Racial and Ethnic 

Differences in 

Housing Search

Final Report

U.S. Department of Housing and Urban Development | Office of Policy Development and Research 
Racial and Ethnic Differences in Housing Search

Final Report

Maria Krysan

University of Illinois, Chicago

Kyle Crowder

University of Washington

Molly M. Scott

Urban Institute

Carl Hedman

Urban Institute

With Sade Adeeyo, Somala Diby, and Sierra Latham
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Acknowledgments

This report was funded by the U.S. Department of Housing and Urban Development. We are 

grateful to them and to all our funders.

The views expressed are those of the authors and should not be attributed to the Urban Institute, 

its trustees, or its funders.

Further, the authors acknowledge the work of our partners at Decision Information Resources 

Inc., our survey research firm. Lenin Williams, David Getman, Silvia Epps, Pamela Wells, and 

many others worked with the research team to adapt to changing circumstances and field the 

Housing Search Study. In addition, this study would not have been possible without the support 

of many other Urban Institute staff and academic interns during the past 3 years, including Rolf 

Pendall, Simone Zhang, Josh Leopold, Martha Galvez, Kristina Fenton, Kevin McLain, Daron 

Holman, Vanessa Ramirez, Sumin Kim, and Krista Cavanagh. 

Finally, our expert panel provided crucial guidance on the research design and the interpretation 

and implications of the results of this study.

Disclaimer

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

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

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

Chapter 1: Background ..........................................................................................................1 

Chapter 2: Methodology ........................................................................................................5 

Housing Search Study ................................................................................................5 

Indepth Interviews .....................................................................................................11 

Secondary Data Analyses ..........................................................................................13 

Chapter 3: The Search Process ..............................................................................................16 

Context .......................................................................................................................16 

Decision Making ........................................................................................................20 

Information Gathering ...............................................................................................23 

Evaluating Options .....................................................................................................30 

Final Outcome ............................................................................................................35 

Chapter 4: Racial and Ethnic Differences in Housing Searches ............................................41 

Context .......................................................................................................................41 

Decision Making ........................................................................................................46 

 Information Gathering ...............................................................................................48 

Evaluating Options .....................................................................................................51 

Final Outcome ............................................................................................................53 

Connections Among the Search Stages .....................................................................60 

Chapter 5: Identifying Other Clues About How and Why Racial Differences Arise 

in Housing Searches ...............................................................................................................62 

 Prioritizing Neighborhood Quality ............................................................................63 

 Avoiding Bad Treatment ............................................................................................66 

 Expectations of Discrimination..................................................................................68 

 Credit History.............................................................................................................74 

 Postvisit Challenges ...................................................................................................76 

Chapter 6: Tying It All Together: Identifying Search Typologies and 

Their Implications ..................................................................................................................79 

 The Key Features of Each Housing Search Dimension .............................................79 

 Identifying a Typology of Searches ...........................................................................80 

 Racial Differences in Search Types ...........................................................................82 

 The Relationship Between Searcher Type and Outcomes .........................................83 
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Chapter 7: Implications for Research ....................................................................................86 

 Preinquiry Phase ........................................................................................................87 

 Inquiry Phase .............................................................................................................89 

 Postinquiry Phase .......................................................................................................89 

 Proposed Methodologies ............................................................................................93 

Appendix A. Lessons Learned From Housing Search Study Recruitment ............................A-1 

Appendix B. Housing Search Study Screener .......................................................................B-1 

Appendix C. Recent Mover Cognitive Test Protocol ............................................................C-1 

Appendix D. Current Searcher Cognitive Test Protocol .......................................................D-1 

Appendix E. Demographic Questions ....................................................................................E-1 

Appendix F. Secondary Data Analysis Figures .....................................................................F-1 

Appendix G. Regression Analysis of Racial and Ethnic Differences in Factors 

That May Shape Housing Searches .......................................................................................G-1 

Appendix H. Regression Analysis of Search Typologies ......................................................H-1 

Appendix I. References ..........................................................................................................I-1
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Executive Summary

Since the passage of the Fair Housing Act in 1968, the U.S. Department of Housing and Urban 

Development (HUD) has been charged with ensuring people of all racial and ethnic backgrounds 

have equal access to housing. As part of this work, HUD commissions decennial studies of 

housing discrimination in the rental market to estimate the prevalence of discrimination across 

the country. Although the kind of discrimination measured in these studies is declining, evidence 

suggests that discrimination against racial minorities persists. Moreover, discrimination is 

becoming increasingly difficult to detect, and other academic research shows that residential 

segregation, although declining, still divides Americans by race.

We know little about one of the core mechanisms through which this inequality may be 

perpetuated: how people search for housing. This is problematic because HUD’s decennial 

studies of housing discrimination make assumptions about how people search for housing and 

conducts rigorous tests of a particular slice of the housing market at a particular stage in the 

process. HUD’s tests of racial discrimination in housing are based on a random sample of 

apartment listings (usually advertised online or through newspapers) to which pairs of wellqualified renters are asked to inquire and attempt to visit. The studies document what happens to 

these matched pairs (matched on all dimensions except the tested characteristic, in this case race

or ethnicity) from the initial inquiry and then visit to the unit. These studies have provided 

invaluable data that have been critical in HUD’s efforts to identify the scope and scale of housing 

discrimination and to design policies that attempt to eliminate it.

What is missing from the methods used in the HUD’s Housing Discrimination Studies, however,

is what happens at the stages before and after the inquiry. The design does not capture what 

funnels people into inquiring about particular units and neighborhoods. The studies assume equal 

probability of inquiring about the same rental locations. Additionally, because the test concludes 

after one visit to see the unit, the study does not uncover discriminatory treatment during 

application and negotiation.1 Moreover, this focus on one part of a housing search means that 

these studies cannot tap the overall costs of discrimination. For example, being denied access to 

a unit at the inquiry stage likely means a lengthier (and more costly) search or a less desirable 

unit for the searcher. Without understanding discrimination in other parts of the search, we are 

left with an incomplete story about how discrimination affects renters who experience it.

This project aims to understand how racial or ethnic groups differ in their search for rental

housing to help HUD detect differential treatment and design policies and programs to better 

meet its goals. We seek to answer four research questions.

1. How do people search for rental housing?

2. How do housing searches differ by race and ethnicity?

3. What are the consequences of these differences for relative housing outcomes?

4. What are the implications for future research?

To answer these core questions, we need to go beyond the inquiry stage. Although the Housing 

Discrimination Studies have proven crucial in efforts to dismantle discrimination, the results of 

our study highlight how the story of how housing becomes racially segregated—and the role of 

 1 Of course, ethical issues are associated with continuing the rental process beyond the initial inquiry or visit in this experimental 

context. Therefore, the limitations of the audit-style method are understandable.
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discrimination in it—is masked by focusing on a particular style of housing search and particular 

assumptions about how discrimination affects that process. Our multimethods research design 

draws on statistically rigorous analyses of probability-based samples and large-scale cognitive 

interviews with convenience samples of renters in a single metropolitan area. It is an exploratory 

study that provides not definitive conclusions but promising directions for the next generation of 

HUD research, building on the foundation provided by the Housing Discrimination Studies.

In this report, we analyze original and secondary data to provide a portrait of the housing search 

process in general, followed by an intensive assessment of how White home seekers and racial or 

ethnic minorities compare in conducting those searches. Beyond having different experiences 

when inquiring into a housing unit, what happens before that inquiry (preinquiry phase) and after 

it (postinquiry phase) can be different for White, Black, and Latino renters. These differences 

reveal additional moments where discrimination can occur, but they also point to areas that may 

cause the racially stratified housing outcomes that define American cities. For example, we find 

racial differences in the resources brought to a search and the motivation for a search. 

Additionally, people consult different sources. Many racial minorities rely on social networks not 

only to identify units, but also to get information about neighborhoods, find landlords who will 

not discriminate against them, and identify neighborhoods that are open to people of their racial

or ethnic group. Relying on these sources means that some searchers do not access housing units 

through the means that audit studies assume (for example, seeing an apartment unit listed in a 

newspaper or on the internet). Our study also reveals that race shapes the kinds of neighborhoods 

people choose from; people do not randomly inquire about units, and in many cases, White, 

Black, and Latino renters search in altogether different neighborhoods.

Racial minorities often anticipate discrimination, which can shape how they search. The 

possibility of discrimination may funnel them away from certain possibilities and toward others 

in the preinquiry stage when deciding what units or neighborhoods to investigate. This 

anticipation can also influence the inquiry stage in how units are inquired about (for example, by 

email or phone). Expecting discrimination can also shape how searchers present to the landlord 

during an inquiry.

Anticipated discrimination may shape how searchers interpret what happens at the postinquiry 

stage, in terms of whether they will apply for a unit, and if they do, what they expect to happen. 

Searchers may decide not to apply for a unit or may not have their application accepted if they 

do. Our analyses provide both direct and indirect evidence of postvisit challenges that racial 

minorities face. Housing searches for racial minorities take longer, possibly because the 

motivation for the search is less time-urgent and more of a diffuse desire to improve 

neighborhood quality, or because searchers experience landlords who give them the runaround, 

stalling until a “more qualified” applicant is identified. Finally, many searchers identify credit 

history and its associated challenges as shaping their search at the preinquiry, inquiry, and 

postinquiry stages. The effects of credit history are underappreciated in current research and need 

more attention, particularly because our data suggest that, across income categories, Black 

renters are more likely to experience these challenges compared with White renters.

In this report, we outline a conceptual framework for the complexity of housing searches, 

describe the research methods used to answer our core research questions, and then provide our 

analysis, beginning with a general overview of the complexity of housing searches, followed by 

analyses that pinpoint racial or ethnic differences in the searches and describe five factors that 

highlight the unique experiences of racial and ethnic minorities. We conclude with a cluster 
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analysis to characterize three types of searchers. Our final chapter brings together all these 

results to map out the next generation of research that will allow HUD to design additional 

studies and policies that take into account the greater complexity of a housing search—and the 

myriad ways that race shapes them—to fulfill its mission of “building inclusive and sustainable 

communities free from discrimination.”

Chapter 1: Background

To guide our investigation, we draw on a conceptual model designed to learn how people search 

for housing. This model highlights five dynamic dimensions of a housing search:

Context: Renters’ characteristics and circumstances as well as local housing market conditions 

shape all aspects of the search.

Decision making: Throughout a search, renters make decisions about what to look for and what 

steps to take.

Information gathering: Searchers gather a variety of information using various sources and 

methods.

Evaluating options: Searches involve assessing how options match expectations.

Final outcome: The end result of the search includes whether renters find a unit to rent as well as 

the subjective and objective features of the unit.

Chapter 2: Methodology

To answer these research questions, our team employed a mixed-methods approach, drawing on 

three components:

The Housing Search Study (HSS): We fielded telephone interviews with a convenience sample 

of 135 recent movers and 351 current searchers in the Washington, D.C. metropolitan area. This 

data set permits a full exploration of all the dimensions of a housing search.

Indepth interviews: We conducted face-to-face interviews with a subsample of 40 HSS 

respondents to provide depth and nuance to the quantitative data gathered in the more structured 

telephone interviews.

Secondary data analysis: We drew from three large, random-sample surveys: the Panel Study of 

Income Dynamics (PSID), the America Housing Survey (AHS), and the Chicago Area Study 

(CAS). These surveys provided statistically rigorous tests of racial or ethnic differences on a 

limited set of variables.

The research team also consulted with a panel of experts including HUD staff at both the 

beginning and final stages of the study to design the methodology and appropriately frame our 

findings. The result is this report, which presents a comprehensive picture of the housing search 

process with an eye toward future research to improve policy and practice.

Chapter 3: Complexity of Housing Searches

Because we know little about the housing search process, we begin our investigation with basic 

questions: what are the dimensions of a search, and what kind of variety is there in how people 

conduct their searches? This discussion will form the conceptual backdrop to explore if and how 

racial groups differ in how they conduct housing searches. By analyzing the convenience sample 

data from the HSS and indepth interviews, we illustrate how housing searches can vary. Because 
viii

of the sampling, we must be cautious of generalizing the results and instead focus on how 

searches can differ, without claims made of the prevalence of particular search behaviors.

Context

Housing searches are shaped by personal constraints and the circumstances surrounding the 

move. Constraints include household income, credit, family status, disability, and access to 

transportation. Circumstances can vary in the degree to which moves are planned or foreseen and 

renters’ reasons for looking for housing. Searches are also affected by local housing market 

conditions. Our HSS and indepth interview data amplify the ways housing searches can be 

undertaken under varying circumstances with consequences for both the process and its 

outcomes.

• When asked what barriers they faced when searching, renters frequently mentioned 

poor credit history, insufficient funds for a security deposit, and not having 

transportation to get to units.

• Unplanned moves vary; some afford more agency to the searcher (for example, 

moving because of a conflict with neighbors) whereas others are reactions to events 

beyond the searcher’s control (for example, rent increases or house fires).

• People move for a variety of reasons (for example, family, job, or a search for better 

quality housing) and can be either pushed out of their current arrangement or pulled 

by the possibility of better options.

• Even if two people move for the same apparent reason, the context can differ. For 

example, one mover might be looking for something more affordable so he or she can 

save for homeownership, whereas another may be looking to move because the 

building’s laundry facilities were shut down, and the increased cost of doing laundry 

made rent unaffordable.

Decision Making

This dimension focuses on people’s decisions about the types of neighborhoods they will search 

in and the number of units and neighborhoods they will inquire about, they will visit, and for 

which they will submit applications.

• The number of units people decide to inquire about, visit, and submit applications for

varies considerably. Most people contact and visit many units but apply to relatively 

few. However, 18 percent of people in our HSS submitted applications to four or

more units in a single search.

• In the HSS, searchers varied quite a bit in how many neighborhoods they considered 

in their search. Just a few considered only the neighborhood in which they were 

already living.

• Because a housing unit cannot be separated from its neighborhood, we question 

whether people place more importance on one or the other when conducting their 

search. In our study, most searchers said these were of equal importance, a response 

that remained stable throughout their search.
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Information Gathering

Renters looking for housing vary not only in the sources of information they use (for example, 

internet, personal networks, or newspaper ads), but also in the types of information they gather 

(for example, on neighborhoods or units). Renters also vary in how they pursue opportunities,

from the inquiry to the visit, and in their subsequent negotiations with landlords or property 

managers. Our HSS captured this dimension, focusing on how people drew on social networks to 

get information and what kinds of information were important to them.

• Renters use their social networks to find rental vacancies and learn more about 

neighborhoods. Some searchers used these networks to identify rentals that did not 

require credit checks. Many searchers who used social networks to identify the units 

they moved into or visited received help from a friend, and most searchers received 

help from someone of the same racial or ethnic background.

• To identify the neighborhoods to search in, people relied on personal knowledge of 

the area and concrete experiences. Indepth interview participants talked about 

filtering searches by neighborhood name or zip code, which they associated with 

beliefs about affordability, safety, and racial composition.

• Searchers look for a variety of information about neighborhoods. Our convenience 

sample of D.C. searchers prioritized information about public transportation, parks 

and other amenities, and crime levels.

• Identifying specific rental units was largely done through social networks (word of 

mouth) and advertisements—with a very heavy reliance on online advertisements. 

However, when communicating with the landlord, the division between renters who 

used email and those who used the telephone was greater.

Evaluating Options

Throughout a search, renters evaluate potential neighborhoods and units using several criteria; 

the importance of any criterion to their decision making can change in light of the options 

available to them and uncovered during their search. The HSS questioned searchers about the 

criteria important to them, and then tracked if and how the criteria changed through the course of 

the search. These data reveal a few suggestive patterns.

• Searchers tend to prioritize building security, landlord responsiveness, and rent. 

Safety crops up again in neighborhood criteria that are considered top priority, along 

with transportation.

• Over the course of a search, renters frequently expand the neighborhoods they are 

considering and the price they are willing to pay—the latter is likely because of the 

high cost of rental housing in the Washington, D.C. metropolitan area.

• Some priorities shift in importance during a search, including building type, size of 

the home, and building amenities. Safety and convenience to public transportation, 

however, remain quite stable in their level of importance.
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Final Outcome

Not all renters find a new unit. If they do, the length of their housing search and their satisfaction 

with the search process, as well as with the unit and neighborhood, vary significantly. Our HSS

sheds light on these dimensions of a housing outcome.

• Most of the HSS participants cited finding a unit in their price range as the reason 

their search was difficult.

• Housing searches vary in length, from less than a week to many months. The variety 

is so substantial that our attempts to interview current searchers were challenged 

because many searches were not completed before our field period ended. It may be 

that many people search with low intensity for an extensive period, on the lookout to 

improve their housing but not with urgency.

• Our indepth interviews conveyed how time-consuming searches can be. Finding time 

to contact landlords, get questions answered, inspect units, submit applications, and 

gather paperwork were examples of why searches can be challenging, in terms of 

time.

• Satisfaction with units and neighborhoods was high, although our searchers found 

what they hoped for in their neighborhood more than in the unit.

The HSS data illuminate the ingredients of the housing search. A housing search is a dynamic 

process, so the order and number of applicable dimensions—and variables within these 

dimensions—may vary significantly for different types of searchers. With this background, we 

turn to the extent to which housing search ingredients vary based on the searcher’s racial or 

ethnic background.

Chapter 4: Racial and Ethnic Differences in Housing Searches

Our most rigorous assessment of the extent to which racial or ethnic groups have different 

experiences relies on the secondary analysis of three data sets—the AHS, PSID, and CAS. 

Although we could draw on statistical principles to determine if racial or ethnic differences exist 

beyond other characteristics (income, in particular), the data sets do not provide complete data on 

all aspects of the complex housing search process. However, the data do reveal systematic racial

or ethnic differences, even after controlling for group differences in demographics, 

socioeconomic status, and—in some cases—housing market conditions.

Context

Searches are affected by searcher characteristics, the circumstances of a move, and the larger 

housing market. Specifically,

• Black and Latino renters have lower incomes than White renters.

• Black and Latino renters are more likely than White renters to have children in their 

household.

Other factors, such as transportation and disability status, also create challenging circumstances. 

In some cases, these racial or ethnic differences can be explained by social class, income, the 

presence of children, age, and gender. In other cases, however, the racial or ethnic differences 

persist, such as for the reasons a move is initiated.
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• Black and Latino renters are more likely than White renters to move to improve the 

quality of their living conditions.

• White renters are more likely than Black or Latino renters to move to be closer to 

work or school, a difference that cannot be explained by background characteristics.

Decision Making

Decisions about which neighborhoods to search are critical to housing outcomes. Understanding 

the differences in how Black, White, and Latino home seekers approach this decision is crucial 

for understanding racialized patterns of housing. Our secondary data analyses provide a glimpse 

into this issue and reveal that—

• Although Latino and White renters are equally likely to look outside their current 

neighborhood, Black renters are more likely than White renters to do so, suggesting a 

more expansive search among Black renters.

• When looking at the neighborhoods people search in, White renters are more likely to 

search only in places where White residents are the majority, whereas Black and 

Latino renters are more likely to search in a mix of places, some where their racial or 

ethnic group is in the majority and others where their group is in the minority.

Information Gathering

A crucial step in searching involves gathering information about neighborhoods and units. Racial

or ethnic differences may emerge in how people to find this information—information that can 

translate into different options and opportunities. Our secondary data provide a glimpse into the 

ways that groups differ in this regard.

• Drawing on social networks (word of mouth) is the most common method used by 

Black and Latino renters, and Black and Latino renters are significantly more likely 

than White renters to use this method to identify the unit they moved into.

• White renters use online resources the most and are significantly more likely than 

Black and Latino renters to use this source to identify the unit they moved into.

• Black and Latino renters are more likely than White renters to have first heard about 

their unit by seeing a sign on a building, although this source is uncommon.

Evaluating Options

Although secondary data do not provide much information about how people evaluate their 

options and which factors are primary and secondary, they do provide a few hints based on a 

question asking renters why they ended their search.

• Black and Latino renters appear to settle on a unit that does not satisfy their criteria, 

because they are less likely than White renters to say they ended their search because

they were happy with the unit.

• Latino renters’ evaluation process may be truncated because of too few options or too 

little time, suggested by the finding that they are more likely than White renters to say 

they stopped their search because they had to move quickly or because they knew no 

other options. Differences between Black and White renters in this regard are 

explained by racial differences in background characteristics.
xii

• Challenges getting transportation to possible units, although rare, was cited more 

often by Black than White renters. This challenge may have resulted in an abridged 

evaluation process.

Final Outcomes

The secondary data analysis sheds light on the racial or ethnic differences in both objective and 

subjective aspects of a search’s final outcome.

• In any given year, White renters are more likely to move than are Black and Latino 

renters.

• White renters are less likely to have a failed search than are Black and Latino renters.

• When they do move, Black and Latino renters are more likely than White renters to 

stay in the same neighborhood. Some of these racial or ethnic differences can be 

explained by background characteristics, particularly having children in the 

household and receiving housing assistance.

• Black renters report longer searches than White and Latino renters.

• Black and Latino renters rate their new neighborhood as lower in quality and less safe 

than White renters, although the Latino-White difference becomes non-statistically 

significant when background controls are used.

• Despite giving negative neighborhood ratings, Black renters are more likely than 

White renters to say their new places are better than their old ones. However, 

objective neighborhood measures demonstrate little to no improvement in the quality 

of the destination neighborhoods when compared with the origin ones.

Connections Among Search Dimensions

The secondary data analyses provide important insights into the ingredients of housing searches 

and point us to racial or ethnic differences. Because of the scattered nature of existing data—with 

limited information on only selected aspects in separate data sets—we cannot achieve a 

comprehensive view of the consequences of these different housing search strategies on the 

outcomes of the search or the possible racial or ethnic differences in them. However, we can 

examine bivariate relationships among those aspects of housing searches identified as having 

substantial and persistent differences based on race or ethnicity. Although these bivariate 

relationships cannot be interpreted as causal links, the connections we uncover point us to clues 

worthy of further investigation.

• People who move for work or school are more likely to use online resources than 

people who move for other reasons.

• Involuntary movers are more likely to end a search with a less-than-ideal outcome 

and to say the new dwelling was chosen because the alternatives were constrained.

• Renters who searched only in the immediate neighborhood saw fewer units and were 

more likely to report that they chose their new dwelling because they perceived no 

better options.

• Searchers who were constrained in the preceding ways rated their new dwelling and 

neighborhood as lower in quality.
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The secondary data provide solid evidence that racial or ethnic differences exist in virtually 

every dimension of housing searches: the reason for the search, how information is gathered, the 

decisions made about where to search, and the eventual outcomes of the search. These results are 

based on probability samples of White, Black, and Latino renters and permit rigorous statistical 

tests. However, the data sets were limited in the scope and range of measures of housing search 

processes, so our knowledge about housing search processes has substantial gaps. For a more 

indepth look, we turn to the original data collection we conducted from the HSS and indepth 

interviews.

Chapter 5: Identifying Other Clues About How and Why Racial Differences Arise in 

Housing Searches

In addition to racial differences in some of the mechanics and approaches of a housing search 

that we uncovered in our secondary data analysis of probability sample data, in our analysis of 

the HSS and indepth interviews we also uncovered several clues about how and why racial 

difference in housing searches occur.

Because of their exploratory nature and convenience sampling methods, our conclusions based 

on the HSS and indepth interview data are less definitive and take a different approach than the 

statistically rigorous analysis of the probability-based samples in the PSID, AHS, and CAS. The 

HSS cannot draw on the principles of statistics to determine significant findings, given its 

convenience sample. We instead identify search strategies and experiences that show racial 

differences at the bivariate level that are not substantially diminished when controls for income 

and other background characteristics are included. We use this approach to draw general 

conclusions about the patterns across different questions and dimensions of a housing search to 

highlight clusters of search features that appear to be distinct for racial minorities. The indepth 

interview data draw out some of these themes and give nuance and insight to why some of the 

racial differences emerge.

Together, the HSS and indepth interviews cast a spotlight on how and why Black renters’

experiences are distinct. Although some strategies and approaches are not exclusive to Black 

renters, they reflect experiences and concerns that, when taken together, pinpoint ways Black 

renters’ search experiences and processes are unique. The themes that emerged in our HSS and 

indepth interviews highlight (1) the role of neighborhood quality in shaping a search, (2) efforts 

and strategies used to avoid bad treatment, (3) the role of anticipated discrimination in shaping 

stages of a search, (4) the emerging challenges associated with credit history, and (5) the 

challenges faced by searchers after visiting a housing unit.

Prioritizing Neighborhood Quality

Our convenience sample of searchers in D.C. revealed that improving housing quality was a 

primary motivation for undergoing a move or search for Black respondents, a finding similar to

the national data. Perhaps because of the fundamental differences also identified in national data 

about quality of initial neighborhoods and neighborhoods searched, Black renters in the HSS—

• Were more likely than White renters to seek information about crime and school 

quality.

• Were more concerned about neighborhood safety and more likely to rate its 

importance at the same level throughout the search.
xiv

• Considered building security significantly more important than White renters 

throughout their search.

Our indepth interview study also affirmed the significance of safety and revealed that Black and 

White searchers spoke differently about safety, with White searchers more likely to refer to areas 

they wish to avoid using hypotheticals, whereas Black searchers gave more details of the exact 

characteristics they wanted to avoid and related personal experiences with unsafe neighborhoods 

that drove these priorities.

Avoiding Bad Treatment

Black renters are especially likely to approach a search focused on ways to avoid bad treatment 

from landlords.

• Black renters more than White renters prioritize landlord responsiveness.

• Black renters more than White renters take special efforts to learn more about a 

potential landlord by using social networks to gather information about landlords and 

communicating by phone (rather than email) with the potential landlord, which gives 

more information about what kind of person the landlord is.

Expectations of Discrimination

Our study included some of the most detailed and direct questions about perceptions of fairness 

or discrimination in housing that exist. We asked questions about general perceptions of 

discrimination, past perceived discrimination, and methods used to avoid discrimination. We 

found the following.

• Many Black renters perceive that Black people do not enjoy equal access to housing 

in the D.C. area, and a large share report having personally experienced what they 

perceived to be housing discrimination.

• Black renters prefer diverse neighborhoods but want to avoid those that are too 

White. One indepth interview participant explained, “I like a blend of people. ... I 

think that’s always better because you can get a taste of everything, the good, the bad, 

and the different. You all could blend together, grow together, working it out 

together.” He went on to explain that he avoided all-White neighborhoods because “I 

always feel like I have to walk on eggs. In the sense that if I mess up or something go 

wrong ... I don’t want that pressure.”

• Learning about how welcoming a community would be to their racial group was an 

important step for more than one-half of our Black sample of current searchers. Some 

assessed the level of welcome by visiting the neighborhood to do “informal surveys”

of residents.

• Black renters attempt to avoid discrimination by relying on social networks to help 

them find landlords who would not discriminate against them. One-fourth of our 

sample of Black recent movers and 60 percent of our sample of Black current 

searchers report they used social networks for this purpose.
xv

Despite these efforts to avoid discrimination—

• About one-fourth of Black recent movers and 15 percent of Black current searchers 

reported experiencing racial discrimination during their housing search.

• Black recent movers and current searchers were substantially more likely than their 

White counterparts to report difficulty finding a landlord who would rent to them. 

Indepth interviews reveal how challenging it is for minority searchers to determine if 

the difficulty they experience is because of racial discrimination, such as wondering if 

the landlord is being truthful when they are told that a unit has just been rented. Our 

interviews also highlighted the steps minority searchers take to try to convey their 

middle-class status (for example, dressing up when they meet the landlord or driving 

a nice car) to try to overcome stereotypes landlords have about them based on race.

Credit History

Black searchers work hard to find landlords who do not require credit history and sometimes 

suspect that landlords prioritize credit history more than other criteria (for example, income) to 

dismiss their applications. Our HSS and indepth interviews uncovered the salience of this issue 

in several ways and at several points in the search process.

• Even after controlling for demographic and socioeconomic differences, our Black 

respondents were more likely than our White respondents to indicate that they had 

poor credit history.

• Black renters were more likely than White renters to use their social networks to find 

rental units that did not require a credit check.

• Some searchers limit where they search because of their credit history, avoiding the 

“little bit nicer places” that they believe will be out of reach because their credit has 

been “dinged.” Some searchers feel a need to wait until their credit improves before 

they search. One respondent reported being unable to rely on referrals from 

coworkers because of a reluctance to share private information about credit status.

• Poor credit history can also increase the cost of leasing or renting. Some indepth 

interview participants had to put up a bigger security deposit or pay more in monthly 

rent to compensate for poor credit.

Postvisit Challenges

Previous HUD Housing Discrimination Studies (HDS) have measured the moment of an inquiry 

and initial visit to a housing unit but do not measure anything after that point. Our HSS captures 

reports and experiences of searchers throughout the entire process, and our results suggest 

several ways that Black experiences are unique.

• Despite a multiplicity of differences in terms of context, search parameters, and 

methods of gathering information, Black and White renters nevertheless inquire about 

and visit similar numbers of units during the course of their searches.

• However, even after controlling for income and other personal factors, Black renters 

submit many more applications than White renters.
xvi

• Indepth interviews offer rich stories of experiences after initial visits to housing units

that paint a picture of landlords giving the runaround to minority renters. Interviewees 

referred to the length of time it took to hear back from landlords; this ranged from 

renters being told that they met all the criteria but being informed after a month of 

back and forth that “someone more qualified” got the unit, to the complexity and 

ambiguity of the process, with credit reports, paperwork, and application fees. None 

of these searchers can be certain their race caused these problems, but the perception 

is there. One Latina renter said, “I have great references, both professional and 

personal ... it’s not just bias like again—it’s not just a class thing. It very much 

intersects with race and ethnicity.”

Chapter 6: Tying It All Together: Identifying Search Typologies and Their Implications

A housing search is more than the sum of the ingredients of context, decision making, 

information gathering, and evaluating options. These ingredients come together to shape a final 

outcome. We used the recent movers in our HSS to craft a typology of searchers, characterizing 

three different general patterns of how these ingredients come together in individual searchers. 

We identified nine key indicators: two related to the context of the search (difficult search 

circumstances, like reasons for the search or barriers to searching, and the importance of push 

and pull factors to the search motivation), three related to information gathering (reliance on 

online activities, reliance on interpersonal context, and use of professional services), two related 

to evaluating options (persistent importance of neighborhood conditions and persistent emphasis 

on unit characteristics), and two related to decision making (volume of units inquired about, 

visited, and applied for; and number of neighborhoods involved). Using these nine indicators, we 

conducted a cluster analysis that allowed us to characterize how these different indicators 

grouped together in individuals to describe general types of searchers.

We use that typology to explore if Black and White renters are more or less likely to be different 

kinds of searchers, and then we compare consequences of particular searcher types for a handful 

of outcomes. We identified three kinds of searchers: open searchers, high-threshold searchers, 

and constrained searchers. There are similarities and differences in how these groups approach 

their search.

Open Searchers

Thirty-eight percent of searchers in the recent mover sample of the HSS engage in open 

searches.2 Open searchers are likely what many people imagine is the prototypical housing 

searcher: he or she has relatively few constraints, uses a range of methods for gathering 

information, and assesses many units across a wide set of neighborhoods. In our study—

• Black and White renters in the HSS (recent movers) were equally likely to be open 

searchers.

• Open searchers report the highest level of unit improvement and report that their new 

neighborhood is the same or better than their prior one.

 2 Because our sample is a convenience sample, the percentages of searchers in each of these three categories is not necessarily 

representative of their prevalence in the population—either among D.C. recent movers or among movers in other areas of the 

country. We report the percentages to characterize the distribution of different searcher types in the data set analyzed in this 

study.
xvii

High-Threshold Searchers

The 22 percent of our convenience sample of recent movers in the D.C. area who fall into this 

category embark on a search with a high level of planning and research, confront few constraints, 

and demand new locations that satisfy long lists of unit and neighborhood criteria. They use 

many tools to perform the search and investigate many neighborhoods, but they inquire, visit, 

and apply for relatively few units.

• White recent movers are more likely to be classified as high-threshold searchers than 

Black recent movers, a difference that does not appear to be attributable to differences 

in income or to the different age or gender profile.

• High-threshold searchers tend to report that their new unit is worse than where they 

started, which may be because of the high quality of the original unit or because 

searchers in this category have particularly high expectations.

• High-threshold searchers generally report that their new neighborhoods are the same 

or better than their old ones.

Constrained Searchers

Constrained searchers tend to report a high number of push and pull factors and are likely to 

have to search for housing unexpectedly and with limited resources; they also draw on fewer 

tools (and a limited reliance on online tools) in their search, often relying on interpersonal 

contacts. Neighborhood characteristics are of comparatively lower priority, whereas a relatively 

large number of unit characteristics are top priorities. They search fewer neighborhoods and unit 

options compared with our other searcher types. This group was the largest in our study (40 

percent).

• In our study, Black recent movers were more likely than White recent movers to be 

constrained searchers, and this racial difference persists even when accounting for 

racial differences in income.

• Constrained searchers report unit improvements but are most likely to report that their 

new neighborhood is worse than their original one.

Chapter 7: Implications for Research

Every 10 years, HUD conducts audit studies of the rental housing market to determine whether 

housing discrimination influences the housing outcomes of racial or ethnic minorities. These 

audit studies have tracked societal changes and have been instrumental in the efforts to uphold 

the mandates of the 1968 Fair Housing Act. However, on its own, the research design—which 

determines whether discrimination occurs at the point of inquiry into a housing unit advertised in 

a particular way—may not be the most effective, relevant, and appropriate means for tracking 

housing discrimination.

The results of our research, of which the goal was to conduct an exploratory study that would 

permit as detailed and comprehensive picture of housing searches as possible, suggest several 

directions for the next generation of research studies. Our results call into question the simplistic 

model of a housing search that underpins the design of prior HDS. The HDS focus only on the 

step at which a searcher inquires of a landlord about a housing unit advertised online or in a 

newspaper and attempts to visit it. Our Housing Search Study (HSS) draws attention to all that 
xviii

happens before and after that inquiry. We describe the myriad ways that racial considerations 

shape which units and neighborhoods a person will inquire about and how they are treated after 

the inquiry. Our results suggest that the road map for future research needs to have this more 

expansive model of housing searches in mind to pinpoint the processes most in need of 

investigation. The inquiry stage is important, but so are the ways people come to inquire about a 

unit and how they are treated after they inquire about it.

Preinquiry Phase

Before inquiring about a particular housing unit, searchers identify neighborhoods to search in, 

gather information from several sources, uncover units to inquire about, and set priorities. Our 

study identified several aspects of this phase of a housing search that are influenced by race or 

ethnicity and may shape whether someone is exposed to, or shielded from, discrimination. The 

widespread anticipation of discrimination on the part of racial and ethnic minorities, the 

differential use of social networks, and the tendency for minority and White home seekers to 

look in different neighborhoods all point to important future directions for research into the 

preinquiry phase.

• What is the effect of anticipated discrimination on the decisions searchers make about 

how they will search and what units they will inquire about?

• What impact does a reliance on particular information sources have on the places 

where people end up inquiring about and living in?

• How often does discrimination occur when searchers use methods other than online 

listings to find out about units?

• How often does discrimination occur in neighborhoods where minorities are most 

likely to search for housing?

Inquiry Phase

Of the three phases, it is the inquiry phase where the HDS is the strongest. Even so, our 

exploratory study of housing searches uncovered additional areas for future HUD studies to 

consider when gauging the extent of discrimination experienced by racial or ethnic minorities.

• To what degree does discrimination vary based on the mode of inquiry used by the 

searcher (that is, by phone, internet, or in person)?

• To what degree do socioeconomic status and credit history shape racial or ethnic 

discrimination when inquiries are made?

Postinquiry Phase

What happens after the initial inquiry into a housing unit is crucial and is an area ripe with the 

potential for discrimination. That Black renters submit more applications than White renters 

despite inquiring about and visiting the same number of units hints at discrimination. Minority

renters’ housing searches last longer than White renters’. The hassles and roadblocks that racial 

or ethnic minorities experience are described in great detail in our indepth interviews. These 

findings suggest that the point when the HDS studies stop tracking searchers may be precisely 

when contemporary forms of discrimination start. HUD might consider studies that address the 

following research questions.
xix

• What role does credit history play, and how does it affect how landlords treat renters 

in the application process?

• What happens at the application stage, and how often does discrimination occur at 

this stage?

• How do landlords decide to whom to rent, and how often does discrimination occur at 

this final stage?

• What is the cumulative cost of discrimination during the entire course of a housing 

search?

To address these research questions, HUD can adapt the American Housing Survey (AHS), 

conduct new analyses of existing 2012 Housing Discrimination Study data, analyze complaint 

data, rethink the design of paired testing, design a longitudinal study of searchers, and research 

landlord decision making.

What follows is our effort to provide an empirical and conceptual foundation for future research, 

policy, and practice that understands the microprocesses of rental housing searches that, in the 

aggregate, contribute to the stratified housing outcomes that impede HUD’s ability to “build 

inclusive and sustainable communities free from discrimination.”
1

Chapter 1: Background

Research bears out that White, Black, and Latino people have significantly different housing 

outcomes—that is, they live in different neighborhoods, and those neighborhoods have, on 

average, quite different characteristics (Peterson and Krivo, 2010; Sharkey, 2013). We also know 

that racial and ethnic minorities may experience continued discrimination when they attempt to 

buy or rent a place to live (Turner et al., 2013). However, remarkably little is known about one of 

the core mechanisms through which this inequality is perpetuated: the manner by which people 

search for housing. It is the process of people searching for and moving into homes that, when 

taken in the aggregate, is a driver of the racially segregated neighborhoods that define much of 

the residential landscape in our nation’s cities. The purpose of this project is to understand how 

racial or ethnic groups differ in their search for rental housing in order to design policies to better 

meet HUD’s goal of “building inclusive and sustainable communities free from discrimination.”

We know from decades of research largely using census data that White, Black, and—to a lesser

(although, in some cases, increasing) extent—Latino citizens live in different neighborhoods.

Racial residential segregation, although declining, nevertheless continues to characterize many of 

our nation’s cities, suburbs, and even small towns (Reardon et al., 2009). The consequences of 

segregation have also been the topic of substantial research, demonstrating the many and varied 

ways in which segregated housing negatively affects individuals and communities (Massey and 

Denton, 1993; Peterson and Krivo, 1993; Williams et al., 2001). Racial minorities are 

disadvantaged in their neighborhood and community outcomes; they are exposed to more crime

(Peterson and Krivo, 2010), greater environmental hazards (Crowder and Downey, 2010; Jones 

et al., 2014), poorer-quality schools and job opportunities (De la Roca et al., 2014; Frankenberg,

2013; Kneebone and Holmes, 2015; Logan et al., 2012), and generally less desirable community 

services and amenities (Charles, Dinwiddie, and Massey, 2004; Cutler and Glaeser, 1997; 

Massey, 2004; Santiago and Wilder, 1991). Property values in minority neighborhoods increase 

less rapidly, segregation translates into poorer health outcomes, and racial or ethnic minorities 

living in segregated communities have fewer opportunities—that is, they are more likely than 

White residents to live in “low-opportunity” areas (Kershaw and Albrecht, 2015; Musterd and 

Andersson, 2005; Williams et al., 2001). In short, where one lives affects so many different 

aspects of one’s life outcomes that a racially stratified housing system has ripple effects that 

serve to perpetuate what are persistent problems of racial inequality in our country (Chetty, 

Hendren, and Katz, 2016); indeed, some scholars have called housing the “structural linchpin” of 

racial inequality in the United States (Bobo, 1989).

Although there is little debate as to whether segregation has negative consequences, there is 

substantial debate about what causes these segregated patterns. The most frequently identified 

causes in the literature are people’s preferences about where they want to live, the persistence of 

racial discrimination in the housing market, racial differences in economic outcomes (income 

and wealth), and public policies and historical trajectories (see Krysan et al., 2014, for review).

However, no consensus exists as to which of these factors is most important.

Unfortunately, most studies, regardless of their findings, have one thing in common—they are 

unmoored from the reality of actual housing searches. Assumptions are generally made about 

how the housing search process unfolds, but those assumptions have rarely been systematically 

assessed. Take audit studies of housing discrimination as an example. These studies are 

considered the gold standard, in terms of research design, for measuring discrimination because 

they pair housing searchers who are matched on all characteristics except for their race, and send 
2

the searchers to inquire about available housing. Because the paired auditors are (in theory) 

identical in all ways except for their race, any differences in treatment that are observed (lessfriendly reception, being told the unit is unavailable, being quoted a different rent) can be 

interpreted as evidence of discrimination. These studies have provided invaluable insights as to 

the question of whether the 1968 Fair Housing Act successfully rooted out housing 

discrimination (Turner et al., 2002, 2013). We know from these rigorous audit experiments that, 

although housing discrimination is decreasing, it is still the case that people are treated 

differently by landlords and real estate agents based on their racial or ethnic background.

Nevertheless, we do not know whether the stages of the process captured by the audit studies 

map onto what people actually do and onto the moments in a search where discrimination occurs.

To be sure, at some point in a housing search, most individuals interact with a landlord in the 

way that the audit studies capture, but the interaction may not come about in the manner assumed 

by an audit study methodology (for example, finding a listing on the internet or in the 

newspaper). We also know nothing about how the searcher decided to inquire about the unit, 

what factors shaped that decision, how the searcher interprets the interaction with the landlord, 

and how this interaction shapes the searcher’s subsequent housing search decisions. In other 

words, although we know a great deal about what happens in terms of race or ethnicity at the 

point of the interaction between a prospective tenant and landlord, we know very little about 

what precedes and follows that interaction and how these factors ultimately affect housing 

outcomes. Thus, although audit studies have demonstrated that housing discrimination exists 

(Fischer and Massey, 2004; Massey and Lundy, 2001; Turner et al., 2013, 2002), further 

exploration is needed to understand how discrimination—anticipated or experienced—factors 

into the multidimensional process of a housing search. Discrimination might influence the 

decisions individuals make about which neighborhoods they search in, which units within which 

neighborhoods people inquire about, or any number of other aspects of a search.

Another body of research that helps inform our understanding of race and housing includes studies 

of the preferences people hold about a neighborhood’s racial or ethnic composition. Research on 

racial residential preferences teaches us that people do hold preferences about the racial or ethnic 

composition of the neighborhood, and for racial or ethnic minorities, these preferences are shaped 

in important ways by concerns about possible discriminatory treatment (Charles, 2006; Farley et 

al., 1994; Krysan and Farley, 2002). Although we know a great deal about how people react to 

neighborhoods of varying racial or ethnic compositions (both in attitudinal surveys and housing 

mobility studies), however, we know nearly nothing about how a neighborhood’s racial or ethnic 

composition actually factors into an individual’s housing search, particularly compared with other 

neighborhood characteristics like housing costs, location, and school quality.

In sum, although existing studies provide important insights into the larger questions about race

or ethnicity and questions related to housing, they fail to consider directly the question of how 

these factors—preferences and discrimination, for example—play out when people actually 

undergo a housing search. We know little about how people approach a housing search—what 

shapes the context of the search, how people make decisions during a search, what information 

they draw on, and how are options evaluated. These factors may have an important role in 

shaping the outcomes that, in turn, affect segregation. For example, do people rely on social 

networks to identify available units? If they do, and if those networks are racially homogeneous, 

then the set of possible apartments that are identified through this search process may be racially 

distinct (Krysan, Crowder, and Bader, 2014).
3

Our attempts to understand the factors that differentiate racial or ethnic groups in terms of their 

housing search patterns is hampered by the fact that we know very little in general about the 

details and nuances of housing searches. Therefore, we begin our study with a conceptual 

framework that identifies core dimensions and describe what we learn about the way that 

searches unfold and how people approach them. From that point, we can then explore whether

housing searches differ for racial or ethnic groups. Specifically, in this exploratory study, we 

seek to answer four key research questions.

1. How do people search for rental housing?

2. How do housing searches differ by race or ethnicity?

3. What are the consequences of these differences for relative housing outcomes?

4. What are the implications for future research?

To make progress on these questions, we develop a conceptual model to guide our investigations.

Existing research leads us to believe that recognizing—and tapping into—the range of 

complexities and the dynamic nature of the process is critical to uncovering the existence and 

consequences of racial or ethnic difference in housing searches (Krysan, 2008). The housing 

search process is anything but linear and static. As a result, the conceptual framework that 

provides the foundation of this project (exhibit 1.1) is constructed in a way to draw attention to

the complex and dynamic process that we seek to capture.

Exhibit 1.1: Conceptual Framework for Housing Search Process

The core structure of the housing search is reflected in the three outer circles: information 

gathering, evaluating options, and decision making. All three of these circles are connected, with 

no clear starting or stopping point. One might move through this cycle once or many times 

before arriving at a final outcome (as shown in the circle at the center of the figure). The housing 

search is also undertaken in a particular context—reflected by the box that surrounds the 

figure—a context that is shaped by the individual’s personal circumstances, the characteristics of 

the search, and the conditions of the housing market.
4

Although our conceptual model clearly demonstrates the structure of housing searches and the 

core dimensions of a housing search, it is important to keep in mind that the process of the search 

can unfold altogether differently, even if it includes all these components. For example, some 

housing searches are extremely truncated—the entire search could happen in a single day—or 

the search could extend for weeks and even months. Time, or the length of the search, is 

therefore a critical variable that can severely affect the process and outcome. Similarly, some 

people will undergo searches that are information rich, in that they generate lots of options, 

undergo considerable information gathering, and face numerous intermediate decisions. Others 

may find a single option and sign a lease without much information gathering or intermediate 

decision making.

For decades, HUD has used paired audit studies to detect discrimination in the housing market.

Additionally, researchers have explored important questions about the causes and consequences 

of racial residential segregation, with substantial focus on the role of discrimination and racial 

residential preferences. Little attention is paid, however, to the housing search process, which is 

an important mechanism in all these efforts and studies. For example, existing large-scale studies 

like the American Housing Survey (AHS) and the Panel Study of Income Dynamics (PSID) were 

not designed specifically to examine housing searches. The purpose of this study is to sketch out 

a much fuller picture of all the dimensions of housing searches by drawing on existing literature, 

original analyses of existing secondary data, and original data that were collected for this specific 

purpose. The design of our original data collection—the Housing Search Study (HSS)—was 

informed by existing literature and relies on a conceptual model of housing searches that are

complex and dynamic, with four key dimensions: context, decision making, information 

gathering, and evaluating options. Because the HSS was a convenience sample (see chapter 2), 

we cannot make claims that these data are representative of trends in the general population.

However, they capture the housing search process in significant detail. Where appropriate, we 

also leverage data from a series of indepth interviews to flesh out nuanced aspects of the 

dimensions in our conceptual framework.

Our report therefore draws on a mixed-methods approach to develop a detailed conceptual model 

of housing search, identify discrete racial or ethnic differences in housing search and outcomes, 

sketch out themes that emerge to explain these differences, and develop a typology of searchers.

This data collection and analysis provides the insights needed to inform future research that can 

be used to inform policy and practice.
5

Chapter 2: Methodology

To construct the strongest research design possible for exploring these issues, the research team 

engaged in a series of preliminary information-gathering tasks, including an indepth literature 

review, exploratory qualitative analyses of transcripts from three focus groups and 12 interviews, 

and a detailed review of existing secondary data sources. The information gleaned from these 

tasks provided not only the substance to create our conceptual framework (see chapter 3), but 

also important insights into the appropriate methods for answering our research questions.

Given the dynamic and complex nature of housing searches and the paucity of existing data that 

can shed light on them, we use a mixed-methods approach that allows us to capitalize on the 

specific strengths of each method. At the same time, such an approach allows us to overcome the 

weaknesses of one method by also drawing on a different method. Together, the results generate 

the most comprehensive exploratory study of these issues, providing a foundation for future 

research and policy agenda on this topic.

This chapter describes each of the methodologies—the Housing Search Study (HSS), the indepth 

interviews, and the secondary data analyses. For each, we present an overview of their 

contributions to the overall study, as well as a description of the sample and the analyses 

performed. For primary data collection activities like the HSS and indepth interviews, we also 

describe recruitment activities and the instruments used for data collection.

Housing Search Study

In order to gain nuanced information about all the aspects of housing search as delineated in our 

conceptual framework, the research team fielded an original data collection. This process 

consisted of large-scale cognitive phone interviews with a convenience sample in the 

metropolitan D.C. area of two kinds of individuals: recent movers and current searchers. We 

conducted studies of both recent movers and current searchers for both conceptual and practical 

reasons. First, housing searches have a number of dynamic elements (for example, assessments 

of specific units and evolving search criteria). In order to produce the highest-quality data of a 

“moving target,” a real-time data collection would yield the most accurate responses. Thus, we 

felt it was critical to include in our design a data collection effort that sampled from people who 

were actively engaged in a search. This inclusion would allow us to capture the fluidity of a 

search as it was happening, rather than after it was over, and it would limit recall issues and posthoc rationalizations. We refer to this as our Current Searcher Study. Although the current 

searchers held the most promise conceptually, we anticipated that finding participants during 

what was probably a busy and stressful time would be extremely difficult. Thus, we decided to 

also recruit a sample of participants who we expected would be an easier group to find and 

recruit—people who had already completed their search but had done so relatively recently, thus 

reducing recall biases. We refer to this as our Recent Mover Study. The two have distinct 

designs.

• Recent Mover Study: We administered these one-time telephone interviews to people 

who had moved into a new rental residence within the past 2 months. This narrow time 

window allowed us to improve on the reliability of responses in existing data and to ask 

much more detailed questions about the search process.

• Current Searcher Study: We interviewed current searchers by telephone up to three times.

These were individuals who, on first contact, were actively searching for a place to live.
6

Wave 1 was the initial point of contact with a searcher; interviewers attempted a wave 2 

interview a specified number of weeks3 after wave 1; if the searcher had not yet found a 

place to live at wave 2, he or she was then contacted for a wave 3 interview. Throughout 

the course of the study, as described below, we modified the time that elapsed among the 

three waves of data collection.

Sample

Our final HSS sample included 135 recent movers and 351 current searchers who were 

interviewed between September 2015 and June 2016. It is worth noting that our study period did 

not include two particularly busy months in terms of the rental market—July and August. Our 

target study area was the Washington, D.C. metropolitan region, which we defined as the District 

of Columbia; Montgomery County, Maryland, Prince George’s County, Maryland; Fairfax 

County, Virginia (including the independent cities of Fairfax and Falls Church); Arlington 

County, Virginia; and the City of Alexandria. This sample differed from our plans both in the 

mix of recent movers versus current searchers and on some of the intended demographic 

characteristics. We describe these methodological issues below.

Our original design for the HSS called for recruiting a total of 700 unique respondents—525 

recent movers and 175 current searchers—evenly distributed across three racial and ethnic 

groups (Black, White, and Latino) and stratified by three levels of household income.4 In the 

next section, we describe extensively our recruitment efforts. The research team used these initial 

numbers as recruitment targets, but the realities of the recruitment process required flexibility.

Unexpectedly, it was current searchers who were much easier to find and recruit than recent 

movers; as a result, we ultimately allocated more resources to this group. These resources 

allowed us to secure more interviews from a group that is, arguably, the more interesting one 

because the search is in process and is not at all subject to possible retrospective reporting biases.

The second challenge, which was less unexpected, was that Latino respondents were very 

difficult to recruit. After many attempts to target this population, we eventually diverted 

resources to focus on ensuring that we attained as economically diverse a sample of White and 

Black respondents as we could get. The third challenge was that, because of a combination of 

slow startup, sudden surges in participation, and varying levels of success recruiting different 

types of respondents, our ideal balance in groups by race and by household income was not 

achieved.

As these challenges surfaced during the field period, we took a flexible approach and tweaked 

our targets and quotas in response. Specifically, we eliminated caps to the strata by mover status, 

race, and income until a critical mass of respondents had participated, and we had a better sense 

of overall recruitment trends. We reached this stage roughly 6 months into data collection. At 

this point, we closed recruitment for the most oversubscribed groups—low- and moderateincome Black current searchers—and allocated the remaining interviews to achieve a more 

diverse sample for the recent mover, current searcher wave 2, and current searcher wave 3 

interviews.

 3 As described in exhibit 2.2, the number of days among waves 1, 2, and 3 varied through the course of the study. This variation was because we 

discovered that our current searchers were not completing their search by the time of wave 3, so we stretched out the time among the waves to try 

to ensure that our current searchers had completed their searches.

4 The research team executed this strategy by asking each respondent whether his or her household income was higher or lower than two 

household income cutoff points, $25,000 and $65,000, which respectively correspond roughly to the 33rd and 67th percentile of renter household 

incomes in the Washington, D.C. metropolitan area, based on 2008–2012 American Community Survey data.
7

Exhibit 2.1: Description of Housing Search Study Samples

Recent 

Movers

Current Searchers

Wave 1 Wave 2 Wave 3

Geography Washington, 

D.C. 

metropolitan 

area

Washington, 

D.C. 

metropolitan 

area

Washington, 

D.C. 

metropolitan 

area

Washington, 

D.C. 

metropolitan 

area

Type of renter or mover Moved into 

rental unit 

within the past 

two months

Currently 

searching for 

rental housing

Current 

searchers 

Current 

searchers 

Still searching (%) 100 86 49

Done searching (%) 100 14 51

Total sample N 135 351 257 110

Race/ethnicity (%)

Black 52 71 70 73

White 45 26 27 25

Latino 3 3 3 3

Income—total (%)

Low (< $25,000) 22 36 36 39

Middle ($25–65,000) 45 45 42 40

High (>$65,000) 33 19 22 21

Income—African American (%)

Low (< $25,000) 33 41 40 45

Middle ($25–65,000) 46 46 44 38

High (>$65,000) 21 13 16 17

Income—White (%)

Low (< $25,000) 10 23 27 26

Middle ($25–65,000) 46 40 36 44

High (>$65,000) 44 37 37 30

The resulting HSS sample included 135 recent movers and 351 current searchers. Across all 

samples, roughly one-third of the sample was White, a little less than two-thirds Black, and less 

than 3 percent Latino. However, the current searcher sample had a higher proportion of Black

respondents (71 percent) compared with the sample of recent movers (52 percent). Few signs of 

differential attrition existed across the waves of our longitudinal study. Most of the sample of 

current searchers did not move into housing by the point of last contact. For the wave 2 sample, 

86 percent was still searching at point of interview, and for wave 3 respondents, 49 percent was

still searching at point of last contact. Middle-income households were most common between

both major racial groups; but fewer low-income households were among White respondents, and 

fewer high-income households were among Black respondents. Our interpretation of the results 

will need to be attentive to the way in which the recent movers and current searcher samples are 

different.

Recruitment

Our recruitment strategy was to identify a convenience sample of people who had recently 

moved into a rental unit (Recent Mover Study) or who were currently undergoing a search for a 

new rental unit (Current Searcher Study). As a result of our convenience sampling approach, our 

HSS sample cannot be understood as representative of housing searchers in the Washington,
8

D.C. metropolitan area, and we cannot draw on the inferential benefits of tests of statistical 

significance. The decision to do a convenience sample was based on the expected challenges and 

prohibitive costs of creating a probability sample of housing searchers. The prohibitive costs are 

that no sample frame (list) of people actively engaged in a housing search that could be used to 

draw a probability sample is easily available. In addition, using a general population sample and 

screening for this relatively rare trait (being currently involved in a housing search) was outside 

the scope of the budget parameters of this exploratory study. Therefore, we had to undertake 

wide-ranging recruitment efforts in order to find individuals meeting our criteria and encourage 

them to participate in the study.

In an initial recruitment stage starting in September 2015, we offered $30 incentives for 

completion of each 20-minute phone interview and advertised the study using a variety of 

methods, including—

• HSS website.

• Ads in local targeted newspapers.

• Mass mailings of postcards to addresses listed for rent in prior months in the Washington, 

D.C. metropolitan area on Craigslist.

• Distributing fliers at retail locations that might be frequented by movers, including UHaul, storage facilities, Target, rental properties.

• Targeted ads on Facebook and Google.

• Twitter.

• Craigslist advertisements.

• Postings on local community pages or listservs.

• Partnerships with property management companies and nonprofit organizations.

