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Assessment of the 

Economic and Social 

Characteristics of LIHTC 

Residentsand Neighborhoods 

Final Report 

Prepared for: 

U.S. Department of Housing and Urban 

Development 

Office of Policy Development and Research 

Prepared by: 

Larry Buron 

Sandra Nolden 

Kathleen Heintzi 

Julie Stewart 

Abt Associates, Inc. 

Cambridge, MA 

August 2000 
Foreword 

Createdas part of the Tax Reform Act of 1986, the Low Income Housing Tax Credit 

(LIHTC) program is currently the largest affordable rental housing construction program 

in the United States. It is estimated that the program has produced nearly one million 

affordable units since its inception. 

Although there have been some major studies of the LIHTC program, few focus on 

residents and neighborhoods. This study, by Abt Associates Inc., presents an assessment 

of the economic and social characteristics of residents and neighborhoods in 39 LIHTC 

developments located in five metropolitan areas - - Boston, Kansas City, Miami, 

Milwaukee, and Oakland - - that were put into service between 1992 and 1994. 

The 39 properties included in this study suggest that the program serves households with 

low incomes. Ninety-four percent of the units studied were reserved for tenants with 

incomes below 60 percent of their area’s median family income. LIHTC may be used to 

subsidize developments that have as few as 20 percent of their units reserved for lower 

income families, but 37 of the 39 the developments studied here served a predominantly 

low income population. 

Although the LIHTC properties served a low income population, there was a mix of 

incomesin the developments. For example,in more than half the developmentsat least 

20 percent of the families were in poverty and & least another 20 percent of the families 

had incomes at least 1.3 times the poverty level. And although the LIHTC developments 

served families with low incomes, on average these families had higher incomes than 

residents of public housing or recipients of Section 8 who lived in the same metropolitan 

areas. The LIHTC families also were more likely to be working and less likely to be on 

welfare than families in public housing or receiving Section 8. There is some overlap 

between LIHTC and Section 8, as 37 percent of the LIHTC residents in this study also 

received Section 8 assistance. 

This study also examines the rent burden experienced by residents of the developments, 

the characteristics of the residents relative to their neighborhoods, and the impact of the 

developments on the neighborhoods in which they are located. This report is a useful 

first look at how this important housing development program benefits the people it is 

intended to serve. 

Susan M. Wachter 

Assistant Secretary for Policy 

Development and Research 
Acknowledgments 

We would like to thank those who helped with the design and implementation of the data 

collection and review of the report. At Abt Associates, Diane Stoner of Survey Group did a 

wonderful job directing the telephone survey of residents in 39 properties. Louis Kiefor, also 

in the Survey Group, did an excellent job cleaning and checking the raw survey data. 

Satyendra Patrabansh did a yeoman’s job putting all the data from multiple sources into a 

consistent and useable format. He was also responsible for most of the programming needed 

for the analysis. Jim Wallace provided helpful comments and insights on drafts of the report. 

Finally, Michele Robinson did an excellent job formatting the report. At HUD, Priscila 

Prunella (the government technical monitor) gave us helpful comments and advice over the 

course of the study. She also helped the project stay on schedule by performing the 

necessary administrative functions and coordinating the review process in a timely manner. 

Finally, we would like to thank the site managers, owners, and residents of the LIHTC 

properties who took the time to be interviewed. 

Larry Buron 

Sandra Nolden 

Kathleen Heintz 

Julie Stewart 

Abt Associates Inc. 
Table of Contents 

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

Chapter 1 - Introduction ..................................................................................................... l-l 

1.1 Overview of the LIHTC ............................................................................................. 1. 1 

1.2 Objectives of the Research......................................................................................... l-3 

1.3 Overview of Study Sample ........................................................................................ l-6 

1.4 Study Data.. ................................................................................................................ l-9 

1.5 Report Overview.. .................................................................................................... l-l 1 

Chapter 2 - The Study Properties ....................................................................................... 2-1 

2.1 Overview of the Study Properties ............................................................................. .2-l 

2.2 Properties and Neighborhoods by MSA ................................................................... .2-6 

Chapter 3 - Social, Financial, and Housing Circumstances of LIHTC Residents.. .......3-l 

3.1 Profile of Residents in Study Tax Credit Properties.. ............................................... .3-l 

3.2 Rent Burdens and Project Rent-Setting Practices.. ................................................. .3-l 1 

3.3 Housing Circumstances and Satisfaction with Housing and Neighborhood.. .........3-11 

3.4 Characteristics of Section 8 Households .................................................................. 3-27 

3.5 Summary .................................................................................................................. 3-32 

Chapter 4 - Role of the LIHTC in Fostering Diverse Communities ............................... 4-l 

4.1 Characteristics of LIHTC Neighborhoods ................................................................ .4-2 

4.2 Income Mixing within LIHTC Properties and Compared to Neighborhoods ...........4-4 

4.3 Racial/Ethnic Diversity within Properties and Compared to Neighborhoods.. .......4-16 

4.4 Residents’ Satisfaction with their Neighborhood .................................................... 4-20 

4.5 Summary.. ............................................................................................................... .4-24 

Chapter 5 - Property Management and Resident Services .............................................. 5-l 

5.1 Profile of Property Management Entities .................................................................. 5-l 

5.2 Profile of Management Operations ............................................................................ 5-2 

5.3 Property Amenities and Social Services .................................................................... 5-6 

5.4 Resident Satisfaction with Management and Services ........................................... .5-l 1 

5.5 Service Needs/Impacts of Welfare Reform.. ............................................................ 5-l 1 

5.6 Summary.. ................................................................................................................ 5-l 1 

i 
Chapter 6 - Developer Motivation and Project Impact .................................................... 6-l 

6.1 Characteristics of Study Developers.. ....................................................................... .6-l 

6.2 Development Objectives and Site Selection Process................................................ .6-3 

6.3 Project Impact ........................................................................................................... .6-7 

6.4 Compliance Period.. ................................................................................................ .6-l 1 

6.5 Summary.. ............................................................................................................... .6-l 1 

Appendix A - Sample Selection Procedures ..................................................................... A-l 

A.1 Identification of Study MSAs ................................................................................... A-2 

A.2 Selection of Study Properties.. .................................................................................. A-5 

A.3 Selection of Residents for Survey.. ......................................................................... A-10 

A.4 Weights ................................................................................................................... A-l 1 

A.5 Tests of Significance.. ............................................................................................. A-12 

Appendix B - Data Collection Methods ............................................................................ B-l 

B. 1 In-Depth Interviews .................................................................................................. B-2 

B.2 Collection of Resident Information from Property Manager Files.. ......................... B-3 

B.3 Site Visitor Observations of Properties and Neighborhoods .................................... B-4 

B.4 Data Collection for Non-Sample Properties ............................................................. B-5 

B.5 Resident Survey.. ...................................................................................................... B-6 

B.6 Collection of Secondary Data ................................................................................... B-9 

Appendix C - Low Income Housing Tax Credit Survey ................................................. C-l 

ii 
List of Exhibits and Figures 

Exhibit 1-1: Five Study MSAs.. ......................................................................................... l-7 

Exhibit 2- 1: Study Properties by MSA and Sponsor Type ............................................... .2-2 

Exhibit 2-2: Characteristics of Study Properties.. ............................................................. .2-3 

Figure 2- 1: Map of LIHTC Projects in Boston MSA ...................................................... .2-9 

Exhibit 2-3 : Boston MSA Study Properties.. .................................................................. .2- 10 

Figure 2-2: Map of LIHTC Projects in Kansas City MSA.. .......................................... .2-14 

Exhibit 2-4: Kansas City MSA Study Properties.. .......................................................... .2-15 

Figure 2-3: Map of LIHTC Projects in Miami MSA.. ................................................... .2-2 1 

Exhibit 2-5: Miami MSA Study Properties.. ................................................................... .2-20 

Figure 2-4: Map of LIHTC Projects in Milwaukee MSA ............................................. .2-27 

Exhibit 2-6: Milwaukee MSA Study Properties ............................................................. .2-28 

Figure 2-5 : Map of LIHTC Projects in Oakland MSA.. ............................................... .2-32 

Exhibit 2-7: Oakland MSA Study Properties.. ................................................................ .2-33 

Exhibit 3- 1: Race and Ethnicity of Heads of Household by Sponsor Type.. .................... .3-3 

Exhibit 3-2: Race and Ethnicity of Study, 

Public Housing, and Section 8 Households By MSA.. .................................. 3-4 

Exhibit 3-3: Income and Income Sources 

of Study Property Households by Sponsor Type.. ........................................ .3-5 

Exhibit 3-4: Income of Study, Public Housing, and Section 8 Households By MSA.. .....3-7 

Exhibit 3-5: Characteristics of Study Property Households by Sponsor Type.. ............. .3-10 

Exhibit 3-6: Households with Section 8 Assistance by Sponsor Type .......................... .3-12 

Exhibit 3-7: Rent Restrictions on Study Property Units ................................................. .3- 13 

Exhibit 3-8: Rents as a Percentage of FMRs and Tax Credit Maximum Rents.. ........... .3-14 

Exhibit 3-9: Rent Burdens Among Study Property Households by Sponsor Type.. .......3-15 

Exhibit 3-10: Rent Burdens Among Study Property 

Households by Income as a Percentage of Area Median.. .......................... .3-l 8 

Exhibit 3- 11: Prior Housing Situation of Study Property Households ............................. .3- 19 

Exhibit 3-12: Primary Reason for Moving to LIHTC Property.. ...................................... .3-20 

Exhibit 3- 13: Resident Ratings of LIHTC Property Apartment ........................................ 3-2 1 

Exhibit 3-14: Resident Comparison of LIHTC 

Property Apartment to Previous Residence ................................................ .3-23 

Exhibit 3-15: Resident Ratings of LIHTC Property Neighborhood ................................. .3-24 

Exhibit 3-16: Location of Previous Apartment or 

Home Compared to Current Apartment.. .................................................... .3-25 

iii 
Exhibit 3-17: ResidentComparisonof LIHTC 

Neighborhood to Previous Neighborhood ................................................... 3-26 

Exhibit 3-18: Race and Ethnicity of Heads of Household by Section 8 Status ................. 3-28 

Exhibit 3-19: Income and Income Sources of Study Property 

Households by Section 8 Status................................................................... 3-29 

Exhibit 3-20: Resident Ratings of LIHTC Property Apartment by Section 8 Status......... 3-30 

Exhibit 3-2 1: Resident Ratings of LIHTC Property Neighborhood by Section 8 Status... 3-3 1 

Exhibit 4- 1: Characteristics of LIHTC Neighborhoods ..................................................... 4-3 

Exhibit 4-2: Distribution of Income and Related Characteristics at LIHTC Properties .... 4-6 

Exhibit 4-3: Percent of Properties Meeting Alternative 

Definitions of Mixed-Income Property ..........................................................4-9 

Exhibit 4-4: Percent of Properties Defined as 

Mixed-Income by Various Characteristics .................................................. 4-13 

Exhibit 4-5: Income Levels at LIHTC Property Compared to Neighborhood .................4-14 

Exhibit 4-6: Racial/Ethnic Diversity in LIHTC Properties ..............................................4-l 8 

Exhibit 4-7: Racial/Ethnic Diversity in LIHTC Properties 

Compared to Neighborhoods .......................................................................4-l 9 

Exhibit 4-8: Residents’ Perceptions of their Neighborhood by Sponsor Type ................4-2 1 

Exhibit 4-9: Residents’ Perceptions of their Neighborhood 

by Mixed-Income Status of Property ........................................................... 4-23 

Exhibit 5-l: Profile of Management Entities and Operations.. ......................................... .5-3 

Exhibit 5-2: Property Amenities and Conditions.. ............................................................ .5-7 

Exhibit 6- 1: Breakdown of Properties by Primary Development Objective.. ................... .6-4 

Exhibit 6-2: Breakdown of Sites by Neighborhood Impact.. ............................................ .6-8 

Exhibit A-l: MSAs Included in the Study.. ....................................................................... A-4 

Exhibit A-2: Study Properties by MSA and Sponsor Type ............................................... A-6 

Exhibit A-3: Sample Properties Compared to All Eligible Properties in Study MSAs ..... A-9 

Exhibit A-4: Survey Sample and Response Rate by MSA.. ............................................ A-l 1 

Exhibit B-l: Data Collected from Property Manager Files ............................................... B-4 

Exhibit B-2: Data Collected for Non-Sample Properties.. ................................................. B-5 

iv 
Executive Summary 

Created by the Tax Reform Act of 1986, the Low-Income Housing Tax Credit (LIHTC) 

program is the primary affordable housing production program in the U.S. This study 

explores the social and economic characteristics of LIHTC residents and the neighborhoods 

in which these properties are located. It is intended to provide both new information on who 

is served by the tax credit program and to explore tenant and project characteristics in relation 

to the neighborhoods where the properties are developed. 

The findings of this report are based on a sample of LIHTC properties placed in service 

between 1992 and 1994 in five MSAs: Boston, Kansas City, Miami, Milwaukee, and 

Oakland. In total, 39 properties are included in the study with between six and nine 

properties in each MSA. Properties with fewer than 10 units, FmHA Section 515 projects, 

and projects serving special needs populations were not included in the study. The properties 

were selected to include a relatively even share of both for-profit and nonprofit-sponsored 

properties in each MSA (however, the results were weighted to reflect all eligible properties 

in the five study MSAs). Data collection included field visits and interviews with site 

managers and owners of each property and a telephone survey of 832 residents in the study 

properties. 

Profile of Study Properties 

The 39 properties included in the study reflected some distinct LIHTC development patterns 

by MSA, in part driven by the priorities set by states in their Qualified Application Plan 

(QAP). For example, in the Boston MSA, most properties in the study were previously 

troubled properties, several with project-based Section 8 and state financed mortgages. The 

State’s QAP targeted such properties. In the Oakland MSA, an overwhelming majority of the 

projects placed in service between 1992 and 1994 were developed by nonprofit sponsors, 

reflecting the strong presence of nonprofits in the Bay area. The basic characteristics of the 

properties in our study are as follows. 

l The vast majority (94 percent) of units in LIHTC properties are reserved for 

qualifying tenants. Two for-profit properties in Oakland elected the minimum 

20 percent qualifying units; the rest of the study properties have over 80 

percent qualifying units with a majority having 100 percent. 

l Section 8 assistance plays a substantial role in the LIHTC properties. Threequarters of the properties had at least one resident with Section 8 assistance, 

including eight properties that had project-based Section 8 units. Of the eight 

V 
properties with project-based Section 8 units, five were located in the Boston 

MSA. 

l LIHTC properties are relatively small. Even though properties with fewer than 

10 units were excluded from the study, most of the study properties have fewer 

than 100 units and are less than three-stories tall. Only two study properties are 

high-rises (both serve non-elderly families). The for-profit properties tend to 

be larger than the nonprofit properties with about half having more than 100 

units compared to less than 20 percent of the nonprofit properties. 

l Most of the properties served non-elderly families or singles. Of the six elderly 

properties, five were developed by for-profit sponsors. 

l Most tax credit properties use experienced, professional management 

companies. The typical property management entity in our study properties is 

a for-profit management company with around 2,000 units in its portfolio. 

Although most management companies have a mix of market-rate and 

subsidized units, about half of the companies could be considered to have a 

tax-credit specialty (at least one-third of their portfolio). 

l Vacancy rates were low at almost every property, but several properties had 

high turnover rates. The average vacancy rate was only 4 percent, but the 

average annual turnover rate was 24 percent per year. However, the median 

turnover rate was 15 percent. (The average turnover rate is driven by seven 

properties-primarily serving either young, mobile populations or seasonal 

farm workers-with reported turnover rates over 50 percent.) 

Who lives in LIHTC properties? 

As noted earlier, all but two of the study properties have set aside over 80 percent of their 

units for qualifying tenants. One possible incentive is for LIHTC properties to serve the 

highest-income households qualifying for tax credit units in order to maximize the amount of 

rent that can be collected from their tenants. However, this is not the case in our sample 

properties. 

l LIHTC properties serve primarily extremely- and very-low-income households. 

Approximately 40 percent of the households have income below 30 percent of 

the area median and 34 percent have income between 3 1 and 50 percent of the 

vi 
median. There are a number of factors that contribute to the tendency of 

LIHTC properties to serve such low-income families. These include the use of 

Section 8 subsidies, restrictions from non-LIHTC sources of funding (e.g., 

HOME), priorities in state Qualified Allocation Plans (QAPs), the difficulty in 

attracting higher-income tenants to some locations, and the mission of some 

developers to serve very low-income families. 

l Over one-third (37 percent) of tax-credit households received Section 8 

assistance. LIHTC households were much more likely to have project-based 

Section 8 assistance (3 1 percent) than tenant-based assistance (6 percent). 

l LIHTC households receiving Section 8 assistance were much poorer than nonSection 8 households in LIHTC properties. Over two-thirds of the Section 8 

households have extremely low incomes (less than 30 percent of the area 

median) whereas only 23 percent of the other residents have income that low. 

In addition to being very or extremely low-income, LIHTC residents tend to be working 

families who are members of a racial or ethnic minority. 

l Nearly 70 percent of LIHTC households reported an adult working for pay at 

the time of the survey. Only 10 percent reported receiving income from public 

assistance in the past year, a finding confirmed by site managers and owners 

who reported that so few residents received welfare that TANF time limits 

would not affect the financial viability of their properties. Compared to other 

LIHTC residents, the Section 8 residents were less likely to be working and 

more likely to receive welfare or disability assistance. 

l Just over 80 percent of the residents are minorities, primarily AfricanAmerican (56 percent) and Hispanic (20 percent). Within LIHTC properties, 

Section 8 residents are much more likely to be minorities (95 percent) than 

other residents (71 percent). 

l Residents of for-profit-sponsored properties are less likely to be members of a 

minority or to have a working adult in the household and more likely to be 

elderly than those in nonprofit-sponsored properties. 

Most residents of LIHTC properties have incomes low enough to qualify for public housing 

or Section 8 assistance. However, compared to public housing residents and Section 8 

recipients, LIHTC residents in all five MSAs have higher incomes, are more likely to have 

earned income, and are less likely to be on welfare. 

vii 
l The extent to which LIHTC residents have higher incomes than public housing 

residents and Section 8 recipients varies considerably by MSA. The income 

differences range from $3,400 in Boston to $15,000 in Oakland. The small 

difference in Boston is likely due to the predominance of project-based Section 

assistance among the study properties and the large difference in Oakland is 

due, in part, to the presence of two properties with substantial proportions of 

non-qualifying units. 

Rent Levels and Rent Burdens in LIHTC Properties 

Rents in LIHTC units (including utilities) are limited to 30 percent of the tax-credit 

maximum income (usually 60 percent of the area median). While this is intended to promote 

affordability, families with incomes below the 60 percent benchmark could pay a large share 

of their income for rent if it is set at the maximum allowed. However, the research shows 

many properties set rents well below the maximum. 

l Rents in approximately 40 percent of the units in LIHTC properties were 

substantially below (less than 80 percent of) the maximum set by the program. 

On the other hand, 35 percent of the units had rents above the tax credit 

maximum, which is due to the presence of market-rate and Section 8 units. 

Note that gross rents in Section 8 units can be above the tax credit maximum 

since tax credit rules require only that the tenant-paid portion of rent fall under 

the tax credit maximum. 

l A small share of units in LIHTC properties (9 percent) have rent restrictions 

beyond those required by the LIHTC program because they also received nonLIHTC subsidies such as HOME funding. Only 6 percent of the units had 

totally unrestricted rents (market-rate units) and the remaining 85 percent had 

just the LIHTC rent restrictions. 

l Most of the LIHTC units (78 percent) had gross rents below the local FMR 

level. Since FMRs are set at the fortieth percentile of area rents, this indicates 

that most LIHTC properties are at least in the modest range. 

l Rents in the nonprofit properties are substantially lower than in the for-profit 

properties. For example, 45 percent of nonprofit units have rents below 70 

percent of the FMR whereas only 9 percent of for-profit units are this low. The 

higher rents in for-profit properties are due to a number of factors, including 

the higher likelihood of for-profit properties to be in higher-rent suburban 

. . . 

VIII 
areas, the prevalence of project-based Section 8 units among for-profit 

properties, and the larger share of non-qualifying units in for-profit properties. 

l LIHTC households are evenly divided between those who pay 30 percent or 

less of their gross income for rent and those who pay more than 30 percent of 

their gross income for rent. Despite rents below the tax credit caps, some 13 

percent of households reported rent burdens over 50 percent, and thus are 

estimated to have worst case housing needs as defined by HUD. An analysis of 

the inputs into the rent burden calculation indicates that many tenants report 

utility costs above the utility allowances set for the property and incomes 

below those reported by the manager (however manager-reported incomes were 

not available in several properties and many of the manager-reported incomes 

were verified more than a year before the resident survey). While the data are 

subject to possible tenant reporting error, it still appears that rent burdens in 

LIHTC properties are high for many tenants. 

How do the LIHTC properties compare to tenants’ previous residences? 

As part of this study we asked tenants to rate their LIHTC units and neighborhoods on 

several dimensions and compare them to the previous place they lived. The results showed 

overall satisfaction with LIHTC units, but less satisfaction with their neighborhood. 

l A majority of the residents rated their LIHTC unit favorably, both overall and 

on specific criteria (e.g., size and condition). For example, 68 percent gave 

their apartment an overall rating of good or excellent while 32 percent rated it 

as poor or fair. 

l Residents in project-based Section 8 units were more likely than other residents 

to rate their LIHTC unit unfavorably. Over 60 percent of residents in projectbased Section 8 units rated their LIHTC unit as fair or poor compared to 

approximately 20 percent of other residents. 

l Most residents thought that their LIHTC apartment was as good or better than 

their previous residence. For example, 54 percent of residents thought their 

current unit was better than their previous residence and 24 percent rated it 

about the same. Only 22 percent thought their previous residence was better. 

l A substantial share of LIHTC residents (23 percent) reported that they lived in 

public housing immediately prior to moving into their tax credit units. It 

ix 
appears that LIHTC properties may serve as an important stepping stone for 

many residents moving from public housing to the private sector. 

l Residents did not rate their neighborhoods as favorably as their apartments. 

Less than half (46 percent) rated their LIHTC neighborhood as good or 

excellent, whereas 54 percent rated it as poor or fair. Residents were evenly 

divided on whether their current neighborhood was better, the same, or worse 

than their previous neighborhood. 

l Similar to the overall apartment ratings, residents in project-based Section 8 

units were much more likely than other residents to rate their LIHTC 

neighborhood unfavorably. Nearly 80 percent of residents in project-based 

Section 8 units rated their neighborhood as fair or poor overall, whereas less 

than half of the other residents rated their neighborhood unfavorably. 

In what types of neighborhoods are LIHTC properties located? 

In the LIHTC program, owners decide where to develop LIHTC properties, subject only to 

local laws that apply to all residential development. There are no additional location 

restrictions in the LIHTC program. Nevertheless, there are several incentives at the federal 

and state level that may influence where a project is located. If a property is located in a 

qualified census tract (QCT) or in a difficult development area (DDA), investors are eligible 

to receive a 30 percent increase in the basis on which tax credits are allocated. Furthermore, 

most state agencies receive more ,applicants for tax credits than they have available to award, 

thus, states must establish their own policies to award credits among eligible projects. The 

result is that LIHTC properties are built in a variety of areas with significant differences in 

the type of neighborhood by sponsor type. 

l A large share of properties were developed in locations that made them eligible 

for a 30 percent increase in the tax credit basis. Roughly half of the properties 

in the five study MSAs were located in QCTs. In addition, both the Boston 

and Oakland MSAs were designated DDAs in the early 199Os, so all of the 

properties developed at that time were eligible for the increased tax credit 

basis. While it is not possible to know what properties would have been 

developed without these incentives, several owners of properties in QCTs said 

the increased tax credits were necessary to make development feasible. 

l LIHTC properties are typically located in city neighborhoods with a majority 

of rental units and residents who have lived there a short time. These 

neighborhood characteristics were more common for nonprofit than for-profit 

X 
properties. For example, 86 percent of the nonprofit properties are located in 

city neighborhoods whereas only 56 percent of the for-profit properties are in 

city neighborhoods. 

l LIHTC neighborhoods are evenly divided between very low-income 

neighborhoods (median income less than 50 percent of area median) and more 

moderate-income neighborhoods. The patterns were significantly different by 

sponsor type. For example, nonprofit properties (67 percent) were much more 

likely to be in extremely low-income neighborhoods than for-profit properties 

(29 percent). 

l LIHTC neighborhoods tend to have a high proportion of minority residents. 

Roughly half of the neighborhoods had predominately (greater than 80 percent) 

minority residents and only 12 percent had predominately white residents. 

l There are distinct race/ethnicity patterns in the LIHTC neighborhoods by 

sponsor type. Over one-quarter of the for-profit properties in our study are in a 

predominately white neighborhood whereas none of the nonprofits are in such 

neighborhoods. At the other extreme, 61 percent of nonprofit properties are in 

predominately minority neighborhoods compared to only 35 percent of forprofits. 

Do LIHTC properties foster economic diversity? 

Several recent housing policy initiatives have been aimed at reducing the spatial 

concentration of very poor households. The impetus for these policies is the consensus 

among policy makers and scholars that high concentrations of very low-income households 

in large developments and/or neighborhoods leads to negative social and behavioral 

outcomes. Hence, the economic diversity of LIHTC properties and their contribution to 

economic diversity in the neighborhood are important policy issues. 

l In approximately half the properties, a majority of the households are 

extremely low-income (income less than 30 percent of the area median). 

However, there are distinct patterns by sponsor type. For-profit properties tend 

to have either many (more than 50 percent) extremely low-income households 

or very few (less than 10 percent), whereas the percentage of extremely lowincome households in nonprofits tends to fall between these extremes (10 to 50 

percent). 

xi 
l Only two LIHTC properties are economically diverse in terms of having a 

substantial proportion of qualifying and non-qualifying renters. However, 

within the qualifying range, there is substantial mixing of higher- and lowerincome groups. Overall, 40 percent of the properties can be considered mixed 

income using a combination of several measures of income diversity. 

Economic diversity is more common in nonprofit properties (55 percent) than 

for-profit properties (23 percent). This is consistent with the above finding that 

for-profits tended to serve primarily extremely low-income residents or 

primarily relatively higher-income residents. 

l Most of the LIHTC properties (76 percent) have residents with Section 8 

assistance, and they typically represent a small share (less than one-third) of the 

tenant population. Only 15 percent of the properties are entirely Section 8, all 

of which are project-based. 

l LIHTC residents at most properties have lower average incomes than their 

neighbors. At 72 ‘percent of the properties, the median income of LIHTC 

residents is substantially lower than the neighborhood median. The median 

income of LIHTC residents is substantially higher than neighborhood residents 

at only 10 percent of the properties 

l Residents of nonprofit properties are much more likely than residents of forprofit properties to be economically better off than their neighbors. This is 

consistent with the findings that nonprotits tend to be in poorer neighborhoods 

than for-profits and that for-profit properties in poorer neighborhoods tend to 

serve primarily extremely low-income residents. 

l Overall, many LIHTC properties (42 percent) serve either to provide housing 

for residents who reduce the level of poverty in the neighborhood or to provide 

housing for lower-income residents in low-poverty neighborhoods. In total, 27 

percent of the properties are located in high-poverty neighborhoods where the 

poverty-rate of the LIHTC residents is substantially lower than in the 

neighborhood. Another 15 percent of properties are located in low-poverty 

areas (poverty rate less than 10 percent) and serve residents whose incomes are 

much lower than their neighbors. Interestingly, it is primarily the nonprofit 

properties that serve to reduce the poverty level in the neighborhood (consistent 

with their more neighborhood revitalization focus) and the for-profit properties 

that provide housing for LIHTC residents in low-poverty neighborhoods. 

xii 
Do LIHTC properties foster racial diversity? 

Earlier, we reported that approximately 60 percent of LIHTC residents were AfricanAmerican (non-Hispanic), 20 percent were Hispanic, and less than 20 percent were white, 

non-Hispanic, However, these results do not indicate whether people of different race/ethnic 

groups live together in the same LIHTC properties or whether properties tend to be 

dominated by one race/ethnic group. Furthermore, it does not tell us how the race and 

ethnicity of LIHTC residents compares to that of neighborhood residents. 

l A small proportion of properties (9 percent) have a substantial proportion of 

white and minority residents. All of these are for-profit properties. Most 

properties have either predominantly white residents (14 percent) or 

predominantly minority residents (77 percent). However, 42 percent of the 

properties have a substantial proportion of both African Americans and 

Hispanics. 

l Only a few properties have a lower proportion of minority households than the 

neighborhood. Over half of the properties have a higher share of minority 

households than the neighborhood, 44 percent have about the same share 

(within 10 percentage points), and only 5 percent have a substantially lower 

share of minority households than the neighborhood. 

l All of the properties with a lower share of minorities than the neighborhood are 

for-profit sponsored properties. For-profits are also much more likely than 

nonprotits to have a similar share of minorities as the neighborhood (65 percent 

versus 26 percent) and less likely to have a substantially higher proportion of 

minorities than the neighborhood (24 percent versus 74 percent). 

How do LIHTC properties fit into the housing market? 

Given that the LIHTC program is a publicly subsidized program, but one that is highly 

decentralized and has private-sector owners and managers, a important question addressed by 

the study is how LIHTC properties fit in to the local housing market. Do the properties serve 

the same or different clientele as other subsidy programs ? Is property management in contact 

with the local Section 8 program ? Are the properties more like private sector units in terms 

of amenities and services? 

l As discussed previously, 74 percent of LIHTC residents have incomes below 

50 percent of median making them eligible for other types of subsidized 

. . . 

XIII 
housing, including public housing and Section 8. In fact, there appears to be 

considerable overlap in the programs. About 37 percent of the tenants in study 

properties receive Section 8 (31 percent project based and 6 percent tenant 

based) and nearly a quarter reported that their previous residence was public 

housing. Despite this overlap, LIHTC residents have higher average incomes, 

are more likely to be working, and are less likely to be on welfare than public 

housing residents and Section 8 recipients in the same MSA. 

l There appears to be very little contact between the LIHTC properties and the 

local Section 8 Voucher program. Only 25 percent of the Tax Credit property 

managers indicated that they had listed vacant units in their developments with 

the Section 8 office over the past two years. This was confirmed by Section 8 

staff who also reported little contact with LIHTC managers. 

l A majority of site managers indicated that their primary competition in the 

market was other subsidized housing-other tax credit projects or Section 8 

developments. Public housing was not viewed as competition, although about 

36 percent of the residents surveyed said they were on the public housing 

waiting list at the time that they found their tax credit unit. 

l In terms of their physical characteristics, LIHTC properties are smaller (i.e., 

fewer units and floors) than most public housing developments in large MSAs 

and for the most part fit in well with the scale and style of the neighborhood. 

Most were well maintained and had good “curb appeal.” 

l The amenities offered at LIHTC properties appear to be basic, not luxurious 

but similar to standard (i.e., non-luxury) market-rate properties. The most 

common amenities reported are off-street parking, laundry facilities in the 

building, and air conditioning. Less than 10 percent reported access to a pool 

or a washer/dryer in the unit. 

How did developers’ objectives shape the properties produced? 

While states, through their Qualified Allocation Plans, broadly shape the types of properties 

that receive tax credits, prospective developers respond to many different types of incentives, 

and, in the end, the units produced with the LIHTC reflect a broad range of motivations and 

considerations. 

l Overall, the study found that 51 percent of the properties were developed 

primarily to improve the neighborhood, 18 percent were developed primarily 
to meet affordable housing goals, and 31 percent were developed with more 

traditional real estate objectives (including profit) in mind. Consistent with 

their social missions and local focus, nonprofit sponsors were most likely to 

identify “neighborhood improvement” or “increasing the supply of affordable 

housing” as their primary objectives in developing. For-profit sponsors often 

identified multiple reasons for pursuing the development, but, overall, were 

most likely to identify financial benefit as the primary goal. 

l Different sponsor objectives led to different types of developments being 

undertaken and noticeable variations in project locations. Nonprofits tended to 

focus development in blighted areas and on problem properties. Sites included 

vacant city lots, drug infested buildings, or non-residential uses considered an 

eyesore or source of crime and other problems. For-profits developed 

properties in areas ranging from blighted to upscale, but were more likely than 

nonprofits to develop properties in higher-income neighborhoods or on 

undeveloped land in more outlying areas. 

l Roughly two-thirds of the study properties were found to have a positive 

impact on the neighborhoods in which they were located. In most cases the 

positive impact included removing a blighting influence or nuisance property, 

and in several cases there was reported evidence of classic neighborhood ripple 

effects such as nearby redevelopment or reductions in crime. 

l The properties considered to have no impact included very small properties and 

properties located in stable neighborhoods or outlying areas where there was no 

real neighborhood to impact. The three properties considered to have negative 

impacts suffered from poor screening and/or management and were viewed as a 

source of drugs or crime in the neighborhood. 

Issues for Future Research 

The current study represents an exploratory study of the relationship between tax credit 

projects and their neighborhoods. The results are applicable to the five MSAs and study 

period selected for the study, but cannot be reliably projected to the program nationally. In 

addition to verifying the results of this study with larger, nationally representative samples, 

future research on the tax credit program might include the following. 

A study of the extent to which the decentralized allocation structure of the LIHTC 

program is succeeding in meeting local housing needs. This study would have two basic 

xv 
components. The first would be an examination of state Qualified Application Plans to 

understand the common elements and variations in state priorities for the LIHTC program. 

This component could also include an investigation of how states develop and modify their 

QAPs and the methods they use to implement their priorities. A second component could 

examine the correlation between state policies and the types of projects actually undertaken. 

It would include an analysis of the extent to which LIHTC properties being built are meeting 

the needs of the area. This analysis would have to be done in the context of the housing 

needs of the State or even sub-state areas. For example, the study could examine whether 

properties are built in areas where a high share of households have excessively high rent 

burdens, in areas with a lack of new housing, or in areas with low concentrations of poverty. 

A more in-depth investigation of the impacts of LIHTCproperties on their neighborhoods 

is also needed. The current study has provided a qualitative assessment of the impact of the 

39 sample properties, however, it would appear that a more extensive analysis is warranted 

given that over 50 percent of the study developers cited neighborhood improvement as their 

primary objective. Some researchers argue that LIHTC is an inefficient tool for providing 

affordable housing to low-income families, but this research does not usually take into 

account the impact of LIHTC properties on the neighborhood. Hence more research is 

needed to understand the extent of neighborhood impact of the LIHTC program. Such 

research should focus on quantitative measures of neighborhood impact and the perspectives 

of stakeholders in the neighborhood who are not involved in the LIHTC property. There are 

a number of possibilities. For example, 2000 Census data should allow analysis of various 

trends in LIHTC neighborhoods. Data on construction activity in the neighborhood prior to 

and after the LIHTC project was begun could be compared to overall construction activity in 

the broader area such as the MSA. Trends in LIHTC neighborhood property values or rent 

levels relative to other neighborhoods in the city could also be examined. Although there are 

many practical difficulties in identifying the relevant population, it might also be possible to 

conduct a survey of non-LIHTC landlords and/or homeowners in the immediate 

neighborhood to discern the impact of LIHTC properties and any new investment or 

improvement activity that might be attributable to the presence of the LIHTC development. 

Finally, it would be useful to examine neighborhood impact in the context of non-LIHTC 

funding sources that are also used in the development of LIHTC properties. Are LIHTC 

properties with tax-exempt bonds, CDBG funding, or other sources of funding more likely to 

have a positive neighborhood impact ? In addition to raising the potential level of subsidy 

available (which by itself may influence the impact of the development), the use of these 

funding sources may indicate that the LIHTC project has support from key actors involved in 

local community development or may indicate that the LIHTC property is part of a 

comprehensive redevelopment effort. 

Research on the LIHTC properties and neighborhoods where Section 8 recipients live 

compared to where other Section 8 recipients live would help to clarify the role of the 

xvi 
LIHTC program in serving Section 8 residents. One issue is whether LIHTC properties are 

providing opportunities for Section 8 recipients to live in better, worse, or similar properties 

and neighborhoods than other places where Section 8 recipients live. Such a study might 

compare the size of LIHTC units, property amenities, and building characteristics such as the 

number of units and the number of floors as well as either objective measures of maintenance 

or resident perceptions of maintenance. Neighborhood characteristics to be compared 

include poverty levels, concentration of Section 8 households, income sources, and quality of 

the housing stock (e.g., average rent levels or age of structures). 

Finally, it would be useful to conduct additional research regardirtg those LIHTC 

properties with a large proportion of market-rate units. Although a relatively small share of 

the total, these projects represent “mixed income” developments in the broadest sense, and 

should be examined given the current policy emphasis on income mixing. Research 

questions would focus on the extent of economic and racial diversity in such projects, the 

neighborhoods where such properties are located, and the characteristics of residents (both 

qualified and unqualified) living in these properties. It would also be important to understand 

better the owners’ motivation in developing a mixed qualified/non-qualified property as well 

as resident perceptions of their property and neighborhood. 

xvii 
Chapter 1 

Introduction 

The Low-Income Housing Tax Credit (LIHTC) program is the primary affordable housing 

production program in the U.S. The Department of the Treasury provides states with 

approximately $3 15 million in new allocation authority each year, representing a total 

commitment of $3.2 billion over the ten years that investors can use the credit. Given the 

size of the LIHTC program, surprisingly little is known about LIHTC tenants, properties, 

management, and neighborhoods. The relative paucity of research is the result of a 

decentralized program-tax credits are awarded by individual state allocating agencies-and 

the corresponding lack of a central data repository available to researchers. This study 

collected and analyzed original data on LIHTC properties in five metropolitan areas to 

address some of the gaps in our knowledge about the LIHTC program. 

This Chapter begins with an overview of the LIHTC program, followed by a brief summary 

of the history of LIHTC research and the research objectives of this study. The next two 

sections describe the study sample and the sources of data used. The final section provides 

an overview of the remainder of the report. 

1.1 Overview of the LIHTC 

The Low-Income Housing Tax Credit was created by the Tax Reform Act of 1986. The act 

eliminated a variety of tax provisions that had favored rental housing and replaced them with 

a program of credits for the production of rental housing targeted to lower income 

households. Under the LIHTC program, the states were authorized to issue federal tax 

credits for the acquisition, rehabilitation, or new construction of affordable rental housing. 

The credits can be used by property owners to offset taxes on other income, and are generally 

sold to outside investors to raise initial development funds for a project. To qualify for 

credits, a project must have a specific proportion of its units set aside for lower income 

households with the rents on these units limited to 30 percent of qualifying income.’ The 

amount of the credit that can be provided for a project is a function of development cost 

(excluding land), the proportion of units that is set aside, and the credit rate (which varies 

based on development method and whether other federal subsidies are used). LIHTC 

I Owners may elect to set aside at least 20 percent of the units for households at or below 50 percent of area 

median income or at least 40 percent for households with incomes below 60 percent of area median. Rents 

in qualifying units are limited to 30 percent of the elected 50 or 60 percent of median income. 

l-l 
investors claim credits to offset taxes otherwise owed for each year of a lo-year period.’ 

However, the IRS can recapture the credits if the property does not stay in compliance over 

the required period. 

Congress initially authorized state agencies to allocate roughly $900 million in credits over 

three years: 1987, 1988, and 1989.3 Subsequent legislation modified the credit, both to make 

technical corrections to the original act and to make substantive changes in the program.4 For 

example, the commitment period (during which qualifying units must be rented to lowincome households) was extended from 15 years to 30 years.’ States were also required to 

ensure that no more credit was allocated to a project than was necessary for financial 

viability. The credit was also made a permanent part of the Federal tax code (Section 42), 

providing the states with roughly $3 15 million in new allocation authority each year. 

Because credits are taken over a IO-year period, the total amount committed from the 

Treasury per year is 10 times the amount allocated--or approximately $3.2 billion per year. 

Since the first year of the tax credit program in 1987, the LIHTC has become the principal 

mechanism for supporting the production of new and rehabilitated rental housing for lowincome households. However, information on the number of units actually developed and 

their characteristics has been difficult to assemble. Given the decentralized nature of the 

program, there is no single federal source of information on tax credit production.6 Most of 

the data about the program available in the past have been compiled by the National Council 

of State Housing Agencies (NCSHA), an association of state housing finance agencies, the 

The credit percentages are adjusted monthly, but fall in the neighborhood of 4 percent or 9 percent of 

qualifying basis. In general, credits are intended to provide a discounted stream of benefits equal to either 

30 percent (for the 4 percent credit) or 70 percent (for the 9 percent credit) of the property’s qualifying 

basis. The 4 percent credit is used for the acquisition of an existing building or for federally subsidized 

new construction or rehab. The 9 percent credit is used for non-federally subsidized rehabilitation or 

construction. The credit percentage is fixed at the point of final allocation of the tax credits. 

This is a commitment from the Treasury of $9 billion, based on annual credits taken for 10 years if all the 

credits are allocated and used. 

See Technical and Miscellaneous Revenue Act of 1988 and the Omnibus Budget Reconciliation Acts of 

1989, 1990, and 1993. 

The Omnibus Reconciliation Act of 1989 extended the commitment period from 15 to 30 years. However, 

project owners are allowed to sell or convert the project to conventional market housing if they apply to the 

state tax credit allocation agency and the agency is unable to find a buyer (presumably a nonprofit) willing 

to maintain the project as low-income for the balance of the 30 year period. If no such buyer is found, 

tenants are protected with rental assistance for up to three years. 

States are required to report on tax credit projects to the IRS. However, these data are not available for 

analysis due to the confidentiality of tax-related submissions. 

1-2 
entities responsible for allocating tax credits in most states. However, NCSHA data often 

suffered from incomplete reporting and key data were not consistently available for all years. 

Regarding program volume, NCSHA data suggest that some 800,000 LIHTC units (around 

90,000 per year on average) were allocated through 1995. However, not all projects that 

receive initial tax credit allocations are actually completed and placed into service. (A 

property must receive a certificate of occupancy and be “placed in service” in order to obtain 

its “final allocation” and begin receiving credits.) Estimates suggest that closer to 55,000 

units per year were placed in service during this period.’ Some of the difference between 

units receiving allocations and units placed in service is accounted for by time lags-project 

developers have two years from the initial allocation to complete the buildings and place 

them in service. However, it also appears that there is a significant fall-out rate for properties 

receiving LIHTC allocations, reflecting difficulties and risks associated with this complex 

development program. 

The best data currently available on LIHTC properties is the project-level database collected 

for HUD by Abt Associates Inc. Using data obtained from state agencies-supplemented in 

certain states with similar data collected by the GAO-the database covers the universe of 

properties developed between 1992 and 1994.8 The database also contains a broad range of 

geographic identifiers and demographic data, which were used to develop the first 

comprehensive portrait of the nature and location of recent tax credit production. The sample 

for this study was derived from these data. 

1.2 Objectives of the Research 

Until recently, there has been little systematic analysis of the LIHTC program. A HUD study 

undertaken very early in the program provided an initial evaluation of the LIHTC, based on a 

sample of properties developed in 1987 and 1988.9 Subsequent to this initial work, very little 

new research was available until HUD commissioned the creation of the National LIHTC 

7 James E. Wallace. 1998. “Evaluating the Low-Income Housing Tax Credit.” In Evaluating Tax 

Expenditures: Tools and Techniquesfor Assessing Outcomes. pp. 43-62. San Francisco: Jossey-Bass. 

8 The initial database also includes available state data (partial) on projects placed in service between 1987 

and 1991. An Abt Associatesfollow-upstudyfundedby HUD is creating a database of all projects placed 

in service from 1995 to the present. 

9 ICF, Inc. 1991. Evaluation of the Low-Income Housing Tax Credit, Final Report. ICF 
Database, completed by Abt Associates in 1996.” At about the same time, GAO was asked 

conduct its own study of the efficacy of the credit resulting in the GAO report: Tax Credits: 

Opportunities to Improve Oversight of the Low Income Housing Program.” Threats to the 

program in 1995 and 1996 (including a potential sunset) spawned additional efforts to 

understand and assessthe credit usage, including an NCSHA-sponsored response to the GAO 

study on the first 10 years of the LIHTC program.12 More recent work on the subject 

includes Newman and Schnare (1997) on locations of LIHTC properties compared with other 

assisted housing, Cummings and Denise DiPasquale (1998, 1999) on the financial and 

investment characteristics of these properties, and Abravanal and Johnson (1999) on the 

owners’ development objectives and future plans for the property.13 

An important theme running through many of these efforts, particularly the HUD/Abt, 

Newman and Schnare, and Cummings and DiPasquale research, is an effort to understand 

LIHTC properties in the context of their neighborhoods. There are many complex factors 

that potentially affect the location of these properties. For example, initial concerns that 

program financial incentives would favor suburban development in relatively high rent/low 

cost areas led to changes in the program to increase the level of subsidy available to 

developments in “difficult development areas” (where construction costs are high relative to 

incomes) and in areas where lower income renters predominate (qualified census tracts). 

