher, most 

commonly from a regular rent-to-FMR of 80-100 percent into the 100-120 percent category for 

rent-to-SAFMR. In those cases, therefore, the properties were located in surrounding 

neighborhoods with relatively low rents compared to the overall metropolitan area. In 12 percent 

of cases, the property moved down one category when converting from rent-to-FMR to rent-toSAFMR. Again, the most common shift was between the two mid-range categories, in this case, 

properties moving from a rent-to-FMR category of 100-120 percent down to the 80-100 percent 

category for rent-to-SAFMR. Less than six percent of properties moved by two or more 

categories.

We then re-ran the regression models from Table 13 and calculated the odds ratios for the rentto-SAFMR categories. The specification of the other variables in the model did not change. As 

Table A3.2 shows, the results were very similar to the rent-to-FMR results from Table 13, albeit 

with a smaller metropolitan-only dataset.

Table A3.2. Odds Ratios for Estimated Small Area Rent-to-FMR Categories

Replication of 

original model

(Section 8 Only)

Model without 

Housing Market 

Variables (Section 8 

Only and Section 8 

+ 236/BMIR)

Model with 

Housing Market 

Variables (Section 

8 Only and Section 

8 + 236/BMIR)

Odds 

Ratio

PvalueOdds 

Ratio

P-value Odds 

Ratio

Pvalue

Small Area Rentto-FMR

(ref. 100-

119.9%)

Rent-to-FMR less than 80% 2.223 *** 2.376 *** 2.331 ***

Rent-to-FMR 80-99.9% 1.511 ** 1.485 ** 1.470 **

Rent-to-FMR 120-129.9% 0.589 0.719 0.744

Rent-to-FMR 130-139.9% 0.578 0.584 0.605

Rent-to-FMR 140-159.9% 0.938 0.948 0.999

Rent-to-FMR 160% or more 0.364 * 0.355 * 0.363 *

In this preliminary experiment, therefore, use of the Small Area FMRs did not change the results 

substantially. Most properties did not change FMR category, and results of the regression 

analysis were similar. In a future study, we recommend calculating a more precise average Small 

Area Rent-to-FMR ratio for each property using adjustment factors for all unit sizes rather than 

just two-bedrooms, and using property datasets in the regression that are directly comparable 

(e.g., a metropolitan-only dataset for both the regular rent-to-FMR study and the SAFMR study).

57