After receiving a limited response in the first 2 months of the data collection period, the research 

team met with experts from the White House Social and Behavioral Sciences Team, who 

provided feedback on print and web promotional materials, both of which were then redesigned 

to reflect their recommendations. Urban Institute staff also relaunched the study website to 

include a form that site visitors could use to securely submit their contact information for the 

research team to use to follow up with them. 6D Global, a digital marketing firm who oversees 

the Urban Institute’s social media outreach, began managing the social media outreach and 

advertising accounts for Facebook and Google. The advertising budgets for social media were 

significantly increased, and the research team tested their original strategies with the new 

materials.

In early 2016, with no measurable change, the incentive was raised from $30 to $50 in order to 

improve the likelihood of successful recruitment. The website, social media ad campaigns, and 

other promotional material were redesigned to reflect the incentive increase and were used for 

several new large-scale and higher-cost recruitment methods.

• Bus advertising campaign consisting of 21-by-22-inch posters placed on 405 WMATA 

buses with routes across the Washington, D.C. metropolitan area.
9

• A print advertising campaign that was a one-half-page color advertisement placed in 

three editions of the Express—a free weekday newspaper published by the Washington 

Post that has an average daily readership of 314,600 and more than 1,700 distribution 

points. The ad was placed in the Monday and Tuesday papers on the week of 2/29/16. 

The following week, the same ad was placed in the Monday edition and the Friday “Rent 

to Rent”—an insert in the paper that is specifically targeted toward renters in the region.

For an indepth description of recruitment and the efficacy of different methods, see appendix A.

Instruments

In the first stage of the HSS, interviewers administered a consent protocol and a short screening 

interview to determine eligibility and funnel the participant into modules suited either to recent 

movers or current searchers (see appendix B).

Recent Movers

Eligible recent movers completed a single 30-minute cognitive interview (appendix C). The 

questionnaire consisted of those elements of the housing search process that are not well 

documented by the AHS and other existing studies. Thus, one of the contributions of this study is 

that we have created instruments that can be used in future studies to effectively tap housing 

search processes. In general, the questions focused on overall and summary responses and 

evaluations rather than specific detailed questions about individual housing units, neighborhoods, 

or visits.

Current Searchers

Despite the generally similar content of the modules administered to recent movers and current 

searchers, including a series of concluding demographic questions (appendix E), the ways they

differ are important. Current searcher cognitive interviews took place in three waves. Wave 1 

was the initial interview, and its contents were identical for all respondents (see appendix D for 

the baseline instrument). When we contacted these respondents for a wave 2 interview, we asked 

them questions from either a still-searching or a done-searching module, depending on the status 

of their search (see appendix D for the text of both modules). Respondents who had still not 

completed their search at wave 2 were interviewed for a third time in wave 3. We either 

administered them a still-searching or a done-searching module as appropriate. However, 

because those respondents who were still searching would not be interviewed again (we were 

limited to three waves), we asked a set of questions at the end of the still-searching module that 

were triggered by the fact that it was a wave 3 interview, despite the search not being complete.

These questions focused on how respondents thought the search would end (see appendix D, 

questions at end).

In order to facilitate the recollection process involved in generating detailed information about 

the locations a respondent inquired about and visited, and to create rapport and increase 

motivation for continued participation in subsequent interviews, we mailed a housing search log 

that could be used to keep track of search locations, as well as a pen with the study logo and 

name on it, along with current searchers’ first incentive payments. In order to increase 

motivation for respondents to use the log, we designed it so that it doubled as a useful “housing 

search” notebook, with sections for notetaking, which might include reflections on what they 

thought of a unit they visited, the address of the unit, or maps of the region.
10

Initially, the research design called for followup with current searchers within 10 to 12 days 

between the wave 1 and 2 interviews and the wave 2 and 3 interviews. However, during the field 

period, we realized that these time windows were not allowing us to capture the end of the search 

for many, if not most, of our current searchers. Consequently, we adjusted our design. We fixed 

a 3-week followup for the wave 2 and wave 3 interviews. In addition, at the initial point of 

contact at wave 3, interviewers asked respondents about the status of their search. Only 

respondents who reported that they were finished with their search were interviewed at that time; 

respondents who reported they were still searching were placed back in the queue to receive a 

followup another 3 weeks later. These decisions were made in hopes of increasing the chances 

that the interviews would capture the end of the search. During the final 3 weeks of data 

collection, the survey firm interviewed all respondents slated for a wave 3 followup, regardless 

of the status of their housing search.

Because of these variations in our approach to the timing of the current searcher followup

interviews, the timing between waves varies substantially across our sample and is described in 

exhibit 2.2.

Exhibit 2.2: Description of Timing of Wave 2 and Wave 3 Interviews

Wave 2 Wave 3

Minimum days since last interview 10 13

Average days since last interview 29.0 37.2

Maximum days since last interview 127 209

N 254 110

Source: Housing Search Study survey, 2016

Analyses

We analyzed the HSS data on recent movers and current searchers separately in order to exploit 

the unique advantages of the data collection instruments of the two groups. Because of their very 

small sample size, Latino renters were excluded from HSS analyses altogether but are included 

in discussions of indepth interviews where possible (see indepth interviews for further 

explanation).

In the both the Recent Mover Study and Current Searcher Study, respondents were asked to 

identify the neighborhoods or communities in which they searched, lived, and ended up moving 

into, and we were interested in being able to describe the racial composition of these 

neighborhoods. We used the 2010–14 American Community Survey (ACS) data to do this. In 

some cases, the names of the communities corresponded directly to official U.S. Census 

designations (such as incorporated places or Census Designated Places). Specifically, for

Montgomery County, Prince George’s County, and Fairfax County, which are more suburban in 

character, respondents were asked to identify incorporated places (such as towns, villages, or 

cities) or Census Designated Places, so we were easily able to use the ACS data, which provided 

racial composition information for these geographic entities. For the more urban District of 

Columbia, Arlington County, and the city of Alexandria, respondents were asked to identify 

areas by referencing the name of the neighborhoods within these areas. In order to link the racial 
11

composition to these nonofficial U.S. Census designations, the research team used a crosswalk 

that matched census tracts to neighborhood geographies.

The first step in our analysis of the HSS is to use these data sets to shed light on the basic 

patterns in frequencies of search dimensions within all the constructs—context, decision making, 

information gathering, evaluating options, and final outcomes. We also used cross-tabulations 

and comparisons of means to gauge those dimensions where racial differences might be present.

Subsequently, we employed multiple regression to determine whether the evidence of racial 

differences in the housing search process in the bivariate analyses (information gathering, 

evaluating options, and final outcomes) held up when we controlled for income and household 

composition. Because of the nature of our convenience sample, tests of statistical significance 

are inappropriate. Nevertheless, in our analysis, we inspect multiple regression models and 

assess racial differences based on the reduction in the size of the coefficients between the 

unadjusted and adjusted effects of being Black on the dependent variable of interest. Finally, in 

order to explore how the different dimensions of housing searches are interconnected, and 

ultimately to identify a typology of searches, we conducted a two-stage data reduction process, 

beginning with principal components analyses of each of the core housing search dimensions 

(context, decision making, evaluating options, and information gathering) and then a cluster 

analysis of those dimensions in order to identify a housing search typology.

Indepth Interviews

The indepth interviews complement the cognitive interviews by providing additional insights 

into the nuances of housing searches. They also present an opportunity to explore themes or 

patterns that emerged in the analyses of the HSS data.

Sample

Our first challenge in designing the indepth interview sample was to identify the individuals who 

would most likely be able to share a full picture of their housing search. Recent movers were 

excellent candidates, but they made up a relatively small part of the overall sample. On the other 

hand, our current searchers sample was larger, but the time window for their searches varied 

widely. In order to capture the most detailed and nuanced information possible, we chose to 

constrain the indepth interviews to recent movers and current searchers who had either finished 

their search, had been searching for less than 2 months at the point of wave 1 contact, or had a 

set date to finish their search (such as the end of a lease or the birth of a new child) that was less 

than 3 months away from the wave 1 interview.

The literature review and expert panel discussion during the research design period highlighted 

two particularly important factors in exploring racial or ethnic differences in housing searches:

household income and family composition (for the importance of distinguishing income and 

family composition, see Owens, 2016). For this reason, we sought to stratify our indepth 

interview sample not only by race, but also by income and the presence of children under age 18 

in the household.

However, we made an exception to both the inclusion criteria and our stratification guidelines for 

Latino renters. The HSS sample did not have enough Latino respondents to take the same 

approach. Consequently, research staff contacted all Latino HSS respondents to try to maximize 

their overall participation.
12

As shown in Exhibit 2.3, we conducted a total of 40 indepth interviews, comprised of 16 White, 

16 Black, and 8 Latino interviewees. The number of cases within each stratum reflects the 

characteristics of the HSS respondents. For example, households without children were most 

common among both White and Black interviewees. Moreover, within the low-income group, 

we were unable to recruit White interviewees with children; the Latino sample consisted 

exclusively of middle- and high-income households, although we were able to achieve an even 

balance between households with and without children. In addition, the research team worked to 

balance the sample as much as possible in other respects, such as recent mover versus current 

searcher, gender, and age.

Exhibit 2.3: Description of Sample for Indepth Interviews

All White Black Latino

Low income

With children 2 0 2 0

Without children 8 4 4 0

Medium income

With children 6 3 2 1

Without children 8 4 3 1

High income

With children 6 2 2 2

Without children 10 3 3 4

Total 40 16 16 8

Recruitment

Indepth interview respondents were recruited exclusively through their participation in the HSS. 

At the conclusion of each cognitive test, the interviewer asked all eligible movers if they would 

be willing to share their contact information directly with the Urban Institute and participate in 

an additional approximately 1-hour in-person interview about their housing search process.5

The Urban Institute research team contacted respondents who expressed interest in the indepth 

interviews, gave consent to share their contact information, and met the basic selection criteria 

described previously in the section about the sample. Urban Institute staff explained the goals of 

the indepth interviews and scheduled appointments, making adjustments throughout the process 

to fill the sampling goals. All interviews were conducted in person: in the respondents’ home or 

a location of their choosing, such as a library, coffee shop, or their workplace. The researchers 

provided an additional $50 to thank respondents for their time.

Instruments

The interviews encouraged respondents to tell the detailed story of their move, from the initial 

decision or impetus to find new housing and through the search process. The interviews 

prompted respondents to reflect on all aspects of our conceptual framework, including the 

specific search strategies used for the completed move, any constraints that affected their search 

strategies or decisions, and the final outcome of the move. These interviews also asked 

respondents to elaborate about their feelings and impressions of neighborhoods and their 

 5 Note that our indepth interview-recruitment efforts excluded individuals who were recipients of subsidized housing assistance.
13

demographics, experiences with landlords and property management companies at all stages of 

the process, and perceptions of and experiences with discrimination.

Analyses

All interviews were audio-recorded and transcribed. Transcriptions were coded using NVivo 

using a structured set of codes. At the document level, the interviews were coded with the 

characteristics of the individual, including their race, income, and household composition.

Within the text of each interview, we coded common themes with regard to the primary 

dimensions of interest within our conceptual framework.

Secondary Data Analyses

In addition to the original data collection efforts in the HSS and indepth interviews, we also 

conducted secondary data analyses of existing large-scale surveys. No existing large-scale 

surveys focus primarily on housing search processes. For this reason, the surveys that do include 

it as a construct capture only a handful of elements explored in our conceptual framework. 

Moreover, respondents are often asked to describe housing searches that may have been several 

years before the survey and may be subject to both recall issues, as well as post-hoc 

rationalizations of past residential decisions that limit the scope and quality of the data.

However, the great strength of leveraging existing survey data is that their representative samples 

allow us to create a more generalizable portrait and identify statistically reliable differences 

between racial or ethnic groups in some key elements of the housing search. Because two of the 

three secondary data sets draw on national samples, we are also able to assess how key processes 

vary depending on housing market conditions in some cases.

Sources

Our primary data sets for this analysis are the 2011 Metropolitan Sample of the American 

Housing Survey (AHS) and the 1997–2011 waves of the Panel Study on Income Dynamics 

(PSID). We also draw on the 2004–2005 Chicago Area Study (CAS) to provide more detailed 

information about the places searched during recent moves.

2011 American Housing Survey’s Metropolitan Sample

The AHS provides representative samples of approximately 4,500 housing units in each of 29 

metropolitan statistical areas for a total sample of more than 130,000 housing units. We rely 

primarily on the AHS recent mover module, which asks householders who moved into an AHS 

unit within the 24 months preceding the survey a series of questions about their housing search. 

The AHS provides the opportunity to assess racial or ethnic differences in the housing search 

process both before and after while controlling for other factors, such as income, education, and 

household composition.

We utilize the AHS data specifically because they enable us to assess the extent to which 

housing market conditions affect search processes and racial or ethnic differences therein. We 

attach information on key characteristics of the metropolitan housing market—the overall 

vacancy rate, the ratio of renter- to owner-occupied housing, and the ratio of median gross rent to 

median income in the area—to the individual records of the AHS and assess the extent to which 

these metropolitan characteristics affect key features of the housing search or alter racial 

differences in search processes. All of the measures of housing market conditions are drawn 

from U.S. census data.
14

The main weaknesses of the AHS for our purposes are that it does not allow for detailed analysis of 

the types of units and neighborhoods movers visited or how they adapted their search and expectations

during the process. Also, because the AHS is a sample of housing units, recent movers are only 

included in the data set by virtue of having moved into a sampled unit. Thus, it is not possible to 

analyze the data longitudinally to compare their housing conditions before and after their moves.

Panel Study of Income Dynamics

In contrast to the cross-sectional nature of the AHS (in terms of respondents), the PSID is a 

longitudinal survey of nearly 9,000 families distributed across hundreds of metropolitan areas. 

The PSID traces, prospectively, renters who move from one housing unit to another in the 2-year 

period between PSID interviews. Although the PSID lacks questions about the housing search 

process, it does allow us to track households’ mobility patterns over time and to distinguish 

between different types of moves—including those between neighborhoods and those between 

housing units within the same neighborhood—and to examine several moves carried out by the 

same householder at different points in time. The PSID also captures information on renters’

anticipation of a move and the correspondence between plans for mobility and actual mobility.

The wide geographic dispersion of the PSID panel, in combination with geographic identifiers in 

the PSID’s Supplemental Geospatial File, also allows us to assess the role of housing market 

conditions in these search processes.

2004–2005 Chicago Area Study

The CAS is, to our knowledge, the only existing data set that asked detailed questions about the 

communities in which people have actually searched for housing. The limitations of the CAS are 

that it focuses on a single metropolitan area, and the renter subsample of the study is relatively 

small. In addition, respondents were asked about searches that may have been as many as 10 

years before the interview. However, the data provide a rare glimpse into racial differences in 

where people actually search for housing. The CAS is an area probability sample that is 

representative of adults (ages 21 and older) living in Cook County, Illinois (which includes the 

city of Chicago). This face-to-face survey oversampled Black and Latino home seekers and 

involved detailed questions gauging respondents’ perceptions and experiences of places in the 

Chicago metropolitan area, including those places in which they had searched for housing in the 

prior 10 years.

Samples

Basic descriptive information for the AHS, PSID, and CAS are provided in exhibit 2.4.

Exhibit 2.4: Description of Secondary Data Samples

AHS PSID CAS

Type of renter or mover

Renters who moved 

within the past 24 

months

Renters who moved 

within the past 24 

months

Renters who moved in 

the past 10 years

Geography National National Cook County, Illinois

Year(s) of study 2011 1997–2011 2004–2005

Total sample N 9,685 7,647 188

Race or ethnicity (%)

African-American 18% 41% 25%

White 54% 51% 30%

Latino 28% 8% 45%

AHS = American Housing Survey. CAS = Chicago Area Study. PSID = Panel Study of Income Dynamics.
15

Analyses

Our report analyzes the AHS, PSID, and CAS to assess the housing search processes used by 

minority renters and how those processes differ from those of White, non-Latino renters. Where 

possible, we also analyze correlations between the search processes and both subjective and 

objective outcomes of the housing search.

By focusing on recent movers, we are able to examine the factors associated with the search for 

housing. In the AHS, recent movers refer to renters who moved into the sampled housing unit 

within the 2 years preceding the interview. With the PSID, we trace, prospectively, renters who 

move from one housing unit to another in the 2-year period between PSID interviews. In the 

CAS, we have a single point-in-time survey of an area probability sample of renters in Cook 

County (which includes the city of Chicago) who had searched for housing in the 10 years before

the survey.

We incorporate information about the characteristics of the individual renter, their family and 

household, and, for the PSID and AHS, the metropolitan housing market conditions in which 

their housing searches take place. We consider three central housing market conditions—overall 

housing vacancy rate, the ratio of renter- to owner-occupied housing, and the ratio of median 

gross rent to median income in area—on several aspects of the housing search and mobility 

process. We also examine interactions between these contextual characteristics and individual 

race or ethnicity to assess group differences in the effects of housing market conditions.

We utilize a three-stage process in the analyses of the AHS and PSID data. First, we compare 

group-specific statistics and bivariate regression models to examine basic racial or ethnic 

differences within each of the constructs of our conceptual framework—context, decision

making, information gathering, evaluating options, and final outcomes. Second, we assess 

whether racial differences persist (or emerge) after adding statistical controls for individual 

characteristics (for example, income and education), household composition, and rental market 

conditions. Third, we use group-specific models and regression analyses with product terms to 

examine if race and ethnicity interact with market factors to affect housing search processes and 

outcomes. Finally, we do limited analyses to investigate whether any relationship exists between 

racial or ethnic differences in the search process and search outcomes. Because of small sample 

sizes, the analyses of the CAS are more limited and include a focus on racial or ethnic 

differences in the makeup of the neighborhoods in which they had searched.

In sum, given the relative lack of existing research on the question of how people search for 

housing and how this might differ by race and ethnicity, we take a multi-methods approach that 

brings together insights from background scholarship on the topic, original analyses of secondary 

data sources, and an original data collection (HSS) that included large-scale cognitive interview 

tests of recent movers and current searchers and complementary indepth interviews. The HSS

involved developing an innovative instrument that captures the important dimensions of housing 

searches as reflected in the conceptual framework that guides study. Researchers fielded the 

study using extensive outreach techniques in an attempt to identify a convenience sample of 

people who had either recently moved in the Washington, D.C. metropolitan area or who were 

currently undertaking a housing search. Our report uses each of these sources of information to 

paint as detailed a picture possible of housing searches, and the extent to which people of 

different racial or ethnic backgrounds undertake them differently.
16

Chapter 3. The Search Process

Existing research leads us to believe that recognizing—and tapping into—the range of 

complexities and the dynamic nature of the housing search process is critical to uncovering the 

existence and consequences of racial or ethnic differences in housing searches (Krysan, 2008).

The housing search process is anything but linear and static. As a result, the conceptual 

framework that provides the foundation of this project (exhibit 1.1) is constructed in a way to 

draw attention to this complexity. The Housing Search Study (HSS), by design, provides a 

window into this complexity. Before we can launch into an assessment of the primary focus of 

this report—the extent to which housing searches vary by race or ethnicity—we must take a step 

back, because we know little about the various dimensions of the search outlined in our 

conceptual model. These dimensions include the context of the search, the decision making, 

information gathering, evaluating of options, and the final outcome in general. In this chapter, we 

define and explore these complex and dynamic elements of searches to get a grasp of what 

features of housing searches might matter. In chapters 4 through 6, we will return to the question 

of how housing searches differ across racial or ethnic groups.

Therefore, in this chapter, we limit our discussion to overall patterns that emerged in the HSS

that was conducted for this report—setting aside an analysis of differences between Black and 

White searchers and across different income levels. Bear in mind, because the HSS and indepth 

interviews used in this chapter are convenience samples, we cannot make claims that these data 

are representative of trends in the general population.

Context

All housing searches happen within a particular context, which includes both fixed individual 

characteristics and the circumstances surrounding the move, as well as the characteristics of the 

housing market in which the search is undertaken. On this note, it is important to understand that 

the HSS data we describe in this chapter come from searches that took place in the Washington, 

D.C. metropolitan area; the context of that housing market must be kept in mind—specifically, 

D.C. is an area with low vacancy rates, high rental costs, and substantial levels of racial 

residential segregation.

Not All Renters Start Their Search With the Same Advantages

Of primary consideration as a search context are the economic resources someone brings to the 

search. Clearly, housing searches are profoundly shaped by social class considerations. The 

financial resources at the searcher’s disposal influence the parameters, choices, and expectations 

about housing searches. Indeed, whether it is even appropriate to characterize the process as one 

of a person “choosing” a place to live depends a great deal on their financial and other 

circumstances (Deluca, Rosenblatt, and Wood, forthcoming). The literature also indicates other 

individual characteristics, besides social class, that are likely to affect aspects of a housing 

search, including family composition, employment status, age, and gender (Breugel, 1996; 

Jarvis, 1999; Kohlhase, 1986).

In addition to income, family status, age, and gender, other factors include disability status and 

automobile accessibility, which we captured in the HSS. Exhibit 3.1 shows the details for each of 

these individual characteristics. In terms of income, our samples of current searchers and recent 
17

movers differ significantly from each other, with the latter having a higher percentage of lowincome searchers. Our samples contain disproportionately more women. It is notable that among 

our sample of recent movers, disability is relatively rare, but among the sample of current 

searchers, fully 1 in 10 respondents reported having someone in their household with a disability.

Exhibit 3.1: Demographic Characteristics of Housing Search Study Samples

Recent 

Movers

Current 

Searchers

Household income 

Low (<$25,000) 22.1 36.1

Middle ($25–65,000) 45 44.5

High (>$65,000) 32.8 19.4

Children in household 19.1 24.2

Married or cohabitating 29 17.3

Female 60.3 66

Mean age 36.4 42.6

Person in household with disability 1.5 10.5

Number of automobiles kept at home for use by household 

members

0 27.5 50.1

1 48.1 36.7

2 20.6 9.6

3 1.5 2.4

4 2.3 1.2

N 131 335

Source: Housing Search Study, 2016

Perhaps not surprisingly, given their relative socioeconomic disadvantage, our current searchers 

also have more limited access to cars than recent movers; one-half of current searchers have no 

automobiles kept at home for use by household members, as opposed to only about 28 percent of 

recent movers.6

In the HSS, we also asked searchers directly what constraints they felt affected their ability to 

search for housing. Exhibit 3.2 shows for our sample of recent movers that transportation 

challenges were the most common—with one in four indicating this constraint. Criminal records 

and evictions were quite rare, and the rest of the constraints asked about were equally common.

The sample of current searchers, consistent with their less-advantaged economic status, reported 

poor credit history and lack of security deposit funds as the most common challenges—with 31 

and 42 percent, respectively.

 6 In addition to the samples of recent movers and current searchers being different from each other, it is useful to understand—given that the HSS 

is a convenience sample—how the HSS samples are different from the movers in the national surveys used in this report (PSID and AHS). First, 

the sample of recent movers in the HSS has a much greater share of high-income households and report having children or a partner living in the 

household much less frequently than households in the PSID and AHS. Women also make up fully 60 percent of the recent mover sample in the 

HSS, as opposed to only 36 and 48 percent in the PSID and AHS, respectively. Similar trends, relative to the PSID and AHS samples, are present 

among current searchers sample in the HSS, although the sample of current searchers tends to have lower household income, more children, and 

more older, female respondents than the HSS recent mover sample. See appendix F.15 for specific comparisons.
18

Exhibit 3.2: Constraints on Housing Search

Source: Housing Search Study survey, 2016

CIRCUMSTANCES SURROUNDING MOVES VARY WIDELY

An important search context is the circumstances behind a move. Searches for moves that are 

planned likely unfold quite differently than those that are unplanned; searches that are 

involuntary probably look different than those that are driven by a desire to improve the quality 

of one’s neighborhood. We gauged both of these contextual features. We found that 35 percent

of our sample of recent movers had unplanned moves, whereas the sample of current searchers

were much less likely undergo a search that was for an unplanned (19 percent). This finding is 

likely because our current searchers were actively involved in executing their move at the time of 

data collection.

Our indepth interviews shed light on the heterogeneity of unplanned moves, and they point to 

how the kind of unplanned move can shape the resulting search process. That is, some searchers 

have unplanned moves that come about from having to react to a sudden event beyond their 

control, such as landlords who would not renew their leases, rent increases, family circumstances 

(having to take on new family members), or house fires. Other reasons for unplanned moves 

provide more of a sense of agency on the part of the mover—trying to get away from neighbors 

with whom someone has a conflict or living with family members indefinitely while conducting 

a search. Each of these may be classified as an unplanned move, but they have distinctive 

features that may have consequences for the kind of search one is able to undertake.

Apart from being planned or unplanned, people also move for different reasons. The recent 

mover and current searcher interviews asked respondents specifically about their reasons for 

moving, including (1) involuntary moves because their home was destroyed, condemned or 

unsafe, or because their landlord said they had to move; (2) a desire for better-quality homes or 

neighborhoods; (3) family reasons, including forming their own household, being closer to 

25%

18%

17% 16%

10%

4% 3%

29%

42%

31%

9%

16%

3%

5%

Transportation Money for a

security deposit or

other fees

Poor credit history Not having child

care

Internet access Criminal record History of prior

eviction

Recent movers (N= 131) Current searchers (N = 338)
19

family, or changes in households like marriage, divorce, separation, or child birth or adoption;

(4) a new job or school, or reducing commute time; (5) finding a less expensive place to live; or 

(6) some other reason. After listing all the options that applied, respondents then indicated which 

one was most important.

We find that the most important reason given for a move varies substantially between our recent 

movers and current searchers samples (exhibit 3.3). The top three responses for our sample of 

recent movers have to do with family, job or school, and other reasons. In contrast, current 

searchers reported better quality—either of their unit or neighborhood—as the most important 

reason for their move, followed by family and lower rent. This may offer important insights into 

our current searchers’ housing search processes. Searches based mostly on a desire for better 

quality may feel less urgent than other kinds of moves, and this desire may result in the lengthy 

searches that this group, on average, was undertaking. This pattern highlights the importance of 

understanding how search context can significantly shape the entire search process.

Exhibit 3.3: Most Important Reason for Moving, by Analytic Sample

HSS = Housing Search Study.

Source: HSS survey, 2016

The indepth interviews provide additional insights into the importance of the reason for 

respondents’ moves. First, some people are pushed out of their current arrangements; others are 

being pulled by the possibility of better options elsewhere. Some people are moving for positive 

reasons—getting married or taking a new job—and others are moving to get away from 

antagonistic landlords or increasing rents. A second theme from our indepth interviews is that 

although people were able to identify their “primary” reason for undertaking a search, more than 

one-third of the searchers we talked to in our indepth interviews told stories that portrayed a 

layered or cascading set of reasons for moving. For example, one respondent was moving to 

change her unit type but also cited that it was becoming unaffordable. Another said they were 

moving to find something more affordable but also to pursue homeownership and to downsize.

26%

22%

17%

12%

15%

8%

21%

10%

15%

17%

32%

5%

Family Job or school Other Lower rent Better quality Involuntary

HSS recent movers HSS current searchers
20

Thus, “housing affordability” as a reason for a move can look very different for different 

searchers. Some might explain their search as a function of the high cost of rent because it is 

making it more difficult to save downpayment money for the house they want to buy; for others, 

affordability may mean that the building’s laundry facilities were not working, and the additional 

cost burden of doing laundry was making the rent unaffordable. Again, this variability 

complicates the way in which we understand constraints like housing cost and how it motivates 

moves for positive or negative reasons.

Decision Making

Throughout their housing search, individuals must make two key decisions over and over again: 

1) whether to start, continue, or stop searching, and 2) what parameters they will apply to their 

search, including both neighborhood and unit characteristics. At the beginning of the search, 

these decisions are shaped by the information gathered and evaluated from lived experience.

Afterward, these iterative decisions drive the ways in which individuals gather information and 

evaluate their options until the search ends.

People make a number of different decisions in the course of a housing search, and in this 

section, we are able to use the HSS data to describe the basic patterns in terms of how many units 

people consider, what neighborhoods they consider, and other decisions made during the 

process.

Recent Movers Contact and Visit Many Units, but Submit Relatively Few Applications

In this first section, we get insight into the scope of housing searches, tapping into the number of 

units that people inquire about, visit, and for which they submit applications. On average, 

searchers inquired about and visited five units; and applied for slightly more than two.

As shown in Exhibit 3.4, a great deal of variation is in the volume of searchers’ contacts, visits, 

and applications. About one-half of the recent movers inquire about and visit zero to three units,

whereas the other half does more than this. The number of applications is, not surprisingly, 

fewer, but even here, 45 percent of recent movers submitted applications for more than one unit,

and nearly one in five submits four or more applications in the course of a single housing search.
21

Exhibit 3.4: Number of Units Contacted, Visited, and Applied To

Total

Number of units for which respondents contacted the landlord to find 

out more information

0–1 26.6

2–3 30.4

4–5 18.4

6 or more 24.8

N 125

Number of units visited in person during search

0 2.4

1 13.7

2–3 31.4

4 or more 52.4

N 124

Number of units for which submitted applications

0 6.5

1 50.0

2–3 25.8

4 or more 17.7

N 124

Source: Housing Search Study survey, recent movers, 2016

Most Recent Movers Search in Neighborhoods With Relatively Large Representations of Their 

Racial or Ethnic Group

Our Recent Movers Study asked respondents in what neighborhoods they had searched, and we 

were able to characterize the average racial or ethnic composition of those neighborhoods. For 

the overall sample, people searched on average in neighborhoods where 45 percent of the 

residents were of the searcher’s racial background. The range was from 4 percent own-group to 

95 percent own-group.

Most Look Outside Their Neighborhood

Searchers also differ in terms of the number of neighborhoods they visit. As exhibit 3.5 shows,

only 8 percent of our searcher sample considers only the neighborhood in which they were living 

when they searched. The rest of the searchers were about evenly distributed across the remaining 

options, with about 20 percent searching in one, two, and three other neighborhoods. Seventeen 

percent considered five or more other neighborhoods during their search.
22

Exhibit 3.5: Number of Other Neighborhoods Considered

Note: N = 369.

Source: Housing Search Study survey, recent movers and current searchers, 2016

Most Current Searchers Put Equal Importance on Housing Units and Neighborhoods

Housing is a bundled good, which means that the unit and the neighborhood it is within cannot 

be separated. Prior research draws attention to the possibility that individuals vary in whether 

they prioritize the unit or the neighborhood in which it is when they are making decisions about 

their search. However, when directly asked which feature was more important—the unit or the 

neighborhood—the response was clearly dominant: fully 80 percent of the searchers in this study 

reported that both the unit and the neighborhood were equally important (exhibit 3.6). The 

remaining was about evenly split between selecting the unit or the neighborhood as most 

important.

Exhibit 3.6: Unit and Neighborhood Priorities 

Notes: Current searchers wave 1. N = 336.

Source: Housing Search Study, current searchers, 2016

One of the central reasons we interviewed people in the middle of a housing search was because 

we were interested in capturing the dynamic nature of a housing search. This allows us, for 

example, to find out if during the course of a housing search people change their priorities. Do 

the experiences of a search have an effect on how people prioritize various aspects of it? In terms 

8%

80%

11%

Unit more important

Unit and neighborhood equally important

Neighborhood more important

8%

21%

20% 20%

14%

17%

0 1 2 3 4 5 +

Number of neighborhoods
23

of whether the unit or neighborhood is more important, the searches have quite a bit of stability;

eighty percent of searchers maintain their same priority throughout the search process (exhibit 

3.7).

Exhibit 3.7: Changes in Unit and Neighborhood Priorities for Current Searchers

Notes: Still-searching current searcher respondents in wave 2 and wave 3. N = 185.

Source: Housing Search Study Survey, current searchers, 2016

Information Gathering

Like decision making, information gathering is an iterative process. Qualitative interview data 

suggest that, at the outset, searchers set initial search parameters informed by passive 

information gathering strategies, such as personal observations of different neighborhoods.

However, after a housing search for a particular unit begins, individuals may use many different 

strategies for finding potentially available units, including media, social networks, brokers or 

intermediaries, or among other methods (Farley, 1996; Krysan, 2008). At later stages, 

individuals seek more indepth information, calling or emailing landlords or property 

management, and visiting the specific property. Searchers may have varying degrees of difficulty 

progressing from one stage to another during the search process (that is, from a friend’s referral 

to a landlord, to a returned phone call, or to a scheduled visit) and may have to apply different 

groupings of methods at different frequencies to achieve the same result.

However, it is not only the way searchers gather information that is important. The type of 

information sought and obtained also shapes individuals’ housing searches. One can seek a wide 

range of information about both neighborhoods and units, including their location, application 

process, amenities, building types, costs, school quality, racial or ethnic composition, and crime 

levels. This information may or may not line up with the corresponding search parameters at any 

given time. Further, searchers may experience varying degrees of success getting the information 

that they seek to inform their decisions.

People Leverage Social Networks Within Their Racial or Ethnic Group To Find Rentals or 

Learn About Neighborhoods

The HSS included measures of several different aspects of the social networks people use to 

gather information as a way to better understand how and why social networks are used. In both 

the Recent Mover and Current Searcher Study, searchers were asked if they used the people they 

knew to learn more about neighborhoods, help find vacancies, and find rentals that did not 

require a credit check. The results for both samples, in exhibit 3.8, are similar in which things are 

10%

81%

9%

Unit more important

No Change

Neighborhood more important
24

most common, but the magnitude is quite different. Both the recent movers and current searchers 

use social networks to help them learn more about neighborhoods and to help them find specific 

vacancies, but it is one-half of recent movers and fully 80 percent of current searchers who do 

this. In addition, one-half of the current searchers said they used social networks to help them 

find rentals that do not require a credit check.

Exhibit 3.8: How People Use Their Social Networks

Recent Movers

(%)

Current Searchers

(%)

To find rental vacancies 58 85.1

To find rentals that do not require credit check 16.2 51.4

To learn more about specific neighborhood 51.9 84.7

N 131 249

Source: Housing Search Study survey, 2016

One of the reasons that the use of social networks as an information source is likely to be 

consequential for racially stratified housing outcomes is that social networks tend to be racially 

homogenous. Thus, if searchers receive information about potential vacancies and possible 

neighborhoods from people of their same racial or ethnic background, the resulting moves may 

be similarly racially stratified. Our Recent Mover Study sheds some light on this possibility 

because respondents who found out about the unit they rented from “someone they knew” were 

asked to identify the racial or ethnic background of the person who referred them. As shown in 

exhibit 3.9, the pattern is suggestive of racially disparate social networks—disparate in a way 

that likely funnels people to places about which members of their own racial group are more 

likely to know.

Exhibit 3.9: Racial Background of Person Who Helped, Recent Movers Sample

Percent

Racial background of person who helped find rental vacancies 

Did not learn about units from someone 42.3

Different 16.9

Same 40.8

Racial background of person who referred to unit rented

Did not learn about unit rented from someone 76.2

Different 3.8

Same 20

N 130

Source: Housing Search Study survey, 2016

Beginning with the broad question of the racial background of people who helped searchers find 

vacancies, we see that searchers who used social networks to find vacancies very likely used 

someone of their own racial group from within that network; 41 percent of searchers learned 

about vacancies from someone of their racial group. Turning to the perhaps more consequential 

question of who referred the searcher to the unit they ended up renting, the racial homogamy 

persists; of the 24 percent of searchers who learned about a unit from someone they knew, 83

percent learned from someone who was of the same race.

Searchers who used social networks to help find units to inquire about were also asked how they 

were related to the person who helped them—family member, friend, coworker, neighbor, or 
25

roommate (exhibit 3.10). Our recent movers who used their networks to help find units to inquire 

about were most likely to rely on friends (51 percent), followed by family members (22 percent) 

and coworkers (20 percent). Very few relied on roommates or neighbors. The only difference 

between recent movers and current searchers is that they used friends more often (62 percent

relied on friends), and much fewer (10 percent) used family members, coworkers, and neighbors.

This finding means that people generally rely on relatively close ties—but not family members—

for assistance in finding units to consider. A small number of recent movers (n = 31) found the 

actual unit they ended up moving into through their social networks. The results show that 

friends, again, were the most common source (61 percent), however in this case, family members 

were the second most common source (23 percent).

Exhibit 3.10: Social Relationship of Person Who Helped Respondents Find Rental Vacancies 

Recent 

Movers (%)

Current 

Searchers

(%)

Relationship to the person who helped find rental vacancies

Family member 21.6 11.4

Friend 51.4 62.3

Coworker 20.3 10.5

Roommate 1.4 4.4

Neighbor 5.4 11.4

N 74 114

Note: Sample limited to respondents who identified that they used social networks to find out about rental vacancies.

Source: Housing Search Study survey, 2016

SEARCHERS RELY ON PERSONAL KNOWLEDGE AND EXPERIENCES TO PINPOINT 

NEIGHBORHOODS TO SEARCH

On the question of how people obtain information about the neighborhoods they will search, the 

indepth interviews reinforced existing research pointing to the importance of lived experiences.

Searchers who were not new to the Washington, D.C. area talked a lot about personal knowledge 

and experiences. This experience often involved filtering their search activity based on the 

neighborhood name or zip code, which often cued their beliefs about affordability, safety, and 

racial composition. As one low-income man with no children explains, “So we were pretty much 

just looking at finances—at the cost, and then we kind of—I would put in the cost and then I 

would go through judging on neighborhoods, and I know the city pretty well. So seeing the name 

of the neighborhood or the name of the intersection, I would think like either I wanted to live 

there or if I didn’t want to live there.”

When asked how she narrowed down her options, one middle-income woman with children 

reports, “Demographics and proximity and certain addresses, yeah. Certain cities that everyone 

just knows.... We searched for certain areas that wasn’t too bad with our credit in mind. We—

because we already knew in certain areas what might be a problem. (And so, did you use like 

geographic locations to decide that?) Yeah. Just living here because I grew—born and raised 

around this area.” Many indepth interview respondents talked about relying on their knowledge 

of the city to filter out particular options and narrow down their search.

In other cases, personal research, through visits to possible neighborhoods, was an important 

strategy. One middle-income woman with children explains her approach to gathering 
26

information about neighborhoods: “We would go at odd times to see what was going on in the 

neighborhood, to see who was standing outside, what that activity was outside. So, we want to—

that was important so we would have to go out at odd times throughout the day. That’s what we 

were looking for and to see just what it would look like. Then we’ll schedule an 

appointment...In the evening at night, like, you know, like I said, a lot of these guys outside.”

MOST RECENT MOVERS GATHER INFORMATION ABOUT PUBLIC TRANSPORTATION, AMENITIES, 

AND CRIME

In addition to gathering information about possible vacancies, searchers also seek information 

about the neighborhoods in which they might search. It is clearly an important step in the process 

for most searchers; about 70 percent of the recent movers in our sample gathered information 

about neighborhoods. Exhibit 3.11 sheds light on what kinds of information searchers are trying 

to uncover; the vast majority are seeking information on public transportation, parks and other 

amenities, and crime.

Exhibit 3.11: Information Gathered About Neighborhoods, Recent Movers Sample

Percent

School quality 17.8

Public transportation 88.9

Parks and other amenities 88.9

Crime 76.7

Other features 13.3

N 90

Source: Housing Search Study survey, 2016

PERSONAL CONTACTS AND ADS ARE POPULAR SOURCES OF INFORMATION ABOUT AVAILABLE 

UNITS

The HSS asked searchers about (1) all the methods used throughout the search, as well as those 

used to (2) identify the units to visit, and to (3) identify the unit into which the respondent 

moved. As shown in exhibit 3.12, recent movers most frequently used the two following 

methods “someone you knew” and “ads”; well more than one-half of the searchers (58 percent–

74 percent) used each. Among Current Searchers, more than one-half of the searchers used the 

following three methods: “someone you knew,” “ads,” and “signs on buildings.”

Exhibit 3.12: Methods Ever Used To Identify Housing Units

Recent Movers (%) Current Searchers (%)

Someone you knew 58 51.7

Ads 74 82.8

Signs 29.8 56.3

Professional service 28.2 34

Social media 27.5 38

Community or religious organization 10.7 23.5

Other 4.6 3.4

N 131 238

Source: Housing Search Study survey, 2016
27

It is important to look more closely at the use of “ads” because people can find ads in a number 

of places—from websites to newspapers to bulletin boards and billboards. For the recent movers, 

the most common source, by far, was online or on websites; overall, 91 percent of recent movers 

who used ads used the internet to find the ads. This finding is compared with about 14 percent

who used newspaper ads. Clearly, online search processes have come to be a dominant source 

for housing searchers.

Our indepth interview participants also talked at length about the online resources they used, 

with many favoring Craigslist, but others saying “95 percent of it (Craigslist), is garbage. You 

don’t really know what it is...and then, by the time you get to it, it’s either gone or, I don’t 

know.... We were primarily looking through Zillow, which seemed to work out really well.”

Other people had a trust issue with Craigslist: “It was nerve wracking; I did not want to use 

Craigslist, that was my last resort just because there’s a lot of creepy people on there. So I was 

hesitant at first, but it worked out.” Despite these kinds of reservations, almost all searchers used 

online resources of one kind or another (Zillow, Apartments.com, and so on) to locate units and 

learn about neighborhoods.

Another advantage of online searches that our indepth interviews drew our attention to was that 

they were considered more efficient, as one renter explains, “email ... most of the time I would 

email, sometimes I would call. But my guess is that it was probably 80/20 as the first choice just 

because it was easier. There was an email contact, I could do that on my lunch break. I can do it.

It was harder to carve out a phone time, and that was just an easier way. Sometimes I also 

wanted to make sure I kept track of what I was doing because if I did something three or four 

contacts in a day, I didn’t want to forget which one I was doing or what it was referring to.” This 

respondent, however, explains what he prefers about the phone: “Most of the time when I see 

something, either I’ll call or send an email. Then the communication over the phone, normally I 

can tell if it’s good or bad.” Other searchers use the in-person and phone communication to get a 

better sense of the landlord, and what kind of a person they will be able to rent from.

MOST INQUIRE BY EMAIL OR PHONE, BUT LANDLORDS ARE NOT ALWAYS RESPONSIVE

Once potential units are identified, whether from a friend, a Craigslist ad, or a community 

organization, a searcher may gather additional information by contacting the landlord or 

management company. During the initial contact, a searcher can get questions answered and 

arrange to see the unit. Each of these stages of information gathering can be accomplished 

differently and with potentially different consequences for the outcomes. Given the 

predominance of online sources of information, it is interesting to see more variation in terms of 

whether that initial inquiry is made through email or by telephone.

As shown in exhibit 3.13, our sample was evenly split between respondents who primarily 

inquired about units by phone and respondents who primarily inquired via email; in-person visits 

were much less common. So, although our searchers rely heavily on the internet to identify units, 

the next step of the process—reaching out to the landlord or property management firm—is not 

as dominated by an online approach. Instead, substantial numbers of respondents use the 

telephone to contact the landlord, bypassing the internet for this stage of the search.
28

Exhibit 3.13: How Searchers Inquire About Units, Recent Movers Sample (percent)

Note: N = 116.

Source: Housing Search Study survey, 2016

GETTING INFORMATION FROM LANDLORDS CAN BE DIFFICULT

Current searchers were asked for assessments of how difficult it was to get information through 

their inquiries with landlords or property managers. The results are reported in exhibit 3.14 and 

show that nearly 40 percent of our sample of current searchers had a difficult time getting 

information from landlords.

Exhibit 3.14: Difficulty Getting Information Needed Through Inquiries

Current Searchers (%)

Not at all difficult 61.7

A little difficult 19.1

Somewhat difficult 15.7

Very difficult 2.1

Extremely difficult 1.3

N 235

Source: Housing Search Study survey, 2016

MANY USE ACTIVE STRATEGIES TO MAKE THEMSELVES APPEAR MORE ATTRACTIVE AS 

TENANTS TO LANDLORDS

In the HSS, we specifically asked respondents to indicate whether they used any of six different 

strategies while gathering information in order to be treated more fairly: dress differently; speak 

differently; bring or leave a partner, child, or friend at home; mention career or professional 

background; and send someone else to check out the unit.

Overall, from both our current searchers and recent movers samples, most searchers said they 

mentioned their career or professional background when meeting with a potential landlord 

(exhibit 3.15). Among recent movers, about 30 percent of the searchers reported bringing 

37.9 39.7

3.4

17.2

1.7

Email Phone Text In-person visits Some other way
29

someone along, whereas one in five said they dressed differently. Our current searchers (who are 

generally lower income than the recent movers) were more likely to use several of these 

strategies, with 41 percent saying they dressed differently, and about the same percent indicated

they brought along someone to the visit. One in five said they “spoke differently” and that they 

“left someone at home.”

Exhibit 3.15: How Searchers Change Their Behavior When They Inquire About Units

Recent Movers

(%)

Current Searchers

(%)

Dress differently 20.8 40.6

Speak differently 10.8 20.5

Bring a partner, friend, or children 29.2 36.8

Leave a partner, friend, or children at home 6.2 21.3

Mention your career or professional background 52.3 60.1

Send someone else to check out a place for you 9.2 6.7

N 130 239

Source: Housing Search Study survey, 2016

MOST CURRENT SEARCHERS FEEL THEY ARE TREATED WELL WHEN VISITING UNITS

Current searchers were also asked about how they were treated during visits (in part, to gather 

more information) to potential housing units. In general, current searchers were quite satisfied 

with their treatment when visiting units. On a scale from 1 to 4 (with 4 representing “very 

satisfied” and 1 “not at all satisfied”), the average rating was a 3.34.

As shown in exhibit 3.16, out of the 577 visits current searchers recorded in waves 2 and 3, 

respondents indicated that they were “very satisfied” with their treatment in more than one-half

(54 percent) of the visits. Respondents reported feeling “a little satisfied” or “not at all satisfied”

with their treatment in only 13 percent of visits, and the remainder—32 percent—were somewhat 

mixed in their assessment.

Exhibit 3.16: Assessment of Treatment During Housing Visit

Recent Movers (%)

Very satisfied 54.4

Somewhat satisfied 31.7

A little satisfied 7.1

Not at all satisfied 5.9

N 577

Note: Sample (N) represents total number of units visited by current searchers in wave 2 and wave 3.

Source: Housing Search Study survey, current searchers, 2016

Searchers in the indepth interviews described how stressful it was in terms of coordinating the 

logistics of connecting with landlords and visiting units. One woman paints an especially vivid 

picture of the stressful aspects of searches: “Sometime I might have one [scheduled visit] at 9:00, 

then I have one at 2:30, then I have another one at 5:00 and then that might get cancelled. I have 

to reschedule, but I couldn’t do it until the next weekend. There were things like that along the 

way that I hadn’t expected. In my mind, all the work was going to be in the prep and then I 

would just show up to the place, and go thumbs up, thumbs down. But there was so much more 

time trying to visit a location and time spent waiting around for somebody else to let me in to the 
30

unit. Then, those kinds of like little details really added up. It felt like every Saturday was a 

wasted day. I couldn’t take off from work to go and do it. I just started a job. It wasn’t that easy 

and trying to set things up on the weekends wasn’t always convenient.”

Another searcher explains, “Like one guy, he wasn’t really like responsive to us, and like he just 

gave us this Montgomery County applications form that I’ve never seen before. Told us to mail it 

back with like a check to process. It was like a weird process and I didn’t feel comfortable just 

having him as a landlord.”

Evaluating Options

Data from the HSS provide unique information on the criteria used by individuals as they enter 

the housing search. Because they define the basic parameters of the search, these criteria are 

likely quite important in determining housing outcomes. The first part of evaluating options is a 

qualitative assessment of the information gathered about units and neighborhoods against 

corresponding search parameters and the current place of residence (Brown and Moore, 1970; 

Landale and Guest, 1985; Simon, 1957; Speare, 1974). Second, individuals evaluate their 

experience with the housing search—how long it has taken, how difficult or easy it has been, and 

how satisfied they have been with their experience. Lastly, searchers constantly assess the 

efficacy of their information gathering methods.

Regardless of how extensive or lengthy it is, this evaluation directly informs decision making

about what happens next in the search.

Current Searchers Prioritize Building Security, Landlord Responsiveness, and Rent

We asked searchers to rate a number of different housing unit features on a scale from very 

important to not at all important. These features included building amenities, security of the 

building, responsiveness of the landlord, style of the building, size of the unit, and the rent asked.

As shown in exhibit 3.17, three features stand out as being rated as very important by more than 

one in three searchers in our sample: security of the building, responsiveness of the landlord, and 

the rent being asked. Whether a unit is a house or apartment is the least likely to be rated as 

“very important.”
31

Exhibit 3.17: Importance of Housing Unit Search Criteria

Note: N = 336.

Source: Housing Search Study survey, current searchers, 2016

Transportation and Safety Are Top Priorities in Terms of Neighborhood Criteria

A similar style of question was asked that focused on the neighborhood characteristics and asked 

about safety of the neighborhood, quality of schools, quality of public services, convenience to 

public transportation, amenities in the neighborhood, location near friends or family, and 

convenience to workplace or school. Given that families with children are nearly certainly 

different from those without, in terms of the relevance of school quality, we present the results 

both for the total sample and for only those searchers with children in the household in exhibit 

3.18. First, for the overall sample, two neighborhood features stand out: convenience to public 

transportation and safety of the neighborhood. More than 80 percent of searchers rated these 

features as “very important.” In our sample, being close to family or friends is the feature least 

likely to have been rated as “very important.” Not surprisingly, however, the pattern is quite 

different among families with children in the household. Specifically, although neighborhood 

safety was also a top criterion among those with children, school quality replaced convenience to 

transportation as the other highly rated feature, with both being evaluated as very important by at 

least 90 percent of the current searcher sample.

66%

85%

82%

44%

59%

84%

Building amenities

Security of building

Responsiveness of the landlord

Whether house or apartment

Size of the home/number of rooms

Rent asked

Reporting "Very Important"
32

Exhibit 3.18: Importance of Neighborhood Search Criteria

Source: Housing Search Study survey, current searchers, 2016

Most Recent Movers Expanded the Geographic Scope or Budget During Their Search

After assessing available options and facing potential difficulty in locating housing that meets all

the initial search criteria, searchers may modify the importance of these various neighborhood 

and unit features. We asked our sample of recent movers to look back on their search and 

(retrospectively) report whether they adjusted their housing criteria in three specific ways: 

expanded their search to other neighborhoods, changed their mind about how important certain 

unit criteria were, and increased their price range (exhibit 3.19). A majority (or near majority) of 

recent movers adjusted their search criteria in each of these ways. Nearly 60 percent expanded 

the neighborhoods in which they searched, and nearly one-half had to increase the price they 

would pay in order to find a place to rent. The particular salience of the price of rental units is 

likely due at least in part to the particular context of this study: the Washington, D.C.

metropolitan area has very high median rent levels. The 2011 median gross rent for the 

Washington metropolitan area was $1,391, the second most expensive rental housing market in 

the country. The national median gross rent during the same period was $871, nearly 40 percent 

lower than the median rent for the Washington region (Flanagan and Schwartz, 2013).

77%

28%

54%

74%

73%

90%

94%

60%

25%

60%

83%

48%

39%

85%

Convenience to workplace and/or school

Location near friends or family

Amenities in the neighborhood

Convenience to public transportation

Quality of public services

Quality of schools

Safety of neighborhood

Reporting "very important"

Total current searcher sample (N = 335)

Current searcher sample with children (N = 82)
33

Exhibit 3.19: Adjustments to Search Criteria for Recent Movers (percent)

Notes: Figure shows total number of responses. N = 129.

Source: Housing Search Study survey, recent movers, 2016

Many Criteria Change in Importance Throughout Search, but the Biggest Priorities Do Not 

Change

Beginning with unit characteristics, we see that as a search unfolds, some features are more 

malleable in their importance than others. Preferences related to amenities in the building, the 

kind of building (apartment or single-family home), and the size of the home or number of 

bedrooms are relatively unstable. In each of these cases, 35 to 40 percent of searchers 

reevaluated their importance—in some cases, it became more important, and in others, it became 

less important. At the other extreme, the two most stable preferences are the security of the 

building and the rent being asked. In both cases, more than 80 percent of searchers remained 

consistent across their search in terms of the importance of these two features (exhibit 3.20).

Exhibit 3.20: Changes in the Importance of Unit Search Criteria

Note: N = 227.

Source: Housing Search Study survey, current searchers, 2016

58.1

45.0

48.8

Expanded your search to look

at other neighborhoods?

Changed your mind about

how important some unit

features?

Increased your price range?

0 10 20 30 40 50 60 70 80 90 100

The rent asked

The size of the home or the number of rooms.

Kind of building

Responsiveness of the landlord

Security of the building.

Amenities in the building

Less important

No change

More important
34

Safety of the neighborhood and convenience to public transportation are the most stable of the 

neighborhood features in terms of their importance; 80 percent of searchers maintained the same 

level of importance for each of these features during the course of their search (exhibit 3.21).

Conversely, the quality of public services was least stable and tended to become less important 

for searchers.

Exhibit 3.21: Changes in Importance of Neighborhood Search Criteria

Note: N = 227.

Source: Housing Search Study survey, current searchers, 2016

Recent Movers and Current Searchers Eliminate Neighborhoods From Their Search for 

Different Reasons

Our HSS asked both recent movers and current searchers if they eliminated any neighborhoods 

from their search at any point. Among those who stopped searching in particular neighborhoods, 

we further asked them why they stopped considering it. Exhibit 3.22 shows that the reasons 

differed substantially between the sample of recent movers and that of current searchers. For the 

sample of recent movers, the most common reason was “did not feel safe.” The next most 

common reasons were either that its location was too far from school or work or too far from 

public transportation. For the sample of current searchers, however, the dominant reason for 

eliminating a neighborhood was financial; on closer inspection, the searchers typically 

determined that they could not afford the neighborhood. The next most common reasons for this 

sample were the “too far from school or work” and also the “did not feel safe” response.

0 10 20 30 40 50 60 70 80 90 100

Convenience to workplace and/or school

Location near friends or family

Amenities in the neighborhood

Convenience to public transportation

Quality of public services

Quality of schools

Safety of neighborhood

Less important

No change

More important
35

Exhibit 3.22: Reasons for Eliminating Neighborhoods From Search

Recent 

Movers (%)

Current 

Searchers

(%)

Reasons stopped considering

Did not feel safe 40.0 25.0

Too far from school or work 32.5 33.3

Too far from public transportation 22.5 17.1

Racial or ethnic mix of the neighborhood 12.5 11.1

Financial reasons 12.5 47.2

Quality of housing 12.5 5.6

Neighborhood amenities and quality 17.5 —

Something else 5.0 19.4

N 40.0 36.0

Note: Sample limited to respondents who identified that they eliminated neighborhoods from search.

Source: Housing Search Study survey, 2016

The indepth interviews illuminated the variety of ways that people adjusted their search 

preferences to make rent fit within their budget. Some individuals compromised on the unit 

features, lease terms, or living arrangements. For example, one woman with children says, “A lot 

of stuff is really out the price range because everything is becoming just gentrified; even bad 

neighborhoods ask for outrageous sums of money for two bedrooms. I’m like now changing, after 

like almost 6 months, I’m changing maybe one bedroom with like den, and then maybe that will 

bring down the price, but I really did want a two bedroom because I have a teenager.... We’re 

going from a house to a two bedroom to a one bedroom. It would be really difficult.” Another 

woman with children explains, “We accepted the one [bathroom] because we needed a place. We 

really didn’t want the washer and dryer not to be in the house, but we accepted that, too.”

Affordability also resulted in one middle-income woman with children adjusting her lease terms:

“To be honest, I wanted to have a 6-month lease at first, but then later on, if I was the one to take 

6-month, I have to pay like $100 extra per month. That’s why I switch my terms to 1 year. If it was 

6-month, with the same price, it was better.” One middle-income man with no children puts it quite 

succinctly: “Price versus safety. Price versus convenience. Price, cost versus location.”

Final Outcome

In the simplest sense, the final outcome is whether the searcher successfully found a unit and leased

up. However, the characteristics of the unit and neighborhood chosen also to add an important 

dimension. This dimension includes both objective characteristics, like the size of the unit and the 

location of the neighborhood, as well as subjective characteristics, such as how satisfied the searcher is 

with the unit relative to his or her initial preferences and expectations as well, as relative to his or her 

old unit. The final outcome also includes an assessment of the housing search process, its length, the 

reasons for stopping, and the searcher’s reports of unfair treatment during the process.