Information from HUD’s LIHTC database shows that 54 percent of all LIHTC units (and 49 

percent of projects) in the early 90s were developed in central city areas, and another 26 

percent of units (21 percent of projects) were developed in suburban locations.‘4 Roughly 

two-thirds of the units are in neighborhoods that would be considered “low-income” (a 

IO Abt Associates. 1996. Development and Analysis of the National Low-Income Housing Tax Credit 

Database. U.S. Department of Housing and Urban Development, Office of Policy Development and 

Research: Washington DC. 

II U.S. General Accounting Office. 1997. Tax Credits: Opportunities to Improve Oversight of the LowIncome Housing Program. (GAO/GGD/RCED-97-55). 

12 Ernst and Young, 1997. The Low Income Housing Tax Credit: The First Decade. E&Y Kenneth 

Leventhal. 

13 Newman and Schnare. 1997. A >... And a Suitable Living Environment=: The Failure of Housing 

Programs to Deliver on Neighborhood Quality.@ Housing Policy Debate, 8(4), 703-741. Cummings and 

DiPasquale. 1998. “Building Affordable Rental Housing: An Analysis of the Low-Income Housing 

Credit.” City Research. Cummings and DiPasquale. 1999. “The Low-Income Housing Tax Credit: An 

Analysis of the First Ten Years.” Housing Policy Debate, 10(2), 25 l-307. Abravanel and Johnson. 1999. 

The Low-Income Housing Tax Credit Program. A National Survey of Property Owners. A Report 

Prepared for the Office of Policy Development and Research, U.S. Department of Housing and Urban 

Development. 

14 Abt Associates (1996), cited earlier. 

I-4 
majority of households with income less than 80 percent of the area median) by HUD’s 

community development program standards. The data also indicate an increase over time in 

the share of units developed by nonprofit sponsors.15 However, little is known about the 

differences in the tenants and neighborhoods served by nonprofit and for-profit sponsors. 

The relationship of LIHTC tenants to their neighborhoods is another important, but little 

explored, area for investigation. The program generally serves higher income residents than 

other HUD rental programs; however, little is known about the extent of income mixing 

within properties or how tenant composition relates to that of the neighborhood. Regarding 

racial and ethnic characteristics, there was evidence of a bimodal distribution of 

neighborhoods where LIHTC projects are located, with many project neighborhoods having a 

very high share of either white or non-white residents. What was missing, however, was any 

information on who is served by different projects in different neighborhoods. Put more 

broadly, do the properties serve residents much like others living in the neighborhood or does 

the program promote mobility by enabling lower income and minority households to live in 

higher income, non-impacted areas? 

The objective of this study is to address some of these research gaps by collecting and 

analyzing in-depth data on tenants, neighborhoods, and properties for 39 projects in five 

different metropolitan areas (the procedures for selecting these properties are discussed in the 

next section). The principal research issues covered in this report are: 

Who lives in LIHTC properties ? What are the economic, race/ethnic, family composition, 

and other characteristics of LIHTC households? Do LIHTC residents rely on earnings, 

welfare, or some other source as their primary source of income? Do many receive Section 8 

assistance? How does the income and race/ethnicity of LIHTC residents compare with that 

of Public Housing and Section 8 residents in the same MSA? 

How does the living environment compare to tenant’s previous residence? Is their 

current apartment larger, better maintained, or have better amenities than their previous unit? 

Is their neighborhood safer, closer to good schools, more convenient or otherwise better (or 

worse) than their previous neighborhood? What is the primary reason they moved to the 

LIHTC property ? Where did they live before moving to their current unit (e.g., public 

housing, a private rental unit, or their own home)? 

Cummings and DiPasquale (1999), cited earlier. 

I-5 
What are rent levels in LIHTC properties ? How do rents compare to FMRs? Do any 

residents have an excessive rent burden.7 Are rents close to the maximum allowed for tax 

credit units? Are there other restrictions on the rents that can be charged? 

In what types of neighborhoods are LIHTC properties located? Do the neighborhoods 

consist primarily of renters or homeowners.3 Are properties located in high poverty or lowpoverty neighborhoods.7 Are the neighborhoods racially diverse or dominated by one racial 

group? 

Do LIHTC properties contribute to racial/ethnic or economic diversity? Do households 

with a broad range of incomes live in the same property? How do the income levels of 

LIHTC residents compare with income levels of neighborhood residents? Do properties tend 

to be dominated by one racial group or are they diverse? Do properties have the same 

race/ethnic composition as the rest of the neighborhood? 

How do LIHTC properties fit into the housing market? Do they compete with other 

subsidized developments or market rate developments? What amenities or services do 

properties offer on their premises? How do they attract tenants? Does management have a 

relationship with the Section 8 office or community organizations? 

What were the main objectives in developing the LIHTC property? Was it purely a 

profit-motivated investment or did the developer have an affordable housing or community 

revitalization mission? How do developers choose the sites for LIHTC developments? Do 

the properties appear to have a positive impact on the neighborhood? 

This study is the first to collect and analyze data on many of these issues that are crucial to 

understanding the types of properties developed under the LIHTC program and the role of the 

program in meeting overall housing policy goals. The study also provides insights into the 

role of for-profit versus nonprofit sponsors in the program and the ways that their 

developments differ along the dimensions identified above. 

1.3 Overview of Study Sample 

This report is based on a sample of 39 properties in five metropolitan areas. It is important to 

understand that this exploratory study is not intended to produce program-wide statistical 

inferences about LIHTC projects or their residents. Thus, we are not able to draw programwide generalizations about, for example, resident incomes or other characteristics, 

affordability of LIHTC units, or project rent levels. To do so would require a national 

probability sample of properties and their residents and would require a study of substantially 

1-6 
larger scope. At the same time, however, a study using a large national probability sample 

would be less likely to capture neighborhood and market context available from this more indepth, but limited-site investigation. This section provides a brief description of how the five 

metropolitan areas and the 39 properties within those five MSAs were selected for the study. 

(See Appendix A for a detailed description of sample selection procedures.) 

Identification of MSAs 

The study design called for the selection of eight LIHTC properties placed in service between 

1992 and 1994 in each of five different MSAs. In choosing the five MSAs, we were guided 

by a desire to select: 

l MSAs with sufficient production in the 1992 to 1994 period to ensure an adequate 

number of properties in the study sample; 

l MSAs containing a variety of property types in terms of project sponsor-type (forprofit or nonprofit), city and suburban location, and qualified and non-qualified 

census tract; and 

l MSAs that are geographically balanced (e.g., at least one from each Census region) 

and represent a wide spectrum of housing market conditions. 

We used HUD’s National LIHTC Database of properties placed in service between 1992 and 

1994 to generate a list of MSAs having substantial levels of overall production as well as 

enough diversity of projects and neighborhoods to meet these criteria. The final stage in the 

selection was based primarily on: geographic distribution, the number of for-profit and 

nonprofit sponsored properties, the mix of central city and suburban properties, the presence 

of qualified census tract and non-qualified census tract properties, tightness of the rental 

market, and level of production (to better represent areas where LIHTC volume is the 

greatest). The result is shown in Exhibit l-l which identifies the five MSAs included in this 

study. 

Exhibit l-l 

Five Study MSAs 

Region 

Northeast 

Midwest 

West 

South 

Open (Midwest) 

MSA Selected 

Boston I 

1Milwaukee 

I 

1Oakland 

Miami 

Kansas City 
Selection of Properties within MSAs 

Once the MSAs were identified, properties and neighborhoods were selected. We stratified 

properties by sponsor type (nonprofit versus for-profit) and then selected a simple random 

sample of four nonprofit and four for-profit properties in each MSA.i6 With a maximum 

sample of 40 LIHTC properties, this study could not reliably represent the whole range of 

LIHTC properties in these MSAs. Therefore, to provide meaningful results for a coherent 

population of LIHTC properties, we excluded the smallest properties (under 10 units) as well 

as properties operating under special rules (e.g., the special occupancy and rent rules 

associated with the FmHA 515 program) or serving special needs populations (e.g., homeless 

or people with AIDS). 

Overall, to be eligible for the study, a property: 

l had to be placed in service between 1992 and 1994; 

l had to contain 10 or more units; 

a could not have received FmHA Section 515 funding (special occupancy and rent 

rules); 

l could not be an SRO or serve a special needs population (e.g., homeless); and 

l had to be located in the metropolitan area of one of the five MSAs in the study. 

After selecting the sample, a few properties were found to be ineligible or refused to 

participate in the study. Where possible, the ineligible and refusal properties were replaced 

with a similar property. The initial selection plan was to select four nonprofits and four forprofits in each MSA for a total of 40 properties evenly split between nonprofits and forprofits. However, Oakland had only three eligible for-profit properties and one refused to 

participate, resulting in two for-profit properties in Oakland. In addition, a nonprofit property 

in both Kansas City and Boston was incorrectly listed as a for-profit in the LIHTC database, 

thus we ended up with five nonprofits in those two MSAs. The discrepancy was discovered 

early enough to recruit a fourth for-profit in Kansas City, but not in Boston. 

Thus, the final sample includes 39 developments: 22 nonprofit properties and 17 for-profit 

properties. We conducted a brief telephone canvas of non-selected properties to determine 

eligibility status and confirm the sponsor type. No other sponsor type discrepancies were 

discovered. From our canvas, we found that 85 properties in the five metropolitan areas met 

the eligibility requirements to be included in the study, of which 46 were nonprofits and 39 

were for-profits. Property-level weights were developed such that the weighted sample 

I6 In some MSAs, propertieswere further stratified into large (more than 100 units) and small properties and 

then randomly selected within these groups. 

I-8 
accurately represents the distribution of eligible properties by MSA and sponsor type.” These 

weights are used for property-level estimates in this report. 

We also selected a sample of residents within these 39 properties for a resident survey.18 We 

separately selected households in each property where the number of households selected in 

each property was directly proportional to the number of households in all eligible properties 

that were represented by that property. This means that a nonprofit in an MSA with a lot of 

nonprofit units in the eligible properties would have a larger number of households in the 

sample than a nonprofit of the same size in an MSA with fewer nonprofit units in the eligible 

properties (because the nonprofit in the MSA with a lot of nonprofit units is representing 

more units). Within properties, households were simply selected randomly. With this 

sample design, household-level estimates (when appropriately weighted) are unbiased 

estimates of all LIHTC households in the five MSAs and the estimates are more precise 

(lower standard errors) than would be obtained from a simple random sample of the same 

number of households in the study properties. See Appendix A for more detail on the 

sampling plan and the derivation of household-level weights. 

1.4 Study Data 

This study relied on data from a number of different sources, including: 

l In-depth interviews with owners and managers of each property; 

l Collection of tenant and rent data from manager files; 

l Observations of properties and neighborhoods; 

l A telephone survey of LIHTC residents; and 

l Secondary data. 

The in-depth interviews, collection of data from manager files, and site visitor observations 

occurred during field visits to each of the 39 study properties and five MSAs. Field visits 

were conducted between March and May 1999, with study team members spending 

approximately one week in each MSA. The resident survey was conducted by telephone 

between May and August 1999. 

” SeeAppendix A for a full descriptionof how weightswerecalculated. 

” Approximately 1,325 householdswere selectedfor the survey. Of those, 832 completed the survey 

representinga 63 percentresponserate. The residentsurveyis includedasAppendix C. 

1-9 
Appendix B contains a more detailed description of our data collection procedures. The 

following sections briefly describe the major types of data collected, beginning with 

information about LIHTC residents. 

Resident Data 

The primary source of information on residents is the LIHTC Resident Survey, which was 

completed by 832 respondents. The survey was administered over the telephone by Abt 

Associates Survey group and achieved a response rate of 63 percent. The major topics 

covered by the survey were: satisfaction with current apartment and neighborhood, prior 

living situation, comparison of current and prior apartment and neighborhood, factors 

involved in choosing their LIHTC apartment, rental payment and assistance information, and 

demographic information about the household. 

A second -major source of information on LIHTC residents is data collected from property 

manager files. Where possible, we collected information on gross monthly income of the 

household, Section 8 assistance, gross rent, household size, number of bedrooms in the unit, 

whether unit is a tax-credit qualifying unit, and contact information for the survey. We also 

explored the availability of data on race/ethnicity information, however this was rarely 

available at the unit level. The advantage of file sources for resident information is that these 

data cover all residents in the property. However, not all of the properties could provide all 

of the requested information and certain items (e.g., income for tenants in market rate units) 

were not generally available. 

Property Data 

Property data were primarily collected from interviews with site managers and owners and 

from the National LIHTC database or similar information from state housing finance 

agencies. From the interviews, we gathered information on the property’s background, 

development objectives, characteristics of the management entities and owners, 

characteristics of the tenant population, tenant assistance and rent setting practices, marketing 

and tenant selection practices, amenities and services provided, and neighborhood and market 

characteristics. The National LIHTC database provided information on the total number of 

units in the property, qualifying fraction, and other property features, all of which were 

confirmed during the interviews. In addition, site visitors recorded observations on the 

condition of the property, upkeep, design quality, and curb appeal relative to nearby 

structures. Finally, in interviews conducted with a range of other local actors (e.g., 

community development representatives, PHA staff, state HFA staff) we asked about specific 

properties or neighborhoods included in the study. 

I-IO 
Neighborhood Data 

Information on neighborhoods in which LIHTC properties are located was collected from 

interviews with key actors, site visitor observations, and 1990 Census Data. In interviews 

with managers, owners, community development representatives, and local Section 8 staff, 

we discussed the characteristics of the neighborhood, trends in the neighborhood, and the 

impact of the LIHTC property on the neighborhood. Site visitors also filled out a windshield 

survey on the physical condition of the neighborhood, types of residential buildings in the 

area, and patterns of land use (e.g., commercial, institutional, retail). Census data from 1990 

were obtained on the homeownership rate, income levels, poverty rates, and the race/ethnic 

composition of the census tract where the property was located. Although Census data were 

nine years old at the time of the study, it is the only source of information available at the 

census tract level for all MSAs in the study. 

MSA Data 

MSA-level data were collected primarily from secondary data sources. For comparing the 

characteristics of LIHTC residents to Public Housing and Section 8 residents, HUD provided 

data from their MTCS database for each of the MSAs in the study. Data on HUD Fair 

Market Rents (FMRs) and Gross Median Income were obtained from the HUD website. 

Estimates of MSA vacancy rates were obtained from the Census website. In addition, we 

asked the HUD Regional Economist, Community Development Department Representatives, 

and other knowledgeable actors about the state of the housing market in the MSA. 

1.5 Report Overview 

This report is organized into six chapters. Chapter 2 provides an overview of the properties 

in our sample. It includes an MSA-by-MSA discussion of the housing market, characteristics 

of the study properties, and how the sample properties compare to the universe of eligible 

LIHTC properties in the MSA. There is also a brief narrative description of each of the 39 

study properties and the neighborhoods in which they are located. 

Chapter 3 presents a profile of LIHTC households. It describes who lives in LIHTC 

properties and compares the economic and racial/ethnic composition of LIHTC residents to 

public housing and Section 8 residents in the same MSA. This chapter also examines the 

rent-setting practices of LIHTC properties and estimates the rent burden for LIHTC residents. 

Finally, it presentsresident perceptions of their LIHTC apartmentsand neighborhoods and 

how these compare to their previous living environment. 
Chapter 4 examines the role of the LIHTC in fostering diverse communities. It begins by 

describing the economic and racial characteristics of the neighborhoods in which the sample 

properties are located. Then it explores the extent of economic and racial/ethnic diversity 

within LIHTC properties and compares the composition of property residents to the residents 

in the surrounding neighborhood. Finally, it investigates whether the economic diversity of a 

property is associated with resident perceptions of their neighborhood. 

Chapter 5 provides a profile of property management and other services offered at properties. 

It describes the amenities offered in LIHTC properties, resident satisfaction with 

management, and managers’ and owners’ perceptions of the impact that changes in welfare 

legislation will have on the financial viability of their property. 

Chapter 6 describes the characteristics of property developers, then investigates their 

objectives for developing the LIHTC property and their site selection procedures. The final 

section explores the impacts of the sample LIHTC properties on the neighborhoods in which 

they are located. 

1-12 
Chapter 2 

The Study Properties 

The current study is based on information collected for 39 properties located in five 

metropolitan areas-Boston, Kansas City, Miami, Milwaukee, and Oakland. This chapter 

introduces the properties and neighborhoods included in the study. We begin with a brief 

overview of the developments and their characteristics, followed by a description of 

individual properties, organized by MSA. 

2.1 Overview of the Study Properties 

As discussed in Chapter 1, the five study sites were selected to include areas with substantial 

levels of both nonprofit and for-profit LIHTC activity. Profit status was expected to play a 

major role in explaining development objectives and patterns because nonprofit and for-profit 

organizations have inherently different motivations. Because of the different motivations of 

nonprofit and for-profit developers, we expected them to undertake different types of LIHTC 

projects, resulting in different patterns of project location, resident characteristics, and other 

features of development. Therefore, an attempt was made to include sufficient numbers of 

each type property in each market. 

As shown in Exhibit 2-1, the study properties are fairly evenly divided between nonprofit and 

for-profit sponsors-22 (56 percent) versus 17 (44 percent), respectively. This breakdown is 

similar to that of the universe of properties placed in service in the five MSAs during the 

study years (54 percent with nonprofit and 46 percent with for-profit sponsors). Within 

individual MSAs, the ratio of nonprofit to for-profit properties is fairly comparable to that of 

the study properties as a whole, with the notable exception of Oakland, where more than twothirds of the properties have nonprofit sponsors. The proportion of nonprofit-sponsored 

study properties is higher in Oakland because over three-quarters of the tax credit properties 

placed into service in that MSA from 1992 to 1994 were sponsored by nonprofit 

organizations. 

2-1 
Exhibit 2-1 

Study Properties by MSA and Sponsor Type 

Exhibit 2-2 presents basic information about the properties by sponsor type, including 

information on the fraction of units reserved for qualifying tenants, property size, geographic 

area, construction and building type, primary population served, presence of Section 8, and 

vacancy and turnover rates. Appendix A shows a comparison between the characteristics of 

the study sample to the characteristics of the entire universe of eligible properties in the five 

MSAs. It shows that the weighted estimates from the study sample are similar to the 

characteristics of the all eligible properties in the five MSAs on the dimensions for which we 

have data on all properties. 

Qualifying Fraction 

As shown in Exhibit 2-2, the overwhelming majority of properties-both nonprofit and forprofit-elected the 40/60 LIHTC set aside, meaning that a minimum of 40 percent of the 

units must be affordable to residentshaving incomes 60 percent or less of the area median 

income. All 22 of the nonprofit properties and 15 of the 17 for-profit sponsored properties 

(88 percent) use the 40160 set-aside, while just two properties (both for-profit) have a 

minimum set-aside of 20 percent of units affordable to residents earning 50 percent of area 

median (the 20/50 set-aside). Unlike the rest of the study properties, in these two properties, 

most of the units are market rate units serving middle- or upper-middle-income residents. 

While LIHTC rules require a minimum set-aside, almost all of the study properties have a 

much higher percentage of qualifying units than the minimum. In fact, 74 percent of the 

study properties had 100 percent qualifying units. The average percentage of qualifying units 

is 94 percent, with a somewhat higher fraction among nonprofit-sponsored-properties (98 

percent) than for-profit sponsored properties (89 percent), although the average among forprofit properties is skewed downward by the presence of the two properties with 20/50 set2-2 
Exhibit 2-2 

Characteristics of Study Properties 

Nonprofit For-Profit 

Number Percent Number Percent 

All Properties 22 100% 17 100% 

Minimum Set-Aside 

20150 0 0% 2 12% 2 5% 

40160 22 100% 15 88% 37 95% 

Average Percent Qualifying Units 98% 89% 94% 

Number of Units 

lo-29 5 23% 1 6% 6 15% 

30-49 7 32% 4 24% 11 28% 

50-99 6 27% 4 24% 10 26% 

100-199 2 9% 6 35% 8 21% 

200 or more 2 9% 2 12% 4 10% 

Average Number of Units 70 102 84 

Geographic Area 

Urban 19 86% 8 47% 27 69% 

Suburban 3 14% 9 53% 12 31% 

Construction Type 

New Construction 9 41% 8 47% 17 44% 

Rehab 10 45% 8 47% 18 46% 

Both 3 14% 1 6% 4 10% 

Building Type 

Average 

Elevator/high-rise only 1 5% 1 6% 2 5% 

Walk-up/low-rise only 16 73% 14 82% 30 77% 

Townhouse only 2 9% 0 0% 2 5% 

Mixed 3 14% 2 12% 5 13% 

Primary Population Served 

Family 21 95% 12 71% 33 85% 

Elderly 1 5% 5 29% 6 15% 

Properties with Project-Based Section 8 4 18% 4 24% 8 21% 

Of these, Average % Project-Based S8 64% 88% 76% 

Units” 

Properties with Tenant-Based Section 8 17 77% 9 53% 26 67% 

Of these, Average % Tenant-Based S8 15% 18% 16% 

Units” 

Properties with Any Type of Section 8 18 82% 12 71% 30 

Of these, Average % S8 Units” I 1 28% 1 1 45% 

)verall % of Units with Any Section gb 33% 55% 

Average Vacancy Rate 4.6% 3.1% 

Turnover Rate 22% 27% F[ I I 

Notes: Figures areunweighted,thus representthe study sample,not the universeof eligible properties 5 MSAs. “Average 

of property-level percentages.bAverageacrossall study properties(not an averageof property-level percentages).Source: 

Propertymanagerinterviews. 

2-3 
asides. By way of comparison, in its national database for the same time period, Abt 

Associates (1996) found the average proportion of qualifying units in LIHTC properties to be 

98 percent.’ 

Property Size 

The study properties range in size from 12 to 328 units, with an average size of 84 units. 

While a majority of the sample properties have between 30 and 99 units (54 percent), almost 

one-third have 100 units or more (3 1 percent) and a few (15 percent) have between 10 and 30 

units.* The nonprofit-sponsored properties in the sample tend to be smaller than their forprofit-sponsored counterparts. The average number of units among nonprofit-sponsored 

properties in the study is 70, versus an average of 102 units in the for-profit-sponsored 

properties. 

In general, it can be assumed that smaller properties are less likely to physically overwhelm 

the immediate area of the property. However, it should be noted that four of the five largest 

study properties in Boston-three of which have more than 100 units-are low-rise, 

scattered-site rehab properties which blend in to their surroundings. At the same time, some 

properties are part of larger, phased developments, which may be more imposing than would 

be suggested by the number of units in the portion sampled for this study. 

Property Location 

Overall, 69 percent of the study properties are located in central city areas and the remaining 

31 percent are in suburban locations, as shown in Exhibit 2-2. There is some variation by 

MSA. While all of the Boston MSA properties and eight of the nine Kansas City MSA 

properties are in central city locations, the proportions of central city and suburban properties 

are more evenly split in the Miami, Milwaukee, and Oakland MSAs. (The study excluded 

properties in rural areas,) Properties in suburban locations were more likely to have for-profit 

sponsors than nonprofit sponsors. In fact, nine of the 12 study properties located in suburban 

areas (75 percent) have for-profit developers. 

Construction and Building Type 

When viewed as a group, the study properties are fairly evenly divided between new 

construction and rehab, with four mixing elements of both. However, there are regional 

differences. Only one of the Boston area properties was fully new construction (five were 

’ Development and Analysis of the National Low-Income Housing Tax Credit Database, Abt Associates Inc., 

July 1996. 

2 Properties with fewer than 10 units were excluded from the study. 

2-4 
rehabilitated and two included both rehab and new construction), reflecting the state’s 

priority for preserving existing housing in the city. By contrast, all but one of the Oakland 

area properties were newly constructed. 

In terms of building type, walkup buildings dominate: 30 of the 39 properties consist 

exclusively of walkup buildings (less than four stories), and another five properties consist of 

walkup buildings combinedwith duplexesor townhouses/rowhouses. Two properties are all 

townhouses, and two properties consist only of buildings with five or more stories. 

Interestingly, neither of the two high-rise buildings serves the elderly. There is no particular 

pattern of construction or structure type by sponsor status; presumably these decisions are 

driven by local market factors. 

Population Served 

As shown in Exhibit 2-2, most of the properties in the study sample serve non-elderly family 

households (including single person households): 33 properties (85 percent) serve primarily 

families, while six (15 percent) serve primarily elderly residents. The population served by 

our sample properties differs by sponsor type. Nonprofit properties are more likely to serve 

families. Of the six properties serving primarily elderly populations, five were developed by 

for-profit entities and serve exclusively the elderly. The sixth property, developed by a 

nonprofit, serves a mix of families and elderly. Population served also differs by location. 

Of the six elderly properties, three are in suburban areas within the Milwaukee MSA. 

Section 8 Assistance 

Many of the properties have Section 8 assistance, either in the form of project-based Section 

8 or residents holding Section 8 certificates or vouchers. Eight properties (21 percent) have 

project-based Section 8, of which five are in the Boston MSA. Properties with project-based 

assistance are generally rehabilitated properties with pre-existing Section 8 contracts or with 

loan management Section 8 contracts for troubled properties, or they have project-based 

certificates. While the properties with project-based Section 8 are evenly split among 

nonprofit and for-profit sponsors, those developed by for-profit entities have higher 

percentages of units with project-based Section 8. Among for-profit-sponsored properties 

with project-based Section 8, an average of 88 percent of the units are Section 8, compared 

with 64 percent for properties with nonprofit sponsors. 

Turning to tenant-based Section 8, 59 percent of the properties have at least some residents 

with Section 8 certificates or vouchers. The nonprofit-sponsored properties are more likely 

to have tenant-based assistance: 68 percent of the nonprofit-sponsored properties have at least 

one resident with tenant-based Section 8, compared with 47 percent of for-profit properties. 

Among properties with tenant-based Section 8, the average percentage of households with 

certificates or vouchers is 16 percent, which does not vary much by sponsor type. 

2-5 
Overall, 77 percent of the properties have at least one resident with Section 8 assistance, 

(either project-based or tenant-based Section 8). In terms of units, we found that nearly half 

the units (44 percent) across all study properties either have project-based Section 8 or are 

occupied by voucher or certificate holders.3 

Vacancy and Turnover 

On the whole, the study properties have fairly low vacancy rates, with a mean property-level 

vacancy rate of four percent. As expected, vacancy rates vary by market, with the lowest 

vacancy rates in the Boston and Oakland MSAs. The relatively low vacancy rates are 

consistent with the notion that LIHTC properties represent newer and more desirable housing 

relative to the overall stock of affordable units. As shown in Exhibit 2-2, vacancies are 

slightly higher in nonprofit-sponsored properties (4.6 percent) compared to properties with 

for-profit developers (3.1 percent), although the average vacancy among nonprofit-sponsored 

properties is skewed upward by two properties with vacancy rates exceeding 20 percent. 

Turnover rates in the properties are fairly high, with an average annual turnover rate of 27 

percent among the for-profit-sponsored properties and 22 percent among the nonprofit 

properties. However, the median turnover rate of 15 percent is considerably lower, 

suggesting that the relatively high average is driven by a relatively few properties. Of the 

seven properties with reported turnover rates of 50 percent or greater, four serve highly 

mobile singles or seasonal farm workers, and one recently changed management. 

2.2 Properties and Neighborhoods by MSA 

This section briefly describes each of the 39 study properties and the neighborhoods in which 

they are located. The research shows some distinct patterns by MSA which greatly influence 

the nature of LIHTC developments. For example, in the Boston MSA, most LIHTC 

properties placed into service from 1992 to 1994 were previously troubled properties in the 

urban core, several with project-based Section 8, which were targeted under the state’s 

Qualified Allocation Plan. The tax credit program was used to preserve or “bail out” these 

state-financed multifamily properties. In Kansas City, the sample properties are almost all 

located in the urban core, with two for-profit properties in largely redeveloped downtown 

areas serving primarily singles working in the service industry, and the nonprofit properties 

generally serving families in poorer inner city neighborhoods. The Miami MSA has a wide 

variety of projects in both inner city and outlying areas, all of which serve primarily families. 

In Milwaukee, the for-profit sponsored properties tend to serve primarily elderly people in 

suburban locations, whereas the nonprofit properties tend to serve families in the city of 

Milwaukee. The Oakland MSA is characterized by a large proportion (over three-quarters) of 

3 SeeChapter3 for furtherdetailsaboutSection8 assistance. 

2-6 
LIHTC properties developed by nonprofit sponsors. In addition, two of the four for-profitsponsored properties in the Oakland MSA (both of which are in the study) have a 20/50 setaside. These are the only two properties in the five MSAs that were placed in service 

between 1992 and 1994 and have a 20/50 rather than 40160 set-aside. 

In the discussion that follows, we begin by characterizing the overall housing market within 

each MSA. This is followed by a description of the individual study properties and their 

neighborhoods. For each MSA, we present a table summarizing the study properties and 

their key characteristics. In addition, accompanying maps show the locations of the study 

properties within the MSA as well as the locations of all eligible LIHTC properties 

developed during the period covered by the study 

Boston MSA 

Overview of Housing Market 

The Boston metro area has one of the tightest housing markets in the country. The overall 

vacancy rate for rental units in the Boston metro area was 4.1 percent in 1998, compared with 

6.7 percent for the Northeast region and 7.9 percent for the country as a whole.4 Rents rose 

by about 5 percent in 1998, continuing an increase that has lasted for several years.’ High 

rents are reflected in high FMRs: the 1999 FMR for a two-bedroom apartment in the Boston 

area was $906.‘j 

The Boston MSA has been designated by HUD as a difficult development area (DDA), 

which qualifies tax credit projects for higher tax credit subsidies. As defined in IRS Section 

42, a DDA is an area that has high construction, land, and utility costs relative to area median 

gross income. Tax credit projects developed in DDAs are eligible to receive an increase (30 

percent) in the eligible basis on which tax credits are calculated. In 1994 for example, some 

17 percent of the LIHTC projects developed nationwide were located in a HUD-designated 

DDA. 

The 32 tax credit properties placed into service in the Boston MSA during 1992-1994 reflect 

state priorities (embodied in Massachusetts’ Qualified Allocation Plan) for rehabilitation of 

troubled inner city properties and for properties designed to serve special needs populations. 

As shown in Figure 2-1, most of the properties are located in central city areas. More than 

4 CensusBureaurental vacancyrates. 

5 Michael Baker, Multifamily Housing Market: Northeast Region, Multifamily Trends, Spring 1999. 

6 1999 FMRs are used for comparison in this study because the time frame is comparable to that of the rent 

data collected. 
four-fifths of the properties placed in service during this period were rehabilitated rather than 

newly constructed, and nearly half are project-based Section 8 developments. At the time, 

the Massachusetts Housing Finance Agency (MHFA) was faced with a large number of 

troubled Section 8 multifamily properties on which it held the mortgages. The tax credit 

program was viewed as an important resource for getting these units under new ownership 

and for financing for needed rehabilitation. 

Another important feature of tax credit development in the Boston area during this period is 

use of the credit for properties designed to serve special needs populations. Although 

properties identified as serving special needs populations were excluded from the study, we 

found that more than one quarter of the Boston properties produced during this three-year 

period serve predominantly special needs populations such as people with AIDS or with a 

history of homelessness. A final feature of tax credit production in the Boston market is the 

strong participation in the program of nonprofit sponsors. More than half of all properties 

developed there in 1992-l 994 were sponsored by nonprofit developers, which are particularly 

numerous and active in the Boston area compared with most other metropolitan areas. 

Study Properties and Neighborhoods 

The eight properties selected for the study in the Boston area include three for-profit 

sponsored properties and five nonprofit sponsored properties. As shown in Exhibit 2-3, the 

properties range from 12 to 220 units, and all serve primarily families, as is reflected in the 

large unit sizes (all eight properties include three-bedroom units, five include four-bedroom 

units, and two offer five-bedroom units). Six of the properties have 100 percent qualifying 

tax credit units, with the two remaining properties having more than 80 percent qualifying 

units. 

In terms of geographic distribution, seven of the properties are located in the city of Boston, 

and the eighth property is located in Cambridge (see Figure 2-l). Among the Boston 

properties, five are rehab properties in the impoverished, inner-city neighborhoods of 

Dorchester and Roxbury. 

Five of the properties are located in two of Boston’s oldest neighborhoods, Dorchester and 

Roxbury-two high-poverty, high-crime residential areas characterized by poor-quality 

housing and a high concentration of minority residents. Dorchester lies south of Boston 

along Dorchester Bay, and Roxbury lies just west of Dorchester. While we define the 

borders of Dorchester and Roxbury as the Boston Redevelopment Authority defines them, 

some consider parts of Roxbury to be Dorchester. One long-time representative of a local 

2-8 
Counties in B Figure 2-l 

Map of LIHTC Projects 

in Boston MSA 

Middlesex by Census Tract Poverty Rate 

0 1.5 3 

miles 

Types of Eligible Properties* 

APropertyin Sample OPropwty Notin Sample 

City of Boston 

in Suffolk County Poverty Rate (Census Tract) r* 

2-9 
Exhibit 2-3 

Boston MSA Study Properties 

Source: Propertymanagerinterviews. 

nonprofit housing developer attributed this phenomenon to the fact that Dorchester has less 

of a stigma than Roxbury. Four of the study properties-all of which are scattered-site and 

project-based Section 8-are located in eastern Roxbury close to the Dorchester border and 

are considered by some locals to be in Dorchester. A fifth property is located near the 

western edge of Roxbury not far from the border of Jamaica Plain, another Boston 

neighborhood featured in the study. 

The remaining Boston properties include two properties that were newly constructed in 

Jamaica Plain by local nonprofits under a unique city-sponsored coop initiative. Jamaica 

Plain is a very mixed neighborhood in terms of income and race/ethnicity and is the site of 

considerable gentrification over the past few years. Finally, the eighth property was 

originally developed by a nonprofit organization in the CambridgeporVRiverside 

neighborhood of Cambridge to serve previously homeless women. The City of Cambridge 

has an extremely tight housing market, owing in part to the presence of Harvard and MIT. 

Abutting Harvard Square, Central Square, and the Charles River, this neighborhood is one of 

the more mixed Cambridge neighborhoods in terms of income and race, and, like Jamaica 

Plain, it is undergoing considerable gentrification. 

Below are descriptions of each Boston area property in the study: 

Hope Bay. This 45unit property is a loo-percent project-based Section 8 property 

developed and managed by a local for-profit developer together with two other 

Section 8 properties (one of which, Morrant Bay, is also a study property). The units 

are scattered across several blocks in 16 mostly three-unit, three-story brick or stone 

walkup buildings, The 85- to lOO-year-old buildings are arranged in row-house 

2-10 
configurations, interspersed with other non-LIHTC buildings, so that the LIHTC 

buildings are indistinguishable from (and connected to) other privately owned 

structures. Located in Roxbury/Dorchester, this property was acquired in very 

distressed condition from the Massachusetts Housing Finance Agency (MHFA) and 

rehabilitated using tax credits and HUD’s Flexible Subsidy program. The residents 

almost all have extremely low incomes (less than 30 percent of HUD area median). 

They are approximately 60 percent African-American and 40 percent Hispanic, 

mirroring the neighborhood in terms of income and racejethnicity. 

Morrant Bay. This property is similar to Hope Bay and is owned by the same for 

profit developer. Containing 130 units (loo-percent project-based Section 8) and 

located in Roxbury/Dorchester, the property was acquired in very distressed condition 

from the Massachusetts Housing Finance Agency (MHFA) and rehabilitated using tax 

credits and HUD Flexible Subsidy. Like Hope Bay, the buildings are mostly threeunit brick or stone structures scattered over several blocks in row-house 

configurations. The residents almost all have extremely low incomes (less than 30 

percent of HUD area median) and are approximately 60 percent African-American 

and 40 percent Hispanic, also mirroring the neighborhood in terms of income and 

racelethnicity. 

Prang Estates. This two-building, 33-unit rehab development in the western part of 

Roxbury was developed and is managed by a local for-profit construction developer. 

The wood-frame Victorian building and plain brick walkup adjacent to it are 

architecturally incongruous with each other and offer no curb appeal, although they 

appear to be better maintained than other housing in the area, which appears to be 

distressed. The residents are mostly African American with some Hispanic and have 

extremely low incomes, with nearly two-thirds having tenant-based Section 8. 

Cottage Brook. Cottage Brook is a 147-unit, loo-percent project-based Section 8 

property developed by a local community development corporation (CDC) active in 

Roxbury and Dorchester. Like Hope Bay and Morrant Bay, discussed above, this 

property consists primarily of units in 85 to loo-year old, scattered-site brick walkup 

buildings. The property is located in the Dudley Street Triangle, a very small area 

straddling North Dorchester and Roxbury which has been the focus of a major 

housing redevelopment effort since the early 1990s and is now being targeted for 

economic development. At the time it was acquired, Cottage Brook was an existing 

project-based Section 8 development in very poor physical condition and a haven for 

drug activity, and, like Hope Bay and Morrant Bay, it was rehabilitated using HUD 

Flexible Subsidy. The developer enjoyed strong support from the City of Boston, 

MFHA, and numerous local organizations. Cottage Brook residents are about 60 

percent African-American and 40 percent Hispanic, and most have extremely low 

incomes. 

2-11 
Franklin Park. Also located in Roxbury!Dorchester, Franklin Park Apartments is a 

220-unit scattered-site development developed and managed by a nonprofit housing 

developer. Like other several other Boston properties in the study, this property 

consists primarily of units in old, scattered-site brick walkups. About three-quarters 

of the units are project-based Section 8, with the other units covered by a state 

housing assistance program. The developer worked closely with MHFA (the 

mortgagee) in developing this existing project-based Section 8 property, which was 

physically and financially distressed due to deferred maintenance and poor 

management. The residents, mostly extremely low-income and about 70 percent 

African American, 20 percent Hispanic, and 10 percent Haitian immigrants, reflect 

the surrounding area. 

Hyde Square Coop. Hyde Square Coop is a 41-unit development located in Jamaica 

Plain, a very mixed and somewhat gentrifying neighborhood. This property was 

developed by a nonprofit CDC under the Boston Coop Initiative, a city-sponsored 

program which used the LIHTC program to develop a total of five properties 

structured such that they will become coops when the compliance period is over. The 

two- and three-unit, new construction wood-frame buildings were built in pockets 

over a two-block area adjacent to a large public housing development. The units 

appear to be in good condition. Resident income ranges from extremely low to 

moderate income, and the upper income range at this property is higher than at most 

of the other Boston study properties. Compared with the somewhat mixed and 

gentrifying neighborhood, the tenancy of this property is lower income and has a 

higher proportion of minorities (roughly three-quarters Hispanic and a quarter 

African-American). 

Stony Brook Gardens. Stony Brook Gardens is an attractive 50-unit townhousestyle coop development in Jamaica Plain, a few blocks from Hyde Square Coop in a 

slightly nicer part of the neighborhood. All but one of the 10 buildings are new 

construction. Like Hyde Square Coop, this property was developed (and is currently 

managed) by a nonprofit housing developer under the Boston Coop Initiative (see 

above). Although the average income at Stony Brook Gardens is very low, incomes 

range from extremely low to moderate, and this is the most mixed Boston area study 

property in terms of incomes. As with Hyde Square Coop, the tenancy of this 

property is lower income and has a higher proportion of minorities (about half 

Hispanic and half African-American) compared with the neighborhood. 

Putnam Place. Putnam Place is a 12-unit, two-building development located in the 

RiversideKambridgeport area of Cambridge. The property, which is in good 

condition and in a desirable location, includes a century-old wood-frame building and 

a new construction building in a similar style. While Putnam Place was originally 

2-12 
developed by a nonprofit housing developer to serve women with a history of 

homelessness, it no longer targets this population. All 12 households have Section 

8-eight have project-based certificates for participants in the Family SelfSufficiency program, and four have tenant-based Section 8. This property houses 

mostly extremely low-income residents, significantly poorer than the surrounding 

neighborhood. The property also has a higher concentration of minorities: the 

residents are three-quarters African-American, 15 percent white, and 10 percent 

Hispanic, compared to the neighborhood, which is about half white and half AfricanAmerican and other minorities. 

Kansas City 

Overview of Housing Market 

The Kansas City metro area has a moderately tight rental housing market. The vacancy rate 

for rental units in the Kansas City metro area was 7.5 percent in 1998, slightly lower than the 

rate of 7.9 percent for both the Midwest region and for the country as a whole.’ The 1999 

FMR for a two-bedroom apartment in the Kansas City area was $534. 

The Kansas City area tax credit properties placed in service from 1992 to 1994 are almost all 

family developments. As shown in Figure 2-2, about two-thirds of the properties in the 

universe are located in the central city, with about one-third in the suburbs. The properties in 

the central city reflect the City’s goal to revitalize the urban core of Kansas City, which is 

home to some of Kansas City’s worst housing stock and lowest-income citizens. Most of the 

housing in this area was built in the late teens, 1920s and 193Os,with some structures as old 

as the 1890s. The City’s strategy is to rebuild the city core through mixed-income 

communities and economic development, with housing tax credits playing a pivotal role. As 

the largest city in the state of Missouri, Kansas City receives 33 percent of the state tax credit 

allocation. In making allocations, the Missouri Housing and Development Corporation 

(MHDC) asks the Kansas City Community Development Department to rate applicant 

projects. From the City’s perspective, projects which involve local funds receive a higher 

rating than those that do not. 

7 Census Bureau rental vacancy rates. 

2-13 
Figure 2-2 

( Map of LIHTC Projects 

in Kansas City MSA 

bYr Census Tract Pove 

2 4 

miles 

Counties in 

Kansas City MSA 

Clinton 

8 

Platte( Clay 

P 

A A 0 

0 

Johnson 

Cass 

rypes of Eligible Properties’ 

AProperlyin Sample

0PropertyNotlnssnlple 

Poverty Rate(CwwusTract).. 

-A 0% lu 10% 

i lO.l%to 30% 

30.1%to 50% 

: !N.1%tolw36 

“ponnrnranbanuscwM(lopo). 

2-14 
Study Properties and Neighborhoods 

The nine Kansas City area properties selected for the study include four for-profit sponsored 

properties and five nonprofit-sponsored properties. As shown in Exhibit 2-4, eight of the 

properties serve families, and one (a for-profit) serves primarily elderly residents. Eight of 

the properties have 100 percent qualifying tax credit units, and one has 92 percent qualifying 

units. 

Exhibit 2-4 

Kansas City MSA Study Properties 

Property Total LIHTC Sponsor Construction Primary Projeet- TenantUnits Units Type Type Population Based Based 

Served Section 8 Section 8 

Askew Saddlery 60 55 For-profit Rehab Families 0% 0% 

Quality Hill IIB 84 84 For-profit New Families 0% 5% 

Squire Park 16 16 Nonprofit New Families 0% 0% 

The Courtyard 39 39 Nonprofit Rehab Families 0% 26% 

MLK Village 108 108 For-profit NewiRehab Elderly 50% 3% 

Blue Hills 20 20 Nonprofit Rehab Families 0% 35% 

Rockford Hill 78 78 Nonprofit Rehab Families 0% 5% 

Jefferson Place 15 15 Nonprofit NewlRehab Families 0% 0% 

Westchester 33 33 For-profit New Families 0% 9% 

Source: Property managerinterviews. 

Eight of the nine study properties in the Kansas City MSA are located central city locations, 

while one is located in the small town of Oak Grove. The eight central city properties are 

located in the urban core of the city, a 1lo-square-mile area of Kansas City, defined as the 

river to 75th Street, StateLine to I-435, excluding the Country Club Plaza area. Within the 

urban core exists some of Kansas City’s worst stock and lowest income citizens. As 

mentioned earlier, most of the housing stock in this areawas built in the late teens, 1920sand 

1930s. This area has been an area of focus for city revitalization efforts for some time. The 

urban core could be describedasprimarily low income and African American. 

Within this urban core, the study properties are located within distinct neighborhoods. One 

property (Askew Saddlery) is located in the River Market neighborhood, bordering 

downtown, where the city was originally founded. The area is now home to the city’s 

Farmers Market, renovated historic buildings, nightlife, museums, and an eclectic 

commercial area. Many of the old hotels in the neighborhood have also been converted to 

loft style apartments. The area is in demand (properties maintain 99 percent occupancy) by 

2-15 
young office workers looking for housing near downtown. 

neighborhood is mixed. 

Demographically the 

Another property (Quality Hill Phase IIB) is located in Quality Hill, a small neighborhood 

within the lo-block Hospital Hill area that is home to the large Truman medical center which 

was jointly developed by the city and the University of Missouri at Kansas City (UMKC). 

Bordered by the river, an interstate highway, and a bluff, the neighborhood was the focus of a 

major revitalization effort undertaken by the City and private developers to turn around a 

declining historic neighborhood. The neighborhood has a heavy population of young, single 

workers and students. 

About two miles south of downtown on opposite sides of Troost Avenue are two distinct 

neighborhoods. The Beacon Hill neighborhood, east of Troost Avenue, is primarily African 

American longtime residents and is home to one study property. The Longfellow 

neighborhood, west of Troost Avenue, is a more diverse “melting pot” with longtime 

residents, but also young professionals moving into the neighborhood. Two properties are 

located in the area straddling the Beacon Hill and Longfellow neighborhoods, and another is 

located about six miles further down along the Troost Avenue corridor. 