Most Report Some Difficulty With Their Housing Search

About a third of our recent mover sample said their housing search was “not at all difficult,”

which means that 70 percent said it was “somewhat,” “very,” or “extremely difficult” (exhibit 

3.23). Searchers who rated their search at least “a little” difficult were then asked to explain what 

made it difficult. Their responses are reported in exhibit 3.24.
36

Exhibit 3.23: Difficulty of Search for Recent Movers

Overall, would you say this housing search was... Percent

Not at all difficult 30.0

A little difficult 26.2

Somewhat difficult 30.0

Very difficult 7.7

Extremely difficult 6.2

N 130

Source: Housing Search Study survey, recent movers, 2016

As shown in exhibit 3.24, a few key factors emerge as very common reasons for recent movers 

to report that their search was difficult. Affordability again emerges as crucial; it is, far and 

away, the most common reason why a search was difficult (for those who had a difficult search).

About 90 percent of our sample reported that finding units in their price range made the search 

difficult. Timing was the second most common response, with most recent movers indicating 

that “feeling rushed to move” was the reason it was a difficult search.

Exhibit 3.24: Reasons for Difficulty of Search

Note: N = 91.

Source: Housing Search Study, recent movers, 2016

The Time It Takes To Find a Unit Varies, and Success Is Far From Guaranteed

Our recent mover sample revealed a great deal of variation on search length; a few people (12

percent) took less than a week for their search (exhibit 3.25). The rest of the sample was about 

evenly spread across the other three categories: more than a week or less than a month; 1 to 2

months; and more than 2 months. This spread reveals a heterogeneity that is reinforced when we 

consider the length of searches in our Current Searcher Study.

90.1

54.9

31.9

19

33

30.8

27.5

Finding units in price range

Feeling rushed to move

Finding transportation

Finding childcare during search

Meeting the application requirements

Finding landlords that would rent to me

Other reasons

Percentage of recent movers
37

Exhibit 3.25: Length of Housing Search, Recent Movers

How long did it take you to find your new place? %

Less than a week 11.5

More than a week but less than a month 30.5

1–2 months 29.8

More than 2 months 28.2

N 131

Source: Housing Search Study, recent movers, 2016

Exhibit 3.26 shows that most current searchers are long-term searchers. Almost one-half had 

already been searching for 2 months by the time they called us for the first interview. Another 30 

percent had been searching for 1 to 2 months. Moreover, during the field period, we found 

ourselves needing to extend the time between waves of the Current Searcher Study because we 

found that our searchers had not yet completed their search when we tried to interview them for 

the third time according to our original schedule (see chapter 2). By the close of the survey, only 

39 percent of current searchers had actually found a unit.

This finding may also be explained by the unusually high number of people who gave as their 

reason for searching to find “better quality housing.” This group of people may be on the lookout 

for better options, but they are not urgently looking for new housing. This category of searchers 

is interesting—that it exists has implications both for understanding how search processes unfold

and sampling for researchers interested in studying searchers.

Exhibit 3.26: Length of Housing Search at Point of Wave 1 Contact, Current Searcher Study

How long have you been searching at this point? 

Would you say... %

Less than a week 2.9

More than a week but less than a month 19.2

1–2 months 29.5

More than 2 months 48.4

N 339

Source: Housing Search Study, current searchers, 2016

Many indepth interview participants talked about how time-consuming a housing search is—

both in terms of the hours of a day (visiting units, scheduling appointments, and so on) and in 

terms of the length of the process that can come from protracted searches. For example, one 

middle-income woman with children describes why the search was protracted: “I guess another 

part was trying, we wanted to stay in the same school district and just trying to find something 

that was semi-affordable. You’re in Bethesda, so it’s expensive here. So, um, trying to find 

something that was a nice house, not gross because there’s a lot of old houses here in this area 

also. I probably searched, well...I probably started in like June, and I found this place in 

September, so it took me a number of months to find something that was between our location, 

the niceness of the place, and the rent price.”

Others focus on how grueling the process can be. One woman recognizes that she was fortunate 

that she had ample time to conduct the search, and in describing the process, illustrates how 

time-consuming it can be: “If you are somebody who doesn’t have a lot of time in—either 
38

because you are moving or maybe you’ve been evicted or maybe, you know, for whatever 

reason, if you don’t have time to wade through every listing on Craigslist, like I did, because I 

was doing it for like every day for like 2 weeks, you know. I think that would make the housing 

process much more difficult because then, you know, you are looking for place that pop up more 

readily and those places are more competitive.”

Finally, one of the searchers reflects on how fortunate she was because she had a job that 

allowed her some flexibility and how crucial that was to making her search work: “If you have a 

job where you don’t have any control over your hours and you’re working shifts and weekends, I 

don’t even know how you move. I just think that would be so much worse. I don’t really have 

any sage wisdom or advice or thoughts other than thank goodness that’s not my situation.”

Most Are More Satisfied With Their New Neighborhoods Than With Their Units

In the Recent Mover Study, respondents were asked a series of questions asking if they felt they 

got “better than they hoped for,” “not as good as they hoped for,” or “about what they hoped for”

in terms of a number of different unit and neighborhood characteristics.

Movers generally reported being able to get what they hoped for or more on most neighborhood 

dimensions. As shown in exhibit 3.27, the percentage of searchers who “settled for less” on any 

of these dimensions ranged from 7 percent (on convenience to public transportation) to 18 

percent (on amenities).

Exhibit 3.27: Assessment of New Neighborhood Features Compared With What Was Hoped For

Recent Movers (%)

Convenience to your workplace or school

Better than hoped for 31.3

About what hoped for 42.7

Not as good as hoped for 13.0

N/A: Convenience to workplace or school did not matter 13.0

N 131

Location near friends or family

Better than hoped for 25.2

About what hoped for 33.6

Not as good as hoped for 16.8

N/A: Location near friends or family did not matter 24.4

N 131

Amenities like restaurants, grocery stores, theaters, shopping, and doctor’s offices

Better than hoped for 44.6

About what hoped for 34.6

Not as good as hoped for 17.7

N/A: neighborhood amenities did not matter 3.1

N 130

Convenience to public transportation

Better than hoped for 54.2

About what hoped for 33.6

Not as good as hoped for 9.2

N/A: Convenience to public transportation not important 3.1

N 131
39

Recent Movers (%)

Quality of public services like libraries, playgrounds, and community centers

Better than hoped for 34.9

About what hoped for 33.3

Not as good as hoped for 15.9

N/A: Quality of public services not important 15.9

N 126

Quality of the schools

Better than hoped for 13.9

About what hoped for 15.6

Not as good as hoped for 7.4

N/A: Quality of schools not important 63.1

N 128

Safety 

Better than hoped for 27.3

About what hoped for 55.5

Not as good as hoped for 13.3

N/A: Quality of schools not important 3.9

N 131

Source: Housing Search Study survey, recent movers, 2016

Overall, our sample of recent movers also felt positively about their unit feature preferences.

Sixty percent rated their new unit as better than their prior one. However, more searchers 

appeared to express dissatisfaction with unit features (rent, size of the home, security of the 

building, and other amenities in the building) than of neighborhood features. First, one-third of 

searchers felt that they were paying more than they hoped to pay; this finding is consistent with 

prevailing themes around cost and income. In addition, 1 in 5 respondents got less than they 

hoped for, in terms of the amenities in the building (see exhibit 3.28).

Exhibit 3.28: Assessment of Various New Unit Features Compared With What Was Hoped For

Recent Movers

(%)

Rent paid is... 

More than thought would pay 33.6

About what thought would pay 38.9

Less than thought would pay 21.4

N/A: Rent did not matter 6.1

N 131

The size of the home or number of rooms...

Better than hoped for 42.7

About what hoped for 38.9

Not as good as hoped for 11.5

N/A: Size did not matter 6.9

N 131

The security of the building is... 

Better than hoped for 33.8

About what hoped for 46.9

Not as good as hoped for 13.1

N/A: Building security did not matter 6.2
40

Recent Movers

(%)

N 130

The amenities in the building are... 

Better than hoped for 38.2

About what hoped for 38.2

Not as good as hoped for 20.6

N/A: Amenities did not matter 3.1

N 131

Source: Housing Search Study survey, 2016

It is clear from this discussion of the ingredients of a housing search that it is a complicated and 

dynamic process that is challenging to measure and describe. However, the Housing Search 

Study (HSS), an instrument designed specifically with housing searches in mind, provides a 

window into this complexity, and the complementary indepth interviews provide nuance to the 

quantitative descriptions. With this more complex and complete picture of the different 

dimensions of a housing search, we now turn to a discussion of the secondary data to provide our 

first picture of racial differences in these processes.
41

Chapter 4: Racial and Ethnic Differences in Housing Searches

With the complexities of housing searches described in chapter 3 in mind, we now turn to our

detailed investigation of racial differences in housing searches. We begin with the method that 

allows us to provide the most rigorous tests of racial differences using representative samples of 

either the nation or a major metropolitan area: secondary data analyses of three surveys.

Specifically, this chapter reports the results of our analyses of the American Housing Study 

(AHS), the Panel Study of Income Dynamics (PSID), and the Chicago Area Study (CAS). We 

draw on original analyses of these survey data sets to assess, to the extent that the data permit,

the housing search processes used by minority renters and how those processes differ from those 

of White, non-Latino renters. In appendix F.15, we report some basic comparisons between the

nationally representative PSID and AHS data sets. Where possible, we also analyze correlations 

between the search processes and both subjective and objective outcomes of the housing search.

Context

As described in our conceptual framework, the housing search process is highly contingent on its 

context: the individual and family characteristics associated with residential mobility and the 

circumstances related to the decision to move. In this section, we assess racial differences in the 

characteristics of renters and the metropolitan housing structures they face, as well as differences 

in the extent to which a residential move was planned or unplanned—that is, related to factors 

outside of the individual’s control. It is important to keep these circumstances and characteristics 

in mind because racial or ethnic differences in these are likely to help shape group differences in 

search processes.

Black and Latino Renters Are Demographically and Economically Different From White Renters

As exhibit 4.1 shows, by comparison with both White and Black renters, Latino renters tend to 

have substantially lower levels of education, and household income is higher for White renters 

than for both Black and Latino renters. A larger share of Black renters (56 percent) than Latino 

(27 percent) and White renters (31 percent) are female, and White renters are less likely than 

Black and Latino renters to have children in the household. By comparison with White and 

Latino renters, Black renters are also more likely to receive housing assistance—either in the 

form of receiving rental assistance or by living in public housing.
42

Exhibit 4.1: Descriptive Statistics for Black, Latino, and White Renters in PSID, 1997–2011

Black Latino White

Mean SD Mean SD Mean SD

Education of head 12.1 (2.3) 10.2 (4.2) 13.2 (2.9)

Family income ($1000s) 24.7 (22.6) 29.7 (22.2) 37.9 (34.1)

Age of householder 38.8 (13.6) 37.8 (13.9) 38.9 (17.0)

Female householder 

(1=yes) 0.562 (0.50) 0.265 (0.44) 0.313 (0.46)

Married or cohabiting 

(1=yes) 0.237 (0.43) 0.566 (0.50) 0.391 (0.49)

Whether children in house 

(1=yes) 0.568 (0.50) 0.586 (0.49) 0.323 (0.47)

Receiving housing 

assistance (1=yes) 0.292 (0.46) 0.086 (0.28) 0.079 (0.27)

Metropolitan-area variables

Housing vacancy rate 11.5 (4.1) 10.3 (4.2) 11.9 (5.8)

Homeownership 66.7 (5.6) 59.9 (7.1) 67.2 (6.5)

Rent-to-income ratio 0.188 (0.02) 0.212 (0.03) 0.184 (0.02)

N 9,314 1,372 8,379

PSID = Panel Study of Income Dynamics. SD = standard deviation.

Source: PSID, 1997–2011

Latino Renters Tend To Reside in Metropolitan Areas With Tighter Housing Markets

Although Black, Latino, and White renters generally face similar housing market conditions, as 

can be seen in exhibit 4.1, Latino renters do tend to live in areas where a lower percentage of 

housing units are vacant, overall levels of homeownership are lower, and median rent levels are 

higher relative to median incomes.

These tighter housing market conditions and group differences in individual- and family-level 

characteristics are important to consider in developing our understanding of group differences in 

housing search processes. Accordingly, we control for all these variables in our subsequent 

analyses of the PSID data. A similar set of controls is included in analyses of the AHS and CAS 

data.

7

Renters and Recent Movers Are Not That Different From Each Other

Also, important to our analysis is the possibility that recent movers are not a random crosssection of the full rental population, and the factors associated with mobility in general represent 

important features of the context in which housing searches occur. Appendix exhibit F.1 presents 

descriptive statistics (means and standard deviations) derived from PSID data for two groups of 

 7 That having been said, the CAS reflects a single metropolitan area, so housing market conditions are not controlled for in the 

analyses of this data set. Other individual-level characteristics are, however, included in analyses of the CAS.
43

renters: those who moved between PSID interviews (recent movers) and those who did not 

(nonmovers). These statistics indicate that relatively few individual- and family-level 

circumstances differentiate renters who moved in the 2 years between PSID interviews and 

renters who did not.

Housing search processes are likely influenced by the extent to which the search was planned or 

anticipated, as opposed to being precipitated by some unexpected event. Fully anticipated moves 

may be more likely to involve more planning, the use of more comprehensive search strategies, 

and consideration of a wider set of options. In contrast, an unanticipated move may involve a 

more truncated search process and a lower likelihood of favorable residential outcomes. Given 

these expectations, assessing racial or ethnic differences in the likelihood of experiencing an 

unplanned search helps us better understand the extent to which this contextual factor might 

explain group differences in search strategies and outcomes.

Black and Latino Movers Are More Likely To Make an Unplanned Move Than White Movers,

but This Difference Is Explained by Differences in Background Characteristics

Using the longitudinal data in the PSID, we examine the link between the anticipation of a move 

at one interview and the likelihood of a move by the subsequent interview (2 years later). Basic 

racial and ethnic differences are displayed in exhibit 4.2, and the more detailed logistic 

regression analysis predicting the log-odds that a move was unplanned (among mobile PSID 

renters) is presented in appendix exhibit F.2.

Exhibit 4.2: Group Differences in Whether a Move Was Planned or Anticipated

Note: N = 9,789.

Source: Recently mobile renters in the Panel Study of Income Dynamics, 1997–2011

The results point to potentially important racial or ethnic differences; about 24 percent of White

respondents who moved between interviews had not anticipated a move 2 years earlier (exhibit 

70.3% 67.9% 76.5%

29.7% 32.2% 23.5%

Black Latino White

Planned move Unplanned move
44

4.2). In contrast, about 30 percent of moves by Black and 32 percent of moves by Latino

respondents were unanticipated. These racial or ethnic differences are statistically significant (p

< 0.001; see model 1 of appendix exhibit F.2). However, the differences disappear after 

controlling for other factors that influence the likelihood of an unplanned move (see model 2 of 

appendix exhibit F.2). This change is because Black and Latino renters tend to have lower levels 

of income and education, as well as other characteristics that increase their risk of unanticipated 

moves.

In Tighter Housing Markets, the Latino-White Contrast Is Particularly Strong

Also, some evidence exists that the Latino-White contrast is especially strong in metropolitan

areas with relatively few vacancies and high levels of homeownership (see model 3 of appendix 

exhibit F.2), indicating that, in tighter housing markets with relatively few rental units and few 

vacancies, Latino renters are more likely to have to make unanticipated moves than are White

renters.

The observed racial or ethnic differences in unanticipated moves raise important questions about 

the specific reasons why renters move. We address this topic with the more detailed information 

provided in the AHS, which asked recent movers the main reason they moved into the new unit 

during the preceding 2 years (exhibit 4.3).

Exhibit 4.3: Racial or Ethnic Differences in Primary Reasons for Moving (percent)

Notes: N = 9,833.

Source: Recently mobile renters in the American Housing Survey, 2011

0.00

5.00

10.00

15.00

20.00

25.00

30.00

35.00

40.00

Involuntary Other

housing

Job or school Family Better quality Lower rent Owner to

renter

Other

Black Latino White
45

Black and Latino Renters Are More Likely To Make Involuntary Moves, Largely Because of 

Socioeconomic Standing

Compared with 4.7 percent of White respondents, 6.0 percent of Latino respondents and 6.5 

percent of Black respondents reported making a move for involuntary reasons, such as an 

eviction, foreclosure, fire or other disaster damage to the previous housing unit, or because they 

were forced to move because the government or private party wanted to use the property. 

Although small, both the difference between Black and White renters and the difference between 

Latino and White renters are statistically significant.

These differences are consistent with the PSID analysis indicating that Latino and Black renters 

are more likely to experience an unplanned move. Moreover, like in the PSID, supplemental 

analyses indicate that these racial or ethnic differences in the likelihood of making a forced move 

are rooted in group differences in socioeconomic resources; racial or ethnic differences disappear 

when individual characteristics—especially income and education—are introduced, indicating 

that the relatively lower socioeconomic standing of Black and Latino renters puts them at greater 

risk for a forced move.

White Renters Are More Likely To Move To Improve Convenience Than Are Black or Latino 

Renters 

The AHS also shows (exhibit 4.3) sharp racial or ethnic differences in the likelihood of making a 

clearly purposive move—changing residence with the intent to improve convenience or living 

conditions. For example, 31 percent of White recent movers, but only 18 percent of Black and 

23.5 percent of Latino recent movers, reported that their primary reason for moving was to be 

closer to work or school. Unlike other differences in reasons, these statistically significant 

differences cannot be explained by group differences in socioeconomic characteristics, family 

composition, or features of the broader housing market. 

Black and Latino Renters Are More Likely To Move To Improve the Quality of Their Housing

Although the percentages are quite low for all groups, a slightly larger share of Black and Latino

renters report that they moved in order to find better quality housing. Specifically, 6 percent of 

White renters, but 8 percent of Latino and nearly 10 percent of Black renters, report that the main 

reason for a move was to find better-quality housing. Again, these racial or ethnic differences are 

statistically significant and persist even with controls for individual characteristics and housing 

market conditions. Although the absence of data on residential origins in the AHS prevents 

analysis of the topic, these differences in reporting moves for higher-quality housing likely 

reflects racial or ethnic differences in the quality of housing from which renters moved.
46

Decision Making

As described in our conceptual framework, racial or ethnic differences in residential outcomes 

might emerge out of variations in decision making during the housing search. For example, 

important considerations may be racial or ethnic differences in the development of desired 

features of housing units and neighborhoods sought during the search process, the criteria used to 

decide whether to investigate specific housing options, decisions about how many housing 

options and which neighborhoods to investigate, and decisions about when to end the search.

Variations at any of these decision-making points may have important implications for racial 

stratification in the types of units and the type of neighborhoods renters occupy. Existing surveys 

provide very little information about these aspects of the search process, but we report here what 

is available, and note that the Housing Search Study (HSS) data will shed more light.

Black and White Renters Look at Comparable Numbers of Units, but Latino-White Differences 

Persist

The AHS survey measured how many housing units recent movers considered when they made 

their move. By comparison with recent White movers (those moving into the current unit within 

the past 2 years), recent Black and Latino movers report looking at significantly fewer units 

before moving (see model 1 of appendix exhibit F.3). Specifically, White movers looked at, on 

average, 6.56 other units before choosing the current location, whereas Black movers considered 

5.48 other options, and Latino movers considered 4.89 other options. However, the difference 

between Black and White renters appears to be a function of group differences in 

sociodemographic characteristics and the conditions of the housing market because it becomes 

statistically insignificant when controls for these factors are introduced (see model 2 of appendix 

exhibit F.3). In contrast, the difference between Latino and White renters remains statistically 

significant even with the full slate of controls. Thus, available evidence suggests that even 

among those with similar economic resources, family characteristics, and housing market

conditions, Latino renters tend to consider fewer units than do White renters during the search 

process.

Latino and White Renters Are Equally Likely To Look Outside Their Current Neighborhoods, but

Black Renters Are More Likely Than White Renters To Do So

By comparison with White renters, Latino renters are also less likely to have looked at other 

neighborhoods outside of the one into which they moved. According to the AHS, about 50

percent of Latino renters looked in other neighborhoods before choosing the neighborhood of 

residence, compared with about 57 percent among both White and Black renters. After 

controlling for other factors, however, the Latino effect disappears (see model 2 of appendix 

exhibit F.4), suggesting that the difference between Latino and White renters is a function of 

differences in economic resources and other individual and family characteristics.
47

In contrast, although the basic model showed no differences between Black and White renters, 

once these factors are controlled, a Black-White difference emerges in the likelihood of having 

considered other neighborhoods (see model 2 of appendix exhibit F.4). Specifically, the odds of 

having considered other potential neighborhood destinations during the search process are about 

28 percent higher for Black renters than for White renters with similar characteristics. Thus, 

available evidence suggests that, all else being equal, Black renters tend to take a more inclusive 

search approach—at least in terms of employing the simple strategy of considering options 

outside of the current neighborhoods—than do White renters.

Beyond the simple number of units considered and whether other neighborhoods were 

considered, the location of these potential residences and the characteristics of the neighborhoods 

in which they are may have crucial effects on racial differences in residential outcomes. We 

know that White, Black, and Latino renters end up living, to a great extent, in segregated 

neighborhoods. What is unclear is the extent to which actions made during the step before a 

move—during which people decide the places they wish to search—also lead to segregation.

Alternatively, for example, it may be that people decide to search in a wide range of different 

neighborhoods with different racial characteristics but for one reason or another end up living in 

a segregated own-group neighborhood. The CAS is the only existing data set that sheds light on 

the decisions that White, Black, and Latino renters make about which neighborhoods to consider 

during the search process.8 Taking into account that people often search in more than one 

neighborhood or community, we classify a person into one of the following mutually exclusive 

categories: searched only in communities where their own group was the majority (> 50 percent); 

searched only in communities where some other group was in the majority; searched in a 

combination of communities—some where their group was the majority and others where 

another group was in the majority; searched only in communities without a majority group; or 

did not search in any of the communities labeled on the map. Using this, we can gauge the extent 

to which the kinds of places people search varies by race or ethnicity. 

Black and Latino Renters Search in Communities With a Wider Variety of Racial Compositions 

Than Do Whites

As shown in exhibit 4.4, whereas most White renters (65 percent) searched only in 

neighborhoods in which their own group was the majority, only 10 percent of Black and 13

percent of Latino renters did the same. Conversely, fully 55 percent of Black renters had in their 

search set both communities where Black residents were the majority and communities where 

White residents were the majority. Likely owing to the small percentage of communities that 

were majority Latino on our maps (and in the metropolitan area in general), Latino renters’

patterns are different; the modal category for Latino renters is to search only in communities 

where some other group is in the majority (49 percent). Latino renters are also much more likely

than Black or White renters to search only in communities without a racial or ethnic majority 

 8 The CAS provides information on a random sample of adults ages 21 and older living in Cook County, Illinois (which includes the city of 

Chicago). Survey respondents were presented with a simplified and colorful map of the Chicago metropolitan area. A large number—but by no 

means all—of the communities in the region (41) were labeled on the map and had a checkbox next to them. Respondents were asked to check 

(using a pen) the boxes next to all the communities in which they had searched for housing in the last 10 years. The communities labeled on the 

map reflect a range of racial compositions, social class characteristics, and geographies.
48

group. Controlling for background characteristics does little to diminish these racial or ethnic 

differences. For example, the greater likelihood of Latino and Black renters for searching in 

“Other only” neighborhoods compared with “Own only” neighborhoods persists even after 

controlling for individual and family characteristics. One exception is that for Black, but not 

Latino, renters the greater likelihood of looking in both “Own” and “Other” group majority 

neighborhoods (compared with “Own only”) also holds after controlling for background 

differences.

Exhibit 4.4: Racial-Ethnic Differences in Types of Neighborhoods Searched by Renters

Note: N = 188.

Source: Chicago Area Study, 2004–2005

Looked at another way, about one-fourth of White renters reported search locations that included 

at least one community in which their own group was not in the majority, compared with 72 

percent of Black and fully 81 percent of Latino renters.

Information Gathering

Racial or ethnic differences in the types of neighborhoods searched and ultimately occupied by 

renters may emerge out of group differences in the type of information gathered during the 

search process. The use of different sources of information—for example, from newspapers, 

rental agents, or online resources—may lead to racial-ethnic variation in the number, type, and 

location of housing options identified during the search and, by extension, contribute to racialethnic differences in residential outcomes. In this sense, assessing racial-ethnic differences in 

methods of gathering information is potentially important for understanding broader patterns of 

residential stratification.

0%

10%

20%

30%

40%

50%

60%

70%

Own only Other only Mixed only Own and other None on Map

Black Latino White
49

Black and Latino Renters Are More Likely Than White Renters To Find a Place To Live by Word 

of Mouth

Among recent movers, based on the American Housing Survey (AHS) data (exhibit 4.5), Black

and Latino renters are more likely than White renters to have used word of mouth to find their 

current residence: about 39 percent of both Black and Latino renters first heard about their new 

place through this type of interpersonal contact, compared with 28 percent of White renters.

These Black-White and Latino-White differences are statistically significant and remain so even 

after controlling for education, income, and other family-level characteristics, as well as 

differences in housing market conditions (see appendix exhibit F.5). Although the AHS data 

provide no information on the rationale for utilizing particular search methods, this finding is 

consistent with the expectation that minority renters are more likely than White renters to rely on 

social networks and other information sources that allow them to avoid discrimination in the 

search process.

Exhibit 4.5: Racial-Ethnic Differences in Method Used To First Identify the New Housing Unit 

(percent)

Note: N = 9,833.

Source: Recently mobile renters in the American Housing Survey, 2011

Black and Latino Renters Are More Likely Than White Renters To Use Signs on Buildings To 

Find an Apartment

By comparison with White renters, Black and Latino renters are more likely to have found their 

current residence by viewing “For Rent” signs posted on buildings; more than 20 percent of 

Latino and nearly 13 percent of Black renters found their home through this method, by

comparison with 10 percent of White renters. Again, these differences remain statistically 

significant with controls for the full slate of individual, family, and metropolitan characteristics 

0

5

10

15

20

25

30

35

40

45

Word of mouth Saw sign Newspaper Agent Online Other

Black Latino White
50

(see appendix exhibit F.6). These differences are important in that they point to a potential 

source of racial or ethnic differences in the geographic scope of the housing search process. If, as 

these results suggest, Latino and Black searchers are more likely than their White counterparts to 

rely on building signs to identify housing options, then their housing search is likely to be more 

circumscribed to the areas in which they already spend time: their home neighborhood or the 

areas in which they go for work, school, or other activities. However, the AHS is limited because 

it provides information only on the strategy through which the renter first heard of the current 

residence and cannot shed light on the broader set of strategies employed during the search.

Black Renters Are More Likely To Use Newspapers, and Latino Renters Are Less Likely To Use 

Rental Agents

The AHS data also point to some group differences in the likelihood of methods that, as of 2011, 

were less common approaches for searching for housing. Black respondents in the AHS are 

significantly more likely than White respondents to have heard about their new residence in the 

newspaper,9 a difference that remains significant even with controls for market conditions and 

sociodemographic characteristics (see appendix exhibit F.7). Latino and White respondents are 

not different in terms of the likelihood of using a newspaper for the housing search. However, 

Latino renters are significantly less likely than White renters to have used a rental agent to find 

their current residence, and this difference is statistically significant both with and without 

controls for other search factors. In contrast, recent Black movers (5.96 percent) are slightly 

more likely than their White (5.85 percent) counterparts to have used an agent during the housing 

search. Although modest, this difference becomes statistically significant after controls for 

education, income, and other individual-level characteristics (see appendix exhibit F.8),

suggesting that Black renters rely on agents more than do White renters with similar 

characteristics.

Black and Latino Renters Use Online Resources Much Less Than White Renters

The more pronounced racial or ethnic differences in search methods are in the use of the online 

resources. Black and Latino renters are significantly less likely than White renters to have used 

online resources to find their current residence. Specifically, only about 14 percent of Black and 

15.5 percent of Latino renters found their new home online by comparison with more than onethird of White renters. Again, these differences are only partially explained by group differences 

in sociodemographic characteristics and whether the move was undertaken involuntarily. Even 

after controlling for individual- and family-level characteristics, as well as the basic reason for 

the move, the odds of having found the dwelling online are about 51 percent lower for Black and 

41 percent lower for Latino renters than for their White counterparts (see model 2 of appendix 

exhibit F.9). Metropolitan-level housing market conditions do little to explain these differences 

(see model 3 of appendix exhibit F.9), nor do the differences appear to vary significantly across 

metropolitan contexts (see model 4 of appendix exhibit F.9).

 

9 The AHS item does not specify whether newspaper ads used in the housing search were online or print.
51

Evaluating Options

Little data are available on the question of how people evaluate their options, although some 

hints are provided in a question in the AHS that asks people why they ended their search—

essentially the endgame of the evaluation process. These reasons say nothing about the process 

of evaluation or the evolution of priorities, but racial or ethnic differences in the reasons for why 

people end their search do hint at an important additional line of stratification.

White Renters Are More Likely Than Black and Latino Renters To End a Search Because They 

Are Happy With What They Found 

As shown in exhibit 4.6, most recent movers who provided a reason for ending the search 

reported that they did so because they were happy with the unit into which they actually moved.

However, racial differences in this response are important. About 58 percent of White recent 

movers reported that they stopped looking for housing because they were happy with the unit 

they found. The comparable percentages among Black and Latino recent movers are 47 percent

and 49 percent, respectively. As shown in appendix exhibit F.10, these differences are 

statistically significant (p < .001).

Exhibit 4.6: Racial or Ethnic Differences in Reasons for Stopping the Search (percent)

Note: N = 9,833.

Source: Recently mobile renters in the American Housing Survey, 2011

Moreover, although the likelihood of ending a search because renters were happy with the unit is 

higher for those with relatively higher levels of education and income and for those who move 

voluntarily, controlling for group differences in these and other individual- and family-level 

circumstances only partially explains racial or ethnic differences (see model 2 of appendix 

0

10

20

30

40

50

60

Happy w/this

unit

Had to move

quickly

Had trouble

traveling

No other

options

Too busy Don't like

looking

No particular

reason

Other reason

Black Latino White
52

exhibit F.10). Even among those renters with similar personal characteristics, the odds of ending 

the search because of satisfaction with the unit are about 22 percent lower for Black renters, and 

15 percent lower for Latino renters than for White renters.

The Housing Markets in Which People Search Affects Whether They End a Search Because They 

Are Happy With What They Found

The likelihood of stopping the search because of being happy with the unit is, not surprisingly, 

significantly affected by the conditions of the broader housing market. As shown in appendix 

exhibit F.10, the likelihood of ending a search for this ideal reason is lower in metropolitan areas 

with higher housing costs (ratio of median rent to median income), a relatively low number of 

rental units, and a high percentage of homeowners. Controlling for these metropolitan-level 

variables reduces the size of the racial or ethnic differences only modestly. Moreover, evidence 

to suggest that the magnitude of these racial differences varies across housing market contexts is

limited; only the Black-White contrast is significantly (p = 0.043) moderated by the level of 

homeownership in the metropolitan area.

Latino Renters Are More Likely Than White Renters To Stop a Search Because They Had To 

Move Quickly and Because They Knew of No Other Options

Exhibit 4.6 also shows racial or ethnic differences in other less-than-ideal reasons for stopping 

the search. For example, whereas about 13 percent of White renters reported stopping their 

search because they had to move quickly, 17 percent of Black and 16 percent of Latino renters 

report this kind of desperation decision. Supplemental analysis shows that these differences are 

largely explained by group differences in the likelihood of moving for involuntary reasons, but 

the difference between White and Latino renters remains significant even when the reason for 

moving and other individual- and family-level circumstances are controlled.

Similarly, by comparison with White recent movers, Black and Latino recent movers are more 

likely to report that they stopped searching because they knew of no other housing options.

Specifically, slightly less than 1 percent of White recent movers reported this reason for stopping 

compared with 2.2 percent of Black and 3.0 percent of Latino recent movers. Once again, 

although modest, these differences are statistically significant, and the Latino-White contrast 

remains so even after controlling for racial or ethnic differences in education, income, and the 

reason for moving.

Black Renters Are More Likely Than White Renters To Report Ending Their Search Because of 

Transportation Problems

Black renters who are recent movers (2 percent) are also more likely than their White

counterparts (0.9 percent) to report that they stopped searching because they had trouble 
53

traveling to look at other options. This small but statistically significant difference is completely 

explained by the lower tendency for Black respondents to have access to a car.

Overall, these results suggest that, by comparison with White renters, Black and Latino renters 

may feel more constrained during the housing search process, leading them to settle for a new 

location for less-than-ideal reasons. The extent to which this racial or ethnic stratification 

reflects, and emerges out of, differences in more nuanced features of the search process deserve 

considerably more attention.

Final Outcome

Housing searches vary in terms of the length of time it takes to find a destination and the 

characteristics of the destination; the AHS and Panel Study of Income Dynamics (PSID) data 

provide a range of insights into a variety of racial or ethnic features of search outcomes, 

beginning with the basic question of whether people move at all.

White Renters Are More Likely To Move Than Either Black or Latino Renters

According to the PSID, the likelihood of moving varies substantially by race. As shown in 

exhibit 4.7, whereas 55 percent of White renters make some kind of move during the 2-year 

period, the mobility rate is around 49 percent for both Black and Latino renters. Results in 

appendix exhibit F.11 indicate that these racial or ethnic differences are statistically significant 

and persist, even after accounting for a wide range of factors shown in past research to affect 

mobility. Controlling for individual and family characteristics, as well as key characteristics of 

the local housing market (vacancy rate, homeownership rate, and ratio of income to median rent 

in the metropolitan area), both Latino and Black renters are still significantly less likely than 

White renters to move to a different housing unit during a 2-year period (see model 2 of 

appendix exhibit F.11). However, the extent of these group differences varies significantly across 

metropolitan area contexts (see model 3 of appendix exhibit F.11). The lower mobility of Latino 

renters (compared with White renters) is especially pronounced in metropolitan areas in which 

the median rent is high relative to local income levels. In contrast, a high level of 

homeownership in the metropolitan area diminishes the Black-White difference in mobility.
54

Exhibit 4.7: Group Differences in Residential Mobility Between Interviews

Note: N = 19,065.

Source: Renters in the Panel Study of Income Dynamics, 1997–2011

Black and Latino Renters Seem Less Likely Than White Renters To Move to a Different 

Neighborhood

Roughly comparable percentages of White, Black, and Latino renters move to a different 

housing unit within the same neighborhood during a typical 2-year period (exhibit 4.7).

However, by comparison with White renters, both Black and Latino renters are less likely to 

move to a different neighborhood; on average, about 29 percent of White renters, but only about 

23 percent of Latino and 24 percent of Black renters, move to a different neighborhood in the 2-

year period between PSID interviews. These differences in mobility type are potentially 

important for understanding stratification in the search process, suggesting that housing searches 

may be more spatially circumscribed for renters from minority groups than for White renters.

Appendix exhibit F.12 presents results of a logistic regression analysis predicting the log-odds 

that a move is between neighborhoods (rather than within the same neighborhood) among renters 

making any kind of move during the 2-year period between PSID interviews. The results in this 

table show that the racial or ethnic differences in the tendency to move to a different 

neighborhood are statistically significant (see model 1 in appendix exhibit F.12) but can be 

partially attributed to group differences in individual- and family level circumstances.

Specifically, the Black-White difference in the likelihood of a move to a different neighborhood 

is cut in half (from -.143 in model 1 to -.056 in model 2) and reduced to statistical insignificance

when individual and family controls are introduced. Most influential is that Black movers are 

more likely than White movers to be receiving housing assistance and to have children in the 

50.7% 51.1% 45.0%

25.2% 26.0%

26.1%

24.1% 22.9% 28.9%

Black Latino White

Moved to a different

neighborhood

Moved to different unit

in the same

neighborhood

Did not move
55

household, both factors of which tend to reduce longer-distance moves.

10 In contrast, LatinoWhite differences in the tendency to move to a different neighborhood remain significant and 

even increase slightly in magnitude, with controls for individual- and family level characteristics.

Appendix exhibit F.12 also shows that the conditions of the metropolitan area have strong effects 

on the distance renters travel when they move; likely reflecting the search for more affordable 

options, renters are more likely to switch neighborhoods in housing markets with low vacancy 

rates, high rates of homeownership, and more expensive housing. However, these market 

characteristics do not appear to alter patterns of racial stratification in this outcome; only high 

rent levels appear to moderate the racial difference such that the reduced likelihood for Black

renters to move to a different neighborhood is more pronounced in areas where the ratio of 

median rents to median incomes is high.11

Black and Latino Renters Are More Likely To Have a Failed Search Than White Renters

As outlined in our conceptual framework, constraints on housing options can increase the risk 

that a housing search ends without an actual move taking place. Although existing data sources 

provide little direct information about such failed searches, the PSID does provide information 

about the extent to which the expectation of moving at one interview is followed by an actual 

move at the next interview. Our analysis of this information shows that about 71 percent of the 

White PSID renters who reported the expectation of a move at one interview actually moved by 

the subsequent interview. Although search intensity likely varies greatly among those who did 

not move, these figures indicate that up to 29 percent of searches for White renters can be 

considered failed searches. In contrast, as much as 39 percent of searches by Black and 38

percent of searches by Latino renters might be considered failed searches. As shown in appendix 

exhibit F.13, these racial or ethnic differences in unconsummated searches remain pronounced 

and statistically significant even after controlling for family economic resources, demographic 

conditions, and characteristics of the housing market, and no evidence that the racial difference is 

softened by favorable housing market conditions exists. Thus, not only are Black and Latino

renters less likely than White renters to end their search because they find a unit they like, they 

may also be more likely to end a search without moving at all.

Black Renters Have Longer Searches Than White and Latino Searchers

As noted previously, American Housing Survey (AHS) data suggest that Black and Latino recent 

movers are more likely than their White counterparts to have ended their housing search for lessthan-ideal reasons. Complementing this finding, data from CAS also show that Black renters 

 10 The relatively low likelihood of inter-neighborhood mobility among those receiving housing assistance likely reflects, at least in part, the 

limited portability of vouchers and the geographic clustering of subsidized housing. See Turner, Popkin, and Cunningham (2000). 11 Supplemental information on the geographic distance between origin and destination neighborhoods suggests that when they do move to a 

different neighborhood, Black and Latino renters tend to move shorter distances than do White renters who change neighborhoods. This tendency 

for Black and Latino renters to move over shorter geographic distances than their White counterparts is also more pronounced in more-expensive 

housing markets, and lower vacancy rates and lower concentrations of rental units in the housing market and probably by less access to a car also 

enhances the Black-White difference in distance moved.
56

reported significantly longer searches than did White renters (exhibit 4.8). Whereas 76 percent of 

White renters (and 79 percent of Latino renters) completed their search in a month or less, only 

44 percent of Black renter does. The Black-White differences persist even with individual-level 

characteristics included as controls. In contrast, Latino renters appear to be statistically similar to 

White renters in terms of the length of search.

Exhibit 4.8: Group Differences in Length of Most Recent Housing Search

Note: N = 188.

Source: Renters in the Chicago Area Study, 2004–2005

Our secondary data also provide insight into the residential destinations of those renters who do 

move. Here, we draw on the PSID Child Development Supplement (PSID-CDS)—administered 

in 1997, 2002, and 2007 to families with children in the household—to assess mobile renters’

satisfaction with their current unit and neighborhood, as well as their assessment of the new 

location relative to their location before the move. Our results point to substantial racial or ethnic 

stratification on all these outcomes.

Black and Latino Renters Seem To Rate Their New Neighborhood as Lower in Quality Than Do 

White Renters

Slightly more than one-half (54 percent) of recent White movers rate their new neighborhood as 

either excellent or very good, whereas only 39 percent of Black and 40 percent of Latino recent 

movers rate their neighborhood in this way (exhibit 4.9). Results of ordered logit models reveal

that overall racial or ethnic differences in neighborhood ratings are statistically significant, and 

the contrast between Black and White movers (but not between Latino and White movers) in 

13% 14% 14%

16%

32% 38%

15%

33% 24%

23%

3%

20%

11%

11%

3%

22%

7%

1%

Black Latino White

More than 6 months

Between 2 and 6 months

2 months

1 month

Less than 1 month

Less than 1 week
57

neighborhood ratings remains statistically significant, even with controls for individual, family, 

and metropolitan characteristics.

Similar racial or ethnic differences are apparent in data on the new neighborhoods of recent 

movers in the AHS data. By comparison with White recent movers, Black recent movers provide 

lower ratings for both their new neighborhood and their new housing unit, and these differences 

persist even with controls for individual and metropolitan conditions that may affect residential 

outcomes. The data show no significant difference between White and Latino recent movers in 

their ratings of their new unit or new neighborhood.

Exhibit 4.9: Group Differences in Rating of New Neighborhood

Note: N = 584.

Source: Recently mobile renters in the Panel Study of Income Dynamics-Child Development Supplement, 1997–2011

At First Glance, Black and Latino Renters Are More Likely Than White Renters To Say Their 

New Neighborhood Is Unsafe

In terms of specific characteristics that might affect overall neighborhood ratings, data from the 

PSID-CDS indicate that Black and Latino renters are more likely than White renters to indicate 

that it is “somewhat dangerous” or “extremely dangerous” to walk in their new neighborhood 

alone at night. As shown in exhibit 4.10, fully 28 percent of Black and 33 percent of Latino

renters indicate these lowest levels of neighborhood safety by comparison with 15 percent of 

White renters. Once again, these racial or ethnic differences are statistically significant and, 

although controlling for housing market conditions forces the Latino-White contrast to statistical 

nonsignificance (p = 0.06) at conventional levels, the contrast between Black and White renters 

remains significant even when controls for individual-, family-, and metropolitan-level 

characteristics are taken into account. Thus, even among renters with similar levels of education 

5.6% 7.7% 3.2%

27.0% 25.0%

15.4%

28.1% 26.9%

27.1%

26.7% 26.9%

33.2%

12.6% 13.5%

21.1%

Black Latino White

Excellent

Very good

Good

Fair

Poor
58

and income, similar age and family characteristics, and facing similar housing market conditions, 

Black renters are less likely than White renters to move into neighborhoods they perceive as safe.

Exhibit 4.10: Group Differences in Perceptions of Whether It Is Safe To Walk Alone in the New 

Neighborhood

Note: N = 584.

Source: Recently mobile renters in the Panel Study of Income Dynamics-Child Development Supplement, 1997–2011

Black Renters Are More Likely To Say Their New Places Are Better Than Their Old

Interestingly, even though they tend to move to units that are rated lower than the destination 

units of White movers, Black recent movers are more likely to report that their new unit and their 

new neighborhood are still better than the units and neighborhoods they left. However, the 

differences are slight; about 42 percent of Black recent movers in the AHS report that their new 

neighborhood is better than the neighborhood from which they moved, compared with 37 percent

of White recent movers. Similarly, 52 percent of Black recent movers say that their new housing 

unit is an improvement over the old one, compared with 45 percent of White recent movers. 

These Black-White differences, although modest, remain statistically significant even with 

sociodemographic controls, and hint at racial differences in the perceived returns to residential 

mobility that may be important to consider in the assessment of the search processes that lead to 

these outcomes.

5.6% 7.7% 2.0%

22.5%

25.0%

13.1%

53.7% 50.0%

60.8%

18.3% 17.3%

24.1%

Black Latino White

Completely safe

Fairly safe

Somewhat dangerous

Extremely dangerous
59

Black and Latino Recent Movers End Up in More-Disadvantaged Neighborhoods Than White

Recent Movers

Putting into context the observed racial difference in subjective neighborhood ratings, our 

analysis of the PSID data shows substantial racial or ethnic differences in the objective 

characteristics of residential destinations for mobile renters. Specifically, as shown in exhibit 

4.11, by comparison with White renters who moved in the preceding 2-year period, Black and 

Latino recent movers ended up in neighborhoods with higher levels of poverty and lower levels 

of average household income. They also entered neighborhoods characterized by higher levels of 

racial or ethnic diversity (as measured by the multigroup entropy index) and lower levels of 

racial isolation (percent own-group) than those neighborhoods White recent movers entered.

Supplemental analyses indicate that all these differences are statistically significant and remain 

so even with controls for individual-, family-, and metropolitan-level factors.

Exhibit 4.11: Differences in Characteristics of Destination Neighborhoods

Note: N = 9,879.

Source: Recently mobile renters in the Panel Study of Income Dynamics, 1997–2011

Destination Differences Are Not Much Different Than the Differences in Origins

These racial or ethnic differences in destinations largely reflect group differences in the 

neighborhoods these movers left. For members of all groups, differences between the 

sociodemographic characteristics (average income, poverty levels, and racial composition) of 

their old and new neighborhoods are only modest. However, the group differences in changes in 

neighborhood characteristics resulting from mobility are all quite small, and none are statistically 

0

10

20

30

40

50

60

70

80

% in poverty 0

10

20

30

40

50

60

70

Average household

income ($1000s)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Racial-ethnic entropy

0

10

20

30

40

50

60

70

80

% own racial-ethnic group

Black

Latino

White
60

significant. The similarities between origins and destinations for members of each group help 

explain why racial or ethnic differences in the comparison of old and new neighborhoods are so

modest, despite substantial differences in destination characteristics. For Black and Latino recent 

movers, the search tends to culminate in residence in neighborhoods with higher levels of 

poverty, lower safety, and lower overall quality than the neighborhoods entered by White recent 

movers, but these new neighborhoods are so similar to their old neighborhood that, for all 

groups, the residential mobility tends to result in little change in objective conditions or 

subjective rating of these conditions. In addition to highlighting profound residential inequity, 

these results suggest that housing search processes for members of different races likely involve 

churning within sets of neighborhoods that are highly circumscribed by race.

Connections Among the Search Stages

Overall, the results of our analysis point to potentially important racial or ethnic differences in 

various individual aspects of the housing search process, including the context of the move, the 

strategies used to search for housing, and reasons for ending the search. Ultimately, however, our 

goal is to understand the connections between these various features of the search process and 

their effects on racially differentiated residential outcomes.

Unfortunately, the scattered nature of existing data—with limited information on only selected 

aspects in separate data sets—prohibits the comprehensive view that we seek. Although falling 

well short of a full view on the topic, data from the AHS provide some clues about the 

interconnections between aspects of a housing search. Appendix exhibits F.14A and F.14B 

present basic bivariate correlations between the key aspects of the search process shown in the 

preceding analyses to vary substantially by race or ethnicity. This analysis is, of course, limited 

in several ways but primarily because it focuses solely on bivariate correlations that cannot be 

interpreted as causal links, and because it is based on AHS data that contain only limited and 

completely retrospective information on only a small selection of aspects of the search process, 

and only for successful movers (rather than all searchers).

Despite these limitations, the data show several interesting patterns of association, particularly 

between reasons for mobility and other search processes and outcomes.

People Who Move for Work or School Are More Likely To Use Online Resources

Those respondents who report that a move for work or school was the primary reason were less likely 

to find their new residence through word of mouth or by seeing a building sign, and more likely to have 

found their housing with online resources. This finding is to be expected in that these types of moves 

likely involve moving to more distant locations that are not part of the individuals’ daily activities 

before the move and about which previous social contacts likely had little specific information. In these 

situations, where the respondent had to find housing far away from their previous location, 

online resources were an important tool. Moving for school or a job is also associated with 

considering a larger number of potential units and other neighborhoods, but also with reporting 
61

that the current housing unit was chosen because of constraints on options, that the search ended with 

less than an ideal outcome, and that the new dwelling is not better than the previous residence.

In contrast to those respondents moving for school or a job, reporting a move for involuntary 

reasons—because of a foreclosure, eviction, disaster, or demanded use by another party—is not 

as clearly associated with specific search strategies; it has only a weak positive correlation with 

finding the current unit by seeing a sign on a building.

Involuntary Movers Are More Likely To End a Search With a Less-Than-Ideal Outcome

However, involuntary moves are strongly associated with a number of the factors related to the 

outcome of the search. Reporting an involuntary reason for moving is positively correlated with 

the likelihood of reporting that the housing search ended with a less-than-ideal outcome, and that 

the new dwelling was chosen because the set of alternative options was constrained. Just as 

important is the fact that moving involuntarily is negatively associated with the rating of the new 

neighborhood and the new housing unit and with the likelihood of reporting that the new 

dwelling is better than the old.

Constrained Searchers Look Different in Terms of Decisions and Outcomes

Finally, these results point to some potentially important interconnections between search 

constraints and residential outcomes. Renters who searched only in the immediate neighborhood 

of residence viewed smaller numbers of units during the search process and were also more 

likely to report that they chose their new dwelling because they perceived no better options.

These constrained choices are, in turn, negatively associated with the rating of both the new 

dwelling and the new neighborhood.

Given the data limitations, these results should be interpreted with some caution. These results 

do suggest that the significant racial or ethnic differences in search parameters, and processes 

revealed in previous analyses—especially those related to decision making, information 

gathering, and evaluating options—likely play an important role in shaping substantial group 

differences in residential outcomes.

The analysis of secondary data provides us with solid evidence that racial or ethnic differences 

are in virtually every dimension of housing searches: the reason for the search, the way 

information is gathered, the decisions that are made about where to search, and the eventual 

outcomes of the search. Importantly, these results are based on probability samples of White, 

Black, and Latino renters and permit rigorous statistical tests; although some of the racial or 

ethnic differences in housing searches can be explained by differences in background 

characteristics like income. However, the existing data sets were limited in the scope and range 

of measures of housing search processes, so what we know about housing search processes has

substantial gaps. For this more indepth look, we turn to the original data collection we 

conducted, the Housing Search Study (HSS) and indepth interviews.
62

Chapter 5: Identifying Other Clues About How and Why Racial Differences 

Arise in Housing Searches

The previous chapter identifies a number of clear but limited racial or ethnic differences in both 

housing search processes and outcomes. This chapter leverages data from the Housing Search 

Study (HSS) and indepth interviews to describe the factors that might be driving these 

differences and clues to the parts in the process that might be most ripe for additional 

exploration. It is important to remember that, unlike chapter 4, which drew mainly on nationally 

representative samples, our HSS data are a convenience sample of searchers in a single urban 

context: the Washington, D.C. metropolitan area. As noted in chapter 3, the Washington, D.C.

area has vacancy rates about one-half of those in the average areas occupied by Panel Study of 

Income Dynamics (PSID) renters, for example. In addition, the individual characteristics of the 

sample differ from national samples (see appendix F.15 and discussion in chapter 3). However, 

despite their differences, racial trends among the recent movers and current searchers in our HSS

are similar to what we see in the PSID and AHS. Compared with White respondents, Black

respondents in our HSS samples tend to be older, disproportionately female, and report lower 

household income, less frequent marriage or cohabitation, and more children in their household.

The implications of our convenience sample mean that the insights based on the HSS are

thematic and exploratory in nature. The HSS is a convenience sample of renters in the 

Washington, D.C. metropolitan area that does not lend to prevalence estimates or tests of 

statistical significance. The themes identified in this chapter represent an assessment of patterns 

we observed in related variables across the HSS and were also echoed in the indepth interviews.

Given that housing search contexts are importantly shaped by the economic resources available 

to a searcher, it becomes crucial in our efforts to paint a picture of racial differences; using this 

convenience sample, that we take measures to determine which racial differences remain, after 

taking into consideration the racially distinct economic characteristics of our samples. Our 

strategy in this report is to refrain from drawing conclusions about racial differences without 

taking social class into consideration. Rather than rely on the presence or absence of statistical 

significance, we instead compare the size of the coefficient for the variable identifying racial 

background between a regression model that includes race as the sole independent variable 

(bivariate) with the size of the coefficient of this same variable (racial background) in a 

regression model in which background characteristics, including, importantly, social class, 

presence of children, and gender. Our rule of thumb is that dependent variables, in which the 

coefficient for racial background is reduced by about one-half in the multivariate model, are 

instances where we are uncomfortable asserting that race, per se, matters. However, where the 

size of the coefficient retains its strength (does not get reduced by more than one-half) in the 

multivariate context, we believe our assertion of racial difference is justified. After running these 

comparative models, we performed a synthesis across the different housing search dimensions

and incorporated insights from the indepth interviews to identify the larger patterns of racial 

difference. The themes that emerged are (1) the role of neighborhood quality in shaping a search,

(2) conscientious efforts and strategies used to avoid bad treatment, (3) the important role of 

anticipated discrimination in shaping many different stages of a search, (4) the emerging 

challenges associated with credit history, and (5) the challenges faced by searchers after a visit to 

a housing unit.
63

Although the results presented in our exhibits in this chapter do not incorporate searchers’

income in them (instead, they show the basic results for each racial group), bear in mind that we 

are only reporting in this chapter those racial differences that, based on the criteria described 

previously, do not appear to be explained away by income differences.

Prioritizing Neighborhood Quality

Consistent with the results from the secondary data analyses, our convenience sample of 

searchers in D.C. revealed that improving the quality of their housing was a primary motivation 

for undergoing a move or search for Black respondents. Specifically, the modal reason for 

moving among our sample of current searchers was a desire for better-quality housing, and Black

searchers (37 percent) were much more likely than White searchers (18 percent) to indicate that 

they were moving primarily to secure higher-quality housing. The coefficient for racial 

background in a multiple regression model is only slightly reduced when controlling for social 

class. This finding is consistent with the long history of research showing that Black and White

renters of similar social class live in markedly different kinds of neighborhoods.

For Black Renters, Safety and Good Schools Are Highly Valued Features

Perhaps because the impetus for the move was to get a better-quality living arrangement, 

information about neighborhood crime and other features are substantially more likely to be 

sought by Black than White searchers. For example, 87 percent of Black searchers indicated that 

they gathered information about the crime levels in a neighborhood, compared with only 64

percent of White searchers. In a regression analysis, the size of the coefficients that reflect these 

racial differences are not reduced—indeed, they are increased—once background characteristics 

are controlled.

Black renters also disproportionately prioritize building security and school quality; in both 

cases, Black renters rate these as “very important” at much higher rates than White renters do 

(exhibit 5.1). Moreover, the importance of building security and neighborhood safety remained 

much more stable for Black than for White renters throughout their search process (exhibit 5.2).

In the case of neighborhood safety, rather than disappear or reduce in size when background 

characteristics are included as control variables, the coefficient for race increases in magnitude.
64

Exhibit 5.1: Racial Differences in Importance of Housing Unit and Neighborhood Search Criteria

Source: Housing Search Study survey, 2016

Exhibit 5.2: Changes in the Importance of Unit and Neighborhood Search Criteria (percent)

Notes: Black renters (N = 163), White renters (N = 64).

Source: Housing Search Study survey, 2016

These differences in the importance of safety and schools for White and Black searchers may 

simply reflect important racial differences in the geographic parameters of the housing search.

For White searchers, living in a safe neighborhood with good-quality schools is likely as 

important as it is for Black searchers. Because these neighborhood characteristics are fairly 

standard features of the neighborhoods in which White renters conduct their search for housing, 

these characteristics never rise to the level of being listed as key search criteria; White renters are 

45.8

94.4

93.9

12.9

60

60

Quality of schools

Safety of neighborhood

Security of the building

Reporting "very important" (%)

Whites (N = 70)

Blacks (N=179)

0

10

20

30

40

50

60

70

80

90

100

Security of the

building - blacks

Security of the

building - whites

Safety of

neighborhood -

blacks

Safety of

neighborhood -

whites

More Important No Change Less Important
65

likely to achieve these neighborhood features without prioritizing them in the housing search. In 

contrast, because Black renters are likely to search for housing in a much more diverse set of 

neighborhoods—some with higher levels of crime, relatively low safety, and weak public 

services—and prioritizing neighborhood safety and schools is likely more important to Black

renters’ likelihood of ending up in an acceptable neighborhood.

Black Renters’ Perceptions of Safety Are Often Influenced by Personal Experiences in HighCrime Neighborhoods

The indepth interviews lend support for this interpretation. When White respondents talked about 

neighborhood safety considerations in their search, they tended to frame them in terms of 

impressions of a hypothetical type of area they wanted to avoid; Black searchers tended to be 

more likely to clearly indicate specific neighborhood characteristics that they associate with an 

unsafe environment, and to point to specific experiences and examples. That is, Black searchers 

were more likely to report actual encounters with unsafe neighborhoods than White searchers.