Further south is another central city neighborhood called Squire Park, located along the 

Paseo, the oldest boulevard in Kansas City. The area, which is home to one of the study 

properties, has always had a large multifamily housing stock. This neighborhood has been a 

target for city revitalization for a number of years evidenced by the mix of new construction, 

rehabilitation, and for-sale development. 

The Westside neighborhood, which is home to one study property, is different from the rest 

of the city in several ways. First, demographically, the residents of the west side are 

Hispanic, and the neighborhood has the lowest median income in the city. The neighborhood 

is also characterized by its very old and poor housing stock-some of the oldest in the city. 

The other distinguishing characteristic of the neighborhood is that it was cut off from the rest 

of the city and nearly destroyed by highways developed in the 1950s and 1960s. 

Finally, the only study neighborhood located outside of Kansas City is the small town of Oak 

Grove. Though becoming a bedroom community of Kansas City, Oak Grove is still very 

much a small rural town, surrounded by farmland. Oak Grove has a main street, small 

businesses, and has been spared the strip malls that characterize suburban neighborhoods. 

The town is primarily white and low to middle income. 

Below are descriptions of each Kansas City area property in the study: 

Askew Saddlery. This 84-unit development, located in an up-and-coming, fairly 

trendy neighborhood just north of downtown called the River Market area, is a 

2-16 
converted warehouse that once served as a saddle factory. A local for-profit 

developer transformed the warehouse into a six-story building offering loft 

apartments with large windows and 16- to 34-foot ceilings, as well as a host of other 

amenities. The residents are mostly young, working singles without children. Their 

incomes are low (50 to 80 percent of area median income) or very low (30 to 50 

percent of area median), considerably less than their middle- and upper-income 

professional neighbors. None have Section 8. The racial mix, which includes both 

African Americans and whites, is reportedly reflective of the neighborhood in which 

the project is located. 

Quality Hill Phase IIB. Quality Hill Phase IIB is a very nice 84-unit garden-style 

property newly constructed in Quality Hill, a largely redeveloped inner city 

neighborhood just west of downtown. Although the property is 100 percent tax credit 

units, it is part of the larger New Quality Hill, a mostly market-rate development that 

includes rehabilitated historic buildings and infill housing, developed by a large forprofit developer. The property is very well maintained, and all the units have highquality appliances and amenities, including access to a pool. Like the rest of the 

neighborhood, the residents are mostly working, childless singles in their 20s or early 

3Os, about five percent of whom have Section 8. On the whole, the property’s 

residents are lower income than their professional neighbors. 

MLK Village Apartments. Located in the low-income, inner city neighborhood of 

Beacon Hill, a few miles south of downtown, MLK Village Apartments is the only 

study property in the Kansas City MSA to serve primarily elderly and disabled 

residents. The property, developed by a for-profit entity created by the Black 

Economic Union and the Citizens Housing Information Center, consists of a 

rehabilitated mid-rise building that once served as a hospital, a newly constructed 

mid-rise apartment building, and eight new townhouse units, for a total of 108 units. 

Security is a major emphasis and a major draw of the property, providing relative 

safety in a neighborhood where crime is perceived to be a problem. Half the units 

have project-based Section 8 certificates. The residents are over 90 percent African 

American and extremely low income, like the surrounding neighborhood. 

The Courtyard Apartments. This nonprofit-sponsored property (also referred to as 

Take Part III and unrelated to Blue Hills Take Part III) is a 39-unit rehab property on 

Troost Avenue, straddling the Beacon Hill and Longfellow neighborhoods. The three 

attractive three-story walkup buildings facing a common courtyard offer considerable 

curb appeal, although the interior hallways and units show some signs of wear. The 

property serves very low- and extremely low-income single-headed AfricanAmerican families, some working and some on public assistance, about a quarter of 

whom have Section 8. The neighborhood, although primarily African American, is 

somewhat more mixed racially than the property. 

2-17 
Blue Hills. Blue Hills Take Part III (Blue Hills) is a 20-unit rehab property 

developed by a local nonprofit in the area straddling Longfellow and Beacon Hill, 

near the Courtyard Apartments. The property received CDBG financing, and the 

“Take Part” reference comes from a city-wide effort to provide low-income housing 

in the City during the early 1990s. The property’s three two-story buildings, which 

are on different sites a few blocks apart, are well maintained but offer little curb 

appeal. Its residents are mostly very low- or extremely low-income single-headed 

families with children and elderly residents. About one-third of the residents receive 

Section 8 assistance. 

Squire Park. Squire Park is a very attractive townhouse property newly constructed 

by a nonprofit housing developer in Kansas City’s Squire Park neighborhood about 

two miles south of downtown. The well maintained property is striking from the 

street, with its well tended lawn and a wrought iron fence fronting the street. All 16 

of the units in this 100 percent tax credit property are large, three-bedroom townhouse 

units with individual decks. The working families who live at Square Park tit in well 

to the surrounding neighborhood, which is low to moderate income and fairly mixed 

racially. None of the households have Section 8. 

Rockford Hill. This 78-unit property is located along the Troost commercial corridor 

about six miles south of The Courtyard, in the high-crime area of south central 

Kansas City. The nonprofit developer secured a sizable amount of CDBG funding for 

the rehabilitation of this property. Situated amidst a heavy concentration of 

subsidized housing, its eight brick buildings are densely configured with very little 

open space and no curb appeal. The residents, almost all African American, are 

mostly working families and some singles and seniors, all of whom have very low or 

extremely low incomes. About five percent have tenant-based Section 8. In terms of 

income and race/ethnicity, the residents largely reflect the surrounding neighborhood. 

Jefferson Place. This 15-unit property was developed in the mostly Hispanic 

Westside neighborhood by a local nonprofit housing developer. The small scale of 

the three new construction buildings and rehabilitated duplex fits in well to this 

mostly single-family neighborhood. The residents are primarily working Hispanic 

families with extremely low incomes, reflective of the surrounding neighborhood, and 

none of the households have Section 8. 

Westchester Village. Located in the small town of Oak Grove about 30 miles east of 

Kansas City, Westchester Village is the only study property in the Kansas City MSA 

that is not in Kansas City proper. Built by a small for-profit developer in a quiet 

town, this 33-unit property borders farmland on one side. Its 12 attractive and well 

maintained one-story serve primarily working families with children. Amenities 

2-18 
include carport parking, central air conditioning, microwave ovens, garbage disposals, 

and two bathrooms. The mostly white residents reflect the racial composition of the 

greater community. Three households have tenant-based Section 8. 

Miami 

Overview of Housing Market 

The Miami metro area includes the City of Miami, unincorporated Miami/Dade County, and 

several other towns and cities. The Miami metro area has the softest rental housing market of 

any in the study, with a vacancy rate for rental units in the Miami metro area of 9.8 percent in 

1998, slightly lower than the 1997 rate of 10.0 percent. Miami’s 1998 vacancy rate is 

slightly higher than the rate of 9.6 percent for the South region and considerably higher than 

the 7.9 percent rate for the nation.* The 1999 FMR for a two-bedroom apartment in the 

Miami area was $566. 

The properties placed in service in Miami from 1992 to 1994 reflect Florida’s Qualified 

Allocation Plan’s priorities for projects serving families. All but one of the properties serve 

primarily families, although three properties have a number of units set aside for the elderly 

under the State Apartment Incentive Loan (SAIL) program. As shown in Figure 2-3, the 

properties placed into service during the study period are scattered throughout the greater 

Miami area. 

Study Properties and Neighborhoods 

The eight Miami area properties selected for the study include four for-profit-sponsored 

properties and four nonprofit-sponsored properties. As shown in Exhibit 2-5, all of the 

properties serve primarily families, although two have a number of units set aside for the 

elderly under the state’s SAIL program. Seven of the eight properties have 100 percent 

qualifying units, and the eighth property has 83 percent qualifying units. 

The Miami MSA is home to the study’s three largest properties. Another notable 

characteristic is the surprising amount of rehab, given the amount of new construction in the 

overall apartment market in the greater Miami area. Four of the properties were rehabilitated: 

one with Section 8, one as a historic building, and two after heavy damage by Hurricane 

Andrew. 

8 Census Bureau rental vacancy rates. 

2-19 
Exhibit 2-5 

Miami MSA Study Properties 

Project- TenantBased Based 

Section 8 Section 8 

0% 6% 

0% 5% 

100% 0% 

0% 0% 

0% 0% 

Teal Pointe 45 45 Nonprofit Rehab Families 0% 0% 

Homestead Plaza 28 28 For-profit Rehab Families 0% 25% 

Riviera 56 56 For-profit Rehab Families 0% 38% 

Source:Propertymanagerinterviews. 

The Miami MSA properties are spread out over a large geographical area stretching 50 miles 

north to south. Two of the properties are located in the City of Miami; one is in Opa-Locka; 

two are in unincorporated Miami; two are in the city of Homestead; and one is in Miami 

Beach. The two City of Miami properties are nonprofit-sponsored new construction 

developments located in poor, African-American neighborhoods. The Opa-Locka property is 

a for-profit Section 8 project in one of the most troubled areas of Dade County. Two 

properties were newly constructed in working class areas of unincorporated Miami, and 

another two properties were rehabilitated (one by a nonprofit and one by a for-profit 

developer) in marginal areas of Homestead after heavy damage from Hurricane Andrew. The 

last property is a for-profit sponsored historic rehab project in Miami Beach. 

The study neighborhoods in the City of Miami include Liberty City and Over-town, both of 

which are impoverished, almost exclusively African-American neighborhoods with little 

viable commercial activity. Despite its central location adjacent to the downtown core, 

Over-town is a largely decayed neighborhood, with poor housing quality, almost no 

commercial activity, rampant crime, and a declining population. A few miles north of 

downtown just off the north-south interstate highway, Liberty City is slightly less 

impoverished, with somewhat better housing conditions and slightly more commercial 

activity. Outside the city limits, a few miles northwest of. Miami’s Liberty City 

neighborhood, is the small city of Opa-Locka, one of greater Miami’s most distressed and 

crime-ridden areas. Characterized by poor-quality single-family homes and Section 8 

buildings, Opa-Locka has almost no economic base and seems to be declining. 

2-20 

Farther north is the area of North Dade County, a sprawling, low-to-moderate-income area of 

unincorporated Miami which is home to a large sports arena and racetrack as well as one of 

the study properties. At the opposite end of unincorporated Dade County, to the south, is 

Cutler Ridge, a another sprawling, low-to-moderate-income area located just off the Florida 

turnpike. A few miles south of Cutler Ridge is Homestead, a small working-class city 

devastated by Hurricane Andrew in 1992. The two Homestead properties are located in the 

southeast and northeast neighborhoods of the city, both of which are fairly low income and 

home to migrant farm workers. 

The last study neighborhood in the Miami area is South Beach in the City of Miami Beach. 

South Beach is a tourist destination with a healthy commercial area and a very mixed 

population in terms of income, race/ethnicity, and age. South Beach’s demographics have 

shifted over the past several years from mostly elderly residents to more young professionals 

and growing Hispanic and gay populations. The quality of housing has improved since a 

decade ago, when there were several abandoned buildings and several vacant hotels. 

Below are descriptions of each Miami area property in the study: 

St. John Apartments. This property is a 35unit development consisting of three 

walkup buildings on two sites a block apart in the impoverished inner city 

neighborhood of Overtown, adjacent to downtown. Developed by a local nonprofit 

CDC, St. John is the first new construction in Overtown in more than 50 years. 

Managed by a small for-profit management company, the property is in fair condition 

but is considerably better maintained than other housing in the neighborhood, and it 

offers central air conditioning. The residents have mostly very low or extremely low 

incomes and are African American, mirroring the neighborhood in terms of income 

and race. 

Edison Terraces II. This 60-unit property consists of two five-story buildings in the 

struggling inner city neighborhood of Liberty City. The development is the fifth new 

construction tax credit property developed in a two-block area by the same nonprofit 

CDC-together, the five developments total 341 units-and is currently managed by 

the same out-of-state for-profit management company that manages the Gardens, 

another study property. With 50 tax credit units, Edison Terraces II is the only Miami 

area property in the study that is not 100 percent qualifying, although the non-tax 

credit units are covered by HOME rent restrictions. The residents, who are all 

African American and generally have very low incomes, reflect the neighborhood in 

terms of racial/ethnic mix and income level. 

Gardens. This 32%unit development located in the distressed city of Opa-Locka was 

acquired as a vacant, dilapidated HUD-held property and rehabilitated by a local forprofit developer using tax credits. The development package included a 15-year loan 

2-22 
managementSection8 contracton 100 percentof the units. The development 

consists of eight very large barracks-style buildings on two sites a few blocks apart, 

and one of the sites is surrounded on two sides by a junkyard. Despite having been 

called the “Jewel of Opa-Locka” by the mayor, this property is the target of sting 

operations to eliminate drug activity and is in perhaps only slightly better condition 

than much of the poor quality housing stock found throughout this small city. The 

residents generally have extremely low incomes and are almost all African American. 

As with the other Miami area properties, the residents are a reflection of the 

surrounding neighborhood. 

Walden Pond. This pleasant and well maintained 290-unit property in North Dade 

County consists of 14 walkup buildings situated at the edge of a small artificial lake 

with a fountain. One of the more upscale properties in the study, this property offers 

a swimming pool, clubhouse, fitness center, and tot lot, as well as individual 

balconies and central air conditioning. The property was developed by ajoint venture 

that included the same nonprofit CDC that built Edison Terraces II and the same forprofit firm that developed Cutler Hammock (see below). It is managed by one of the 

country’s largest management companies, which also manages the two latter 

properties. The neighborhood is sprawling and poorly defined, and the immediate 

area of the property includes a troubled apartment building, a well-maintained seniors 

building, and a struggling condo development. The residents are mostly working 

single-parent-headed families with low or very low incomes. The racial/ethnic mix is 

about half African American and half Hispanic. 

Cutler Hammock. Cutler Hammock is another large (262 units) multi-building 

property newly constructed by the same for-profit developer that helped build Walden 

Pond. This property lies near the southern border of unincorporated Dade County in 

an area known as Cutler Ridge. The property, which is well maintained and offers a 

pool and central air conditioning, is located in a pocket of mostly small, modest 

single-family homes and three other tax credit properties in good condition. This 

pocket straddles two neighborhoods: a middle-income neighborhood and the poor, 

mostly African-American, neighborhood of Goulds. The property’s residents are 

mostly very-low-income families with children, although 53 units are set aside for 

elderly. The racial mix is about a third African American, a third Hispanic, and a 

third white, which is similar to the immediate area. 

Teal Pointe. Teal Pointe is a two-building 45-unit property located in Homestead. 

After heavy damage from Hurricane Andrew, it was rehabilitated by a nonprofitheaded joint venture that included the same for-profit enterprise that built Cutler 

Hammock and Walden Pond. Like those two properties, Teal Pointe offers a 

swimming pool and central air conditioning. The property’s mostly Hispanic and 

2-23 
Haitian residents, many of whom are seasonal farm workers, reflect the greater 

community. 

Homestead Plaza. Like Teal Pointe, Homestead Plaza is located in Homestead and 

was rebuilt using tax credits after major hurricane damage. The developer is a small 

construction contractor with no experience with the tax credit program who acquired 

the property in uninhabitable condition and rehabbed it with intent to sell the units as 

condominiums after the compliance period. The modest property consists of a single 

2%unit concrete-block building and a pool that has been permanently closed due to 

the high costs of maintenance. A small management company keeps the property in 

fair condition. The residents are mostly Hispanic and African American with 

extremely low incomes, and, like the residents of Teal Pointe, many are seasonal farm 

workers. About one-fourth have tenant-based Section 8. 

Riviera. Located in Miami Beach within walking distance of the beach and 

shopping, this well maintained historic art deco building was restored using tax 

credits by a local for-profit developer who specializes in historic residential buildings. 

The residents are about half elderly and half families generally without children. 

Over a third have tenant-based Section 8, and most of the certificate and voucher 

holders are elderly. Resident income is generally low or extremely low. About twothirds of the tenants are Hispanic, close to a third are white, and two families are 

African American. The property’s racial/ethnic mix is similar to the neighborhood, 

but it has proportionally more elderly than the neighborhood, whose elderly 

population is declining. 

Milwaukee 

Overview of Housing Market 

The Milwaukee metro area has a relatively soft rental housing market. The vacancy rate for 

rental units in the Milwaukee metro area was 8.9 percent in 1998, somewhat higher than the 

rate of 7.9 percent for both the Midwest region and for the country as a whole, and a half a 

point higher than the 1997 rate of 8.4 percent for the MSA.’ The 1999 FMR for a twobedroom apartment in the Milwaukee area was $605. 

Twelve LIHTC properties were placed in service between 1992 and 1994 in the Milwaukee 

metropolitan area.” The 12 properties were evenly split between nonprofit and for-profit 

9 CensusBureaurentalvacancy rates 

lo In the Milwaukee metropolitan area, there were 13 separately funded LIHTC projects placed in service 

between1992and 1994with 10or more units. However, we treat MarinersPointe I and II as oneproject, 

2-24 
sponsoredowners. As shown in Figure 2-4, five of the properties are located in low-poverty 

areas outside the city of Milwaukee. By contrast, the Milwaukee City properties tend to 

border extremely high poverty areas, either in the outermost census tract with a poverty rate 

above 50 percent or in the first census tract outside the extremely high-poverty area. 

The Milwaukee LIHTC properties placed in service between 1992 and 1994 also show a 

clear distinction between the nonprofit and for-profit sponsored properties. The for-profit 

sponsored properties tend to serve primarily elderly people in suburban locations, whereas 

the nonprofit properties tend to serve families in the city of Milwaukee. Four of the six forprofit properties are located in low-poverty suburbanareaswith three of those four serving 

elderly tenants. In contrast, all six of the nonprofit properties serve families in the city of 

Milwaukee with the exception that one nonprofit property is about evenly split between 

elderly and family residents. The distinction between city and suburban properties, although 

not necessarily the sponsor type, appears to be driven by two factors. First, almost half of 

Milwaukee’s public housing serves elderly and disabled residents, and the local housing 

authority is having difficulty maintaining high occupancy rates in these projects.” 

Meanwhile, there is a long waiting list for family public housing units, so city policy makers 

are more receptive to tax credit properties serving families in the city. Second, developers 

reported that the suburban areas were more receptive to elderly developments, because they 

increase the property tax base without utilizing school resources and because elderly people 

are perceived to be much less likely to engage in criminal activities. 

Study Properties and Neighborhoods 

Two-thirds of the Milwaukee-area properties were selected for the study, and as shown on the 

Map (Figure 2-4), they are representatively spread throughout the metropolitan area. The 

Milwaukee MSA properties and their basic characteristicsare shown in Exhibit 2-6. The 

eight Milwaukee area properties selected for the study include four for-profit sponsored 

properties and four nonprofit sponsored properties. As shown in the exhibit, four of the 

properties serve primarily seniors and four serve primarily families. This concentration of 

elderly properties is unique among the five MSAs included in the study. 

The study properties have between 37 and 115 units, almost all of which are qualifying units. 

Five of the eight properties have 100 percent qualifying units, with the other three having 88 

percent, 92 percent, and 99 percent qualifying units. All of the developments consist of 

townhouses or low-rises/walkups, none higher than three stories. Nevertheless, the new 

because they are part of the same development and managed as one development, hence we refer to the 

universe as containing 12 LIHTC projects. 

” They are studying whether or not there is a shortage of housing for elderly residents who qualify for 

LIHTC properties, but tend to be higher-income than the public housing residents. (Interview with HUD 

Regional Economist, April 30, 1999). 

2-25 
Figure 2-4 

ap of LIHTC Projects 

in Milwaukee WA 

- I by Census Tract poVerty Rate 

a 

miles 

Counties in Milwaukee MSA 

1 

1 Types of Eligible Properties* 

I AProperty in Sample 

OProperty Not in Sample 

1 Poverty Rate (Census Tract) l * 

I ; 0% to 10% 

10.1% to 30% 

g$ 30.1% to 50% 

II 50.1% to 100% 

2-26 
construction properties tend to stand out in their neighborhoods, because they are larger or 

more spread out than the surrounding housing, are built in relatively undeveloped areas, and 

because of their newness. 

Consistent with the universe of eligible LIHTC properties in Milwaukee, the for-profit study 

properties tend to serve elderly people in suburban areas, whereas the nonprofit properties 

tend to serve families in the city. Furthermore, as shown in Exhibit 2-6, the City of 

Milwaukee properties tend to be rehabilitated developments rather than new construction. 

Only two of the five city developments are new construction. Milwaukee City development 

officials prefer rehabilitated properties because of the large amount of multifamily housing 

with deferred maintenance needs and vacancies in the city. All three suburban properties 

were new construction and built on previously undeveloped land. Two of them were built on 

former wooded or marshy areas, whereas the third was built on property owned by a 

manufacturing company that had unused property on the outskirts of its facility. 

Exhibit 2-6 

Milwaukee MSA Study Properties 

Property Total LIHTC Sponsor Construction Primary Project- TenantUnits Units Type Type Population Based Based 

Served Section 8 Section 8 

YW Village EastII 100 100 Nonprofit Rehab Elderly” 16% 2% 

YW Village West 71 65 Nonprofit Rehab Families 0% 7% 

Garden West 40 40 Nonprofit Rehab Families 0% 3% 

Parkwest 38 38 Nonprofit New Families 0% 26% 

Mariner’s Pointe 115 115 For-profit New Families 0% 0% 

Maple Crest 112 112 For-profit New Elderly 0% 2% 

Williamstown Bay 52 46 For-profit New Elderly 0% 0% 

River Oaks 37 37 For-profit New Elderly 0% 3% 

a This property includes a 56-unit building serving elderly and a 44-unit building serving families. 

Source: Property manager interviews. 

Among the for-profit study properties in the Milwaukee MSA, three are suburban new 

construction developments for the elderly and one is a new construction family development 

in Milwaukee. The nonprofit properties, all in central city locations, include two rehab 

developments serving primarily families, one rehab property serving elderly and families, 

and a new construction property serving families. 

As shown on the map (Figure 2-4), the study properties are located in three suburban areas 

and four Milwaukee neighborhoods with two properties several blocks apart in the same 
neighborhood of Milwaukee. The five family developments are all located in the city of 

Milwaukee, three within a mile of each other in the low-income and predominantly AfricanAmerican Concordia neighborhood to the west of Marquette University. The city, the 

university, the YWCA and other nonprofit organizations, and several large employers (e.g., 

Harley Davidson) are trying to revitalize these areas. One of the family developments is in 

the Parkwest neighborhood, an extremely distressed neighborhood with lots of vacant land, 

but bordering one of the more racially diverse and middle income areas of Milwaukee, 

Sherman Park. Two of the three suburban senior properties are located in different middleto-upper income, predominately white towns about a 30 minute drive from Milwaukee. The 

third is a few minutes south of Milwaukee in a working class area. 

Below are descriptions of each Milwaukee area property in the study: 

YW Village East II. YW Village East is a loo-unit property developed by YWCA 

through the rehabilitation of a vacant, dilapidated, and crime-ridden project in a very 

low-income area a few blocks west of Milwaukee’s Marquette University. This 

property was developed in conjunction with YW Village West, a 72-unit development 

about five blocks west, and while the neighborhood still has a crime problem, the two 

developments have been a force in removing most of the conspicuous signs of crime 

and gang activity. One of the two buildings of YW Village East serves exclusively 

elderly residents (56 units), while the other building serves primarily single-headed 

households with children (44 units). Sixteen of the senior units have project-based 

Section 8 certificates. Overall, the facilities and grounds seem very well maintained, 

but the studio units are extremely small (342 square feet). The residents, who are 

very low- and extremely low-income and a mix of about 70 percent African American 

and 30 percent other minorities, reflect the surrounding neighborhood. 

YW Village West. YW Village West was developed by the YWCA along with YW 

Village East II. Like its eastern counterpart five blocks away, this property was once 

a vacant project taken over by drugs and prostitution. Overall, the facilities and 

grounds seem well maintained, but the apartments are small (even smaller than YW 

East). There is a learning center in the main building open to the entire neighborhood 

and a courtyard with a play ground and barbecue facilities between the other two 

buildings. The residents are very low income, with rent restricted to people earning 

less than 50 percent of the median. The income level and racial mix of the residents 

is similar to the surrounding neighborhood-very low and extremely low income and 

about 70 percent African American. 

Garden West. Garden West is a rehabbed two-building development in the 

Concordia neighborhood of Milwaukee, about two blocks west of YW Village West 

and seven blocks west of YW Village East II. Prior to the redevelopment, the area 

was known as “drug central,” and while the property manager claims that the 

2-28 
landlords and residents have cleaned it up, there are still crime problems just to the 

west of the development. The buildings are quite old with minimal curb appeal, like 

most of the other developments on the street. The apartments are small, bare, and 

worn, and the only amenity other than carpet and blinds is an outside parking lot. 

One or two adults live in each unit, and eight units are rented to participants in a 

homeless assistance program. The residents are extremely low-income AfricanAmericans, although few receive rental assistance other than those in the homeless 

assistance program. 

Parkwest Townhomes. This attractive town house development is a nonprofit 

sponsored 3%unit property built on land cleared 25 years ago to construct a freeway 

that was never built and that had been vacant ever since. The area was a poor, central 

city location with 90 percent African-American residents, boarded-up housing and 

commercial space, and rampant crime. The goal of the developer was to build highquality units to attract working and other relatively higher-income people to the 

neighborhood. Today, Parkwest is credited with reducing reported crime and 

providing significant symbolic and actual reinvestment in the neighborhood, although 

there are still several boarded-up houses and commercial buildings in the area 

surrounding the property. Two more phases of the development will be eventually 

built on other nearby land that was cleared for the freeway. The residents of Parkwest 

are mostly African American families with very low or extremely low incomes, 

similar to the neighborhood. 

Mariner’s Pointe. Mariner’s Pointe is a newly constructed, for-profit development 

serving primarily families on the north side of Milwaukee. The 112-unit town house 

development was built on previously undeveloped land and is still bordered by open 

space but is just blocks away from major roads and shopping centers. The town 

homes look luxurious, with private entrances,attached garages,central air, 

dishwashers, and washers and dryers in every unit. The residents are low-income in a 

neighborhood that has a mix of low- and middle-income residents. 

Maple Crest. Maple Crest is a 112-unit for-profit property in upscale Port 

Washington (the jewel of the Great Lakes), about 40 minutes north of downtown 

Milwaukee. The four-building development, which is very well maintained, takes up 

10 acres of previously wooded land in western Port Washington, just inside the city 

limits. The residents are all low-income, white elderly residents, although one 

building contains 10 SRO units that house some younger disabled tenants. Services 

for the elderly, such as on-site prepared meals and van service, are provided for a fee 

by social service agencies. 

Williamstown Bay. Williamstown Bay-Cudahy II contains 52 units and is the 

second phase of a three-phase, for-profit sponsored tax credit project (totaling 144 

2-29 
units) servingelderlyresidentsin the working andmiddle-classsuburbof Cudahy. 

The three newly constructed three-story properties, all blue with the same design, 

stand out compared to the single-family homes and smaller apartment buildings in the 

area. The lawns and building are in excellent condition, and each unit is completely 

disabledaccessible.Themostlywhite elderlyresidentsreflectthe racialmakeupof 

the surrounding neighborhood but have lower incomes. None of the residents have 

Section 8. 

River Oaks. River Oaks is a 37-unit, for-profit development serving elderly tenants 

in Hartland, an upscale suburb about 30 minutes from downtown Milwaukee. Built 

on previously undeveloped wooded and marshy land, the buildings look out over a 

scenic marsh. Almost all of the tenants are elderly (average age of 80) widowed, 

white females who grew up nearby and have a family support network. They have 

low incomes, tending to be between 50 and 60 percent of the median. 

Oakland 

Overview of Housing Market 

The Oakland metro area has a relatively tight rental housing market. The vacancy rate for 

rental units in the Oakland metro area was 6.0 percent in 1998, somewhat lower than the rate 

of 6.7 percent for the West region and considerably lower than the nationwide vacancy rate. 

However, the vacancy rate in Oakland rose sharply from 4.4 percent the previous year.‘* The 

1999 FMR for a two-bedroom apartment in the Oakland area was $86 1. Like Boston, the 

Oakland MSA has been designated by HUD as a DDA. 

The properties placed in service from 1992 to 1994 reflect California’s Qualified Allocation 

Plan’s priorities for properties developed with the participation of local tax-exempt entities 

and serving special needs populations. More than three-quarters of the projects placed in 

service during that period have nonprofit sponsors, and over a third of the developments in 

the universe serve special needs populations or are single-room occupancy projects (SROs) 

(again, the study excluded properties serving primarily special needs residents). As shown in 

Figure 2-5, the properties in the universe are evenly distributed in central city and suburban 

locations. 

CensusBureaurentalvacancyrates 

2-30 
Figure 2-5 

Map of LIHTC Projects 

in Oakland MSA 

by Census Tract Poverty Rate 

Types of Eligible Properties’ 

APropertyin Sample

oprowltv Not In SamDIe I 

Poverty Rate (Census Tract) .. 

no% tolo% 

lO.l%to 30% 

3 Ez: 1% 
Study Properties and Neighborhoods 

As shown in Exhibit 2-7, the six properties selected for the study include two for-profitsponsored properties and four nonprofit-sponsored properties. The two for-profit-sponsored 

properties are the only properties in the study with a 20/50 set-aside, and one of them is the 

only study property in the Oakland MSA that serves primarily the elderly (the other five 

properties serve primarily families). All the study properties were newly constructed with the 

exception of one nonprofit-sponsored property that was rehabilitated after major earthquake 

damage. 

Property 

Drasnin Manor 

Hismen Hin-Nu 

SantanaApts 

Richmond 

Del Norte 

Exhibit 2-7 

Oakland MSA Study Properties 

Total LIHTC Sponsor Construction TenantUnits Units Type Type 1Based 

Served Section 8 

26 26 Nonprofit New Families 0% 

92 92 Nonprofit New Families 0% 

30 30 Nonprofit Rehab Families 0% 

64 64 Nonprofit New Families 0% 

135 27 For-profit New Families 0% I I 

Villa San Ramon 1 118 1 24 For-profit New Elderly 0% --K-I 

Source:Propertymanagerinterviews. 

Geographically, three of the properties are located.in Oakland, and three are in the cities of 

Richmond, El Cerrito, and San Ramon. Two of the Oakland properties and the Richmond 

property were all newly constructed in very distressed neighborhoods by experienced 

nonprofit CDCs or housing developers. The third Oakland property is located in a somewhat 

more mixed neighborhood and was rehabilitated after a major earthquake by a nonprofit 

organization with no prior housing development experience. The property in El Cerrito was 

developed in a mixed neighborhood by a for-profit firm with extensive experience in the 

LIHTC program. Finally, the San Ramon property is a fairly luxurious home for the elderly 

developed in an almost all-white, upper-middle-income community by a for-profit 

partnership. 

The study neighborhoods include two different Oakland neighborhoods, as well as one 

neighborhood in each of the other three cities. The first Oaklandneighborhood,which is 

home to two of the study properties, is located in the eastern part of the San Antonio district 

along the East 14th Street (also called International Boulevard) commercial corridor. A very 

low-income neighborhood characterized by rampant crime, poor housing quality, and 

struggling mom and pop stores lining the corridor, this neighborhood is one of the most 

2-32 
distressed areas in the Oakland MSA. The second neighborhood, also located in the San 

Antonio district but closer to the moderate-income area of Lake Merritt, is somewhat more 

mixed than the first neighborhood in terms of income level and quality of housing. The 

racial/ethnic makeup of both neighborhoods is mostly African American with some Asian 

and Hispanic residents. 

The city of Richmond is a fairly low-income community located just north of Berkeley and 

Oakland, and the Richmond study neighborhood is another of the most distressed 

neighborhoods in the Oakland MSA. Called the Iron Triangle, this crime-ridden 

neighborhood is cut off from the rest of the city by railroad tracks on the northeast and 

northwest, and by a freeway to the south. The quality of housing and commercial areas in the 

Iron Triangle is very poor, with many abandoned structures. The only viable commercial 

activity seems to be the retail development built with federal funds in conjunction with and 

adjacent to the study property. 

Also to the north of Berkeley is the moderate-income suburb of El Cerrito, located just east 

of Richmond. The study property is located along a major artery that divides two 

neighborhoods. A block to the west lies a freeway, beyond which are the blue collar/lowincome flatlands of Richmond, where mostly African Americans live. A block to the west is 

the beginning of a moderate-income hillside residential neighborhood which houses mostly 

white and some Asian residents. The last study neighborhood is in the upper-middle income, 

almost exclusively white suburb called San Ramon, which lies in a fairly rural area about an 

hour east of Oakland. The immediate area of the property is a community for the elderly. 

Below are descriptions of each Oakland area property in the study: 

Drasnin Manor. Developed and managed by Oakland’s oldest nonprofit housing 

developer, Drasnin Manor’s single, rather plain-looking 26-unit walkup building was 

newly constructed in the distressed San Antonio district along the East 14th Street 

corridor. The property offers no services and no amenities other than a laundry room, 

community room, and limited parking, but it is in better condition than other housing 

in the area. The residents are mostly extremely low-income and about 85 percent 

African American, 11 percent Hispanic, and 4 percent white, reflecting the 

surrounding neighborhood. As required by the state Rental Housing Construction 

Program, which provided a deferred payment loan for the project’s development, 

about one-third of the units have rents affordable to residents with 50 percent of 

median income. Three residents have tenant-based Section 8. 

Hismen Hin-Nu Terrace. This property, whose Native American name means 

“Sungate Terrace, ” is an attractive 92-unit property built along the East 14th Street 

corridor across the street from Drasnin Manor, on a site previously occupied by an 

abandoned supermarket. Developed and managed by a well respected local CDC 

2-33 
using CDBG funds, a city grant, and a deferred payment loan from the state’s Rental 

Housing Construction Program, this property offers many amenities, including 

garaged parking, dishwashers and garbage disposals, wall-to-wall carpeting, and 

balconies, as well as several on-site services, including Head Start and after-school 

tutoring. The residents are mostly families with children, and about one-forth have 

tenant-based Section 8. Resident income is generally extremely low or very low, and 

the racial mix is about 85 percent African American, 10 percent Asian, and 5 percent 

Hispanic, mirroring the immediate neighborhood. The state loan program requires 

that 28 units have rents affordable to those with 50 percent of median income, and 20 

units have tenant-based Section 8. 

Santana Apartments. Santana Apartments is located in a relatively mixed 

neighborhood within the generally low-income San Antonio district. Devastated by a 

major earthquake in 1989, this 30-unit property was subsequently rehabilitated as a 

tax credit property by a nonprofit religious charity organization with no prior housing 

development experience. Although eight of the units were originally developed using 

HUD’s Permanent Housing for the Homeless (PHH) program, the property lost this 

subsidy because of poor management by the developer. Six residents with HIV still 

occupy these previously subsidized units, protected by a local rent board decision 

requiring management to subsidize the residents as long as they choose to live there. 

A new nonprofit organization recently took over management, trying to address the 

additional problems of deferred maintenance and on-site drug activity. The residents, 

who are mostly extremely low income (although none have tenant-based Section 8) 

and about 90 percent African American, are poorer and more likely to belong to 

minority groups than their neighbors. 

Richmond City Center Apartments. This 64-unit walkup building was built in 

Richmond’s notorious Iron Triangle neighborhood by one of the nation’s leading 

nonprofit housing developers. The property was built as part of a larger community 

development effort which included the publicly funded construction of home 

ownership units behind the property and an adjacent shopping area anchored by a 

major chain supermarket, fast food restaurant, and drug store. Secured by a police 

substation on the ground floor of the apartment building, the block of development 

forms an island of relative health and safety in a very distressed neighborhood. 

Similar to Hismen Hin-Nu Terrace in Oakland, this property offers secured garage 

parking, a nicely landscaped inner courtyard, dishwashers, balconies, and wall-to-wall 

carpeting. The residents’ very low or low incomes are somewhat higher than the 

surrounding neighborhood, but the racial mix of 90 percent African American, 5 

percent Hispanic, and 5 percent white is similar to that of the neighborhood. 

Del Norte Place. One of two for-profit study properties in the Oakland MSA, Del 

Norte Place is a mostly market-rate development newly constructed in the moderate2-34 
income city of El Cerrito. The 108~unitdevelopmentincludes 27 tax credit units and 

consists of four low-rise buildings and commercial space on the ground floor. The 

property is located on a major commercial corridor, straddling two neighborhoods: 

the low-income Richmond flatlands which lie just past the freeway two blocks to the 

west, and a moderateincome residential neighborhood rising east on the hillside just 

behind the property. Adjacent to a stop on the Bay Area’s rapid transportation rail 

and close to major throughways, the location is very convenient to Oakland, 

Berkeley, and San Francisco. One of the most mixed properties in the study, Del 

Norte Place serves families, students, commuters, and the elderly, with household 

incomes ranging from $8,000 to over $100,000 per year. Among the tax credit units, 

household income is extremely low or very low. The property is also racially diverse, 

with 20 percent African American, 40 percent white, and 40 percent Asian residents. 

In terms of both income and race/ethnicity, the property’s residents reflect this mixed 

area that straddles two distinctly different neighborhoods. 

Villa San Ramon. The only other for-profit study property in the Oakland MSA, this 

118-unit property is a luxury residence for the elderly located in the upper income 

suburb of San Ramon, about an hour east of Oakland. Like Del Norte Place, this 

development is mostly market, with 20 percent tax credit units. The market unit rents 

of $2,000 to $3,000 per month, including meals and services, illustrate the high 

income levels of most of the residents (or their families). According to the developer, 

the property could not have been built without the equity raised through tax credits. 

The Villa San Ramon residents reflect the surrounding neighborhood, a senior 

community characterized by White, upper-income residents at least 55 years old, most 

of them living in single-family homes. 
Chapter 3 

Social, Financial, and Housing 

Circumstances of LIHTC Residents 

This chapter examines the social, financial, and housing circumstances of residents living in 

tax credit properties. It begins with a profile of LIHTC residents with respect to race and 

ethnicity, income, and other household characteristics. Section 3.2 examines the extent to 

which LIHTC residents receive rental assistance, discusses rent-setting practices in LIHTC 

properties, and examines rent burden levels among LIHTC residents. Section 3.3 presents 

information on the previous housing situations of study households and their satisfaction with 

their current LIHTC apartments and neighborhoods. Finally, Section 3.4 compares 

household characteristics of Section 8 residents in the study with those of LIHTC residents 

who do not receive rental assistance. 

This chapter uses weighted data to represent all residents of eligible tax credit properties 

across the five MSAs. It draws on a survey of 832 residents and, to a lesser extent, on 

administrative records collected from property managers.’ In order to provide context for the 

results, we compare our findings to the national study published by GAO in 1997.’ It is 

important to remember that our sample was chosen to be representative of the five selected 

MSAs, whereas the GAO study is nationally representative. In addition, because there is 

limited national information available on the issues covered by this report, only a few results 

can be compared. 

3.1 Profile of Residents in Study Tax Credit Properties 

Race and Ethnicity 

The majority of residents in the 39 study properties identified themselves as African 

American. As shown in Exhibit 3-1, 60 percent are African American, 25 percent are white, 

I Only differences in sample estimates that are statistically significant at the 10 percent level of significance 

using a t-test (for means) or a chi-square test (for categorical data) are discussed in the text. Using the 10 

percent level of significance means that there is less than a 10 percent chance that, although the sample 

estimates are different, the actual (unknown) population values are the same. See Appendix A for more 

detailed description of the significance tests performed. 

2 GAO, 1997. Opportunities to Improve Oversight of the Low-Income Housing Program, 

GAO/GGD/RCED-97-55 Low Income Housing Tax Credit, March 1997). 

3-1 
and 16 percent identify themselves as being a member of another racial group.3 On the 

whole, there is a considerable difference between the study properties developed by nonprofit 

sponsors and those with for-profit developers. For example, only 8 percent of residents in the 

nonprofit-sponsored properties are white, compared to 37 percent of those in the for-profitsponsored properties. (When elderly properties are excluded, the difference narrows to 8 

percent and 31 percent, respectively.) Similarly, while more than two-thirds of residents in 

nonprofit-sponsored properties (69 percent) are African American, only about half of 

residents in study properties with for-profit sponsors (52 percent) are African American. 

In terms of ethnicity, 21 percent of the residents in the study properties identified themselves 

as Hispanic or Latino.4 As with race, ethnicity differs by sponsor status, although not as 

dramatically. While 26 percent of residents in nonprofit-sponsored properties are Hispanic or 

Latino, about 17 percent of those in for-profit-sponsored properties are Hispanic or Latino. 

Combining race and ethnicity, Exhibit 3-l shows the proportion of households who are nonminority (white, non-Hispanic) compared to the proportion who are members of any ethnic or 

racial minority. Overall, 81 percent of study households are members of a racial or ethnic 

minority. This percentage is significantly higher in nonprofit properties (96 percent) as 

compared to for-profit properties (70 percent). 

As a benchmark, we compared the racial and ethnic characteristics of tax credit residents to 

those of public housing and Section 8 residents in the same metropolitan areas. As shown in 

Exhibit 3-2, the proportion of minorities is considerably higher in the 39 tax credit properties 

than in public housing or among Section 8 certificate or voucher holders in two of the five 

MSAs. 

In both Boston and Kansas City, the proportion of ethnic and racial minorities in tax credit 

properties is considerably higher than in public housing or tenant-based Section 8. In Miami, 

the proportion of African Americans is dramatically higher but the proportion of Hispanic 

residents is strikingly lower than in public housing or Section 8. Oakland’s mix is just the 

opposite, with proportionally more Hispanics but fewer African Americans in LIHTC 

households compared to public housing and Section 8. Finally, Milwaukee LIHTC 

properties have proportionally fewer racial and ethnic minorities than public housing or 

Section 8 households. 

3 By comparison, the GAO study reported 33 percent African-American and 53 percent white heads of 

household in tax credit properties (GAO, 1997). 

4 This compares with 11 percent Hispanic heads of household in tax credit properties reported by GAO, 

1997. 

3-2 
Race and Ethnicity 

Race* 

White 

African American 

Other 

Ethnicity” 

Hispanic 

Non-Hispanic 

Race and Ethnicity Together* 

White Non-Hispanic 

Minority 

Source: LIHTC Resident Survey (1999) 

Exhibit 3-I 

of Heads of Household by Sponsor Type 

Nonprofit For-profit Total 

Sponsor Sponsor (n=832) 

(n=391) (n=441) 

8% 37% 25% 

69% 52% 60% 

22% 11% 16% 

26% 17% 21% 

74% 83% 79% 

4% 30% 19% 

96% 70% 81% 

Notes: Estimates weighted to reflect households in eligible LIHTC properties in five study MSAs. 

*Indicates significant difference between residents of nonprofit and for-profit properties at the 10 percent significance level 

(see Appendix A for a description of the significance tests performed). 

Totals may not sum to 100 because of rounding. 

As a benchmark, we compared the racial and ethnic characteristics of tax credit residents to 

those of public housing and Section 8 residents in the same metropolitan areas. As shown in 

Exhibit 3-2, the proportion of minocties is considerably higher in the 39 tax credit properties 

than in public housing or among Section 8 certificate or voucher holders in two of the five 

MSAs. 

In both Boston and Kansas City, the proportion of ethnic and racial minorities in tax credit 

properties is considerably higher than in public housing or tenant-based Section 8. In Miami, 

the proportion of African Americans is dramatically higher but the proportion of Hispanic 

residents is strikingly lower than in public housing or Section 8. Oakland’s mix is just the 

opposite, with proportionally more Hispanics but fewer African Americans in LIHTC 

households compared to public housing and Section 8. Finally, Milwaukee LIHTC 

properties have proportionally fewer racial and ethnic minorities than public housing or 

Section 8 households. 

3-3 
Exhibit 3-2 

Race and Ethnicity of Study, Public Housing, and Section 8 Households By MSA 

MSA LIHTC Heads of Public Housing Section 8 

Boston MSA 

Race 

White 

African American 

Other 

Ethnicity 

Hispanic 

Non-Hispanic 

Kansas City MSA 

Race 

White 

African American 

Other 

Ethnic@ 

Hispanic 

Non-Hispanic 

Miami MSA 

Race 

White 

African American 

Other 

Ethnicity 

Hispanic 

Non-Hispanic 

Milwaukee MSA 

Race 

White 

African American 

Other 

Ethnicity 

Hispanic 

Non-Hispanic 

Oakland MSA 

Race 

White 

African American 

Other 

Ethnicity 

Hispanic 

Non-Hispanic 

Household Heads of Certificate/Voucher 

Household Holders 

(n=194) 

6% 67%* 68%* 

70% 27%* 28%* 

24% 6%* 3%* 

34% 21%* 17%* 

66% 79%* 82%* 

(n=l54) 

43% 61%* 48%* 

53% 34%* 52%* 

5% 5%* l%* 

8% l%* 2%* 

92% 99%* 98%* 

(n=179) 

23% 62%* 75%* 

61% 38%* 25%* 

16% o%* o%* 

29% 61%* 71%* 

71% 39%* 28%* 

(n=143) 

40% 30%* 330/o* 

53% 69%* 66%* 

6% l%* o%* 

2% 17%* 3% 

98% 83%* 97% 

(n=162) 

27% 19%* 25%* 

52% 65%* 63%* 

22% 16%* 12%* 

14% 7%* 6%* 

86% 93%* 94%* 

Sources: LIHTC Resident Survey (1999) and MTCS Data (January 1999) Notes: Estimates weighted to reflect households in eligible 

LIHTC properties in five study MSAs. *Indicates significant difference from LIHTC residents at the 10 percent significance level (see 

Appendix A for a description of the significance tests performed). Totals may not sum to 100 because of rounding. 

3-4 
Income and Income Sources of LIHTC Property Households 

In general, households in tax credit properties have very low-income levels, far below the 

eligibility cutoff of 50 or 60 percent of area median income. As shown in Exhibit 3-3, the 

average annual gross income of LIHTC residents in the five study MSAs is $18,449.5 Over a 

quarter of the study property households (27 percent) have annual incomes of $10,000 or 

less, and 71 percent have incomes of $20,000 or less. Only 11 percent earn more than 

$30,000 per year, and these are likely to be predominately residents in non-qualifying units. 

Exhibit 3-3 

Income and Income Sources of Studv Prooertv Households bv Soonsor e . s . 

Nonprofit For-Prok 

sponsor Sponsor 

(n=391) (n=441) p 

Annual Household Income* 

$10,000 or less 22% 29% 

$lO,OOl-$20,000 46% 42% 

$20,001-$30,000 21% 17% 

More than $30,000 11% 12% 

Income as a % of HUD Area Median+* 

O-30% 41% 

31-50% 38% 

5 l-80% 18% 

Greater than 80% 4% 

Mean Annual Household Income $18,222 

Median Annual Household Income $16,800 

Adult in Household Currently Working for 75% 

Pay* 

Social Security, Retirement, or Pension* 14% 29% 

Public Assistance 11% 10% 

Disability Payments* 11% 8% 

Type 

Total 

(n=832) a 

27% 

44% 

19% 

11% 

40% 

34% 

20% 

7% 

$18,449 

$16,138 

69% 

23% 

10% 

9% 

ource: LIHTC Resident Survey (1999). +Adjusted for family size. 

Notes: Estimates weighted to reflect households in eligible LIHTC properties in five study MSAs. 

* Indicates significant difference between residents of nonprofit and for-profit properties at the 10 percent significance level 

(see Appendix A for a description of the significance tests performed). 

Totals may not sum to 100 because of rounding. 

a Some survey respondents did not know their income but were able to identify a range in which their income lies. These 

respondents are not included in the calculations of income as a percentage of HUD area median or of mean or median 

annual household income. 

All income is in 1999dollars. This comparesto an averagehouseholdincomeof $13,300(1996 dollars)in 

tax credit properties reported in the nationally representative study by GAO in 1997. 

3-5 
Nearly three-quarters of the households in tax credit properties (74 percent) have incomes at 

or below 50 percent of area median income: 40 percent of study property households are 

considered to be extremely low income (household income is 30 percent or less of area 

median income), and another 34 percent are very low income (household income is 31 to 50 

percent of area median income).6 Roughly 20 percent of households have incomes between 

51 and 80 percent of median income, and just 7 percent have incomes exceeding 80 percent 

of area median income. There is little difference in household income by sponsor type. 

Most households in LIHTC properties have at least one employed adult. Overall, 69 percent 

have an adult currently working for pay. Residents living in properties developed by 

nonprofit entities are somewhat more likely to be working, with 75 percent currently working 

compared to 65 percent in for-profit properties. Turning to other sources of income, almost 

one-fourth of the residents (23 percent) receive social security, retirement income, or a 

pension. The percentage is considerably higher among for-profit properties (29 percent) than 

among nonprofit properties (14 percent), reflecting the relatively large share of for-profit 

properties serving exclusively elderly populations. 

Given that most LIHTC residents work, only a small fraction (10 percent) of the households 

receive public assistance. There is no significant difference by sponsor type. Approximately, 

9 percent receive disability payments, with a slightly higher proportion in nonprofit 

properties (11 percent) compared to for-profit properties (8 percent). About two-thirds of 

residents on disability are under age 65. 

Because several of the study’s for-profit properties serve predominately the elderly, we reexamined the income measures shown in Exhibit 3-3 excluding these developments. When 

the elderly properties are excluded, the percentage of households in for-profit properties 

working for pay increased from 65 percent to 76 percent, and the percentage of households 

on social security, retirement or pension decreased from 29 percent to 16 percent. 

Exhibit 3-4 compares household income among LIHTC residents to incomes of public 

housing households and Section 8 certificate and voucher holders. In all five MSAs, the tax 

credit households have higher incomes, are more likely to have earned income, and are less 

likely to be on welfare or TANF than households in public housing or receiving tenant-based 

Section 8 assistance. However, these differences vary considerably by MSA. 

6 This is similar to GAO’s 1997 estimate of three-quarters of tax credit households with incomes at or below 

50 percent of area median income. 

3-6 
Exhibit 3-4 

Income of Study, Public Housing, and Section 8 Households By MSA 

MSA LIHTC Public Housing Section 8 

Households Households Certificate/Voucher 

Households 

Boston MSA 

(n=194) 

Mean Annual Household Income $16,137 $11,091* $12,743* 

Income as a % of HUD Area Median 

30% or below 62% 74%* 71%* 

31-50% 26% 21%* 22%* 

51-80% 10% 3%* 5%* 

Greater than 80% 2% 1%* o%* 

Earned Income 64% 28%* 42%* 

Public Assistance 13% 14% 20%* 

Kansas City MSA 

(n=l54)” 

Mean Annual Household Income $18,303 $8,894* $8,760* 

Income as a % of HUD Area Median 

30% or below 35% 77%* 78%* 

31-50% 28% 19%* 19%* 

51-80% 33% 3%* 2%* 

Greater than 80% 4% o%* o%* 

Earned Income 68% 22%* 37%” 

Public Assistance 4% 14%* 29%* 

Miami MSA 

(n=179)” 

Mean Annual Household Income $16,071 $7,794* $8,831* 

Income as a % of HUD Area Median 

30% or below 31% 86%* 77%* 

3l-50% 39% 13%* 20%* 

5 l-80% 24% l%* 3%* 

Greater than 80% 6% o%* o%* 

Earned Income 77% 19%* 27%* 

Public Assistance 14% 19%* 21%* 
Exhibit 3-4 (continued) 

Income of Study, Public Housing, and Section 8 Households By MSA 

MSA 

Milwaukee MSA 

Mean Annual Household Income 

Income as a % of HUD Area Median 

30% or below 

31-50% 

51-80% 

Greater than 80% 

EarnedIncome 

Public Assistance 

Oakland MSA 

Mean Annual Household Income 

Income as a % of HUD Area Median 

30% or below 

31-50% 

51-80% 

Greater than 80% 

EarnedIncome 

Public Assistance 

LIHTC Public Housing Section 8 

Households Households Certificate/Voucher 

Households 

(n=143) 

$18,494 $10,133* $11,540* 

33% 78%* 72%* 

46% 18%* 26%* 

14% 3%* 2%* 

6% o%* o%* 

48% 33%* 45%* 

3% 13%* 14%* 

(n=l62)” 

$26,989 $11,340* $12,246* 

33% 79%* 81%* 

32% 20%* 16%* 

17% 2%* 3%* 

18% o%* o”41* 

59% 29%* 36%* 

12% 44%* 39%* 

Sources: LIHTC Resident Survey (1999); MTCS Data (public housing: Jan. 1999; Section 8: Sep. 1999). 

Notes: MTCS data for public housing have the following rates of unavailable data: Boston, 2 percent; Kansas City, 6 

percent; Milwaukee, 2 percent; Oakland, 1 percent. MTCS data for Section 8 have the following rates of unavailable data: 

Boston, 2 percent; Kansas City, 7 percent; Miami, 1 percent; Milwaukee, 1 percent; Oakland, 2 percent. 

Estimates weighted to reflect households in eligible LIHTC properties in five study MSAs. 

*Indicates significant difference from LIHTC residents at the 10 percent significance level (see Appendix A for a 

description of the significance tests performed). 

Totals may not sum to 100 because of rounding. 

Yome survey respondents did not know their income but were able to identify a range in which their income lies. These 

respondents are not included in the calculations of income as a percentage of HUD area median or of mean annual 

household income. 

In Boston, for example, the incomes of LIHTC households are more similar to those of 

public housing residents or Section 8 recipients than in the other MSAs. The mean annual 

income among Boston tax credit households is approximately $5,000 larger than the mean 

income of Boston public housing households and $3,400 larger than the mean for Section 8 

certificate and voucher holders. This is the smallest difference between tax credit and 

assisted-housing households in any of the five study MSAs. In terms of income relative to 
the MSA median, 62 percent of tax credit households are classified as extremely low income 

(income below 30 percent of the median) compared to 74 percent of public housing 

householdsand 71 percent of tenant-basedSection 8 households. Again, these are the most 

similar proportions of any MSA in the study. The relative similarity of Boston tax-credit and 

public housing/Section 8 households’ incomes is likely due to the predominance of projectbased Section 8 assistance among the study properties in Boston, a pattern which is not found 

in the other study sites. 

By contrast, in the Oakland MSA the incomes of study property households exceed those of 

public housing and tenant-based Section 8 households by dramatic margins. The mean 

income among Oakland tax credit householdsis $26,989-about $15,000 higher than that of 

public housing and Section 8 residents. While around 80 percent of Oakland’s public 

housing and Section 8 families have extremely low incomes, only one-third of the LIHTC 

households do. Similarly, while none of the public housing or Section 8 households have 

incomes that exceed 80 percent of area median, 18 percent of the LIHTC households do. The 

proportion of study households with incomes exceeding 80 percent of area median is 

considerably larger in the Oakland MSA than the other MSAs due to the presence of the two 

study properties with a large share of non-qualifying units. 

The other three MSAs lie between these extremes. Mean household income among tax credit 

households in Kansas City and Miami is about twice that of public housing and Section 8 

tenants. In Milwaukee, there is a somewhat smaller difference. In all live MSAs, the 

proportion of households with earned income is significantly higher for LIHTC residents 

than for public housing or Section 8. Similarly, the proportion on public assistance(welfare 

or TANF) is lower. Overall, LIHTC properties serve a higher income population that is more 

likely to be working and less likely to be on public assistance. 

Size and Composition of LIHTC Households 

Exhibit 3-5 presents key characteristics of the LIHTC households, by sponsor type, including 

household size, number of children, and age, marital status, and education level of the head 

of household. 

As shown, the size and composition of the study households differ greatly by sponsor type. 

Nonprofit properties serve more large families, while for-profit properties serve more oneand two-person households. For example, there are nearly twice as many households with 

four or more persons living in the nonprofit properties as compared to for-profit properties. 

Similarly, there are twice as many one-personhouseholds in the for-profit properties (42 

percent) as in properties with nonprofit sponsors (21 percent). And while fewer than half the 

households in for-profit properties (46 percent) have children, nearly two-thirds of 

households in nonprofit properties (64 percent) have children. 

3-9 
Exhibit 3-5 

Characteristics of Study Property Households by Sponsor Type 

Household Size* 

One 

Two 

Three 

Four or more 

Number of Children* 

None 

One 

Two 

Three or more 

Age of Head of Household+* 

Less than 25 

25-34 

35-44 

45-59 

60 or older 

Marital Status of Head of 

Household* 

Single, not living with partner 

Single, living with a partner 

Married 

Divorced or separated 

Widowed 

Highest Level of Education by Head* 

No high school degree 

High school degree 

Trade school 

Some college, no degree 

Associate degree 

Bachelor or higher degree

^..-^a. r TT,Tt-D-“:,l--r P..-.^..,,nnn\ 

Nonprofit For-Profit 

Sponsor Sponsor 

(n=391) (n=441) 

21% 42% 

21% 26% 

27% 17% 

31% 16% 

36% 54% 

20% 22% 

25% 13% 

19% 10% 

12% 13% 

33% 35% 

24% 12% 

22% 10% 

10% 31% 

48% 44% 

7% 5% 

19% 15% 

21% 20% 

5% 16% 

21% 23% 

29% 28% 

5% 8% 

27% 20% 

9% 6% 

8% 16% 

Total 

(n=832) 

33% 

24% 

21% 

22% 

46% 

21% 

18% 

14% 

12% 

34% 

17% 

15% 

22% 

45% 

6% 

16% 

21% 

11% 

22% 

28% 

7% 

23% 

7% 

13% 

+Age of Head of Household is as of December 3 1, 1999 

Notes: Estimates weighted to reflect households in eligible LIHTC properties in five study MSAs. 

*Indicates significant difference between residents of nonprofit and for-profit properties at the 10 percent significance level 

(see Appendix A for a description of the significance tests performed). 

Totals may not sum to 100 because of rounding. 

3-10 
These differences in household composition stem in large part from the fact that the heads of 

households in for-profit properties are three times as likely to be elderly. Only 10 percent of 

households in nonprofit properties are headed by someone 60 or older, compared to 31 

percent in for-profit properties. Despite the sizable population of elderly in for-profit 

properties, the largest proportion of households (about one-third) in both types of properties 

is in the 25to-34-year-old category. 

In terms of marital status, nearly half the study households in both types of properties are 

single and not living with a partner, and another 21 percent are divorced. Many of these 

households are single-parent households: close to two-thirds of the single-headed households 

(62 percent) and over half of the divorced heads of households (56 percent) have children. 

Not surprisingly, because of the relatively high proportion of elderly residents, the proportion 

of widowed heads of households is three times higher in for-profit-sponsored properties than 

in nonprofit-sponsored properties (16 percent compared to 5 percent). 

Overall, the LIHTC households are fairly well educated. The vast majority of the heads of 

household (78 percent) have at least a high school degree. A full 43 percent have had at least 

some college, and 20 percent have a college degree. There is not a dramatic difference in 

education level by sponsor type. 

3.2 Rent Burdens and Project Rent-Setting Practices 

This section examines the rent burdens of study property households. To provide a context 

for the analysis of rent burdens, we begin with a summary of the extent to which LIHTC 

households receive rental assistance and of how rents are set in different types of properties. 

Housing Assistance Among LIHTC Property Households 

Section 8 housing assistance plays an important role in the housing situation of many LIHTC 

households. As shown in Exhibit 3-6, 37 percent of the residents in the study properties have 

Section 8 assistance, either in the form of project-based Section 8 or a Section 8 certificate or 

voucher.’ The proportion of residents with Section 8 differs by sponsor type. About 31 

percent of residents in nonprofit properties have some form of Section 8, as compared to 41 

percent of the residents in for-profit properties. 

7 This is comparable to GAO’s estimate of 39 percent of tax credit households receiving rental assistance, 

GAO, 1997. 
Exhibit 3-6 

Households with Section 8 Assistance by Sponsor Type 

Nonprofit For-Profit 

Sponsor Sponsor Total 

(n=1,466) (n=l,708) (n=3,174) 

Section 8” 

Project-based 23% 36% 31% 

Tenant-based 8% 5% 6% 

None 69% 59% 63% 

Source: Administrative records and property manager interviews for all occupied units. 

Notes: Estimates weighted to reflect households in eligible LIHTC properties in five study MSAs. 

*Indicates significant difference between residents of nonprofit and for-profit properties at the 10 percent significance level 

(seeAppendix A for a description of the significancetestsperformed). 

Totals may not sum to 100 because of rounding. 

Project Rent-Setting Practices 

Exhibit. 3-7 summarizes the types of rent restrictions that apply to units in the study 

properties. For purposes of discussion, we group these units into four categories: 

l Units covered by project-based Section 8 

l Units funded by programs such as HOME or state housing programs that require 

affordability for residents with incomes 35 to 50 percent or less of area median 

income’ 

l Units whose rents are restricted by the tax credit maximum only 

l Units with no rent restrictions (market-rate units) 

The use of Section 8 certificates or vouchers may also affect owners’ rent-setting practices, 

because units occupied by Section 8 certificate or voucher holders must meet PHA rent 

reasonableness standards. 

The rent restrictions applicable to the study properties vary by sponsor type. Of the units in 

properties with nonprofit sponsors, 22 percent have project-based Section 8, 20 percent have 

rent restrictions associated with HOME or state program financing, and 56 percent have no 

8 Under the HOME Program, there are two types of HOME-assisted rental units: Low HOME units and High 

Home units. Projects with five or more HOME units must have at least 20 percent Low HOME units. Low 

HOME units must be affordable for residents with incomes 50 percent of area median income, while High 

HOME units must be affordable to those with incomes 65 percent of median. For the purposes of this 

category, we included only Low HOME units. 

3-12 
rent restrictions beyond the tax credit maximum rents (i.e., rents set at 30 percent if 50 or 60 

percent of the area median). Only 2 percent of units in properties with nonprofit sponsors are 

market-rate units. 

Rent Restrictions 

Rent Restriction* 

Project-based Section 8 

HOME or Similar Program 

(35-50% of Area Median Income) 

LIHTC Maximum Only 

(50 or 60% of Area Median Income) 

Exhibit 3-7 

on Study Property Units 

Nonprofit For-Profit Total 

Sponsor Sponsor (n=3,264) 

(n=1,529) (n=1,735) 

22% 36% 30% 

20% 0% 9% 

56% 55% 55% 

No Rent Restrictions (market rate units) 2% 9% 6% 

Source: Administrative records and property manager interviews for all study property units. 

Notes: Estimates weighted to reflect households in eligible LIHTC properties in five study MSAs. 

*Indicates significant difference between residents of nonprofit and for-profit properties at the 10 percent significance level 

(see Appendix A for a description of the significance tests performed). 

Totals may not sum to 100 because of rounding. 

By contrast, units in for-profit properties are more likely to be market-rate (no income or rent 

restrictions) and more likely to have project-based Section 8, but less likely to have any rent 

restrictions from participating in the HOME, or similar state programs. As shown in Exhibit 

3-7, 36 percent have project-based Section 8, none have rent restrictions associated with 

HOME or state program financing, 55 percent have no restrictions beyond the tax credit 

maximum rents, and 9 percent have no rent restrictions (i.e., they are market-rate). 

Exhibit 3-8 shows gross rents in the study property units as a percentage of area Fair Market 

Rents (FMRs) and tax credit maximum rents. These measures provide a market-based 

indication of affordability, except in project-based Section 8 units and in units occupied by 

Section 8 certificate holders, where residents pay 30 percent of income regardless of gross 

rent. 

As shown, LIHTC rents vary considerably by sponsor type. Nearly half of the nonprofit 

units (45 percent) have rents that are 70 percent or less of FMRs, compared with 9 percent of 

for-profit property units. Similarly, only 21 percent of units in nonprofit properties have 

rents above 90 percent of FMRs, compared to 58 percent of for-profit property units. The 

higher rents in for-profit properties is consistent with locational differences by sponsor type. 

For-profit properties are much more likely than nonprofits to be located in low-poverty and 

suburban neighborhoods where rents tend to be higher (See Exhibit 4-l). 
Exhibit 3-8 

Rents as a Percentage of FMRs and Tax Credit Maximum Rents, By Sponsor Type 

Nonprofit For-Profit 

Sponsor Sponsor 

(n=1,439) (n=1,591) 

45% 9% 

17% 14% 

18% 19% 

7% 26% 

14% 32% 

Total 

(n=3,050) 

26% 

16% 

18% 

17% 

22% 

5% 

36% 

24% 

35% 

Gross Rent as a % of FMR* 

70% or less 

71-80% 

81-90% 

91-100% 

Greater than 100% 

Gross Rent as a % of Tax Credit Maximum* 

50% or less 

51-80% 

81-100% 

Greater than 100% 

source: Administrative records, HUD. 

10% 1% 

48% 24% 

20% 28% 

22% 47% 

Notes: Estimates weighted to reflect households in eligible LIHTC properties in five study MSAs. 

*Indicates significant difference between residents of nonprofit and for-profit properties at the IO percent significance level 

(see Appendix A for a description of the significance tests performed). 

Totals may not sum to 100 because of rounding. 

In addition, for-profit units are more likely than nonprofit units to be near or above the tax 

credit maximum.’ Specifically, three-quarters of units in for-profit properties have rents 

above 80 percent of the tax credit maximum, compared to less than half the units in nonprofit 

properties, and 47 percent of the for-profit units are above the tax credit maximum, compared 

to 22 percent of units in nonprofit properties. (Gross rents may exceed the tax credit limit in 

units with Section 8 assistance [project- or tenant-based], and in non-qualifying units.) 

Rent Burden 

An important policy issue is the extent to which households in “shallow subsidy” programs, 

such as the LIHTC, have rent burdens that are within an acceptable range. This is considered 

to be 30 percent of adjusted income in HUD assistance programs. In analyzing rent burden 

for this study, we examined tenant rent includ&g tenant-paid utilities as a percentage of gross 

9 Tax credit maximum rents are 30 percent of 50 percent or 60 percent of area median income, depending on 

the property’s minimum set-aside. 

3-14 
household income.lo We were also interested in the change in rent burden associatedwith 

moving to a tax credit property. Although change in rent burden could not be measured 

directly, as a proxy, we askedsurvey respondentsto comparetheir current rent to their rent 

before they moved to the LIHTC property, and the relative difficulty in paying the rent now 

comparedto their previous residence. 

As shown in Exhibit 3-9, half of the study households (50 percent) pay 30 percent or less of 

their gross income for rent and utilities. One-quarter(25 percent) pay 31 to 40 percent, 12 

percent pay 41 to 50 percent, and 13 percent pay over 50 percent of their gross income 

toward rent and utilities. Rent burdens among households living in the study properties do 

not vary dramatically based on sponsor type. 

Exhibit 

Rent Burdens Among Study Property 

Rent Burden* 

20% or Less of Gross Income 

21-30% 

3l-40% 

41-50% 

Greater than 50% 

Rent Compared to Previous Apartment 

Lower than Previous Apartment 

About the Same 

Higher than Previous Apartment 

Perceived Rent Burden Compared to 

Previous Apartment 

Easier to Pay than Previous Apartment 

About the Same Rent Burden 

Harder to Pay than Previous Apartment 

3-9 

Households by Sponsor Type 

13% 16% 15% 

40% 31% 35% 

25% 25% 25% 

11% 13% 12% 

11% 15% 13% 

48% 46% 47% 

13% 12% 13% 

39% 42% 40% 

34% 33% 34% 

39% 38% 39% 

26% 29% 28% 

ource: LIHTC Resident Survey (1999) Notes: Estimates weight1 3 reflect households in eligible LIHTC properties in five study 

MSAs. *Indicates significant difference between residents of nonprofit and for-profit properties at the 10 percent significance level (see 

Appendix A for a description of the significance tests performed). Totals may not sum to 100 because of rounding. 

lo Tenant rent, tenant-paid utilities, and gross income for rent-burden calculations were all self-reported by 

survey respondents. Where utilities data were missing, the utility allowance was used as a proxy. Where 

tenant-reported income was missing, manager-reported income was used. 

3-15 
This finding is surprising,suggestingthat 13percentof LIHTC householdshaveworstcase 

housing needs (i.e., rent burdens exceeding 50 percent). In order to understand better the 

reasons for these apparent high rent burdens, we examined the 95 households with calculated 

rent burdens of greater than 50 percent. More than one-third (35 percent) have elderly heads 

(65 or older), who are more likely to have fixed incomes than non-elderly. A substantial 

portion of these elderly households (39 percent) are in Villa San Ramon, the mostly marketrate luxury residence for the elderly in California. It is possible that these households may 

have substantial asset income or family resources that they may not have included in their 

incomes reported in the survey. Surprisingly, however, 20 percent of the households with 

rent burdensof at least50 percentreceiveSection8 rental assistance(project-or tenantbased). 

To identify possible sources of data error, we examined more closely each input to the rent 

burden calculation-tenant-paid rents, tenant-paid utilities, and gross household income. For 

each of these inputs, we used tenant-reported data from the survey. Where available, we 

compared these tenant-reported data with data provided by the property manager. 

At several properties, managers provided information on tenant-paid rents as well as gross 

rents. For theseproperties,we comparedtenant-paidrentsfrom the surveyand from the 

managers to assess whether there was a significant discrepancy. In a few cases, it was clear 

that residents pay for some additional service, such as parking, along with their basic rent, 

and therefore overstated their actual rent. For the most part, however, the two sources were 

in agreement, thus eliminating tenant-paid rents as a significant source of data error. 

We also compared tenant-reported utilities with the utility allowance reported by the property 

manager. In a large number of cases, tenant-reported utilities exceeded the utility allowance, 

and in several instances, particularly among Section 8 residents, high utilities were the 

primary driver of high rent burden. There are severalpossible explanationsfor the 

discrepancy between tenant-reported utilities and the utility allowance. First, very often, 

actual consumption of electricity, gas, and other utilities exceeds the utility allowance 

provided for the unit. This may be because the allowance is insufficient or because the 

tenant’s usage is excessive, or a combination of the two. It is also possible that some tenants 

overestimated the amount they spend on utilities, in which case rent burden would be 

overstated. 

Finally, we compared gross household income as reported by the resident with income as 

reported by the property manager, where available. Very often, there was a significant 

discrepancy between the two sources, and more often than not, the tenant-reported income 

was lower than the manager-reported income. A number of factors may be at work. It is 

possible that residents did not include asset income when they provided their gross household 

income for the survey (as noted above, many of those with worst case housing needs are 
elderly, and these households may have substantial asset income). The discrepancy may also 

be the result of simple reporting error. However, it is important to note that the income data 

provided by property managers was often over a year old by the time of our survey, and that 

the households’ financial circumstances may have changed between the last recertification 

and the time of the study survey. 

To determine the effect of the income data source on the estimation of rent burden, we 

recalculated rent burden using management-reported tenant income instead of tenant-reported 

income. Using this approach, we found that 50 percent of households had calculated rent 

burdens exceeding 30 percent, and 10 percent had rent burdens exceeding 50 percent (as 

compared to 13 percent using tenant-supplied data). We also compared the two rent burden 

calculation approaches to determine the percentage of households that had high rent burdens 

according to both approaches. We found that 38 percent had rent burdens exceeding 30 

percent according to both approaches, and 8 percent had rent burdens exceeding 50 percent. 

With regard to changes in rent burden, the majority of households pay less rent or similar rent 

in their current apartment compared to their previous residence. Specifically, 47 percent 

indicated that they pay less rent in the LIHTC property than they did in their previous 

housing situation, and 13 percent said they pay about the same. However, about 40 percent 

of residents reported that they pay more now than in their previous residence. There was 

little variation based on sponsor type. 

Overall, most residents reported that their current rent was easier to pay than their previous 

rent or about the same level of difficulty considering both changes in income and rent since 

they moved. About one-third of the households (34 percent) said their current rent was easier 

to pay now than in their previous residence, and 39 percent said it was about the same. 

About one-quarter (28 percent) said it was harder since they moved to their LIHTC 

development. 

Exhibit 3-10 shows rent burden by income as a percentage of area median income. As 

shown, residents with the highest rent burdens are those with the lowest incomes. Nearly 

one-fourth (23 percent) of extremely low-income households (earning 30 percent or less of 

median income) have rent burdens exceeding 50 percent, more than twice the proportion of 

households in higher income groups. Furthermore, while a substantial proportion of the 

extremely low-income households (43 percent) and very low-income households (38 percent) 

have rent burdens below 30 percent, low-income households (74 percent) and more moderate 

income households (79 percent) are almost twice as likely to have rent burdens of 30 percent 

or below. 
Exhibit 3-10 

Rent Burdens Among Study Property Households 

by Income as a Percentage of Area Median 

Income as a Percentage of Area Median 

30% or 31%- 51%- >80% Total 

less 50% 80% 

Rent Burden* 

20% or Less of Household Income 10% 13% 18% 54% 15% 

21-30% 33% 25% 56% 25% 34% 

31-40% 21% 31% 22% 5% 24% 

41-50% 14% 20% 2% 3% 13% 

Greater than 50% 23% 10% 3% 13% 14% 

Source: LIHTC Resident Survey (1999) 

Notes: Estimates weighted to reflect households in eligible LIHTC properties in five study MSAs. 

*Indicates significant difference at the 10 percent significance level (see Appendix A for a description of the significance 

tests performed). Totals may not sum to 100 because of rounding. 

Data on income as a percentage of area median was not available for all residents. For this reason, the number of 

households in the Total column is slightly lower than that in Exhibit 3-9 and the percentages in the Total column are 

slightly different (no more than one percentage point different in any category) in the two exhibits. 

3.3 Housing Circumstances and Satisfaction with Housing and 

Neighborhood 

In this section, we examine the previous housing situations of LIHTC households, and why 

they moved to the tax credit property. We then examine resident satisfaction with their 

current LIHTC apartment and how it compares to their previous place of residence. Finally, 

we discuss resident satisfaction with their current neighborhood and how the current 

neighborhood compares with the neighborhood in which they previously lived. 

Prior Housing Situation 

Exhibit 3-l 1 shows the prior housing situation of the LIHTC households. Overall, close to 

one-fourth of the residents in the study properties (23 percent) reportedly lived in public 

housing before moving to their LIHTC property and 7 percent received rental assistance. 

Half the residents (50 percent) previously rented an apartment with no assistance, 9 percent 

owned their own home, and 10 percent shared rent with another family or lived with friends 

and family and did not pay rent. 

3-18 
Exhibit 3-11 

Prior Housing Situation of Study Property Households 

Prior Housing Situation* 

Owned Home 

Rented with No Housing Assistance 

Rented with Housing Assistance 

Lived in Public Housing 

Shared Rent with Another Family 

With Friends/Family (paid no rent) 

Other 

Type of Building* 

Single-family Home 

Two-to-four Unit Building 

Five-to-nine Unit Building 

Building with Ten or More Units 

^.._. . . TTUI-PD,,:,A,..+P......n.,II one\ 

Nonprofit For-Profit 

Sponsor Sponsor 

(n=391) (n=441) 

4% 13% 

52% 48% 

7% 7% 

29% 18% 

3% 7% 

5% 6% 

0% 1% 

26% 37% 

33% 22% 

12% 9% 

30% 31% 

Total 

(n=832) 

9% 

50% 

7% 

23% 

5% 

5% 

0% 

32% 

27% 

10% 

30% 

Notes:Estimates weightedto reflecthouseholds ineligibleLIHTCproperties in five studyMSAs.

*Indicates significant difference between residents of nonprofit and for-profit properties at the 10 percent significance level 

(see Appendix A for a description of the significance tests performed). 

Totalsmaynotsumto 100becauseof rounding. 

Residents living in nonprofit-sponsored properties were more likely to have lived in public 

housing or received rental assistance in their prior housing situation (36 percent) compared to 

residents living in for-profit properties (25 percent). Conversely, households living in forprofit-sponsored properties were much more likely to have been homeowners in their 

previous housing situation. While only 4 percent of household heads in nonprofit properties 

owned their previous place of residence, 13 percent in for-profit properties were homeowners 

prior to moving into their tax credit apartment. 

Regardless of tenure type or assistance status, about one-third of the households lived in a 

single-family home prior to moving into their tax credit apartment. This figure is somewhat 

higher among households in for-profit properties (37 percent) compared to nonprofit 

properties (26 percent). 

Primary Reason for Moving to LIHTC Property 

Exhibit 3-12 presents information on the primary reasons residents moved into their tax 

credit developments. As shown, the most commonly cited reasons for moving were to pay 

less rent and to have a nicer or bigger apartment. Overall, 29 percent of survey respondents 

3-19 
said the main reasonthey moved into their current apartmentwas becauskthe rent was lower, 

and 27 percent said they chosetheir apartmentbecauseit is nicer or bigger. 

Exhibit 3-12 

Primary Reason for Moving to LIHTC Property 

Nonprofit For-Profit Total 

Sponsor Sponsor (n=832) 

(n=391) (n-441) 

Reason for Moving to LIHTC Apartment* 

Lower Rent 26% 31% 29% 

Nicer or Bigger Apartment 37% 19% 27% 

Safer Place 8% 9% 9% 

Closer to Job or Work 6% 8% 7% 

Smaller, More Easily Managed Place 3% 8% 6% 

Closer to Friends or Relatives 4% 7% 5% 

To Have Own Apartment 3% 6% 5% 

Other 13% 13% 13% 

Source: LIHTC Resident Survey (1999) 

‘Notes: Estimates weighted to reflect households in eligible LIHTC properties in five study MSAs. 

*Indicates significant difference between residents of nonprofit and for-profit properties at the 10 percent significance level 

(see Appendix A for a description of the significance tests performed). 

Totals may not sum to 100 because of rounding. 

Other reasonsgiven were that it was a saferplace (9 percent) or becauseit was closerto ajob 

or workplace (7 percent). Among households in for-profit properties, another commonly 

cited reasonwas to move into a smaller,more easily managedplace (8 percent),reflecting the 

elderly population who often moved from siligle-family homes. 

Motivations for moving differ basedon property sponsortype. While the largest proportion 

of households in nonprofit properties (which are more likely to serve families) chose their 

apartment becauseit was nicer or bigger than where they were living previously, the most 

commonly cited reasonamonghouseholdsin for-profit propertieswas to pay lower rent. 

Ratings of LIHTC Property Apartment 

For the most part, the LIHTC residents are satisfied with their tax credit apartments. As 

shown in Exhibit 3-13, more than two-thirds of the study households (68 percent) rated their 

apartment as good or excellent overall, and one-third (32 percent) rated it as fair or poor. 

Roughly three-quarters of the households rated their apartment as good or excellent in the 

categoriesof size, layout, and condition of appliances,while two-thirds rated their apartment 

as good or excellent in terms of amenities and the condition of the building. In addition, 

three-quarterssaid they were somewhator very satisfiedwith the property management. 
Exhibit 3-13 

Resident Ratings of LIHTC Property Apartment 

Overall Rating of Apartment 

Fair or Poor 

Good or Excellent 

Size of Apartment 

Fair or Poor 

Good or Excellent 

Layout 

Fair or Poor 

Good or Excellent 

Condition of Appliances 

Fair or Poor 

Good or Excellent 

Condition of Building* 

Fair or Poor 

Good or Excellent 

Amenities* 

Fair or Poor 

Good or Excellent 

Good Place for Children* 

Fair or Poor 

Good or Excellent 

Satisfaction with Management 

Somewhat or Very Dissatisfied 

Neither Satisfied nor Dissatisfied 

Somewhat or Very Satisfied 

ource: LIHTC Resident Survey (1999) 

Nonprofit For-Profit Total 

Sponsor Sponsor (n=832) 

(n=391) (n=441) 

30% 33% 32% 

70% 67% 68% 

26% 25% 26% 

74% 75% 74% 

20% 26% 23% 

80% 74% 77% 

18% 21% 20% 

82% 79% 80% 

27% 33% 31% 

73% 67% 70% 

30% 40% 35% 

70% 60% 65% 

42% 59% 51% 

58% 41% 49% 

18% 22% 20% 

4% 5% 5% 

78% 72% 75% 

Notes: Estimates weighted to reflect households in eligible LIHTC properties in five study MSAs. 

*Indicates significant difference between residents of nonprofit and for-profit properties at the 10 percent significance level 

(see Appendix A for a description of the significance tests performed). 

Totals may not sum to 100 because of rounding. The categories of fair and poor, and of good and excellent, were combined 

to enhance readability of the table without losing important information. 

Despite these high overall ratings, only about half the households said they thought the 

development was a good place for children, with a higher proportion of households in 

nonprofit properties (58 percent) voicing this opinion than in for-profit properties (41 

percent). In general, the households in the nonprofit properties rated their apartments slightly 

3-21 
higher than the households in the for-profit properties, with the widest margin of difference 

in the category of being a good place for children. 

Comparison to Previous Residence 

Exhibit 3-14 presents survey results showing how residents compared their tax credit 

development unit to their previous residence. On the whole, they rated their tax credit 

property apartment favorably. Roughly half the households (54 percent) said their tax credit 

apartment was better than the place they used to live, about a quarter (24 percent) said it was 

about the same, and 22 percent said their current apartment is not as good as their previous 

residence. A similar pattern was found for most of the individual rating categories listed in 

Exhibit 3-14. However, again the ratings fell short in the category of being a good place for 

children: only 36 percent of all the households said their tax credit apartment was better than 

their previous residence, while 38 percent said it was not as good. 

In all categories of comparison, the nonprofit-sponsored properties scored higher than the forprofit-sponsored properties. The proportion of households in nonprofit properties who said 

their apartment is better than their previous residence was 10 to 14 percentage points higher 

than the proportion of households in for-profit properties in the category of size, layout, 

condition of appliances, and amenities. Similarly, in all categories of comparison, a higher 

proportion of households in for-profit-sponsored properties than in nonprofit properties rated 

their tax credit apartment as not as good as their prior housing. 

One factor driving the relatively unfavorable comparison of the current apartment to the 

previous residence by households in for-profit properties may be the relatively large 

proportion of these residents who lived in a single-family home prior to living in the tax 

credit development. As noted earlier in this section, households in for-profit properties were 

more likely than those in nonprofit properties (37 percent versus 26 percent) to have lived in 

a single-family home prior to moving into the tax credit property.” And, residents who 

previously lived in a single-family home were more than twice as likely as others to compare 

their current apartment unfavorably with their previous residence. 

Ratings of LIHTC Property Neighborhood 

Exhibit 3-15 shows how residents in the study properties rated their neighborhood along 

various dimensions. In general, residents did not rate their neighborhoods as positively as 

” If we excludepropertiesservingprimarily the elderly, the percentageof householdsin for-profit properties 

who previously lived in a single-family homedropsslightly from 37 percentto 35 percent. 

3-22 
they did their LIHTC apartment. Overall, 46 percent of the households rated their 

neighborhood as good or excellent, while 54 percent rated it as fair or poor. In both for-profit 

and nonprofit properties, households rated their neighborhood more favorably in specific 

categories than they did overall. Within the specific categories, neighborhoods of both types 

of projects fared best in the categories of access to public transportation and transportation to 

work, and less well in terms of safety and accessto good schools and recreation. 

Exhibit 3-14 

Resident Comparison of LIHTC Property Apartment to Previous Residence 

Nonprofit For-Profit Total 

Sponsor Sponsor (n=832) 

(n=391) (n=441) 

Overall Comparison* 

LIHTC Apartment Not as Good 19% 25% 22% 

LIHTC Apartment About the Same 22% 25% 24% 

LIHTC Apartment Better 59% 50% 54% 

Size of Apartment* 

LIHTC Apartment Not as Good 24% 34% 29% 

LIHTC Apartment About the Same 18% 21% 20% 

LIHTC Apartment Better 58% 45% 51% 

Layout* 

LIHTC Apartment Not as Good 20% 30% 26% 

LIHTC Apartment About the Same 25% 25% 25% 

LIHTC Apartment Better 55% 45% 49% 

Condition of Appliances* 

LIHTC Apartment Not as Good 7% 14% 11% 

LIHTC Apartment About the Same 39% 45% 42% 

LIHTC Apartment Better 54% 40% 47% 

Condition of Building* 

LIHTC Apartment Not as Good 18% 24% 21% 

LIHTC Apartment About the Same 32% 35% 34% 

LIHTC Apartment Better 49% 41% 45% 

Amenities* 

LIHTC Apartment Not as Good 17% 26% 22% 

LIHTC Apartment About the Same 31% 36% 34% 

LIHTC Apartment Better 51% 38% 44% 

Good Place for Children* 

LIHTC Apartment Not as Good 30% 44% 38% 

LIHTC Apartment About the Same 27% 25% 26% 

LIHTC Apartment Better 42% 31% 36% -I rurce: LIHTC Resident Survey (1999) 

Notes: Estimates weighted to reflect households in eligible LIHTC properties in five study MSAs. 

*Indicates significant difference between residents of nonprofit and for-profit properties at the 10percent significance level (see 

Appendix A for a description of the significance tests performed). Totals may not sum to 100 because of rounding. 

3-23 
Resident Ratings 

Overall Neighborhood Rating* 

Fair or Poor 

Goodor Excellent 

Access to Good Schools* 

Fair or Poor 

Good or Excellent 

Access to Public Transportation* 

Fair or Poor 

Good or Excellent 

Transportation to Work* 

Fair or Poor 

Goodor Excellent 

Access to Grocery Stores* 

Fair or Poor 

Good or Excellent 

Access to Social Services* 

Fair or Poor 

Good or Excellent 

Accessto Recreation 

Fair or Poor 

Goodor Excellent 

Proximity to Friends and Family* 

Fair or Poor 

Good or Excellent 

Proximity to Job Opportunities* 

Fair or Poor 

Good or Excellent 

Safety* 

Fair or Poor 

Goodor Excellent 

Environment for Children 

Fair or Poor 

Goodor Excellent 

Exhibit 3-15 

of LIHTC Property Neighborhood 

Nonprofit For-Profit Total 

Sponsor Sponsor (n=832) 

(n=391) (n=441) 

59% 50% 54% 

41% 50% 46% 

44% 52% 48% 

56% 48% 52% 

11% 31% 22% 

89% 69% 78% 

16% 24% 23% 

84% 71% 77% 

26% 46% 37% 

74% 54% 63% 

32% 42% 38% 

68% 58% 62% 

45% 47% 46% 

55% 53% 54% 

32% 40% 36% 

68% 60% 64% 

41% 49% 45% 

59% 51% 55% 

58% 48% 52% 

42% 52% 48% 

59% 64% 61% 

41% 36% 39% 

iource: LIHTC Resident Survey (1999). Notes: Estimates weighted to reflec Duseholds in eligible LIHTC properties in five study 

MSAs. *Indicates significant difference between residents of nonprofit and for-profit properties at the 10 percent significance level (see 

Appendix A for a description of the significance tests performed). Totals may not sum to 100 because of rounding. 

3-24 
The households in for-profit properties were more satisfied than those in nonprofit properties 

with their neighborhoods overall, with 50 percent rating their neighborhood as good or 

excellent compared to 41 percent of households in nonprofit properties. However, 

households in the nonprofit properties were more satisfied than their counterparts in for-profit 

properties in every specific rating category except safety, including access to good schools, 

access to public transportation, transportation to work, access to grocery stores, access to 

social services, access to recreation, closeness to friends and family and jobs, and the 

neighborhood as a good environment for children. This suggests that the perception of 

neighborhood safety may be driving residents’ overall neighborhood rating. 

Comparison to Previous Neighborhood 

Exhibit 3-16 shows the proportion of households whose previous residence was in the same 

neighborhood, a different neighborhood in the same city, a different city in the same state, a 

different state, or a different country. As shown, 19 percent of the residents moved into a tax 

credit property in the same neighborhood in which they were already living, 58 percent lived 

in a different neighborhood within the same city, and 23 percent moved there from a different 

city, state, or country. 

Location of Previous Apartment 

Same Neighborhood 

Different Neighborhood, Same City 

Different City, Same.State 

Different State 

Different Country 

Source: LIHTC Resident Survey (1999) 

Exhibit 3-16 

or Home Compared to Current Apartment* 

Nonprofit For-Profit Total 

Sponsor Sponsor (n=832) 

(n=391) (n=441) 

22% 17% 19% 

62% 54% 58% 

14% 22% 18% 

1% 7% 4% 

1% 1% 1% 

Notes: Estimatesweighted to reflect householdsin eligible LIHTC propertiesin five study MSAs. 

*Indicates significant difference between residents of nonprofit and for-profit properties at the 10 percent significance level 

(see Appendix A for a description of the significance tests performed). 

Totals may not sumto 100becauseof rounding. 

For those residents who moved to the LIHTC property from a different neighborhood or area, 

Exhibit 3-17 presents residents’ ratings of their current neighborhood compared to their 

previous neighborhood. Again, residents were far less favorable about the change in 

neighborhood than the change in property. Overall, about a third of the households (34 

percent) said their current neighborhood was better than the previous neighborhood, a third 

(34 percent) said it was not as good, and a third (33 percent) said it was about the same. 
In almost all the specific categoriesof comparison,the largest proportion of residentsrated 

the LIHTC neighborhood as being about the sameas the previous neighborhood. However, 

in most areasof comparison,householdsin nonprofit properties were more likely to compare 

their current neighborhood favorably and less likely to compare it unfavorably with their 

previous neighborhood than householdsin for-profit properties. 