For example, one high-income Black woman with children describes the difference in 

neighborhood safety by only moving across the street: “We’re on Georgia Avenue in the center 

of Wheaton. So, I guess they call it Wheaton Triangle. And our apartment was in Wheaton 

Triangle in a cluster of apartment buildings. And so on that side of Georgia Avenue, the crime 

rate is very high. Our neighbor was raped at gunpoint. Our bikes were stolen. Someone was 

robbed at gunpoint in our garage. And it’s right on top of the Metro. So, the crime rate is just 

ridiculous. So, we moved across the street where the Metro isn’t directly located. And it’s all 

houses and townhouses, and it’s very safe. It’s very safe. You don’t see any sketchy activity. I’m 

a part of this neighborhood group online. Ever since I moved to this side of the street, I don’t 

hear about the same things that I heard on that side of the street.”

Black Renters Tended To Be More Satisfied Than White Renters With Their New Units

Along a number of different dimensions, Black searchers felt they did better than they hoped 

for—and this was truer of Black than of White searchers. For example, in terms of the 

neighborhoods they moved into, Black searchers were more likely than White searchers to say 

they got “better than they hoped for” in terms of the quality of schools (exhibit 5.3), an effect 

that is unchanged with the inclusion of background characteristics as control variables.

When asked about their unit’s features, in our recent mover sample, most searchers reported that 

they improved on their unit’s features relative to their prior one; 60 percent rated their new unit 

as better than their prior one, with another one-fourth indicating that the new unit was about the 

same (exhibit 5.3). Our results hint that Black searchers are more satisfied with their new unit, 

vis-à-vis their old unit; for example, 21 percent of White searchers rate their new unit as worse 

than the old one compared with only 9 percent of Black searchers. This difference (worse versus 

all others) remains at essentially the same magnitude when controls for background 

characteristics are included.

The only neighborhood feature that Black searchers were less likely than White searchers to get 

what they were hoping for was in terms of location. Black searchers were more likely than White

searchers to say that their new unit was “not as good as they hoped for” in terms of its location 

near family or friends.
66

Exhibit 5.3: Assessment of New Neighborhood Features Compared With What Was Hoped 

For

Black

Searchers

White

Searchers

Total

Quality of the schools

Better than hoped for 20.9 5.5 13.9

About what hoped for 19.4 10.9 15.6

Not as good as hoped for 9 5.5 7.4

N/A: Quality of schools not important 50.7 78.2 63.1

Assessment of unit in comparison to previous home

Better 64.3 55.7 60.3

Worse 8.6 21.3 14.5

About the same 27.1 23 25.2

Location near friends or family

Better than hoped for 27.1 23 25.2

About what hoped for 24.3 44.3 33.6

Not as good as hoped for 22.9 9.8 16.8

N/A: Location near friends or family did not matter 25.7 23 24.4

N 70 61 131

N/A = not applicable.

Source: Housing Search Study survey, recent movers, 2016

Avoiding Bad Treatment

A second theme that emerges from our HSS is that Black renters may be especially likely to 

approach a search with the intentional goal of avoiding bad treatment at the hands of landlords.

This goal is accomplished by picking particular techniques with which to conduct a search or by 

selecting certain criteria to prioritize.

Black Renters More Than White Renters Prioritize Landlord Responsiveness as an Important 

Criterion Throughout Their Search

These concerns are expressed in many ways throughout our data set. For example, Black renters 

are concerned about having potentially negative experiences interacting with landlords or 

property managers (exhibit 5.4). Compared with White searchers, Black searchers appear to 

enter the housing search process with much greater emphasis on the treatment they receive from 

the prospective landlord, and the importance of landlord interactions remained unchanged for 

most Black searchers—and for a handful, this criterion became more important during the 

search.
67

Exhibit 5.4: Racial Differences in the Importance of Treatment by Landlords

Black

Searchers

White

Searchers

Total

Responsiveness of the landlord or management company to 

respondent’s concerns 

Very important 89.9 60 81.5

Somewhat important 9.5 31.4 15.7

Not very important 0 5.7 1.6

Not at all important 0.6 2.9 1.2

N 179 70 249

Responsiveness of the landlord or management company to 

respondent’s concerns

Less Important 8.6 18.8 11.5

More Important 6.1 17.2 9.3

No change 85.3 64.1 79.3

N 163 64 227

Source: Housing Search Study survey, recent movers, 2016

Black Renters Are More Likely Than White Renters To Execute Their Search in a Way That 

Helps Them Learn More About Their Potential Landlord

For this reason, Black searchers in the indepth interviews talked about taking time to actually 

talk with landlords to figure out what kind of landlord the person would be. This may explain 

why Black searchers disproportionately favor social networks when information gathering and

their preference for personally communicating by phone, rather than online sources of 

information and email communication (exhibit 5.5). One low-income Black man explains, “I 

have a good eye, it’s just the relationship that I have with the landlord. If we have a good 

communication, we can talk. I can tell if we are going to bump heads or something like that. The 

place can always be nice, but if you don’t have a good relationship with the landlord or leasing 

office or something like that, then eventually it’s not going to work out.”
68

Exhibit 5.5: Racial Differences in How Searchers Inquire About Units, Recent Movers Sample

(percent)

Source: Housing Search Study survey, recent movers, 2016

Expectations of Discrimination

Another theme that emerged in our Housing Search Study (HSS) focuses on a particular type of 

bad treatment—racial discrimination. Our study included one of the most detailed and direct sets 

of questions about perceptions of fairness or discrimination in housing that exists. We asked 

questions across a range of different steps in the search, from before the search even starts to 

past, perceived discrimination to methods used to avoid the possibility of discrimination in their 

search. We found the following results.

Black Renters Often Start Their Search Expecting Unfair Treatment

One of the unique contributions of the HSS is the detailed questions about perceived housing 

discrimination it contains. Researchers asked Black respondents if they thought that Black

people had as good a chance as White people to get any housing they could afford. One-half of 

the Black recent movers and nearly two-thirds (63 percent) of Black current searchers reported 

that they felt that Black people in their community did not enjoy equal access to housing in the 

D.C. area.

These views are often informed by direct experiences. As exhibit 5.6 shows, about 40 percent of 

Black current searchers reported at least one experience with racial discrimination during a prior 

housing search. The most common experience was steering; about 30 percent felt that a rental 

agent or landlord had shown them only apartments in certain neighborhoods because of their race 

or ethnicity. A little less than 20 percent of Black current searchers had other types of 

experiences, including believing they were denied housing because the rental agent or landlord 

didn’t want to rent to them or feeling that other residents in the building or neighborhoods that 

they visited treated them unfairly because of their race or ethnicity.

27.1

49.1

37.9

50.8

28.1

39.7

15.3

19.3

17.2

6.8

3.6 5.1

0

10

20

30

40

50

60

Blacks (N = 59) Whites (N = 57) Total (N = 116)

Email

Phone

In-person visits

Other source
69

Exhibit 5.6: Current Searchers’ Perceptions of Discrimination in the Past

Black Searchers

White

Searchers

Total

Ever felt...

were denied housing because the rental agent or landlord 

didn’t want to rent to you because of your race or 

ethnicity? 18.5 4.4 14.7

a rental agent or landlord was showing them only 

apartments in certain neighborhoods because of your race 

or ethnicity? 29.7 8.9 24.1

other residents in the building or neighborhoods visited 

treated you unfairly because of your race or ethnicity? 17.5 3.3 13.7

Count of experiences of perceived discrimination in earlier 

searches

0 59.8 86.7 67

1 22 11.1 19

2 11.4 1.1 8.6

3 6.9 1.1 5.4

N 246 90 336

Source: Housing Search Study survey, 2016

Black Searchers Tend To Prefer Diverse Neighborhoods but Avoid Areas That Are Too White

and Prioritize Finding Neighborhoods That are Welcoming to Black Residents

When directly asked to reflect on their racial residential composition preferences, Black

searchers in the indepth interviews explained their preferences in a way that mirrors what other 

research has found (Krysan and Farley, 2002). First, desire for diversity is strong, as one Black

man we interviewed describes: “I like a blend of people. I like multi-whatever. I think that’s 

always better because you can get a taste of everything, the good, the bad, and the different. You 

all could, blend together, grow together, working it out together. I like that much better. I will 

say more on the even side all the way across the board. I don’t want too much of anything.”

Also, some desire to avoid areas that are too White, as this same respondent, when asked if any 

demographic groups made him feel uncomfortable, explains, “Yeah. Caucasian. Because I 

always feel like I have to walk on eggs. In the sense that if I mess up or something go wrong. ... 

I don’t want that pressure.” The desire for diversity is coupled with a desire to avoid places 

where the other residents might make life unpleasant.

This theme is also uncovered in the HSS, where we were interested in learning which

neighborhood features searchers viewed as important to learn more about during their search. In 

addition to finding widespread interest among our samples of searchers for information on 

transportation, neighborhood amenities, and crime levels, we asked whether people sought 

information on the openness of the community to people of their racial or ethnic group. Our 

results showed that most Black searchers selected this as an important part of their informationgathering process; 56 percent of the recent movers said they sought such information during their 

search.

Our indepth interviews revealed a specific strategy that some Black renters use in order to figure 

out if a neighborhood would be welcoming: in-person visits to the neighborhood. They do this to 

find out directly, sometimes conducting an informal “survey.” One of the Black men (low 

income, with children) we interviewed said he would ask other residents, “How hard was it, 
70

how’s the neighborhood? If there’s someone outside of my race or whatever, I’ll ask about that.

‘How many [of] my race is there; do you think I could fit in in that? I want you to be honest with 

me,’ and that kind of thing.”

To Avoid Discrimination, Black Renters Rely on Social Networks To Identify Units

White and Black renters alike draw on family, friends, and coworkers to help them in their 

search, and these networks tend to belong to their same racial group. However, Black renters use 

their social networks specifically to help them find landlords who will not discriminate against 

people of their racial or ethnic group. Among our sample of recent movers, one-fourth of Black

searchers reported using their social networks to help them in this way. Among the current 

searchers in our sample, it is much more prevalent; more than 60 percent indicated that they had 

used their networks to find nondiscriminatory landlords during their search.

Despite Their Efforts To Avoid It, Many Renters Still Perceive Racial Discrimination During 

Their Housing Search

Between 10 and 13 percent of Black Housing Search Study respondents (recent movers and 

current searchers) reported the most extreme case of being denied housing based on their race or 

ethnicity during this most recent search (exhibit 5.7). A higher percentage (15 percent of recent 

movers and 9 percent of current searchers) reported that “a rental agent or landlord was showing 

you only units in certain neighborhoods because of your race or ethnicity”—that is, reported 

being steered. A small percentage reported discrimination at the hands of other residents (7

percent and 4 percent, respectively).

Exhibit 5.7: Reports of Perceived Housing Discrimination Based on Race or Ethnicity

Recent Mover Current Searcher

Black White Total Black White Total

During your search, did you ever feel that...

you were denied housing because the 

rental agent didn’t want to rent to you 

because of your race or ethnicity? 13.2 0 7 10 3.8 8.5

a rental agent or landlord was showing you 

only units in certain neighborhoods 

because of your race or ethnicity? 14.5 1.7 8.5 8.8 3.8 7.5

the other residents in the buildings or 

neighborhoods that you visited treated you 

unfairly because of your race or ethnicity? 7.4 1.6 4.7 3.8 3.8 3.8

Count of perceived racial biases perceived 

during search

0 76.5 98.4 86.8 85 96.2 87.7

1 14.7 0 7.8 8.8 0 6.6

2 5.9 1.6 3.9 5 0 3.8

3 2.9 0 1.6 1.3 3.8 1.9

N 68 61 129 80 26 106

Source: Housing Search Study Survey, 2016
71

Taken together, only about one-fourth of Black recent movers and 15 percent of Black current 

searchers reported experiencing one or more of these kinds of discriminatory treatment in the 

search they just completed.

12

An important part of a housing search is the visit to potential units. The HSS asked searchers 

detailed questions about individual visits to possible rentals, and one of the questions was 

whether the searcher ever felt that that they were treated unfairly during their visit because of 

their race or ethnicity. Fourteen percent of our Black current searchers said yes to this during one 

of their interviews (exhibit 5.8).

Black and Latino Renters Describe the Complexity of the Visits and the Ambiguity of 

Perceived Discrimination

The following extended excerpt of our interview with a high-income Black woman with children 

is rich with insights about the complexity of contemporary forms of discrimination and the 

conscientious efforts some Black searchers must go through to try to ensure they are treated 

fairly. 

Interviewer: Some folks have certain aspects of like their personal circumstances 

that make it difficult for them to secure housing. Do you have any of those? You 

and your husband?

Respondents: Yeah, I mean, our names get us really far. My name is Stephanie,13

so no one ever is hesitant to talk to me I think. My husband’s name is Leroy, so 

they don’t know what he is. He could be any race. And we always made sure that 

we were wearing our work attire when we met with people.

Interviewer: I don’t want to make any assumptions. So, why did you feel like you 

needed to do that?

Respondent: Because if we weren’t wearing our work attire, we would just look 

like a young Black couple, they don’t know if we’re married. Even if I do have 

my ring on, they don’t know if we’re responsible. I mean, we’re not naïve. We 

know how it works.

Interviewer: How do you think it works?

Respondent: For example, my brother is homeless. He’s single. When he looks 

for a place, he doesn’t wear work attire, and he made a comment 2 days ago. He 

was like, “No one is going to let a young Black man rent a basement from them”

or “be a roommate to them.” And that’s his experience. My experience with my 

husband is it’s not too much different from that. We have to look responsible, and 

we know that. And when we’re dressed in our work attire—he’s a manager, he 

wears suits. And I’m a consultant, so except for Fridays, I wear my dresses and 

heels. And that is what we made sure we were wearing when we met with the 

people at the different locations.

 12 As discussed previously, it is important to remember that these are questions about perceptions and experiences; we lack an 

ability to ascertain if these were instances of discrimination, and we also cannot know if individuals who said “no” to this 

question were victims of subtle forms of discrimination that are impossible to detect by the individual. 13 Stephanie and Leroy are pseudonyms but chosen to convey the point that the respondent is making by selecting names that are 

similar in terms of being “White-sounding” (Stephanie) and “ambiguous” (Leroy).
72

Interviewer: And how do you think people received you because you were 

dressed in that kind of attire?

Respondent: That we made money and we were a responsible couple. Sometimes 

we even have our son with us. And then my current landlord, he was actually 

standing outside when we pulled up, and I have a Mercedes. And we came out in 

our work attire. So, I already know he had probably no negative thoughts about us 

because we kind of have this appearance that we have it all together, we’re 

responsible, we’ve obviously making money together, and I think that added to 

the trust. I think if we pulled up in a car that was like maybe raggedy, and my 

husband had on his jeans with his like ball shirt, and I had on like skinny jeans, 

and some regular T-shirt with like Jordans, I don’t think we would have received 

the same feedback from him. And somebody was viewing the house when we 

pulled up. And he said he liked us so much he just stopped responding to 

everybody. He did. He said we just seemed so well put together. He didn’t say 

that, but we did seem well put together, and my son put on his little cuteness. And 

he just said he liked us very much.

Interviewer: When you say again, I just don’t want to assume, but when you say 

your name gives it away or it makes people comfortable, what do you mean by 

that?

Respondent: My name is popularly like given to White girls, and the way I talk is 

very proper because I’m from Montgomery County. And people don’t always 

know that they’re talking to a Black girl or a Black woman. And even when they 

do see me and see that I’m Black, I talk so proper and I kind of have this 

appearance and etiquette that even if I am Black, they can always tell I’m 

educated or I’m well put together.

Interviewer: And why would that be important for somebody who’s leasing 

something to you?

Respondent: Right. I think property managers have—just like getting a car. They 

think you’re not going to pay rent, you’re not going to pay your car loan. I’ve 

been a victim of discrimination. I received a check from Department of Justice ... 

for being discriminated against for my car. They inflated my interest rate because 

I’m Black, because they expected me to—what’s it called—like bounce my 

checks, default on my loan. So that same thing could happen with renting a home.

They could have made me pay higher—put more money down or safety deposit 

down for the house. I mean, they didn’t, but I know those things happen. So, we 

give off this appearance that we don’t hold the same stereotypes that most African 

Americans have.

This detailed exchange highlights the disadvantages this respondent perceives—starting with the 

advantage she perceives because her name does not signal to a potential landlord (before meeting 

her) that she is Black. It also includes an insight into the complicated role that the trappings of 

her social class play in shaping how she is treated. She believes that she has to dress a particular 

way and emphasize her middle-class status in order to overcome the discriminatory treatment 

that Black people generally receive. She couches this in efforts to ensure that landlords do not 

make assumptions about her ability to pay rent based on their stereotypes about Black renters.
73

Clearly the mechanics of her search are affected by these perceptions, and it is within this 

context of a discriminatory housing system that she adapts her behavior during the visits to units 

to hopefully ensure fair treatment and overcome the tendency to discriminate against Black

people.

Black Renters Have Difficulty Finding Landlords To Rent to Them

Similarly, and perhaps because of this, Black HSS respondents disproportionately identified 

“finding landlords who would rent to them” as one of their biggest difficulties during their 

housing search (exhibit 5.8). Indeed, with background controls, the effect of being Black on 

whether this reason was selected, was actually greater than it was in a bivariate context. Black

respondents were substantially more likely to say “finding landlords to rent to me” was the 

reason a search was difficult. This difference is consistent with the notion that Black renters

continue to face significant discrimination at the hands of potential landlords.

Exhibit 5.8: Reason for Search Difficulty

Recent Movers Current Searchers

Black White Total Black White Total

Finding landlords that 

would rent to respondent 45.8 14 30.8 41.3 15.2 32.3

N 48 43 91 63 33 96

Respondent felt he or she 

was treated unfairly during 

a visit because of his or her 

race or ethnicity — — — 13.6 7.4 11.9

N 147 54 201

Source: Housing Search Study survey, 2016

In the indepth interviews, many minority respondents described experiences where they sensed 

unfair treatment during their search. One high-income Latino woman (with no children), for 

example, relates a story about how she sent a friend to look at a potential rental unit because she 

was unable to do it herself because she was out of town for work. She explains, “And she was 

there first, and she knocked on the door. The woman didn’t answer, and she was like, ‘are you 

sure this is the address?’ I’m like, ‘that’s the address she gave me.’ I sent her a screen shot.

Anyway, the next couple showed up, and it was two White people, and then the woman opened 

the door, and then that’s when my friend kind of went, like, ‘Hey, like I have been out here.’ The 

woman was like, ‘Oh, I didn’t see you. Come in.’ She goes in. I’m like, ‘Was it a mistake to like 

invite my Black friend?’ I told my friend, Bridget, I’m like, ‘Girl, you are Black. I’m brown. I 

don’t know if that was a good combo.’ And she was like, right. I’m like, yeah. I could have 

asked another friend, you know. I have like diverse friends.”

This respondent later refers to the instance again and is very explicit about how she understands 

what was going on and also how it shapes her search strategies. The interviewer asks, “Do you 

feel like you’re more comfortable going to like kind of a one-on-one meeting versus this group 

meeting, like an open house?” She responds: “If I’m competing with like young White couples, 

yeah” and then elaborates: “What I have a problem with is that you are only telling me this now 

when you are not going to review my applications, but like, whatever, whatever. I actually didn’t 

think that was going to happen to me, because I’m all like college educated and shit and I have a 

decent job, but you are not immune to this stuff. It’s crazy.”
74

Our indepth interviews also highlighted experiences with discrimination after their initial visit, 

and we describe them in more detail in our discussion of postvisit challenges.

Credit History

Our study uncovered the many ways that credit history shapes Black searchers’ experiences.

They work hard to find landlords who did not require it and suspect that landlords prioritize 

credit history more than other criteria (such as income) to dismiss their applications. Our HSS

and indepth interviews uncovered the salience of this issue in several different ways and at 

several different points in the search process.

Across Income Groups, Black Renters Disproportionately Report Having Issues With Their 

Credit That Makes Their Search Difficult and Affects Their Search Strategy

Our HSS provides consistent evidence that, even when comparing Black and White renters with 

similar economic profiles and family circumstances, Black housing searchers in our sample are 

substantially more likely to report that problems with credit history make it difficult to search for 

housing (exhibit 5.9). Among our sample of recent movers, more than five times more Black

renters than White renters mention having poor credit history. The levels are higher among the 

sample of current searchers, but racial disparities are still apparent, with twice as many Black

renters than White renters (35 percent versus 17 percent) reporting poor credit history.

Exhibit 5.9: Racial Differences in Credit History

Recent Movers Current Searchers

Black White Total Black White Total

Constraint on search—Poor credit 

history 27.1 4.9 16.8 35.9 16.7 30.7

N 70 61 131 248 89 337

Respondent uses social networks to 

find rentals that do not require credit 

check 24.6 6.6 16.2 55.9 40 51.4

N 69 61 130 179 70 249

Source: Housing Search Study survey, 2016

Credit Issues Affect How Black and Latino Renters Approach Their Search in Different Ways

Our indepth interviews confirm that problems with credit are an overarching issue for Black

renters. The topic came up more with our Latino and Black searchers than with our White

searchers and was mentioned by low-, medium-, and high-income minority searchers.

Searchers gave examples of how they alter their housing search in different ways to 

accommodate or address their credit issues. For example, a high-income Latino woman with no 

children explains what happened when she discovered she had credit issues: “In December I 

learned that my credit took a big hit. It was actually really—it wasn’t very good. I have been 

working to fix it, because I know—and that’s actually one of the reasons I’m waiting till late 

summer, to have more time to better it.... It was pretty low, and I’m talking like low 600s, like 

590 something. I was like, ‘shit, it’s never been this low,’ so I’m working on bettering or 

repairing it. And so, in addition to everything... I’m aware of that kind of information a potential 
75

landlord could see about me that I would have like to justify to them or like address, you know.”

When asked if she limits the places she’s searching based on her credit history: “No. I’m just 

going to hope for the best. I think I’m a desirable tenant. I know I am, so I’m kind of focusing on 

that versus the things that might like weigh me down.”

Another searcher, a low-income Black male with children, explains the awkwardness he 

experienced around following up on his coworkers’ suggestions for possible places to rent: “You 

only could share so much with coworkers when it comes to your credit and things like that. And 

obviously they must’ve thought my credit was better than it was, and obviously you’re not going 

to share that. Most of that was like my credit was up to par. Obviously, theirs must have been.

Most of that was just a waste of my time to be honest with you.” Thus, relying on this social

network for leads on apartments was rendered ineffective because of the credit issues he faced, 

but did not want to share that with his coworkers. As the next examples illustrate, some renters 

use their social networks to avoid such problems.

Black Renters Use Social Networks To Identify Units Where Credit Will Not Be an Issue

As with avoiding racial discrimination, Black renters disproportionately use social networks to 

help identify units where their credit won’t be an issue (exhibit 5.6). In both the recent mover 

and current searcher samples, Black searchers are markedly more likely than White searchers to 

indicate that they use their social networks in this way. Indeed, most Black current searchers do 

this. By including background characteristics in a regression model, this racial difference is 

reduced, but only minimally.

Problems With Credit Disadvantage Minority Renters in Many Different Ways

Bad credit can affect housing search outcomes in many ways. First, it restricts the kind of 

properties renters seek out. One middle-income Black woman with no children explains 

“[Private landlords] are a little more lenient with credit because they’re paying mortgage on a 

place that they’re not living in and they want to hurry up and get it filled.” Credit issues may also 

limit the properties that are open to them, as one middle-income Latino with no kids describes it: 

“My credit is dinged. That’s affecting probably if I want to live in a little bit nicer place. People, 

I’ll explain to them. I can pay. I can show you bank statements, stuff, but that did come up. So, 

yeah, and you have—it’s a constraint. I think it’s a constraint. It’s a huge problem.” In another 

case, a middle-income Black woman with no children and good credit explains how credit issues 

nevertheless constrain her search: “No, I have good credit. It’s just with Craigslist, I don’t trust 

anyone to check my credit. So if I seen anything like that, I normally didn’t look at that ad any 

longer—it’s not that I have anything to hide. It’s just like, you know, I don’t know you and why 

do you need that information. So no.”

Other indepth interview participants elaborate on how their credit situation can result in a costlier 

or administratively more complicated rental arrangement, even if they ultimately get the unit. For 

example, one low-income Black man with no kids explains, “You might have to put an extra 

security deposit, or you might have to put up another month’s rent.... I let them know up front, 

you know, ‘Look, my credit score is not that good, but I do want to live here, what’s required?

Do I need a co-signor? Do I need to put down like another month’s rent?’”

Some renters explained that when landlords pull credit checks on them, it can only make their 

issues with credit worse. As one low-income Black male with children explains, credit checks 

can create a vicious cycle: “They say they’re going to do a soft pull, and it’s not a soft pull and it 
76

cost you, it will affect your credit. I knew what I was working with [in terms of credit score] 

before I started looking. I tell them that over the phone before I come and see you and when I get 

there, they switch up on me. I get kind of really livid over that, but at this point, you are already 

three-quarters through your paperwork and everything. To me, it’s beyond silly, waste of time.”

One middle-income Latino man with no children succinctly summarizes the precariousness that 

comes from the emphasis on credit reports in determining who would be a good tenant: “People, 

I’ll explain to them, ‘I can pay. I can show you bank statements’, stuff, but that didn’t come up... 

I think it’s a constraint. It’s a huge problem, because everyone has been through that before. I 

had very, very, very, very good credit, and lost a job, lost a house. I mean, it’s life. Things 

happen, and then your credit gets dinged and people don’t want to rent to you.”

Postvisit Challenges

Several decades of Housing Discrimination Studies have measured the moment of an inquiry and 

initial visit to a housing unit, but it does not measure everything that happens after that point.

The Housing Search Study (HSS) conducted in this project captures reports and experiences of 

searchers throughout the entire process; our results revealed that this is another area where Black

searchers have unique experiences.

One of the indicators we used to measure the decision-making process of searchers is the number 

of applications they submitted during their search. Despite the fact that Black and White renters 

inquire about similar numbers of units and actually visit similar numbers of units, the differences 

in the average number of applications submitted during the search were pronounced.

As shown in exhibit 5.10, nearly one-third of our sample of Black recent movers reported 

applying for four or more units, compared with only one percent of White recent movers. These 

data—all for individuals who recently completed their housing search—suggest that, although

White and Black renters seek information on and visit similar numbers of units during the search 

process, it takes Black renters more tries at the application process before successfully landing a 

new home. Taking into consideration the searchers’ economic resources has essentially no effect

on the magnitude of the coefficient representing the effect of racial background on number of 

applications, suggesting that the need for multiple applications cannot be explained away by 

racial differences in income. We believe that this pattern points to the possibility of a range of 

different ways in which White and Black searchers are treated differently after visiting units and 

deciding to submit applications.
77

Exhibit 5.10: Number of Units Contacted, Visited, and Applied To

Black

Searchers

White

Searchers

Total

Number of units for which contacted the landlord to find out more 

information

0–1 32.4 20 26.6

2–3 26.5 35 30.5

4–5 19.1 18.3 18.8

6 or more 22.1 26.7 24.2

Number of units visited in person during search

0 2.9 6.6 4.7

1 17.6 8.2 13.2

2–3 27.9 34.4 31

4 or more 51.5 50.8 51.2

Number of units for which submitted applications

0 6.0 6.9 6.4

1 32.8 70.7 50.4

2–3 29.9 20.7 25.6

4 or more 31.3 1.7 17.6

N 67 58 125

Source: Housing Search Study survey, recent movers, 2016

Indeed, it may be that this is the point in the process where much discrimination occurs—a point 

that the design of audit-style housing discrimination studies that have been conducted to date 

cannot uncover (because they do not include the stage of the process when applications are 

submitted). It may be that other factors like credit scores are affecting the success of these 

applications. More research focused on identifying why Black searchers end up submitting more 

applications is called for, but our indepth interviews shed light on the kinds of postvisit

experiences that may be at play.

Black and Latino Renters Describe Getting the Runaround When They Are Interested in a 

Unit

Some interviewees talked about being rejected for unclear reasons, although often they suspect it 

is racial discrimination. For example, one low-income Black man describes one experience: “I 

qualified for everything and all of that, but at the end from nowhere, no real excuse, they just 

told me, ‘Well, we found someone that’s more qualified’ after we’ve been dealing together for 

almost a month or better. I tell you that was crushing. Unless you have a lawyer or you’re going 

to put more money to that, what can you really do? You move on.”

In another example, one middle-income Latino woman with children describes the layers of 

requirements she had to navigate: “I went—the same day that I applied, I got a money order to 

do the background check and a hundred-something-dollar holding fee. And I applied—I brought 

my pay stubs and everything. The only thing that I forgot was my social.... So, there was a 

bunch of us in the office and the leasing consultant put my information in, put my application in.

And she printed out, I guess, an approval sheet. So, she took me in the back and she told me that 

I was approved contingent upon my background check coming back, if that was clean. I wasn’t 

worried about that because I hadn’t had anything on it. And then she told me the building that I’d 

be moving in and everything. And the next thing I know—well, she said that she—she told me 

my security deposit. She said because of my credit history, that I may have to pay a full month.

And I said that’s fine. But she said she was going to talk to her property manager to see if she 
78

could get it down to at least half.... So, the next thing I know I’m waiting, waiting, waiting, 

waiting to hear back. And the next thing I know, I’m being told that I have to have a cosigner 

because they said that my income wasn’t enough. I made enough, but my income—I didn’t meet 

the income requirements. It was kind of thing after thing.”

Ultimately, this same respondent is explicit about the role of discrimination: “I think I’ve 

experienced barriers I hadn’t anticipated as someone who—I perceive myself, again, to be like a 

good tenant. I have great references, both professional and personal...it’s not just bias like 

against—it’s not just a class thing. It very much intersects with race and ethnicity.”

Together, the HSS and indepth interviews cast a spotlight on how and why Black renters’

experiences are distinct. Although some of the specific strategies and approaches are not 

exclusive to Black renters, they reflect a cluster of experiences and concerns that, when taken 

together, pinpoint ways in which Black renters’ search experiences and processes are unique.

The themes that emerged in our HSS and indepth interviews highlight (1) the role of 

neighborhood quality in shaping a search, (2) conscientious efforts and strategies used to avoid 

bad treatment, (3) the important role of anticipated discrimination in shaping many different 

stages of a search, (4) the emerging challenges associated with credit history, and (5) the 

challenges searchers face after a visit to a housing unit.

Our final exploration of housing searches and racial differences puts the pieces of a search 

(context, decisions, evaluations, and information gathering) back together again, and develops a 

typology of searchers. Using this typology, we then ask whether differences in the kinds of 

searches people undertake are racial.
79

Chapter 6: Tying It All Together: Identifying Search Typologies and Their 

Implications

As our conceptual model (exhibit 3.1) reminds us, housing searches are dynamic and nonlinear.

In order to capture the complexity of housing searches and to bring together the key conceptual 

dimensions that we have been describing to this point, in this chapter, we describe our efforts to 

develop search typologies. That is, we use the richness of our Housing Search Study (HSS), 

which attempted to capture all these different dimensions of search, and ask how these 

dimensions hang together—are certain kinds of search characteristics and behaviors more likely 

to cluster together in any given searcher? Once we have identified these different search types, to 

what extent are certain kinds of searches more common among Black and White renters? Finally, 

what is the connection between these different search types and outcomes? To do this, we use a 

cluster analysis based on the Recent Mover Study (because of our interest in the connection 

between search processes and outcomes, which are unavailable for the full set of current 

searchers). By tying all this information across dimensions together, we are able to paint an even 

richer picture of the variation in types of searches people undergo—and how Black and White

searchers might vary in them. Moreover, we are also able to answer, using a different approach

than described in chapter 4, the question of what the implications of those searches are for 

residential outcome.

Our conceptual framework highlights four dimensions of the housing search process: the search 

context, information gathering, evaluating options, and decision making. Our goal is to examine 

how these dimensions are interconnected in order to create a typology of typical searches.

Because the HSS data contain multiple measures of each of these, we must begin by identifying

the key aspects of each search dimension.

The Key Features of Each Housing Search Dimension

To simultaneously take advantage of these rich data and provide an accessible typology of 

searches, we employ a two-stage data reduction process. In the first stage, we used a principal 

components analysis (PCA) to reduce our many measures of each dimension to a core set of the 

key aspects of each search dimension. The PCA, by examining the statistical associations 

between multiple variables within a given dimension, enables us to condense the data to provide 

the key indicators for each dimension. The statistics from the PCA can be found in appendix H, 

but the substantive results are described next, following the four key dimensions of the search: 

context, decision making, information gathering, and evaluating options.

CONTEXT

To identify key components of the first search dimension—the context of the search—we used 

PCA to examine the association between four broad sets of variables: (1) whether the respondent 

reported that the search was made difficult because of transportation difficulties, a shortage of 

money, insufficient child care, a physical disability, credit issues, a criminal record, or a past 

eviction; (2) whether the respondent reported that he or she had to move because of unforeseen 

circumstances; (3) push factors indicating that the respondent reported that the most important 

reason for moving was the current rent is too expensive, or the quality of the current housing is 

too low; and (4) pull factors indicating whether the respondent reported the most important 

reason for moving was to be closer to a job, school, or family. When combined into a single 
80

PCA, two distinct components emerge14 from these indicators: (1) an index that combines 

variables that reflect difficult search circumstances, including variables related to starting the 

housing search because of unforeseen circumstances and barriers to carrying out the search; and 

(2) an index of variables related to the relative importance of push and pull factors. Together,

these indexes appear to highlight a contrast between search constraints and the volitional pushpull motivations for the search.

INFORMATION GATHERING

To portray the information-gathering dimension of the search process, we use a PCA to assess 

interconnections between variables related to the tools and processes searchers use to identify 

and investigate housing options. These variables come from survey items asking respondents 

how often they use advertisements, professional services (real estate agents and community 

organizations), direct contact (phoning or in-person meetings), and online tools (online ads, 

social media, and email) to collect information about housing options. Two strong dimensions 

emerged from this analysis as well, one indicating the reliance on online activities to gather 

information and one referring to the level of reliance on interpersonal contact. The variable 

indicating whether the searcher used professional services loaded on neither of these two 

dimensions and was retained as a separate dimension of the information-gathering process.

EVALUATING OPTIONS

Several variables were used to identify key aspects of the process through which individuals 

evaluate the options identified during their search: the number of characteristics of housing units 

that were rated by the respondent as “very important,” the number of neighborhood 

characteristics rated as “very important,” the stated relative importance of unit and neighborhood 

characteristics, and questions related to the stability of selection criteria during the search 

process. The PCA suggests two key dimensions from this set of variables: one set of variables 

load strongly on a dimension related to the persistent importance of neighborhood conditions in 

evaluating housing options and another set that suggests an emphasis on housing-unit 

characteristics in the evaluation process.

DECISION MAKING

Finally, the PCA related to the decision-making component of the search process identifies a 

dimension related to the numbers of housing units inquired about, visited, and applied for before 

a decision was made, and a separate orthogonal dimension related to the number of different 

neighborhoods involved in the search process.

Identifying a Typology of Searches

With the results of this data reduction in hand, we use a cluster analysis to identify groups of 

individual searchers who are similar in terms of the combination of these various features of the 

housing search process. The result of this second stage is a typology that characterizes common 

 14 We use a varimax rotation and follow the standard convention of retaining components with eigenvalues above 1 and for which 

the eigenvalue produced is stronger in the observed data than in the average of 10 randomly generated data sets.
81

types of housing searches completed by the respondents to the Recent Mover Study. Key 

statistics from the cluster analyses can be found in appendix H, but here we focus on the 

substantive meaning.

The nine key indicators emerging out of the PCAs—two related to the context of the search, 

three related to information gathering, two related to evaluating options, and two related to the 

decision-making process—were used as the basis of a cluster analysis. The goal of this cluster 

analysis was to understand the structure of the data to identify groups (clusters) of recent 

searchers with similar characteristics along the dimensions of the search. In essence, the cluster 

analysis assisted in the identification of a core set of search types that vary in terms of constraints 

they face, factors pushing or pulling them, the priorities of units or neighborhoods, and their 

orientation toward information-gathering processes.

15

OPEN SEARCHERS

The group we classify as open searchers tend to report relatively few characteristics that either 

compelled them to leave their old residence or draw them toward a new location. They also have 

only a moderate level of constraints on their search (relatively unlikely to report that the move is 

unplanned and moderate barriers to searching). Although they tend not to rely on interpersonal 

contact (phoning or making in-person inquiries) when they gather information, they do tend to 

rely heavily on both online tools and professional services. Open searchers also tend to 

emphasize a balance of neighborhood and unit characteristics in assessing their housing options 

and tend to visit a relatively large number of units spread across a large number of 

neighborhoods as they conduct their search. Thirty-eight percent (42 of 111) of the individuals 

for whom we have complete search information are classified as open searchers.

HIGH-THRESHOLD SEARCHERS

High-threshold searchers have much in common with open searchers; they tend to report 

relatively few push or pull motivators for their move, likely reflecting at least a moderate level of 

residential satisfaction with their previous residence, and they report a heavy use of online tools 

to conduct their search. However, high-threshold searchers also differ from open searchers in 

several important ways. First, they are much less likely to use real estate professionals to identify 

housing options. More importantly, they are even less likely to report that their search is 

unplanned or is affected by limitations on access to time or search resources. In contrast to open 

searchers, high-threshold searchers tend to place a high level of importance on both 

neighborhood and unit characteristics in weighing their residential options, and although they 

report searching in a relatively large number of neighborhoods, they investigate, visit, and apply 

for relatively few units. High-threshold searchers are the smallest group in our sample, at 22 

percent.

 15 We used an iterative K-means cluster approach to identify the cluster model that best fits the Recent Mover Study data (that is, 

had lowest entropy and highest purity). Based on these criteria, the result was a three-cluster solution.
82

CONSTRAINED SEARCHERS

Constrained searchers tend to report a high number of push and pull factors that motivate their 

search, but are also much more likely than members of other search types to report that the 

search was unplanned and that their opportunities to search for housing are limited by a shortage 

of resources, little access to childcare or transportation, low access to online resources, and other 

constraints. Constrained searchers report a limited reliance on online resources to search for 

housing options but are even less likely than high-threshold searchers to rely on professionals.

They are also much more likely than other groups to use interpersonal contacts such as phone 

calls or in-person visits to investigate housing options.16 Whereas they tend to report a low 

priority on neighborhood characteristics, constrained searchers tend to report a relatively large 

number of unit characteristics as priorities in investigating their housing options. Finally, the 

scope of the housing search tends to be relatively limited for constrained searchers in terms of 

investigating relatively few neighborhoods and a relatively low number of housing-unit options.

Slightly more than 40 percent of our sample of searchers was constrained searchers.

Racial Differences in Search Types

Having identified a common set of search types, we now turn to the question of whether the 

likelihood of engaging in a particular type of search varies by the race of the respondent. The 

statistics in exhibit 6.1 suggest that such racial differences are fairly prominent for some searcher 

types.

Exhibit 6.1: Racial Differences in Search Type

Source: Housing Search Study survey, recent movers, 2016

 16 Data from the Current Searcher Study also indicate that constrained searchers tend to use a larger number of friends and 

personal contacts than do individuals who do not face constraints, and frequently use their social networks to find housing 

options. These network measures are not available in the Recent Mover Study and are, therefore, not included in the cluster 

analysis presented here.

0

10

20

30

40

50

60

70

80

90

100

Black (N=58) White (N=53)

Open search High-threshold search Constrained search
83

BLACK AND WHITE RECENT MOVERS ARE ABOUT EQUALLY LIKELY TO BE OPEN SEARCHERS

About 40 percent of Black searchers and 36 percent of White searchers fall into the open 

searcher cluster, with few constraints and expansive searches using a wide range of search tools.

As shown in the linear-probability regression model in appendix exhibit H.1, this racial 

difference widens a bit after accounting for racial differences in age, gender, and income but is 

generally modest.

WHITE RECENT MOVERS ARE MORE LIKELY THAN BLACK RECENT MOVERS TO BE HIGHTHRESHOLD SEARCHERS

Although open searchers are equally prevalent among our White and Black recent movers, 

among high-threshold searchers, the racial differences are much more striking. These are 

individuals who tend to emphasize both neighborhood and unit characteristics in weighing their 

residential options, tend to employ a wide range of tools to perform the search, investigate 

options in a wide range of neighborhoods, but ultimately investigate, visit, and apply for 

relatively few units. As shown in exhibit 6.1, nearly 38 percent of White recent movers can be 

characterized as high-threshold searchers. In contrast, only about 7 percent of Black recent 

movers engaged in a high-threshold search. Interestingly, as shown in the comparison of model 1 

and model 2 of appendix exhibit H.2, this racial difference remains largely unchanged after 

controlling for income, gender, and age. Thus, the stronger tendency toward high-threshold 

searches cannot be attributed to the fact that White recent movers, compared with recently 

mobile Black renters, tend to have higher incomes or a different age or gender profile.

BLACK RECENT MOVERS ARE MORE LIKELY THAN WHITE RECENT MOVERS TO BE 

CONSTRAINED SEARCHERS

Exhibit 6.1 shows that more than one-half (53.4 percent) of Black recent movers but only onefourth (26.4 percent) of White movers are in the constrained-searcher category. Again, this 

category includes searchers who are likely to have reported that their search was unplanned or 

made difficult by a lack of money, childcare, transportation, or other resources; rely on a limited 

set of tools for the search process; and assess relatively few units in a restricted set of 

neighborhoods before making their residential decisions. As shown in appendix exhibit H.3, lowand middle-income movers are more likely than higher-income movers to have engaged in a 

constrained search. However, the sharp racial difference in the likelihood of experiencing this 

kind of search remains even after controlling for income, gender, and age of the individual.

The Relationship Between Searcher Type and Outcomes

To the extent that search strategies have effects on actual residential outcomes, these racial 

differences in search type have potentially important implications for racial-residential 

stratification. Hints of these potential implications are provided by examining the association 

between search type and the extent to which the resulting move was viewed as an improvement 

in residential location. Recent movers were asked two questions about their new homes: (1) Is 

this unit better, worse, or about the same as your old home? and (2) Is the quality of your new 

neighborhood better, worse, or about the same as your old neighborhood? We examine how 

these ratings are related to the searcher type.
84

OPEN SEARCHERS REPORT THE HIGHEST LEVEL OF UNIT IMPROVEMENT

As shown in exhibit 6.2, individuals classified as open searchers tend to report the highest level 

of unit improvement; about 62 percent of these open searchers report that their new unit is better 

than their old place, and less than 1 in 10 report that the new unit is worse. In contrast, among 

those engaged in constrained searches, 60 percent report that their new unit is better, and about 

16 percent report that it is worse. High-threshold searchers express the least amount of 

residential improvement with a move; only one-half report an improvement of unit quality after 

the move, and one-fourth report that the new unit is worse than the old. This tendency for highthreshold searchers to report that their new unit is worse than where they started may reflect 

either the quality of the residential origin (recall that these searchers tended to report relatively 

few push factors) or the fact that individuals in this category have especially high residential 

expectations that are not easily met.

Exhibit 6.2: Change in Unit Perception After Residential Move, by Searcher Type

Note: Question: “Is this unit better, worse, or about the same as your old home?”

Source: Housing Search Study survey, recent movers, 2016

CONSTRAINED SEARCHERS TEND TO HAVE THE WORST NEIGHBORHOOD OUTCOMES

Searcher-type differences in neighborhood improvement are even more pronounced. As shown 

in exhibit 6.3, more than one-half of each search-type group report that their new neighborhood 

is better than the neighborhood they left, although the percentage is slightly lower for 

constrained searchers (52.5 percent) than for open searchers (56.8 percent) and high-threshold 

searchers (54.6 percent). However, although only 8 percent of open searchers and 9 percent of 

high-threshold searchers report that their new neighborhood is worse, nearly one in four (22.5

percent) of constrained searchers report this kind of neighborhood downward mobility. This 

tendency for constrained movers to experience downward neighborhood mobility may reflect, in 

part, their focus on unit characteristics in assessing residential options.

0

10

20

30

40

50

60

70

80

90

100

Open searcher (N = 25) High-threshold searcher (N =

40)

Constrained searcher (N = 46)

Better Worse Same
85

Exhibit 6.3: Change in Neighborhood Perception After Residential Move, by Searcher Type

Note: Question: “Is the quality of your new neighborhood better, worse, or about the same as your old 

neighborhood?”

Source: Housing Search Study survey, recent movers, 2016

The results of our cluster analysis reinforce many of the points emerging from the analysis of 

individual dimensions of the search process but enable us to illustrate how a search is more than

only the sum of its ingredients. We find that roughly equal proportions of Black and White

searchers engage in the kinds of searches that are often envisioned in the conceptual models of 

residential searching—the open searcher, whose search is extensive—and results in moves to 

neighborhoods and units that are, at least subjectively, improvements over the previous location.

These kinds of searches are not the norm. Rather, most Black and White renters in our sample of 

recent movers group engaged in a type of search that does not neatly match the theoretically

standard depiction of an extensive, open search. Racial differences are large here. Among White

movers, a slightly more common search strategy is the high-threshold searcher, characterized by 

a high level of planning and research, few constraints, and a demand for new locations that 

satisfy relatively long lists of unit and neighborhood criteria. These high-threshold searchers tend 

to have high residential standards that are often not met in the search for housing. Among Black

movers, it is the constrained searcher that is more common. Constrained searchers tend to 

employ fewer tools in their search for housing, often relying on interpersonal contacts during the 

search.

The results from this analysis are, of course, limited by our reliance on a convenience sample of 

searchers in a specific metropolitan area. To more fully flesh out the prevalence and 

consequences of these different searcher types requires a new generation of research studies that 

focus specifically on housing search processes. In the next chapter, we turn to the more general 

question of the research implications of this project.

0

10

20

30

40

50

60

70

80

90

100

Open searcher (N = 23) High-threshold searcher (N =

35)

Constrained searcher (N = 41)

Better Worse Same
86

Chapter 7: Implications for Research

The U.S. Department of Housing and Urban Development (HUD) is charged with ensuring equal 

opportunity in housing for all racial or ethnic groups and must find ways to both accurately 

estimate the prevalence of housing discrimination and effectively detect it for purposes of 

enforcement. Much of HUD’s research on housing discrimination has focused on how home 

seekers are treated by rental agents or realtors when they search for rental housing or a home to 

purchase. The characteristics of those interactions, the incidence of differential treatment 

(between housing testers paired on all aspects but one, such as race, family status, source of 

income, and so on) and therefore the extent of discrimination was chronicled during four 

decades. Researchers paid little attention, however, to how people search for a home. Indeed, 

HUD’s decennial studies of housing discrimination make explicit and implicit assumptions about 

how this process happens, and conduct rigorous tests of a particular slice of the housing market 

and at a particular stage in the process. For example, HUD’s tests of racial discrimination in 

housing are based on a random sample of apartment listings (usually advertised online or through 

newspapers) to which pairs of highly qualified renters are asked to inquire and attempt to visit.

The studies document what happens to these matched pairs of housing searchers (matched on all 

dimensions but the tested characteristic: in this case, race or ethnicity) from the initial inquiry to 

the visit of the unit.

What is missing from the methods used in the Housing Discrimination Studies is what happens 

at the preinquiry and postinquiry stages. In particular, the design does not capture the processes 

that funnel people into inquiring about particular units and neighborhoods—in other words, the 

existing studies assume equal probability of inquiring about the same rental locations.

Additionally, because the test concludes at the end of one visit to see the unit, the study does not 

uncover possible discriminatory treatment during the application and negotiation process. In 

short, it is unclear whether the stage of the housing search that is measured by these studies (the 

inquiry stage), is sufficient to tell us whether people of all racial or ethnic backgrounds do, in 

fact, enjoy equal access to housing. Moreover, this focus on one part of a housing search means 

that these studies cannot tap the overall costs of discrimination. For example, being denied 

access to a unit at the inquiry stage likely translates for the searcher into a lengthier (and more 

costly) search, and a less desirable unit. Without understanding the fuller housing search in 

which any instance of discrimination occurs, we are left with an incomplete story about how 

discrimination affects those who experience it.

The present study, therefore, explored the methods, experiences, and outcomes of current 

housing searchers and how they differed by race and ethnicity, with an eye toward better 

understanding the obstacles to equal access to quality housing. As a practical matter, as we 

approach the year 2020, HUD will likely be asked, as it has each decade, to evaluate the extent 

of housing discrimination in the rental and sales markets. The purpose of this commissioned 

study is a better understanding of how people search for housing in today’s marketplace; this 

understanding will help inform the design of HUD’s housing discrimination research.

The results of this exploratory research, with its detailed and comprehensive picture of housing 

searches, suggest several directions for the next generation of research studies to pursue. At the 

most abstract level, the results call into question the quite simplistic model of a housing search 

that underpins the design of prior Housing Discrimination Studies, which have focused on a 

particular moment—the step at which a searcher inquires of a landlord about an advertised

(internet or newspaper) housing unit and attempts to visit it. Our study draws attention to all that 
87

happens before that inquiry (preinquiry) and after that inquiry (postinquiry). We describe the 

myriad ways that racial background and racial considerations shape which units and 

neighborhoods a searcher will inquire about and how searchers are treated after the inquiry. Our 

results suggest that the road map for future research needs to have this more expansive model of 

housing searches in mind in order to pinpoint the processes most in need of investigation.

In the following, we bring together key findings from our research into housing searches so that 

we can highlight new research directions that encourage HUD to move beyond the traditional 

focus of the Housing Discrimination Studies on the inquiry stage and include a call for studies 

that examine the preinquiry and postinquiry phases as well. By acknowledging the full 

complexity of a housing search, it becomes clear that each of these phases has features that lend 

to potential discriminatory treatment, and to ignore them is to provide an incomplete picture of 

how discrimination can shape unequal housing outcomes for racial or ethnic minorities.

Preinquiry Phase

Much happens before a searcher inquires about a particular housing unit; searchers identify 

neighborhoods to search in, gather information from a number of possible sources, identify units 

to inquire about, and set their priorities. As a result of the full picture of the entire search process 

that was the purpose of this study, we have identified many ways that the preinquiry phase is 

shaped by race or ethnicity. To focus on the preinquiry phase means to consider all the factors 

that shape the places where people ultimately do inquire about housing. In particular, based on 

the results of this study, we should consider the relationship between certain search approaches 

(in the preinquiry phase) and the units where inquiries are actually made. To be clear, when we 

shift the focus to the preinquiry phase, we are no longer investigating the direct effect of 

instances of discrimination in the sense of denying people access to particular units. Instead, the 

emphasis is on how the anticipation of discrimination influences what people do in the 

preinquiry phase by, for example, funneling people away from certain neighborhoods and into 

others. Of course, where searchers inquire about units has a direct effect on where they end up 

living and the extent to which they are exposed to discrimination. To date, we know quite little 

about how either of these processes unfolds. Based on the results of this study, fruitful research 

would address the following questions.

• What is the effect of anticipated discrimination on the decisions searchers make 

about how they will search and what units they will inquire about? One clear 

conclusion from our analysis of housing searches is that racial or ethnic minorities often 

anticipate and perceive racially discriminatory treatment in the housing market.

Regardless of whether the perceived discrimination is real, or the anticipated 

discrimination comes to pass, the anticipation of discrimination can affect search 

strategies.

• The expectation of discrimination can funnel searchers to use certain sources of 

information (social networks) more than others (internet).

• The desire to avoid discriminatory treatment once a person moves into a 

neighborhood can deter searchers from inquiring about units in certain 

neighborhoods if they learn something about the neighborhood that suggests it may 

be uncomfortable for people of their racial or ethnic group.
88

• An interest in assuring that a landlord will treat them fairly may result in a 

lengthier and more costly process in the form of desiring a face-to-face meeting 

with a potential landlord more than an online interaction.

• What effect does a reliance on particular information sources have on the places 

where people end up inquiring about and ultimately living in? To address the larger 

question of how discrimination affects residential segregation patterns, HUD studies that 

examine the effect of using different information sources on the final outcome would be 

valuable. For example, we know that racial minorities are less likely to use the internet 

and more likely to use word-of-mouth. This may be partly because of anticipated 

discrimination. Given the importance of social networks throughout the search process 

and, particularly so, in terms of funneling people to inquire about units in particular 

places, what are the consequences of using social networks on the outcomes of the 

housing search? To fully understand how this more expansive understanding of how 

discrimination shapes housing outcomes requires that research connect the source of 

information about units to the final outcome, both in terms of where the units are and

whether they are likely to experience discrimination.

The next two questions focus specifically on the prevalence of discrimination that arises out of 

the decisions searchers made in the preinquiry phase.

• How often does discrimination occur when searchers use methods other than online 

listings to find out about units? Both secondary data analysis and the Housing Search 

Study (HSS) indicate that minorities are less likely to find their units online; however, the 

most recent paired-testing study depended exclusively on online listings. Using 

alternative methods to identify units—such as through word of mouth or bulletin 

boards—may expose searchers to very different sets of landlords and circumstances.

Thus, HUD studies that explore the preinquiry stage—that is, how the person found out 

about the potential unit to rent—would better capture the extent of discrimination that 

reflects the variety of ways people find out about units and therefore to which they are 

exposed.

• How often does discrimination occur in the neighborhoods where minorities are 

most likely to search for housing? In light of the findings that anticipated 

discrimination can shape where people inquire about housing, and given racial 

differences in where people inquire about housing, an important consideration when 

attempting to gauge the real-world consequences of discrimination is the prevalence of 

discrimination in the types of areas where minorities are most likely to search. Existing 

paired-testing studies provide estimates of discrimination at the metropolitan level using 

a random sample of listings. However, analysis of secondary data clearly shows that 

minorities are not equally likely to look for housing in all neighborhoods. The probability 

of experiencing discrimination may vary by neighborhood demographics, and more 

nuanced assessments of discrimination’s prevalence would incorporate measures that 

capture the prevalence of discrimination across different neighborhood types.

Each of these different research questions, which focuses on what happens before an inquiry 

takes place, pushes HUD to begin to consider a more expansive understanding of how and where 

discrimination can affect the housing process in a way that may impinge on the equal access to 

housing promised by the Fair Housing Act. Some of these ways are indirect effects of 

discrimination, whereas others push to fine-tune our estimates of the extent to which racial 
89

minorities face discrimination in the real world, adjusting for the realities of how units are 

identified and pursued.

Inquiry Phase

Where the Housing Discrimination Study is strongest is in the ability to estimate the prevalence 

of discrimination at the inquiry phase. Even at this phase, the results of this study of housing 

search processes have identified some additional areas to focus research attention on the inquiry 

phase.

• To what degree does discrimination vary based on the mode of inquiry used by the 

searcher—phone, internet, or in person? Some evidence in this study suggests that 

racial minorities may favor inquiring about units over the phone or in-person rather than 

by internet. Although studies have been conducted to compare rates of discrimination in 

online settings, to our knowledge, no studies directly compare different modes of inquiry 

to the same housing unit. It is useful to ask the extent to which one or another mode of 

inquiry protects searchers from discrimination or, conversely, exposes them to heightened 

risks.

• To what degree do socioeconomic status and credit history mask racial or ethnic 

discrimination when inquiries are made? Analysis of secondary data shows clearly 

that White and minority renters have fundamentally different socioeconomic 

characteristics; minorities have lower household income and credit scores, on average. As 

a result, landlords who intend to discriminate against people of a particular race or 

ethnicity can, in theory, successfully exclude these potential tenants by applying criteria 

having to do with income or credit history, which are not protected classes. In existing 

paired-testing studies, testers are all well-qualified renters. Indepth interview participants 

in our study referred to the challenges they faced because of assumptions they felt 

landlords made about their social class status because of their racial background. Studies 

that seek to disentangle these effects would help clarify the prevalence of discrimination 

faced at the inquiry phase.

Postinquiry Phase

Perhaps the most compelling set of results with the strongest research implications from this 

study comes in the form of the many findings that suggest that what happens after the inquiry 

into a unit is of crucial importance to the outcomes. Among these findings is the fact that,

although racial or ethnic minority searchers inquire about and visit comparable numbers of units 

as White searchers, they apply to many more units. Their searches also last longer. They describe 

the many ways that, after they initiate the process of applying to a unit, they are given 

misinformation, are told they meet the criteria but then are passed over in favor of a “more 

qualified” applicant, or in general, are dodged or given the runaround, making the application 

process sometimes last weeks and months. In other words, at the point where the Housing 

Discrimination Studies stop—the inquiry—is exactly where contemporary forms of 

discrimination may start.