Exhibit 3-17 

Resident Comparison of LIHTC Neighborhood to Previous Neighborhood 

Overall Comparison 

LIHTC Neighborhood Not As Good 

LIHTC Neighborhood About the Same 

LIHTC Neighborhood Better 

A.ccessto Good Schools* 

LIHTC Neighborhood Not As Good 

LIHTC Neighborhood About the Same 

LIHTC NeighborhoodBetter 

Access to Public Transportation* 

LIHTC Neighborhood Not As Good 

LIHTC Neighborhood About the Same 

LIHTC Neighborhood Better -

Transportation to Work* 

LIHTC Neighborhood Not As Good 

LIHTC Neighborhood About the Same 

LIHTC Neighborhood Better 

Accessto Grocery Stores* 

LIHTC Neighborhood Not As Good 

LIHTC Neighborhood About the Same 

LIHTC Neighborhood Better 

Access to Social Services* 

LIHTC Neighborhood Not As Good 

LIHTC Neighborhood About the Same 

LIHTC Neighborhood Better 

Nonprofit For-Profit 

Sponsor Sponsor 

(n=304) (n=358) 

33% 34% 

33% 32% 

33% 34% 

18% 31% 

56% 43% 

25% 26% 

6% 21% 

50% 43% 

44% 37% 

10% 19% 

52% 44% 

38% 37% 

18% 31% 

40% 42% 

43% 27% 

9% 16% 

55% 53% 

37% 31% 

Total 

(1~362) 

34% 

33% 

34% 

25% 

49% 

26% 

15% 

46% 

40% 

15% 

48% 

37% 

25% 

41% 

34% 

13% 

53% 

34% 

3-26 
Exhibit 3-17 (continued) 

Resident Comparison of LIHTC Neighborhood to Previous Neighborhood 

Access to Recreation* 

LIHTC Neighborhood Not As Good 

LIHTC Neighborhood About the Same 

LIHTC Neighborhood Better 

Proximity to Friends and Family* 

LIHTC Neighborhood Not As Good 

LIHTC Neighborhood About the Same 

LIHTC Neighborhood Better 

Proximity to Job Opportunities* 

LIHTC Neighborhood Not As Good 

LIHTC Neighborhood About the Same 

LIHTC Neighborhood Better 

Safety 

LIHTC Neighborhood Not As Good 

LIHTC Neighborhood About the Same 

LIHTC Neighborhood Better 

Environment for Children* 

LIHTC Neighborhood Not As Good 

LIHTC Neighborhood About the Same 

LIHTC Neighborhood Better 

ource: LIHTC Resident Survey (1999) 

24% 32% 28% 

39% 37% 38% 

38% 31% 34% 

16% 27% 23% 

55% 42% 48% 

29% 30% 30% 

13% 25% 20% 

51% 46% 48% 

36% 29% 32% 

28% 34% 31% 

37% 32% 34% 

35% 34% 35% 

Notes: Estimates weighted to reflect households in eligible LIHTC properties in five study MSAs. 

*Indicates significant difference between residents of nonprofit and for-profit properties at the 10 percent significance level 

(seeAppendix A for a description of the significancetestsperformed). 

Totals may not sum to 100 because of rounding. 

3.4 Characteristics of Section 8 Households 

While the previous sections have examined household characteristics based on property 

sponsor type, this section focuses on differences in tax credit households according to the 

households’ Section 8 status-whether they receive Section 8, and if so, whether the 

assistanceis project-basedor tenant-based.We presentonly those characteristicswhere there 

is a notable difference by Section 8 status, including race, income and income sources, and 

satisfactionwith housing and neighborhood. 

- 3-27 
Race and Ethnicity by Section 8 Status 

One area of notable difference by Section 8 status is race. Tax credit households with 

Section 8 aremuch more likely to be membersof a racial minority than tax credit households 

without Section 8. As shown in Exhibit 3-18, 71 percentof headsof householdswith tenantbasedSection 8 and 84 percent of those with project-basedSection 8 are African American, 

comparedwith lessthan half (47 percent) of those without Section 8. Strikingly, 99 percent 

of tax credit householdsin project-basedSection 8 properties and 95 percent of those with 

certificates or vouchers are either members of a racial minority or are Hispanic. This 

comparesto 71 percentof unassistedhouseholds. 

Exhibit 3-18 

Race and Ethnicity of Heads of Household by Section 8 Status

Race” 

White 

African American 

Other 

Ethnicity 

Hispanic 

Non-Hispanic 

Race and Ethnicity Together* 

White Non-Hispanic 

Minority 

Source: LIHTC Resident Survey (1999) 

No Section 8 

(n=513) 

36% 

47% 

17% 

21% 

79% 

29% 

71% 

Tenant-Based Project-Based 

Section 8 Section 8 

(n=78) (n=204) 

8% 5% 

71% 84% 

21% 11% 

24% 19% 

76% 81% 

5% 1% 

95% 99% 

Notes: Estimates weighted to reflect households in eligible LIHTC properties in five study MSAs. 

*Indicates significant difference at the 10 percent significance level (see Appendix A for a description of the significance 

tests performed). 

Totals may not sum to 100 because of rounding. 

Income and Income Sources 

There are also significant differences in the income levels of LIHTC households,depending 

on whether they receive Section 8 assistance.As shown in Exhibit 3-19, the median income 

of study households without Section 8 ($19,200) is approximately twice that of Section 8 

recipients. Similarly, approximately half the study householdson Section 8 have incomesof 

$10,000 or less, comparedwith only 12 percent of householdswithout Section 8. Roughly 

two-thirds to three-quartersof those with Section 8 have extremely low incomes (30 percent 

or lessof areamedian income), comparedto lessthan a quarter of thosewithout, 

3-28 
Income sourcesdiffer, as well. More than two-thirds (71 percent) of households without 

Section 8 have at least one adult currently working, compared to just over half of those with 

Section 8. Section 8 householdsare also roughly three times more likely than non-Section 8 

households to receive welfare or TANF or disability payments. 

Exhibit 3-19 

Income and Income Sources of Study Property Households by Section 8 Status 

No Section 8 Tenant-Based 

Annual Household Income* 

$10,000 or less 

$lO,OOl-$20,000 

$20,001-$30,000 

More than $30,000 

Income as a % of HUD Area Median+* 

O-30% 

31-50% 

51-80% 

Greater than 80% 

MeanAnnualHouseholdIncome 

Median Annual Household Income 

Adult in Household Currently Working 

for Pay* 

Social Security, Retirement, or Pension 

Welfare or TANF* 

Disability Payments* 

;ource:LIHTC Resident Survey (1999) 

+Adjusted for family size. 

(n=513)” Section 8 

(n=78)a 

12% 54% 

45% 36% 

26% 6% 

17% 4% 

23% 74% 

39% 18% 

28% 6% 

10% 2% 

$22,337 $12,366 

$19,200 $9,780 

71% 51% 

23% 24% 

6% 21% 

5% 18% 

47% 

42% 

8% 

3% 

55% 

16% 

18% 

14% 

Notes: Estimates weighted to reflect households in eligible LIHTC properties in five study MSAs. 

*Indicates significant difference at the 10 percent significance level (see Appendix A for a description of the significance 

tests performed). Totals may not sum to 100 because of rounding. 

a Some survey respondents did not know their income but were able to identify a range in which their income lies. These 

respondents are not included in the calculations of income as a percentage of HUD area median or of mean or median 

annual household income. 

Satisfaction with Housing and Neighborhood 

Another area of significant difference for Section 8 and non-Section 8 households is 

satisfactionwith the tax credit apartmentand neighborhood. In general,residentsin projectbasedSection 8 developmentsrated their tax credit apartmentmuch lower thancertificate or 

voucher holders or residents with no Section 8. As shown in Exhibit 3-20, 78 percent of 

3-29 
those without Section 8 and 86 percent of certificate and voucher holders rated their 

apartment as good or excellent, compared with only 38 percent of residents with proj’ectbased assistance. Similarly, in every rating category, project-based Section 8 residents 

consistently rated their apartments significantly lower than certificate and voucher holders 

and those without Section 8. For example, in the category of amenities, project-based 

Section 8 residents were three times less likely to rate their apartment as good or excellent 

thanthosewith tenant-based assistance or no assistance.

Exhibit 3-20 

Resident Ratings of LIHTC Property Apartment by Section 8 Status 

Overall Rating of Apartment* 

Fair or Poor 

Good or Excellent 

Size of Apartment* 

Fair or Poor 

Good or Excellent 

Layout* 

Fair or Poor 

Good or Excellent 

Condition of Appliances* 

Fair or Poor 

Good or Excellent 

Condition of Building* 

Fair or Poor 

Good or Excellent 

Amenities* 

Fair or Poor 

Good or Excellent 

Good Place for Children* 

Fair or Poor 

Good or Excellent 

Satisfaction with Management* 

Somewhat or Very Dissatisfied 

Neither Satisfied nor Dissatisfied 

Somewhat or Very Satisfied 

No Section 8 Tenant-Based Project-BaSed 

(n=513) Section 8 Section 8 

(n=78) (n=204) 

22% 14% 62% 

78% 86% 38% 

22% 18% 39% 

78% 82% 61% 

16% 9% 44% 

84% 91% 56% 

14% 10% 37% 

86% 90% 63% 

25% 20% 49% 

75% 80% 51% 

21% 27% 73% 

79% 73% 27% 

43% 34% 73% 

57% 66% 27% 

18% 9% 29% 

4% 6% 5% 

78% 85% 66% 

Source: LIHTC Resident Survey (1999). Notes: Estimates weighted to reflect households in eligible LIHTC properties in 

five study MSAs. *Indicates significant difference at the 10 percent significance level (see Appendix A for a description of 

the significance tests performed). Totals may not sum to 100 because of rounding. 

3-30 
At the same time, those with certificates or vouchers rated their apartments somewhat higher 

than those without Section 8. This may be because they are comparing their tax credit 

apartment with another privately owned unit previously rented with their certificate or 

voucher, or with public housing (28 percent of certificate and voucher holders in the study 

properties lived in public housing prior to moving into their tax credit apartment, compared 

with 16 percent of those without Section 8). 

With regard to neighborhood, project-based Section 8 residents rated their neighborhoods 

significantly lower than residents with tenant-based assistance or no assistance. As shown in 

Exhibit 2-1, 79 percent of tax credit households in project-based Section 8 developments 

rated their neighborhood overall as fair or poor, compared with 44 percent of those without 

Section 8 and 55 percent of certificate and voucher holders. Again, in each of the specific 

rating categories, residents in Section 8 projects rated their neighborhood lower. The biggest 

differences were found in the categories of access to grocery stores, proximity to job 

opportunities, safety, and the suitability of the neighborhood for children. Large differences 

in these areas highlight the distressed nature of the neighborhoods in which many of the 

study’s project-based Section 8 developments are located. 

Exhibit 3-21 

Resident Ratings of LIHTC Property Neighborhood by Section 8 Status 

No Section 8 Tenant-Based Project-Based 

Overall Neighborhood Rating* 

Fair or Poor 

Good or Excellent 

Access to Good Schools* 

Fair or Poor 

Good or Excellent 

Access to Public Transportation* 

Fair or Poor 

Good or Excellent 

Transportation to Work* 

Fair or Poor 

Good or Excellent 

Access to Grocery Stores* 

Fair or Poor 

Good or Excellent 

(n=513) 

44% 

56% 

42% 

58% 

18% 

82% 

18% 

82% 

27% 

73% 

Section 8 Section 8 

(n=78) (n=204) 

55% 79% 

45% 21% 

57% 60% 

43% 40% 

11% 35% 

89% 65% 

9% 38% 

91% 62% 

33% 59% 

67% 41% 
Exhibit 3-21 (continued’ 

Resident Ratings of LIHTC Property Neighborhood by Section 8 Status 

No Section 8 Tenant-Based Project-Based 

(n=513) Section 8 Section 8 

Access to Social Services* 

Fair or Poor 

Good or Excellent 

Access to Recreation* 

Fair or Poor 

Good or Excellent 

(n=78) (n=204) 

33% 38% 50% 

67% 62% 50% 

40% 41% 62% 

60% 59% 38% 

Proximity to Friends and Family* 

Fair or Poor 

Good or Excellent 

Proximity to Job Opportunities 

Fair or Poor 

Good or Excellent 

Safety 

Fair or Poor 

Good or Excellent 

Environment for Children 

Fair or Poor 

Good or Excellent 

Source: LIHTC Resident Survey (1999) 

33% 31% 45% 

67% 69% 55% 

37% 45% 63% 

63% 55% 37% 

43% 57% 73% 

57% 43% 27% 

53% 54% 84% 

47% 46% 16% 

Notes: Estimates weighted to reflect households in eligible LIHTC properties in five study MSAs. 

*Indicates significant difference at the 10 percent significance level (see Appendix A for a description of the significance 

tests performed). 

Totals may not sum to 100 because of rounding. 

3.5 Summary 

Data from the residentsurveyand from propertymanagerfiles were usedto examinethe 

social, financial, and housing circumstances of households living in tax credit properties. In 

general, LIHTC households are very-low- or extremely low-income working families who 

are members of a racial or ethnic minority. Residents of for-profit-sponsored properties are 

less likely to be members of a minority group and more likely to be elderly than those in 

nonprofit properties. Comparedto public housingand tenant-basedSection8, tax credit 

propertieshavea higherpercentageof racialminoritiesin threeof the MSAs, anda lower 

percentage in two MSAs. 

3-32 
Overall, almost one-third of LIHTC households have project-based Section 8, and about 5 

percent have tenant-based Section 8. Project-based Section 8 is more common among forprofit properties, while rent restrictions associated with HOME and other capital subsidy 

programs are much more common in nonprofit properties. In general, gross rents in 

nonprofit properties are considerably lower than rents in for-profit properties in relation to 

FMRs or to the tax credit maximum rents. Approximately 50 percent of study households 

have rent burdens exceeding 30 percent, and 13 percent have rent burdens exceeding 50 

percent, while 50 percent have rent burdens of 30 percent or less. 

Interestingly, close to one-third of tax credit residents lived in public housing or received 

rental assistance prior to moving into their tax credit development. This suggests that LIHTC 

units may be a stepping stone to the private market for some public housing residents. On 

the whole, residents in nonprofit properties are more likely to have moved from subsidized 

housing and less likely to have been homeowners than their counterparts in for-profit 

properties. They were also more likely to have moved into their tax credit property in order 

to live in a bigger or nicer apartment, while those in for-profit developments were more likely 

to move there to pay lower rent. Overall, residents in tax credit properties are satisfied with 

their housing: most households rated their LIHTC unit as good or excellent overall and 

compared their unit favorably with their previous housing situation. However, survey 

respondents were less positive about their neighborhoods than they were about their 

development. In general, residents in nonprofit properties were slightly more likely to be 

satisfied with both their housing and their neighborhood than were residents of for-profit 

properties. 

Finally, when we examined resident characteristics by Section 8 status, we found that Section 

8 residents of tax credit properties are considerably more likely to be members of a racial or 

ethnic minority, less likely to be working, and have lower incomes than those without 

housing assistance. We also found that residents in tax credit properties with project-based 

Section 8 were more dissatisfied with both their apartment and their neighborhood than were 

residents with tenant-based assistance or no housing assistance. 

3-33 
Chapter 4 

Role of the LIHTC in Fostering Diverse Communities 

To be eligible for tax credits, a property owner must agree to allocate at least 20 percent of 

the units to families below 50 percent of the area median income or 40 percent of the units to 

families below 60 percent of the area median income.’ Other than meeting this commitment 

and following fair housing laws, LIHTC properties are not restricted in the population they 

serve. Furthermore, owners decide where to build (or rehabilitate) LIHTC properties, subject 

only to the same local zoning laws that apply to all housing construction. There are no 

additional location restrictions in the LIHTC program. 

Nevertheless, there are several incentives at the federal and state level that may influence 

where a property is located and who resides at the property. For example, the amount of tax 

credits that can be awarded increases with the percentage of units set aside for families 

meeting the tax-credit income limit. Also, if a property is located in a qualified census tract 

(any tract where at least half of the households have incomes below 60 percent of the area 

median) or in a difficult development area (a metro area designated by HUD as having high 

construction costs relative to rents), investors can receive a 30 percent increase in the basis on 

which tax credits are allocated. State tax-credit allocation policies also influence who is 

served and where properties are located. Most state allocation agencies receive more 

applications for credits than the state has available, thus they must establish procedures to 

award credits among eligible projects. States can institute their own preferences or set asides 

beyond the minimum eligibility and set aside requirements established at the federal level. 

As noted in Chapter 2, state policies (as articulated in their Qualified Allocation Plans) do 

seem to influence the types of projects undertaken by developers. Finally, if LIHTC 

developers receive additional subsidies from non-LIHTC sources, there may be additional 

restrictions that are imposed by the funding source. For example, if a LIHTC developer 

obtains HOME funding, 20 percent of the HOME units must be affordable to tenants with 

incomes below 50 percent of the MSA median. 

Thus, except for the restrictions noted in the first paragraph, the property owners choose the 

population to target for their development and where to locate properties based on their 

knowledge of the market, their objectives, their response to the incentives provided by 

federal and state policies, and their decision to agree to restrictions attached to any nonLIHTC subsidies obtained. 

In addition,rent in the tax-credit qualifying units canbe no higherthan 30 percentof the income-eligibility 

limit (either 50 or 60 percent of the median, depending on the allocation chosen). 

4-1 
This chapter explores the location of LIHTC properties and the characteristics of the residents 

in the context of the neighborhoods in which the properties are located. It begins with a 

description of the neighborhoods where LIHTC properties are located then investigates the 

extent of income and racial diversity both within properties and relative to the neighborhood. 

The final section investigates the relationship between the residents’ perceptions of their 

neighborhood and income diversity within the LIHTC property. 

4.1 Characteristics of LIHTC Neighborhoods 

One of the important questions about any assisted housing program is the quality of the 

neighborhoodswherethehousingis provided. However,existingresearchon neighborhood 

effects has not reached consensus on what combination of neighborhood characteristics 

matter and how neighborhoods interact with individual and family characteristics to influence 

socio-economic outcomes. Hence, there is no definitive measure of neighborhood quality. In 

a study of the quality of neighborhoods where public housing and other residents of assisted 

housing live, Newman and Schnare (1997)* examined the quality of the housing stock, 

socioeconomic status, racial diversity, and the concentration of assisted housing in the 

neighborhood. We examine similar measures of neighborhood quality for the LIHTC 

properties represented by this study, except we did not collect data on the concentration of 

assisted housing and we use homeownership rates and the percent of vacant housing units as 

a proxy for the quality of housing stock rather than the median gross rent in the census tract.3 

The results are presented in Exhibit 4-l. 

LIHTC properties in the five study MSAs are in neighborhoods dominated by rental units 

according to the 1990 U.S. Census. In three-fourths of the neighborhoods, a majority of the 

units arerentalunits, andconsistentwith the natureof rentalunits, a majority of residents 

have lived in the neighborhood less than five years. In addition, in nearly one-fifth of the 

neighborhoods more than 20 percent of the units are vacant. For-profit sponsored properties 

appear to be more likely to be located in more stable neighborhoods (lower turnover) than 

nonprofit properties. A much higher proportion of for-profit propertiesthan nonprofit 

properties are in majority homeownership neighborhoods (42 percent versus 8 percent) and 

neighborhoods where a majority of residents have lived in the neighborhood for over five 

years (3 1 percent versus 17 percent). 

2 Newman, SandraJ. and Ann B. Schnare. 1997. “And a Suitable Living Environment: The Failure of 

HousingProgramsto Deliver on NeighborhoodQuality.” Housing Policy Debate, 8(4), 703-74 1. 

3 Their publishedresultsarenot comparableto the resultsin this report. Newman and Schnare(1997)usea 

nation-widedatasetof assistedhousingandusehousingunits asthe unit of analyses,whereasthis report is 

basedon LIHTC propertiesin five MSAs andthis chapterusestheproperty asthe unit of analysis. 

4-2 
Exhibit 4-l 

Characteristics of LIHTC Neighborhoods 

Neighborhood Characteristic Nonprofit 

Sponsor 

(n=22) 

Percent owner-occupied housing* 

O-19% 31% 

20-50% 61% 

5 1% or higher 8% 

Percent of vacant housing units 

O-9% 49% 

1O-20% 30% 

2 1% or higher 21% 

Residents who have lived in neighborhood five 

years or longer 

O-50% 82% 

5 1% or higher 18% 

Median income relative to MSA median 

O-29% 13% 

30-50% 54% 

51% or higher 33% 

Poverty rate* 

Low (O-9%) 0% 

Moderate (1O-29%) 45% 

High (30% or higher) 55% 

Location* 

Central city 86% 

Suburbs 14% 

Percent minority* 

O-20% 0% 

21-79% 39% 

80-100% 61% 

Neighborhood type 

Low-poverty, white, suburban 0% 

Other suburban neighborhoods 14% 

Low/moderate-poverty, city, minority (> 40%) 29% 

Low/ moderate-poverty, city, white 9% 

High-poverty, city, minority 48% 

For-Profit Total 

Sponsor (n=39) 

(n=17) 

22% 27% 

36% 49% 

42% 24% 

51% 50% 

35% 32% 

14% 18% 

69% 76% 

31% 24% 

7% 13% 

22% 37% 

71% 50% 

31% 14% 

34% 40% 

35% 46% 

56% 72% 

44% 28% 

26% 12% 

39% 39% 

35% 49% 

28% 13% 

17% 15% 

15% 23% 

11% 10% 

30% 40% 

median income; poverty rate; location; race/ethnicity)andHUD data(1998: MSA medianincome). 

Notes: Estimatesweighted to reflect all eligible LIHTC propertiesin 5 study MSAs. 

*Indicates significant difference between nonprofit and for-profit properties at the 10 percent significance level (see 

Appendix A for a description of the significancetestsperformed). 

4-3 
LIHTC neighborhoods are evenly split between very-low income and more moderate-income 

neighborhoods. Approximately one-half of the neighborhoods have a median income below 

50 percent of the MSA median and just under half were considered high-poverty 

neighborhoods (poverty-rate of 30 percent or higher) based on the 1990 census.4 In terms of 

sponsor-type, nonprofit properties are located in substantially poorer neighborhoods than forprofit properties. Over half (55 percent) the nonprofit properties are located in high-poverty 

neighborhoods compared to one-third (35 percent) of for-profit properties. 

LIHTC properties tend to be located in city (rather than suburban) neighborhoods and in 

neighborhoods where a majority of the residents are minorities. Nearly three-fourths of the 

LIHTC properties (72 percent) are located in the city and about half the properties (49 

percent) are in neighborhoods where the population is at least 80 percent minority. Nonprofit 

properties are significantly more likely than for-profit properties to be in city neighborhoods 

(86 percent versus 56 percent) and to be in predominately minority neighborhoods (61 

percent versus 35 percent). 

The bottom panel of Exhibit 4-l combines the poverty-rate, city/suburban location, and the 

racial composition to describe the LIHTC neighborhoods. As can be seen in the exhibit, 

high-poverty, city neighborhoods with at least 40 percent minorities are the most common 

neighborhood type (40 percent) followed by similar neighborhoods except they are 

low/moderate poverty-rate (23 percent). Two patterns stand out by sponsor-type. First, 28 

percent of for-profit properties are in low-poverty, white, suburban neighborhoods, but no 

nonprofits are located in such neighborhoods. A majority of these for-profit suburban 

properties serve primarily elderly residents. Second, 48 percent of the nonprofit properties 

are in high-poverty, city neighborhoods with at least 40 percent minorities compared to only 

30 percent of for-profit properties. 

4.2 Income Mixing within LIHTC Properties and Compared to 

Neighborhoods 

Several recent housing policy initiatives have been aimed at reducing the concentration of 

very poor households. The impetus for these policies is the consensus among policy makers 

and scholars that high concentrations of very low-income households in large developments 

and/or neighborhoods leads to negative social and behavioral outcomes. This argument is 

4 In 1989 (the year for which annual income data is collected as part of the 1990 census), 10.4 percent of 

U.S. families had income below the poverty line. By 1998, the poverty rate of families had declined to 

10.0 percent. (U.S. Census Bureau website) 

4-4 
advanced,for example, by Wilson (1987) in his book The Truly Disadvantaged.5 Wilson 

attributes the rise of inner-city neighborhoods characterized by high rates of welfare 

dependency, weak labor force attachment, and crime to the increasing isolation of poor 

communities from the middle- and working-class role models.6 

As described earlier, the federal eligibility rules for the LIHTC program do not address the 

issue of whether or not the LIHTC properties will be mixed-income properties or contribute 

to neighborhood income diversity. Instead, it is left up to the individual developers and the 

state agencies overseeing the program. In this section, we investigate the resident income 

levels and extent of income diversity within LIHTC properties and compare the income 

levels of LIHTC residents to income levels of residents in the surrounding neighborhood. 

Income Level of LIHTC Residents 

As noted earlier, LIHTC properties can have both qualifying units (which must be rented to 

families under 50 or 60 percent of the MSA median income) and market rate units (which 

have no restrictions on who can rent them). If all of a property’s units are qualified units, 

then we know that all of the families will have incomes below 60 percent of the MSA median 

income.’ The top panel of Exhibit 4-2 shows the percent of qualified units in LIHTC 

properties. This is where the investigation of income levels and income-mixing of residents 

in LIHTC properties begins. Most of the LIHTC properties (82 percent) consist entirely of 

qualified units. Furthermore, even among properties that have some market-rate units, most 

still have more than 80 percent qualified units. Only 4 percent of all the properties were 

predominately market-rate (two Oakland properties). Overall, these results indicate that 

LIHTC properties serve almost exclusively very low-income families. 

The next two panels of Exhibit 4-2 show the proportion of extremely low-income families 

(income below 30 percent of the MSA median) and the poverty rate at LIHTC properties. 

Wilson, William Julius. 1987. The Truly Disadvantaged: The Inner City, the Underclass, and Public 

Policy. Chicago: University of Chicago Press 

The research in this field has very little empirical evidence supporting or refuting the purported benefits of 

mixed-income housing and neighborhoods. For a discussion of the empirical research on mixed-income 

housing and poverty deconcentration strategies, see Schwartz and Tajbakhsh. 1997. “Mixed-Income 

Housing; Unanswered Questions.” Cityscape, 3(2): pp. 71-91; and Popkin, Buron, and Levy. 1999. 

“Mixed Income and Dispersal Strategies: Will They Help the Most Distressed Public Housing Residents?” 

manuscript. 

All but two properties in the study sample chose the 40160 regime (set aside a minimum of 40 percent of 

their units for households below 60 percent of the MSA median). Both properties that chose the 20150 

regime are for-profits in Oakland. See Chapter 2 for a description of the study properties. 

4-5 
Exhibit 4-2 

Distribution of Income and Related Characteristics at LIHTC Properties 

Property Characteristics Nonprofit For-Profit Total 

Sponsor Sponsor (n=39) 

(n=22) (n=17) 

Percent of tax-credit qualifying units in property 

20-25% 0% 8% 4% 

26-79% 0% 0% 0% 

80-90% 11% 4% 8% 

9 l-99% 8% 7% 7% 

100% 82% 81% 82% 

Percent of households in property with extremelylow income (C 30% of the MSA median)* 

40 % 6% 34% 20% 

1O-29% 40% 19% 30% 

30-49% 37% 19% 28% 

50% or higher 16% 28% 22% 

Poverty rate in property* 

Low poverty (<lo% of households) 14% 42% 27% 

Moderate poverty (lo-29%of households) 58% 8% 33% 

High poverty (30-49% of households) 18% 8% 13% 

High poverty (>49% of households) 10% 43% 26% 

Percent of households in property with at least 

one adult working for pay 

O-49% 4% 30% 17% 

so-74% 39% 24% 32% 

75% or higher 57% 46% 51% 

Percent of households in property receiving 

iources: 

Section 8 assistance 

0% 20% 28% 24% 

l-33% 62% 29% 47% 

34-66% 3% 22% 12% 

67-99% 4% 0% 2% 

100% . . . . ^ ,.A,.,. 

10% 

..C , .. n 

21% 

.. n 

15% 

1 . . Administrative data trOm Site managers or owners (1 YYY: quallrlea umts; section 6 assistance, most reslaenl 

income data) and LIHTC Resident Survey (1999: adults working for pay; resident incomes for properties with 

predominately market-rate units and several properties where administrative records with resident incomes were not 

provided). 

Notes: Estimates weighted to reflect eligible LIHTC properties in 5 study MSAs. 

Two nonprofit properties did not have enough reported data (either administrative data or Resident survey data) to 

determine the income distribution of residents and hence are missing for median income and poverty rate calculations. 

*Indicates significant difference between nonprofit and for-profit properties at the 10 percent significance level (see 

Appendix A for a description of the significance tests performed). 

4-6 
Most properties have at least a sizeable share of extremely low-income residents, but only 22 

percent have a majority of extremely low-income residents. On the other extreme, 20 percent 

of the properties have fewer than 10 percent extremely low-income residents. The 

comparison of residents’ income relative to the national poverty line shows that properties 

are fairly evenly divided among properties with low (27 percent), moderate (33 percent), and 

high poverty rates (39 percent). 

There are distinct patterns in the income levels of residents at by sponsor type. For-profit 

properties are more likely than nonprofits to have fewer than 10 percent extremely lowincome residents (34 percent versus 6 percent), but for-profits are also more likely than 

nonprofits to have a majority of extremely low-income residents (28 percent versus 16 

percent). The pattern for poverty rates is similar. Almost all of the for-profit properties have 

either a low internal poverty rate (less than 10 percent of households with income below the 

poverty line) or a very high-poverty rate (50 percent or more of the households with income 

below the poverty line). In contrast, nonprofit properties tend to have moderate poverty rates 

(between 10 and 29 percent of households below the poverty line). 

Most of the LIHTC properties are dominated by working families. At 83 percent of the 

properties, over half of the households had at least one working adult at the time of the 

survey. Nonprofit properties are substantially more likely than for-profit properties to have a 

majority of working households (86 percent versus 58 percent), which is, in part, due to the 

higher share of elderly properties in the for-profit portfolio. Taken together, the above results 

indicate that LIHTC properties are serving the working-poor or near poor population. 

Consistent with the low-incomes of LIHTC households, a substantial portion receive Section 

8 assistance - 37 percent in all (see Exhibit 3-6). However, as can be seen in the bottom row 

of Exhibit 4-2, most LIHTC properties have a low proportion of residents receiving Section 8 

assistance. At 71 percent of the properties, less than one-third of the residents receive 

Section 8 assistance, including one-quarter of the properties with no Section 8 recipients. On 

the other extreme, at 15 percent of the properties, all the residents are Section 8 recipients. 

All but one of the properties in this category are 100 percent Project-Based Section 8 

developments. At the other 100 percent Section 8 property, 75 percent of the households live 

in Project-Based Section 8 units and the other residents are Section 8 Certificate and Voucher 

holders. For-profit properties tend to have a higher share of residents receiving Section 8 

assistance than nonprofits. 

Income Mixing within LIHTC Properties 

LIHTC properties have a low-incidence of non-qualifying units, that is very few families 

have incomes above 60 percent of the MSA median. This rules out the presence of many 

LIHTC properties with income-diversity ranging from extremely poor residents traditionally 
served by the Public Housing and Section 8 program and higher-income residents above the 

80 percent of area median eligibility-cutoff for assisted housing. Any diversity in family 

income levels within an LIHTC property has to be primarily among families below 60 

percent of the area median income level. However, even within that portion of the income 

distribution, family income levels can vary considerably and properties can serve a diverse 

income mix of residents. 

In the discussion that follows, we present four definitions of a mixed-income property that 

are applicable to LIHTC properties. These definitions are patterned after (but not exactly the 

same as) the definitions used by Khadduri and Martin (1997)8 to define properties as mixedincome in their study of privately-owned rental housing projects subsidized by HUD 

programs. 

Mixed-income measures are based on qualifying fraction, income relative to the MSA 

median, and income relative to the poverty line. Each of the definitions requires that at least 

20 percent of the property’s households fall into the extremes: a very low-income group and 

a relatively higher-income group. The choice of a 20 percent minimum in each group, rather 

than either a higher or lower threshold, is based on several factors. First, several states have 

used the 20 percent cutoff in programs designed to promote mixed-income development.’ 

Second, it is consistent with recent research categorizing subsidized housing as either mixedincome or not.” Third, 20 percent is a large enough proportion that it indicates a substantial 

number of a property’s residents fall into this category, not just a unique family or two. 

See Khadduri and Martin. 1997. “Mixed-Income Housing in the HUD Multifamily Stock.” Cityscape, 

3(2), pp. 33-69. 

The Massachusetts Housing Finance Agency (MHFA) has an explicit goal of promoting economic 

integration. Since 1990 MHFA has structured most of its mixed-income projects to include 20 percent 

low-income units and 80 percent market rate units (See page 90 of Schwartz and Tajbakhsh. 1997. 

“Mixed-Income Housing: Unanswered Questions.” Cityscclpe, 3(3), pp. 71-92). Under the New Jersey 

Fair Housing Act of 1985 many municipalities provide density bonuses for mixed-income housing that set 

aside at least 20 percent of the units for low- and moderate-income households. Similarly, the 

Montgomery, Maryland’s Moderately Priced Dwelling Unit program requires that all large (50 or more 

units) residential developments make 12.5 to 15 percent of the units affordable to low- and moderateincome households and provides density bonuses for properties that set aside more than 12.5 percent of 

their units for low- and moderate-income households. In the New York City Vacant Cluster Program, the 

city finances rehabilitation of large clusters of tax-foreclosed building in low-income neighborhoods for 

formerly homeless and low-income households, but reserves 25 percent of the units for moderate-income 

households (50 to 80 percent of area median). See Schwartz and Tajbakhsh (1997) for more details on 

state and local programs encouraging mixed-income housing. 

Khadduri and Martin’s (1997) “Broad Range of Incomes” definition of a mixed-income property required 

that 20 percent of the households earn below $10,000 and 20 percent earn above $20,000. 

4-8 
The percent of properties that are categorizedas mixed-income using alternative definitions 

are shown in Exhibit 4-3. The first definition of a mixed-income property is that at least 20 

percent of the residents are in subsidized units (either tax-credit qualified or otherwise 

subsidized) and 20 percent are in unsubsidized units with no rental assistance.” As noted 

earlier, almost all the properties have more than 80 percent tax-credit qualifying units, thus 

only 4 percent of all properties are categorized as mixed-income by this definition. 

Exhibit 4-3 

Percent of Properties Meeting Alternative Definitions of Mixed-Income Property 

Definition of Mixed-Income Property Total 

(n=37) 

ources: 

At least 20% of units are market rate and 20 percent 0% 8% 4% 

are tax-credit qualifying units* 

At least 20% of households have income below 30 25% 19% 22% 

percent of MSA median income and 20% have 

income above 50 percent of MSA median income. 

At least 20% of families have income below the 40% 8% 24% 

poverty line and 20% have income 1.85 times the 

poverty line.* 

At least 20% of families have income below the 70% 39% 55% 

poverty line and 20% have income 1.30 times the 

poverty line.* 

Meet at least one of the first 3 definitions of a 55% 23% 40% 

mixed-income property. * 

^ . ..-. Admuustrative data from site manager/owner (1999: tax credit qualifymg umts and most income data) and 

LIHTC Resident Survey (I 999: resident incomes for properties with predominately market-rate units and several properties 

where administrative records with resident incomes was not provided). 

Notes: Estimates weighted to reflect eligible LIHTC properties in 5 study MSAs. 

Two nonprofit properties did not have enough reported data to determine the income distribution of residents. 

*Indicates significant difference between nonprofit and for-profit properties at the 10 percent significance level (see 

Appendix A for a description of the significance tests performed). 

The second definition of a mixed-income property requires that 20 percent of the families 

have incomes below 30 percent of the MSA median and 20 percent have incomes above 50 

” Khadduri and Martin (1997) used a “Partly Subsidized” definition of mixed-income housing which 

categorized a subsidized development as mixed-income if it had at least 10 market-rate units. Given the 

various sizesof the LIHTC developments,we use a definition requiring 20 percentmarket-rateunits for 

our definition. 

4-9 
percent of the MSA median. The 30 percent of the median ceiling for low-income families 

was selected because it is the cutoff for housing programs targeting extremely low-income 

families (e.g., the 1998 housing bill established set asides in both the Public Housing and 

Section 8 program for families below 30 percent of the median). The 50 percent of the 

median income floor for high-income families was chosen because it was the cutoff for very 

low income families that were given federal preferences for public housing through the mid1990s. Hence families above 50 percent of the MSA median are eligible for housing 

assistance but were not given preferences in the past nor in the current federal law. In dollar 

terms, the income difference between a family of three at exactly 30 percent and exactly 50 

percent of the MSA median ranges from $7,632 in Miami to $11,826 in Oakland.” Overall, 

22 percent of the LIHTC properties meet the “under 30/ over 50 median income” definition 

of mixed-income housing. Using this definition, the proportion of mixed-income properties 

does not vary much by sponsor type (25 percent of nonprofits versus 19 percent of forprofits). 

The third and fourth definitions are based on family’s income relative to the poverty line. In 

both cases, households with income levels below the poverty line are defined as poor. The 

non-poor category is defined as families above 185 percent of the poverty line for the third 

definition and above 130 percent of the poverty line for the fourth definition. These cutoffs 

were chosen based on cutoffs for food assistance programs: 130 percent of the poverty line is 

the eligibility cutoff for the Food Stamp program and 185 percent of the poverty line is the 

cutoff for the free and reduced-price school lunch program. Both cutoffs reflect policy 

makers’ decisions on the income levels below which families are most in need of assistance. 

For a family of three, the poverty threshold in 1998 is $13,003. Thus a family at exactly 185 

percent of the poverty line would have income of $24,056. Therefore, a three-person family 

in the non-poor group has a minimum of $11,053 higher income than a three-person family 

with income below the poverty line. The percent of LIHTC properties defined as mixedincome using the “under lOO/ over 185 poverty line” definition is 24 percent, as shown in 

Exhibit 4-3. Nonprofit properties (40 percent) are significantly more likely to be mixed 

income than for-profit properties (8 percent).13 

‘* For a family of three in 1998, the difference between 30 and 50 percent of the median is $7,632 in Kansas 

City, $10,026 in Miami, $10,368 in Milwaukee, $11,286 in Boston, and $11,826 in Oakland. 

I3 This measure is conceptually similar to Khadduri and Martin’s (1997) “Broad Range of Incomes” 

definition of mixed-income housing which used dollar-income categories roughly based on the poverty 

line. A mixed-income property was defined as a property where 20 percent of the families have income 

below $10,000 and 20 percent have income above $20,000. Using their definition, we found 19 percent of 

properties were defined as mixed-income (not shown). 

4-10 
The fourth alternative for defining a property as mixed-income also relies on the poverty line 

as a cutoff for the poor group, but uses 130 percent of the poverty line as the floor for the 

non-poor group. This alternative is considerably more liberal than the other definitions. That 

is, the required difference in income levels between the low- and high-income groups is 

considerably smaller than the other definitions-only $3,900. Overall, 55 percent of the 

properties are categorized as mixed-income with the “under 1001 over 130 poverty line” 

definition. As with the previous definition, nonprofit properties (70 percent) are significantly 

more likely than for-profit properties to be mixed-income (39 percent). Since this definition 

of mixed-income is more liberal than the prior definitions, the result is that it includes most 

of the properties defined as mixed-income from the three prior definitions plus a substantial 

number of additional properties. 

Overall, even though most family incomes are below 60 percent of the MSA median these 

results suggest a sizeable proportion of LIHTC properties in the five study MSAs have 

residents with a diverse mixture of incomes. The more conservative definitions suggest 

about one-fifth of the properties are mixed-income while the more liberal definition suggests 

more than half the properties are mixed-income. For analysis of mixed-income properties in 

the next section, we define a property as mixed-income if meets any one of the first three 

definitions of a mixed-income property (i.e., definitions based on market/qualified-units, 

under 30/ over 50 median income, or under lOO/ over 185 poverty-line). As can be seen in 

the bottom panel of Exhibit 4-3, 40 percent of the LIHTC properties are classified as mixedincome with this combined definition. Nonprofit properties (55 percent) are significantly 

more likely than for-profits (23 percent) to be defined as mixed-income using the combined 

definition. 

Factors Associated with Mixed-Income Properties 

This section explores the neighborhood and property characteristics that are associated with 

properties with a diverse mix of incomes. For this analysis we classify a property as mixedincome if it meets any one of the first three criteria for a mixed-income property as discussed 

in the previous section (see Exhibit 4-3). 

Exhibit 4-4 shows the percent of properties in different groups that are mixed-income for 

groups defined by key characteristics of the property (sponsor type, primary population 

served, and the proportion of residents on Section 8) and neighborhood (city or suburban 

location, poverty rate, and homeownership rate). The exhibit presents the results in 

descending order of the proportion of properties in the group that are mixed-income (shown 

in right most column) so that the group with the highestproportion of mixed-income 

properties is in the top row and the group with the lowest proportion is in the bottom row. 

Note that the groups are not mutually exclusive and each property is in multiple categories. 

4-11 
Overall, 40 percent of properties are classified as mixed-income. The two groups with the 

highest proportion of mixed-income properties are properties sponsored by nonprofit owners 

(55 percent) and properties located in moderate-poverty neighborhoods (54 percent). These 

are the only two groups to have a majority of mixed-income properties. However, almost 

half of the properties in neighborhoods with less than a 50 percent homeownership rate and 

properties located in the city are also classified as mixed-income. On the other end of the 

spectrum, properties that are sponsored by for-profit owners, or serve primarily elderly 

residents, or are in suburban areas, or in low-poverty neighborhoods, or in neighborhoods 

with over a 50 percent homeownership rate are the least likely to be mixed-income: all have 

less than 25 percent mixed-income properties. The remaining groups, including whether or 

not over 20 percent of the residents receive Section 8 assistance, have about the same 

percentage of mixed-income properties as the overall average of 40 percent. 

The above results control for one factor at a time (e.g., nonprofit or for-profit sponsored). 

We also estimated a multivariate model with all (or subsets) of the variables in Exhibit 4-4 to 

try to distinguish the neighborhood and property factors that were significantly correlated 

with mixed-income properties when controlling for other factors. In general, moderatepoverty neighborhood was the only variable that was usually significantly correlated (in 

positive direction) with mixed-income properties across model specifications. Properties 

with nonprofit sponsors or with over 20 percent Section 8 residents also were consistently 

positively associated with the likelihood of a property being mixed-income, but the estimates 

were usually not significantly different from zero. The coefficient estimates on most of the 

other variables varied considerably across models and were rarely significant. Since the 

results were very sensitive to minor changes in the model specification, they are not reported 

here. With only 39 property-observations, the factors associated with mixed-income 

properties when controlling for other relevant factors are difficult, if not impossible, to 

discern. 

Income of LIHTC Residents Relative to Neighborhood 

Earlier results have shown that residents of LIHTC properties and the neighborhood in which 

they are located tend be low-income, but there is substantial variation across properties and 

across neighborhoods. In this section, we explore how the incomes of LIHTC residents 

compare to the income of residents in the surrounding neighborhood (where neighborhood is 

defined as the Census Tract where the property is located). 

4-12 
Exhibit 4-4 

Percent of Properties Defined as Mixed-Income by Various Characteristics 

Subpopulation based on Property Characteristic Unweighted Percent of these 

Sample Size properties that 

are mixedincome 

Nonprofit property sponsor 20 55% 

Moderate-poverty neighborhood (1O-29% poverty rate) 12 54% 

Under 50% homeownership rate in neighborhood 

Central city location 

Property serves predominately families 

More than 20% of residents receive Section 8 

All Properties 

Less than 20% of residents receive Section 8 

High-poverty neighborhood (>29% poverty rate) 

For-profit property sponsor 

Property serves predominately elderly 

Suburbanlocation 

Low-poverty neighborhood (O-9% poverty rate) 

Over 50% homeownershiprate in neighborhood 

28 49% 

26 49% 

32 42% 

14 42% 

37 40% 

23 38% 

18 37% 

17 23% 

5 17% 

11 14% 

7 12% 

9 8% 

Sources: Census(1990; censustract median income inflated to $1999), LIHTC Resident Survey (1999) and property tiles 

(1999) for resident income. 

Notes: Property is defined as mixed-income if it meets any one of the following criteria: (1) At least 20% of the units are 

market-rate and 20% are tax-credit qualified units; or (2) At least 20 percent of the household incomes are below 30% of the 

MSA median and 20 percent are above 50% of the MSA median; or (3) At least 20 percent of the households have income 

below the poverty line and 20 percent have income above 185% of the poverty line. Two nonprofit properties did not have 

enough reported data to determine the income distribution of residents. Estimates weighted to reflect eligible LIHTC 

properties in 5 study MSAs. 

As shown in Exhibit 4-5, LIHTC households on average have substantially lower incomes 

than neighborhood residents. At 72 percent of the LIHTC properties, the median income of 

LIHTC residents is lower than the median income of neighborhood residents. By contrast, 

only 10 percent of the LIHTC properties have a higher household median income than the 

neighborhood and 19 percent have about the same level (within 10 percent). All of the 

properties with higher median income than the neighborhood are located in extremely poor 

neighborhoodswherethemedianincomeof the neighborhoodis lessthan30 percentof the 

area median. Hence, these properties are providing housing for families who are raising the 

income level of the neighborhood which may have a positive influence on the neighborhood. 