• What happens at the application stage, and how often does discrimination occur at 

this stage? Our quantitative analyses drew attention to the fact that racial minorities had 

longer housing searches, applied to more units in a single search (despite inquiring about 

and visiting the same number of units as White searchers), and describe in indepth 

interviews that they were often given the “runaround” by landlords after initiating the 
90

application process (for example, prospective landlords requested additional requirements 

and paperwork, were slow to return phone calls, or would string them along but 

ultimately select someone else who the landlord said was “more qualified”). All of these 

pieces of evidence point to the need to expand studies of housing discrimination beyond 

the initial inquiry phase and into the application (postinquiry) phase.

• Why do existing paired-testing studies, which track testers up until the viewing of

units, document only very modest instances of discrimination against minority 

renters? Our analyses of secondary data points to a greater probability of ending a search 

without finding a unit, as well as systematically longer housing searches for minority 

renters. The HSS offers that Black renters inquire about and visit the same number of 

units as White renters, but submit more applications; yet, we know little or nothing about 

this part of the process and how minorities may experience discrimination at this stage 

(for example, differences in fees and requirements).

• What role does credit history play, and how does it affect how the landlord during 

the application process treats the renter? Racial minority renters more often than 

White renters refer to credit history as an area of challenge when conducting a housing 

search. Understanding how credit history shapes both the places people inquire about (in 

the preinquiry phase) and how the applicant is evaluated by the potential landlord should 

be an important area of concern for HUD, because it may be another criterion along 

which discrimination can occur. Because credit history is not necessarily an indicator of 

whether a person currently has the financial resources to pay their rent, it may be that 

racial minorities are affected disproportionately by the use of credit history as a screener 

for rental housing. Alternatively, landlords may use credit history as an excuse to pick a 

different applicant for their unit. A study that examined the extent to which credit history 

shapes the outcome of an inquiry or application and whether this varies based on the 

racial or ethnic background of the searcher would shed light on what may be a 

contemporary form of housing discrimination.

• How do landlords make decisions about which person to rent to, and how often does 

discrimination in this final stage occur? Similar to the application process, no research 

documents the landlord or property management side of the last stage of filling a 

vacancy. Moreover, paired-testing studies never measure differences in outcomes—that 

is, whether landlords actually offer the tester the unit.

• What is the cumulative cost of discrimination over the course of a housing search?

Analysis of secondary data shows that searches last longer for minority renters, but the 

HSS was unable to capture the consequences of these longer searches, in terms of their 

direct costs (that is, application fees, gas, parking, or bus fares) and indirect costs (that is,

time). Studies that examine the prevalence of discrimination rarely explore the full extent 

of the consequences of discrimination. Such studies would be valuable complements to 

the studies described here that focus on pinpointing the various stages and phases when 

discrimination can occur.

In short, in order to determine the full extent of discrimination that racial minorities experience 

in the housing market, the measurement of discrimination cannot be limited to a single point in 

what is a complicated and dynamic process. HUD investigations would be well served by 

broadening their conceptual framework of housing searches to include preinquiry and 

postinquiry phases that importantly shape the housing search experience. Understanding how the 

myriad racial or ethnic differences throughout the housing search process translate into points 
91

when discrimination can happen or anticipated discrimination can affect the housing outcomes is 

needed. To do this, an expanded methodological toolkit is in order. In the next section, we 

suggest some specific study designs that could be used to begin to tap into these more 

complicated processes.
92

Exhibit 7.1: Possible Methods for Future Studies of Housing Search and Discrimination

Adapting 

the AHS

New 

Analysis 

of HDS 

Data

Analysis 

of 

Complaint 

Data

Redesigned 

Paired

Testing

Longitudinal 

Study of 

Searchers

Research on 

Landlord

Decision Making

Preinquiry phase

Effects of anticipated discrimination X X

Impact of information sources on outcomes X X

How often does discrimination occur when 

source of unit information is online versus 

other?

X

How often does discrimination occur in

neighborhoods minorities are likely to search for 

housing?

X X

How often does discrimination occur when 

searchers use methods other than online 

listings to find out about units?

X X

Inquiry phase

To what degree is discrimination based on the 

mode of inquiry used by the searcher?

To what degree do socioeconomic status and 

credit history affect racial or ethnic 

discrimination at the inquiry stage?

X

Postinquiry phase

What role does credit history play, and how 

does it affect how the renter is treated at the 

application process?

X

What happens at the application stage, and how 

often does discrimination occur at this stage? X X X X

How do landlords make decisions about to 

whom to rent, and how often does 

discrimination in this final stage occur?

X X

What are the cumulative costs of discrimination

during the course of a housing search? X

AHS = American Housing Survey. HDS = Housing Discrimination Studies.
93

Proposed Methodologies

The appropriate method to answer each of these questions varies substantially. Exhibit 7.1 

summarizes the possible methods for each of the questions.

Adapting the American Housing Survey (AHS). The AHS, a biennial survey sponsored by 

HUD, presents a great opportunity to generate national prevalence estimates of perceptions and 

anticipation of discrimination as well as differences in housing search process and outcomes.

HUD administers the existing instrument to occupants of a representative sample of American 

housing units, and a smaller module asks a set of targeted questions to people who report moving 

into their unit within the past couple of years. Using this survey as a platform would be 

considerably less expensive than fielding an original survey of housing searches. Our HSS 

developed and tested an extensive battery of questions that cover the broad scope of housing 

searches; these instruments are a valuable resource for designing the targeted module, with an 

ability to draw on the large-scale cognitive interview instruments to guide the identification of 

the most useful survey questions for tapping the most relevant concepts. Researchers may add 

some questions for the entire population, whereas they could administer others only to recent 

movers. In addition, this vehicle is likely to generate the kind of sample size that would enable

for sophisticated multivariate analyses that our HSS was only able to begin to tap. The AHS 

modules could also be adapted to include questions about the selection of units and 

neighborhoods (preinquiry phase) for further exploration by the searcher, thus permitting 

linkages among anticipated discrimination, types of communities and units searched, and final 

outcomes.

Analysis of existing HDS data. The 2012 Housing Discrimination Study documented the 

location of listings as well as the mode of inquiry— that is, in-person visit, email, or phone call.

The research team did some exploration of differences in discrimination by type of 

neighborhood; however, it may be useful to revisit these data to focus on the types of 

neighborhoods where minority renters are most likely to look for housing. Further, other 

opportunities to explore how the mode of inquiry is related to instances of discrimination may 

exist.

Analysis of complaint data. Paired testing only documents up to the viewing of units at a visit 

to a particular property. However, much of our analyses suggest that discrimination may be 

occurring after that point in the process. Analyzing trends in existing fair-housing complaint data 

could be an important first step in understanding how discrimination at the application stage and 

beyond may manifest. These insights from complaints could then be used to design experimental 

studies for documenting discrimination.

Redesign of paired testing. Paired testing has many advantages, not least of which is its 

experimental design and its strength of documenting microcomponents of housing search at the 

individual property level. Its weakness, as it is currently conceptualized, is that it may be missing 

many important areas where discriminatory treatment may occur. Our exploratory work on 

housing searches suggests that HUD may want to evaluate the feasibility of three different ways 

that together or separately could be used to adapt the testing methodology to measure 

discrimination more expansively throughout the course of a housing search.

First, HUD could consider a change in the way it matches testers to better understand the 

interaction of race, income, and credit. One way would be to consider multipart tests, where two 

sets of different race pairs present at each property: one pair with income and credit similar to the 
94

typical White renter, and another pair with income and credit similar to the typical Black or 

Latino renter. Although methodologically sound, these kinds of tests would also be costly and 

difficult to implement. Alternatively, HUD could try a nontraditional approach, sending out two 

purposefully dissimilar testers: a White tester with income and credit typical of Black or Latino 

renters, and a Black or Latino tester with income and credit typical of a White renter. Either of 

these designs would help distinguish the application of economic criteria from racial or ethnic 

discrimination.

Second, HUD could consider changing the underlying assumptions about housing searches to 

match minority search patterns. The tests could focus on the kinds of neighborhoods and the 

information strategies that minorities are more likely to be looking in and using. Testers could be 

assigned to specific neighborhoods, asked to use methods other than online sources to identify 

units, and follow up by phone or in-person.

Third, HUD could consider expanding tests beyond the in-person inquiry stage to document the 

application process and potential differential treatment at this stage. This expansion could 

include keeping track of landlord’s requirements for next steps, including fees, paperwork, credit 

checks, and background checks, as well as negotiations about rent amounts, security deposits, 

and timing of move-in. Because of elevated risk for detection and cost, it is not practical to 

expect testers to actually go through this process to completion, but much can be learned by 

expanding the testing protocol to include questions about these next steps. Even landlords’

willingness to provide this kind of information would provide valuable insights.

Longitudinal study of searchers. We learned many lessons from the design and implementation 

of our study of current searchers for this project. The strength of a longitudinal study of searchers 

is that by tracking a search as it is happening, we can capture the full, ongoing scope of a 

housing search. However, it has several methodological weaknesses: its convenience sample, its 

geographic scope, and its difficulty capturing the full length of a search because of wide variance 

among respondents. To identify people who expect to start searching for rental housing within a 

given window of time, HUD could piggyback on a nationally representative survey like the AHS 

to recruit individuals from their sample of respondents. This action would generate nationally 

representative samples without the tremendous cost associated with trying to recruit and identify 

this unique population. HUD might also consider a different format to record observations, to 

complement or even replace interviews, such as online logs or self-administered surveys, about 

interactions with particular properties, as well as to document time and costs in real time. The 

burden of this kind of data collection is high; HUD would likely have to put in place substantial, 

ongoing incentives or offer technology (computers or smart phones) that respondents could use 

to record their experiences.

Research on landlord decision making. The only way to learn about how landlords make 

decisions from the application stage on is to talk directly with landlords about these processes, 

their rationales, and how they are communicated to potential tenants. The last survey of landlords 

took place in 1995. HUD may want to consider partnering with the National Association of 

Rental Property Owners, American Rental Properties Owners and Landlords Association, or 

other trade groups to convene conversations on these topics and field an updated survey 

equipped to capture information about landlord decision-making processes.

In conclusion, HUD has for decades provided critical information about the dynamics of housing 

discrimination in the United States. Racial dynamics in the United States, however, have 
95

changed, and so too have the ways that people search for housing. What has not changed is that 

our nation’s housing, for the most part, remains substantially segregated on the basis of race and 

ethnicity. The purpose of this exploratory study was to step back and consider how people 

actually search for housing and use that to frame a discussion of how we can best identify the 

moments in that process where discrimination might emerge. What we have uncovered is 

evidence that race permeates the process—moving well beyond the single moment in time 

captured by existing studies of discrimination to include both aspects of the search that precede 

and follow that moment in time that has been measured and studied for decades. The next 

decades of research into housing discrimination must be cognizant of these many ways that 

interfere with the rights of all Americans, regardless of race or ethnicity, to access high-quality 

housing of their choosing. We hope this report provides the road map for the next generation of 

HUD studies, and that these future studies, in turn, provide the framework for policies that can 

ultimately help break down the segregation and discrimination that have affected generations of 

Americans.
A-1

Appendix A. Lessons Learned From Housing Search Study Recruitment

Exhibit A.1: Types of Outreach Efforts

Sept. Oct. Nov. Dec. Jan. Feb. Mar. April

Website and print materials

Launch

Redesign 

Print media advertisement

El Tiempo Latino 

El Pregonero

Metro Express

Social media

Facebook

Twitter

Direct mailings

Targeted for rent addresses posted on 

Craigslist

Material distribution

Storage and retail facilities

Rental properties

Online advertisements

Craigslist and Google

Neighborhood message boards

Partner outreach

Community organizations

Property management companies

Bus Advertising 

WMATA—bus ads

WMATA = Washington Metropolitan Area Transportation Authority.
A-2

Exhibit A.2: Interview Tracking 

Month

New Callers 

(Wave 1) Wave 2 Wave 3

All Complete 

Interviews

September 1 0 0 1

October 46 8 1 55

November 28 37 16 81

December 17 14 20 51

January 16 6 9 31

February 53 8 2 63

March 248 80 1 329

April 36 31 21 88

May 32 50 40 122

Total 477 234 100 811

Note: Survey launched Sept. 22, 2015.

Exhibit A.3: Total Interviews and Unique Respondents

0

100

200

300

400

500

600

700

800

900

Cumulative

interviews complete:

total

Cumulative

interviews complete:

wave 1
A-3

Exhibit A.4: Stated Source of Referral

Percent

Saw poster or newspaper ad 39.3

Received postcard in the mail 0.9

Handed flyer 12.9

Online mailing list or listserv 3.2

Twitter 0.3

Facebook 1.4

Craigslist 13.8

Through a friend 6.2

Other 21.7

Note: Includes all call-ins. 

Exhibit A.5: High-Cost, High-Exposure Outreach Efforts

Outreach Description Audience Date

Metro bus 

advertisement

405 interior bus 

cards (22”x21”) 

placed on bus 

routes throughout 

eligible counties in 

the DC area. 

Northern-15

Western-70

Bladensburg-10

Southern-15 

Shepherd-40

Landover-5

Four-Mile-100

West Ox-50

Montgomery-100

2/29/16-

04/24/2016

Metro Express, 

high-volume days 

and/or strategic 

placement in 

“Ready to Rent”

supplement

Express is a free 

commuter daily 

(Monday-Friday) 

newspaper 

targeting readers 

and commuters in 

the D.C. metro 

area.

314,600 average 

daily readership

2/29/16, 3/1/16,

3/7/16, 3/11/16
B-1

Appendix B. Housing Search Study Screener

CATI: TIME STAMP

QTYPE.

INTERVIEWER: WHAT IS CALL STATUS?

1 RESPONDENT CALLED IN (GO TO QLINTRO)

2 CALL INITIATED BY PHONE CENTER (GO TO INTRO1)

3 ANSWERING MACHINE (GO TO QANSW)

4 CALL INITIATED BY PHONE CENTER THROUGH SNOWBALL

RECRUITING (GO TO INTRO1)

QANSW.

ANSWERING MACHINE SCRIPT

Hello, my name is Holly/Henry Smith and I am calling from Decision Information Resources for the //DIR 

INSERT NAME OF SURVEY//. I am trying to reach [FLNAME] for a short paid interview. 

Please have [FLNAME] call us at 1-866-986-1968. Thank you.

INTERVIEWER: PRESS “NEXT” TO EXIT AND RECORD CALL DISPOSITION.

CATI: GO TO END INTERVIEW.

QLINTRO.

Hello, thank you for calling about the Housing Search Study. My name is Holly/Henry Smith and I work at 

Decision Information Resources, Inc.; we are a working with the Urban Institute on this study of people’s 

experiences searching for housing, which is funded by the U.S. Department of Housing and Urban 

Development.

INTERVIEWER: PRESS “NEXT” TO CONTINUE.

CATI: GO TO INTRO1a.

INTRO1.
B-2

Hello, my name is Holly/Henry Smith and I work at Decision Information Resources, Inc.; we are working with 

the Urban Institute on this study of people’s experiences searching for housing, which is funded by the U.S. 

Department of Housing and Urban Development [CATI: IF QTYPE=4 SHOW (“and one of our participants 

nominated you as someone who might want to participate in this study and might be eligible”)]. There is a 

thank-you gift for eligible people who participate.

CATI IF QTYPE=4 [Disclose if the subject asks who nominated them]

INTERVIEWER: IF NECESSARY, READ: “[FLNAME] has agreed to help with a study of renters who have 

recently moved or who are planning to move to a new home within the Washington, DC, Virginia, Maryland 

metropolitan area.

1 RESPONDENT AVAILABLE – CONTINUE

2 RESPONDENT NOT AVAILABLE – ARRANGE CALLBACK AND

ENTER CALL NOTE (GO TO RCONU)

8 REFUSED – ENTER DISPOSITION CODE AND CALL NOTE

DESCRIBING SITUATION IN THE CALL RECORD TEXT BOX (GO

TO END INTERVIEW)

INTRO1a.

I’d like to ask you a few screening questions to see if you are eligible. This will take just a few minutes. Of 

course, if you get started and are uncomfortable with any questions, you don’t have to answer them. You can 

stop your participation at any time. The researchers involved in the study respect your privacy and your name

will not be linked to any of the answers you give us. Can I go ahead and get started with the screening 

questions? 

INTRO2A

Screening Questions 

CATI: TIMESTAMPS

In case we get disconnected, please provide me a name and phone number so I can call you back: 

NAME: ________________________ CATI: CREATE FLNAME

PHONE: ______-______-__________ [CATI: CREATE RPHONE]
B-3

CATI: TO MINIMIZE “DON’T KNOW”/” REFUSED” RESPONSES IN SCREENER PROVIDE THE FOLLOWING 

INSTRUCTIONS TO IVWR TO PROMPT R WHEN DON’T KNOW/REFUSED GIVEN: “In order to determine if you 

are eligible for the study, we need to know this information. Even if you aren't completely sure, what is 

your best guess?”

RENTER STATUS

S1. Do you currently rent or own the place in which you are living?

1. Rent (SKIP TO S2)

2. Own

3. Some other arrangement? SPECIFY _________

8. DON’T KNOW OR1ST REFUSAL, READ PROMPT

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

S1a. Are you undergoing a housing search for a house or apartment that you wish to 

RENT?

1. YES (CATI, CREATE CURSRCH=1) (SKIP TO S2c)

2. NO (SKIP TO SCRNFAIL)

8. NO ANSWER GIVEN, READ PROMPT

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

SCRNFAIL INTERVIEWER: R IS NOT ELIGIBLE FOR THE STUDY

Thank you for your participation. 

Without an answer to all of the screener questions, I’m unable to determine your eligibility. 

Thank you for your time.

 S1a: This study is for people who are renters or planning to become renters, so I am sorry 

but you are not eligible.

S2: This study is for people who are currently searching for a place to live or who have 

recently moved, so I am sorry but you are not eligible.

S3A: This study is for people who are currently searching to rent outside of a public housing 

development, so I am sorry but you are not eligible. (ONLY WHEN YES IS THE ANSWER)

S4: This study is for people living in the area for a long-term basis, so I am sorry but you are 

not eligible.

S5a: This study is for people who are currently searching in specific areas, so I am sorry but 

you are not eligible.
B-4

S6: This study is for people who were involved in searching for your current home, so I am 

sorry but you are not eligible.

S8: This study is for people in specific groups, so I am sorry but you are not eligible.

S9: This study is for people who are at least 21 years old, so I am sorry but you are not 

eligible.

S2. Have you moved in the last two months, that is, since [DATE] (2 MONTHS BEFORE CURRENT DATE) 

OR are you currently searching for housing? 

1. MOVED WITHIN THE LAST 2 MONTHS (SKIP TO S2a)

2. CURRENTLY SEARCHING FOR HOUSING (SKIP TO SCRNFAIL)

3. BOTH SEARCHING AND RECENTLY MOVED (CATI, CREATE

CURSRCH=1) (SKIP TO SCRNFAIL)

4. NONE OF THE ABOVE (SKIP TO SCRNFAIL)

5. SIGNED A LEASE, BUT NOT YET MOVED IN [IF VOLUNTEERED] (SKIP TO S2a)

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ PROMPT

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

MOVED WITH IN THE LAST TWO MONTHS

S2a. [CATI: IF S2=5 READ Although you have not yet moved into your new place, for the rest of 

the survey, I would like to ask you to answer questions about your current unit or neighborhood in 

terms of the new place that you will soon be moving into. Questions about your past unit should 

refer to where you are living right now. First, what day will your new lease begin?” OTHERWISE, 

READ “When did you move into the place where you are living now?”

DD____ MM _____ YYYY ____

98. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ

PROMPT

99. CONFIRMED REFUSAL

CATI: IF DATE IS WITHIN 2-MONTH TIMEFRAME FROM CURRENT DATE CREATE RECMVR=1, SKIP TO S3

CATI: IF DATE IS OUTSIDE 2-MONTH TIME FRAME SKIP TO SCRNFAIL

S2b. Are you planning to buy or rent?
B-5

1. RENT (CATI: CREATE CURSRCH=1, SKIP TO S3a.)

2. BUY (SKIP TO SCRNFAIL)

3. OPEN TO BOTH RENTING OR BUYING (CATI: CREATE CURSRCH=1, SKIP TO S3)

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ PROMPT

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

S2C. Is there a set date when you need to finish your search, such as the end of a lease, the birth of 

a new child, or another date?

INTERVIEWER: IF A DTE IS SELECTED, YOU MUST SELECT “YES, SPECIFY DATE” TO CONTINUE, 

OTHERWISE YOU MUST SELECT NO TO CONTINUE

1. YES, SPECIFY DATE__________________ (GET DATE)

2. NO

8. DON’T KNOW

9. REFUSED (SKIP TO SCRNFAIL)

CATI: IF S2C IS > 90 DAYS FROM TODAY, SKIP TO SCRNFAIL

IF RECMVR=1

S3. Are you currently living in a unit owned by the local housing authority? [CATI: IF S2=5 ALSO ADD

“Remember, by current we are referring to the home you will soon be moving into.”]

1. YES (SKIP TO SCRNFAIL)

2. NO

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ PROMPT

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

IF CURSRCH=1

S3a. Are you currently searching for housing only through the local housing authority?

1. YES (SKIP TO SCRNFAIL)

2. NO

3. DON’T KNOW/NOT SURE

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ PROMPT

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)
B-6

S4. Did you recently move to the DC area for a short-term internship or educational

program lasting less than four months? 

1. YES (SKIP TO SCRNFAIL)

2. NO

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ PROMPT

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

LOCATION QUESTIONS 

CURRENT SEARCHERS MODULE: IF CURSRCH=1, SHOW:

S5a. In what counties are you looking or considering for housing? [CATI: IF S2=5 

ALSO ADD “Remember, by current we are referring to the home you will soon 

be moving into.”]

INTERVIEWER: DO NOT READ LIST; CHECK ALL THAT APPLY

1. WASHINGTON, DC [RESPONDENT MAY SAY, “DISTRICT OF 

 COLUMBIA” OR “DC”]

2. MONTGOMERY COUNTY, MD

3. PRINCE GEORGE'S COUNTY, MD, OR “PG COUNTY”

4. FAIRFAX COUNTY, VA (INCLUDING THE INDEPENDENT CITIES

OF FAIRFAX AND FALLS CHURCH)

5. ARLINGTON COUNTY, VA

6. CITY OF ALEXANDRIA (INDEPENDENT CITY ADJACENT TO

ARLINGTON AND FAIRFAX COUNTIES)

7. NONE OF THE ABOVE (SKIP TO SCRNFAIL)

8. NOT SURE _____

9. OTHER, SPECIFY_________________ (GO TO S5A2)

98. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ

PROMPT

99. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)
B-7

S5a2. So, just to be clear, this is not in either the DC area, or Montgomery

County, Prince George’s County, Fairfax County, Arlington County, or the

City of Alexandria?

1. YES, SEARCH AREAS ARE NOT IN ANY OF THE

COUNTIES/AREAS LISTED (SKIP TO SCRNFAIL)

2. NO, SEARCH AREAS ARE IN ONE OF THE

COUNTIES/AREAS LISTED

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL,

READ PROMPT (SKIP TO SCRNFAIL)

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

RECENT MOVERS MODULE: IF RECEMVR=1, SHOW:

S5b. In what county do you currently live? [CATI: IF S2=5 ALSO ADD “Remember, by 

current we are referring to the home you will soon be moving into.”]

INTERVIEWER: DO NOT READ LIST

1. WASHINGTON, DC [RESPONDENT MAY SAY, “DISTRICT OF 

COLUMBIA” OR “DC”]

2. MONTGOMERY COUNTY, MD

3. PRINCE GEORGE'S COUNTY, MD, OR “PG COUNTY”

4. FAIRFAX COUNTY, VA (INCLUDING THE INDEPENDENT CITIES

OF FAIRFAX AND FALLS CHURCH)

5. ARLINGTON COUNTY, VA

6. CITY OF ALEXANDRIA (INDEPENDENT CITY ADJACENT TO

ARLINGTON AND FAIRFAX COUNTIES)

7. NONE OF THE ABOVE (SKIP TO SCRNFAIL)

8. NOT SURE _____

9. OTHER, SPECIFY_________________ (GO TO S5B2)

98. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ

PROMPT
B-8

99. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

S5b2. So, just to be clear, this is not in either the DC area, or Montgomery

County, Prince George’s County, Fairfax County, Arlington County, or the

City of Alexandria?

1. RESPONDENT IS NOT CURRENTLY LIVING IN ANY OF THE

COUNTIES/AREAS LISTED (SKIP TO SCRNFAIL)

2. RESPONDENT IS CURRENTLY LIVING IN ONE OF THE

COUNTIES/AREAS LISTED

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ

PROMPT (SKIP TO SCRNFAIL)

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

INVOLVEMENT IN SEARCH

RECENT MOVERS MODULE VERSION

S6. CATI: IF RECMVR=1, SHOW: Thinking about the search you did for your current home, how much were you 

personally involved in the search? Would you say you were ...

1. Very involved

2. Somewhat involved

3. Involved a little

4. Not involved at all (SKIP TO SCRNFAIL)

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ PROMPT 

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

CURRENT SEARCHERS MODULE VERSION

S6a. CATI: IF CURSRCH=1, SHOW: How much do you expect to be involved in the search for your 

new home? Would you say ...

1. Very involved

2. Somewhat involved

3. Involved a little

4. Not involved at all (SKIP TO SCRNFAIL)

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL,

READ PROMPT

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

RACE/ETHNICITY (Adapted from Census 2010)
B-9

S7. Are you of Hispanic, Latino, or Spanish origin?

1. YES (SKIP TO N4)

2. NO

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ PROMPT

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

S8R. What is your race? Please tell me all that apply.

INTERVIEWER: ONLY READ OPTIONS IF RESPONDENT DOES NOT VOLUNTEER RACE. SELECT MULTIRACIAL OR 

BIRACIAL ONLY IF RESPONDENT VOLUNTEERS IT AND SELECTS NO OTHER RACE OPTION.

Yes No

S8_1. White 1 (SKIP TO 

SCRNFAIL AFTER 

QUOTA)

2

S8_2. Black or African American 1 (SKIP TO 

SCRNFAIL AFTER 

QUOTA)

2

S8_3. Asian 1 (SKIP TO 

SCRNFAIL)

2

S8_4. American Indian or Alaska Native 1 (SKIP TO 

SCRNFAIL)

2

S8_5. Native Hawaiian or Pacific Islander 1 (SKIP TO 

SCRNFAIL)

2

S8_6. Some other race 1 (SKIP TO 

SCRNFAIL)

2

S8_7. MULTIRACIAL OR BIRACIAL (ONLY IF 

VOLUNTEERED AND NO OTHER RACE IS SELECTED, 

DO NOT READ)

1 (SKIP TO S8A_R) 2

S8_8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST 

REFUSAL

1 (READ PROMPT) 2

S8_9. CONFIRMED REFUSAL 1 (SKIP TO 

SCRNFAIL)

2
B-10

INTERVIEWER: IF R ANSWERS, “DON’T KNOW/REFUSED” PROMPT WITH: “In order to determine if 

you are eligible for the study, we need to know this information. Even if you aren't completely sure, 

what is your best guess?"

 

RACE QUOTA: IF RESPONDENT = WHITE, NON-HISPANIC AND CAP HAS

BEEN REACHED, NOT ELIGIBLE FOR STUDY, SKIP TO

SCRNFAIL

(IF S8R=7)

S8A_R. What racial groups do you identify with? Please tell me all that apply.

INTERVIEWER: ONLY READ OPTIONS IF RESPONDENT DOES NOT VOLUNTEER RACE. SELECT 

MULTIRACIAL OR BIRACIAL ONLY IF RESPONDENT VOLUNTEERS IT AND SELECTS NO OTHER RACE 

OPTION.

Yes No

S8A_1. White 1 (SKIP TO 

SCRNFAIL AFTER 

QUOTA)

2

S8A_2. Black or African American 1 (SKIP TO 

SCRNFAIL AFTER 

QUOTA)

2

S8A_3. Asian 1 (SKIP TO 

SCRNFAIL)

2

S8A_4. American Indian or Alaska Native 1 (SKIP TO 

SCRNFAIL)

2

S8A_5. Native Hawaiian or Pacific Islander 1 (SKIP TO 

SCRNFAIL)

2

S8A_6. Some other race 1 (SKIP TO 

SCRNFAIL)

2

S8A_7. MULTIRACIAL OR BIRACIAL (ONLY IF 

VOLUNTEERED AND NO OTHER RACE IS SELECTED, 

DO NOT READ)

1 (SKIP TO S8A_R) 2

S8A_8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST 

REFUSAL

1 (READ PROMPT) 2

S8A_9. CONFIRMED REFUSAL 1 (SKIP TO 

SCRNFAIL)

2
B-11

INCOME/AGE 

S9. What is your age? Are you... 

1. Under 21 (SKIP TO SCRNFAIL)

2. 21-29

3. 30-39

4. 40-49

5. 50-59

6. 60-69 

7. 70 or older

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ PROMPT

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

AGE QUOTA: CATI: SKIP TO SCRNFAIL IF CAP FOR AGE GROUP HAS

BEEN REACHED, NOT ELIGIBLE FOR STUDY

S10. Are there any children under the age of 18 who live with you?

1. YES

2. NO

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ PROMPT

9. CONFIRMED REFUSAL

CATI: ASK IF RECMVR=1, ELSE SKIP TO S11b_1

S11a_1. In your current living situation, do you have roommates—that is, unrelated adults

with whom you share the house or apartment?

1. YES (SKIP TO S11a_2)

2. NO (SKIP TO S11a_3)

3. BOTH SITUTIONS (IF VOLUNTEERED) (SKIP TO S11a_3)

4. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ PROMPT

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

S11a_2. We are looking for a range of people to participate in this study so we would like to ask

you about your yearly individual income. We are interested in only your individual

income and not that of any of your roommates. Is your individual yearly income...

1. Less than $25,000

2. Between $25,000 and $65,000

3. More than $65, 000

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ PROMPT

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)
B-12

CATI: SKIP TO S12

S11a_3. We are looking for a range of people to participate in this study so we would like to ask

you about your yearly total household income. We would like to know the total

household income that includes all adults. Is your yearly total household income...

1. Less than $25,000

2. Between $25,000 and $65,000

3. More than $65, 000

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ PROMPT

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

CATI: ASK IF CURSRCH=1

S11b_1. Are you planning to move into a living situation where you have roommates –

that is, unrelated adults with whom you will share the house or apartment?

1. YES (SKIP TO S11b_2)

2. NO (SKIP TO S11b_3)

3. BOTH SITUTIONS (IF VOLUNTEERED) (SKIP TO S11b_3)

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL, READ PROMPT

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

S11b_2. We are looking for a range of people to participate in this study so

we would like to ask you about your yearly individual income.

We are interested in only your individual income and not that of

any of your roommates. Is your individual yearly income...

1. Less than $25,000

2. Between $25,000 and $65,000

3. More than $65, 000

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL,

READ PROMPT
B-13

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

CATI: SKIP TO S12

S11b_3. We are looking for a range of people to participate in this study so

we would like to ask you about your yearly total household

income. We would like to know the total household income

amongst the adults that will be sharing the new apartment or house.

Will the yearly total household income be...

1. Less than $25,000

2. Between $25,000 and $65,000

3. More than $65, 000

8. NO ANSWER GIVEN, DON’T KNOW, OR 1ST REFUSAL,

READ PROMPT

9. CONFIRMED REFUSAL (SKIP TO SCRNFAIL)

INTERVIEWER: IF R ANSWERS, “DON’T KNOW/REFUSED” PROMPT WITH: “In order to determine if you are 

eligible for the study, we need to know this information. Even if you aren't completely sure, what is your best 

guess?"

S12. How did you hear about this study?

1. Saw poster or bus ad

2. Received postcard in the mail

3. Handed flyer or saw an ad in a newspaper

4. Online mailing list or listserv

5. Twitter

6. Facebook

7. Craigslist

8. Through a friend

9. Other (specify)

98. DON’T KNOW, READ PROMPT

99. CONFIRMED REFUSAL

RECRUITMENT QUOTA: [CATI: IF CAP FOR RECRUITMENT METHODS HAS BEEN REACHED- E.G. CRAIGSLIST, 

NOT ELIGIBLE, SKIP TO SCRNFAIL]

---END SCREENING QUESTIONS----

S5. Based on the answers you gave me, you are eligible for participating in the Housing

Search Study. Before we begin, could you provide your current street address? 
B-14

STREET: __________________________

CITY: __________________________

STATE: __________________________

ZIP: __________________________

98. DON’T KNOW, READ PROMPT

99. CONFIRMED REFUSAL

S5_1. What is the name of the neighborhood or area you live in?

INTERVIEWER: IF R HAS SIGNED A LEASE BUT NOT YET MOVED THEN WE ARE ASKING ABOUT THE 

NEIGHBORHOOD THEY WILL BE MOVING TO

____________________List neighborhood(s) and Other, specify option

98. DON’T KNOW

99. REFUSED

CATI: IF RECMVR=1, SKIP TO (RECENT MOVERS MODULE, ELSE IF CURSRCH=1 SKIP TO CURRENT SEARCHERS
C-1

Appendix C. Recent Mover Cognitive Test Protocol

INTRODUCTORY TEXT

Q_START. Ok, thanks. This study is for people who have moved to a new home in the DC area within the last 

two months. We would like to ask you some questions about your recent housing search experience, how you 

searched for housing, and how it was that you ended up renting the home that you currently live in. We will 

also ask some basic questions about your background and other experiences. This study will take about 30 

minutes and you will receive a $50 gift card to thank you for your time. We respect your privacy, and your name 

will not be linked to any of your answers. The study is completely voluntary, and you can refuse to answer any 

question or stop the study at any time. May I continue?

1. YES

2. NOT NOW (SKIP TO RCONU)

3. REFUSED (SKIP TO END_INT)

HOUSING BASICS

The first set of questions is about your current housing situation.

 B3. What is your current rent, that is, [CATI: IF S11a_1=1 SHOW: “that part of the rent that

you are responsible for”; IF S11a_1=2 OR 3 SHOW: “the total rent for the unit”?

INTERVIEWER: IF R IS NOT PAYING RENT, SELECT OPTION BOX BELOW.

________ $$ [Range: 1-9997]

NOT PAYING RENT (SKIP TO B5)

9998. DON’T KNOW (SKIP TO B5)

9999. REFUSED (SKIP TO B5)

B4. Is that...

1. Per week

2. Every other week, 

3. Per month, or

4. Something else? Specify

8. DON’T KNOW

9. REFUSED
C-2

B5. Now I’d like to ask you some questions about where you lived before you moved on [INT_1_DATE: 

FROM S2a].

What was your address before you lived at [FROM S5: FULLADD]? 

Number and Street: ________________________ 

City: __________________________

State: _________________________

Zip: __________________________

INTERVIEWER: IF RESPONDENT DOES NOT KNOW ADDRESS, PROBE FOR CROSS STREETS OR GENERAL 

AREA

Cross streets or general area: __________________________

CATI: IF ZIP CODE MATCHES ZIP CODE IN S5 SKIP TO B6

B5b. Would you say this was the same neighborhood as you currently live in, or is it a different 

neighborhood? 

1. SAME NEIGHBORHOOD

2. DIFFERENT NEIGHBORHOOD

98. DON’T KNOW

99. REFUSED

B6. When you lived at [FROM B5: PRIOR_STREET], did you own the place or were you renting?

1. OWN 

2. RENTING 

8. DON’T KNOW 

9. REFUSED 

B7. Now I would like to ask you questions about the neighborhood you recently moved to –

that is, the one you are currently living in.

Overall, how would you rate the quality of your [CATI: IF B5b=2 INSERT “new”}] neighborhood on a 

scale of 1 to 10, with 10 being the best and 1 being the worst? 

________ Rating [Range: 1-10]

98. DON’T KNOW

99. REFUSED

CATI: IF B5b=1 SKIP TO B9
C-3

B8. In general, is the quality of your new neighborhood better, worse, or about the same as your previous 

neighborhood?

1. BETTER

2. WORSE

3. ABOUT THE SAME

8. DON’T KNOW

9. REFUSED

B9. Now we would like to ask you about several features of the neighborhood you are currently living in and 

how they compare to what you were hoping for when you were looking for a place to live.

Better 

than 

you 

hoped 

for

About 

what 

you 

hoped 

for

Not as 

good as 

you 

hoped 

for 

Or did 

that not 

matter 

to you

DON’T 

KNOW

REFUSED

B9. First, how about your current 

neighborhood’s convenience to your 

workplace or school?

Would you say it is...

B10. How about your current 

neighborhood’s location near friends or 

family? Would you say it is...

B11. How about the amenities like 

restaurants, grocery stores, theaters, 

shopping, and doctor’s offices in your 

current neighborhood? 

B12. What about your current 

neighborhood’s convenience to public 

transportation?

B13. What about your current 

neighborhood’s quality of public services 

like libraries, playgrounds, and community 

centers?

B14. What about the quality of the schools 

in your current neighborhood?

B15. What about the safety of your current 

neighborhood?

B16. Now we would like to ask you about one last neighborhood characteristic. How about the mix of racial 

and ethnic groups in your current neighborhood?

Would you say it is...

1. Better than you hoped for, (GO TO B16A)

2. About what you hoped for, (GO TO B16A)

3. Not as good as you hoped for, or (GO TO B16A)

4. Did it not matter to you? (GO TO B18)
C-4

8. DON’T KNOW (GO TO B18)

9. REFUSED (GO TO B18)

IF B16 = 1,2, or 3

B16a. In what way [is it better than you hoped for/is it about what you hoped for/is

it not as good as you hoped for]? 

SPECIFY: ________________________________________

98. DON’T KNOW

99. REFUSED

UNIT-SPECIFIC QUESTIONS

B18a. Now I am going to ask you some questions about the building and home that you currently live in.

What kind of building is your unit in? Is it a...

1. Single family home

2. Small building with four or fewer

3. Medium sized building with 5-50 units

4. Large apartment building or complex with more than 50 units

5. VOLUNTEERED: OTHER (SPECIFY)____________________

8. DON’T KNOW

9. REFUSED

B18. How many bedrooms does your unit have?

________ BEDROOMS [Range: 1-9]

98. DON’T KNOW

99. REFUSED

B21. Is this housing unit better, worse, or about the same as your previous home? 

1. Better

2. Worse

3. About the Same

8. DON’T KNOW

9. REFUSED

B22. Now we would like to ask about a number of features of your new home and how they compare to 

what you were hoping for.

Would you say the rent you pay for it is...
C-5

1. More than you thought you would pay, 

2. About what you thought you would pay, 

3. Less than you thought you would pay, or 

4. Did the rent not matter to you?

8. DON’T KNOW

9. REFUSED

B24-B28.

Better 

than 

you 

hoped 

for

About 

what 

you 

hoped 

for

Not as 

good as 

you 

hoped 

for 

Or did 

that not 

matter 

to you

DON’T 

KNOW

REFUSED

B24. How about the size of the home or 

number of rooms?

Would you say the size of the home or 

number of rooms is...

B27. How about the security of the 

building? Would you say the security of the 

building is...

B28. How about the amenities in the 

building, like laundry facilities, parking, and 

other on-site facilities?

B32. In your search, which was more important to you, ...

1. The unit itself,

2. The neighborhood where it was located, or

3. were both equally important?

8. DON’T KNOW

9. REFUSED

B33. People move for a number of different reasons. I’m going to read several possibilities and I would like 

you to tell me which, if any, was an important reason why you decided to move. 

First, did you move: 

YES NO DON’T 

KNOW REFUSED

a. Because your old home or building was destroyed, 

condemned or unsafe? 1 2 8 9

c. Because your landlord said you had to move? 1 2 8 9

d. Because of a new job or job transfer?

1 2 8 9

e. To form your own household? 1 2 8 9

f. To be closer to family? 1 2 8 9
C-6

YES NO DON’T 

KNOW REFUSED

g. Because of a change in household or family size, 

including marriage, divorce, separation, or child birth or 

adoption?

1 2 8 9

h. To reduce commuting time? 1 2 8 9

i. To upgrade to a larger home or a better-quality home? 1 2 8 9

j. To find a less expensive place to live? 1 2 8 9

k. To live in a better neighborhood? 1 2 8 9

l. Because of some other reason? 1 2 8 9

B33_O. What is the other important reason you moved?

CATI: IF 2 OR MORE =1 (YES) CONTINUE, ELSE SKIP TO B33n

CATI: SHOW B33A-B33K WHEN =1 (YES)

B33_m. Which reason is the most important?

98. DON’T KNOW

99. REFUSED

B33_n. Thinking about your decision to search for a new place to live, would you say this was something you 

had been planning to do for a while or did the need to search for a new place come up unexpectedly?

1. Planning for awhile

2. Came up unexpectedly

3. DON’T KNOW

4. REFUSED

B34. Once you started looking for a new place to live, how long did it take you to find your new place? 

Would you say it took...

1. Less than a week

2. More than a week but less than a month

3. 1-2 months

4. More than 2 months

8. DON’T KNOW

9. REFUSED
C-7

FINANCIAL HEALTH

B36. We would like to know if there are any things that made it difficult for you to search for housing. Please 

tell me if any of the following made it difficult for you. 

YES NO DON’T 

KNOW

REFUSED

a. Not having access to the internet? 1 2 8 9

b. [Skip if S_10 =2]: Not having childcare? 1 2 8 9

c. Not having transportation to get to units? 1 2 8 9

d. History of prior eviction? 1 2 8 9

e. Criminal record for you or anyone in your household? 1 2 8 9

f. Poor credit history? 1 2 8 9

g. Not enough money for a security deposit or other fees? 1 2 8 9

B37. Do you currently have a Section 8 Housing Choice Voucher, or other form of subsidized housing? 

1. YES

2. NO

3. NO BUT AM ON WAITING LIST [ONLY IF VOLUNTEERED]

8. DON’T KNOW

9. REFUSED

CATI: IF RACE = WHITE, SKIP TO B39

B38. In general, do you think that [RACE (add ‘s’ so it is African AmericanS, LatinoS, etc.] have as good a 

chance as whites in this community to get any housing they can afford, or do you think they don't have 

as good a chance?

INTERVIEWER: IF R ALSO IDENTIFIES AS “WHITE”, SAY “I understand. This question is being asked because you 

also identified as Black or African American.”

1. HAVE AS GOOD A CHANCE

2. DON’T HAVE AS GOOD A CHANCE

8. DON’T KNOW [VOLUNTEERED]

9. REFUSED

CATI: IF R IS LATINO AND BLACK/AFRICAN AMERICAN; THEN [RACE] = BLACK/AFRICAN AMERICAN

B38a. In general, do you think that [RACE (add ‘s’ so it is African AmericanS, LatinoS, etc.] have as good a 

chance as whites in this community to get any housing they can afford, or you think they don't have as 

good a chance?
C-8

INTERVIEWER: IF R ALSO IDENTIFIES AS “WHITE”, SAY “I understand. This question is being asked because you 

also identified as Latino.”

1. HAVE AS GOOD A CHANCE

2. DON’T HAVE AS GOOD A CHANCE

8. DON’T KNOW [VOLUNTEERED]

9. REFUSED

CATI: IF R IS LATINO AND BLACK/AFRICAN AMERICAN; THEN [RACE] = LATINO

B39. When you first started searching for a place to live, did you consider any other neighborhoods besides 

the one you moved into? 

1. YES

2. NO (GO TO B42)

8. DON’T KNOW

9. REFUSED

B40. Which neighborhood(s) or areas did you consider? PROBE: Any others?

INTERVIEWER: ONCE SELECTION OF NEIGHBORHOODS IS COMPLETE, YOU MUST SELECT 97: ‘NO 

OTHER NEIGHBORHOODS’ TO PROCEED TO THE NEXT QUESTION

____________________List neighborhood(s) and Other, specify option

97. NO OTHER NEIGHBORHOOD

98. DON’T KNOW

99. REFUSED

B42. We would like to know how you first heard about the unit that you moved into. Would you say you 

heard about it from someone you knew, that you saw or heard an advertisement for it, saw a sign in 

the window, learned about it from a professional service, learned about it through social media, 

through a community or religious organization, or some other way? 

1. SOMEONE YOU KNEW (ASK B43)

2. ADVERTISEMENT (ASK B45)

3. SIGN ON THE WINDOW (SKIP TO B49)

4. PROFESSIONAL SERVICE

5. SOCIAL MEDIA (ASK B47)

6. COMMUNITY OR RELIGIOUS ORGANIZATION (SKIP TO B50)

7. OTHER, SPECIFY: _____________________

8. DON’T KNOW/CAN’T REMEMBER (SKIP TO B50)

9. REFUSED
C-9

CATI: ASK IF B42_1 = 1

B43. What best describes your relationship to the person who told you about this unit, was it:

INTERVIEWER: SELECT ALL THAT APPLY

1. A family member

2. A friend

3. A co-worker

4. A roommate

5. A neighbor

8. DON’T KNOW

9. REFUSED

CATI: ASK IF B42_1 = 1

B44. What best describes the race and ethnicity of the person/people who told you about this unit? Would 

you say...

INTERVIEWER: SELECT ALL THAT APPLY

1. Hispanic/Latino

2. White

3. Black or African American

4. Asian

5. Other, SPECIFY: ____________________

8. DON’T KNOW

9. REFUSED

CATI: ASK IF B42_2 = 1

B45. Where was this ad? Was it in a newspaper, on a website, billboard, or radio?

1. NEWSPAPER, SPECIFY: Which newspaper?

2. WEBSITE, SPECIFY: Which website?

3. BILLBOARD

4. RADIO, SPECIFY: Which station? __________________________

8. DON’T KNOW

9. REFUSED

CATI: ASK IF B42_4 = 1
C-10

CATI: ASK IF B42_5 = 1

B47. Which social media site did you first hear about the unit on?

1. Facebook

2. Twitter

3. Myspace

4. Email listservs (e.g., community listservs, interest group listservs)

5. Other social media

8. DON’T KNOW

9. REFUSED

CATI: ASK IF B42_6 = 1

CATI: ASK ALL

B50. Now instead of just asking about the unit that you ended up moving into, we would like to ask you 

about all of the different things you did during your housing search. What sources of information did 

you use in general to find out about all of the units you considered in your search? 

YES NO DON’T 

KNOW REFUSED

a. First, did you hear about units from someone you knew? 1 2 8 9

b. Did you see or hear advertisements for them? 1 2 8 9

c. Did you see signs in the windows of units or buildings? 1 2 8 9

d. Did you learn about them from a professional service? 1 2 8 9

e. Did you learn about them on social media? 1 2 8 9

f. Did you learn about them through community or religious 

organizations? 1 2 8 9

g. Did you use any other sources? (IF YES: SPECIFY) 1 2 8 9

CATI: ASK IF B50g=1

B50_O. What other sources did you use?

1. Other, Specify: _________________________

98. DON’T KNOW

99. REFUSED

[IF SOMEONE YOU KNEW ASK 51-52]
C-11

CATI: ASK IF B50_1 = 1

B51. Thinking about the person you knew who told you about units, what best describes your relationship to 

this person? If there was more than one person, tell me about your relationship to the person who 

told you about the most units.

INTERVIEWER: SELECT ALL THAT APPLY

Was it...

1. A family member

2. A friend

3. A co-worker

4. A roommate

5. A neighbor

8. DON’T KNOW

9. REFUSED

CATI: ASK IF B50_1 = 1

B52. What best describes the race and ethnicity of the person/people who told you about units?

If there was more than one person, tell me the race/ethnicity of the person who told you

about the most units. 

INTERVIEWER: SELECT ALL THAT APPLY

Would you say...

1. Hispanic/Latino

2. White

3. Black or African American

4. Asian

5. Other, SPECIFY: _____________________________

8. DON’T KNOW

9. REFUSED

CATI: ASK IF B50_2 = 1

B53. Thinking about the ads you used to find out about units, where were these ads? Were they

in a newspaper, on a website, billboard, or radio?
C-12

INTERVIEWER: SELECT ALL THAT APPLY. 

1. NEWSPAPER, SPECIFY: Which newspaper?

2. WEBSITE, SPECIFY: Which website?

3. BILLBOARD

4. RADIO, SPECIFY: Which station? ______________________

8. DON’T KNOW

9. REFUSED

CATI: ASK IF B50_5 = 1

B55. Which social media sites did you use to find out about units?

INTERVIEWER: SELECT ALL THAT APPLY. 

1. Facebook

2. Twitter

3. Myspace

4. Email listservs (e.g., community listservs, interest group listservs)

5. Other social media, SPECIFY: _________________

8. DON’T KNOW

9. REFUSED

B57. Sometimes the people we know help us in different ways during a housing search. Can

you tell me if you used the people you know to assist with your housing search in any of

the following ways?

YES NO DON’T 

KNOW REFUSED

b. To find rentals that did not require a credit check or other 

application requirements? 1 2 7 8

c. To learn more about specific neighborhoods? 1 2 7 8

d. To find landlords that would not discriminate against people of 

your race/ethnicity? 1 2 7 8

B58. Sometimes people gather information about the neighborhoods they are thinking about moving to. 

Did you try to find out anything about the neighborhoods you were thinking of moving into? 
C-13

1. YES

2. NO (SKIP TO B60)

8. DON’T KNOW (SKIP TO B60)

9. REFUSED (SKIP TO B60)

B59. What neighborhood features did you try to learn more about? 

YES NO DON’T 

KNOW REFUSED

a. School quality 1 2 7 8

b. Public transportation 1 2 7 8

c. Parks and other amenities such as restaurants, bars, and shopping 

1 2 7 8

d. Crime 1 2 7 8

e. How open the neighborhood would be to people of your racial or 

ethnic background 1 2 7 8

f. Are there any other neighborhood features you tried to learn 

about? Specify 1 2 7 8

CATI: ASK IF B59f=1

B59_O. What other features did you try to learn about?

1. Other, Specify: _________________________

98. DON’T KNOW

99. REFUSED

B60. People sometimes contact the landlord or property management company to gather additional 

information about a unit to decide if they want to continue considering it. 

How many units did you contact the landlord, management company, or current tenant(s) about to find 

out more information? 

_________ RANGE: [0-99]

998. DON’T KNOW (GO TO B60A)

999. REFUSED 

B60a. Which answer comes closest? Would you say...

1. 0

2. 1

3. 2

4. 3-5
C-14

5. 6-10

6. 11-15

7. 16-25

8. More than 25

998. DON’T KNOW

999. REFUSED

CATI: IF B60=0 OR B60A=1 SKIP TO B63

B60b. Did you primarily inquire about units through... 

1. Email,

2. Phone call,

3. Text 

4. In-person visits, or

5. Some other way? SPECIFY: ________________________

8. DON’T KNOW

9. REFUSED

B63. At any point in your search, did you:

YES NO DON’T 

KNOW REFUSED

a. Expand your search to look at other neighborhoods? 1 2 7 8

b. Change your mind about how important some unit features 

were? 1 2 7 8

c. Increase your price range? 1 2 7 8

B64. Now, I am going to ask you a few questions about your experiences visiting rental units and applying for 

rental units.

How many units did you actually visit in person during your search?

_________ RANGE: [0-99]

998 DON’T KNOW (GO TO B64a)

999 REFUSED (SKIP TO B65)

B64a. Which answer comes closest? Would you say...

1. 0

2. 1

3. 2

4. 3-5

5. 6-10

6. 11-15

7. 16-25

8. More than 25
C-15

98. DON’T KNOW

99. REFUSED

B65. How many units did you attempt to visit in person, but were unable to because you were told they 

weren’t available? 

_________ RANGE: [0-99]

998 DON’T KNOW (GO TO B65a)

999 REFUSED (SKIP TO B66)

B65a. Which answer comes closest? Would you say...

1. 0

2. 1

3. 2

4. 3-5

5. 6-10

6. 11-15

7. 16-25

8. More than 25

98. DON’T KNOW

99. REFUSED

B66. Did you do any of the following when you called or met with a potential landlord in order to increase 

the chance that a landlord would treat you well? Did you...

YES NO DON’T 

KNOW REFUSED

a. Dress differently than you usually do? 1 2 7 8

b. Speak differently than you usually do? 1 2 7 8

c. Bring a partner/friend/children? 1 2 7 8

d. Leave a partner/friend/children at home to increase the chance 

that the landlord would treat you well? 1 2 7 8

e. Mention your career or professional background? 1 2 7 8

f. Send someone else to check out the unit for you? 1 2 7 8

CATI: IF (B64<1 OR B64 = 999) OR (B64A <1 OR B64A = 99) GO TO B70

B68. Did you visit units in more than one neighborhood? 

1. YES

2. NO (GO TO B70)

8. DON’T KNOW (GO TO B70)

9. REFUSED (GO TO B70)
C-16

B69. What areas or neighborhoods did you visit units in? PROBE: Any others?

INTERVIEWER: ONCE SELECTION OF NEIGHBORHOODS IS COMPLETE, YOU MUST SELECT 97: ‘NO 

OTHER NEIGHBORHOODS’ TO PROCEED TO THE NEXT QUESTION

____________________List neighborhood(s) and Other, specify option

97. NO OTHER NEIGHBORHOOD

98. DON’T KNOW

99. REFUSED

B70. Were there any neighborhoods that you stopped considering after visiting? 

1. YES

2. NO (GO TO B72)

8. DON’T KNOW (GO TO B72)

9. REFUSED (GO TO B72)

B70a. Which neighborhoods did you stop considering?

CATI: LIST NEIGHBORHOODS FROM B69 FOR SELECTION

B71. Why did you stop considering those neighborhoods after visiting them? 

Was it because...

INTERVIEWER: IF R INDICATES DIFFERENT REASONS FOR DIFFERENT NEIGHBORHOODS, CHECK ALL 

REASONS MENTIONED

YES NO DON’T 

KNOW REFUSED

a. You did not feel safe? 1 2 7 8

c. It was too far from school/work? 1 2 7 8

d. It was too far from public transportation? 1 2 7 8

f. Of the racial/ethnic mix of the neighborhood? 1 2 7 8

g. Of something else? SPECIFY:_____________________ 1 2 7 8

CATI: ASK IF B71g = 1

B71_O. For what other reasons did you stop considering those neighborhoods?
C-17

1. Other, Specify: _________________________

98. DON’T KNOW

99. REFUSED

CATI: IF (B64<1 OR B64 = 999) OR (B64A <1 OR B64A = 99) GO TO B74

B72. Were there any units that you stopped considering after visiting? 

1. YES

2. NO (GO TO B74)

8. DON’T KNOW (GO TO B74)

9. REFUSED (GO TO B74)

B73. Now I will read several reasons you might have stopped considering a unit. Please tell me if any of 

these apply to you. 

First, did you stop considering any units because you ... 

YES NO DON’T 

KNOW REFUSED

a. Did not like the unit? 1 2 7 8

b. Did not like the building? 1 2 7 8

c. Did not like the landlord or management company? 1 2 7 8

d. Did not like the application process? 1 2 7 8

e. Did not like the neighbors? 1 2 7 8

f. Any other reason? Specify: _______ 1 2 7 8

CATI: ASK IF B73e = 1

B73_O. For what other reasons did you stop considering those units?

1. Other, Specify: _________________________

98. DON’T KNOW

99. REFUSED

B74. How many units did you submit applications for? 

_________ RANGE: [0-99]

998 DON’T KNOW (GO TO B74a)

999 REFUSED 

B74a. Which answer comes closest? Would you say...

1. 0

2. 1
C-18

3. 2-3

4. 4-5

5. 6-10

6. More than 10

98. DON’T KNOW

99. REFUSED

[CATI: If B74=0 OR B74a=1 skip to B77]

B75. How many times was your application denied?

_________ RANGE: [0-99]

998 DON’T KNOW (GO TO B75a)

999 REFUSED 

B75a. Which answer comes closest? Would you say...

1. 0

2. 1

3. 2-3

4. 4-5

5. 6-10

6. More than 10

98. DON’T KNOW

99. REFUSED

[CATI: If B75=0 OR B75a=1 skip to B77]

B76. Why were your application(s) denied? 

INTERVIEWER: CHECK ALL THAT APPLY

1. SOMEONE ELSE APPLIED FIRST 

2. UNIT WAS TAKEN

3. PROBLEM WITH REFERENCES 

4. PROBLEM WITH CREDIT HISTORY

5. PROBLEM WITH RENTAL HISTORY

6. PROBLEM WITH CRIMINAL HISTORY

7. INCOME TOO LOW

8. WAS NOT TOLD WHY/NEVER HEARD BACK FROM LANDLORD OR

MANAGEMENT COMPANY

9. OTHER, SPECIFY: ___________________________________

98. DON’T KNOW

99. REFUSED

B77. Now I am going to ask you a few questions about your housing search overall.

Overall, would you say this housing search was...

1. Extremely difficult
C-19

2. Very difficult

3. Somewhat difficult

4. A little difficult

5. Not at all difficult 

8. DON’T KNOW

9. REFUSED

CATI: IF B77>=5, GO TO B79

B78. Which of the following things would you say made it difficult?

Would you say.... 

YES NO DON’T 

KNOW REFUSED

a. Finding units in your price range? 1 2 7 8

b. Feeling rushed to move? 1 2 7 8

c. Gathering information about units? 1 2 7 8

d. Gathering information about neighborhoods? 1 2 7 8

e. Finding transportation to get to units? 1 2 7 8

f. [Skip if S_10B & S10C=0]: Finding someone to take care of your 

children so you could visit units 1 2 7 8

g. Meeting the application requirements? 1 2 7 8

h. Finding landlords that would rent to you? 1 2 7 8

i. Were there any other reasons why it was difficult? SPECIFY 1 2 7 8

CATI: ASK IF B78 = 1.

B78_O. What else made it difficult?

1. Other, Specify: _________________________

98. DON’T KNOW

99. REFUSED

B79. During your search, did you ever feel that you were denied housing because the rental

agent or landlord didn't want to rent to you because of your race or ethnicity?

1. YES

2. NO 

8. DON’T KNOW 

9. REFUSED 

B80. During your search, did you ever feel that a rental agent or landlord was showing you only units in 

certain neighborhoods because of your race or ethnicity? 

1. YES
C-20

2. NO 

3. DID NOT USE RENTAL AGENT (IF VOLUNTEERED) 

8. DON’T KNOW 

9. REFUSED 

B81. During your search, did you ever feel that the other residents in the buildings or

neighborhoods that you visited treated you unfairly because of your race or ethnicity?

1. YES

2. NO 

8. DON’T KNOW 

9. REFUSED 
D-1

Appendix D. Current Searcher Cognitive Test Protocol

Baseline Module 

INTRODUCTORY TEXT 

C_START Ok, thanks. We will be asking you questions about your current housing search—both your 

experiences and how you are doing your search. The survey also includes basic questions about your 

background, and other experiences. This study will take about 30 minutes and you will receive a $50 gift card 

to thank you for your time. We respect your privacy, and your name will not be linked to any of your answers. 

This study is completely voluntary, and you can refuse to answer any question or stop the study at any time. 

May I continue?

BASELINE QUESTIONS

HOUSING BASICS

The first set of questions is about your current housing situation.

C3. What is your current rent, that is, [CATI: IF S11a_1=1 SHOW: “that part of the rent that you are 

responsible for”; IF S11a_1=2 OR 3 SHOW: “the total rent for the unit”]? 

INTERVIEWER: IF R IS NOT PAYING RENT, SELECT OPTION BELOW BOX

$________ [Rent Amount] [Range: 1-9997] 

00. NOT PAYING RENT (GO TO C5)

9998. DON’T KNOW (GO TO C5)

9999. REFUSED (GO TO C5)

C4a. Is that...

1. Per week,

2. Every other week,

3. Per month,

4. Something else? Specify: ______________

8. DON’T KNOW

9. REFUSED

C5. How many years have you lived in the D.C. metropolitan area?
D-2

__________________[years] [Range: 1-100]

INTERVIEWER: ROUND UP TO NEAREST WHOLE NUMBER, IF LESS THAN ONE YEAR, RECORD 0

9997. ALL MY LIFE [IF VOLUNTEEERED]

9998. DON’T KNOW

9999. REFUSED

C6. People move for a number of different reasons. I’m going to read several

possibilities and I would like you to tell me which, if any, are an important reason

you are looking to move. 

Are you moving: 

YES NO

DON’T 

KNOW

REFUSED

a. Because your old home or building was destroyed, 

condemned or unsafe?

1 2 8 9

c. Because your landlord said you had to move? 1 2 8 9

d. Because of a new job or job transfer? 1 2 8 9

e. To form your own household? 1 2 8 9

f. To be closer to family? 1 2 8 9

g. Because of a change in household or family size, 

including marriage, divorce, separation, or child birth 

or adoption?

1 2 8 9

h. To reduce commuting time? 1 2 8 9

i. To upgrade to a larger home or a better-quality

home?

1 2 8 9

j. To find a less expensive place to live? 1 2 8 9

k. To live in a better neighborhood? 1 2 8 9

l. Because of some other reason? 1 2 8 9

C6_O. What is the other important reason you are moving?

_____________________________

8. DON’T KNOW

9. REFUSED
D-3

CATI: IF 2 OR MORE =1 (YES) CONTINUE, ELSE SKIP TO C6_n

C6_m. Which reason is the most important?

CATI: SHOW C6A-C6K WHEN =1 (YES)

__________________[Reason]

98. DON’T KNOW

99. REFUSED

C6_n. Thinking about your decision to search for a new place to live, would you say this

was something you had been planning to do for a while or did the need to search

for a new place come up unexpectedly?

1. Planning for a while

2. Came up unexpectedly

8. DON’T KNOW

9. REFUSED

C7. How long have you been searching at this point? Would you say...

1. Less than a week

2. More than a week but less than a month

3. 1 – 2 months

4. More than 2 months

8. DON’T KNOW

9. REFUSED

C8. Is there a set date when you need to finish your search, such as the end of

a lease, the birth of a new child, or another date? 

1. YES, SPECIFY DATE__________________ (GET DATE)

2. NO

8. DON’T KNOW

9. REFUSED

C9. /C9_2.We would like to know if there are any things that are making it difficult for you

to search for housing. Please tell me if any of the following is making it

difficult for you. 
D-4

YES NO DON’T 

KNOW

REFUSED

a. Not having access to the internet? 1 2 8 9

b. [Skip if S10=2 (NO)]: Not having childcare? 1 2 8 9

c. Not having transportation to get to units? 1 2 8 9

d. History of prior eviction? 1 2 8 9

e. Criminal record for you or anyone in your household? 1 2 8 9

f. Poor credit history? 1 2 8 9

g. Not enough money for a security deposit or other fees? 1 2 8 9

C10. Do you currently have a Section 8 Housing Choice Voucher, or other form of

subsidized housing? 

1. YES

2. NO

3. NO BUT AM ON WAITING LIST [ONLY IF VOLUNTEERED] 

8. DON’T KNOW

9. REFUSED

CATI: IF RACE = WHITE, SKIP TO C12

C11. In general, do you think that [RACE add ‘s’ to end of racial group name to

match ‘whites’] have as good a chance as whites in this community to get any housing they can 

afford, or do you think they don’t have as good a chance?

INTERVIEWER: IF R ALSO IDENTIFIES AS “WHITE”, SAY “I understand. This question is being asked because you 

also identified as Black or African American.”

1. HAVE AS GOOD A CHANCE

2. DON’T HAVE AS GOOD A CHANCE

8. DON’T KNOW [VOLUNTEERED]

9. REFUSED

CATI: SHOW IF R IS LATINO AND BLACK/AFRICAN AMERICAN; THEN [RACE] = BLACK/AFRICAN AMERICAN
D-5

C11a. In general, do you think that [RACE add ‘s’ to end of racial group name to

match ‘whites’] have as good a chance as whites in this community to get any housing they can 

afford, or you think they don’t have as good a chance?

INTERVIEWER: IF R ALSO IDENTIFIES AS “WHITE”, SAY “I understand. This question is being asked because you 

also identified as Latino.”

1. HAVE AS GOOD A CHANCE

2. DON’T HAVE AS GOOD A CHANCE

8. DON’T KNOW [VOLUNTEERED]

9. REFUSED

CATI: SHOW IF R IS LATINO; THEN [RACE] = LATINO

C12. In your previous housing searches, did you ever feel that you were denied housing

because the rental agent or landlord didn't want to rent to you because of your

race or ethnicity?

1. YES

2. NO 

8. DON’T KNOW

9. REFUSED

C13. In your previous housing searches, did you ever feel that a rental agent or landlord

was showing you only apartments in certain neighborhoods because of your race

or ethnicity?

1. YES

2. NO 

8. DON’T KNOW

9. REFUSED

C14. In your previous housing searches, did you ever feel that other residents in the

building or neighborhoods that you visited treated you unfairly because of your

race or ethnicity?

1. YES

2. NO 

8. DON’T KNOW

9. REFUSED

---End Contextual Questions---
D-6

Baseline Parameters

These next few questions are about the kind of home and neighborhood you think you want to live 

in.

C16. Which neighborhood(s) or areas are you considering? PROBE: Any others?

INTERVIEWER: ONCE SELECTION OF NEIGHBORHOODS IS COMPLETE, YOU MUST SELECT 97: ‘NO 

OTHER NEIGHBORHOODS’ TO PROCEED TO THE NEXT QUESTION

____________________List neighborhood(s) and Other, specify option

97. NO OTHER NEIGHBORHOOD

98. DON’T KNOW

99. REFUSED

C16a. In which neighborhood do you currently live? 

____________________List neighborhood(s) and Other, specify option

98. DON’T KNOW

99. REFUSED

C17. How important are the following neighborhood features to you in deciding where

to live? 

For each, tell me if you consider it very important, somewhat important, not 

very important, or not at all important. 

Very 

Important

Somewhat 

Important

Not Very 

Important

Not at All 

Important

DON’T 

KNOW

REFUSED

a. Convenience to 

workplace and/or school.

1 2 3 4 7 8

b. Location near friends or 

family.

1 2 3 4 7 8

c. Amenities like 

restaurants, grocery 

1 2 3 4 7 8
D-7

Very 

Important

Somewhat 

Important

Not Very 

Important

Not at All 

Important

DON’T 

KNOW

REFUSED

stores, theaters, shopping, 

and doctor’s offices in the 

neighborhood.

d. Convenience to public 

transportation. 

1 2 3 4 7 8

e. Quality of public 

services like libraries, 

playgrounds, and 

community centers.

1 2 3 4 7 8

f. Quality of the schools. 1 2 3 4 7 8

g. Safety of the 

neighborhood.

1 2 3 4 7 8

h. The mix of racial and 

ethnic groups in the 

neighborhood.

1 2 3 4 7 8

C19. What is the maximum amount you are willing to pay per month in rent? 

_________ RANGE: [$0-$9997]

9998. DON’T KNOW (GO TO C19A)

9999. REFUSED (SKIP TO C20)

C19A. Which answer comes closest? Would you say...

1. 0-$799

2. $800-$1,599

3. $1,600-$2,399

4. $2,400-$3,199

5. $3,200 or more

98. DON’T KNOW

99. REFUSED
D-8

C20. What size unit are you looking for? 

INTERVIEWER: CHECK ALL THAT APPLY

1. Studio

2. 1 bedroom

3. 2 bedrooms 

4. 3 bedrooms

5. 4 bedrooms 

6. 5 or more bedrooms 

8. DON’T KNOW

9. REFUSED

C20a. What kind of building are you looking for a unit in? Is it a...

1. Single family home

2. Small building with four or fewer

3. Medium sized building with 5-50 units

4. Large apartment building or complex with more than 50 units

5. VOLUNTEERED: OTHER (SPECIFY)____________________

8. DON’T KNOW

9. REFUSED

C21. How important are the following characteristics in deciding which place you will choose to rent? 

Very 

Important

Somewhat 

Important

Not Very 

Important

Not at All 

Important

DON’T 

KNOW

REFUSED

a. The rent you pay for it. 

Would you say that is?

1 2 3 4 7 8

c. The size of the home or 

number of rooms. 

1 2 3 4 7 8

cc. The kind of building like a 

single-family home or a 

particular size apartment 

building or complex.

1 2 3 4 7 8

e. Responsiveness of the 

landlord or management 

company to your concerns.

1 2 3 4 7 8
D-9

Very 

Important

Somewhat 

Important

Not Very 

Important

Not at All 

Important

DON’T 

KNOW

REFUSED

f. Security of the building. 1 2 3 4 7 8

g. Amenities in the building 

like laundry facilities, parking, 

and other on-site facilities.

1 2 3 4 7 8

C21_O. Are there any other unit features that you considered in deciding where to live?

INTERVIEWER: IF RESPONDENT ADDS FEATURE, TYPE FEATURE INTO TEXT BOX 

AND ASK: “Do you consider this feature very important, somewhat important, not very 

important, or not at all important?” THEN SELECT R’S CHOICE FROM DROP DOWN 

NEXT TO EACH FEATURE ADDED.

Very 

Important

Somewhat 

Important

Not Very 

Important

Not at All 

Important

DON’T 

KNOW

REFUSED

ENTER 

TEXT

1 2 3 4 7 8

ENTER 

TEXT

1 2 3 4 7 8

ENTER 

TEXT

1 2 3 4 7 8

1. OTHER FEATURES HAVE BEEN ADDED

2. NO OTHER FEATURES HAVE BEEN ADDED

98. DON’T KNOW

99. REFUSED

C22. Now I am going to ask you some questions about how you are gathering information about 

apartments/homes during your housing search. 

Please tell me if you use the people you know to assist with the housing search in

any of the following ways.
D-10

YES NO

DON’T 

KNOW

REFUSED

a. To find out about rental vacancies? 1 2 7 8

b. To find rentals that do not require a credit check or other 

application requirements? 

1 2 7 8

c. To learn more about specific neighborhoods? 1 2 7 8

d. To find landlords that would not discriminate against people of 

your race/ethnicity?

1 2 7 8

C30. Which is more important to you at this point in your search: 

1. The unit itself,

2. The neighborhood where it is located, or

3. Are both equally important?

8. DON’T KNOW

9. REFUSED

C31. People sometimes contact the landlord or property management company to gather additional 

information about a unit to decide if they want to continue considering it. 

How many units have you contacted the landlord, management company, or current tenant(s) to find 

out more information?

_________ RANGE: [0-99]

998. DON’T KNOW (GO TO C31A)

999. REFUSED (SKIP TO C33)

C31A. Which answer comes closest? Would you say...

1. 0

2. 1

3. 2

4. 3-5

5. 6-10

6. 11-15

7. 15-25

8. More than 25

98. DON’T KNOW

99. REFUSED

CATI: IF C31 = 0 OR C31A = 1 SKP C33
D-11

C32. Have you primarily inquired about units through...

1. Email,

2. Phone call,

3. Text,

4. In-person visits, or

5. Some other way? SPECIFY

8. DON’T KNOW

9. REFUSED

C33. Now I am going to ask you a few questions about your experiences visiting

apartments.

How many units have you actually visited in person during your search so far?

_________ RANGE: [0-99]

998. DON’T KNOW (GO TO C33A)

999. REFUSED (SKIP TO C34)

C33A. Which answer comes closest? Would you say...

1. 0

2. 1

3. 2

4. 3-5

5. 6-10

6. 11-15

7. 15-25

8. More than 25

98. DON’T KNOW

99. REFUSED

C34. How many units have you attempted to visit in person, but were unable to because

you were told they weren’t available? 

_________ RANGE: [0-99]

998. DON’T KNOW (GO TO C34A)

999. REFUSED (SKIP TO C35)

C34A. Which answer comes closest? Would you say...

1. 0

2. 1

3. 2

4. 3-5
D-12

5. 6-10

6. 11-15

7. 15-25

8. More than 25

98. DON’T KNOW

99. REFUSED

CATI: IF C33=0 OR 999 GO TO C46

CATI: IF C33=998 & (C33A = 0 OR 99) GO TO C46

C35. Have you visited apartments in any neighborhood other than where you currently

live? 

1. YES

2. NO (GO TO C37)

8. DON’T KNOW (GO TO C37)

9. REFUSED (GO TO C37)

C36. What areas or neighborhoods did you visit apartments in? PROBE: Any others?

INTERVIEWER: ONCE SELECTION OF NEIGHBORHOODS IS COMPLETE, YOU MUST SELECT 97: ‘NO 

OTHER NEIGHBORHOODS’ TO PROCEED TO THE NEXT QUESTION

____________________List neighborhood(s) and Other, specify option

97. NO OTHER NEIGHBORHOOD

98. DON’T KNOW

99. REFUSED

C36A. Thinking of the most recent unit you visited, what neighborhood was it in?

____________________List neighborhood(s) and Other, specify option

8. DON’T KNOW

9. REFUSED

C37. Thinking of the most recent unit you visited, how did you hear about it? Would you say you first

heard about it from someone you knew, saw or heard an advertisement for it, saw a sign in the 

window, learned about it from a professional service, learned about it through social media, through 

a community or religious organization, or some other way?
D-13

1. SOMEONE YOU KNEW 

2. ADVERTISEMENT (GO TO C40)

3. SIGN ON THE WINDOW (GO TO C45)

4. PROFESSIONAL SERVICE (GO TO C45)

5. SOCIAL MEDIA (GO TO C42)

6. COMMUNITY OR RELIGIOUS ORGANIZATION (GO TO C45)

7. OTHER, SPECIFY (GO TO C45)

8. DON’T KNOW/CAN’T REMEMBER (GO TO C45)

9. REFUSED (GO TO C45)

CATI: ASK IF C37_1 = 1

C38. What best describes your relationship to the person who told you about this unit,

was it:

INTERVIEWER: SELECT ALL THAT APPLY

1. A family member

2. A friend

3. A co-worker

4. A roommate

5. A neighbor

8. DON’T KNOW

9. REFUSED

CATI: ASK IF C37_1 = 1

C39. What best describes the race and ethnicity of the person who told you

about this unit? Would you say...

INTERVIEWER: SELECT ALL THAT APPLY

1. Hispanic, Latino

2. White

3. Black or African American

4. Asian

5. Other, SPECIFY: ____________________

8. DON’T KNOW

9. REFUSED

CATI: ASK IF C37_2 = 1
D-14

C40. Where was this ad? Was it in a newspaper, on a website, billboard, or radio?

1. NEWSPAPER, SPECIFY: Which newspaper?

2. WEBSITE, SPECIFY: Which website?

3. BILLBOARD

4. RADIO, SPECIFY: Which station? __________________________

8. DON’T KNOW

9. REFUSED

CATI: ASK IF C37_4 = 1

CATI: ASK IF C37_5 = 1

C42. Which social media site did you use to find the unit?

INTERVIEWER: SELECT ALL THAT APPLY

1. Facebook

2. Twitter

3. Myspace

4. Email listservs (e.g., community listservs, interest group listservs)

5. Other social media

8. DON’T KNOW

9. REFUSED

CATI: ASK ALL
D-15

C45. When you contacted the landlord and then visited this unit, did you do any of the following in order 

to increase the chance that the landlord would treat you well? 

YES NO

DON’T 

KNOW

REFUSED

a. Dress differently than you usually do? 1 2 7 8

b. Speak differently than you usually do? 1 2 7 8

c. Bring a partner/friend/children? 1 2 7 8

d. Leave a partner/friend/children at home to increase the chance 

that the landlord would treat you well?

1 2 7 8

e. Mention your career or professional background? 1 2 7 8

f. Send someone else to check out the unit for you? 1 2 7 8

C46. How many apartments or rental houses have you submitted applications for so

far?

_________ RANGE: [0-99]

998. DON’T KNOW (GO TO C46A)

999. REFUSED (SKIP TO C47)

C46A. Which answer comes closest? Would you say...

1. 0

2. 1

3. 2-3

4. 4-5

5. 6-10

6. More than 10

98. DON’T KNOW

99. REFUSED

CATI: ASK IF C46 > 0 OR C46A = 2, 3, 4, OR 5, ELSE GOTO D1

C47. How many times have you had an application denied so far?
D-16

_________ RANGE: [0-99] (SKIP TO C48)

997. DON’T KNOW (GO TO C47a)

998. REFUSED (SKIP TO C48)

C47A. Which answer comes closest? Would you say...

1. 0 (SKIP TO C48)

2. 1

3. 2-3

4. 4-5

5. 6-10

6. More than 10

98. DON’T KNOW

99. REFUSED

CATI: ASK IF C47 >0 OR C47A >1

C48. Why was / were your applications denied? 

INTERVIEWER: SELECT ALL THAT APPLY

1. SOMEONE ELSE APPLIED FIRST 

2. UNIT WAS TAKEN

3. PROBLEM WITH REFERENCES 

4. PROBLEM WITH CREDIT HISTORY

5. PROBLEM WITH RENTAL HISTORY

6. PROBLEM WITH CRIMINAL HISTORY 

7. INCOME TOO LOW

8. WAS NOT TOLD WHY/NEVER HEARD BACK FROM LANDLORD OR MANAGEMENT COMPANY

9. OTHER, SPECIFY: ___________________________________

88. DON’T KNOW

99. REFUSED

CATI: C SECTION WILL BE FOLLOWED BY THE DEMOGRAPHICS (D SECTION), IN DEPTH INTERVIEW (N 

SECTION), AND CONTACT INFORMATION (Z SECTION) AT THE END OF THE APPENDIX A, B, D DOCUMENT
D-17

Still Searching Module

Search Background

[IF WAVE 2: IF CURRENT SEARCHER BASELINE ANSWER TO C8 NOT EQUAL TO 1 (NO SPECIFIC SEARCH END 

DATE0] GO TO SS1

[IF WAVE 3: IF STILL SEARCHING ANSWER TO S1 EQUAL TO 1 (NO SPECIFIC SEARCH END DATE0] GO TO SS1

[IF WAVE 2: IF CURRENT SEARCHER BASELINE ANSWER TO C8 = 1, YES, (SPECIFIC END DATE)] GO TO SS2

[IF WAVE 3: IF STILL SEARCHING ANSWER TO S1 = 2, NO OR TO S2 = 2, (SPECIFIC END DATE)] GO TO SS2

SS1. These first few questions are about whether there have been any changes to your housing search 

since your last interview.

When we last talked you said that you had no set date by which you had to finish your housing 

search, is that still the case?

1. YES (GO TO SS4)

2. NO (SKIP TO SS3)

8. DON’T KNOW (SKIP TO SS3)

9. REFUSED (SKIP TO SS3)

SS2. These first few questions are about whether there have been any changes to your housing search since 

your last interview.

The last time we talked to you, you said you wanted to finish the search by [ENDSEARCHDT].

Is this still the goal? 

1. YES (GO TO SS4)

2. NO (SKIP TO SS3)

8. DON’T KNOW

9. REFUSED

SS3. By what date do you need to finish your search for housing? 

1. ____________________ CATI: GET DATE

8. DON’T KNOW

9. REFUSED

SS4. When we last spoke, you said that the maximum amount you were willing to pay per month in rent is 

[WAVE 2:C19 MAX_C /C19A MAX_CA / WAVE 3: S5 MAX_S /S5A MAX_SA].]. 
D-18

Is that still the case?

1. YES (GO TO SS6)

2. NO (SKIP TO SS5)

8. DON’T KNOW

9. REFUSED

SS5. What is the new maximum amount you are willing to pay per month in rent? 

_________ RANGE: [$0-$9,997]

9998. DON’T KNOW (GO TO SS5A)

9999. REFUSED (SKIP TO SS6)

SS5A. Which answer comes closest? Is it...

1. 0-$799

2. $800-$1,599

3. $1,600-$2,399

4. $2,400-$3,199

5. $3,200 or more

9998. DON’T KNOW (SKIP TO SS6)

9999. REFUSED (SKIP TO SS6)

CATI: IF WAVE 2: SS5 > C19, MAX_C=increased

CATI: IF WAVE 2: SS5 < C19, MAX_C=decreased

CATI: IF WAVE 2: SS5A > C19A, MAX_CA=increased

CATI: IF WAVE 2: SS5A < C19A, MAX_CA=decreased

CATI: IF WAVE 3: SS5 > SS5, MAX_S=increased

CATI: IF WAVE 3: SS5 < SS5, MAX_S=decreased

CATI: IF WAVE 3: SS5A > SS5A, MAX_SA=increased

CATI: IF WAVE 3: SS5A < SS5A, MAX_SA=decreased

SS5B. In comparing your response to last time, it looks like you [MAX_C/MAX_S] the 

amount of rent you were willing to pay. Can you tell me why you [MAX_C/MAX_S] 

it? 

_______________________

98 DON’T KNOW

99 REFUSED

CATI: IF C16 = 98 OR 99, SKIP TO SS8

SS6. Now I am going to ask you a few questions about the kind of neighborhood and home you would like 

to live in. 
D-19

I am going to read to you the neighborhood[s] or area[s] that you said you were interested in at your 

last interview; please tell me if you are still searching in these neighborhoods or areas. 

[CATI: SHOW LIST FROM PRIOR WAVE 2 C16 OR WAVE 3 SS6 AND ASK

YES/NO FOR EACH NEIGHBORHOOD] NOTE: THE INDICATOR “INTERVIEWER: MAKE 

NO SELECTION FOR THIS ROW” WILL APPEAR IN ROWS WHERE NO SELECTION WAS 

MADE AT C16.

[CATI: SHOW UP TO THREE NEIGHBORHOODS R IS NO LONGER CONSIDERING AND ASK SS7 FOR 

EACH ONE. IF R IS NO LONGER CONSIDERING MORE THAN THREE NEIGHBORHOODS, RANDOMLY 

SELECT THREE.]]

SS7. Why are you no longer searching in [CATI: SHOW CHOICE FROM SS6]?

[INTERVIEWER: DO NOT READ; SELECT ALL THAT APPLY]

1. AVAILABLE UNITS WERE NOT IN MY PRICE RANGE

2. SCHOOL QUALITY

3. CRIME/SAFETY

4. NEIGHBORS

5. DID NOT THINK IT WOULD BE WELCOMING TO PEOPLE OF MY

RACE/ETHNICITY

6. TOO FAR FROM FAMILY OR FRIENDS

7. TOO FAR FROM WORK OR SCHOOL

8. LACKED AMENITIES (E.G., PARKS, RESTAURANTS, SHOPPING)

9. OTHER, SPECIFY: _______________________

98. DON’T KNOW

99. REFUSED

SS8. Are there any additional neighborhoods or areas that you have started considering since our last 

interview? 

1. YES

2. NO (GO TO SS10)

8. DON’T KNOW (GO TO SS10)

9. REFUSED (GO TO SS10)

SS9. What neighborhood or neighborhoods are you now considering? PROBE: Any others?

____________________List neighborhood(s)

1. Other, Specify: _________________________

97. NO OTHER NEIGHBORHOODS
D-20

98. DON’T KNOW

99. REFUSED

SS10. How important are the following neighborhood features to you in deciding where to live? 

Very 

Important

Somewhat 

Important

Not Very 

Important

Not at All 

Important

DON’T 

KNOW

REFUSED

a. Convenience to 

workplace and/or school. 

Would you say that it is: 

1 2 3 4 7 8

b. Location near friends or 

family.

1 2 3 4 7 8

c. Amenities like 

restaurants, grocery 

stores, theaters, shopping, 

and doctor’s offices in the 

neighborhood.

1 2 3 4 7 8

d. Convenience to public 

transportation. 

1 2 3 4 7 8

e. Quality of public 

services like libraries, 

playgrounds, and 

community centers.

1 2 3 4 7 8

f. Quality of the schools. 1 2 3 4 7 8

g. Safety of the 

neighborhood.

1 2 3 4 7 8

h. The mix of racial and 

ethnic groups in the 

neighborhood.

1 2 3 4 7 8
D-21

CATI: IF WAVE 2: ASK ABOUT R/E COMPOSITION, IF 1 DEGREE OR MORE CHANGE IN C17h; IF WAVE 3: ASK 

ABOUT R/E COMPOSITION, IF 1 DEGREE OR MORE CHANGE IN S10h

SS10_i. Sometimes people change their minds about what they are looking for in

their housing. In comparing your responses to your last interview, it looks

like [CATI: INSERT NEIGHBORHOOD FACTOR] has become

[more/less] important to you. Can you tell me why you changed your mind

about how important it is? 

_______________________

98. DON’T KNOW

99. REFUSED

SS11. How important are the following characteristics in deciding which place you will choose to rent?

Very 

Important

Somewhat 

Important

Not Very 

Important

Not at All 

Important

DON’T 

KNOW

REFUSED

a. The rent you pay for it. 

Would you say that is:

1 2 3 4 7 8

c. The size of the home or 

number of rooms. 

1 2 3 4 7 8

d. The kind of building like a 

single-family home or a 

particular size apartment 

building or complex.

1 2 3 4 7 8

e. Responsiveness of the 

landlord or management 

company to your concerns.

1 2 3 4 7 8

f. Security of the building. 1 2 3 4 7 8

g. Amenities in the building 

like laundry facilities, parking, 

and other on-site facilities.

1 2 3 4 7 8
D-22

SS11_O. Are there any other characteristics that you consider in deciding where to live?

INTERVIEWER: IF RESPONDENT ADDS FEATURE, TYPE FEATURE INTO TEXT BOX 

AND ASK: “Do you consider this feature very important, somewhat important, not very 

important, or not at all important?” THEN SELECT R’S CHOICE FROM DROP DOWN 

NEXT TO EACH FEATURE ADDED.

Very 

Important

Somewhat 

Important

Not Very 

Important

Not at All 

Important

DON’T 

KNOW

REFUSED

ENTER 

TEXT

1 2 3 4 7 8

ENTER 

TEXT

1 2 3 4 7 8

ENTER 

TEXT

1 2 3 4 7 8

1. OTHER FEATURES HAVE BEEN ADDED

2. NO OTHER FEATURES HAVE BEEN ADDED

98. DON’T KNOW

99. REFUSED

SS12. Which is more important to you at this point in your search: 

1. The unit itself,

2. The neighborhood where it is located, or

3. Are both equally important?

8. DON’T KNOW

9. REFUSED

[CATI: COMPARE TO ANSWER IN BASELINE]

SS12A/B. Sometimes people change their minds about what they are looking for in

their housing. In comparing your responses to your last interview, it looks like

[CATI: INSERT CHANGE IN PREFERENCE FOR UNIT OR 

NEIGHBORHOOD]. Can you tell me why you changed your mind about how 

important it is?

_________________________________

8. DON’T KNOW

9. REFUSED 
D-23

SS13. People sometimes contact the landlord or property management company to

gather additional information about a unit to decide if they want to continue

considering it. 

Since we last talked to you, how many housing units have you contacted the landlord, management

company, or current tenant(s) about to find out more information? 

_________ RANGE: [0-99]

998. DON’T KNOW (GO TO SS13A)

999. REFUSED

SS13A. Which answer comes closest? Is it...

1. 0

2. 1

3. 2

4. 3-5

5. 6-10

6. 11-15

7. More than 15

98. DON’T KNOW

99. REFUSED

SS14. We would like to know what sources of information you have been using to find out about possible 

units. 

YES NO

DON’T 

KNOW

REFUSED

a. First, do you hear about units from someone you know? 1 2 8 9

b. [Have you been] ... seeing or hearing advertisements for 

them? 

1 2 8 9

c. ...seeing signs in the windows of units or buildings? 1 2 8 9

d. ... learning about them from a professional service? 1 2 8 9

e. ... learning about them on social media? 1 2 8 9

f. ... learning about them from community or religious 

organizations? 

1 2 8 9

g. Or have you been hearing about them from some other 

sources? (IF YES: SPECIFY)

1 2 8 9
D-24

SS14_O. What other sources have you been using?

98. DON’T KNOW

99. REFUSED

CATI: ASK IF SS14_a = 1

SS15. Thinking about the person who has told you about units, what best describes your relationship to this 

person? If there is more than one person, tell me about your relationship to the person who has 

told you about the most units.

Are they:

1. A family member

2. A friend

3. A co-worker

4. A roommate

5. A neighbor

8. DON’T KNOW

9. REFUSED

CATI: ASK IF SS14_a = 1

SS16. What best describes the race and ethnicity of the person who has told you about units? If

there is more than one person, tell me the race and ethnicity of the person who has told

you about the most units. Would you say...

1. Hispanic, Latino

2. White

3. Black or African American

4. Asian

5. Other, SPECIFY: ____________________

8. DON’T KNOW

9. REFUSED

CATI: ASK IF SS14_b = 1

SS17. Thinking about the ads you used to find out about units, where were these ads? Were they

in a newspaper, on a website, billboard, or radio?

1. NEWSPAPER, SPECIFY: Which newspaper? _________________________

2. WEBSITE, SPECIFY: Which website? _________________________
D-25

3. BILLBOARD

4. RADIO, SPECIFY: Which station? __________________________

8. DON’T KNOW

9. REFUSED

CATI: ASK IF SS14_e = 1

SS19. Which social media sites have you used?

INTERVIEWER: SELECT ALL THAT APPLY

1. Facebook

2. Twitter

3. Myspace

4. Email listservs (e.g., community listservs, interest group listservs)

5. Other social media, SPECIFY: _________________

8. DON’T KNOW

9. REFUSED

SS22. Sometimes the people we know help us in different ways during a housing search. Can

you tell me if you used the people you know to assist with your housing search in any of

the following ways?

YES NO

DON’T 

KNOW

REFUSED

a. To find out about rental vacancies? 1 2 7 8

b. To find rentals that did not require a credit check or other 

application requirements? 

1 2 7 8

c. To learn more about specific neighborhoods? 1 2 7 8

d. To find landlords that would not discriminate against people of 

your race/ethnicity?

1 2 7 8

CATI: IF SS13 = 0 OR 999 OR SS13A = 1 OR 99 SKP SS24
D-26

SS23. In general, since our last interview, have you inquired about units through...

INTERVIEWER: SELECT ALL THAT APPLY

1. Email,

2. Phone call,

3. Text

4. In-person visits, or

5. Some other way? SPECIFY: ______________________

8. DON’T KNOW

9. REFUSED

SS24. In general, how difficult has it been to get the information you needed through your

inquiries? Has it been...

1. Extremely difficult

2. Very difficult

3. Somewhat difficult

4. A little difficult

5. Not at all difficult

8. DON’T KNOW

9. REFUSED

SS25. Now I am going to ask you some questions about your experiences visiting apartments or homes. 

Since our last interview, how many units have you actually visited in-person?

_________ RANGE: [0-99]

998. DON’T KNOW (GO TO SS25A)

999 REFUSED (SKIP TO SS26)

SS25A. Which answer comes closest? Would you say...

1. 0

2. 1

3. 2

4. 3-5

5. 6-10

6. 11-15

7. More than 15

98. DON’T KNOW

99. REFUSED

CATI: IF SS25=0 OR 999, OR SS25A=1 OR 99, SKIP TO SS26
D-27

SS35. Thinking about the visits to units you have made since the last interview, please tell me

if you did any of the following when you called or met with potential landlords to

increase the chance that the landlord would treat you well? 

YES NO

DON’T 

KNOW

REFUSED

a. Dress differently than you usually do? 1 2 7 8

b. Speak differently than you usually do? 1 2 7 8

c. Bring a partner/friend/children? 1 2 7 8

d. Leave a partner/friend/children at home to increase the chance 

that the landlord would treat you well.

e. Mention your career or professional background? 1 2 7 8

f. Send someone else to check out the unit for you? 1 2 7 8

SS26. How many units have you attempted to visit in person, since the last interview, that is since [DATE OF 

LAST INTERVIEW (MM_DD)], but were unable to because you were told they were unavailable?

_________ RANGE: [0-99]

998. DON’T KNOW (GO TO SS26A)

999 REFUSED (SKIP TO SS26)

SS26A. Which answer comes closest? Would you say...

1. 0

2. 1

3. 2

4. 3-5

5. 6-10

6. 11-15

7. More than 15

98. DON’T KNOW

99. REFUSED

CATI: IF SS25=0 [IF WAVE 2 INTERVIEW PROCEED TO EXIT_W2] [IF WAVE 3 INTERVIEW PROCEED TO SS41]
D-28

CATI: If SS25 >3 OR SS25A>3, SHOW: Now I am going to ask you about the 3 units you most recently visited. 

Let’s start with the one you visited most recently. [THEN SUBSEQUENTLY ASK ABOUT THE SECOND MOST 

RECENTLY VISITED; THEN THE THIRD MOST RECENTLY VISITED]

CATI: IF SS25 < 4 OR SS25A < 4, SHOW: Now I am going to ask you about each of the units you visited starting 

with the most recent one.

SS28. What was the address of this unit? PROBE: Do you remember the cross streets or general area?

INTERVIEWER: ATTEMPT TO CAPTURE STREET ADDRESS, CITY, AND STATE.

Street: ________________________ 

City: __________________________

State: _________________________

INTERVIEWER: IF RESPONDENT DOES NOT KNOW ADDRESS, PROBE FOR CROSS STREETS OR GENERAL AREA

Cross streets or general area: __________________________

98. DON’T KNOW (GO TO SS137)

99. REFUSED (GO TO SS137)

CATI: IF SS28= 98, 99 ASK SS29, ELSE SKIP TO SS137

SS29. What neighborhood or community was it in?

____________________List neighborhood(s)

97. Other, Specify: _________________________

98. DON’T KNOW

99. REFUSED

SS137. How did you first hear about the unit that you visited? Would you say you first heard about it from 

someone you knew, that you saw or heard an advertisement for it, saw a sign in the window, learned 

about it from a professional service, learned about it through social media, through a community or 

religious organization, or some other way?

1. SOMEONE YOU KNEW (GO TO SS138)

2. ADVERTISEMENT (GO TO SS140)

3. SIGN ON THE WINDOW (GO TO SS32)

4. PROFESSIONAL SERVICE (GO TO SS32)

5. SOCIAL MEDIA (GO TO SS142)

6. COMMUNITY OR RELIGIOUS ORGANIZATION (GO TO SS32)

7. OTHER, SPECIFY (GO TO SS32)
D-29

8. DON’T KNOW/CAN’T REMEMBER (GO TO SS32)

9. REFUSED (GO TO SS32)

CATI: ASK IF SS137_1 = 1

SS138. What best describes your relationship to the person who told you about this unit,

was it:

INTERVIEWER: SELECT ALL THAT APPLY

1. A family member

2. A friend

3. A co-worker

4. A roommate

5. A neighbor

8. DON’T KNOW

9. REFUSED

CATI: ASK IF SS137_1 = 1

SS139. What best describes the race and ethnicity of the person who told you

about this unit? Would you say...

INTERVIEWER: SELECT ALL THAT APPLY

1. Hispanic, Latino

2. White

3. Black or African American

4. Asian

5. Other, SPECIFY: ____________________

8. DON’T KNOW

9. REFUSED

CATI: ASK IF SS137_2 = 1

SS140. Where was this ad? Was it in a newspaper, on a website, billboard, or radio?

1. NEWSPAPER, SPECIFY: Which newspaper?

2. WEBSITE, SPECIFY: Which website?

3. BILLBOARD

4. RADIO, SPECIFY: Which station? __________________________
D-30

8. DON’T KNOW

9. REFUSED

CATI: ASK IF SS137_5 = 1

SS142. Which social media site did you use to find the unit?

INTERVIEWER: SELECT ALL THAT APPLY

1. Facebook

2. Twitter

3. Myspace

4. Email listservs (e.g., community listservs, interest group listservs)

5. Other social media, SPECIFY: ________________________

8. DON’T KNOW

9. REFUSED

SS32. Which of the following best describes the type of visit you had?

1. You went to an open house,

2. You went to a private viewing with a rental agent,

3. You had a private viewing with landlord, or

4. Something else? Specify: _______________________________

8. DON’T KNOW

9. REFUSED

SS33. How satisfied were you with the way you were treated during this visit?

Were you:

1. Very satisfied

2. Somewhat satisfied

3. Only a little satisfied

4. Not at all satisfied

8. DON’T KNOW

9. REFUSED

SS34. Did you feel you were treated unfairly during your visit because of your race or ethnicity?

1. YES

2. NO
D-31

8. DON’T KNOW

9. REFUSED

SS36. Did you decide to submit an application for this unit?

1. YES (GO TO SS37)

2. NO (GO TO SS36A)

8. DON’T KNOW (GO TO END OF LOOP)

9. REFUSED (GO TO END OF LOOP)

SS36A. Why did you decide not to apply for this unit? 

YES NO

DON’T 

KNOW

REFUSED

a. Price was too high? 1 2 7 8

b. Did not like the unit? 1 2 7 8

c. Did not like the building? 1 2 7 8

d. Did not like the landlord or management company? 1 2 7 8

e. Did not like the neighbors? 1 2 7 8

f. Were there any other reasons why you decided not to apply for 

this unit?

1 2 7 8

CATI: IF SS36AF=1

SS36A_O. What were the other reasons you decided not to apply for this unit?

______________________________

98. DON’T KNOW

99. REFUSED 

CATI: ALL GO TO END OF LOOP

SS37. Was your application accepted?

1. YES (GO TO SS39)

2. NO (GO TO SS38)

8. DON’T KNOW/DON’T KNOW YET (SKIP TO SS39)

9. REFUSED (SKIP TO SS39)
D-32

SS38. Why was your application denied? 

INTERVIEWER: CHECK ALL THAT APPLY

1. SOMEONE ELSE APPLIED FIRST 

2. UNIT WAS TAKEN

3. PROBLEM WITH REFERENCES 

4. PROBLEM WITH CREDIT HISTORY

5. PROBLEM WITH RENTAL HISTORY

6. PROBLEM WITH CRIMINAL HISTORY

5. INCOME TOO LOW

6. WAS NOT TOLD WHY/NEVER HEARD BACK FROM LANDLORD OR

MANAGEMENT COMPANY

7. OTHER, SPECIFY: ___________________________________

8. DON’T KNOW

9. REFUSED

CATI: IF SS37=2 SKIP TO END OF LOOP

SS39. Are you hoping to move into this unit?

1. YES (GO TO END OF LOOP)

2. NO (GO TO SS40)

8. DON’T KNOW (GO TO END OF LOOP)

9. REFUSED (GO TO END OF LOOP)

CATI: ASK IF SS39=2

SS40. Why not? 

_______________________

98. DON’T KNOW

99. REFUSED

[CATI: REPEAT QUESTIONS 28-40 FOR ALL VISITS MADE BY RESPONDENT OR, IF RESPONDENT VISITED MORE 

THAN 3 UNITS, THE 3 MOST RECENT VISITS]

[CATI: IF WAVE 2 INTERVIEW—PROCEED TO EXIT_W2]

[CATI: IF WAVE 3 INTERVIEW PROCEED TO SS41]

EXIT SCRIPT for Wave 2

EXIT_W2 Thank you for completing the survey! Your answers will help researchers better understand how 

and why people search for housing. 
D-33

[CATI: INSERT INSTRUCTIONS ON RECEIPT OF INCENTIVE PAYMENT]

Is it OK to contact you again on or about [2 weekends from interview date] for another paid interview?

[INTERVIEWER: NOTE IF CLIENT DOES NOT CONSENT TO NEW INTERVIEW]

1. OK TO CONTACT

2. NOT OK TO CONTACT

[INTERVIEWER: IF CLIENT AGREES TO BE CONTACTED, SAY: “Thanks! And don’t forget to keep logging your 

housing search in the log we sent you.”

SUMMARY QUESTIONS [ASKED AT WAVE 3 ONLY]

This is our last interview, so even though your search is still going on, I am going to ask you a few questions 

about how the search has gone so far and how you think it will end. 

SS41. What statement best describes why you think you are still searching for a unit?

1. You haven’t found the perfect unit yet,

2. You haven’t found a unit that meets your basic needs,

3. You don’t need to move yet,

4. You enjoy searching?

8. DON’T KNOW

9. REFUSED

SS42. Would you say this housing search has been...

1. Extremely difficult

2. Very difficult

3. Somewhat difficult

4. A little difficult

5. Not at all difficult (SKIP TO SS44)

8. DON’T KNOW

9. REFUSED

[CATI: IF SS42 = 5 (Not at all difficult) SKIP TO SS44]

SS43. Which of the following things would you say made it difficult? 
D-34

YES NO

DON’T 

KNOW

REFUSED

a. Finding units in your price range? 1 2 7 8

b. Feeling rushed to move? 1 2 7 8

c. Gathering information about units? 1 2 7 8

d. Gathering information about neighborhoods? 1 2 7 8

e. Finding transportation to get to units? 1 2 7 8

f. [Skip if S10 = 2 (No Children)]: Finding someone to take care of 

your children so you could visit units 

1 2 7 8

g. Meeting the application requirements? 1 2 7 8

h. Finding landlords that would rent to you?

1 2 7 8

i. Were there any other reasons why it was difficult? SPECIFY: 

_________________________ 1 2 7 8

CATI: ASK IF SS43k=1: 

SS43_O. What else made it difficult?

_________________________

98. DON’T KNOW

99. REFUSED

[CATI: IF SS1=1 OR SS3=8,9 (SEARCH HAS NO DEFINITE END DATE) SKIP TO SS45]

SS44. How confident are you that you will be able to find a new home or apartment before you have to 

move out of your current residence? Would you say...

1. Extremely confident

2. Very confident

3. Somewhat confident

4. Not at all confident

8. DON’T KNOW

9. REFUSED
D-35

SS45. During your search, did you ever feel that you were denied housing because the rental

agent or landlord didn’t want to rent to you because of your race or ethnicity?

1. YES

2. NO

8. DON’T KNOW

9. REFUSED

SS46. During your search, did you ever feel that a rental agent or landlord was showing you

only units in certain neighborhoods because of your race or ethnicity?

1. YES

2. NO

8. DON’T KNOW

9. REFUSED

SS47. During your search, did you ever feel that the other residents in the building or

neighborhoods that you visited treated you unfairly because of your race or ethnicity?

1. YES

2. NO

8. DON’T KNOW

9. REFUSED

(WAVE 2) EXIT_SUMM

Thank you for completing the interview survey! Your answers will help researchers

better understand how and why people search for housing. 

GOTO Z1

(WAVE 3) EXIT_SUMM

Thank you for completing the survey! Your answers will help researchers better understand how and why 

people search for housing. 

We are still looking for more people who have recently moved or who are currently looking for housing and 

we are hoping that the people we’ve talked to already can help spread the word. In addition to your $50$50 

for this interview, we will mail you fliers about the Housing Search Study. We would appreciate it if you could 
D-36

give these fliers and the information about this study to anyone you know—this might be neighbors, friends, 

acquaintances—who might also be searching for housing or have recently moved. 

Would you be willing to share anyone’s contact information with us so that we can reach out to them? We will 

share your name as the referring person if they ask how we received their contact information. 

1. YES

2. NO GOTO Z1

SNOW1.

Thank you very much. In order to make this as easy as possible, could you please tell me the best phone 

number to reach them? 

CATI: PROVIDE TEXT BOX FOR PHONE

Is that a cell phone, home phone, or other?

1. Cell phone

2. Home phone

3. Other

8. DON’T KNOW

Do you have an email address of theirs that you could share with me?

CATI: PROVIDE TEXT BOX FOR email

Thank you, you should expect to get a package from us within a few days. 

Z1. Those are all the questions I have to ask you today. Thank you for the time you’ve spent talking with me 

and for your participation in this study. In order to send your $50$50 gift card, I need to confirm your name 

and address. You gave your contact information as:

CATI: SHOW NAME FROM FLNAME AND ADDRESS FROM S5] [FULLADD, RCITY, RSTATE, RZIP] 

INTERVIEWER: READ R’S NAME AND ONLY STREET ADDRESS TO CONFIRM AND ASK:

Is that correct?

1. YES (SKIP TO THANK)

2. NO

8. DON’T KNOW

9. REFUSED

CATI: IF Z1=8 OR 9 DISPLAY “In order to send your $50$50 gift card, I am required to ask for your full name 

and address.”
D-37

Z1a. OK, could you give me the name and address you would like me to use to send

your gift card?

CATI: SHOW NAME FROM FLNAME AND ADDRESS FROM S5] [FULLADD, RCITY, RSTATE, RZIP] 

INTERVIEWER: ENTER CORRECTED NAME AND/OR ADDRESS. READ BACK THE ADDRESS INFORMATION TO R, 

SPELLING THE WORDS.

First Name: ________________________

Last Name: ________________________

Street: ________________________

City: __________________________

State: _________________________

Zip: __________________________

CATI: PROVIDE TEXT BOX FOR ENTRY OF CORRECTED FULL NAME AND ADDRESS WHILE ORIGINAL NAME 

AND ADDRESS IS DISPLAYED FOR COMPARISON PURPOSES

CATI: CREATE CDMO USING DATA ENTERED AT Z2 TO REPLACE:

FULLADD – FULL ADDRESS

RCITY – CITY

RSTATE – STATE

RZIP – ZIP

THANK. Thank you very much for your time and assistance.
D-38

Done Searching Module

UI: Instructions to DIR screener: Prior to the interview record the following information:

RESPONDENT NAME: 

DATE OF WAVE 1 INTERVIEW:

DATE OF WAVE 2 INTERVIEW: 

DATE OF WAVE 3 INTERVIEW: 

INTRO3

Hello, this is Holly/Henry Smith and I am calling from Decision Information Resources for the Housing Search 

Survey. I am trying to reach [FLNAME] for a short-paid interview. Is h/she available?

INTERVIEWER: IF NECESSARY, READ: “[FLNAME] has agreed to help with a study of renters who have recently 

moved or who are planning to move to a new home within the Washington, DC, Virginia, Maryland 

metropolitan area.

1 RESPONDENT AVAILABLE – CONTINUE (GO TO INTRO3a)

2 RESPONDENT NOT AVAILABLE – ARRANGE CALLBACK AND

ENTER CALL NOTE (GO TO RCONU)

9 REFUSED – ENTER DISPOSITION CODE AND CALL NOTE

DESCRIBING SITUATION IN THE CALL RECORD TEXT BOX (GO 

TO END INTERVIEW)

INTRO3a

Hello, we interviewed you via phone on [date of baseline interview] about your housing search process. At 

that time, we said we’d contact you 14 days later to follow up on your housing search. This set of questions 

will take about 20 minutes. Are you still willing to participate?

1. YES (GO TO DS_a)

2. NO (GO TO END INTERVIEW)

3. NOT NOW [INTERVIEWER: ARRANGE CALLBACK AND ENTER

CALL NOTE (GO TO RCONU)
D-39

RCONU: 

IF R IS CONFIRMED BUT UNAVAILABLE READ:

Could you tell me a good time to call back to reach him/her? 

_______________________________ [CALL BACK DATE/TIME]

Thank you, I will call back then. 

IF SPEAKING WITH R READ:

What is the best time for us to call you back to complete the survey? 

_______________________________ [CALL BACK DATE/TIME]

IF R NO LONGER LIVES THERE READ:

Is there a number where he/she can be reached? May I have it please?

________________________________ [NEW CONTACT NUMBER FOR R]

INTERVIEWER: MAKE NOTE OF NEW NUMBER AND MAKE PERMANENT MESSAGE. THEN TRY NEW NUMBER. 

ENTER 1 TO EXIT AND SET APPOINTMENT. 

IN WAVE 3 ONLY

INTERVIEWER: IF R IS STILL SEARCHING OR DOES NOT KNOW READ:

If you don’t mind, we would like to continue this interview once your search has ended. May we call you back 

in two weeks to check in?

Thanks you, I will call back then.

END INTERVIEW

Thank you for your time. We are sorry that you do not want to complete the survey. If you change your mind

or have any questions, please contact DIR at 1-866-986-1968.

Screener – Follow-up Waves
D-40

DS_b. Are you still searching for housing?

1. YES (GO TO DS_ba)

2. NO (GO TO DS_a)

8. DON’T KNOW (GO TO RCONU)

9. REFUSED (GO TO RCONU)

FOR WAVE 3 ONLY

DS_ba It's really important to our research that we talk to you when you have finished 

your search so we can learn the most about this process. Since you aren't done yet, 

we would like to wait for our final interview until your search is over. 

What is your best guess as to when your search will be done?

1. R GIVES DATE

2. R DOES NOT GIVE DATE

8. DON’T KNOW 

9. REFUSED 

FOR WAVE 3 ONLY

DS_bb [CATI: IF DS_ba=1 SAY “Thank you so much, we will call you back then to check in. 

INTERVIEWER: SELECT NEXT, CODE AS 11 AND SCHEDULE AN APPOINTMENT FOR 

DATE INDICATED BY R”]

[CATI: IF DS_ ba=2 SAY “OK, we would like to call you back in three weeks to check 

in and conduct the final interview then. 

INTERVIEWER: SELECT NEXT, CODE AS 11 AND SCHEDULE AN APPOINTMENT FOR 

THREE WEEKS FROM TODAY’S DATE”] 

INTERVIEWER: IF R IS RELUCTANT TO SCHEDULE A CALL BACK OR WANTS TO 

COMPLETE NOW, SAY: “I understand. However, the final interview is meant to 

gather information about the end of your search, so we will have to wait until your 

search is over to conduct that interview. If you have any questions you may call the 

project director, Lenin Williams at 1-888-864-1425 x132.”

IF R REFUSES TO SCHEDULE A CALL BACK, SELECT NEXT AND CODE AS 35 HARD 

REFUSAL
D-41

CATI: DS_bb IS A CLOSING QUESTION END INTERVIEW 

DS_a. Do you have your housing search log handy?

1. YES

2. NO

8. DON’T KNOW

9. REFUSED

INTERVIEWER: IF R ANSWERS “YES” SAY “It would be helpful to have it in front of you, would you like to get 

it?” GIVE THEM TIME TO RETRIEVE THEIR LOG BEFORE PROCEEDING; IF NOT CONTINUE AS NORMAL

STILL SEARCHING

DS_c. Thank you. I am going to ask you a series of questions about your ongoing

housing search 

[CATI: GO TO CURRENT SEARCHERS: STILL SEARCHING MODULE]

DONE SEARCHING

DS_d. Thank you. I am going to ask you a series of questions about your housing search

and your current housing situation.

Are you done searching because you found a new place to live or because you

decided to stop searching and stay in your old place?

1. FOUND A NEW PLACE 

2. STAYING IN OLD PLACE 

Done Searching – Introductory Text

DS_START 

We would like to ask you some questions about how your housing search ended and your housing search 

activities since your last interview. This will only take about 20 minutes and you will receive a gift of $30 to 

thank you for your time. We respect your privacy, and your name will not be linked to any of your answers. 

The study is completely voluntary, and you can refuse to answer any question or stop the study at any time. 

May I continue?
D-42

Housing Basics

DS1. When did you stop your housing search?

_____________________________ CATI: GET DATE

8. DON’T KNOW

9. REFUSED

CATI: IF DS_d=2 (OLD PLACE) SKIP TO DS27

DS2. Have you actually moved into your new home yet or are you in the process of moving?

1. Actually moved

2. Still in the process

8. DON’T KNOW

9. REFUSED

[Note to DIR: this is just to determine the phrasing of the questions about the respondent’s new home, the 

rest of the questions assume that the respondent has actually moved but there needs to be a fill logic included 

so that we can fill it appropriately]

DS3. What is [CATI: IF DS2>=2 SHOW: “the address of your new home”; OTHERWISE SHOW: “your 

current street address”]?

INTERVIEWER: ATTEMPT TO CAPTURE STREET ADDRESS, CITY, STATE AND ZIP CODE.

Street: ________________________ 

City: __________________________

State: _________________________

Zip: __________________________

INTERVIEWER: IF RESPONDENT DOES NOT KNOW ADDRESS, PROBE FOR CROSS STREETS OR 

GENERAL AREA

Cross streets 

or general area: __________________________

98. DON’T KNOW

99. REFUSED

 
D-43

DS3a. Would you say this is the same neighborhood you previously lived in, or is it a different 

neighborhood? 

1. SAME NEIGHBORHOOD

2. DIFFERENT NEIGHBORHOOD

98. DON’T KNOW

99. REFUSED

DS6. What is [CATI: IF DS2>=2 SHOW: “the rent at your new home”; OTHERWISE SHOW: “your current 

rent”]? 

INTERVIEWER: IF R IS NOT PAYING RENT, SELECT OPTION BOX BELOW.

________ $$ [Range: 1-2000]

NOT PAYING RENT (GO TO DS8)

9998. DON’T KNOW (GO TO DS8)

9999. REFUSED (GO TO DS8)

DS7. Is that...

1. Per week,

2. Every other week

3. Per month, or

4. Something else? Specify: _________________

8. DON’T KNOW

9. REFUSED

DS8. Now I would like to ask you questions about the neighborhood your new unit is in.