4-13 
Exhibit 4-5 

Income Levels at LIHTC Property Compared to Neighborhood 

Income Measure of Nonprofit For-Profit Total 

Property and Neighborhood being Compared Sponsored Sponsored (n=37) 

Median income* 

Lower property income 

About the same (within 10 percent) 

Higher property income 

Poverty rate* 

Higher poverty rate in property 

About the same(within 10percentagepoints) 

Lowerpovertyratein property 

Poverty rate classification (low: O-9%; moderate: 

lo-29%; high: 30% or higher)* 

High-poverty neighborhood 

High-poverty property 

Low/moderate-poverty property 

Moderate-poverty neighborhood 

High-poverty property 

Low/moderate-poverty property ’ 

Low-poverty neighborhood 

High-poverty property 

Low/moderate-poverty property 

(n=ZO) (n=17) 

55% 89% 72% 

26% 11% 19% 

19% 0% 10% 

31% 55% 42% 

17% 31% 24% 

52% 14% 33% 

12% 28% 20% 

43% 7% 27% 

13% 23% 19% 

32% 11% 19% 

0% 0% 0% 

0% 31% 15% 

‘ources: Census(1990: censustract poverty rate in 1989and median income inflated to %1998),Administrative data from 

site manager/owner(1999: most residentincome), and LIHTC ResidentSurvey (1999: residentincomesfor propertieswith 

predominately market-rate units and several properties where administrative records with resident incomes was not 

provided). 

Notes: Estimates weighted to reflect eligible LIHTC properties in 5 study MSAs. 

Two nonprofit properties did not have enough reported data to determine the income distribution of residents 

*Indicates significant difference between nonprofit and for-profit properties at the 10 percent significance level (see 

Appendix A for a description of the significance tests performed). 

The properties with lower income than the neighborhood are evenly split between 

neighborhoods with median income less than 30 percent of the area median, income between 

30 and 50 percent of the area median, and income above 50 percent of the area median (not 

shown in exhibit). Hence, in approximately one-third of the situations where the property 

income is lower than the neighborhood, the LIHTC properties are providing an opportunity 

4-14 
for lower-income families to live in relatively higher-income neighborhoods which may 

increase their own economic opportunities. 

A comparison of poverty rates also indicates that LIHTC residents tend to be economically 

worse off than neighborhood residents,but the tendency is not as strong as it appearsto be 

when comparing median incomes. While 42 percent of the properties have a higher poverty 

rate than the surrounding neighborhood, 33 percent have a lower rate and 24 percent have 

about the same rate (within 10 percentage points). 

There are distinct differences based on the sponsor-type of the property. Residents of 

nonprofit properties tend to have higher incomes than residents of their neighborhood, 

whereas the opposite holds true for for-profit properties. While 45 percent of nonprofit 

properties have about the same or higher median income levels than the neighborhood, only 

11 percent of for-profit properties do. Furthermore, 55 percent of nonprofit properties have 

lower poverty rates than the neighborhood, whereas only 31 percent of the for-profit 

properties have lower poverty rates. This result may be due to the tendency of for-profit 

propertiesto be in higher-incomeneighborhoodsthan nonprofit properties (seeExhibit 4-l). 

The bottom panel of Exhibit 4-5 shows the distribution of properties (basedon poverty-rate 

classification) across high-, moderate-, and low-poverty neighborhoods.‘4 The results 

indicate that 20 percent of LIHTC properties have high, internal poverty rates and are located 

in high-poverty neighborhoods and that 27 percent of properties have low/moderate, internal 

poverty rates, but are located in high-poverty neighborhoods. Only 15 percent of the 

propertieshave low/moderatepoverty ratesand are locatedin low-poverty neighborhoods. 

However, the most interesting results are the patterns by sponsor type. Nonprofit properties 

tend to have lower poverty rates than the neighborhood, but are exclusively located in 

moderate- or high-poverty neighborhoods. On the other hand, for-profit properties are evenly 

spreadout acrossneighborhood types classifiedby poverty rate (seeExhibit 4-l), but tend to 

have higher poverty rates than the neighborhood. For example, 43 percent of nonprofit 

properties have low/moderate poverty rates, but are located in a high-poverty neighborhood, 

whereas only 12 percent are both high-poverty properties and neighborhoods. In contrast, 

I4 Neighborhoods and properties are classified by poverty rate as follows: O-9 percent is low-poverty; lo-29 

percent is moderate poverty; 30 percent or higher is high-poverty. The choice of these particular categories 

was guided by definitions of low- and high-poverty neighborhoods used in Section 8 mobility counseling 

programs. The Moving to Opportunity Demonstration (MTO) defined a low-poverty neighborhood as one 

with a poverty rate below 10 percent, thus we chose this definition for low-poverty neighborhoods. In the 

Regional Opportunity Counseling (ROC) program, each PHA used their own defkition of low-poverty 

and high-povertyneighborhoods.In the 15 ROC sites,the definition of high-povertyneighborhoods 

ranged from neighborhoods with poverty rates above 10 percent to only neighborhoods with poverty rates 

above 40 percent, but a majority of the definitions were between 20 and 30 percent. Hence, we chose to 

define a high-poverty neighborhood as one with a poverty rate above 30 percent. 

4-15 
only 7 percent of for-profit properties are low/moderate poverty properties in high-poverty 

neighborhoods, but 28 percent are both high-poverty properties and neighborhoods. So even 

though a smaller proportion of for-profits than nonprofits (35 percent versus 55 percent) are 

located in high-poverty neighborhoods, a higher proportion of all for-profits are high-poverty 

properties in high-poverty neighborhoods (28 percent versus 12 percent). A second 

prominent difference is that 31 percent of for-profit properties are low/moderate poverty 

properties in low-poverty neighborhoods, whereas none of the nonprofit properties in our 

sample are even located in low-poverty neighborhoods. 

The distinctly different patterns by sponsor-type may be related to the differences in the 

development objectives of nonprofit and for-profit properties. As discussed later in this 

report (Chapter 6), owners of nonprofit properties tend to report community revitalization as 

the objective of their LIHTC development efforts whereas, for-profit owners more frequently 

report traditional real estate objectives (e.g., profit or interesting development opportunity) as 

the objective of their LIHTC development efforts. Given the community revitalization goals 

of nonprofit owners, it makes sense that they have a propensity to develop properties in 

moderate- or high-poverty neighborhoods (where revitalization is needed) and serve a higherincome clientele than the surrounding neighborhoods (to raise the income level of the 

neighborhood). At the same time, given the traditional real estate objectives of the for-profit 

owners, it makes sense that they would be more spread out across neighborhood types to take 

advantage of profit opportunities wherever they exist. It is not evident why for-profit 

properties tend to have higher-poverty rates than the neighborhood in moderate- and highpoverty neighborhoods. 

4.3 Racial/Ethnic Diversity within Properties and Compared to 

Neighborhoods 

In Chapter 2, we reported that approximately 60 percent of LIHTC residents were AfricanAmerican, 20 percent were Hispanic, and 20 percent were white. However, these results do 

not indicate whether or not African-Americans, Hispanics, and whites live together in the 

same LIHTC properties or whether properties tend to be dominated by one race/ethnic group. 

In this section, we investigate the racial/ethnic composition of LIHTC properties then 

compare it to the composition of the neighborhood. 

Racial/Ethnic Diversity within LIHTC Properties 

The proportion of the LIHTC property’s residents who are minorities is presented in the top 

panel of Exhibit 4-6. Given the high proportion of minority residents in our study properties 

that was mentioned earlier, it is not surprising that over half of the properties (52 percent) 

4-16 
have entirely minority residents. Another 26 percent of the properties have between 70 to 99 

percent minority residents, thus 78 percent of the properties have more than 70 percent 

minority residents. On the other extreme, 5 percent of the properties have only white (nonHispanic) residents. The racial composition of properties differs significantly by sponsor 

type. All of the nonprofit properties have more than 70 percent minorities whereas only 52 

percent of the for-profit properties show this high concentration of minorities. However, 

while none of the nonprofit properties have more than 70 percent white residents, 29 percent 

of for-profit properties show this high concentration of whites. 

The bottom half of Exhibit 4-6 presents the categorization of properties by whether they are 

racially/ethnically diverse or dominated by a particular race/ethnic group. Properties are 

classified as diverse if at least 20 percent of the residents are from two different race/ethnic 

groups. Consistent with the results just discussed, we found few properties with a mixture of 

whites and minorities (defined as at least 20 percent whites and 20 percent minorities in the 

property). Overall, only 9 percent of the properties have a mixture of whites and minorities. 

However, a substantial proportion of the properties had a diverse mix of minority groups 

(defined as at least 20 percent in two different minority groups).15 Over forty percent of the 

properties are mixed-minority, usually because they have a substantial proportion of AfricanAmericans and Hispanics. Residents at one-half of the properties are predominately one 

race/ethnic group. At 36 percent of the properties, over 80 percent of the residents are 

minorities (usually African-American) while at 14 percent of the properties, over 80 percent 

of the residents are white. 

The pattern for race/ethnic diversity varies significantly by sponsor type. Approximately 

one-half of both nonprofit (53 percent) and for-profit (46 percent) properties are 

predominately one race/ethnic group, but in these properties, nonprofits are predominately 

minority whereas for-profit properties tend to be predominately white. None of the nonprofit 

properties are predominately white compared to 30 percent of for-profit properties. On the 

other hand, 53 percent of the nonprofit properties are predominately minority whereas this is 

true for only 16 percent of for-profit properties. The racially/ethnically diverse nonprofit 

properties (47 percent) are all mixed-minority while 36 percent of for-profits are mixedminority and an additional 19 percent are a mixture of whites and minorities. 

The mixed-minority definition is based on the definition used by Khadduri and Martin (1997) in their 

analysis of the race/ethnic diversity of HUD-subsidized private developments. The minority groups we use 

are African-American (non-Hispanic), Hispanic, and Asian/Native American/other non-whites. 

4-17 
Exhibit 4-6 

Racial/Ethnic Diversity in LIHTC Properties 

Property Characteristic Nonprofit For-Profit Total 

Percent minority* 

0% 

l-29% 

30-69% 

70-99% 

100% 

Racial/Ethnic diversity* 

Mixture of whites and minorities (at least 

20% of each) 

Mixed-Minority (at least20% from two 

different minority groups) 

Predominately minority (over 80%) 

Predominantly white (over 80%) 

Sponsor Sponsor (n=39) 

(n=22) (n=17) 

0% 11% 5% 

0% 19% 9% 

0% 19% 9% 

41% 9% 26% 

59% 43% 52% 

0% 19% 9% 

47% 36% 42% 

53% 16% 36% 

0% 30% 14% . ^ ources: LIHTC Resident Survey (1999: primary source for racelethmcity) and admmistrative data trom managers/owners 

(1999: racial/ethnic composition for a few properties) 

Notes: Estimates weighted to reflect eligible LIHTC properties in 5 study MSAs. 

*Indicates significant difference between residents of nonprofit and for-profit properties at the IO percent significance level 

(see Appendix A for a description of the significance tests performed). 

Racial/Ethnic Composition of LIHTC Property Compared to Neighborhood 

Next, we compare the racial/ethnic composition of LIHTC properties to the composition of 

the neighborhood. First, we compare the percent of minorities in each property to the 

neighborhood, then we categorize properties based on a combined measure relating property 

and neighborhoodrace/ethniccomposition. 

As shown in Exhibit 4-7, properties are fairly evenly split between those that have a 

proportion of minorities similar to their neighborhood and those that have a higher proportion 
of minorities than their neighborhood.16 Very few properties have a lower proportion of 

minorities than their neighborhood. Overall, 44 percent of properties have about the same 

race/ethnic composition of their neighborhood while 

minorities and only 5 percent have a lower proportion 

Exhibit 4-7 

Racial/Ethnic Diversity in LIHTC Properties 

Percent of minorities living in project compared to 

neighborhood* 

Lower proportion of minorities in property 

About the same (within 10 percentage points) 

‘Higher proportion of minorities in property 

Racial/Ethnic composition in property compared to 

neighborhood* 

Majority of whites in both the property and 

neighborhood 

Majority of minorities in both the property and the 

neighborhood 

Majority of whites in property, but majority of 

minorities in the neighborhood 

Majority of minorities in property, but majority of 

whites in neighborhood 

ources: LIHTC Resident Survey (1999) and/or property files (19 

Census(1990) for racial/ethnic composition of neighborhood. 

51 percent have a higher proportion of 

of minorities. 

Compared to Neighborhoods 

Nonprofit For-Profit Total 

Sponsor Sponsor (n=39) 

(n=22) (n=17) 

0% 11% 5% 

26% 65% 44% 

74% 24% 51% 

0% 37% 17% 

77% 47% 63% 

0% 0% 0% 

23% 17% 20% 

^ . ) for racial composition of property residentsand 

Notes: Estimatesweighted to reflect eligible LIHTC propertiesin 5 study MSAs. 

*Indicates significant difference between nonprofit and for-profit properties at the 10 percent significance level (see 

Appendix A for a description of the significancetestsperformed). 

Properties within plus or minus 10 percentage points of the proportion of minorities in the neighborhood 

were classified as having about the same composition as the neighborhood. 

4-19 
The bottom panel of Exhibit 4-7 combines the race/ethnic composition of the property and 

neighborhood into one of four groups: majority of whites (non-Hispanic) in both; majority,of 

minorities in both; majority of whites in property, but majority of minorities in 

neighborhood; or majority of minorities in property, but majority of whites in neighborhood. 

By this classification system, 20 percent of the properties have a majority of minority 

residents, but are located in a neighborhood with a majority of white residents. These are the 

only properties where a majority of the residents in the property and neighborhood are not the 

same, because none of the properties have a majority of white residents in a minority 

neighborhood. The most common combination is a majority of minority residents in both the 

property and neighborhood (63 percent). 

Not surprisingly, the patterns are significantly different by sponsor type. At 77 percent of 

nonprofit properties, a majority of both the property and neighborhood residents are 

minorities while this is true for only 47 percent of for-profit properties. By contrast, none of 

the nonprofits have a majority of white residents in a white neighborhood, while 37 percent 

of the for-profit properties do. About one-fifth of nonprofit (23 percent) and for-profit 

properties (17 percent) have a majority of minority residents but are in neighborhoods with a 

majority of white residents. 

4.4 Residents’ Satisfaction with their Neighborhood 

An important question addressed by this research is LIHTC residents’ perceptions of the 

quality of their neighborhood and whether they feel a part of the neighborhood. In this 

section we investigate residents’ overall rating of their neighborhood, perceptions of social 

cohesion in the neighborhood, and several indicators of whether residents feel a part of their 

neighborhood. We first compare residents’ responses based on whether they live in a 

nonprofit or for-profit property, then we compare responses based on whether or not they live 

in a mixed-income property. 

Residents perceptions of their neighborhood are mixed. Depending on the measure, between 

one-half and three-fourths rated it favorably and the remainder rated it unfavorably. Under 

one-half of the residents gave their neighborhood an overall rating as good or excellent (46 

percent) and over one-half rated it as fair or poor (54 percent) as can be seen in Exhibit 4-8. 

Residents overall rating of their neighborhood were consistent within properties indicating 

some agreement of whether the LIHTC property was in a “good” or “bad” neighborhood. At 

10 of the 39 properties, over two-thirds of the respondents rated their neighborhood as good 

or excellent, while at 15 of the properties over two-thirds of the respondents rated their 

neighborhood as fair or poor. The residents at the other 13 properties were more evenly 

divided between residents who rated their neighborhood favorably and those who rated it 

negatively. 

4-20 
Exhibit 4-8 

Residents’ Perceptions of their Neighborhood by Sponsor Type 

Overall rating of neighborhood as a place to live* 

Good or Excellent 

Fair or Poor 

Indicators of “social cohesion and trust”: 

Percent of residents who somewhat or strongly 

agree with following statements: 

People in this neighborhood generally get along 

with each other 

People around here are willing to help their 

neighbors 

This is a close-knit neighborhood* 

Whether resident feels a part of or isolated from 

neighborhood* 

Feels part of neighborhood 

Feels isolated from neighborhood 

Somewhere in-between 

Socialize with people in neighborhood who do not 

live in the same building* 

Somewhat or very active in neighborhood* 

ource: LIHTC ResidentSurvey (1999). 

Nonprofit For-Profit 

Sponsored Sponsor Total 

(n=391) (n=441) (n=832) 

41% 50% 46% 

59% 50% 54% 

76% 71% 73% 

68% 62% 65% 

61% 52% 56% 

55% 50% 52% 

13% 19% 17% 

31% 31% 31% 

51% 41% 45% 

50% 39% 44% 

Notes: Estimatesweighted to reflect residentsin eligible LIHTC propertiesin five study MSAs. 

*Indicates significant difference between residents of nonprofit and for-profit properties at the 10 percent significance level 

(seeAppendix A for a description of the significancetestsperformed). 

On measures of “social cohesion and trust”” the overall favorable responses ranged from 56 

percent who reported “This is a close-knit neighborhood” to 73 percent who reported that 

” These measures of social cohesion and trust come from: Sampson, Raudenbush, and Earls. 1997. 

“Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy.” Science,v.277 (August 

15),pp. 9 18-924. 

4-21 
“People in the neighborhood generally get along with each other.” The third measure was in 

the middle: 65 percent somewhat or strongly agreed that “People around here are willing to 

help their neighbors.” 

Nevertheless, most residents do not feel isolated from their neighborhood. Over half (52 

percent) reported feeling part of their neighborhood, while only 17 percent reported feeling 

isolated from their neighborhood and 31 percent reported that neither extreme accurately 

portrayed their situation. Approximately 45 percent of the respondents reported being 

somewhat or very active in their neighborhood (in terms of attending meetings and other 

events) and socializing with people in the neighborhood who do not live in the same 

building. 

Many of the results are significantly different by sponsor type. Residents of for-profit 

properties (50 percent) are more likely than residents of nonprotits (41 percent) to give their 

neighborhoodanoverallratingof goodor excellent.However,on the communitymeasures, 

residents of nonproftts are significantly more likely than residents of for-profits to rate their 

neighborhood favorably. For example, 61 percent of nonprofit residents perceive their 

neighborhood as close-knit, whereas 52 percent of for-profit residents do so. Furthermore, 51 

percent of nonprofit residents report socializing with neighborhood residents while only 41 

percent of for-profit residents report socializing with neighborhood residents. 

One of the concerns about mixed-income properties is that residents may not interact with 

eachother (thus defeatingthe presumedbenefitsof mixed-incomehousing)andmay feel 

isolated from their neighborhood. We explore that issue in this section using the combined 

definition of mixed-income properties described earlier. Specifically, we compare the 

residents’ perceptions of their neighborhood by whether or not they live in a mixed-income 

property (see Exhibit 4-9). Since we already discussed the overall results above, here we 

focus on the responses of residents in mixed-income properties versus the residents of other 

properties. 

On most dimensions, residents of mixed-income and non-mixed-income properties have 

similarperceptionsof their neighborhood.Approximatelyone-halfgive their neighborhood 

an overall rating of good or excellent and between one-half and three-fourths rate their 

neighborhood favorably on the social cohesion and trust questions regardless of the mixedincome status of their developments. However, there are significant differences in resident 

reports of how active they are in their neighborhood and whether they socialize with people 

in the building or neighborhood. Residents of mixed-income properties are significantly 

more likely than residents of other properties to report socializing with people in their 

building (63 percent versus 55 percent) and to report being somewhat or very active in the 

neighborhood (57 percent versus 39 percent). But, residents of mixed-income properties are 

4-22 
Exhibit 4-9 

Residents’ Perceptions of their Neighborhood by Mixed-Income Status of Property 

Not in Mixed- In Mixed- Total 

Income Property Income Property (n=825) 

Overall rating of neighborhood as a place 

to live 

Good or Excellent 

Fair or Poor 

Indicators of “social cohesion and trust”: 

Percent of residents who somewhat or 

strongly agree with following statements: 

People in this neighborhood generally 

get along with each other 

People around here are willing to help 

their neighbors 

This is a close-knit neighborhood 

Whether resident feels a part of or 

isolated from neighborhood 

Feels part of neighborhood 

Feelsisolated from neighborhood 

Somewhere in-between 

Socialize with people in the same building* 

Socialize with people in neighborhood who 

do not live in same building* 

Somewhat or very active in neighborhood* 

l._-^^. TTT,‘-pc- n--:?l--r 0..__.^.. ,,nnn\

(n=591) (n=234) 

45% 51% 46% 

55% 49% 54% 

74% 71% 73% 

63% 69% 65% 

55% 57% 56% 

52% 53% 52% 

16% 19% 17% 

32% 28% 31% 

55% 63% 57% 

48% 39% 45% 

39% 57% 44% 

Notes: Estimatesweighted to reflect residentsin eligible LIHTC propertiesin five study MSAs. 

*Indicates significant difference betweenresidentsof nonprofit and for-profit propertiesat the 10 percentsignificance level 

(seeAppendix A for a description of the significancetestsperformed). 

less likely to report socializing with other people in the neighborhood (39 percent versus48 

percent). The survey question on how active a personis in their neighborhood includes both 

activities with building residents and with other neighbors. Hence, taken together, these 

results suggest that residents of mixed-income properties have more interaction than residents 

of non-mixed-income properties with their neighbors in the LIHTC property, but less with 

4-23 
neighbors outside of their building. Thus overall, in mixed-income LIHTC properties, the 

residents appear be interacting with each other and feel as much a part of their neighborhood 

as residents of non-mixed-income properties, although they do not socialize quite as much 

with their neighbors outside of their building. 

4.5 Summary 

This chapter explored the location of LIHTC properties and the characteristics of the 

residents in the context of the neighborhood. 

LIHTC properties in the five study MSAs are in neighborhoods dominated by rental units 

and as is typical of rental neighborhoods, predominately residents who have not lived there 

for more than 5 years. LIHTC neighborhoods are evenly split between low-income 

neighborhoodswith medianincomelessthan 50 percentof the MSA medianand more 

moderate income neighborhoods. Approximately half of the neighborhoods have 

predominately minority residents (80 percent or more) and 12 percent have predominately 

white residents. Overall, 72 percent of the properties are located in the central city of the 

MSA with more than half of those located in high-poverty (poverty rate greater than 30 

percent) neighborhoods with more than 40 percent minority residents. The other LIHTC 

properties located in the city are in low- or moderate poverty areas. Of the 28 percent of the 

properties in the suburbs, half are in low poverty areas with predominately white residents. 

In terms of sponsor type, nonprofit properties were typically located in high-poverty, city 

neighborhoods with predominatelyminority residents.The neighborhoods wherefor-profit

properties were located were more evenly spread out in terms of urban/suburban location, 

poverty rate of the neighborhood, and racial characteristics of the neighborhood residents. 

The residents of LIHTC properties are typically the working poor or near poor. At over 80 

percent of the properties, a majority of the households have at least one wage-earning adult. 

Nevertheless, almost 40 percent of the properties have high internal poverty rates (poverty 

rates greater than 30 percent). Consistent with the low-incomes of LIHTC households, a 

substantial portion receive Section 8 assistance (37 percent). At most properties, only a small 

shareof residentsreceiveSection8 Assistance,but only one-quarterof thepropertieshadno 

Section 8 recipients. On the other extreme, at 15 percent of the properties, all the residents 

receive Section 8 Assistance. Properties with for-profit sponsors tended to have either very 

few households with income below the poverty line or a majority of households with income 

below the poverty line. In contrast, a majority of nonprofit properties had moderate internal 

poverty rates (between 10 and 29 percent). 

4-24 
Only two of the study properties had a substantial share of market-rate units in their property. 

The other properties all had more than 80 percent tax-credit qualifying units with most of 

these properties having 100 percent qualified units. Hence, income diversity in LIHTC 

properties is generally limited to households with income less than the tax-credit qualifying 

maximum (60 percent of the MSA median in most properties). However, within that range, 

40 percent of the properties could be considered mixed-income developments.‘8 Consistent 

with the pattern of for-profit properties to have either primarily extremely low-income 

residents or primarily moderate-income tenants, for-profit properties were significantly less 

likely to be categorized as mixed-income (23 percent) than nonprofit properties (40 percent). 

The median income at 72 percent of LIHTC properties was lower than the neighborhood 

median. These properties were fairly evenly split across extremely low-income 

neighborhoods, low-income neighborhoods, and more moderate income neighborhoods. At 

only 10 percent of the properties, all in extremely low-income neighborhoods, was the 

median property income higher than the neighborhood median. The income at the other 20 

percent of properties was similar to the neighborhood (within 10 percent). Hence, over 40 

percent of LIHTC properties are either providing housing for residents who raise the 

neighborhood income level or provide opportunities for lower-income residents to live in 

more moderate income neighborhoods. 

Most LIHTC properties have either predominantly minority residents or predominantly white 

residents. There is substantial racial diversity in terms of whites and minorities at less than 

10 percent of the properties. However, 42 percent of the properties have substantial racial 

diversity across minority groups. 

Just over half of the properties have a higher proportion of minorities than the neighborhood 

and most of the other properties have about the same proportion of minority residents as the 

neighborhood (within 10 percentage points). The most common scenario is a majority of 

minorities in both the property and the neighborhood (63 percent). Less common, but still 

substantial, is a majority of whites in both the property and the neighborhood (17 percent). 

No properties have a majority of white residents in a neighborhood with a majority of 

minority residents, but one-fifth have a majority of minority residents in a neighborhood with 

a majority of white residents 

‘* A property is defined as mixed-income if it meets any one of the following criteria: (1) At least 20% of the 

units aremarket-rateand 20% are tax-credit qualified units; or (2) At least 20 percent of the household 

incomes are below 30% of the MSA median and 20 percent are above 50% of the MSA median; or (3) At 

least20 percentof the householdshaveincomebelow the poverty line and 20 percenthave incomeabove 

185% of the poverty line 

4-25 
Resident perceptions of their neighborhood are mixed. Depending on the measure, between 

one-half and three-fourths rated it favorably and the remainder rated it unfavorably. Under 

one-half of the residents gave their neighborhood an overall rating as good or excellent and 

over one-half rated it as fair or poor. On measures of “social cohesion and trust”, the overall 

favorable responses ranged from 56 percent who reported “this is a close-knit neighborhood” 

to 73 percent who reported that “people in the neighborhood generally get along with each 

other.” Nevertheless, only 17 percent of the residents reported they felt isolated in their 

neighborhood and approximately 45 percent reported being active in their neighborhood and 

socializing with people in their neighborhood. Many of these results are significantly 

different by sponsor type with residents of for-profit properties more likely than nonprofits to 

rate their neighborhood favorably overall, but less likely to describe it as a close-knit 

neighborhood or to be active or socialize in their neighborhood. 

4-26 
Chapter 5 

Property Management and Resident Services 

This chapter reviews the property management arrangements and operations of the study 

properties, along with the other servicesprovided to residents. The chapteralso examinesthe 

relationship of the study properties with community organizations and the potential impacts 

of welfare reform on the propertiesand their residents. 

5.1 Profile of Property Management Entities 

The 39 study properties are managed by a total of 31 management entities, including both 

for-profit companies and nonprofit organizations. In every MSA except Oakland, there is at 

least one company managing more than one of the sample properties. For example, in 

Miami, one company manages three properties, and a second company manages two of the 

study properties. In Boston there are two companies each managing two sample properties. 

In Kansas City, one nonprofit manages two sample properties, and two organizations in 

Milwaukee each managetwo study properties. This overlap suggeststhat there are certain 

“players” or companies that manage tax credit and affordable housing in each market. This 

conclusion is also supported from information on the managementcompanies’ experience 

with the tax credit program. Overall, nearly half of the property managers can be considered 

to have a tax credit specialty (at least one-third of the units in their management portfolio), 

with most of the companies reporting this specialty being for-profit management entities. It 

should be noted that most companies report a portfolio that includes a mix of units, including 

affordable housing and market rate housing. However, only about 13 percent of the 

management entities report a portfolio composed predominantly of market rate housing. 

An important characteristic of management entities is their relationship to the 

developer/owner. Often, properties are managedby a related company or a division of the 

owner/developer entity. If the management company is independent, it is usually referred to 

as an independent fee manager. The LIHTC properties are evenly divided along this 

dimension, with half of properties managed by an entity that has a relationship with the 

owner/developer,and half managedby independentfee managers. In our sample,nonprofit 

properties are less likely to have a management company related to the owner than the forprofit properties. 

Despite the fairly even numbers of for-profit and nonprofit sponsors in the sample,most of 

the property managementagents(71 percent) are for-profit entities. In fact, all of the forprofit properties are managed by for-profit management companies, while the nonprofit 

5-1 
properties are evenly split between nonprofit and for-profit management companies. In the 

vast majority of cases (8 of 11) where nonprofit properties are managed by nonprofit 

managers, the manager is either the nonprofit sponsor itself or a subsidiary organization. 

Some variations by MSA were observed. For example, Miami was the only MSA where all 

of the properties are managed by for-profit companies; Oakland is the only MSA with more 

nonprofit managers than for-profit managers (4 of the 6 properties are managed by a 

nonprofit, which is consistent with the sponsorship split). Overall, there appears to be a 

strong trend in the study properties for utilizing professional management companies, both 

for-profit and nonprofit. 

The sample properties in the study are managed by companies that range in size from the 

largest in the country with nearly 400,000 units in its portfolio to small organizations or 

individuals, managing fewer than 200 units. However, the typical portfolio is between 1,100 

and 3,000 units, with the for-profit managers having a typical portfolio of approximately 

2,600 units and the nonprofit managers having a portfolio of about 1,800 units. 

The largest management company in the study (and the country), AIMCO, managed three of 

our sample properties in the Miami MSA. On the other extreme, the two smallest 

management entities in the study are a nonprofit in Kansas City, the Westside Housing 

Organization, with approximately 170 units under management, and 2 individuals in Oakland 

managing one property of 118 units. Westside Housing serves both as the developer and 

manager of many of its properties which are located exclusively on the Westside of Kansas 

City, a predominately Hispanic neighborhood. The individual investors in Oakland are the 

developers and managers of Villa San Ramon, a 118 unit luxury elderly property in San 

Ramon, a suburb of Oakland. The two investors are currently adding another 150 units of 

elderly housing to their portfolio. 

Most of the management entities in our study also have a local focus versus a multi-state or 

national focus. Almost three-fourths focus their operation in a single city or at most in one 

state. The greatest degree of local focus can be found in Boston and Oakland. Not 

surprisingly, nonprofit management entities are more likely (77 percent) than for-profits (64 

percent) to have an exclusively local focus. 

5.2 Profile of Management Operations 

How are tax credit properties managed? Exhibit 5-l presents the management data on a 

number of dimensions. In terms of management presence, we found that one-half of the tax 

credit properties in the study had an on-site management office and one-quarter had a 

residentmanager.Management presence did not varysignificantlyby sponsortype.
Exhibit 5-I 

Profile of Management Entities and Operations 

Management Type* 

Nonprofit 

For-profit 

Relationship to Owner 

Related management entity 

Independent fee manager 

On-site Management Office 

Yes 

No 

Resident Manager 

Yes 

No 

Nonprofit For-profit 

Sponsor Sponsor Total 

(n=22) (n=lS) (n=39) 

54% 0% 29% 

45% 100% 71% 

41% 60% 50% 

59% 40% 50% 

50% 51% 51% 

50% 49% 49% 

33% 15% 25% 

67% 85% 75% 

Marketing Methods (in last two years) 

Newspaper 

Signs 

Contacts with Section 8 Office 

Apartment Finder* 

Contacts with community organiz. 

Contacts with social service org. 

Competition 

Other subsidized developments 

Market-rate developments 

Both 

Screening Methods 

Credit Check* 

Rental History/Landlord References* 

Criminal Background Check* . . . ,.n,,n\ ource: lntervlewswith sitemanagers(Irrr). 

33% 48% 40% 

30% 45% 37% 

21% 29% 25% 

9% 35% 21% 

20% 19% 19% 

23% 15% 19% 

63% 70% 66% 

28% 23% 26% 

9% 7% 8% 

100% 88% 95% 

100% 76% 89% 

94% 63% 80% 

Notes: Estimatesare weighted to reflect all eligible properties in 5 study MSAs. 

*Indicates significant difference between nonprofit and for-profit properties at the 10 percent significance level (see 

Appendix A for a description of the significance tests performed). 

Marketing and Finding LIHTC Units 

Exhibit 5-l also shows that the most popular mechanisms for marketing tax credit properties, 

according to propertymanagers, areadvertisingin thenewspaper andposting“for rent” signs

5-3 
on the front of the property-approximately 40 percent report using each of these methods. 

No other marketing method, including contacting the Section 8 office about openings, was 

reported by more than one-quarter of the managers. Given that over one-third of LIHTC 

tenantsreceive some form of Section 8 assistance(see Chapter 3) and over three-fourths of 

the propertieshave at least one tenant on Section 8 assistance(seeChapter 2), it is somewhat 

surprising that only 25 percent of the property managerscontact the Section 8 office when 

they have vacancies. However, the lack of regular contact between most LIHTC managers 

and the local Section 8 office was reported by both site managers and Section 8 staff. It 

appearsthat at most of the LIHTC properties, the residentswho receive Section 8 assistance 

found out about vacancieson their own. 

There is little difference in marketing methodsbetweennonprofits and for-profits. However, 

almost every marketing method asked about was reportedly used by a higher proportion of 

for-profits than nonprofits. Nevertheless, the only significant difference was in the use of 

apartment finder publications which were used by 35 percent of for-profits, but only 9 

percentof nonprofits. 

It should also be noted that 9 properties (just under one-fourth of the study properties) 

reported no formal marketing in the last two years. Seven of these nine properties were in the 

Boston area,reflecting a very tight housing market in Boston and the fact that managersdo 

not need to advertise in order to fill vacancies. 

While managersrelied heavily on newspapersfor advertising, residents reported word of 

mouth as the most common source for finding their LIHTC unit. (No exhibit is provided on 

resident sources for finding their unit.) Overall, 51 percent of the residents found their 

current home through family and friends. This is true for both nonprofit and for-profit 

properties. Residentsidentified newspapersand signage/walking by the property (the most 

common marketing methods reported by managers) as the next most frequent methods for 

learning of their new homes,though neither of thesewas reported by more than 15 percentof 

respondents. No other method for finding out about their new unit was reported by more 

than seven percent of the respondents. 

Competition for LIHTC Properties 

Managers reported that the major competition for their tax credit property is other subsidized 

housing including other tax credit properties and project-based Section 8 housing. None 

specifically reported public housing as their competition. As can be seen in Exhibit 5-1, 

there is no difference on this measure between nonprofit and for-profit properties, with 63 

percent of nonprofits citing other subsidized housing as their major competition and 70 

percent of for-profits reporting the same. Likewise, only one-quarter of nonprofits or for5-4 
profits reported market rate housing as their primary competition, although a few properties 

identified both market rate and subsidized housing as their competition. 

In terms of MSAs, managers in four out of the five areas identified subsidized housing as the 

competition. Oakland is the exception where managers reported that market rate housing was 

the primary competition. Several managers indicated that there was effectively no 

competition in their neighborhood for the types of units they provide. For example, one new 

construction townhouse property in Kansas City provides 3 bedrooms units, with private 

garages, in a nicely landscaped property. The manager said that there are no units in the area 

with comparable features and affordable rents. 

Examining this issue from the perspective of the residents, more than one-third of the 

residents (36 percent) reported they were on a public housing waiting list when they found 

their current apartment. This figure is slightly higher for residents of nonprofit properties40 percent as compared to 32 percent of the residents in for-profit properties. Overall, 27 

percent of residents reported that they were living in public housing prior to moving to their 

current home. Residents of nonprofit properties were more likely to have moved to their 

current unit directly from public housing (32 percent) than residents in for-profit properties 

(22 percent). 

These data demonstrate the relationship between tax credit properties andpublichousingand 

Section 8. Given scarce affordable housing resources, families typically try a number of 

different avenues to secure safe, decent housing at an affordable rent. What is interesting, is 

that even if managers do not consider public housing their competition, a fairly substantial 

number were on the public housing waiting list at the time they found their tax credit unit. 

The fact that roughly one-fourth of the LIHTC residents report they came directly from 

public housing suggests that these properties do serve as a stepping stone or transition out of 

public housing and into more market oriented housing. 

Screening 

Property managers view the screening of applicants as an essential management tool in 

creating a safe and comfortable community for all residents. Good screening on the front end 

eliminates problems later on that may lead to nonpayment of rent and eventually eviction. 

As indicated in Exhibit 5-1, the vast majority of the study properties report using three 

screening methods: a credit check, landlord references and/or rental history, and a criminal 

background check. However, nonprofits are more likely than for-profits to use each of these 

methods. While 100 percent of the nonprofit properties report using credit checks and rental 

history, just 82 percent and 76 percent of for-profits reported using the two methods 

respectively. Likewise, 91 percent of the nonprofits said that they use a criminal background 

check compared to 59 percent of the for-profits who use that method of screening. Almost 

5-5 
all managers report using third party income verifications, but this is a requirement of the tax 

credit program. 

5.3 Property Amenities and Social Services 

There has long been a debate regarding the provision of social and community services 

combined with housing. Traditionally, public housing authorities have provided such 

services but the broader multifamily housing industry has not. Service provision by 

management is a point of interest since tax credit properties are serving primarily the working 

poor or special populations, such as the elderly. This section reviews the amenities and the 

servicesprovided by the study properties as well as the servicesresidentstake advantageof 

in the broadercommunity. 

Security 

Security is an important issue for many of the neighborhoodsin which the study properties 

are located. In fact, when asked about the most important consideration for moving into their 

current building, increased safety was the third most frequently cited reason, following 

paying lower rent and living in a nicer apartment (see Chapter 3). In line with the importance 

of security to residents, eight in ten study properties provide some kind of security system or 

measure. As can be seen in Exhibit 5-2, the two most popular measures are restricted entry 

(63 percent) and buzzer systems (56 percent). In addition, over one-third of the properties 

reported having security guards at the development for at least part of the day, usually the 

evening or overnight hours. The use of security guards varied from a dedicated guard at the 

building entrance for selected shifts (including a few with security cameras)to a security 

service that checks the grounds and entrances each evening while also providing the same 

service for other developments. While there is little difference across sponsor types in the 

use of restricted entry or security systems, for-profits are twice as likely as nonprofits to hire 

security guards (5 1 percent vs. 25 percent). 

Site Visitor Observations of Properties 

A fundamental question is what kind of living environment do tax credit properties provide? 

To supplementthe residentratings of their property discussedin Chapter 3, site visitors rated 

the appearance and curb appeal of the study properties, the maintenance, upkeep, and 

cleanliness of the property and buildings, as well as their “tit” in the larger neighborhood. 

These ratings are shown in Exhibit 5-2 and discussed below. 

5-6 
Exhibit 5-2 

Prc lmenities and Conditions

Nonprofit For-profit Total 

Sponsor (n=22) Sponsor (n=17) (n=39) 

Security 

Restricted Entry 63% 63% 63% 

Buzzer 56% 56% 56% 

Security Guard* 25% 51% 37% 

Site Visitor Observations 

Neighborhood Fit 

Poor 0% 0% 0% 

Fair 3% 8% 5% 

Good 49% 55% 52% 

Excellent 48% 37% 43% 

Overall Curb Appeal 

Poor 0% 5% 2% 

Fair 26% 31% 28% 

Good 46% 42% 44% 

Excellent 27% 22% 25% 

Maintenance/Upkeep 

Poor 0% 6% 2% 

Fair 11% 15% 13% 

Good 51% 31% 42% 

Excellent 38% 48% 42% 

Overall Rating 

Poor 0% 5% 2% 

Fair 8% 15% 12% 

Good 58% 35% 47% 

Excellent 34% 44% 39% 

Property Amenities 

Off-street parking 75% 77% 76% 

Laundry facilities in buildin 70% 74% 72% 

Air conditioning* 69% 42% 55% 

Community Room 51% 54% 52% 

Playground 42% 27% 35% 

Dishwasher 27% 38% 32% 

Pool 7% 9% 8% 

Washer/dryer in unit 0% 11% 5% 

Services provided on-site 39% 38% 39% 

Relationship with Outside Age] 

Provide Services to Residents 27% 24% 26% 

ources: Site manager interviews and site v rations (1999). Notes: I mates are weighted to re ct eligible properties in : 

study MSAs. *Indicates significant difference between nonprofit and for-protit properties at 10 percent significance level (see Appendix 

A for description of significance tests). 

5-7 
In general, site visitors ranked developments as either “good” or “excellent” in each category 

with only small differences between nonprofit and for-profit properties. Almost all of the 

properties (95 percent) were judged good or excellent in terms of neighborhood fit. Although 

site visitors noted that some properties were slightly taller or larger than other residential 

structures in the neighborhood, tax credit properties tend to be smaller than public and 

assisted housing projects built in previous decades and thus fit in better with the 

neighborhood. The rehabilitated properties maintained the original look of the building and 

the new properties were generally built to fit in with the neighborhood. 

Curb appeal was rated as good or excellent at 69 percent of the properties. The lower curb 

appeal ratings relative to neighborhood fit ratings is a reflection of both the standard 

multifamily architecture of many of the newly constructed buildings and the distressed 

neighborhoods in which some of the rehabilitated LIHTC properties are located where even 

recently revitalized buildings are challenged to maintain their initial luster and may have 

limited curb appeal. Nevertheless, several of the properties were described as the bestlooking developments in the area. These ratings did not vary much by sponsor type. 

The study properties were also rated on the maintenance and upkeep of the properties. Most 

of the properties were highly rated on this factor with 89 percent of the nonprofits receiving a 

good or excellent rating and 79 percent of the for-profits receiving the same rating. Finally, 

site visitors were asked to give the properties an overall rating taking into account all of the 

above factors (neighborhood fit, curb appeal, and maintenance/upkeep). The overall ratings 

were also very positive: 86 percent of the properties had an overall rating of good or 

excellent. Similar to maintenance and upkeep, more for-profit properties were rated as 

excellent overall, but more nonprofit properties were rated as good, resulting in more 

nonprofit than for-profit properties receiving a positive (good or excellent) rating (92 percent 

versus 79 percent). 

Amenities 

For the most part, the tax credit properties in our study provide basic amenities for residents 

but are not luxurious. The most common amenities were off-street parking (76 percent) and 

laundry facilities in the building (72 percent). On the other extreme, very few properties 

provided higher-end amenities: only 8 percent of the properties offered access to a pool and 

only 5 percent had washers and dryers in the units. However, air conditioning, a somewhat 

luxurious amenity was available in over half of the properties (55 percent). With respect to 

other amenities, about half of the properties had community rooms (52 percent), one-third 

had playgrounds (35 percent), and a similar share had dishwashers (32 percent). In general, 

the amenities recorded do not differ systematically by sponsor type, however nonprofits (69 

percent) were significantly more likely than for-profits (42 percent) to provide air 

conditioning. 

5-8 
Social Activities 

In terms of social activities (as distinct from social services), we found a handful of 

properties that provide some activities for residents, though these tend to be focused on 

senior residents. For example, Williamstown Bay-Cudahy II in Milwaukee brings in 

speakers and hosts health fairs and other social activities, such as Bingo. Also in Milwaukee, 

the YW Villages have a senior choir and provide various field trips for senior residents. 

On-Site Community and Social Services 

When asked about the needs of their residents, many managers articulated similar needs. 

These include: day care, job training, budget/finance counseling, education, health, and 

nutrition. However, the majority of tax credit properties in our study (more than half) do not 

provide any kind of social services on-site. As shown in Exhibit 5-2, only 39 percent of the 

properties provide services on-site and this does not vary by sponsor type. The kinds of 

services provided on-site include: children’s activities such as summer camp and tutoring; 

adult job training and computer literacy classes; and domestic violence counseling. In 18 

percent of the properties, there is a resident service coordinator who makes referrals and other 

kinds of linkages to services that residents may need. Five of the seven properties providing 

a service coordinator are nonprofit properties. Most of the property managers said that lack 

of interest on the part of residents and lack of budget resources were the primary reasons that 

they did not provide social services on site. 

Interestingly, several organizations said that they had provided services in the past, but that 

there was not enough interest to keep the programs operating. For example, in Kansas City, a 

nonprofit sponsor, KC Neighborhood Alliance (KCNA), indicated that most of their residents 

are single, working mothers who do not have extra time and energy for activities or services. 

KCNA also noted that residents found KCNA’s role confusing when the organization was 

providing services: was KCNA the landlord or a service provider? Both to maintain clarity 

about their role, and as a result of lack of resident interest, KCNA has ceased service 

provision and now makes a community organizer available to residents. 

Relationships with Outside Agencies 

While LIHTC properties are limited in the extent to which they provide on-site services, 

residents may access outside services to meet their needs. In many cases managers were 

unaware of the outside services that residents use. Those that did have some idea mentioned 

common services including medical care, children’s after school and summer programs, 

senior services, daycare, and emergency services (primarily for rental assistance) from local 

churches and agencies such as the Salvation Army. 

5-9 
Approximately one-quarter of the managers at nonprofit and for-profit properties reported 

having some kind of relationship with an outside community group or agency, as indicated in 

Exhibit 5-2. 

Some examples of the kinds of relationships that tax credit properties have with outside 

agencies include Cottage Brook Apartments in Boston that has a direct relationship with an 

organization called Project Hope. Project Hope provides training for non-working mothers 

intended to lead to employment in the in-home day care field. Also in Boston, Putnam Place 

has a relationship with a local family support program, Families First. The program helps 

families to deal with family structure issues. Also, next to the property is a teen center and a 

community health center. Just a Start (the developer) also refers residents to the employment 

services offered by the organization. 

In Oakland, the Richmond City Center Apartments has a resident services coordinator that 

provides referrals to local service providers. There is also a Police Substation in the 

commercial space where they operate Project READ, a tutoring program. 

In Kansas City, the Blue Hills Homes Corporation does not provide direct services, but they 

do have a Community Services staff person who develops networks with local providers to 

establish linkages to needed services. Jefferson Place developer and manger, the Westside 

Housing Organization (WHO) has close relationships with at least four other nonprofit 

organizationsoperatingin theneighborhood.Theseorganizationsprovidecounseling,ESL, 

job skills, and transportation. 

An example of an interesting partnership is Garden West, in Milwaukee, which contracts 

eight units to Safe Haven, a homeless prevention program, which has a case manager living 

on the property. Also in Milwaukee, YW Village East II provides a variety of services to 

their residents including SHARE, a program that drives seniors to food shopping and medical 

appointments. The property also has a relationship with the Senior Development Center that 

works with senior residents on mental and physical development. Finally, the property also 

providesGED andjob trainingon-sitethrougha learningcenterlocatedat their nearbyYW 

Village West property. 

When residents were asked about social services in their neighborhoods, 62 percent reported 

having good or excellent access to social services in their neighborhoods. This was slightly 

higher for nonprofits: 68 percent of nonprofit residents reported good or excellent access to 

services compared to 58 percent of for-profit residents. When comparing their current 

neighborhood to their previous neighborhood, more than half of all residents (53 percent) 

said that the access to services was the same, one-third (34 percent) said the access to 

services was better, and the remaining 13 percent said the access to services in their new 

neighborhood was not as good. However, services did not seem to play a factor in the 

5-10 
selection of their current home. Access to services was very low on the list of reasons for 

choosing their current home (see Chapter 3). 

5.4 Resident Satisfaction with Management and Services 

Given the property features and services they receive, are tax credit residents satisfied with 

the management of their units ? According to our resident survey, 41 percent of the residents 

said they were very satisfied with the management of their current unit. Another 34 percent 

said they were somewhat satisfied, resulting in a total of 75 percent of the residents reporting 

satisfaction with the current management. Nonprofit residents reported slightly higher levels 

of satisfaction with 78 percent indicating that they were satisfied versus 72 percent of the forprofit residents. Overall, 20 percent of the residents report being somewhat or very 

dissatisfied with their current management company. From the data available it is not clear 

what accounts for this dissatisfaction. 

5.5 Service Needs/Impacts of Welfare Reform 

According to the resident survey, only 6 percent of the respondents report someone in their 

household was currently receiving TANF, with no significant difference between for-profits 

and nonprotits. This is consistent with the property managers’ perceptions that there were 

few residents in the study properties that received public assistance. Accordingly, most of 

the managers did not think that welfare reform would have a great impact on the properties. 

A few noted that welfare reform would result in a decrease in resident incomes, and that 

Section 8 would likely pick up the slack. 

5.6 Summary 

The management entities for LIHTC study properties ranged from the largest in the country 

to entities managing only the property in the study, but the typical management entity 

managed portfolios with between 1,000 and 3,000 units. There is an even split between the 

management companies that are related to the owner/developer entity and those that are 

independent fee managers. Most are for-profit management companies, including all the 

managers of for-profit properties and a little under half of the managers of nonprofit 

companies About half of the companies could be considered to have a specialty in tax-credit 

properties (at least one-third of their portfolio), but almost all hada combinationof marketrate and subsidized developments in their portfolio. Approximately three-quarters of the 

residents reported being somewhat or very satisfied with their management company. 
Two-thirds of the managers report other subsidized units, but not public housing, as their 

primary competition. However, only one-fourth report contacting the local Section 8 office 

about vacanciesor regularly receiving referrals from the Section 8 office. Almost all of the 

properties conduct a credit and landlord reference check as part of their tenant screening and 

four-fifths also do a criminal background check with nonprofits more likely than for-profits 

to use each of these screening methods. 

Approximately 40 percent of the properties provided on-site services which tended to be 

services for seniors, children’s activities (including tutoring), or a resident service 

coordinator who made referrals to outside agencies. The properties tend to provide basic 

amenities like off-street parking and laundry facilities in the building, but very few reported 

high-end luxuries like pools or washer/dryers in the unit. However, over half had air 

conditioning with nonprofits more likely than for-profits to provide it. 

Site visitors reported that almost all the properties tit in with the style and scale of the 

surrounding neighborhood and most were clean and appearedto be well maintained. Taking 

into account neighborhood fit, curb appeal, and visible maintenance and upkeep, site visitors 

rated 86 percent of the properties as good or excellent and only 14 percent as fair or poor. 

Finally, the site managers reported very few residents received welfare (consistent with 

resident survey), thus they did not think welfare reform would affect the financial viability of 

their development. 
Chapter 6 

Developer Motivation and Project Impact 

The LIHTC is a highly decentralized program. While states, through their Qualified 

Allocation Plans, broadly shape the types of properties that receive the credits, prospective 

developers respond to many different types of incentives, and, in the end, the units produced 

with the LIHTC may reflect a broad range of motivations and considerations. This chapter 

focuses on the developers who participate in the LIHTC, the factors that shape the specific 

projects they have developed, and how the projects impact the neighborhoods in which they 

are located. Findings presented in this chapter are based on interviews with the developers, 

owners, and managers of the study properties. 

6.1 Characteristics of Study Developers 

As discussed previously, the study sample consists of 39 properties, 22 of which were 

produced by nonprofit developers and 17 of which were produced by for-profit developers. 

Among the 22 nonprofit properties, nearly three-quarters were sponsored by neighborhoodbased CDCs or similar types of organizations serving a broader geographic area--in some 

cases, an entire city. The group of 22 nonprofit properties also included several produced by 

large regional affordable housing developers, as well as a few sponsored by nationally 

affiliated charitable or service organizations. 

Examples of neighborhood-based development organizations include the Jamaica Plain 

Neighborhood Development Corporation (developer of Hyde Square Coop) near Boston and 

Westside Housing (Jefferson Place) in Kansas City. Jamaica Plain Neighborhood 

Development Corporation (JPNDC) was founded in the late 1970s with the mission of 

providing affordable housing opportunities in the context of stabilizing the Jamaica Plain 

neighborhood. The organization is engaged in economic development as well as housing 

activities and is credited with rehabbing some 80 percent of the abandoned structures in 

Jamaica Plain. In Kansas City, Westside Housing (WHO) serves a primarily Hispanic 

community on the west side of the city. This organization was formed in 1973 by a group of 

activists, concerned about the neighborhood, who saw a need for affordable housing 

development to complement social services being brought into the area. By the time of this 

study, WHO had developed nine affordable rental properties and built or rehabilitated 186 

singlefamily homes. 

6-I 
East Bay Asian Local Development Corp (Hismen Hin-Nu Terrace) in Oakland provides an 

example of a CDC with a with a broader, city wide focus. Founded in 1975, EBALDC’s first 

project was the rehabilitation of a warehouse in Oakland’s Chinatown district into a 

multicultural center. Since 1983, EBALDC has completed some 550 units of housing in 

various parts of the city, ranging from SROs to mixed use, multifamily and commercial 

buildings. 

Overall, the local CDCs in the study tended to have substantial experience with housing 

development prior to developing the sample property, although the total numbers of units 

produced tended to be in the hundreds. The least experienced of the local nonprofits were 

two church-related organizations, .both of which were embarking on their first housing 

development projects. Inexperienced organizations often used development consultants to 

assist them in their projects, and, in a few cases, nonprofit sponsors entered into joint venture 

arrangements with more experienced for-profit developers. 

In contrast to the locally-oriented CDCs, the nonprofit developer group also includes three 

affordable housing developers with a more regional reach. The first is Community Builders 

(TCB), which since 1964 has developed over 13,000 units of affordable housing, mostly in 

Massachusetts. TCB is a full service, nonprofit housing organization, providing both 

development and management services. BRIDGE Housing in San Francisco is another, well 

known nonprofit development entity which produces thousands of units per year and also 

serves as a development advisor to many local Bay Area nonprofits. Similarly, the 

Wisconsin Partnership for Housing Development (WPHD) serves as a developer and 

syndicator for LIHTC projects statewide. WPHD served as a development consultant to one 

of the church-related CDCs in Milwaukee and also took over the management of another 

sample property when the original nonprofit disbanded. 

Finally, the nonprofit developer group includes two nationally affiliated service 

organizations: Catholic Charities (Santana Apartments in Oakland) and the YWCA (YW 

Villages East and West in Milwaukee). Despite their national ties, these organizations 

function like neighborhood-based nonprofits and have a distinctly local focus. They are also 

among the least experienced of the nonprofits, relying on local development consultants for 

tax credit expertise.Both organizationshadpreviouslybeenengagedin operatingshelters 

and/or transitional housing facilities for the homeless, but the sample properties were their 

first rental housing developments. 

The for-profit developers in the study cover a broad range of organizations and missions. 

Several of these are companies that specialize in affordable housing (typically Section 8 

project-based) and develop properties to help increase the supply of affordable units. For 

example, the stated mission of Long Bay, a minority-owned development company working 

in the Dorchester area of Boston is to provide quality real estate services and to benefit the 

6-2 
community. Long Bay developed two study properties, both of which were previously 

distressed Section 8 developments. McCormack Baron is another example of a development 

firm which has made its name in affordable housing, working closely with public housing 

authorities (St. Louis) and local governments (Kansas City) to redevelop and revitalize 

deterioratedinner city areas. The McCormack Baron property included in this study is part 

of the company’s New Quality Hill development, a mixed income community consisting of 

renovated historic structures and new garden apartment units located in a formerly blighted 

section of downtown Kansas City. Another revitalization-oriented developer is Creative 

Choice Homes (The Gardens at Opa-Locka) in Miami. This entity works primarily in the 

southeast and has developed some 15 properties in distressed inner city areas as well as 

several troubled public housing sites under HUD’s HOPE VI program. Finally, the study 

includes one case in which the developer entity is a for-profit spin-off of a nonprofit 

organization. Although classified as a for-profit for the purposes of this study, the goals of 

the parent organization (the Black Economic Union, an organization formed in 1967 to 

promote economic development and provide technical assistance to small business in Kansas 

City) clearly drove this development effort. 

The remaining for-profit developers vary tremendously in scope and structure. Most are 

traditional real estate development and management companies, along ’ with a few 

construction-orientedfirms. They include national development and investment companies, 

such as the Related Group-involved in four of the Miami properties-as well as firms 

serving smaller niche markets-such as the RenaissanceGroup which specializesin small 

and rural communities in Wisconsin. While most are corporations, a few were small 

partnershipsor essentiallysolo practitioners. For example,Affordable Landmarks (developer 

of Riviera Plaza in Miami) is a “one man show” which purchases and rehabs vacant historic 

buildings in Miami Beach. 

6.2 Development Objectives and Site Selection Process 

Understandingdevelopermotivations and objectivesin producing their LIHTC developments 

may shed additional light on the types of areas where LIHTC projects are located and the 

ways these projects relate to their neighborhoods. However, untangling these motivations is 

far from simple. While financial benefit is assumed to be a major motivator for for-profit 

developers, those with whom we spoke often identified multiple reasons for developing the 

subject sites, including meeting the need for affordable housing in the project area or the 

desire to revitalize a distressed site. By the same token, nonprofits may pursue housing 

developmentprojects in part to earn developer fees for the organization, which can then be 

reinvested in the properties or in other neighborhood projects. 

6-3 
In this section, we attempt to sort the study properties into three groups: 1) those intended 

primarily to serve community development objectives, i.e., elimination of a blighting 

influence or the improvement of a specific neighborhood or area; 2) those developed 

primarily to increase available affordable housing or serve a particular, under served 

population; and 3) those developed primarily to serve more traditional profit-oriented real 

estate objectives. We also investigate the process that led to the selection of the specific site 

for development using the LIHTC. 

Not surprisingly, nonprofit sponsors were more likely than their for-profit counterparts to 

articulate community development goals for their projects. (See Exhibit 6-l.) For example, 

consistent with its goal of stabilizing the Jamaica Plain neighborhood, JPNDC’s primary 

motive in developing the Hyde Square Coop was a desire to eliminate blight in the 

neighborhood, specifically a number of vacant lots in the lower part of Jamaica Plain near the 

Roxbury line. JPNDC worked with the community to determine which lots to develop as 

housing and which should be used for open space. Similarly, Urban Edge in Boston built 

Stony Brook Gardens on a block that had been acquired by the state for an unbuilt extension 

of an interstate. Prior to Urban Edge’s acquiring the parcels, the site had been essentially 

vacant, with one run down building which was rehabbed as part of the development. 

Exhibit 6-1 

Breakdown of Properties by Primary Development Objective 

Primary Objective Nonprofit For-profit All 

Properties Properties Properties 

(N=22) (N=17) (N=39) 

Neighborhood Improvement I 17 I 3 I 20 I 

Expand Affordable Housing I 5 I 2 I 7 I 

Traditional Real Estate/Profit 0 12 12 

Total 22 17 39 

Altogether, three-quarters (17) of the nonprofit properties in the study were identified by their 

developers as located in a previously blighted area that the organization took on to improve 

the neighborhood. Most commonly these sites were vacant or dilapidated buildings and were 

often described as drug havens. Examples include: Cottage Brook, developed by Dorchester 

Bay EDC, which was a Section 8 project-based building in poor condition and a haven for 

drug activity; Garden West, developed by the Wisconsin Partnership for Housing 

Development, also described as a drug haven; Rockford Hill, developed by the East Meyer 

Development Corporation, described as filled with drugs and gangs; and YW Villages (East 

6-4 
and West), developed by the YWCA, consisting of several vacant buildings at least one of 

which was occupied by gangs. Other developments intended to address blight and improve 

neighborhoods by addressing vacant buildings or lots included: Parkwest, developed by the 

New Covenant Housing Corporation; Jefferson Place, developed by the Westside Housing 

Organization; Blue Hills Take Part III developed by the Blue Hills Homes Corporation; 

Squire Park, developed by Neighborhood Housing Services; and Take Part III (unrelated to 

Blue Hills Take Part III) developed by the Kansas City Neighborhood Alliance. 

In Oakland, two of the study sites were initially identified by design classes from the 

University of California at Berkeley as possible affordable housing sites and were ultimately 

developed by local CDCs. These include Drasnin Manor (developed by Oakland Community 

Housing) and Hismen Hin-Nu Terrace (developed by EBALDC). These properties are 

located directly across the street from each other. The Hismen Hin-Nu site contained a 

abandoned supermarket building with a large parking lot and was the site of many drug deals. 

The Drasnin Manor site was a vacant lot next to a dry cleaners. Another Oakland area 

property, Richmond City Center Apartments was also intended to redevelop what was 

considered one of the worst comers in that city. However, the initiator of the effort in this 

case was the City Redevelopment Agency, which issued an RFP to redevelop the property; 

BRIDGE Housing, a large nonprofit developer, won the competition and built the LIHTC 

property as well an adjacent shopping center. 

The remaining nonprofit properties (7) were developed to serve affordable housing goals, 

with less emphasis on neighborhood improvement. These include properties such as Franklin 

Park in Boston, which was developed by the Community Builders. Franklin Park was 

selected by Community Builders because it fit the organizations “product line” of distressed 

multifamily properties which it could acquire, rehab, and preserve. Others in this group 

include Teal Pointe in Miami and Santana Apartments in Oakland. The former was 

rehabilitated by Greater Miami Neighborhoods after it was damaged during Hurricane 

Andrew. Santana Apartments was rehabilitated by Catholic Charities after it was rendered 

uninhabitable by the 1989 Bay Area earthquake. 

Turning to the for-profit properties, three-quarters (12) appeared to be developed primarily to 

serve traditional real estate objectives, including realizing a profit from developing, owning, 

and/or managing residential properties. Although their product includes a number of 

properties in blighted areas, other factors, such as an upturning market or a unique 

development opportunity were also usually present in the decision to pursue projects in these 

areas. For example, the developers of Askew Saddlery, a vacant warehouse in a blighted but 

revitalizing area of downtown Kansas City, simply loved the building and wanted to restore 

it. Similarly, the developer of Riviera Plaza specializes in saving historic structures. This 

property, located in a gentrifying but still poor area of Miami Beach, is reflective of the 

neighborhood’s tropical/art deco flair and is now beautifully restored. The fact that it now 

6-5 
provides good housing for its primarily elderly residents is an added benefit as is the 

displacement of squatters and crack users who previously controlled the building. 

Others with traditional motives include the developers of Westchester Village near Kansas 

City and Homestead Plaza in the Miami area. Westchester Village is located in Oak Grove, a 

small community that is quickly being swallowed up by Kansas City. The developers were 

looking for new business opportunities, having previously developed a number of FmHA 

Section 515 properties. Westchester Village was their first tax credit property. Homestead 

Plaza was acquired by its current owner in uninhabitable condition after Hurricane Andrew. 

The developer purchased the units, rehabbed them, and converted them to condos, which he 

hopes to sell after renting them for the 15-year rental compliance period. 

While profit motives are a part of the development story in all of these cases, several of the 

for-profit developers have clearly adopted a broader mission. For example, as mentioned 

previously, Long Bay in Boston (developer of Hope Bay and Morrant Bay) worked closely 

with the Massachusetts Housing Finance Agency to preserve units in foreclosed Section 8 

buildings. This developer sees as a part of its mission preserving affordable units and 

returning them to decent condition. The Gardens at Opa-Locka and Quality Hill in Kansas 

City represent projects initiated by developers with broader community revitalization 

missions. Creative Choice Homes, developer of Gardens at Opa-Locka, specializes in 

revitalizing distressed properties, although in this case the results are somewhat mixed, with 

the property still suffering from problems with drugs and poor physical design. McCormack 

Baron, developer of Quality Hill, is nationally recognized for its forward thinking design and 

development work which includes several showcase revitalization projects and some of the 

first mixed-income housing redevelopments in the country. 

Altogether, about half of the properties (20) were developed to meet neighborhood 

improvement goals, 20 percent (7) were developed to increase the supply of affordable 

housing, and the remaining 30 percent (12) were primarily intended to serve traditional real 

estate objectives. Somewhat surprisingly, the vast majority of the sites developed with tax 

credits were already identified or targeted by their developers before a decision was made to 

seek tax credits. This seems natural, for example, for projects developed by neighborhoodbased groups, where the focus is on dealing with a problem site or providing a new housing 

resource; in such casestax credits are simply one of tools available to support redevelopment. 

However, even among traditional profit motivated developers, in only a few cases did we 

find developers seeking out locations specifically to use credits. Two properties in 

Wisconsin (both built on undeveloped land) provide the best examples of this. The 

developers of Maple Crest, an elderly property in Port Washington Wisconsin, search for 

LIHTC development sites. The particular property selected in this case was chosen because 

it was an attractive wooded site in an upscale community, in a growing market, and in an area 

with a shortage of elderly housing. Similarly, the original developer for Williamstown Bay 

6-6 
in Cudahy, seeks out sites for affordable housing, particularly in modest income, blue collar 

communities. In this case, the property was designed for the elderly and the subject site, near 

a grocery store and pharmacy, fit the bill well. 

The fact that so few of the study properties were “located” in this way tends to undercut the 

hypothesis suggested by researchers at the outset of the program that developers would 

systematically seek out ideal tax credit locations-in particular, areas with low construction 

costs but relatively high family incomes. (The idea was that such locations would allow 

developers to minimize construction, financing, and operating costs while maximizing rents 

charged, since the later are based on area incomes.) The concern was that this process would 

ultimately lead to a locational mismatch between housing need and LIHTC projects. In 

response, the Tax Credit program was amended to include additional incentives to produce 

properties in poor neighborhoods (qualified tracts) and metro areas with low incomes relative 

to costs (Difficult Development Areas). While this study cannot assess the effect of these 

incentives, the siting process described by study developers rarely entailed a systematic 

search for the most advantageous location. Rather, it would seem that credits are more 

commonly used-by both types of developers-as one among several tools for addressing 

specific, pre-identified buildings or sites. 

Nevertheless, there are some important locational differences by developer type. As noted in 

Chapter 4, nonprofit properties are overwhelmingly located in central cities; the only real 

exceptions are two nonprofit properties in Miami, where the nonprofit teamed with a forprofit developer to create affordable housing opportunities. Almost all of these central city, 

nonprofit properties were developed explicitly to serve neighborhood improvement goals. 

Types of sites include a mix of vacant city lots and occupied and unoccupied residential 

structures. By contrast, for-profit properties are evenly split between city and suburban 

locations, with the almost half built on undeveloped land in suburban areas or outlying 

towns. These types of properties were most likely to have been developed primarily with 

profit motives in mind. For-profit properties in city locations reflected a mix of motivations 

as well as a mix of previous land uses. 

6.3 Project Impact 

A final question of interest is whether the LIHTC projects included in the study had a 

positive impact on the neighborhoods in which they are located. Like the assessmentof 

developer motivations, the answers are necessarily judgements. They are based on 

discussionswith developersandmanagers,interviewswith objectivelocal observers(e.g., 

CD agency staff or other local experts) where available, and on the observations of site 

visitors who toured the subject properties and the surrounding areas. The study was not 

6-7 
intended to collect any hard data on trends (e.g., property values, crime rates, investment 

patterns, or community perceptions) that might support or refute a claim of impact. Rather 

we looked, in a qualitative way, for elements of potential “neighborhood impact” including a 

positive visual impact, reported improvements in crime (or removal of a source of crime), 

changes in investment climate (i.e., nearby owners undertaking repairs or improvements) and 

subsequent development activity that might have been stimulated or acceleratedby the 

developmentof the tax credit property. 

In reviewing the 39 sample properties, we concluded that about two-thirds (25) could be said 

to have had substantial, positive impact on the neighborhoods in which they are located-that 

is, they treated a problem site, served as a stimulus for improvement activity, or at least 

provided a foothold for future revitalization of an area. (See Exhibit 6-2.) For the remaining 

properties that were not deemed to have had this kind of impact, there were several reasons. 

First, some of the properties, such as Putnam Place in Cambridge were judged to be too small 

to have much impact on their neighborhoods;this 12-unit property was developedto increase 

the stock of affordable units and was not really intended as a neighborhood improvement 

effort. Another example is Blue Hills in Kansas City: comprised of three small buildings (20 

units total) in three different neighborhoods, it is simply too small and dispersed to have any 

critical impact. 

Breakdown 

Type/Level of Impact 

Strong Positive Impact 

Moderate Positive Impact 

No Impact 

Negative Impact 

Total 

Exhibit 6-2 

of Sites by Neighborhood Impact 

2 1 3 

22 17 39 

Second, some of the properties were in areas that were not really neighborhoods at all. An 

example is Maple Crest outside of Milwaukee. This project is located in an undeveloped area 

and, while it enhances the tax base and may be the start of a new development site, there is 

little physical neighborhood for it to impact as of yet. Several Florida and California 

properties fell into this category, located in suburban-feelingareas,bordered by commercial 

uses with no clear neighborhood boundaries. 

6-8 
Third, several of the sample properties were located in very stable areas which showed no 

signs of distress or need for improvement. An example of this situation is Villa San Ramon, 

which is located in a high income senior community near Oakland. Properties such as Villa 

San Ramon provide many benefits to the low income seniors who can occupy the units and 

perhaps stay in a community that has been their long time home, however, it is difficult to 

say that the tax credit property “improved” the area in the senseused in this study. 

Finally, three properties were judged to be so poorly managed that their impact on the 

surrounding neighborhood verged on negative. Two of these were rehabs, one of a distressed 

property that by virtue of its size and design may not have been a good choice for 

reinvestment. Drug dealing was reported at both properties. The third was a new 

construction property, which while physically attractive (though somewhat imposing), had 

been allowed to become a “neighborhood problem” by the previous management. Current 

management attributes the problem primarily to poor tenant screening; many of the units 

sustained tenant damage and neighbors blamed the residents for car break-ins and other theft 

in the area. New management instituted criminal background checks and evicted problem 

tenants in early 1999, but still reports problems with minor theft and vandalism as well as 

domestic violence. 

Of the 25 properties considered to have had a positive neighborhood impact, three-quarters 

(18) were developed by nonprofit sponsors, virtually all of which had neighborhood 

improvement as an explicit goal. The remaining seven properties had for-profit developers 

and reflected the gamut of development objectives. In general, these for-profit properties 

were more likely to be rated as having moderate as opposed to strong impact on the 

neighborhood. In some cases they were located in better (already gentrifying 

neighborhoods); they were also more likely to be part of a larger redevelopment effort and 

therefore not on the cutting edge of neighborhood improvement. 

Examples of breakthrough projects with classic neighborhood ripple effects include Parkwest 

in Milwaukee and the Hyde Square Coops in Jamaica Plain. Parkwest is the only new 

housing in this part of Milwaukee and serves as an important (and visible) symbol of 

reinvestment. Since the development of the townhomes, crime in the area reportedly has 

declined and there has been other improvement activity in the surrounding area, including 

rehab of local single family homes and duplexes. Having proved the feasibility of this type 

of redevelopment, the sponsor, New Covenant Housing Corporation, started a second phase 

of townhomes and is planning a third. New Covenant hopes to convert the units to 

homeownership at the end of the compliance period and once the neighborhood has become 

more stable. 

6-9 
la Stairs for Future 2a Front Porch/ 3a 

Attic Expansion Entry 

1b Steep Sloped Roof 2b Variations in Roof 3b 

1c Flexible/Open Line 

Living Area 2c Landscaping 3c 

1d Expandable Areas 3d 

1e Easy Access/ 

Wider Doorways 3e 

3f 

3g 

Modular 4a Front Porch 

Dimensions (Shading) 

Modest Square 4b Grade Sloped 

Footage A way From 

Kitchen Pantry Foundation 

Lot 4c Roof Overhangs 

Reconfiguration 4d Gutters and 

Strategic Downspouts/ 

Window Sheltered 

Placement Entryway 

OVE Framing 

Techniques 

Stacked 

Bathrooms 

-! 
6.4 Compliance Period 

As a part of this study we asked developers about the length of the compliance/extended use 

period for the property and what was likely to happen to the property once the compliance 

period was over. The original legislation creating the tax credit provided for a 15year period 

during which qualifying units must be rented to low income households. The Omnibus 

Reconciliation Act of 1989 extended the commitment period to 30 years but permits owners 

to sell or convert the property to market rate after 15 years if the state agency which allocated 

the credits is unable to find a buyer (presumably a nonprofit) willing to own and maintain the 

units as low income housing for the balance of the 30 years. Based on their placed in service 

dates, the properties in the study sample should be covered by this later provision. 

Questions regarding the compliance period proved fairly difficult for developers to answer. 

Seven of the respondents could not tell us the compliance or use period for their properties. 

Of the remainder, just under half indicated a 15-year period, one-fifth indicated a 30-year 

extended use period, and another handful indicated that their properties were covered by 

additional use restrictions (some up to 50 years) as a result of state agency requirements or 

the terms of a subsidized loan. Overall, nonprofits seemed to expect the properties to remain 

affordable in perpetuity, although only a few were able to point to specific agreements (e.g., 

right of first refusal) in the LIHTC arrangement that would return the property to nonprofit 

control at the end of the period. Three of the nonprofit properties were intended to be 

converted to homeownership-the two co-ops in Boston and Parkwest Townhomes in 

Milwaukee. 

Among for-profits, there was a similar expectation that the properties would remain low 

income indefinitely. The exceptions were the two Oakland area properties with low 

qualifying percentages (20/50 regime), along with one Miami property that the developer 

hopes to convert to condos. Despite the general sentiment that the properties would remain 

affordable, the process to be followed and the future ownership form were not clear. Several 

of the developers said that the properties would remain low income primarily because the 

neighborhoods were not good enough to support a higher income use. A number also said 

that they expected “another program would come along” to keep the units low income once 

initial use restrictions expired. 

6.5 Summary 

Overall, roughly half of the properties in the sample were developed primarily to serve 

neighborhood improvement goals. The vast majority of these were developed by nonprofit 

6-I 1 
sponsors and were undertaken to address a specific problem site in the neighborhood. Close 

to 20 percent of the properties were developed primarily to increase the supply of affordable 

housing, with less emphasis on improving a neighborhood. Again, most of these properties 

were developed by nonprofits, although two were developed by a community oriented forprofit. Finally, the remaining 30 percent were developed by for-profit developers with 

traditionalrealestateobjectivesin mind. 

Regardless of motivation, roughly two-thirds of the properties could be said to have had a 

substantial,positive impact on their neighborhoods.This assessmentwas basedon our 

discussions with project owners and managers, the views of local experts, and our 

observations of the properties and their surrounding neighborhoods. Nonprofit properties 

accounted for most of this group, with an important difference being that nonprofits tended to 

represent the first new development in their neighborhoods, compared to for-profit 

properties, which were more likely to be part of a larger trend towards revitalization. Most 

developers, both nonprofit and for-profit, expected the properties to remain affordable 

essentially in perpetuity. This expectation appeared to be driven by a combination of 

affordability and use restrictions (both federal and state-imposed), the low-income character 

of someof theneighborhoods,andthesensethatfundingsourceswouldbecomeavailablein 

the future to preserve the units for continued low-income use. 

6-12 
Appendix A 

Sample Selection Procedures 

A-l 
Appendix A 

Sample Selection Procedures 

This appendix explains how our study sample was derived. It starts with a description of the 

criteria used to identify the five metropolitan areas to include in the study. Then we describe 

the sampling procedures for selecting a sample of LIHTC properties. This section includes a 

comparison of our property-level sample with all eligible properties in the five metropolitan 

areas. The next section describes our procedures for selecting a household sample for the 

LIHTC Resident Survey. The final two sections describe the derivation of the property- and 

household-level weights and the tests of statistical significance used in this report. 

A.1 Identification of Study MSAs 

Identifying the MSAs to include in the study was the first step in deriving the study sample. 

This selection was guided by the number and type of properties available for selection in 

subsequent stages. The HUD LIHTC data indicates that about 4,000 LIHTC properties were 

placed in service between 1992 and 1994. However, the focus of the study is on properties in 

metropolitan areas, thus the properties located in non-metropolitan areas (31 percent) were 

excluded from the study universe. Most FmHA Section 515 properties are located in nonmetropolitan areas; however, we also excluded any remaining FmHA properties in 

metropolitan areas. Because these projects operate under their own distinct set of occupancy 

and rent rules, it would not be useful to allocate sample to those few FmHA projects which 

may be located within metropolitan boundaries.’ After removing the properties that are not 

in the scope of this study-non-metropolitan and FmHA Section 5 1.5 properties-2,550 

properties remain in the initial property-level universe. 

Two further cuts to the property list were made for practical reasons. First, we removed 

properties that did not have adequate geographic data to identify the MSA and Census Tract 

in which they were located.* The ability to link the property to the Census tract data is 

’ The HUD database indicates that 3 percent of central city units and 19 percent of suburban metro units use 

FmHA financing. [See Abt Associates. 1996. Development and Analysis of the National Low-Income 

Housing Tax Credit Database. U.S. Department of Housing and Urban Development, Office of Policy 

Development and Research: Washington DC.] 

2 In the HUD project that createdthe LIHTC database,extensive efforts were made to geocodeall the 

LIHTC properties. It would not have been worthwhile to spend additional resources in this project trying 

to geocode the properties with missing MSA and Census Tract information, especially since most of them 

would not end up being located in the MSAs selected for this study. 

A-2 
necessary to describe the community where the property is located and compare the 

characteristics of the property and its residents to the other properties and residents in the 

community-an important part of this study. Second, to increase the number of observations 

in each property/tract, we also excluded properties with fewer than 10 units. With a 

maximum of 40 properties in this study, it would be inefficient to choose properties with 

fewer than 10 units: such properties account for 22 percent of all properties, but only 3 

percent of the LIHTC units. After these cuts, there were 1,304 properties in 236 MSAs from 

which to select the sample. 

To pare down the list to five MSAs, we were guided by the following three principles: 

l Choose MSAs with sufficient production in the 1992 to 1994 period to ensure 

recruitment of an adequate number of properties; 

l Choose MSAs containing a variety of property types in terms of project sponsor-type 

(for-profit or nonprofit), city and suburban location, and qualified and non-qualified 

census tract; 

l Choose five MSAs that are geographically balanced (at least one from each Census 

region) and represent a wide spectrum of housing market conditions. 

Below we explain our implementation of these principles to narrow the list of MSAs and then 

discuss our MSA selections. 

To ensure an adequate number of properties and neighborhoods from which to select, we 

examined areas with substantial LIHTC production. The study design required selection of 

eight properties from at least five different census tracts in each MSA; thus, an MSA must 

have at least five census tracts where a LIHTC property was placed in service between 1992 

and 1994 to be eligible for selection. Furthermore, we set a minimum threshold of 10 

properties in each MSA in order to provide a reasonable expectation that we would gain the 

cooperation of at least eight properties in the MSA. 

Following the process above, we used the LIHTC database to generate a list of 38 

MSAs(containing 739 properties) meeting our criteria of adequate LIHTC production--at 

least 10 properties in five tracts with LIHTC projects. At this stage, we used the 

characteristics of the properties and the need for balancing geographic and economic 

conditions to choose our five MSAs. We considered the following factors: geographic 

distribution; the number of for-profit and nonprofit sponsored properties; the mix of central 

city and suburban properties; the presence of qualified census tract and non-qualified census 

tract properties; and tightness of the rental market. 

A-3 
Selection at this stage was also informed by what was known about Tax Credit production in 

different areas of the country, often the result of different policy preferences reflected in state 

allocation plans. For example, in Los Angeles, state and local officials have a pronounced 

preferencefor usingthe LIHTC programto supportSROdevelopment,with roughlyhalf of 

all units in this area being efficiencies. Thus, even though Los Angeles had more LIHTC 

production overall, Oakland was the first choice in the West because its properties are likely 

to be more representative of the properties in other metropolitan areas in the region. The 

resulting MSA-selection is shown in Exhibit A- 1, which identifies the five MSAs included in 

the study. 

Exhibit 

MSAs Included 

Region 

Northeast 

Midwest 

West 

A-l 

in the Study 

MSA 

Boston I I 

1Milwaukee 

Oakland 

As can be seen in Exhibit A-l, we have two MSAs from the Midwest and one each from the 

other regions. In deciding which region to allocate two MSAs, we considered regional 

distribution of LIHTC properties and the geographic balance of all five sites together. Since 

the Midwest and the South have more properties and units than the other two regions, we 

wanted the fifth MSA to come from one of these regions. Among those MSAs, Kansas City 

was the best fit geographically, had a relatively high production rate and has a looser rental 

market to balance some of the other selections with tighter housing markets. We also chose 

two MSAs in Difficult DevelopmentAreas(DDA)-BostonandOakland--andthreethat are 

not in DDAs. 

It is also worth noting that except for Kansas City, very few properties were excluded 

because they had fewer than 10 units or because they were in the FmHA Section 515 

program. In Kansas City, two-thirds of the properties were excluded because they had fewer 

than 10 units.3 If we had included the smaller properties in the study, many of these 

3 The exclusionof the smallerunitsdoesnot biastheresults,but doesmeanthat theresultsshouldonly be 

interpreted as representative of LIHTC properties with at least 10 units. 

A-4 
properties would have likely been included in the sample and the Kansas City .properties 

would have represented a very small share of the LIHTC units there. In the other four MSAs, 

lessthan sevenpercent of the properties (and a negligible percentageof the total units) were 

excluded because they had fewer than 10 units or were in the FmHA program. The sampling 

universe for these MSAs is a nearly exhaustive list of the properties placed in service 

between 1992 and 1994 according to HUD’s LIHTC database. 

A.2 Selection of Study Properties 

Once the five MSAs for the study were finalized, we selectedthe properties for the study. 

Ideally, we wanted to ensurea mix of central city and suburban locations and qualified and 

non-qualified census tracts in each area, as well as both profit and nonprofit projects. In 

choosing the MSAs, all thesefactors were consideredbecausewe wanted to ensuresufficient 

representation of each in the final property sample. However, if we used all of these 

dimensions for selection of properties (eight combinations in all, e.g., a nonprofit, suburban, 

qualified censustract property), we would be severelyrestricted in our selection processand 

might end up with a sample of properties that is not very representative of the MSA. As a 

result, we selectedproperties based on just the sponsor type (nonprofit versus for-profit), 

allowing neighborhood location to fall out naturally. Because of the inherently different 

motivations of nonprofit and for-profit developers,they are expectedto undertake different 

types of LIHTC projects which might result in different patterns of project location, resident 

characteristics, and other features of the development and this sample selection method 

ensured adequate representation of each sponsor type. The sampling plan called for 

allocating half of the eight observationsin eachMSA to nonprofit properties and half to forprofit properties. 

Within each MSA, selection of properties was randomly chosen from separatelists of forprofit and nonprofit properties. Theoretically, this could have resulted in properties in fewer 

than five census tracts being chosen (e.g., two properties in each of four census tracts); 

however, analysis of the data showed there are almost the samenumber of censustracts as 

properties in the selected MSAs, indicating there are very few census tracts with more than 

one property placed in production between 1992 and 1994. Hence, this was not a concernin 

the selectionprocess. 

We also identified replacementsfor eachfirst-choice property to accommodateproblems due 

to lack of cooperation or to possible errors in the LIHTC database. It is important to 

recognizethat the data in the LIHTC databasewas providedby statesand was largely 

unverified, thus some of the initially-selected properties could be ineligible. In addition, our 

eligibility criteriaregardingspecialpopulationsservedand SROswas not availablein the 

A-5 
LIHTC database. However, only Boston had more than one ineligible property--it had six of 

them. Of the six ineligible properties in Boston, three were serving people with AIDS and 

the other three consisted of a SRO project, a transitional housing property, and a property 

with FmHA financing (not indicated on the LIHTC database). In Oakland, one selected 

property was found to be a SRO and thus was ineligible to be in the sample. None of the 

selected properties in Kansas City, Miami, or Milwaukee were found to be ineligible. 

Exhibit A-2 presents the final property-level sample by MSA and sponsor type. 

Exhibit A-2 

Study Properties by MSA and Sponsor Type 

Nonprofit For-Profit 

MSA Sponsor Sponsor Total 

Boston 5 3 8 

Kansas City 5 4 9 

IMiami I 4 I I 8 I 

Milwaukee 4 4 8 

Oakland 4 2 6 

All Prouerties 22 17 39 

The final property-level sample consists of 39 properties, 22 nonprofit properties and 17 forprofit properties. It comesvery closeto our goal of 4 nonprofitsand 4 for-profits in each 

MSA. However, as can be seen in Exhibit A-2, the Oakland sample ended up with only two 

for-profit properties. This is because the Oakland MSA had only four for-profits placed in 

service between 1992 and 1994. Of those, one was ineligible because it was a SRO property 

(noted earlier) and one property owner declined to participate, hence the final Oakland 

sample includes two of the three eligible for-profit properties in the MSA. Both the Boston 

and Kansas City samples have one more nonprofit property than originally targeted. This is 

due to discrepancies in our original data, which indicated that a selected property was a forprofit sponsored property when it was actually a nonprofit sponsored property.4 In Kansas 

City, we were able to recruit an additionalfor-profit property,but the discrepancywas 

discovered too late to do so in Boston. 

4 Partially as a result of this discrepancy, we collected basic information from all of the properties to 

ascertain the population of eligible nonprofit and for-profit properties in our five MSAs. These turned out 

to be the only two properties where we found a discrepancy in sponsor type. 

A-6 
As mentioned above, some of the initially-selected properties did not end up in the final 

sample because they refused to participate. No more than three property owners refused to 

participate in four of the MSAs, but six property owners refused in Kansas City. Of the six 

that refused in Kansas City, two were nonprofits and four were for-profits.5 The reasons for 

refusals varied: some owners reporting they did not want to further burden their staff or 

tenants because they had just finished a state audit or some other activity; other owners were 

unwilling to participate in any data collection effort (i.e., interviews and/or data from 

property files); and some owners could never be reached.6 Nevertheless, as can be seen in 

Exhibit A-3, the sample properties look very similar to all the eligible (including refusal 

properties) properties in the MSA. 

We compared weighted estimates from the sample to the population figures on all eligible 

properties for 12 different characteristics.’ The characteristics compared include sponsor 

type, central city location, qualified census tract, construction type, number of units, percent 

of qualified units, primary population served, and the type and prevalence of Section 8 

assistance. As can seen in Exhibit A-3, location in a qualified census tract (QCT) is the only 

dimension where there was a significant difference between the sample properties and all the 

eligible properties in the five MSAs. The study sample indicates 58 percent of the properties 

are located in qualified census tracts, whereas the all-eligible sample indicates only 46 

percent of the properties are in qualified census tracts. Examining the results by sponsor type 

indicates that this difference appears for both nonprofit and for-profit sponsored properties, 

but only the nonprofit sample properties are significantly more likely to be in qualified 

census tracts than their all-eligible counterpart (78 percent in nonprofit sample versus 59 

percent of all eligible nonprofits). 

All other comparisons, overall and by sponsor type, indicate no significant differences 

between the study sample and the population of eligible properties. Overall, it appears, at 

We comparedthe observablecharacteristicsof eligible propertiesin KansasCity to the study sampleof 

properties to find out if the refusals skewed the Kansas City in any particular manner. The sample 

characteristicsare similar to the populationof eligible propertiesin terms of constructiontype, size of the 

property, ratio of qualified units, primary populationserved,Section8 assistance,andnon-LIHTC funding 

sources. However, the samplepropertiesaremore likely to be in qualified censustracts within the urban 

corethanthe universeof eligible KansasCity properties. 

We made numerousefforts to contact the owner. We startedwith the name and phone number of the 

developerand owner in the LIHTC database.If that did not work, we tried directory assistanceand asked 

the StateFinanceAgency for updatedcontactinformation. Finally, we used directory assistanceto track 

down the site managerof the property,thenworked throughthem to talk to the owner. In all but onecase, 

we were at leastable to track down a numberfor the owner,but a few did not return multiple phonecalls 

andletters. In the end,we codedthemasrefusal. 

Derivation of the weightsis discussedin the last sectionof this appendix. 

A-7 
least on observable characteristics, that the study sample is fairly representative of the 

eligible properties in the five MSAs. 

A-8 
Sample Properties 

Nonprofit sponsor 

Construction type 

Entirely rehab. 

All or partially new 

Total units 

1O-20 

21-50 

51-99 

100+ 

Mean # of units 

Percent of qualified units 

l-60% 

61-80% 

81-90% 

91-99% 

100% 

Mean percent 

Primary population served 

Family 

Elderly 

Exhibit A-3 

Compared to All Eligible Properties in Study MSAs 

Nonprofit For-profit Tot al 

Ail’ Sample All Sample All Sample 

Eligible (~22) Eligible (~17) Eligible (n=39) 

(n=4I6) (n=39) (Ik=85) 

100% 100% 0% 0% 54% 54% 

78% 86% 51% 56% 66% 72% 

59% 78%* 31% 35% 46% 58%* 

34% 40% 45% 51% 39% 45% 

66% 60% 55% 49% 62% 55% 

17% 15% 6% 0% 12% 8% 

48% 51% 31% 34% 41% 43% 

24% 20% 25% 26% 24% 23% 

12% 14% 38% 40% 23% 26% 

58.0 63.0 97.7 94.6 75.2 77.5 

2% 0% 6% 8% 4% 4% 

0% 0% 0% 0% 0% 0% 

7% 9% 3% 4% 5% 7% 

4% 4% 9% 7% 7% 6% 

86% 87% 81% 81% 84% 84% 

97.5, 98.3 94.0 92.8 96.0 95.8 

90% 97% 77% 78% 85% 88% 

10% 3% 23%. 22% 15% 12% 

Any units with project-based 

(PB) Section 8 12% 18% 25% 28% 19% 22% 

Type of PB Section 8 

No PB Section 8 88% 82% 78% 72% 83% 77% 

Existing 7% 9% 15% 15% 10% 12% 

New 5% 9% 7% 12% 7% 11% 

Any units with tenant-based 

(TB) Section 8 78% 77% 57% 57% 69% 68% 

Percent of Section 8 units 

0 22% 20% 26% 28% 23% 24% 

l-20 51% 46% 37% 22% 45% 35% 

21-100 27% 34% 37% 51% 32% 41% 

Mean percent 18.9 22.7 30.6 35.0 24.6 28.4 

Sources: HUD LIHTC database and inte rviews with site managers and owners. Notes: stimates for imple are weig :d. The “all 

eligible” figures incorporate the entire population of eligible properties, thus are not weighted. *indicates significant difference between 

sample and population parameter (all eligible properties at the 10 percent significance level). 

A-9 
A.3 Selection of Residents for Survey 

This section describes our procedures for selecting a sample of households from the 39 study 

properties for the LIHTC Resident Survey. 

The average property size in five MSAs ranges between 54 (Milwaukee) and 101 (Miami) 

units. Based on the desired precision of the estimates, the expected response rate, and 

resources available, the study design called for selecting an average of 270 units at each of 

the MSAs for the survey. Because the sample properties had far more units than that, we had 

to select a sample of tenants in each property. 