Overall, how would you rate the quality of the neighborhood on a scale of 1 to 10, with 10 being the 

best and 1 being the worst? 

________ Rating [Range: 1-10]

98. DON’T KNOW

99. REFUSED

CATI: IF DS3a=1 SKIP TO DS10
D-44

DS9. Is this new neighborhood better, worse, or about the same as your previous

neighborhood? 

1. BETTER

2. WORSE

3. ABOUT THE SAME

4. SAME NEIGHBORHOOD

8. DON’T KNOW

9. REFUSED

DS10. How important were each of the following neighborhood features to you in deciding where to live? 

For each factor, tell me if you considered it very important, somewhat important, not very important, 

or not at all important. 

Very 

Important

Somewhat 

Important

Not Very 

Important

Not at All 

Important

DON’T 

KNOW

REFUSED

a. Convenience to workplace 

and/or school.

1 2 3 4 7 8

b. Location near friends or family. 1 2 3 4 7 8

c. Amenities like restaurants, 

grocery stores, theaters, shopping, 

and doctor’s offices in the 

neighborhood.

1 2 3 4 7 8

d. Convenience to public 

transportation. 

1 2 3 4 7 8

e. Quality of public services like 

libraries, playgrounds, and 

community centers.

1 2 3 4 7 8

f. Quality of the schools. 1 2 3 4 7 8

g. Safety of the neighborhood. 1 2 3 4 7 8

h. The mix of racial and ethnic 

groups in the neighborhood.

1 2 3 4 7 8
D-45

CATI: IF WAVE 2 ASK ABOUT R/E COMPOSITION [C17h], IF 1 DEGREE OR MORE CHANGE; IF WAVE 3 ASK 

ABOUT R/E COMPOSITION [S10h], IF 1 DEGREE OR MORE CHANGE

DS11. Sometimes people change their minds about what they are looking for in

their housing. In comparing your responses to your last interview, it looks

like [CATI: INSERT NEIGHBORHOOD FACTOR] became

[more/less] important to you. Can you tell me why you changed your mind

about how important it was? 

_______________________

98. DON’T KNOW

99. REFUSED

CATI: ASK FOR EACH: DS10A | DS10B | DS10C | DS10D |DS10E | DS10F | DS10G | DS10H when = 1 or 2

DS12. [CATI: FOR EACH FACTOR RESPONDENT SELECTED AS “very” or

“somewhat important”] How satisfied are you with the following features in the 

neighborhood your new unit is in?

INTERVIEWER: READ FIRST RESPONSE OPTION, THEN “Would you say you were...” THEN READ RESPONSE 

OPTIONS.

 

Very 

satisfied

Somewhat 

satisfied

Only a 

little 

Satisfied

Not at all 

satisfied

DON’T 

KNOW

REFUSED

CATI: INCLUDE EACH FACTOR 

RESPONDENT SELECTED AS 

“very” or “somewhat 

important” AT DS10

1 2 3 4 7 8

DS13a. Now I am going to ask you some questions about the building and home that you [CATI: IF DS2>=2 

SHOW: “will be living in”; OTHERWISE SHOW: “currently live in”]. 

What kind of building is your unit in? Is it a...

1. Single family home

2. Small building with four or fewer

3. Medium sized building with 5-50 units

4. Large apartment building or complex with more than 50 units
D-46

5. VOLUNTEERED: OTHER (SPECIFY)____________________

8. DON’T KNOW

9. REFUSED

DS13, how many bedrooms does your unit have?

________ Bedrooms [Range: 1-9]

98. DON’T KNOW

99. REFUSED

DS16. Is this housing unit better, worse, or about the same as your previous home? 

1. BETTER

2. WORSE

3. ABOUT THE SAME

8. DON’T KNOW

9. REFUSED

DS17. How important were each of the following unit features to you in deciding where to live? 

INTERVIEWER: READ FIRST RESPONSE OPTION, THEN “Would you say it was...”

Very 

Important

Somewhat 

Important

Not Very 

Important

Not at All 

Important

DON’T 

KNOW

REFUSED

a. The rent you pay for it. 1 2 3 4 7 8

c. The size of the home or 

number of rooms. 

d. The kind of building like a 

single-family home or a 

particular size apartment 

building or complex

1 2 3 4 7 8

e. Responsiveness of the 

landlord or management 

company to your concerns.

1 2 3 4 7 8

f. Security of the building. 1 2 3 4 7 8

g. Amenities in the building 

like laundry facilities, parking, 

and other on-site facilities.

1 2 3 4 7 8
D-47

CATI: ASK DS19 IF: DS17A | DS17B | DS17C | DS17D |DS17E | DS17F | DS17G | DS17H | DS17I | DS17J when 

= 1(Very Important) or 2 (Somewhat Important)

DS19. How satisfied are you with the following features in your new home? 

INTERVIEWER: READ FIRST RESPONSE OPTION, THEN “Would you say that you were...”

Very 

satisfied

Somewhat 

satisfied

Only a 

little 

satisfied

Not at all 

satisfied

DON’T 

KNOW

REFUSED

CATI: ASK FOR EACH DS17 

ITEM = 1 or 2:

1 2 3 4 7 8

Gathering Information 

Now I am going to ask you some questions about how you found your new home and how you gathered 

information in general about homes during your housing search.

DS20. We would like to know how you first heard about the unit that you [CATI: IF DS2>=2 SHOW: “will 

be moving to”; OTHERWISE SHOW: “moved into”]. 

Would you say you heard about it from someone you knew, that you saw or heard an

advertisement for it, saw a sign in the window, learned about it from a professional

service, learned about it through social media, learned about it through a community or

religious organization or some other way? 

1. SOMEONE YOU KNEW

2. ADVERTISEMENT

3. SIGN ON THE WINDOW

4. PROFESSIONAL SERVICE

5. SOCIAL MEDIA

6. COMMUNITY OR RELIGIOUS ORGANIZATION

7. OTHER, SPECIFY: _________________
D-48

8. DON’T KNOW/CAN’T REMEMBER

9. REFUSED

CATI: ASK IF DS20_1 = 1

DS21. What best describes your relationship to the person who told you about this unit, was it:

INTERVIEWER: SELECT ALL THAT APPLY

1. A family member

2. A friend

3. A co-worker

4. A roommate

5. A neighbor

8. DON’T KNOW

9. REFUSED

CATI: ASK IF DS20_1 = 1

DS22. What best describes the race and ethnicity of the person who told you about this unit? Would you 

say...

1. Hispanic/Latino

2. White 

3. Black or African American 

4. Asian 

5. Other, SPECIFY: _____________________________

8. DON’T KNOW

9. REFUSED

CATI: ASK IF DS20_2 = 1

DS23. Where was this ad? Was it in a newspaper, on a website, billboard, or radio?

1. NEWSPAPER, SPECIFY: Which newspaper?

2. WEBSITE, SPECIFY: Which website?

3. BILLBOARD

4. RADIO, SPECIFY: Which station? __________________________

8. DON’T KNOW

9. REFUSED

CATI: ASK IF DS20_5 = 1
D-49

DS25. Which social media site did you first hear about the unit on? 

1. Facebook

2. Twitter

3. Myspace

4. Email listservs (e.g., community listservs, interest group listservs)

5. Other social media, SPECIFY: __________________

8. DON’T KNOW

9. REFUSED

CATI: ASK IF DS20_6 = 1

[Failed searchers start here; ask failed searchers DS1as well]

DS27. Between the last time we talked to you and when you ended your search, how

many housing units did you inquire about? That is, for how many units did you

contact the landlord, management company, or current tenant(s) to find out more

information?

_________ RANGE: [0-99]

997. DON’T KNOW (GO TO DS27A)

998. REFUSED

DS27A. Which answer comes closest? Was it...

1. 0

2. 1

3. 2

4. 3-5

5. 6-10

6. 11-15

7. More than 15

98. DON’T KNOW

99. REFUSED
D-50

DS28. Now we would like to ask you about sources of information you used in general to find

out about all of the units you considered during your search. 

YES NO

DON’T 

KNOW

REFUSED

a. First, did you hear about units from someone you know? 1 2 8 9

b. Did you see or hear an advertisement for them? 1 2 8 9

c. Did you see signs in the windows of units or buildings? 1 2 8 9

d. Did you learn about them from a professional service? 1 2 8 9

e. Did you learn about them on social media? 1 2 8 9

f. Did you learn about them though community or religious 

organizations? 1 2 8 9

g. Did you use some other sources? (IF YES: SPECIFY) 1 2 8 9

CATI: ASK IF DS28g=1

DS28_O. What other sources did you use?

1. Other, Specify: _________________________

98. DON’T KNOW

99. REFUSED

CATI: ASK IF DS28a = 1

DS29. Thinking about the person who told you about units, what best describes your relationship to this 

person? If there was more than one person, tell me about your relationship to the person who told 

you about the most units.

INTERVIEWER: SELECT ALL THAT APPLY. 

1. A family member

2. A friend

3. A co-worker

4. A roommate

5. A neighbor

8. DON’T KNOW

9. REFUSED
D-51

CATI: ASK IF DS28a = 1

DS30. What best describes the race and ethnicity of the person who told you about units? If there was 

more than one person, tell me the race/ethnicity of the person who told you about the most units.

1. Hispanic/Latino

2. White

3. Black or African American

4. Asian

5. Other, SPECIFY: ____________________

8. DON’T KNOW

9. REFUSED

CATI: ASK IF DS28b = 1

DS31. Thinking about the ads you used to find out about units, where were these ads? Were they

in a newspaper, on a website, billboard, or radio?

INTERVIEWER: SELECT ALL THAT APPLY. 

1. NEWSPAPER, SPECIFY: Which newspaper?

2. WEBSITE, SPECIFY: Which website?

3. BILLBOARD

4. RADIO: Which station?

8. DON’T KNOW

9. REFUSED

CATI: ASK IF DS28_5 = 1

DS33. Which of the following social media sites did you use to find out about units? 

INTERVIEWER: SELECT ALL THAT APPLY. 

1. Facebook

2. Twitter

3. Myspace

4. Email listservs (e.g., community listservs, interest group listservs)

5. Other social media

8. DON’T KNOW

9. REFUSED
D-52

DS36. Sometimes the people we know help us in different ways during a housing search. Can

you tell me if you used the people you know to assist with your housing search in any of

the following ways?

YES NO

DON’T 

KNOW

REFUSED

a. To find out about rental vacancies? 1 2 7 8

b. To find rentals that did not require a credit check or other 

application requirements? 

1 2 7 8

c. To learn more about specific neighborhoods? 1 2 7 8

d. To find landlords that would not discriminate against people of 

your race/ethnicity?

1 2 7 8

CATI: IF D27=0 or D27a=1, SKIP TO DS39

DS37. In general, since our last interview, did you inquire about units through...

INTERVIEWER: SELECT ALL THAT APPLY.

1. Email,

2. Phone call,

3. Text,

4. In-person visits, or

5. Some other way? SPECIFY_____________

8. DON’T KNOW

9. REFUSED

DS38. Overall, how difficult was it to get the information you needed through these inquiries.

Would you say:

1. Extremely difficult

2. Very difficult

3. Somewhat difficult

4. A little difficult

5. Not at all difficult 

8. DON’T KNOW

9. REFUSED
D-53

Search Goals 

DS39. I am going to read to you the neighborhood or neighborhoods that you said you were

considering in your last interview; please tell me if you stopped considering any of these

neighborhoods during your search.

[CATI: IF WAVE 2 LIST NEIGHBORHOODS FROM PRIOR WAVE [C16] AND ASK YES, STOPPED 

CONSIDERING/NO, DID NOT STOP CONSIDERING FOR EACH NEIGHBORHOOD; IF WAVE 3 LIST 

NEIGHBORHOODS FROM PRIOR WAVE [SS6] AND ASK YES, STOPPED CONSIDERING/NO, DID NOT STOP 

CONSIDERING FOR EACH NEIGHBORHOOD] 

DS131.Why did you stop considering this/these neighborhood(s) after your visit? Was it

because...

YES NO

DON’T 

KNOW

REFUSED

a. You did not feel safe? 1 2 7 8

c. It was too far from school/work? 1 2 7 8

d. It was too far from public transportation? 1 2 7 8

e. Of the racial or ethnic mix of the neighborhood? 1 2 7 8

f. Something else (specify) 1 2 7 8

ASK IF DS13f=1 (YES)

DS131_O. For what other reasons did you stop considering this/these neighborhood(s) after

 your visit? 

1. _____________________

98. DON’T KNOW

99. REFUSED

DS41. Since the last time we talked, did you add any more neighborhoods to the list of places

where you searched for housing? 
D-54

1. YES 

2. NO (SKIP TO DS43)

8. DON’T KNOW (SKIP TO DS43)

9. REFUSED (SKIP TO DS43)

ASK IF DS41=1 (YES)

DS42. What neighborhood or neighborhoods did you add? PROBE: Any others?

____________________List neighborhood(s)

Other, Specify: _________________________

97. NO OTHER NEIGHBORHOODS

98. DON’T KNOW

99. REFUSED 

Visiting Apartments

Now I am going to ask you some questions about your attempts to visit apartments or homes you were 

interested in renting.

DS43. Since our last interview, how many units did you actually visit in person?

_________ RANGE: [0-99] (IF ANS=0 SKIP TO DS44)

998. DON’T KNOW (GO TO DS43A)

999. REFUSED (SKIP TO DS44)

DS43A. Which answer comes closest? Would you say...

1. 0

2. 1

3. 2

4. 3-5

5. 6-10

6. 11-15

7. More than 15

98. DON’T KNOW

99. REFUSED 

CATI: IF DS43=’0’, OR DS43A=1 OR 99 SKIP TO DS44

DS52X. Thinking about the visits to units you have made since the last interview, please tell me

if you did any of the following when you called or met with potential landlords to

increase the chance that the landlord would treat you well?
D-55

YES NO

DON’T 

KNOW

REFUSED

a. Dress differently than you usually do? 1 2 7 8

b. Speak differently than you usually do? 1 2 7 8

c. Bring a partner/friend/children? 1 2 7 8

d. Leave a partner/friend/children at home to increase the chance 

the landlord would treat you well?

1 2 7 8

e. Mention your career or professional background? 1 2 7 8

f. Send someone else to check out the unit for you? 1 2 7 8

DS44. How many units have you attempted to visit in person, since the last interview, that is

since [DATE OF LAST INTERVIEW (MM_DD)], but were unable to because you

were told they weren’t available? 

_________ RANGE: [0-99]

998. DON’T KNOW (GO TO DS44A)

998. DON’T KNOW (GO TO DS44A)

DS44A. Which answer comes closest? Would you say...

1. 0

2. 1

3. 2

4. 3-5

5. 6-10

6. 11-15

7. More than 15

98. DON’T KNOW

99. REFUSED

DS46. CATI: IF DS43 >3 AND < 999 OR DS43A > 4 and < 99, SHOW: Now I am going to

ask you about the 3 units you most recently visited, including the unit you decided to

rent.

CATI: IF DS43 <4 OR DS43A <4, SHOW: Now I am going to ask you about each of

the units you visited starting with the unit you decided to rent.
D-56

CATI: IF DS43=0 OR DS43A=1 OR 99 SHOW: Now I am going to ask you about the

visit you made to the unit you decided to rent.

CATI: BEGIN LOOP: DS46 – DS57 (Loop will cycle a maximum of 3 times)

CATI: DO NOT ASK QUESTION BELOW FOR UNIT RESPONDENT DECIDED TO RENT, WHICH WILL BE THE FIRST

LOOP THROUGH

What was the address of this unit?

INTERVIEWER: ATTEMPT TO CAPTURE STREET ADDRESS, CITY, AND STATE.

Street: ________________________

City: __________________________

State: _________________________

INTERVIEWER: IF RESPONDENT DOES NOT KNOW ADDRESS, PROBE FOR CROSS STREETS OR GENERAL AREA

Cross streets or general area: __________________________

98. DON’T KNOW (GO TO D47)

99. REFUSED (SKIP TO DS47)

CATI: IF DS46= 98, 99 ASK DS47, ELSE SKIP TO DS50

DS47. What neighborhood or community was it in?

_____________________(Neighborhood)

97. OTHER, SPECIFY: ___________________________

98. DON’T KNOW

99. REFUSED

CATI: ASK QUESTION BELOW FOR ALL THREE UNITS

DS50. How satisfied were you with the way you were treated during this visit?

1. Very satisfied

2. Somewhat satisfied

3. Only a little Satisfied

4. Not at all satisfied

8. DON’T KNOW

9. REFUSED 
D-57

CATI: ASK QUESTION BELOW FOR ALL THREE UNITS

DS51. Did you feel you were treated unfairly because of your race or ethnicity?

1. YES

2. NO

8. DON’T KNOW

9. REFUSED

CATI: DO NOT ASK QUESTION BELOW FOR UNIT RESPONDENT DECIDED TO

RENT

DS53. Did you decide to submit an application for this unit?

1. YES (GO TO DS54)

2. NO (GO TO DS55)

8. DON’T KNOW (GO TO DS55)

9. REFUSED (GO TO DS55)

CATI: DO NOT ASK QUESTION BELOW FOR UNIT RESPONDENT DECIDED TO

RENT

DS54. Why did you apply for this unit?

INTERVIEWER: SELECT ALL THAT APPLY. 

1. LIKED THE BUILDING

2. LIKED THE UNIT

3. AFFORDABLE

4. LIKED THE NEIGHBORHOOD

5. LIKED THE LANDLORD/MANAGEMENT COMPANY

6. TIRED OF SEARCHING

7. NEEDED TO MOVE QUICKLY

8. OTHER, SPECIFY: ____________________

98. DON’T KNOW

99. REFUSED

(CATI: ALL GO TO DS56)

CATI: DO NOT ASK QUESTION BELOW FOR UNIT RESPONDENT DECIDED TO RENT

DS55. Why did you decide not to apply for this unit? 
D-58

YES NO

DON’T 

KNOW

REFUSED

a, Price was too high? 1 2 7 8

b. Did not like the unit? 1 2 7 8

c. Did not like the building? 1 2 7 8

d. Did not like the landlord or management company? 1 2 7 8

e. Did not like the application process? 1 2 7 8

f. Did not like the neighbors? 1 2 7 8

g. Were there any other reasons why you decided not to apply for 

this unit? 

1 2 7 8

CATI: IF DS55F=1

DS55_O. What were the other reasons you decided not to apply for this unit?

OTHER, SPECIFY: ______________________________

98. DON’T KNOW

99. REFUSED 

CATI: DO NOT ASK QUESTION BELOW FOR UNIT RESPONDENT DECIDED TO RENT

DS56. Was your application accepted?

1. YES (GO TO DS58)

2. NO (GO TO DS57)

8. DON’T KNOW (GO TO DS58)

9. REFUSED (GO TO DS58)

CATI: DO NOT ASK QUESTION BELOW FOR UNIT RESPONDENT DECIDED TO RENT

DS57. Why was your application denied? 

INTERVIEWER: CHECK ALL THAT APPLY

1. SOMEONE ELSE APPLIED FIRST

2. UNIT WAS TAKEN 

3. PROBLEM WITH REFERENCES 

4. PROBLEM WITH CREDIT HISTORY

5. PROBLEM WITH RENTAL HISTORY
D-59

6. PROBLEM WITH CRIMINAL HISTORY

5. INCOME TOO LOW 

6. WAS NOT TOLD WHY/NEVER HEARD BACK FROM LANDLORD OR

MANAGEMENT COMPANY

7. OTHER, SPECIFY: _____________________________________

8. DON’T KNOW

9. REFUSED

[Repeat questions until you have asked about all units respondent visited or, if respondent visited more than 3 

units, the 3 most recent]

CATI: END LOOP: DS46 – DS57

DS58. Overall, would you say this housing search was...

1. Extremely difficult,

2. Very difficult,

3. Somewhat difficult,

4. A little difficult, or

5. Not at all difficult?

8. DON’T KNOW

9. REFUSED

[CATI: IF DS58 =5 (NOT AT ALL DIFFICULT) SKIP TO DS60]

DS59. Which of the following things made it difficult? Would you say...

YES NO

DON’T 

KNOW

REFUSED

a. Finding units in your price range? 1 2 7 8

b. Feeling rushed to move? 1 2 7 8

c. Gathering information about units? 1 2 7 8

d. Gathering information about neighborhoods? 1 2 7 8

e. Finding transportation to get to units? 1 2 7 8

f. [Skip if S_10B & S10C=0]: Finding someone to take care of your 

children so you could visit units 

1 2 7 8
D-60

YES NO

DON’T 

KNOW

REFUSED

g. Meeting the application requirements? 1 2 7 8

h. Finding landlords that would rent to you? 1 2 7 8

i. Were there any other reasons why it was difficult? SPECIFY 1 2 7 8

CATI: ASK IF DS59i = 1.

DS59_O. What else made it difficult?

1. Other, Specify: _________________________

98. DON’T KNOW

99. REFUSED

DS60. During your search, did you ever feel that you were denied housing because the rental

agent or landlord didn’t want to rent to you because of your race or ethnicity?

1. YES 

2. NO 

8. DON’T KNOW 

9. REFUSED 

DS61. During your search, did you ever feel that a rental agent or landlord was showing you

only units in certain neighborhoods because of your race or ethnicity?

1. YES 

2. NO 

8. DON’T KNOW 

9. REFUSED 

DS62. During your search, did you ever feel that the other residents in the building or

neighborhoods that you visited treated you unfairly because of your race or ethnicity?

1. YES 

2. NO 

8. DON’T KNOW 

9. REFUSED

CATI: IF DS_d=1 (“FOUND A NEW PLACE”) SKIP TO Z1
D-61

DS64. Why did you decide not to move? 

INTERVIEWER: SELECT ALL THAT APPLY

1. WAS TOLD I COULD REMAIN IN MY CURRENT HOME

2. MOVING WAS TOO EXPENSIVE 

3. LIFE CIRCUMSTANCE CHANGED 

4. DID NOT FIND ANY UNITS THAT MET ALL MY CRITERIA

5. OTHER, SPECIFY: ___________________

8. DON’T KNOW

9. REFUSED

DS65. Do you think you might move in the next couple of years? 

1. YES 

2. NO (SKIP TO END)

8. DON’T KNOW (SKIP TO END)

9. REFUSED (SKIP TO END)

CATI: ASK IF DS65=1 (YES)

DS66. Would you say you definitely will move, probably will move, or are you more uncertain?

1. Definitely

2. Probably

3. Uncertain

8. DON’T KNOW

9. REFUSED

(WAVE 2) EXIT_SUMM

Thank you for completing the interview survey! Your answers will help researchers better understand how 

and why people search for housing. 

GOTO Z1

(WAVE 3) EXIT_SUMM

Thank you for completing the survey! Your answers will help researchers better understand how and why 

people search for housing. 
D-62

We are still looking for more people who have recently moved or who are currently looking for housing and 

we are hoping that the people we’ve talked to already can help spread the word. In addition to your $50 for 

this interview, we will mail you fliers about the Housing Search Study. We would appreciate it if you could 

give these fliers and the information about this study to anyone you know—this might be neighbors, friends, 

acquaintances—who might also be searching for housing or have recently moved. 

Would you be willing to share anyone’s contact information with us so that we can reach out to them? We will 

share your name as the referring person if they ask how we received their contact information. 

3. YES

4. NO GOTO Z1

SNOW1.

Thank you very much. In order to make this as easy as possible, could you please tell me the best phone 

number to reach them? 

CATI: PROVIDE TEXT BOX FOR PHONE

Is that a cell phone, home phone, or other?

1. Cell phone

2. Home phone

3. Other

4. Don’t know

Do you have an email address of theirs that you could share with me?

CATI: PROVIDE TEXT BOX FOR email

Thank you, you should expect to get a package from us within a few weeks. 

Z1. Those are all the questions I have to ask you today. Thank you for the time you’ve spent

talking with me and for your participation in this study. In order to send your $50 gift

card, I need to confirm your name and address.

You gave your contact information as:

CATI: SHOW NAME FROM FLNAME AND ADDRESS FROM S5] [FULLADD, RCITY, RSTATE, RZIP] 

INTERVIEWER: READ R’S NAME AND ONLY STREET ADDRESS TO CONFIRM AND ASK:

Is that correct?

1. YES (SKIP TO THANK)

2. NO
D-63

8. DON’T KNOW

9. REFUSED

CATI: IF Z1=8 OR 9 DISPLAY “In order to send your $50 gift card, I am required to ask for your full name and 

address.”

Z1a. OK, could you give me the name and address you would like me to use to send

your gift card?

CATI: SHOW NAME FROM FLNAME AND ADDRESS FROM S5] [FULLADD, RCITY, RSTATE, RZIP] 

INTERVIEWER: ENTER CORRECTED NAME AND/OR ADDRESS. READ BACK THE ADDRESS INFORMATION TO R.

First Name: ________________________

Last Name: ________________________

Street: ________________________

City: __________________________

State: _________________________

Zip: __________________________

CATI: PROVIDE TEXT BOX FOR ENTRY OF CORRECTED FULL NAME AND ADDRESS WHILE ORIGINAL NAME 

AND ADDRESS IS DISPLAYED FOR COMPARISON PURPOSES

CATI: CREATE CDMO USING DATA ENTERED AT Z2 TO REPLACE:

FULLADD – FULL ADDRESS

RCITY – CITY

RSTATE – STATE

RZIP – ZIP

THANK. Thank you very much for your time and assistance. If you have any

questions about this study, please call Lenin Williams at 1-888-864-1425 x 132.
E-1

Appendix E. Demographic Questions

CATI: ASK DEMOGRAPHICS (D SECTION), IN DEPTH INTERVIEW (N SECTION), AND CONTACT INFORMATION 

(Z SECTION) FOR BOTH RECENT MOVERS (B SECTION) AND CURRENT SEARCHERS (C SECTION) 

We are almost done. Now I would like to ask you some basic questions about your background.

DEMOGRAPHICS 

AGE, GENDER, MARITAL STATUS

D1. What year were you born?

__________________________ (YYYY)

[Calculate and confirm age] 

8. DON’T KNOW

9. REFUSED

D2. Are you male or female?

1. MALE

2. FEMALE

8. DON’T KNOW

9. REFUSED

D3. Are you currently...

INTERVIEWER: SELECT ALL THAT APPLY IF MORE THAN ONE RESPONSE IS VOLUNTEERED.

1. Married

2. Widowed

3. Divorced

4. Separated

5. Never married

6. In a civil union or domestic partnership

8. DON’T KNOW

9. REFUSED

S10A. How many other adults age 18 and over live with you in your housing unit?

S10A_1. _______________ Number of adults:

CATI: IF S10 > 1 SHOW “INTERVIEWER: RESPONDENT HAS ALREADY INDICATED THAT 0 CHILDREN 

LIVE IN THE HOME, SELECT 0 FROM THE DROP DOWN BELOW AND CONTINUE”

How many children under the age of 18 live in this home?
E-2

S10A_2. _______________ Number of children:

S10A_CONFIRM That’s [S10A_1] adult(s) and [S10A_2] child/children that live with you in your 

house.

1. YES CORRECT

2. INCORRECT (GO TO S10A)

CATI: IF CURSRCHR=1, ASK S10B, ELSE SKIP TO D4

S10B. When you move, how many other adults age 18 and over will live with you in your

housing unit?

S10B_1. _______________ Number of adults:

How many children under age 18 and over will live with you in your housing unit?

S10B_2. _______________ Number of children:

S10B_CONFIRM. So when you move you plan to live with [S10B] person / people?

1. YES CORRECT

2. INCORRECT (GO TO S10B_1)

SOCIAL CLASS (INCOME/EDUCATION/OCCUPATION)

D4. Was your total [CATI: IF S11A_1=1 SHOW “individual”; IF S11A_1=2 OR 3 SHOW “household family”] 

income over the last 12 months, that is, since [MONTH, PRIOR YEAR], from all sources, before taxes, 

more or less than $60,000?

1. More than $60,000 (SKIP TO D4a)

2. Less than $60,000 (SKIP TO D4b)

8. DON’T KNOW

9. REFUSED

D4a. Was it more than $80,000?

1. YES

2. NO (SKIP TO D5)

3. EXACTLY $80,000 (SKIP TO D5)

8. DON’T KNOW (SKIP TO D5)

9. REFUSED (SKIP TO D5)

D4a_1. Was it more than $100,00?

1. YES

2. NO (SKIP TO D5)

 3. EXACTLY $100,000 (SKIP TO D5)
E-3

8. DON’T KNOW (SKIP TO D5)

9. REFUSED (SKIP TO D5)

D4a_2. Was it more than $150,000?

1. YES (SKIP TO D5)

2. NO (SKIP TO D5)

 3. EXACTLY $150,000 (SKIP TO D5)

8. DON’T KNOW (SKIP TO D5)

9. REFUSED (SKIP TO D5)

D4b. Was it less than $40,000?

1. YES

2. NO (SKIP TO D5)

3. EXACTLY $40,000 (SKIP TO D5)

8. DON’T KNOW (SKIP TO D5)

9. REFUSED (SKIP TO D5)

D4b_1. Was it less than $20,000?

1. YES

2. NO

 3. EXACTLY $20,000

8. DON’T KNOW

9. REFUSED

D5. What is the highest level of school you have completed or the highest degree you have earned? 

1. Less than high school diploma

2. High school graduate - High school DIPLOMA or equivalent (For example:

 GED)

3. Some college but no degree

4. Associate degree

5. Bachelor’s degree [For example: BA, AB, BS]

6. Master's degree, Professional School Degree, Doctorate Degree

98. DON’T KNOW

99. REFUSED

D6. What is your current employment status? 

INTERVIWER: SELECT ALL THAT APPLY
E-4

1. Employed full time (40 HOURS/WEEK OR MORE)

2. Employed part time

3. Unemployed, looking for work

4. Unemployed, other reason (E.G RECEIVES SSI, CANNOT WORK DUE TO

DISABILITY) 

5. Student 

6. Retiree

7. Stay at home parent

8. Other, SPECIFY: ________________

98. DON’T KNOW

99. REFUSED

D7. Were you born in the United States?

1. YES

2. NO

8. DON’T KNOW

9. REFUSED

CATI: FILL PAST TENSE IF RECMVR=1 AND PRESENT TENSE IF CURSRCH = 1

D8. Did/Does anyone in your household who moved/is moving with you have a disability that requires 

special housing accommodations?

1. YES

2. NO

8. DON’T KNOW

9. REFUSED

D9. How many automobiles are kept at home for use by members of your household? (AHS)

1. NONE

2. __________ (Vehicles) [RANGE 1-5] INTERVIEWER: IF R SAYS MORE

 THAN 5 VEHICLES, RECORD AS 5

8. DON’T KNOW

9. REFUSED

[Conclude with corresponding exit script from Recent Mover Module, and Current Searchers Wave 1 Baseline 

Test, and In-Depth Interviews]
E-5

Contact Information

Those are all the questions I have to ask you today. Thank you for the time you’ve spent talking with me and 

for your participation in this study. In order to send your $50 gift card, I need to confirm your name and 

address.

Z1. You gave your contact information as:

CATI: SHOW NAME FROM FLNAME AND ADDRESS FROM S5] [FULLADD, RCITY, RSTATE, RZIP] 

INTERVIEWER: READ R’S NAME AND ONLY STREET ADDRESS TO CONFIRM AND ASK:

Is that correct?

1. YES (SKIP TO Z3)

2. NO

8. DON’T KNOW

9. REFUSED

CATI: IF Z1=8 OR 9 DISPLAY “In order to send your $50 gift card, I am required to ask for your full name and 

address.”

Z2. OK, could you give me the name and address you would like me to use to send

your gift card?

CATI: SHOW NAME FROM FLNAME AND ADDRESS FROM S5] [FULLADD, RCITY, RSTATE, RZIP] 

INTERVIEWER: ENTER CORRECTED NAME AND/OR ADDRESS. READ BACK THE ADDRESS INFORMATION TO R, 

SPELLING THE WORDS.

First Name: ________________________

Last Name: ________________________

Street: ________________________

City: __________________________

State: _________________________

Zip: __________________________

CATI: PROVIDE TEXT BOX FOR ENTRY OF CORRECTED FULL NAME AND ADDRESS WHILE ORIGINAL NAME 

AND ADDRESS IS DISPLAYED FOR COMPARISON PURPOSES
E-6

CATI: CREATE CDMO USING DATA ENTERED AT Z2 TO REPLACE:

FULLADD – FULL ADDRESS

RCITY – CITY

RSTATE – STATE

RZIP – ZIP

Z3. I also need to confirm your phone number in case we have any questions 

 about the interview. 

INTERVIEWER: READ THE PHONE NUMBER. Is this [CATI: DISPLAY RPHONE1] the phone number you would 

like us to use to contact you in the future?

1. YES (SKIP TO Z4)

2. NO 

8. REFUSED (SKIP TO Z4)

CATI: IF Z3=2 PROVIDE TEXT BOX FOR ENTRY OF CORRECTED PHONE NUMBER. DISPLAY ORIGINAL PHONE 

NUMBER FOR COMPARISON PURPOSES. DISPLAY “INTERVIEWER: ENTER CORRECTED PHONE NUMBER. 

READ BACK TO R TO CONFIRM.”

CATI: IF RECMVR=1 SKIP TO Z7

Z4. Is this a home, work, or cell phone number?

1. Home

2. Work

 3. Cell

8. Don’t know

9. REFUSED

Z4A. Is there an additional phone number you would like to provide?

1. YES 

2. NO (SKIP TO Z7)

8. REFUSED (SKIP TO Z7)

CATI: IF Z4A= 1 PROVIDE TEXT BOX FOR ENTRY OF SECOND PHONE NUMBER

INTERVIEWER: ENTER PHONE NUMBER. READ BACK TO R TO CONFIRM.

Z5. Is this a home, work, or cell phone number?

1. Home

2. Work

 3. Cell

8. Don’t know

9. REFUSED 
E-7

CATI: ASK IF Z4=3 OR Z5=3 

Z6. In the future, may we call or send a text message to the cell phone number you provided?

1. YES

2. NO

8. DON’T KNOW 

9. REFUSED

Z7. Is there an e-mail address you would like to provide?

1. YES 

2. NO (SKIP TO Z8)

8. REFUSED (SKIP TO Z8)

CATI: IF Z7= 1 PROVIDE TEXT BOX FOR ENTRY OF EMAIL ADDRESS

CATI: ALL SKIP TO Z11 (INACTIVE UNTIL GIVEN FURTHER INSTRUCTIONS)

Z8. We are still looking for more people who have recently moved or who are currently

looking for housing and we are hoping that the people we’ve talked to already can help

spread the word. In addition to your $50 gift card for this interview, we will mail you

fliers about the Housing Search Study. We would appreciate it if you could give these

fliers and the information about this study to anyone you know—this might be neighbors,

friends, acquaintances—who might also be searching for housing or have recently

moved. 

Would you be willing to share anyone’s contact information with us so that we can reach out to 

them? We will share your name as the referring person if they ask how we received their contact 

information. 

INTERVIEWER: IF YES IS SELECTED, SAY: “Thank you, may I have that person’s name?” AND ENTER 

THE NAME IN THE TEXT BOX PROVIDED

1. YES (GO TO Z9)

2. NO (GO TO Z11)

9. REFUSED

Z9. Thank you very much. In order to make this as easy as possible, could you please

tell me the best phone number to reach them? 
E-8

___________ [PHONE1] ___________ [PHONE2]

1. CELL 1. CELL

2. HOME 2. HOME

3. OTHER 3. OTHER

9. REFUSED 9. REFUSED

Z10. Do you have an email address of theirs that you could share with me?

1. YES

2. NO

9. REFUSED

CATI: IF Z10= 1 PROVIDE TEXT BOX FOR ENTRY OF EMAIL ADDRESS

REQUEST FOR IN-DEPTH INTERVIEW WITH UI

N4. 

[ASKED OF RECENT MOVERS AND CURRENT SEARCHERS WHO ARE DONE SEARCHING AT WAVE 2, STILL

SEARCHING AT WAVE 3 OR DONE SEARCHING AT WAVE 3]

Lastly, we are working with researchers at the Urban Institute on this project, and they will also be conducting 

a small number of in-person interviews with some of the participants in this project. Participants would 

receive a $50 token of appreciation for this interview. We would like to share your contact information with 

researchers at the Urban Institute who are working with us on this research. If you meet their criteria, one of 

the researchers may contact you to explain the study and see if you are interested in participating. I should 

note that your contact information is always kept separate from your survey responses, and we will not share 

the contact information with anyone outside of the researchers involved in the project.

Is it ok for us to share your information with the Urban Institute and have their staff reach out to you for this 

in-person interview?

1. Yes

2. No (GO TO Z11)
E-9

[If YES and RECENT MOVER or CURRENT SEARCHER WHO REFUSED CALL-BACK]: 

N4a. Thank you very much. In order to make this as easy as possible, could you please

tell me if this is the best phone number to reach you? 

That is [insert from Screener] XXX XXX XXXX / AAAAAAA@AAAAAAA.COM? 

1. Yes

2. No, the phone number is incorrect (ENTER CORRECT PHONE

NUMBER)

3. No, the email is incorrect (ENTER CORRECT EMAIL)

N5. Some people find it easier to arrange an interview in advance, and one way we could do

this is by text or email. What is the best way to reach you for this purpose?

1. TEXT 

2. EMAIL 

3. PHONE CALL (GO TO N5a)

INTERVIEWER: IF RESPONDENT SELECTS EMAIL, BUT DECLINED TO PROVIDE AN EMAIL AT PRIOR 

QUESTION, SELECT THE PREV BUTTON BELOW AND COLLECT THE EMAIL ADDRESS.

N5a. [If phone] What time of day is the best to reach you? Morning, Afternoon,

Evening, or anytime?

1. MORNING

2. AFTERNOON

3. EVENING

4. ANYTIME

N5b. Are any days better than others? 

1. MONDAY

2. TUESDAY

3. WEDNESDAY

4. THURSDAY
E-10

5. FRIDAY

6. SATURDAY

7. SUNDAY

8. ANY DAY IS FINE

Z11. [CATI: IF N4=1 SHOW “Thank you for sharing your information with UI. A

researcher will only call you if there is a need for respondents matching your criteria, but

your willingness to participate is greatly appreciated.] You should expect to get a gift

card from us in [CATI: IF CURSRCH=1 SHOW “a few days” IF RECMVR=1

SHOW “about 3 weeks”] with your gift card.

CATI: IF RECMVR=1 SKIP TO THANK; ELSE IF CURSRCHR=1 SKIP TO Z12

Z12. If it is OK with you, we will be contacting you again on or about

[CATI: ADD 10 DAYS FROM INTERVIEW DATE] for another paid interview to ask

you about your search, whether you are still searching or you are done searching. Would

that be OK? 

1. YES, OK TO CONTACT (GO TO Z12a)

2. NO, NOT OK TO CONTACT (SKIP TO Z12b)

Z12. If it is OK with you, depending on our need for more respondents, we may be contacting you again on 

or about

 [CATI: ADD 10 DAYS FROM INTERVIEW DATE] for another paid interview to ask

 you about your search, whether you are still searching or you are done searching. Would

 that be OK? 

 1. YES, OK TO CONTACT (GO TO Z12a)

 2. NO, NOT OK TO CONTACT (SKIP TO Z12b)

Z12a. OK, we will contact you again on CATI: ADD 10 DAYS FROM INTERVIEW DATE, but only if there’s a 

need for more interviews. Along with your gift card we will be sending in the mail a housing search log so that 

you can keep track of your search experience. If we talk to you again you may use the log to help with 

remembering your activities. If you have any questions, please call Lenin Williams at 1-888-864-1425 x 132.
E-11

INTERVIEWER: PRESS NEXT TO CONTINUE TO EXIT SCREEN.

THANK. Thank you very much for your time and assistance. If you have any 

questions about this study, please call Lenin Williams at 1-888-864-1425 x 

132.

CATI: RECORD END DATE (SECTNEDDT) AND TIME (SECNEDTM)

CATI: CALCULATE SECTION TIMES
F-1

Appendix F. Secondary Data Analysis Figures

Exhibit F.1: Descriptive Statistics for Recent Movers and Nonmovers in Panel Study of Income 

Dynamics (PSID)

Nonmovers Recent Movers

Mean SD Mean SD

Race of householder

Black 0.514 0.500 0.465 0.499

Latino 0.076 0.266 0.068 0.252

White 0.410 0.492 0.467 0.499

Education of head 12.249 3.004 12.634 2.716

Family income ($1,000s) 30.425 28.633 31.292 29.162

Age of householder 43.389 16.131 34.521 12.915

Female householder (1=yes) 0.459 0.498 0.405 0.491

Married or cohabiting (1=yes) 0.322 0.467 0.334 0.472

Whether children in house (1=yes) 0.461 0.498 0.462 0.499

Receiving housing assistance (1=yes) 0.215 0.411 0.155 0.362

Metropolitan-area variables

Housing vacancy rate 11.517 4.864 11.667 4.981

Homeownership 66.100 6.714 66.734 6.060

Rent-to-income ratio 0.189 0.023 0.187 0.023

Number of observations 9,186 9,879

SD = standard deviation.

Source: PSID 1997–2011
F-2

Exhibit F.2: Logit Coefficients and Standard Errors From Regression Analysis of Whether

Residential Move Was Unplanned

Recent Movers in Panel Study of Income Dynamics (PSID)

Model 1 Model 2 Model 3

Race (reference=White)

Black 0.319 0.069 0.496

(0.057) (0.063) (0.496)

Latino 0.433 0.187 – 5.003

(0.102) (0.111) (2.287)

Education of head – 0.072 – 0.073

(0.012) (0.012)

Family income ($1000s) – 0.007 – 0.007

(0.001) (0.001)

Age of householder 0.038 0.038

(0.002) (0.002)

Female householder (1=yes) 0.218 0.219

(0.075) (0.075)

Married or cohabiting (1=yes) 0.167 0.162

(0.083) (0.083)

Whether children in house (1=yes) 0.167 0.169

(0.063) (0.063)

Receiving housing assistance (1=yes) 0.041 0.042

(0.075) (0.076)

Year of observation 0.006 0.006

(0.006) (0.006)

Metropolitan-area variables

Housing vacancy rate 0.008

(0.009)

Homeownership rate – 0.008

(0.010)

Rent-to-income ratio – 1.276

(2.329)

Interactions

Metropolitan-area vacancy rate x Black 0.012

(0.015)

Metropolitan-area vacancy rate x Latino – 0.087

(0.039)

Metropolitan-area homeownership rate x Black – 0.006

(0.015)

Metropolitan-area homeownership rate x Latino 0.068

(0.028)

Metropolitan-area rent-to-income ratio x Black – 0.737

(3.633)

Metropolitan-area rent-to-income ratio x Latino 9.316

(5.311)

Constant – 1.180 – 12.619 – 12.514

(0.042) 12.724) (12.776)

BIC 10508.479 9938.164 10009.131

BIC = Bayesian information criterion.

Source: PSID 1997–2011
F-3

Exhibit F.3: OLS Coefficients and Standard Errors From Regression Analysis of Number of Units 

Viewed During the Housing Search

Recent movers in AHS

Model 1 Model 2 Model 3 Model 4

Race (reference: White)

Black – 1.079 – 0.444 – 0.295 – 31.236

(0.23) (0.25) (0.25) (12.36)

Latino – 1.669 – 0.862 – 1.130 – 12.894

(0.21) (0.25) (0.26) (12.05)

Education (reference: < high school) ref. ref. ref.

HS degree 0.938 0.852 0.830

(0.28) (0.28) (0.28)

College degree 1.850 1.736 1.703

(0.33) (0.33) (0.33)

Income (reference: <$5k)

5–10k 1.071 1.155 1.146

(0.47) (0.47) (0.47)

10–15k 0.323 0.366 0.355

(0.44) (0.44) (0.44)

15–20k 0.387 0.405 0.380

(0.45) (0.45) (0.45)

20–30k 0.701 0.721 0.706

(0.37) (0.37) (0.37)

30–40k 1.372 1.370 1.344

(0.41) (0.41) (0.41)

40–50k 0.305 0.250 0.236

(0.42) (0.42) (0.42)

50–60k 0.545 0.450 0.432

(0.46) (0.46) (0.46)

60–70k 1.355 1.228 1.212

(0.50) (0.50) (0.50)

70–80k 1.053 0.919 0.897

(0.52) (0.52) (0.52)

80–90k 0.561 0.274 0.254

(0.58) (0.58) (0.58)

90–100k 0.929 0.715 0.719

(0.65) (0.65) (0.65)

100–125k 1.614 1.392 1.370

(0.57) (0.57) (0.57)

125–150k 1.541 1.207 1.188

(0.68) (0.69) (0.69)

150k+ 1.480 1.110 1.085

(0.56) (0.56) (0.56)

Age – 0.008 – 0.010 – 0.010

(0.01) (0.01) (0.01)

Female (1=yes) – 0.121 – 0.100 – 0.095

(0.19) (0.19) (0.19)

Married (1=yes) 0.557 0.533 0.542 

(0.21) (0.21) (0.21)

Foreign born (1=yes) – 0.650* – 0.821 – 0.830 

(0.27) (0.27) (0.27)

Involuntary move (1=yes) 1.158 1.142 1.144

(0.33) (0.33) (0.33)

Receives housing assistance (1=yes) – 0.514 – 0.533 – 0.510

(0.36) (0.36) (0.36)
F-4

Model 1 Model 2 Model 3 Model 4

Access to car (1=yes) – 0.107 – 0.133 – 0.148

(0.22) (0.22) (0.22)

Single-family building (1=yes) 0.845 0.963 0.962

(0.21) (0.21) (0.21)

Metropolitan-area variables

Homeownership rate – 5.652 – 16.756

(5.31) (7.50)

Rent-to-income ratio 7.717 – 19.000

(10.51) (15.36)

Housing vacancy rate – 2.631 9.778

(7.16) (9.76)

Interactions

Metropolitan-area homeownership rate x Black 30.853

(13.21)

Metropolitan-area homeownership rate x Latino 10.184

(13.71)

Metropolitan-area rent-to-income ratio x Black 73.434 

(27.25)

Metropolitan-area rent-to-income ratio x Latino 30.633

(25.17)

Metropolitan-area vacancy rate x Black – 37.809 

(17.54)

Metropolitan-area vacancy rate x Latino – 10.399

(19.18)

Constant 6.562 4.490 7.171 18.548 

(0.12) (0.50) (4.88) (7.06)

Model R-squared 0.007 0.023 0.027 0.028

AHS = American Housing Survey. OLS = ordinary least squares.

Source: Recent movers in the AHS 2011
F-5

Exhibit F.4: Logistic Regression Coefficients and Standard Errors From Regression Analysis of 

Whether Considered Other Neighborhoods During the Housing Search

Recent movers in AHS

Model 1 Model 2 Model 3 Model 

4

Race (reference: White)

Black 0.021 0.239 0.246 –

2.993

(0.05) (0.06) (0.06) (2.76)

Latino – 0.265 – 0.024 – 0.035 –

3.677

(0.05) (0.06) (0.06) (2.64)

Education (reference: < high school)

HS degree 0.245 0.241 0.241

(0.06) (0.06) (0.06)

College degree 0.552 0.549 0.548

(0.07) (0.07) (0.07)

Income (reference: <$5k)

5–10k 0.098 0.099 0.097

(0.10) (0.10) (0.10)

10–15k 0.099 0.099 0.098

(0.10) (0.10) (0.10)

15–20k 0.222 0.219 0.218* 

(0.10) (0.10) (0.10)

20–30k 0.062 0.058 0.057

(0.08) (0.08) (0.08)

30–40k 0.298 0.292 0.292

(0.09) (0.09) (0.09)

40–50k 0.132 0.126 0.128

(0.09) (0.09) (0.09)

50–60k 0.291 0.282 0.282 

(0.10) (0.10) (0.10)

60–70k 0.206 0.196 0.201

(0.11) (0.11) (0.11)

70–80k 0.116 0.107 0.100

(0.11) (0.11) (0.11)

80–90k 0.341 0.324 0.321

(0.13) (0.14) (0.14)

90–100k 0.405 0.389 0.386 

(0.15) (0.15) (0.15)

100–125k 0.112 0.103 0.098

(0.13) (0.13) (0.13)

125–150k 0.295 0.276 0.270

(0.15) (0.15) (0.15)

150k+ 0.284 0.268 0.261

(0.13) (0.13) (0.13)

Age – 0.008 – 0.008 – 0.008

(0.00) (0.00) (0.00)

Female (1=yes) – 0.068 – 0.068 – 0.069

(0.04) (0.04) (0.04)

Married (1=yes) 0.251 0.246 0.246

(0.05) (0.05) (0.05)

Foreign born (1=yes) – 0.260 – 0.266 – 0.270

(0.06) (0.06) (0.06)

Involuntary move (1=yes) 0.334 0.333 0.332
F-6

Model 1 Model 2 Model 3 Model 

4

(0.07) (0.07) (0.07)

Receives housing assistance (1=yes) 0.024 0.022 0.025

(0.08) (0.08) (0.08)

Access to car (1=yes) 0.205 0.203 0.207

(0.05) (0.05) (0.05)

Single-family building (1=yes) – 0.039 – 0.032 – 0.032

(0.05) (0.05) (0.05)

Metropolitan-area variables 1.349 -0.776

Homeownership rate (1.17) (1.66)

3.375 – 0.687

Rent-to-income ratio (2.32) (3.42)

– 2.373 0.040

Housing vacancy rate (1.57) (2.17)

Interactions

Metropolitan-area homeownership 

rate x Black

4.006

(2.94)

Metropolitan-area homeownership 

rate x Latino

3.697

(3.00)

Metropolitan-area rent-to-income 

ratio x Black

4.984

(6.08)

Metropolitan-area rent-to-income 

ratio x Latino

8.296

(5.53)

Metropolitan-area vacancy rate x

Black

– 4.058

(3.86)

Metropolitan-area vacancy rate x

Latino

– 4.408

(4.20)

Constant 0.262 – 0.164 – 1.469 0.487

(0.03) (0.11) (1.08) (1.57)

BIC 14661.73

9

14603.98

2

14628.10

6

14677.8

11

AHS = American Housing Survey. BIC = Bayesian information criterion.

Source: Recent movers in AHS 2011
F-7

Exhibit F.5: Logistic Regression Coefficients and Standard Errors From Regression Analysis of 

Whether Recently Mobile Householder First Heard of the New Dwelling Through Word of Mouth

Model 1 Model 2 Model 3 Model 4

Race (reference: White)

Black 0.509 0.314 0.280 – 5.383

(0.05) (0.06) (0.06) (2.86)

Latino 0.527 0.252 0.328 4.735

(0.05) (0.06) (0.06) (2.74)

Education (reference: < high school)

HS degree – 0.215 – 0.197 – 0.202

(0.06) (0.06) (0.06)

College degree – 0.552 – 0.524 – 0.524

(0.08) (0.08) (0.08)

Income (reference: <$5k)

5–10k 0.051 0.027 0.032

(0.10) (0.10) (0.10)

10–15k – 0.037 – 0.057 – 0.053

(0.10) (0.10) (0.10)

15–20k – 0.095 – 0.111 – 0.108

(0.10) (0.10) (0.10)

20–30k – 0.073 – 0.087 – 0.089

(0.08) (0.08) (0.08)

30–40k – 0.267 – 0.273 – 0.272 

(0.09) (0.09) (0.09)

40–50k – 0.193 – 0.183 – 0.183

(0.10) (0.10) (0.10)

50–60k – 0.228 – 0.208 – 0.204

(0.11) (0.11) (0.11)

60–70k – 0.480 – 0.448 – 0.435

(0.12) (0.12) (0.12)

70–80k – 0.305 – 0.279 – 0.277

(0.12) (0.12) (0.12)

80–90k – 0.681 – 0.615 – 0.629

(0.16) (0.16) (0.16)

90–100k – 0.363 – 0.313 – 0.309

(0.16) (0.16) (0.16)

100–125k – 0.413 – 0.348 – 0.354 

(0.14) (0.14) (0.14)

125–150k – 0.543 – 0.467 – 0.466 

(0.17) (0.18) (0.18)

150k+ – 0.358* – 0.253 – 0.248

(0.14) (0.14) (0.14)

Age 0.004 0.005 0.005 

(0.00) (0.00) (0.00)

Female (1=yes) – 0.080 – 0.087 – 0.093

(0.04) (0.04) (0.04)

Married (1=yes) – 0.170 – 0.165 – 0.169

(0.05) (0.05) (0.05)

Foreign born (1=yes) 0.245 0.303 0.288

(0.06) (0.06) (0.06)

Involuntary move (1=yes) – 0.218 – 0.219 – 0.217 

(0.08) (0.08) (0.08)

Receives housing assistance (1=yes) – 0.032 – 0.032 – 0.024

(0.08) (0.08) (0.08)

Access to car (1=yes) – 0.059 – 0.048 – 0.045
F-8

Model 1 Model 2 Model 3 Model 4

(0.05) (0.05) (0.05)

Single-family building (1=yes) 0.143 0.110 0.108

(0.05) (0.05) (0.05)

Metropolitan-area variables

Homeownership rate 3.812 3.374

(1.22) (1.79)

Rent-to-income ratio 2.209 1.626

(2.43) (3.71)

Housing vacancy rate – 1.917 – 0.304

(1.66) (2.35)

Interactions

Metropolitan-area

homeownership rate x Black 6.868

(3.05)

Metropolitan-area

homeownership rate x Latino – 5.309

(3.11)

Metropolitan-area rent-to-income 

ratio x Black 8.832

(6.33)

Metropolitan-area rent-to-income 

ratio x Latino – 6.888

(5.76)

Metropolitan-area vacancy rate x

Black – 6.608

(4.03)

Metropolitan-area vacancy rate x

Latino 3.916

(4.34)

Constant – 0.942 – 0.461 – 3.273 – 3.026

(0.03) (0.11) (1.12) (1.69)

BIC 13428.872 13417.389 13390.45 13422.567

BIC = Bayesian information criterion.

Source: Recent movers in the American Housing Survey, 2011
F-9

Exhibit F.6: Logistic Regression Coefficients and Standard Errors From Regression Analysis of 

Whether Recently Mobile Householder First Heard of the New Dwelling by Seeing a Sign

Model 1 Model 2 Model 3 Model 4

Race (reference: White)

Black 0.267 0.207 0.224 – 3.103

(0.08) (0.08) (0.08) (4.21)

Latino 0.833 0.655 0.582 – 2.292

(0.06) (0.08) (0.08) (3.67)

Education (reference: < high school)

High school degree 0.068 0.054 0.054

(0.08) (0.08) (0.08)

College degree – 0.311 – 0.323 – 0.329 

(0.11) (0.11) (0.11)

Income (reference: <$5k)

5–10k 0.089 0.118 0.119

(0.15) (0.15) (0.15)

10–15k 0.353 0.367 0.367 

(0.14) (0.14) (0.14)

15–20k 0.480 0.481 0.483

(0.13) (0.13) (0.13)

20–30k 0.159 0.173 0.177

(0.12) (0.12) (0.12)

30–40k 0.068 0.080 0.082

(0.13) (0.13) (0.13)

40–50k 0.050 0.062 0.066

(0.14) (0.14) (0.14)

50–60k – 0.115 – 0.116 – 0.113

(0.16) (0.16) (0.16)

60–70k 0.018 0.028 0.031

(0.17) (0.17) (0.17)

70–80k – 0.029 – 0.034 – 0.034

(0.17) (0.17) (0.17)

80–90k 0.089 0.061 0.058

(0.20) (0.20) (0.20)

90–100k 0.325 0.303 0.309

(0.21) (0.21) (0.21)

100–125k – 0.936 – 0.948 – 0.948

(0.27) (0.27) (0.27)

125–150k – 0.589 – 0.613 – 0.626

(0.29) (0.29) (0.29)

150k+ – 0.425 – 0.444 – 0.460

(0.23) (0.23) (0.23)

Age 0.003 0.002 0.002

(0.00) (0.00) (0.00)

Female (1=yes) – 0.038 – 0.031 – 0.032

(0.06) (0.06) (0.06)

Married (1=yes) 0.107 0.091 0.093

(0.07) (0.07) (0.07)

Foreign born (1=yes) 0.077 0.055 0.052

(0.08) (0.08) (0.08)

Involuntary move (1=yes) 0.155 0.146 0.145

(0.10) (0.10) (0.10)

Receives housing assistance (1=yes) – 0.582 – 0.587 – 0.590

(0.12) (0.12) (0.12)

Access to car (1=yes) 0.013 – 0.004 – 0.002
F-10

Model 1 Model 2 Model 3 Model 4

(0.07) (0.07) (0.07)

Single-family building (1=yes) – 0.100 – 0.115 – 0.114

(0.07) (0.07) (0.07)

Metropolitan-area variables

Homeownership rate – 1.935 – 3.960

(1.71) (2.74)

Rent-to-income ratio 2.217 – 1.872

(3.30) (5.54)

Housing vacancy rate 4.261 4.960

(2.30) (3.50)

Interactions

Metropolitan-area homeownership rate x

Black 3.432

(4.50)

Metropolitan-area homeownership rate x

Latino 2.799

(4.15)

Metropolitan-area rent-to-income ratio x

Black 5.363

(9.23)

Metropolitan-area rent-to-income ratio x

Latino 5.885

(7.67)

Metropolitan-area vacancy rate x Black – 0.065

(5.79)

Metropolitan-area vacancy rate x Latino – 1.232

(5.72)

Constant – 2.187 – 2.258 – 1.791 0.268

(0.04) (0.16) (1.55) (2.56)

BIC 8264.831 8348.377 8348.778 8401.361

BIC = Bayesian information criterion.

Source: Recent movers in the American Housing Survey, 2011
F-11

Exhibit F.7: Logistic Regression Coefficients and Standard Errors From Regression Analysis of 

Whether Recently Mobile Renter First Heard of the New Dwelling in a Newspaper

Model 1 Model 2 Model 3 Model 4

Race (reference: White)

Black 0.544 0.270 0.260 20.321

(0.10) (0.11) (0.11) (5.28)

Latino – 0.203 – 0.230 – 0.184 – 4.549

(0.11) (0.13) (0.13) (6.61)

Education (reference: < high school)

High school degree – 0.023 – 0.015 – 0.005

(0.12) (0.12) (0.12)

College degree – 0.421 – 0.404 – 0.375

(0.16) (0.16) (0.16)

Income (reference: <$5k)

5–10k 0.098 0.078 0.083

(0.19) (0.19) (0.19)

10–15k – 0.009 – 0.024 – 0.016

(0.19) (0.19) (0.19)

15–20k 0.225 0.213 0.219

(0.18) (0.18) (0.19)

20–30k – 0.228 – 0.244 – 0.242

(0.17) (0.17) (0.17)

30–40k 0.002 – 0.011 – 0.004

(0.18) (0.18) (0.18)

40–50k – 0.161 – 0.165 – 0.155

(0.20) (0.20) (0.20)

50–60k – 0.044 – 0.040 – 0.029

(0.21) (0.21) (0.21)

60–70k – 0.695 – 0.692 – 0.679

(0.28) (0.29) (0.29)

70–80k – 0.476 – 0.470 – 0.474

(0.27) (0.27) (0.27)

80–90k – 0.872 – 0.854 – 0.828 

(0.37) (0.37) (0.37)

90–100k – 0.634 – 0.619 – 0.626

(0.39) (0.39) (0.39)

100–125k – 0.734 – 0.707 – 0.682

(0.34) (0.34) (0.34)

125–150k – 1.673 – 1.655 – 1.623

(0.60) (0.60) (0.60)

150k+ – 1.227 – 1.189 – 1.122 

(0.41) (0.42) (0.42)

Age 0.010 0.010 0.011

(0.00) (0.00) (0.00)

Female (1=yes) 0.256 0.249 0.244

(0.09) (0.09) (0.09)

Married (1=yes) 0.295 0.296 0.297 

(0.10) (0.10) (0.11)

Foreign born (1=yes) – 0.457 – 0.424 – 0.432

(0.15) (0.15) (0.15)

Involuntary move (1=yes) – 0.207 – 0.204 – 0.213

(0.16) (0.16) (0.16)

Receives housing assistance (1=yes) 0.008 0.004 -0.007

(0.14) (0.14) (0.14)

Access to car (1=yes) – 0.066 – 0.058 – 0.055
F-12

Model 1 Model 2 Model 3 Model 4

(0.10) (0.10) (0.10)

Single-family building (1=yes) 0.125 0.122 0.131

(0.10) (0.10) (0.10)

Metropolitan-area variables

Homeownership rate 4.553 10.666

(2.46) (3.56)

Rent-to-income ratio 5.505 15.611

(4.96) (7.38)

Housing vacancy rate – 5.739 – 11.334

(3.37) (4.92)

Interactions

Metropolitan-area homeownership rate x

Black – 21.230

(5.63)

Metropolitan-area homeownership rate x

Latino 3.798

(7.52)

Metropolitan-area rent-to-income ratio x

Black – 43.317

(11.72)

Metropolitan-area rent-to-income ratio x

Latino 14.727

(13.93)

Metropolitan-area vacancy rate x Black 24.348 

(7.53)

Metropolitan-area vacancy rate x Latino – 11.421

(11.10)

Constant – 2.936 – 3.091 – 6.638 – 12.146

(0.06) (0.23) (2.27) (3.35)

BIC 4502.178 4603.566 4624.901 4654.210

BIC = Bayesian information criterion.

Source: Recent movers in the American Housing Survey, 2011
F-13

Exhibit F.8: Logistic Regression Coefficients and Standard Errors From Regression Analysis of 

Whether Recently Mobile Householder First Heard About the New Dwelling From an Agent

Model 1 Model 2 Model 3 Model 4

Race (reference: White)

Black 0.020 0.259 0.261 6.831

(0.11) (0.12) (0.12) (5.71)

Latino – 0.603 – 0.416** – 0.435 7.427

(0.12) (0.14) (0.14) (6.80)

Education (reference: < high school)

High school degree 0.006 – 0.001 0.007

(0.15) (0.15) (0.15)

College degree 0.059 0.062 0.073

(0.17) (0.17) (0.17)

Income (reference: <$5k)

5–10k – 0.392 – 0.391 – 0.395

(0.25) (0.25) (0.25)

10–15k – 0.329 – 0.332 – 0.325

(0.23) (0.23) (0.23)

15–20k – 0.493 – 0.505 – 0.500

(0.25) (0.25) (0.25)

20–30k – 0.430 – 0.439 – 0.439

(0.20) (0.20) (0.20)

30–40k – 0.141 – 0.150 – 0.140

(0.20) (0.20) (0.20)

40–50k 0.021 0.015 0.015

(0.21) (0.21) (0.21)

50–60k – 0.126 – 0.137 – 0.131

(0.23) (0.23) (0.23)

60–70k 0.206 0.198 0.200

(0.23) (0.23) (0.23)

70–80k 0.057 0.049 0.061

(0.24) (0.24) (0.24)

80–90k 0.288 0.268 0.266

(0.26) (0.26) (0.26)

90–100k 0.344 0.326 0.313

(0.28) (0.28) (0.28)

100–125k 0.294 0.293 0.290

(0.25) (0.25) (0.26)

125–150k 0.247 0.229 0.234

(0.29) (0.29) (0.29)

150k+ 0.629 0.625 0.621 

(0.23) (0.24) (0.24)

Married (1=yes) 0.122 0.109 0.101

(0.10) (0.10) (0.10)

Foreign born (1=yes) – 0.107 – 0.104 – 0.095

(0.14) (0.14) (0.14)

Age 0.000 0.000 0.000

(0.00) (0.00) (0.00)

Female (1=yes) – 0.105 – 0.103 – 0.107

(0.09) (0.09) (0.09)

Involuntary move (1=yes) – 0.093 – 0.099 – 0.095

(0.17) (0.17) (0.17)

Receives housing assistance (1=yes) 0.174 0.171 0.164

(0.18) (0.18) (0.18)

Access to car (1=yes) 0.289 0.284 0.296
F-14

Model 1 Model 2 Model 3 Model 4

(0.12) (0.12) (0.12)

Single-family building (1=yes) 0.236 0.232 0.231 

(0.10) (0.10) (0.10)

Metropolitan-area variables

Homeownership rate 3.741 7.262 

(2.63) (3.51)

Rent-to-income ratio 8.725 19.448 

(5.26) (7.15)

Housing vacancy rate – 4.425 – 7.295

(3.55) (4.67)

Interactions

Metropolitan-area homeownership rate x

Black – 5.419

(6.11)

Metropolitan-area homeownership rate x

Latino – 5.308

(7.73)

Metropolitan-area rent-to-income ratio x

Black – 17.930

(12.60)

Metropolitan-area rent-to-income ratio x

Latino – 22.978

(14.22)

Metropolitan-area vacancy rate x Black 5.183

– 8.23

Metropolitan-area vacancy rate x Latino 2.51

– 10.93

Constant – 2.778 – 3.113 – 6.833 – 10.999

(0.06) (0.25) (2.43) (3.29)

BIC 4324.414 4464.401 4489.316 4534.719

BIC = Bayesian information criterion.

Source: Recent movers in the American Housing Survey, 2011
F-15

Exhibit F.9: Logistic Regression Coefficients and Standard Errors From Regression Analysis of 

Whether Recently Mobile Householder First Heard of the New Dwelling From Online Sources

Model 1 Model 2 Model 3 Model 4

Race (reference: White)

Black – 1.131 – 0.711 – 0.669 0.146

(0.07) (0.07) (0.07) (3.83)

Latino – 1.008 – 0.526 – 0.585 – 6.543

(0.06) (0.07) (0.07) (3.59)

Education (reference: < high school)

High school degree 0.620 0.600 0.605

(0.10) (0.10) (0.10)

College degree 1.286 1.255 1.255

(0.10) (0.11) (0.11)

Income (reference: <$5k)

5–10k – 0.083 – 0.064 – 0.071

(0.15) (0.15) (0.15)

10–15k – 0.099 – 0.083 – 0.085

(0.14) (0.14) (0.14)

15–20k – 0.164 – 0.138 – 0.144

(0.14) (0.14) (0.14)

20–30k 0.117 0.129 0.131

(0.11) (0.11) (0.11)

30–40k 0.330 0.333 0.330 

(0.12) (0.12) (0.12)

40–50k 0.230 0.212 0.210

(0.12) (0.12) (0.12)

50–60k 0.454 0.430 0.426

(0.13) (0.13) (0.13)

60–70k 0.558 0.517 0.506

(0.13) (0.13) (0.13)

70–80k 0.449 0.415 0.415 

(0.14) (0.14) (0.14)

80–90k 0.713 0.647 0.653

(0.15) (0.15) (0.15)

90–100k 0.500 0.446 0.441 

(0.17) (0.17) (0.17)

100–125k 0.769 0.704 0.701

(0.15) (0.15) (0.15)

125–150k 0.706 0.615 0.613

(0.17) (0.17) (0.17)

150k+ 0.796 0.674 0.666

(0.14) (0.14) (0.14)

Age – 0.025 – 0.026 – 0.026

(0.00) (0.00) (0.00)

Female (1=yes) 0.099 0.106 0.109

(0.05) (0.05) (0.05)

Married (1=yes) 0.080 0.083 0.086

(0.06) (0.06) (0.06)

Foreign born (1=yes) – 0.329 – 0.381 – 0.371

(0.08) (0.08) (0.08)

Involuntary move (1=yes) 0.226 0.233 0.233

(0.09) (0.09) (0.09)

Receives housing assistance (1=yes) – 0.487 – 0.487 – 0.493

(0.13) (0.13) (0.13)

Access to car (1=yes) 0.095 0.093 0.090

(0.06) (0.06) (0.06)
F-16

Model 1 Model 2 Model 3 Model 4

Single-family building (1=yes) – 0.031 0.026 0.025

(0.06) (0.06) (0.06)

Metropolitan-area variables

Homeownership rate – 3.111 – 4.003

(1.49) (1.87)

Rent-to-income ratio – 3.192 – 4.782

(2.99) (3.85)

Housing vacancy rate – 1.304 – 0.450

(1.98) (2.41)

Interactions

Metropolitan-area homeownership rate 

x Black – 1.363

(4.12)

Metropolitan-area homeownership rate 

x Latino 7.468

(4.10)

Metropolitan-area rent-to-income ratio x

Black 1.315

(8.37)

Metropolitan-area rent-to-income ratio x

Latino 9.584

(7.50)

Metropolitan-area vacancy rate x Black – 1.500

(5.50)

Metropolitan-area vacancy rate x Latino – 7.682

(5.82)

Constant – 0.691 – 1.085 1.744 2.561

(0.03) (0.15) (1.39) (1.77)

BIC 11345.261 10728.223 10711.391 10755.417

BIC = Bayesian information criterion.

Source: Recent movers in the American Housing Survey, 2011
F-17

Exhibit F.10: Logistic Regression Coefficients and Standard Errors From Regression Analysis of 

Whether Stopped Searching Because Happy With Unit

Model 1 Model 2 Model 3 Model 4

Race (reference: White)

Black – 0.466 – 0.243 – 0.232 – 8.420

(0.070) (0.076) (0.076) (3.851)

Latino – 0.322 – 0.164 – 0.157 – 3.053

(0.062) (0.077) (0.078) (3.543)

Education (reference: < high school)

High school degree 0.165 0.164 0.164

(0.076) (0.076) (0.077)

College degree 0.236 0.230 0.223

(0.097) (0.098) (0.098)

Income (reference: <$5k)

5–10k 0.090 0.096 0.094

(0.132) (0.132) (0.132)

10–15k 0.180 0.189 0.188

(0.123) (0.123) (0.124)

15–20k 0.357 0.382 0.381

(0.128) (0.128) (0.128)

20–30k 0.332 0.341 0.349

(0.107) (0.107) (0.107)

30–40k 0.578 0.588 0.588

(0.122) (0.122) (0.122)

40–50k 0.526 0.523 0.530

(0.125) (0.125) (0.125)

50–60k 0.431 0.426 0.428

(0.139) (0.139) (0.140)

60–70k 0.610 0.598 0.601

(0.157) (0.157) (0.158)

70–80k 0.638 0.629 0.627

(0.158) (0.158) (0.159)

80–90k 0.727 0.708 0.698

(0.204) (0.204) (0.205)

90–100k 0.887 0.894 0.908

(0.240) (0.241) (0.242)

100–125k 0.693 0.666 0.654

(0.183) (0.183) (0.184)

125–150k 0.747 0.728 0.700

(0.236) (0.237) (0.237)

150k+ 0.820 0.775 0.742

(0.192) (0.193) (0.193)

Age 0.005 0.005 0.005

(0.002) (0.002) (0.002)

Female (1=yes) – 0.032 – 0.030 – 0.031

(0.057) (0.057) (0.057)

Married (1=yes) – 0.090 – 0.083 – 0.082

(0.066) (0.067) (0.067)

Foreign born (1=yes) – 0.024 – 0.051 – 0.056

(0.080) (0.081) (0.081)

Involuntary move (1=yes) – 0.727 – 0.710 – 0.708

(0.103) (0.103) (0.103)

Receives housing assistance (1=yes) – 0.129 – 0.121 – 0.118

(0.099) (0.099) (0.099)

Access to car (1=yes) 0.096 0.094 0.097
F-18

Model 1 Model 2 Model 3 Model 4

(0.064) (0.064) (0.065)

Single-family building (1=yes) 0.195 0.235 0.238

(0.065) (0.066) (0.067)

Metropolitan-area variables

Homeownership rate – 3.973 – 6.775

(1.613) (2.387)

Rent-to-income ratio – 8.429 – 13.623

(3.197) (4.916)

Housing vacancy rate 1.793 2.606

(2.156) (3.082)

Interactions

Metropolitan-area homeownership rate x

Black 8.293

(4.093)

Metropolitan-area homeownership rate x

Latino 3.116

(3.991)

Metropolitan-area rent-to-income ratio x

Black 15.447

(8.499)

Metropolitan-area rent-to-income ratio x

Latino 4.683

(7.448)

Metropolitan-area vacancy rate x Black – 3.296

(5.300)

Metropolitan-area vacancy rate x Latino – 0.546

(5.516)

Constant 0.459 – 0.360 3.719 6.498

(0.039) (0.143) (1.484) (2.251)

BIC 7802.293 7834.662 7845.037 7886.616

BIC = Bayesian information criterion.

Source: Recent movers in the American Housing Survey, 2011
F-19

Exhibit F.11: Logit Coefficients and Standard Errors From Regression Analysis of Whether Moved 

to a Different Housing Unit Between 2-Year Interviews

Model 1 Model 2 Model 3

Race (reference = White)

Black – 0.229 – 0.190 1.760

(0.042) (0.045) (1.018)

Latino – 0.246 – 0.285 – 2.316

(0.081) (0.088) (1.732)

Education of head – 0.012 – 0.016

(0.008) (0.008)

Family income (thousands in dollars) 0.001 0.000

(0.001) (0.001)

Age of householder – 0.046 – 0.046

(0.002) (0.002)

Female householder (1=yes) 0.058 0.060

(0.055) (0.055)

Married or cohabiting (1=yes) 0.001 0.010

(0.056) (0.056)

Whether children in house (1=yes) – 0.221 – 0.208

(0.043) (0.043)

Receiving housing assistance (1=yes) – 0.271 – 0.281

(0.049) (0.049)

Year of observation 0.081 0.082

(0.004) (0.004)

Metropolitan-area variables

Housing vacancy rate – 0.001 – 0.007

(0.005) (0.007)

Homeownership rate 0.014 0.014

(0.005) (0.007)

Rent-to-income ratio 0.295 3.932

(1.215) (1.782)

Interactions

Metropolitan-area vacancy rate x Black 0.011

(0.011)

Metropolitan-area vacancy rate x Latino – 0.008

(0.031)

Metropolitan-area homeownership rate x Black – 0.011

(0.010)

Metropolitan-area homeownership rate x Latino 0.056

(0.021)

Metropolitan-area rent-to-income ratio x Black – 7.316

(2.611)

Metropolitan-area rent-to-income ratio x Latino – 6.366

(4.124)

Constant 0.202 – 161.268 – 162.750

(0.030) (7.245) (7.263)

BIC 26371.654 24039.458 24035.813

BIC = Bayesian information criterion.

Source: Recent movers in the Panel Study of Income Dynamics, 1997–2011
F-20

Exhibit F.12: Logit Coefficients and Standard Errors From Regression Analysis of Whether 

Residential Move Was to a Different Neighborhood

Model 1 Model 2 Model 3 Model 4

Race (reference = White)

Black – 0.143 – 0.056 – 0.082 2.575

(0.046) (0.052) (0.053) (1.188)

Latino – 0.232 – 0.260 – 0.330 0.547

(0.091) (0.097) (0.102) (1.999)

Education of head 0.019 0.016 0.015

(0.010) (0.010) (0.010)

Family income (thousands in dollars) 0.001 0.000 0.000

(0.001) (0.001) (0.001)

Age of householder – 0.034 – 0.034 – 0.034

(0.002) (0.002) (0.002)

Female householder (1=yes) – 0.003 – 0.002 0.003

(0.062) (0.062) (0.062)

Married or cohabiting (1=yes) – 0.098 – 0.085 – 0.080

(0.066) (0.066) (0.066)

Whether children in house (1=yes) – 0.121 – 0.114 – 0.112

(0.052) (0.052) (0.052)

Receiving housing assistance (1=yes) – 0.357 – 0.360 – 0.364

(0.062) (0.062) (0.062)

Year of observation 0.083 0.084 0.084

(0.005) (0.005) (0.005)

Metropolitan-area variables

Housing vacancy rate – 0.032 – 0.036

(0.006) (0.007)

Homeownership rate 0.014 0.023

(0.006) (0.008)

Rent-to-income ratio 4.064 7.780

(1.396) (1.929)

Interactions

Metropolitan-area vacancy rate x Black 0.006

(0.013)

Metropolitan-area vacancy rate x Latino – 0.017

(0.035)

Metropolitan-area homeownership rate x Black – 0.022

(0.012)

Metropolitan-area homeownership rate x Latino 0.015

(0.023)

Metropolitan-area rent-to-income ratio x Black – 6.648

(3.023)

Metropolitan-area rent-to-income ratio x Latino – 8.065

(4.891)

Constant 0.103 – 165.825 – 167.912 – 170.069

(0.032) (9.903) (9.929) (9.942)

BIC 13705.910 13027.032 13020.245 13059.665

BIC = Bayesian information criterion.

Source: Recent movers in the Panel Study of Income Dynamics, 1997–2011
F-21

Exhibit F.13: Logit Coefficients and Standard Errors From Regression Analysis of Whether an 

Expected Move Was Not Carried Out

Model 1 Model 2 Model 3 Model 4

Race (reference = White)

Black 0.438 0.329 0.304 – 0.909

(0.053) (0.058) (0.059) (1.314)

Latino 0.399 0.479 0.343 1.937

(0.097) (0.100) (0.104) (2.090)

Education of head 0.021 0.018 0.020

(0.010) (0.010) (0.010)

Family income (thousands in dollars) 0.002 0.001 0.001

(0.001) (0.001) (0.001)

Age of householder 0.042 0.042 0.042

(0.002) (0.002) (0.002)

Female householder (1=yes) – 0.080 – 0.075 – 0.079

(0.068) (0.068) (0.068)

Married or cohabiting (1=yes) – 0.196 – 0.188 – 0.192

(0.071) (0.071) (0.071)

Whether children in house (1=yes) 0.192 0.204 0.196

(0.057) (0.057) (0.057)

Receiving housing assistance (1=yes) 0.343 0.335 0.342

(0.068) (0.067) (0.068)

Year of observation – 0.057 – 0.057 – 0.057

(0.006) (0.006) (0.006)

Metropolitan-area variables

Housing vacancy rate – 0.012 – 0.008

(0.007) (0.009)

Homeownership rate – 0.005 – 0.006

(0.006) (0.009)

Rent-to-income ratio 2.675 0.165

(1.526) (2.305)

Interactions

Metropolitan-area vacancy rate x Black – 0.008

(0.015)

Metropolitan-area vacancy rate x Latino 0.029

(0.038)

Metropolitan-area homeownership rate x Black 0.006

(0.013)

Metropolitan-area homeownership rate x Latino – 0.042

(0.026)

Metropolitan-area rent-to-income ratio x Black 4.727

(3.357)

Metropolitan-area rent-to-income ratio x Latino 3.400

(4.877)

Constant – 0.885 111.895 110.986 111.618

(0.039) (11.469) (11.492) (11.500)

BIC 2899.919 12281.533 12289.420 12332.370

BIC = Bayesian information criterion.

Source: Renters in the Panel Study of Income Dynamics, 1997–2011
F-22

Exhibit F.14A: Bivariate Correlation Coefficients for Selected Aspects of the Housing Search

Reason for Move

Forced

Closer to Job or 

School

Get Better 

Quality

Reason for move

Forced 1

Closer to job or school – 0.1427 1

Get better quality – 0.0678 – 0.1691 1

Method of search

Word of mouth – 0.0149 – 0.053 0.0135

Online – 0.0088 0.133 – 0.0239

Saw a sign 0.0237 – 0.046 0.0244

Number of units searched 0.0224 0.0572 – 0.0342

Searched other neighborhoods 0.0245 0.0572 – 0.0126

Constrained choice 0.0331 0.0361 – 0.0382

Rating of unit – 0.047 – 0.0066 0.0513

Rating of neighborhood -0.0252 0.0162 0.0432

Source: Recent movers in the American Housing Survey, 2011

Exhibit F.14B: Bivariate Correlation Coefficients for Selected Aspects of the Housing Search

Method of Search

Word of 

Mouth Online

Saw a 

Sign

Number of 

Units 

Searched

Searched 

Other 

Neighborhoods

Constrained 

Choice

Rating 

of Unit

Method of 

search

Word of mouth 1

Online – 0.4048 1

Saw a sign – 0.2806

–

0.2249 1

Number of units 

searched – 0.1335 0.1517 0.0082 1

Searched other 

neighborhoods – 0.1667 0.2017 0.0453 0.3052 1

Constrained 

choice 0.0568

–

0.0063

–

0.0159 0.0163 0.0176 1

Rating of unit 0.0037 0.0113

–

0.0056 – 0.0388 – 0.0337 – 0.1346 1

Rating of 

neighborhood – 0.0028 0.0311 0.0086 – 0.0316 – 0.0236 – 0.1248 0.5924

Source: Recent movers in the American Housing Survey, 2011
G-1

Appendix G. Regression Analysis of Racial and Ethnic Differences in Factors 

That May Shape Housing Searches

Racial Differences in Importance of Housing Unit and Neighborhood Search Criteria

Exhibit G.1: Baseline Importance, Quality of Schools

Current searcher, ordered logistic regression, multivariate

Coefficient Std. Error

Black 0.987*** (0.29)

Low-income 0.423 (0.35)

Medium-income – 0.067 (0.33)

Age – 0.016 (0.01)

Female 0.178 (0.27)

Children 3.396*** (0.51)

Cut1

Constant – 0.253 (0.48)

Cut2

Constant 0.866 (0.48)

Cut3

Constant 1.581** (0.49)

N 249

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the four ordered categories: 1—“not at all 

important”; 2—“not very important”; 3—“somewhat important”; and 4—“very important.”

Source: Housing Search Study survey, 2016

Exhibit G.2: Baseline Importance, Quality of Schools

Current searcher, ordered logistic regression, multivariate

Coefficient Std. Error

Black 1.340*** (0.27)

Cut1

Constant 0.106 (0.22)

Cut2

Constant 1.043*** (0.23)

Cut3

Constant 1.556*** (0.24)

N 249

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the four ordered categories: 1—“not at all 

important”; 2—“not very important”; 3—“somewhat important”; and 4—“very important.”

Source: Housing Search Study survey, 2016
G-2

Exhibit G.3: Baseline Importance, Safety of Neighborhood

Current searcher, ordered logistic regression, multivariate

Coefficient Std. Error

Black 2.084*** (0.44)

Low-income 0.456 (0.54)

Medium-income 0.827 (0.48)

Age 0.028 (0.02)

Female 1.264** (0.43)

Children 0.172 (0.70)

Cut1

Constant – 1.421 (0.85)

Cut2

Constant 1.709* (0.72)

N 249

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the four ordered categories: 1—“not at all 

important”; 2—“not very important”; 3—“somewhat important”; and 4—“very important.” No participant 

listed “not at all important” so estimates are not available.

Source: Housing Search Study survey, 2016

Exhibit G.4: Baseline Importance, Safety of Neighborhood

Current searcher, ordered logistic regression, bivariate

Coefficient Std. Error

Black 2.409*** (0.41)

Cut1

Constant – 3.332*** (0.59)

Cut2

Constant – 0.416 (0.24)

N 249

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the four ordered categories: 1—“not at all 

important”; 2—“not very important”; 3—“somewhat important”; and 4—“very important.” No participant 

listed “not at all important” so estimates are not available.

Source: Housing Search Study survey, 2016
G-3

Exhibit G.5: Baseline Importance, Security of the Building 

Current searcher, ordered logistic regression, multivariate

Coefficient Std. Error

Black 2.113*** (0.44)

Low-income 0.403 (0.50)

Medium-income 1.424** (0.49)

Age 0.030* (0.02)

Female 0.685 (0.41)

Children – 0.324 (0.60)

Cut1

Constant – 1.47 (0.85)

Cut2

Constant – 0.564 (0.73)

Cut3

Constant 1.657* (0.70)

N 249

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the four ordered categories: 1—“not at all 

important”; 2—“not very important”; 3—“somewhat important”; and 4—“Very important.”

Source: Housing Search Study survey, 2016

Exhibit G.6: Baseline Importance, Security of the Building

Current searcher, ordered logistic regression, bivariate

Coefficient Std. Error

Black 2.301*** (0.39)

Cut1

Constant – 3.364*** (0.60)

Cut2

Constant – 2.482*** (0.41)

Cut3

Constant – 0.421 (0.24)

N 249

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the four ordered categories: 1—“not at all 

important”; 2—“not very important”; 3—“somewhat important”; and 4—“Very important.”

Source: Housing Search Study survey, 2016
G-4

Changes in the Importance of Unit and Neighborhood Search Criteria

Exhibit G.7: Change in Importance, Safety of Neighborhood

Current searcher, ordered logistic regression, multivariate

Coefficient Std. Error

Black – 0.788 (0.52)

Low-income – 1.623* (0.65)

Medium-income – 1.367* (0.62)

Age 0.024 (0.02)

Female 0.108 (0.46)

Children 0.798 (0.56)

Cut1

Constant – 3.298*** (0.92)

Cut2

Constant 2.913** (0.89)

N 227 (0.52)

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the three ordered categories: 1—“less 

important”; 2—“no change”; and 3—“more important.”

Source: Housing Search Study survey, 2016

Exhibit G.8: Change in Importance, Safety of Neighborhood

Current searcher, ordered logistic regression, bivariate

Coefficient Std. Error

Black – 0.753 (0.50)

Cut1

Constant – 3.242*** (0.50)

Cut2

Constant 2.583*** (0.44)

N 227

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the three ordered categories: 1—“less 

important”; 2—“no change”; and 3—“more important.”

Source: Housing Search Study survey, 2016
G-5

Exhibit G.9: Change in Importance, Security of the Building

Current searcher, ordered logistic regression, multivariate

Coefficient Std. Error

Black – 0.562 (0.48)

Low income – 0.156 (0.57)

Medium income – 0.334 (0.54)

Age 0.019 (0.02)

Female 0.77 (0.45)

Children 0.24 (0.52)

Cut1

Constant – 1.907* (0.79)

Cut2

Constant 3.665*** (0.86)

N 227 (0.48)

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the three ordered categories: 1—“less 

important”; 2—“no change”; and 3—“more important.”

Source: Housing Search Study survey, 2016

Exhibit G.10: Change in Importance, Security of the Building

Current searcher, ordered logistic regression, bivariate

Coefficient Std. Error

Black – 0.342 (0.45)

Constant

Cut2 – 2.838*** (0.44)

Constant

N 2.562***

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the three ordered categories: 1—“less 

important”; 2—“no change”; and 3—“more important.”

Source: Housing Search Study survey, 2016
G-6

Assessment of New Neighborhood Features as Compared With What Was Hoped For

Exhibit G.11: What About the Quality of the Schools in Your Current Neighborhood?

Recent mover, ordered logistic regression, multivariate

Coefficient Std. Error

Black 1.150* (0.45)

Low-income 0.558 (0.60)

Medium-income 0.639 (0.49)

Age 0.038* (0.02)

Female 0.135 (0.44)

Children 1.728*** (0.45)

Cut1

Constant 3.583*** (0.84)

Cut2

Constant 4.020*** (0.86)

Cut3

Constant 5.135*** (0.91)

N 121

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the four ordered categories: 1—“did that not 

matter to you”; 2—“not as good as you hoped for”; 3—“about what you hoped for”; and 4—“better than 

you hoped for.”

Source: Housing Search Study survey, 2016

Exhibit G.12: What About the Quality of the Schools in Your Current Neighborhood? 

Recent mover, ordered logistic regression, bivariate 

Coefficient Std. Error

Black 1.271** (0.40)

Cut1

Constant 1.287*** (0.32)

Cut2

Constant 1.647*** (0.34)

Cut3

Constant 2.651*** (0.39)

N 121

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the four ordered categories: 1—“did that not 

matter to you”; 2—“not as good as you hoped for”; 3—“about what you hoped for”; and 4—“better than 

you hoped for.”

Source: Housing Search Study survey, 2016
G-7

Exhibit G.13: Is This Housing Unit Better, Worse, or About the Same as Your Previous Home?

Recent mover, ordered logistic regression, multivariate

Coefficient Std. Error

Black 0.977* (0.41) 

Low-income – 0.762 (0.56)

Medium-income – 1.045* (0.45) 

Age – 0.033* (0.02) 

Female – 0.612 (0.38) 

Children 0.187 (0.49) 

Cut1

Constant – 3.579*** (0.75) 

Cut2

Constant – 2.108** (0.71) 

N 130

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the three ordered categories: 1—“worse”; 2—

“about the same”; and 3—“better”.

Source: Housing Search Study survey, 2016

Exhibit G.14: Is This Housing Unit Better, Worse, or About the Same as Your Previous Home?

Recent mover, ordered logistic regression, bivariate 

Coefficient Std. Error

Black 0.49 (0.35)

Cut1

Constant – 1.524*** (0.30)

Cut2

Constant – 0.153 (0.26)

N 131

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-8

Exhibit G.15: How About Your Current Neighborhood’s Location Near Friends or Family? 

Recent mover, ordered logistic regression, multivariate

Coefficient Std. Error

Black – 0.213 (0.36)

Low-income 0.258 (0.50)

Medium-income 0.007 (0.38)

Age – 0.011 (0.01)

Female 0.324 (0.34)

Children 0.009 (0.40)

Cut1

Constant – 1.424* (0.64)

Cut2

Constant – 0.624 (0.63)

Cut3

Constant 0.849 (0.63)

N 130

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the four ordered categories: 1—“did that not 

matter to you”; 2—“not as good as you hoped for”; 3—“about what you hoped for”; and 4—“better than 

you hoped for.”

Source: Housing Search Study survey, 2016

Exhibit G.16

How About Your Current Neighborhood’s Location Near Friends or Family?

Recent mover, ordered logistic regression, bivariate 

Coefficient Std. Error

Black – 0.215 (0.32)

Cut1

Constant – 1.248*** (0.27)

Cut2

Constant – 0.468 (0.24)

Cut3

Constant 0.982*** (0.25)

N 131

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the four ordered categories: 1—“did that not 

matter to you”; 2—“not as good as you hoped for”; 3—“about what you hoped for”; and 4—“better than 

you hoped for.”

Source: Housing Search Study survey, 2016
G-9

Baseline Racial Differences in the Importance of Treatment by Landlords

Exhibit G.17: Responsiveness of the Landlord or Management Company to Respondent’s 

Concerns

Recent movers, ordered logistic regression, multivariate

Coefficient Std. Error

Black 1.850*** (0.39)

Low-income 0.176 (0.47)

Medium-income 0.771 (0.45)

Age 0.013 (0.01)

Female 0.522 (0.38)

Children – 0.806 (0.47)

Cut1

Constant – 2.511** (0.82)

Cut2

Constant – 1.613* (0.70)

Cut3

Constant 0.668 (0.64)

N 249

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the four ordered categories: 1—“not at all 

important”; 2—“not very important”; 3—“somewhat important”; and 4—“Very important.”

Source: Housing Search Study survey, 2016

Exhibit G.18: Responsiveness of the Landlord or Management Company to Respondent’s 

Concerns

Recent movers ordered logistic regression, bivariate 

Coefficient Std. Error

Black 1.819*** (0.35)

Cut1

Constant – 3.475*** (0.60)

Cut2

Constant – 2.591*** (0.41)

Cut3

Constant – 0.379 (0.24)

N 249

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the four ordered categories: 1—“not at all 

important”; 2—“not very important”; 3—“somewhat important”; and 4—“Very important.”

Source: Housing Search Study survey, 2016
G-10

Exhibit G.19: Change in Importance—Responsiveness of the Landlord or Management Company 

to Respondent’s Concerns

Recent movers ordered logistic regression, multivariate

Coefficient Std. Error

Black – 0.305 (0.39)

Low-income 0.545 (0.47)

Medium-income 0.318 (0.44)

Age 0.012 (0.01)

Female – 0.352 (0.36)

Children 0.948* (0.43)

Cut1

Constant – 1.501* (0.66)

Cut2

Constant 2.998*** (0.70)

N 227

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the three ordered categories: 1—“less 

important”; 2—“no change”; and 3—“more important.”

Source: Housing Search Study survey, 2016

Exhibit G.20: Change in Importance—Responsiveness of the Landlord or Management Company 

to Respondent’s Concerns

Recent movers ordered logistic regression, bivariate

Coefficient

Std. 

Error

Black – 0.036 (0.37)

Cut1

Constant – 2.072*** (0.35)

Cut2

Constant 2.257*** (0.36)

N 227

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: “Cut#” represent the cut points of the estimation for the three ordered categories: 1—“less 

important”; 2—“no change”; and 3—“more important.”

Source: Housing Search Study survey, 2016
G-11

Racial Differences in How Searchers Inquire About Units

Exhibit G.21

Primary Method of Inquiry About Units

Recent movers, multinomial logistic regression, multivariate

Coefficient Std. Error

email

Black – 0.931 (0.49)

Low-income – 0.794 (0.71)

Medium-income 0.084 (0.55)

Age – 0.013 (0.02)

Female – 0.135 (0.49)

Children – 1.12 (0.60)

Constant 1.322 (0.89)

Phone

Black 0 (.)

Low-income 0 (.)

Medium-income 0 (.)

Age 0 (.)

Female 0 (.)

Children 0 (.)

Constant 0 (.)

In-person visit

Black – 0.413 (0.64)

Low-income – 1.808* (0.87)

Medium-income – 1.16 (0.67)

Age 0.014 (0.02)

Female – 0.533 (0.62)

Children – 1.403 (0.85)

Constant 0.285 (1.07)

All other methods

Black 0.708 (1.09)

Low-income – 0.741 (1.22)

Medium-income – 1.323 (1.24)

Age – 0.064 (0.05)

Female – 1.578 (0.98)

Children – 0.019 (1.00)

Constant 1.171 (1.86)

N 116

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-12

Exhibit G.22: Racial Differences in How Searchers Inquire About Units

Recent movers, multinomial logistic regression, bivariate

Coefficient Std. Error

email

Black – 1.188** (0.44)

Constant 0.56 (0.31)

Phone

Black 0 (.)

Constant 0 (.)

In-person visit

Black – 0.829 (0.55)

Constant – 0.375 (0.39)

All other methods

Black 0.065 (0.92)

Constant – 2.079** (0.75)

N 116

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-13

Reports of Housing Discrimination Based on Race or Ethnicity

Exhibit G.23: Did You Ever Feel You Were Denied Housing Because the Rental Agent or Landlord 

Didn’t Want To Rent to You Because of Your Race or Ethnicity?

Current searcher, logistic regression, multivariate

Coefficient Std. Error

Black 1.666** (0.56)

Low-income – 0.027 (0.51)

Medium-income 0.212 (0.48)

Age – 0.004 (0.01)

Female – 0.133 (0.35)

Children – 0.339 (0.40)

Constant – 2.911*** (0.74)

N 330 (0.56)

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.24: Did You Ever Feel You Were Denied Housing Because the Rental Agent or Landlord 

Didn’t Want To Rent to You Because of Your Race or Ethnicity?

Current Searcher, logistic regression, bivariate 

Coefficient Std. Error

Black 1.586** (0.54)

Constant – 3.068*** (0.51)

N 333

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-14

Exhibit G.25: Did You Ever Feel That a Rental Agent or Landlord Was Showing You Only Units in 

Certain Neighborhoods Because of Your Race or Ethnicity?

Current searcher, logistic regression, multivariate

Coefficient Std. Error

Black 1.523*** (0.43)

Low-income – 0.537 (0.41)

Medium-income – 0.656 (0.39)

Age 0.004 (0.01)

Female – 0.645* (0.30)

Children 0.996** (0.32)

Constant – 1.923*** (0.58)

N 333 (0.43)

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.26: Did You Ever Feel That a Rental Agent or Landlord Was Showing You Only Units in 

Certain Neighborhoods Because of Your Race or Ethnicity?

Current searcher, logistic regression, bivariate 

Coefficient Std. Error

Black 1.464*** (0.40)

Constant – 2.327*** (0.37)

N 336

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.27: Did You Ever Feel That Other Residents in the Building or Neighborhoods Visited 

Treated You Unfairly Because of Your Race or Ethnicity?

Current searcher, logistic regression, multivariate

Coefficient Std. Error

Black 1.998** (0.64)

Low-income 0.076 (0.52)

Medium-income 0.057 (0.49)

Age – 0.023 (0.01)

Female – 0.331 (0.36)

Children 0.145 (0.38)

Constant – 2.443** (0.78)

N 333 (0.64)

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-15

Exhibit G.28: Did You Ever Feel That Other Residents in the Building or Neighborhoods 

Visited Treated You Unfairly Because Of Your Race or Ethnicity? 

Current searcher, logistic regression, bivariate 

Coefficient Std. Error

Black 1.815** (0.61)

Constant – 3.367*** (0.59)

N 336

p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-16

Reports of Housing Discrimination Based on Race or Ethnicity

Exhibit G.29: Count of Explicitly Racial Housing Search Outcomes 

Current searcher, logistic regression, multivariate

Coefficient Std. Error

Black 0.506*** (0.11)

Low-income – 0.075 (0.14)

Medium-income – 0.071 (0.13)

Age – 0.003 (0.00)

Female – 0.162 (0.10)

Children 0.167 (0.12)

Constant 0.384* (0.18)

N 335

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.30: Count of Explicitly Racial Housing Search Outcomes

Current searcher, logistic regression, bivariate 

Coefficient Std. Error

Black 0.483*** (0.17)

Constant 0.167 (0.09)

N 338

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-17

Reports of Perceived Housing Discrimination Based on Race or Ethnicity

Exhibit G.31: Did You Ever Feel That You Were Denied Housing Because the Rental 

Agent Didn’t Want To Rent to You Because of Your Race or Ethnicity? 

Recent mover, logistic regression, multivariate

Coefficient Std. Error

Black 0 (omitted)

Low-income 4.877 (350.25)

Medium-income 5.333 (350.25)

Age 0.013 (0.02)

Female 0.794 (0.52)

Children 0.88 (0.54)

Constant – 7.412 (350.25)

N 68

p < 0.05, ** p < 0.01, *** p < 0.001

Note: No White respondents indicated ‘yes’ to this question. 

Source: Housing Search Study survey, 2016

Exhibit G.32: Did You Ever Feel That You Were Denied Housing Because the Rental 

Agent Didn’t Want To Rent to You Because of Your Race or Ethnicity?

Recent mover, logistic regression, bivariate 

Coefficient Std. Error

Black 0 (omitted)

Constant – 1.115*** (0.19)

N 68

* p < 0.05, ** p < 0.01, *** p < 0.001

Notes: No White respondents indicated ‘yes’ to this question. Unable to test differences by race. 

Source: Housing Search Study survey, 2016
G-18

Exhibit G.33: Did You Ever Feel That You Were Denied Housing Because the Rental 

Agent Didn’t Want To Rent to You Because of Your Race or Ethnicity?

Recent mover, logistic regression, multivariate

Coefficient Std. Error

Black 1.107* (0.48)

Low-income – 0.298 (0.50)

Medium-income – 0.135 (0.42)

Age 0.009 (0.01)

Female – 0.051 (0.37)

Children – 0.101 (0.44)

Constant – 2.324*** (0.68)

N 129

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.34: Did You Ever Feel That You Were Denied Housing Because the Rental 

Agent Didn’t Want To Rent to You Because of Your Race or Ethnicity?

Recent mover, logistic regression, bivariate 

Coefficient Std. Error

Black 1.070* (0.44)

Constant – 2.128*** (0.40)

N 129

* p < 0.05, ** p < 0.01, *** p < 0.001

Did You Ever Feel That You Were Denied Housing Because the Rental Agent Didn’t Want 

To Rent to You Because of Your Race or Ethnicity?

Recent mover, logistic regression, multivariate

Coefficient Std. Error

Black 0.608 (0.52)

Low-income 0.44 (0.59)

Medium-income – 0.127 (0.58)

Age – 0.014 (0.02)

Female – 0.195 (0.42)

Children 0.305 (0.46)

Constant – 1.623* (0.82)

N 129

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-19

Exhibit G.36: Did You Ever Feel That You Were Denied Housing Because the Rental 

Agent Didn’t Want To Rent to You Because of Your Race or Ethnicity?

Recent mover, logistic regression, bivariate 

Coefficient Std. Error

Black 0.685 (0.49)

Constant – 2.135*** (0.40)

N 129

p < 0.05, ** p < 0.01, *** p < 0.001

Exhibit G.37: Count of Perceived Racial Biases During the Search

Recent mover, logistic regression, multivariate

Coefficient Std. Error

Black 0.284* (0.11)

Low-income 0.084 (0.15)

Medium-income 0.082 (0.12)

Age 0.002 (0.00)

Female 0.014 (0.11)

Children 0.08 (0.13)

Constant – 0.083 (0.19)

N 129

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.38: Count of Perceived Racial Biases During the Search

Recent mover, logistic regression, bivariate

Coefficient Std. Error

Black 0.320** (0.10)

Constant 0.033 (0.07)

N 129

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-20

Exhibit G.39: Did You Ever Feel That You Were Denied Housing Because the Rental 

Agent Didn’t Want To Rent to You Because of Your Race or Ethnicity?

Current searcher, probit regression, multivariate

Coefficient Std. Error

Black 0.713 – 0.562

Low-income 4.287 – 283.301

Medium-income 4.602 – 283.301

Age 0.002 – 0.015

Female 0.127 – 0.431

Children – 0.88 – 0.535

Constant – 6.26 – 283.302

N 106

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.40: Did You Ever Feel That You Were Denied Housing Because the Rental 

Agent Didn’t Want To Rent to You Because of Your Race or Ethnicity?

Current searcher, probit regression, multivariate

Coefficient Std. Error

Black 0.487 (0.49)

Constant – 1.769*** (0.45)

N 106

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.41: Did You Ever Feel That a Rental Agent or Landlord Was Showing You Only 

Units in Certain Neighborhoods Because of Your Race or Ethnicity?

Current searcher, probit regression, multivariate

Coefficient Std. Error

Black 0.532 (0.53)

Low-income 0.279 (0.60)

Medium-income 0.481 (0.56)

Age – 0.009 (0.02)

Female – 0.101 (0.43)

Children – 0.254 (0.48)

Constant – 1.676* (0.84)

N 106

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-21

Exhibit G.42: Did You Ever Feel That a Rental Agent or Landlord Was Showing You Only 

Units in Certain Neighborhoods Because of Your Race or Ethnicity?

Current searcher, probit regression, bivariate 

Coefficient Std. Error

Black 0.413 (0.49)

Constant – 1.769*** (0.45)

N 106

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.43: Did You Ever Feel That the Other Residents in the Buildings or 

Neighborhoods That You Visited Treated You Unfairly Because of Your Race or 

Ethnicity?

Current searcher, probit regression, multivariate

Coefficient Std. Error

Black 0.321 (0.76)

Low-income 0 (omitted)

Medium-income 0 (omitted)

Age – 0.002 (0.03)

Female 0 (omitted)

Children 0 (omitted)

Constant – 0.743 (1.37)

N 15

* p < 0.05, ** p < 0.01, *** p < 0.001

Note: A very limited sample reported ‘yes’ to this question, so we were unable to test differences across 

income, gender, and presence of children variables.

Source: Housing Search Study survey, recent movers, 2016

Exhibit G.44: Did You Ever Feel That the Other Residents in the Buildings or 

Neighborhoods That You Visited Treated You Unfairly Because of Your Race or 

Ethnicity?

Current searcher, probit regression, bivariate 

Coefficient Std. Error

Black – 0.012 (0.52)

Constant – 1.769*** (0.45)

N 106

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-22

Exhibit G.45: Count of Explicitly Racial Housing Search Outcomes

Current searcher, OLS regression, multivariate

Coefficient Std. Error

Black 0.176 (0.14)

Low-income 0.122 (0.17)

Medium-income 0.308 (0.16)

Age – 0.001 (0.01)

Female 0.082 (0.13)

Children – 0.27 (0.15)

Constant – 0.047 (0.23)

N 106

OLS = ordinary least squares.

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G. 46: Count of Explicitly Racial Housing Search Outcomes 

Current searcher, OLS regression, bivariate 

Coefficient Std. Error

Black 0.11 (0.13)

Constant 0.115 (0.12)

N 106

OLS = ordinary least squares.

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-23

Reason for Search Difficulty

Exhibit G.47: Difficult Finding a Landlord Who Would Rent to You

Recent movers, logistic regression, multivariate

Coefficient Std. Error

Black 1.767** (0.61)

Low-income 0.816 (0.72)

Medium-income 0.375 (0.68)

Age – 0.004 (0.02)

Female – 1.517** (0.57)

Children 0.971 (0.61)

Constant – 1.508 (0.98)

N 91

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G48: Difficult Finding a Landlord Who Would Rent to You

Recent movers, logistic regression, bivariate

Coefficient Std. Error

Black 1.652** (0.53)

Constant – 1.819*** (0.44)

N 91

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.49: Difficult Finding a Landlord Who Would Rent to You 

Current searcher, logistic regression, multivariate

Coefficient Std. Error

Black 1.065 (0.63)

Low-income 2.587** (0.86)

Medium-income 1.613 (0.85)

Age 0.014 (0.02)

Female – 0.312 (0.58)

Children – 0.464 (0.62)

Constant – 3.460** (1.20)

N 96

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-24

Exhibit G.50: Difficult Finding a Landlord Who Would Rent to You

Current searcher, logistic regression, bivariate

Coefficient Std. Error

Black 1.370* (0.55)

Constant – 1.723*** (0.49)

N 96

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.51: Respondents Felt They Were Treated Unfairly During a Visit Because of

Their Race or Ethnicity

Current searcher, logistic regression, multivariate

Coefficient Std. Error

Black 0.462 (0.61)

Low-income 0.346 (0.67)

Medium-income 0.068 (0.66)

Age 0.019 (0.02)

Female 0.752 (0.52)

Children – 0.329 (0.58)

Constant – 3.812*** (1.01)

N 201

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.52: Respondents Felt They Were Treated Unfairly During a Visit Because of 

Their Race or Ethnicity

Current searcher, logistic regression, bivariate

Coefficient Std. Error

Black 0.677 (0.57)

Constant – 2.526*** (0.52)

N 201

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-25

Racial Differences in Credit History

Exhibit G.53: Respondents Report of Search Difficulty Because of Their Poor Credit 

History

Recent movers, logistic regression, multivariate

Coefficient Std. Error

Black 1.797* (0.71)

Low-income 1.049 (0.78)

Medium-income 0.525 (0.75)

Age 0.021 (0.02)

Female – 0.891 (0.54)

Children 0.264 (0.63)

Constant – 3.768*** (1.08)

N 130

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.54: Respondents Felt They Had Search Difficulty Because of Their Poor Credit 

History 

Recent movers, logistic regression, bivariate

Coefficient Std. Error

Black 1.974** (0.65)

Constant – 2.962*** (0.59)

N 131

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-26

Exhibit G.55: Respondents Felt They Had Search Difficulty Because of Their Poor Credit 

History

Current searchers, logistic regression, multivariate

Coefficient Std. Error

Black 0.698* (0.33)

Low-income 0.888* (0.41)

Medium-income 0.658 (0.40)

Age 0.011 (0.01)

Female 0.203 (0.28)

Children 0.435 (0.30)

Constant – 2.687*** (0.56)

N 332

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.56: Respondents Felt They Had Search Difficulty Because of Their Poor Credit 

History

Current searchers, logistic regression, bivariate

Coefficient Std. Error

Black 1.031*** (0.31)

Constant – 1.609*** (0.28)

N 335

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.57: Respondents Used People They Knew To Find Rentals That Did Not Require 

Credit Checks

Recent movers, logistic regression, multivariate

Coefficient Std. Error

Black 1.277* (0.64)

Low-income 1.32 (0.77)

Medium-income 0.652 (0.73)

Age 0.001 (0.02)

Female 0.088 (0.54)

Children – 0.505 (0.71)

Constant – 3.190** (1.06)

N 130

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-27

Exhibit G.58: Respondents Used People They Knew To Find Rentals That Did Not Require 

Credit Checks 

Recent movers, logistic regression, bivariate

Coefficient Std. Error

Black 1.520** (0.59)

Constant – 2.657*** (0.52)

N 131

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.59: Respondents Used People They Knew To Find Rentals That Did Not Require 

Credit Checks 

Current searchers, logistic regression, multivariate

Coefficient Std. Error

Black 0.47 (0.33)

Low-income 1.578*** (0.41)

Medium-income 1.021** (0.38)

Age 0.02 (0.01)

Female – 0.592 (0.31)

Children – 0.005 (0.35)

Constant – 1.752** (0.55)

N 249

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.60: Racial Differences in Credit History

Current searchers, logistic regression, bivariate

Coefficient Std. Error

Black 0.641* (0.29)

Constant – 0.405 (0.24)

N 249

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-28

Number of Units Contacted, Visited, and Applied To

Exhibit G.61: Number of Units for Which the Landlord Was Contacted To Find Out More 

Information

Recent movers, OLS regression, multivariate

Coefficient Std. Error

Black – 1.042 (1.36)

Low-income 0.881 (1.80)

Medium-income 1.791 (1.45)

Age 0.026 (0.05)

Female 0.939 (1.29)

Children – 0.826 (1.56)

Constant 3.378 (2.31)

N 128

OLS = ordinary least squares.

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.62: Number of Units for Which the Landlord Was Contacted To Find Out More 

Information 

Recent movers, OLS regression, bivariate

Coefficient Std. Error

Black – .601 (1.22)

Constant 5.483 *** (0.34)

N 128

OLS = ordinary least squares.

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-29

Exhibit G.63: Number of Units Visited in Person During Search

Recent movers, OLS regression, multivariate

Coefficient Std. Error

Black – 1.380 (0.94)

Low-income – 1.020 (1.25)

Medium-income – 0.129 (1.00)

Age 0.062 (0.04)

Female 0.045 (0.89)

Children – 0.727 (1.08)

Constant 3.908* (1.59)

N 129

OLS = ordinary least squares.

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G.64: Number of Units Visited in Person During Search 

Recent movers, OLS regression, bivariate

Coefficient Std. Error

Black – 1.245 (0.84)

Constant 5.672 *** (0.61)

N 129

OLS = ordinary least squares.

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016

Exhibit G. 65: Mean Number of Units for Which Respondents Submitted Applications 

Recent movers, OLS regression, multivariate

Coefficient Std. Error

Black 1.593** (0.51)

Low-income 0.594 (0.67)

Medium-income 0.334 (0.55)

Age 0.012 (0.02)

Female – 1.235* (0.48)

Children – 0.078 (0.59)

Constant 1.504 (0.87)

N 125

OLS = ordinary least squares.

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
G-30

Exhibit G.66: Mean Number of Units for Which Respondents Submitted Applications

Recent movers, OLS regression, bivariate

Coefficient Std. Error

Black 1.626*** (0.47)

Constant 1.448*** (0.34)

N 125

OLS = ordinary least squares.

* p < 0.05, ** p < 0.01, *** p < 0.001

Source: Housing Search Study survey, 2016
H-1

Appendix H. Regression Analysis of Search Typologies

Exhibit H.1: OLS Regression of the Probability of Experiencing an Open Search

Individuals participating in the Recent Mover Survey

Model 1 Model 2

Coefficient Std. error Coefficient Std. error

Black 0.038 (0.093) 0.051 (0.101)

Low-income 0.103 (0.134)

Medium-income 0.083 (0.111)

Age – 0.005 (0.004)

Female – 0.108 (0.098)

Constant 0.358*** (0.067) 0.543** (0.176)

R2 0.002 0.040

N 111 111

** p < 0.01, *** p < 0.001

Note: OLS = Ordinary Least Squares.

Exhibit H.2: OLS Regression of the Probability of Experiencing a High-Threshold Search

Individuals participating in the Recent Mover Survey

Model 1 Model 2

Coefficient Std. error Coefficient Std. error

Black – 0.308*** (0.073) – 0.305*** (0.077)

Low-income – 0.099 (0.103)

Medium-income 0.185* (0.085)

Age 0.020 (0.075)

Female 0.003 (0.003)

Constant 0.377*** (0.053) 0.185 (0.135)

R2 0.140 0.218

N 111 111

* p < 0.05, *** p < 0.001

Exhibit H.3: OLS Regression of the Probability of Experiencing a Constrained Search

Individuals participating in the Recent Mover Survey

Model 1 Model 2

Coefficient Std. error Coefficient Std. error

Black 0.270** (0.091) 0.253** (0.095)

Low-income – 0.004 (0.127)

Medium-income – 0.268* (0.105)

Age 0.088 (0.093)

Female 0.002 (0.004)

Constant 0.264*** (0.065) 0.272 (0.167)

R2 0.076 0.160

N 111 111

* p < 0.05, ** p < 0.01, *** p < 0.001
I-1

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U.S. Department of Housing and Urban Development

Office of Policy Development and Research

Washington, DC 20410-

6000

May 2018