The sample for the resident survey was selected using proportionate stratified sampling 

procedures. This procedure typically results in more precise estimates (i.e., lower standard 

errors), and never results in less precise estimates than a simple random sample of the same 

sample size. First we divided the population of eligible properties into separate strata based 

on MSA (5 strata). Then, we allocated the survey sample to each MSA in the same 

proportion as their units in the population. For example, LIHTC units in Boston comprise 

approximately 25 percent of units in the five MSAs, thus 25 percent of the survey sample 

was allocated to Boston. Within strata we allocated sample to each property in the same 

proportion as the population represented by that property. However, to increase the chances 

of obtaining and adequate sample size at each property for property-level estimates using 

survey data, we added additional sample for properties with less than 30 units. Prior to 

adjustments for nonresponse to the survey, this sampling procedure results in approximately 

equal sampling probabilities (taking the probability of selection of the property and the tenant 

into account) of all LIHTC residents in the sample properties. The end result is a sample 

with more precision than a simple random sample of households in the sample properties. 

Following the procedures discussed above, we selected our survey sample. Out of 

approximately 1,325 eligible households, we completed surveys with 832 respondents, 

representing a 63 percent response rate. The eligible survey sample and response rate by 

MSA is presented in Exhibit A-4. Response rates by MSA ranged from 53 percent in Miami 

to 73 percent in Oakland. As discussed in the weighting section, tenant weights were 

developed to account for differential nonresponse to the survey. 

A-IO 
Exhibit A-4 

Survey Sample and Response Rate by MSA 

Note: Eligible survey sample excludes vacant units and units where household identified in 

tenantfiles (from March or April 1999)no longerlived in the unit. 

A.4 Weights 

This study required estimates at both the property level and the household level, thus weights 

were developed for each. The property weight is simply the inverse of the probability of a 

property being selected for the sample. For example, there were 8 nonprofit properties in 

Miami and if we randomly selected 4 of the 8 properties, each property would have a 0.5 

chance of being in the sample, thus the property weight would be equal to 2.0 (l/0.5) Since 

properties were randomly selected within MSA-Sponsor type groups, the property weight 

varies across these dimensions.8 All estimates with the property as the units of analysis or 

with administrative data on all households in a property use the property-level weights. 

Household-level weights for estimates using survey data were also developed. Like the 

property weights, these weights are based on the inverse probability of being selected, but in 

this case it is the probability of a household being selected for the survey. In addition, an 

adjustment is made to account for survey non-response. The probability of being selected for 

the survey sample is a product of the probability of the household’s property being included 

in the study, and conditional on that, the probability of a household in the property being 

selected for the survey sample. In sum, the probability of being selected for the survey is the: 

probability of the property being selected multiplied by the probability of the household 

8 There were some extremedifferencesin the sizesof propertiesamongthe Bostonnonprofits andtheMiami 

for-profits, thus in these two situations we further created two separate groups based on size (less than or 

greater than 100 units) and separatelyselectedsampleswithin these groups. A few extremely large 

properties were also chosen with certainty. The property weights account for these sampling procedures. 

A-l 1 
being selected given that the household is in a selected property. In the above example, the 

probability of a Miami nonprofit being selected is 0.5. If at one of the sample properties, 7.5 

percent (0.75 probability), of the households were selected for the survey, then the overall 

probability of that household being selected for the survey is 0.375 (0.5 * 0.75). Thus, the 

household-sampling weight is 2.67 (or 1 / 0.375). To arrive at the final household weight, 

these weights were adjusted for nonresponse within properties. The non-response factor is 

simply the inverse probability of selected properties completing the survey.9 The overall 

response rate was 63 percent (or 0.63 probability), hence, on average sampling weights were 

multiplied by approximately 1.6 (or 1 / 0.63). Thus, in our example the final weight for a 

survey respondent would be 4.3 (2.67 * 1.6) if there was a 63 percent response rate at the 

property. All estimates from survey data used the household-level weights. 

A.5 Tests of Significance 

As in most studies, the findings in this study are based on a sample (a subset) of the 

population rather than the entire population. Hence, we present sample estimates of the 

population parameters of interest. Because the results are from a sample, there is sampling 

error associated with the estimate. That is, there is some uncertainty surrounding the estimate 

because we could have had different estimates if we had chosen a different sample. Hence, in 

comparing two sample estimates (such as mean earnings of residents of nonprofit and forprofit properties) this sampling error (or uncertainty) must be taken into account. The 

sampling error is taken into account in tests of statistical significance. These tests objectively 

indicate whether it is likely that the true population parameters are different as suggested by 

differences in the sample estimates. We have chosen to use the 10 percent level of 

significance as the criteria for reporting differences as statistically significant. Using the 10 

percent level of significance as our criteria for reporting significant results means there is less 

than a 10 percent chance that the population parameters are not truly different even though 

there are statistically significant differences in sample estimates. 

The tests of statistical significance used in this report include both the t-test for comparisons 

of means (e.g., comparison of mean income for residents of nonprofit and for-profit 

properties) and the chi-square test for comparisons of categorical data (e.g., comparisons of 

categorized earnings-such as <=30%, 31-50 %, ~50% of the area median-between 

residents of nonprofit and for-profit properties). 

9 This assumesnomespondentsaremissingat random. 

A-12 
The t-test is based on a comparison of the t-statistic to the critical value from the standard 

normal distribution. The t-statistic (t) is the difference between the two means being 

compared divided by the standard error of the estimate. The formula is shown below. 

t =(X1-X2)/&ar(Xj-X2) 

where 

X, = mean of group 1 

X, = mean of group 2 

Var(X,-X,) = Variance of the estimated difference between group 1 and group 2 means 

K-W. 

&mva = Standard error of estimates difference between group 1 and group 2 

means. 

The t-statistic measures the magnitude of the difference in standard error units rather than the 

natural units of the outcome variable (e.g., dollars). Thus the sampling distribution of the tstatistic is the same for all variables under the null hypothesis that there are no differences in 

the two means being compared. This means that, regardless of the outcome variable, one can 

test hypotheses using the same distribution of t-values. The t-statistic has a mean zero 

(because the null hypothesis is that there is a zero difference between the two means being 

compared) and the 90 percent confidence interval for the t-statistic ranges from -1.64 to 1.64. 

That is, if there is truly no difference in the population means, 90 percent of the samples that 

could have been chosen will have differences in means that will result in a t-statistic within 

the 90 percent confidence interval. If the t-statistic is outside that range (less than -1.64 or 

greater than 1.64), then we reject the null hypothesis (that the means are the same in the 

population) at the 10 percent significance level. Hence, the critical value for a 10 percent 

significance level test is 1.64. If the t-statistic is greater than 1.64 (or < -1.64), then we reject 

the hypothesis that the two population means are equal. If the t-statistic is between -1.64 and 

1.64, the we do not reject the hypothesis that the two population means are the same even 

though the sample estimates may be different. 

Note that because of the complex sample design (stratifying by MSA and sponsor type), it 

was not appropriate to calculate Var(X, -X,) using the standard variance formula for simple 

random samples. Hence, we used the STATA procedure, SVYTEST, to conduct the t-tests. 

SVYTEST uses a linearization based variance estimator that takes into account the stratified 

sampling design in calculating variances used in the significance tests.” The calculation of 

lo See pages 67-72 of STATA Reference Manual, Release 6, Volume 4: Su-Z. (1999) for more details on the 

variance calculation. 

A-l 3 
the t-statistic is the same as it would be with a simple random sample, just the calculation of 

the variance (and thus the standard error) of the estimate is different because of the complex 

sample design. 

Here is an example. Assume the mean income of for-profit households is $18,500 (X,) and 

the mean income of nonprofit households is $18,000 (X,) and the variance of the estimated 

difference between X, and X, [Var(X, -X,)1 is equal to $100,000. To get the standard error 

of the variance, take the square root of $100,000 which is equal to $3 16.20. Then calculate 

the t-statistic. Since the difference between X, and X, is $500 and the standard error of the 

difference is $316.20, the t-statistic is $500 divided by $316.20, which equals 1.58. Since, 

this t-statistic is within the 90 percent confidence interval (between -1.64 and 1.64) under the 

null hypotheses that the population means are the same, we cannot reject the null hypothesis 

at the 10 percent significance level. That is, even though the sample mean incomes are $500 

different, we are not confident that the true population means are not the same. Now, instead 

assume that the for-profit households had a mean income of $19,000 making the difference 

between for-profit and nonprofit households $1000. Now, the t-statistic would be equal to 

3.16 ($1000 divided by $3 16.2). Since this t-statistic is outside the 90 percent confidence 

interval, we say would say the two means are statistically significantly different at the 10 

percent significance level. 

The chi-squared test is used for comparisons of categorical data such as a comparison of the 

proportion of residents of nonprofit and for-profit properties that are extremely low-income, 

very low-income, low income, or moderate income. The chi-square test is based on a 

comparison of the x2 (chi-squared) statistic to the critical value from a chi-squared table. The 

x2 statistic is the sum of the squared deviations of the actual number of households in each 

cell (e.g., one cell is the number of nonprofit households that are extremely low-income) 

from the number expected to be in that cell if residents of nonprofit and for-profit properties 

had the same categorical distribution of the variable of interest. Algebraically, the formula 

for the chi-squared statistic is: 

s 

X2 = 3, [Cxi - eJ’/ e’l1 

where 

subscript i denotes the ith cell in the table and s the total number of cells; 

Xi is the observed value for cell i; and 

8i is the expected value for cell i on the assumption that there is no difference between the 

two groups being compared (e.g., no difference between the percent of residents of 

nonprofit and for-profit properties in that particular income category). 

The calculated x2 is then compared to the critical value at the 10 percent level of significance 

from a chi-square table. If the calculated x2 is greater than the critical value, then we reject 

A-14 
the hypothesis that the two populations have the same distribution of the variable being 

compared. If the calculated x2 is less than the critical value, then we do not reject the 

hypothesis that the two populations have the same distribution of the variable being 

compared. In the chi-squared test, the critical value depends on the sample size and the 

degrees of freedom in the categorical data being compared,” thus it varies across 

calculations. 

As in the case of the t-test, we relied on a procedure available in the STATA software 

package, SVYTAB, to conduct the chi-squared tests. SVYTAB takes into account the 

complex sample design in conducting the significance tests. To account for the survey 

design, the SVYTAB procedure converts the test statistic into an F-statistic with noninteger 

degrees of freedom using a procedure developed by Rao and Scott (1981, 1984).12 The pvalue for the new F-statistic can be interpreted in the same way as the p-value from the 

ordinary x2 statistic.13 

The degrees of freedom are usually determined by (r - 1) * (c - 1) where r equals the number of rows in 

the table (e.g., the number of income categories) and c equals the number of columns (e.g., the number of 

subgroups whose income distribution is being compared). 

I2 Rao and Scott. 1981. “The Analysis of Categorical Data from Complex Sample Surveys: chi-squared tests 

for goodness of fit and independence in two-way tables. Journal of the American Statistical Association 

76: 221-230. 

Rao and Scott, 1984. On Chi-Squared Tests for Multiway Contingency Tables with Cell Proportions 

Estimated From Survey Data.” Annals of Statistics 12: 46-60. 

” See pages 76-92 of STATA Reference Manual, Release 6, Volume 4: Su-Z. (1999) for more details on the 

chi-square test used for this study. 

A-15 

II 
Appendix B 

Data Collection Methods 

B-l 
Appendix B 

Data Collection Methods 

Data collection methodsincluded: 

l In-depth interviews with actorsfor eachproperty; 

l Collection of residentdata from managerfiles; 

l Site visitor observationsof propertiesand neighborhoods; 

l Resident survey; and 

l Collection of secondary data. 

The in-depth interviews, collection of resident data from manager files, and site visitor 

observations of properties and neighborhoods occurred on-site during field visits to each of 

the 39 study properties in the five MSAs.’ Field visits were conducted by study staff from 

March to May 1999, spending approximately one week in each MSA. 

B.l In-Depth Interviews 

During the field visits, the site visitors conducted a series of discussions with key actors for 

eachproperty. Discussionswere usedto gather qualitative information about the properties, 

residents, and neighborhoods and to explore development decisions and motivations. Key 

actors include: 

Property Managers; 

Developers/Owners; 

Representatives of the Local Community Development Department; 

Public Housing Authority (Section 8) Staff; 

Police Department Representatives; and 

StateHousing FinanceAgency Staff. 

Most of the discussions were conducted during the site visits; however, some were conducted 

by telephone. Below, we describethe types of information we gatheredin discussionswith 

each type of actor. 

Appendix A detailsthe processof selectingthe study sample. 

B-2 
Property Manager. In our discussions with the property managers, we gathered 

information on the property’s background, characteristics of the management entity, 

characteristics of the tenant population, tenant assistance and rent setting practices, 

marketing and tenant selection practices, amenities and services provided, and 

neighborhood and housing market characteristics. 

Developer/Owner. In the discussions with developers/owners, we gathered 

background information on the development and financing of the property, with 

particular emphasis on how and why the particular site was selected for development 

(or redevelopment) using the LIHTC. 

Community Development Department Representative. Discussions with 

representatives of the local Community Development Agency/Planning Department 

were conducted while on site in each MSA. In talking to CD department 

representatives, we gathered data on the city’s role in and perspectives on LIHTC 

development in general, the neighborhoods surrounding each sample property, and 

the perceived impact of the property on the areas in which they are located. 

Public Housing Authority Stafl In talking to PHA Section 8 staff, we gathered 

information about whether the PHA refers Section 8 certificate and voucher holders to 

LIHTC properties in general and to the study property in particular. We also explored 

the availability of housing for certificate and voucher holders in general and in tax 

credit properties in particular. 

State Housing Finance Agency. Representatives of the State Tax Credit Allocating 

Agency were contacted via telephone, as they were often in a different locality. We 

obtained copies of the states’ Qualified Allocation Plans (QAPs) and gathered 

information about the states’ overall objectives in allocating tax credits and the 

specific allocation/selection criteria in effect at the time the properties were 

developed. 

HUD Regional Economist. We spoke to the HUD Regional Economist about housing 

market conditions in the study MSA. We also asked about the impact of LIHTC 

projects, if any, on local Public Housing and Section 8 programs. 

B.2 Collection of Resident Information from Property Manager 

Files 

A key element of the on-site data collection was the collection of basic resident datafrom 

property manager files. Exhibit B-l shows the resident information we collected from 

management records. 

B-3 
Exhibit B-l 

Data Collected from Property Manager Files 

Tenant Contact Information 

Tenant Gross Monthly Income 

( Gross Rent I 

Race/ethnicity (if available) 

Household size (if available) 

It should be noted that, while managers were generally cooperative in providing data, there 

weresomelimitationsto thesedata. First, managersgenerallywerenot ableto providedata 

for residents in market rate units because they usually do not have the requested information 

for these residents, since LIHTC compliance regulations indicate that annual certifications 

must be collected for low-income residents only. Second, state policies and certification 

forms vary, therefore, the information was not consistent across MSAs and did not always 

include race or other characteristics. Finally, it is our experience that even if property 

managers collect certain data at application, they are unlikely to update it unless specifically 

required as a part of a reporting requirement. 

In light of theseconcerns,we collectedcompleteincomeanddemographicdataasa part of 

the resident survey. Available data were collected from managers, however, and was used to 

verify or augment the survey data as needed. Resident data obtained from property managers 

were entered into an electronic Tenant Data Spreadsheet. These data formed the basis for 

sampling residents and for conducting the resident survey. 

8.3 Site Visitor Observations of Properties and Neighborhoods 

During site visits, field staff recordeda varietyof observationsabouteachpropertyandits 

immediate neighborhood. The data from this “windshield survey” were used to document the 

design and configuration of the development (relative to surrounding properties), rate 

observable aspects of maintenance and upkeep, and assessphysical conditions in and around 

the development. The latter included noting any major signs of disorder (trash, vacant lots, 

B-4 
boarded up buildings, groups of people hanging out) and assessing the overall quality of the 

housing stock in the area. 

Information was recorded on a Property and Neighborhood Data Coding Sheet. In addition 

to the observations,we used the Property and Neighborhood Data Coding Sheet to record 

(post code) descriptive data about the property and the neighborhood obtained from our 

discussions with key actors and/or any written materials about the property or neighborhood 

obtained during our site visits. 

B.4 Data Collection for Non-Sample Properties 

In addition to the data collected for sample properties, we also collected data on the 

properties not selected for the study in the five MSAs, thus creating a database on the 

universe of properties in the study. Exhibit B-2 shows the data collected for the universe of 

properties. The purpose of this data collection was to place sample properties in context and 

to find out how many properties placed in service between 1992 and 1994 in the MSAs were 

eligible for the study even though they were not selected. 

Exhibit B-2 

Data Collected for Non-Sample Properties 

Category Data Item 

Population Served Age (Family/Elderly) 

SpecialNeeds(specify) 

Relationship with Section Existing Section 8 project at time of development (yes/no) 

8 Program Project-basedSection8 aspartof LIHTC project(yes/no) 

Proportion of tenants with tenant-based Section 8 

Public Subsidies Tax Exempt Bonds (yes/no) 

CDBG Funds (yes/no) 

HOME Funds (yes/no) 

FmHA (yes/no) 

Historic Tax Credits (yes/no) 

Other (yes/no) 

Confirmation of Data in Number of Qualified/Non-Qualified Units 

LIHTC Database Sponsor Type 

Construction Type (New/Rehab/Combination) 

Year Placed in Service 

B-5 
B.5 Resident Survey 

Administration of Survey 

The Resident Survey was carried out by Abt’s Survey Group with oversight from the study 

team. About 20 minutes in length, the survey was administered by telephone only.2 The 

instrument was pre-tested in January 1999, and the survey was conducted from June through 

August 1999. At the end of the survey period, we had completed 832 interviews, with a 

response rate of 63 percent.3 

The biggest challenge faced by the research team in administering a telephone-only survey 

for this study was in obtaining all the resident phone numbers. Our strategy for 

accomplishing this goal began with recruitment, when we discussed the need for contact 

information with the manager and assured him or her that the contact information would only 

be used for the survey. Resident phone numbers were generally collected from the property 

manager while the team was on site. We were able to collect phone numbers for about 70 

percent of the residents. However, in some cases, phone numbers provided were out-of-date 

or incorrect. For eight properties, managers did not provide a roster of phone numbers. At 

three of these properties, however, managers agreed to post sign-up sheets at the property so 

that residentscould sign up if they were interestedin the survey. 

The survey team sent an advance letter to each resident in the survey sample to introduce the 

survey and to expand and improve our database of contact information. The letter, which 

was sent in May 1999, informed respondents of the study effort, indicated that a $20 

incentive payment would be made for each completed interview, and requested that residents 

provide their phone number if we did not have it, or their correct phone number if we had an 

incorrect number. The residents provided this information on a form and sent it back in a 

business reply envelope. This letter also offered a toll free number that they could call 24 

hours per day/seven days a week to schedule a convenient time to complete the survey or 

provide updated contact information. As Spanishwas the first language spoken by a large 

portion of the residents in the Boston and Miami properties, residents living in properties 

with a sizable number of Spanish-speaking residents received bilingual advance letters. For 

households who did not respond to the letter (and for whom no valid telephone number was 

obtained from the property manager), we attempted to obtain their phone numbers from 

2 This method was chosen because it was more cost-efficient, less obtrusive, and did not require relying on 

property managers to gain access to locked properties. 

3 Of the interviews completed, 55 were in Spanish. 

B-6 
directory assistance and reverse directories. In addition, at several properties, we asked the 

managers if updated telephone numbers were available for those residents for whom we had 

incorrect contact information. 

The Survey Group began administering survey on June 1, 1999 and continued through 

August 15, 1999, a total field period of ten and a half weeks. Spanish-speaking residents 

were interviewed by Spanish-speaking interviewers using a translation of the survey. A 

second letter was mailed at the end of the seventh week of the survey to residents in the 

sample who had not yet completed the survey. 

Our telephone procedures included a minimum of 12 attempts to contact each respondent 

including calls at various times of day and on weekends as needed to conduct the interview. 

In the event that the respondent did not want to conduct the interview at the time we called, 

interviewers were instructed to schedule appointments at a more convenient time. The 

interviews were conducted using Abt’s Computer Assisted Telephone Interviewing (CATI) 

process. After the survey period was over, the responses were weighted to account for 

probability of selection and non-response to reflect the universe of eligible properties in the 

five MSAS.~ 

Finally, about three-quarters of the way through the survey, we sent a second advance letter 

to all households we had not reached by telephone. We included a $2 bill to catch their 

attention and again requested and updated telephone number. 

Topics Covered in the Resident Survey 

Appendix C contains the complete resident survey instrument. Major topics covered by the 

survey arehighlighted below: 

Satisfaction with Current Apartment. This section of the survey asked respondents 

to rate their LIHTC apartment, both overall and in terms of specific aspects of the 

apartment, such as size, layout, physical condition, amenities, and building 

management. The section also asked residents about how long they expect to live in 

their LIHTC unit and whether their next residence is likely to be a home that they 

own or another rental unit. 

4 Please refer to Appendix A for a more detailed discussion of selection methods, response rates, and 

weighting. 

B-7 
Satisfaction with Current Neighborhood. This section asked respondents to rate 

their current (LIHTC) neighborhood, both overall and on specific features, such as: 

access to public transportation, shopping, services, and schools; proximity to job 

opportunities; and being a safe environment. This section also included questions 

used in other research as measures of social cohesion (e.g., willingness of neighbors 

to help each other and extent to which they “get along”) as well as direct questions 

related to respondents’ sense of belonging or isolation and the extent to which they 

participate in community activities. 

Comparison of Current Apartment to Previous Apartment or Home. This section of 

the survey asked respondents to compare their current (LIHTC) apartment to their 

previous house or apartment in order to assess changes in housing quality. Again 

these comparisons included an overall rating as well as questions focusing on specific 

items, such as size, layout, physical condition, and amenities. 

Comparison of Current Neighborhood to Previous Neighborhood. This section 

asked respondents to compare their current (LIHTC) neighborhood to their previous 

neighborhood in order to assesschanges in neighborhood quality. Again, questions 

wereboth generaland specific(e.g.,accessto public transportation,shopping,and 

services, proximity to job opportunities, and being a safe environment). 

Factors Involved in Choosing Current Apartment. This section focused on the 

housing search process that led respondents to their LIHTC unit. Questions examined 

key factors in their selection of the particular unit, whether they already had a Section 

8 certificate or voucher, what sorts of properties they looked at, and where they first 

heard about the subject (LIHTC) property. 

Rent Assistance Information on Current Apartment. Thesequestionssolicited 

information on the current rent and utilities and ask whether respondents receive 

rental assistance, such as project-based Section 8 or Section 8 certificates or vouchers. 

Background on Prior Housing Situation. This section included a series of more 

detailed questions about the respondents’ housing situation immediately prior to 

moving into their LIHTC unit, including the location, type, and size of the old 

apartment or home, how many people lived there, and whether the previous unit was 

owned or rented. 

Rent Burden and Assistance Associated with Previous Apartment or Home. These 

questions asked respondents about the rent burden of the prior housing situation 

compared to the current housing situation and solicit information about previous 

rental assistance. The questions were also used to determine whether respondents 
shared rent in the previous unit and whether they previously lived in an institutional 

setting, including a shelter for the homeless or for victims of domestic violence. 

Satisfaction with Previous Apartment or Home. In this section, respondents were 

asked to rate their level of satisfaction with their previous dwelling unit. 

Satisfaction with Previous Neighborhood. This section asked respondents to rate 

their previous neighborhood and collects descriptive information about the income 

and racial mix of this neighborhood. The section also asked respondents about their 

perceptions of belonging or isolation in the previous neighborhood and their level of 

participation in that community. 

Household Information. This final section of the survey gathered demographic 

information about the household, including the respondent’s age, ethnicity, race, and 

household size and composition. It also asked about employment status, receipt of 

welfare, and sources and amount of household income. 

B.6 Collection of Secondary Data 

In addition to the data collected by our field team, we also used secondary data sources in our 

analysis, including MTCS and TRACS, HUD Area Gross Median Income (AGMI), and 

HUD Fair Market Rents (FMRs). Abt staff worked with HUD staff to obtain current MSAlevel MTCS and TRACS data on demographic characteristics for both public housing and 

Section 8 residents. These measures included: 

l distribution by race; 

l distribution by age; 

0 mean household size; 

l median/mean household income and distribution by income range; 

l percentage of residents with earned income; and 

l percentage of residents receiving welfare. 

We obtained FMR and AGMI data from HUD’s webpage. In addition, we used Census data 

to geocode sample property addressesas well as survey respondents’ prior addresses. 

B-9 
Appendix C 

Low-Income Housing Tax Credit Resident Survey 
Case ID# 

Study # 

Stratum 

Interviewer 

Date 

OMB# 

OMB Exp. Date 

# 

ID # 

I I 

2528-0200 

03/25/2000 

ASSESSMENT OF ECONOMIC AND SOCIAL CHARACTERISTICS 

OF LIHTC RESIDENTS AND NEIGHBORHOODS 

Resident Survey 

Survey Research Group 

Abt Associates, Inc. 

May 1999 

c-2 
INTRODUCTION TO RESPONDENT: 

Hello, may I speak with (RESPONDENT NAME)? 

Hi, this is (NAME) calling from Abt Associates. We recently sent you a letter explaining 

that we are working with the U.S. Department of Housing and Urban Development on an 

important study about apartment buildings that were built using federal tax credits.* As we 

explained in the letter, you will receive $20 for participation in this phone survey about what 

makes different apartments or neighborhoods a good place to live. The information from this 

survey is completely confidential; no one will be identified in our report. 

I just want to confirm that you still live in the same building. Is your address still (STREET 

ADDRESS)? 

[IF CONFIRMED, START INTERVIEW. IF NOT, END THE INTERVIEW.] 

*If respondent asks: These are buildings where the developer applied for and received a tax 

subsidy in order to keep rents affordable. 

c-3 
Section A: Current Housing Situation 

I’d like to start by asking you some questions about where you live now. 

Satisfaction with Current Apartment 

Al.Overall, how would you rate the apartment where you currently live? Is it... 

An excellent place to live 4 

A good place to live 3 

A fair place to live, or 2 

A poor place to live? 1 

A2. How would you rate your current apartment in the following areas. Would you say it 

is excellent, good, fair, or poor in terms of... [(ROTATE) START WITH FIRST 

ITEM. CONTINUE UNTIL ALL ITEMS HAVE BEEN RATED.]? 

ITEM Excellent Good Fair Poor Don’t 

Know 

A. the size of the apartment 4 3 8 

B. the layout of the apartment --I- 4 3 8 

C. the condition of appliances like 

stoves and refrigerators, and the 

plumbing and electrical systems 4 3 8 

D. the condition of the common areas 4 3 -q-G- 8 

and hallways in the building + 

E. the availability of amenities like 8 

dishwashers and laundry facilities 4 3 2 1 

F. being a good place for children to 4 

live -I- 3 

-t2 1 8 

A3. How long ago did you move into your current building or development? 

6439 - years, (A3M) plus - months ago 

A4. How many bedrooms does your current apartment have? bedrooms 

A5. How satisfied are you with the building management where you currently live? Are 

you... 

C-4 
Very satisfied 5 

Somewhat satisfied 4 

Neither satisfied nor dissatisfied 3 

Somewhat dissatisfied, or 2 

Very dissatisfied 1 

A6. Do you think you will still be living in this building... 

A. five years from now? YES ...... . [SKIP TO A71 

NO.. ......2 

B. three years from now? YES ...... . [SKIP TO A71 

NO.. .......2 

C. one year from now? YES.. .....l 

NO.. .......2 

A7. [IF SHORTER = 1 THEN GO TO A8 INTRODUCTION] When you move, do you 

think it will be to... 

a house or unit that you own 

another rental apartment 

or someplace else 

DON’T KNOW 

1 

2 

3 

8 

Satisfaction with Current Neighborhood 

I would like to ask you some questions about the neighborhood you live in now. 

A8. Overall, how would you rate your current neighborhood as a place to live? Is it... 

An excellent place to live 4 

A good place to live 3 

A fair place to live, or 2 

A poor place to live 1 

A9. How would you rate your current neighborhood in the following areas. Would you 

say it is excellent, good, fair, or poor in terms of...[ROTATE] 
H. being close to job opportunities 4 3 2 1 8 

I. being safe 4 3 2 1 8 

J. being a good environment for 4 3 2 1 8 

children 

AlO. Please tell me how much you agree or disagree with the following statements about 

Your neighborhood. [(ROTATE) READ ITEM, AND SAY...Do you strongly agree, 

somewhat agree, neither agree nor disagree, somewhat disagree, or strongly disagree.] 

ITEM Strongly Somewhat Neither agree Somewhat Strongly 

agree agree nor disagree disagree disagree 

A. People around here are willing to 5 4 3 2 1 

helptheirneighbors 

B. This is a close-knit neighborhood 5 4 3 2 1 

C. People in this neighborhood 5 4 3 2 1 

generally get along with each other 

Al 1. Which of the following statements seems most true with regard to how you feel about 

your neighborhood? [CHOOSE EITHER VERSION] 

“I feel like I am a part of my neighborhood.“. ................... .3 

“I feel isolated from my neighborhood.“. .......................... .2 

C-6 
Or do neither of these statements seem true.. .................... .l 

OR 

“I feel isolated from my neighborhood”. ........................... .2 

“I feel like I am part of my neighborhood”. ...................... . 

Or do neither of these statements seem true.. ................... .l 

A12. How active are you in the neighborhood in terms of attending meetings and events? 

Would you say you are... 

Very active .............................................................. .3 

Somewhat active or.. ................................................. .2 

Not active ................................................................. .l 

A 13. Do you socialize with people in your building on a regular basis? 

YES ........ 1 NO......2 

A14. Do you socialize with people who live in your neighborhood, but not in your 

building, on a regular basis? 

YES ....... 1 NO......2 

Comparison of Current Apartment to Previous Apartment or Home 

[IF SHORTER = 1 THEN GO TO A20 INTRODUCTION] 

Now, I would like to ask you a few questions comparing your current apartment to your 

previous apartment or home. By previous, I mean the house or apartment you lived in 

immediately before your current apartment. 

Al 5. In general, how would you compare your current apartment to your previous 

apartment or house. Is your current apartment... 

better than the previous place 3 

about the same, or 2 

not as good as the previous place? 1 

A16. For each of the following specific items that I read, tell me whether your current 

apartment is better than your previous house or apartment, about the same, or not as 

good as the previous house or apartment. [ROTATE] 

c-7 
Better than the About the Not as good as Don’t 

previousplace I Same I theprevious Know 

place? 

A. the size of the apartment 3 2 1 8 

B. the layout of the apartment 3 2 1 8 

C. the condition of appliances 

(like stoves and refrigerators), and 3 2 1 

the plumbing and electrical 1 I I 1 8 

systems 

D. the conditionof the common 8 

areas and hallways in the building 3 2 1 

E. the availability of amenities 

such as dishwashers and laundry 3 2 1 8 

facilities I I

I II I 

F. being a good place for children 1 3 2 1 1 8 

to live 

Comparison of Current Neighborhood to Previous Neighborhood 

Now, I would like you ask you a few questions comparing your current neighborhood to your 

previous neighborhood. By previous neighborhood, I mean the neighborhood where you 

lived immediately before your current apartment. 

Al 7. Where was your previous apartment or home located? Was it located... 

In the same neighborhood as your current apartment 1 [SKIP TO A201 

In a different neighborhood but the same city 2 

In a different city, but the same state 3 

In a different state 4 

In a different country 5 

A18. In general, how would you compare your current neighborhood to the neighborhood 

where you used to live? Is your current neighborhood.... 

Better than the previous neighborhood 3 

About the same, or 2 

Not as good as the previous neighborhood 1 

C-8 
1 

2 

3 

4 

5 

6 

7 

8 

9 

A19. For eachof the following items, would you sayyour current neighborhood is better, 

about the same, or not as good as your previous neighborhood? [ROTATE] 

ITEM Better than the about the Not as good DON’T 

previous same asthe KNOW 

neighborhood previous 

place? 

A. having accessto good schools. 3 2 1 8 

B. having access to public 3 2 1 8 

transportation. 

C. having good transportationto work 3 2 1 8 

D. having accessto grocery stores 3 2 1 8 

E. having accessto social services 3 2 1 8 

F. having access to parks and 3 2 1 8 

recreationfacilities 

G. being closeto friends and relatives 3 2 1 8 

H. being closeto job opportunities 3 2 1 8 

1. being safe 3 2 1 8 

J. being a good environment for 3 2 1 8 

children 

Factors Involved in Choosing Current Apartment 

Now, I would like to askyou a few questionsabout how you choseyour current apartment. 

A20. What reasonbest describeswhy you moved into your current building? [(ROTATE) 

CHOOSE ONLY ONE. READ LIST]: 

To pay lower rent 

To live in a nicer or bigger apartment 

To be closerto friends or relatives 

To live in a safer place 

To havebetter accessto social services 

To be closer to job/work 

To be nearbetter schoolsfor your children 

To live in smaller/easier to manage apartment 

For someother reason? Pleasespecify: [RECORD 

VERBATIM] 

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3 

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5 

6 

7 

8 

9 

A2 1. What is the second most important reason you moved into your current building? 

[(ROTATE)CHOOSEONLY ONE.IF NEEDED,READLIST AGAIN]: 

To pay lower rent 

To live in a nicer or bigger apartment 

To be closer to friends or relatives 

To live in a safer place 

To have better accessto social services 

To be closer to job/work 

To be near better schools for your children 

To live in smaller/easier to manage apartment 

For someotherreason?Pleasespecify: [RECORD 

VERBATIM] 

A22. [IF SHORTER = 1 THEN GO TO A241 When you were looking for your current 

apartment, [READ]: 

A. Were you on the public housing waiting list? 

YES .......1 NO .........2 

B. Did you have a Section 8 certificate or voucher you were trying to use? 

YES .......1 NO .........2 

A23. Which of the following considerations best describes the types of buildings you 

looked at or considered most while you were looking for your current apartment? 

ww 

BuildingsthatwouldacceptSection8 1 

Buildings with some other kind of subsidy to help low-income families, or 2 

Buildings not involved with government subsidies 3 

A24. Compared to the other buildings or apartments you looked at, was your current 

apartment: [READ] 

the least expensive? YES .......l NO ........ .2 

the best in terms of size or amount of space? YES .......l NO ........ .2 

in thebestlocation? YES.......l NO.. .......2 

in the best condition? YES ...... .l NO ........ .2 

offering the most/best amenities (like dishwashers, laundry, etc.) 

YES .......l NO ........ .2 

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3 

4 

5 

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A25. How did you first hear about your current apartment? Was it from [READ]: CIRCLE 

ONE RESPONSE 

friends or relatives 

the newspaper 

a realtor 

a sign on the property 

the Section 8 Office 

another social service office or a community group or 

someplace else? Please Specify: [RECORD 

VERBATIM] 

Rent and Assistance Information on Current Apartment 

Now, I would like to askyou somequestionsabout your rental costsand payments. 

A26A. [IF SHORTER = 1 THEN GO TO A271 Do you currently receive any governmental 

rental assistance, such as Section 8 or help from a local government in paying rent? 

YES .......1 

NO .........2 [SKIP TO A271 

A26B. Is this assistance.... 

a Section 8 certificate or voucher that 

you can use in a different place if you move 1 

project-based Section 8 that is tied to the 

apartment or building you live in now, 2 

or some other type of rental assistance Please 3 [RECORD VERBATIM] 

Specify: 

A27. How much do you currently pay each month for rent? 

$ rent per month [IF 0, SKIP TO A291 

A28. Are all, some or no utilities, such as electricity, gas, and water, included in your rent? 

All of the utilities included 1 [SKIP TO SECTION B] 
Some of the utilities included 2 

No utilities included 3 

A29. On average, how much do you currently pay each month for...[READ EACH ITEM 

ONE AT A TIME. ENTER ZEROS AS APPROPRIATE]: 

a) Electricity $ 

b) Gas $ 

c) Heating Oil/Propane $ 

d) Water/Sewer $ 

e) Trash Collection $ 

c-12 
Section 6: Prior Housing Situation 

Now I would like to ask you some questions about the place you lived right before you 

moved to your current apartment. 

Background on Prior Housing Situation 

B 1.SKIPPED ON PURPOSE 

B2. Was the building you lived in a... 

Single-family house 

Two-to-four-unit building 

Five-to-nine unit building or an 

Apartment building with 10 or more units? 

................ 1 

................ 2 

................ 3 

................ 4 

B3 . How many bedrooms did your previous (house/apartment) have? 

bedrooms 

B4. How many people lived with you in that (home/apartment), including yourself? 

total number of people [IF 1, SKIP TO B6] 

B5. Including yourself, how many were adults and how many were children age 17 or 

younger? 

4 number of adults 

9 number of children 

[SUM MUST BE EQUAL TO NUMBER IN B4. IF NOT, WORK WITH RESPONDENT 

TO RECONCILE NUMBERS] 

B6. Did you own the place that you lived in prior to your current apartment? 

YES .. . ..l [SKIP TO B14] 

NO........2 
Rent Burden and Assistance Associated with Previous Apartment 

or Home 

B7. Was your previous apartment or home located in a public housing development or 

scattered-site public housing unit? 

YES ........l [SKIP TO Bll] 

NO.. ........2 

[IF SHORTER = 1 THEN GO TO Bl l] 

B8. Which best describes your living situation before you moved to your current 

apartment? Were you living [READ]: 

In a house or apartment where you or your family were responsible 

for paying the rent ..................................................................................... I 

In a house or apartment where you or your family shared the rent with 

another family .......................................................................................... 2 

With friends or family and not responsible for paying rent 3 [SKIP TO B14] 

In a homeless shelter or shelter for domestic violence, OR 4 [SKIP TO B14] 

In the military or another institution 5 [SKIP TO B14] 

B9A. Did you receive any governmental rental assistance where you used to live, such as 

Section 8 or help from a local government in paying rent? 

YES.. ..........l 

NO ............ .2 [SKIP TO BlO] 

B9B. Was the rental assistance ... 

A Section 8 certificate or voucher that you could take with 

you when you moved 1 

project-based Section 8 tied to that apartment 

or building, or 2 

some other type of rental assistance? 3 [RECORD 

VERBATIM] 

c-14 
Please Specify: 

c-15 
[SKIP BlOA AND BlOB IF B2=1] 

B 1OA. Were there other tenants in your previous building that received Section 8 assistance? 

YES .......... 1 

NO.. .......... 2 

DON’T KNOW ... 8 

B 1OB. Were there other tenants in your previous building that received rental assistance 

Bll. 

B12. 

B13. 

other than Section 8 assistance? 

YES .......... 1 

NO. ........... 2 

DON’T KNOW 8 

Did you pay more or less rent where you used to live compared to your current rent? 

Was... 

The rent higher at the previous place 1 

About the same at the previous place, 2 

or was the rent lower at the previous place? 3 

When you were living in your previous place, was your income, including earned 

income and any public assistance or social security... 

Higher than it is now 1 

About the same as now, or 2 

Was your income lower than it is now? 3 

Thinking about the income you had when you were living at your previous place and 

the rent you had to pay, would you say the rent was... 

Harder to pay than it is now 1 

About the same, or 2 

The rent was easier to pay then than it is now 3 

C-16 
Satisfaction with Previous Apartment or Home 

B14. Overall, how would you rate the (house/apartment) where you used to live? Was it... 

An excellent place to live, 4 

A good place, 3 

A fair place, or 2 

A poor place to live? 1 

[IF A17=1 THEN SKIP TO SECTION C (PREVIOUS NEIGHBORHOOD SAME AS 

CURRENT NEIGHBORHOOD)] 

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2 

3 

4 

5 

6 

Satisfaction with Previous Neighborhood 

B 15. Overall, how would you rate your previous neighborhood 

Was it [READ]: 

An excellent place to live, 

A good place, 

A fair place, or 

A poor place to live? 

as a place to live? 

4 

3 

2 

1 

B16. the 

income level 

of your previous neighborhood. Would you say it was [READ]: 

All or mostly low-income households 1 

A mix of low and moderate income households, or 2 

All or mostly moderate or upper-income households? 3 

[IF SHORTER= 1 THEN GO TO Cl] Whichof the followingbestdescribes 

Other (SPECIFY) 

B 17A. Which of the following best describes the racial mix of your previous neighborhood. 

Would you say it was [READ]: 

Mostly one race or ethnic group, or 

Racially or ethnically mixed 

B 17B. Which group was it? READ LIST 

Mostly White 

Mostly Black or African American 

Mostly American Indian or Alaska Native 

Mostly Hispanic or Latin0 

Mostly Asian 

Mostly Native Hawaiian or Pacific Islander 

1 

2 [SKIP TO B18] 

B18. We would like to look up census data for your previous neighborhood; could you give 

us the mailing address of your previous place? 

Number and Street 

City State - zip 

REFUSED 7 

C-18 
DON’T KNOW 8 

B19. Which of the following statements seems most true with regard to how you felt about 

your previous neighborhood? [CHOOSE EITHER VERSION] 

“I felt like I was part of my neighborhood.“. .............. .3 

“I felt isolated from my neighborhood.“. .................... .2 

Or do neither of these statements seem true.. .............. .l 

OR 

“I felt isolated from my neighborhood.“. .................... .2 

“I felt like I was part of my neighborhood.“. .............. .3 

Or do neither of these statements seem true.. .............. .l 

B20. How active were you in that neighborhood in terms of attending meetings or events? 

Would you say you were... 

Very active .............................................................. .3 

Somewhat active or .................................................. 2 

Not active? ................................................................. 1 

c-19 
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Section C: Household Information 

Finally, I would like to ask you just a few background questions. 

Cl. In what year were you born? 

c2. Would you describe your ethnicity as 

Hispanic or Latino, or 1 

Non-Hispanic or Latin0 2 

REFUSED 7 

c3. How would you describe your race? 

White 1 

Black or African American 2 

American Indian or Alaska Native 3 

Asian 4 

Native Hawaiian or Pacific Islander 5 

Other (SPECIFY) 6 

REFUSED 7 

c4. How many people currently live in your apartment, including yourself? 

person/people [IF “1” IN C4, SKIP TO C6] 

C5. Including yourself, how many household members are adults and how many are 

children under the age of 18? 

a> adults 

b) children 

C6. [IF SHORTER = 1, THEN GO TO C lo] Do you currently work for pay? 

YES 1 

NO 0 [SKIP TO C8] 

REFUSED 7 [SKIP TO C8] 

c7. On average, do you work 20 hours a week or more? 

YES 1 

NO 0 

c-20 
REFUSED 7 

C8. 

c9. 

ClO. 

[If C4=1 SKIP TO ClO.] Does anyone else in your household currently work for pay? 

YES 1 

NO 0 [SKIP TO Cl01 

REFUSED 7 [SKIP TO Cl01 

On average, (does that person/do those persons) work 20 hours a week or more? 

YES 1 

NO 0 

REFUSED 7 

What is your total household income before taxes or any deductions? Please include 

income from all members of your household. [IF NECESSARY: You can give me 

that on a monthly or yearly basis.] 

$ [If DK or REFUSED, GO TO C 1OB] 

ClOA. Is that... 

per month 1 [SKIP TO Cl l] 

or peryear? 2 [SKIP TO Cl l] 

ClOB. [PROBE IF Cl0 IS DK or REFUSED: Is your total annual household earnings 

more than. . . (CONTINUE UNTIL YOU HEAR “AW”)] 

Yes No REF DK 

$10,000 1 2 7 8 

$20,000 1 2 7 8 

$30,000 1 2 7 8 

$40,000 1 2 7 8 

C 11. What amount comes from each of the following sources? Please include all members 

of the household for each source. 

al) Earned income from work $ a2) per month 1 per year 2 

b 1) Public assistance $ b2) per month 1 per year 2 

cl) Social Security, retirement, or a pension $ c2) per month 1 per year 2 

dl) Disability payments $ d2) per month 1 per year 2 

el) Other sources $ e2) per month 1 per year 2 

[INTERVIEWER: SUM COMPONENTS AND CHECK AGAINST TOTAL. IF DIFFERENT, 

PROBE TO RECONCILE TOTAL AND COMPONENTS.] 

c-21 
[IF Cl lb1 =0 (NO PUBLIC ASSISTANCE), SKIP TO C14.1 

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2 

3 

4 

5 

6 

7 

C12. Are you or anyone else in your household currently receiving AFDCor TANF 

payments? 

YES 1 [SKIP TO Cl41 

NO 2 

REFUSED 7 

C 13. Have you or other household members ever received AFDC or TANF payments 

as an adult? 

Yes 1 

No 2 

REFUSED 7 

C14. What is the highest level of education you have achieved? 

Grade/Primary School 

High School, no degree 

High School degree 

Trade School 

Some College, no degree 

Associate Degree 

Bachelor Degree or higher 

C 15. Which of the following best describes your current marital status? 

Single, not living with a partner 1 

Single, living with a partner 2 

Married 3 

Divorced or separated 4 

Widowed 5 

THANK YOU STATEMENT: Thank you for completing the survey. As we said earlier, 

we would like to pay you $20.00 for taking the time to answer these questions. Pleasegive 

me the address where we should send the check. 

NumberandStreet Apt # 

City State Zip 

Thanks again for taking the time to participate in our survey. The $20 check will arrive in 

to - weeks. Have a good (day/evening). -

C-23