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Beyond BLS

Beyond BLS briefly summarizes articles, reports, working papers, and other works published outside BLS on broad topics of interest to MLR readers.

April 2022

Racial discrimination and housing outcomes in the U.S. rental market

Summary written by: Arthak Adhikari

Many of us can attest that having access to quality housing is essential for our economic well-being. Housing not only provides us shelter but also gives us access to additional economic opportunities through proximity to work, school, public transportation, and so forth. Perhaps because of these necessities, housing is also quite expensive and usually the largest expense for an individual or household.

The United States housing market has also become extremely competitive. Outside observers have described it as a “sellers” or “renters” market. In this type of environment, housing is a large portion of an individual’s or a household’s expenditures, and it provides benefits other than shelter. Any ethnic or racial biases that arise in the process of individuals obtaining housing can largely affect their economic and societal outcomes. In their working paper, “Racial discrimination and housing outcomes in the United States rental market” (National Bureau of Economic Research, Working Paper 29516, November 2021), Peter Christensen, Ignacio Sarmiento-Barbieri, and Christopher Timmins analyze potential bias in the rental housing market and discriminatory behavior on the part of property managers and show that this bias is statistically significant in predicting rental housing outcomes.

In their study, the authors used a software bot to analyze over 25 thousand interactions between property managers and fictitious renters. The software bot was used to send inquiries from fictitious renters to 8,476 property managers across the 50 largest U.S. metropolitan areas. The inquiries were sent in unison, which allows for comparing discriminatory constraints while averting regional differences because of seasonal variation. Rental listings in the downtown and suburban areas of each metropolitan market were targeted, and for each listing, the software bot began a 3-day sequence of inquiries, sending one inquiry each day. For the fictitious renters, their identities were randomly chosen from a set of 18 first and last name pairs to elicit associations with one of three racial and ethnic groups—African American, Hispanic or LatinX, and White. Responses were registered if property managers responded within 7 days of the inquiry and they indicated that the property was available.

The authors used a linear probability model to estimate the effects of racial bias. The main variable of interest was the relative response rate differentials. These differentials measured the likelihood of response to an inquiry from an African-American identity and Hispanic or LatinX identity compared with a White identity for the same listing. In their full sample, White identities received the highest response rate. The average response rates for the other identities were much lower. The corresponding relative response rates were –9.3 percent for African Americans and –4.6 percent for Hispanic or LatinX. Looking at geographical differences, Christensen and colleagues found that African-American identities had the highest relative response rate differentials in the Midwest followed by the South, while Hispanic or LatinX identities had the highest in the Northeast and the South.

A feature of this study is that the authors were able to link the listed rental properties in the sample to the racial and ethnic identities of the households that rented them in 2020. To do this linking, they used InfoUSA, a consumer database that tracks 120 million households and 292 million individuals. The authors imputed racial and ethnic identities of each renter using an algorithm that computes the probability of a name corresponding to a racial and ethnic group using individual surnames and county of location. They found that of the rental properties in their sample, most were eventually rented by White renters (around 70 percent), while African-Americans and Hispanic or LatinX households made up an additional 10 percent each. Combining these findings with their experimental findings, the authors observed that a nonresponse to a renter from a given racial and ethnic identity corresponded to a 26-percent reduction in the probability of a subsequent lease by a renter from the same group.

To conclude, the study by Christensen and his coauthors adds to an important discussion regarding racial bias in the housing market. Being able to assess not only responses but also the way the probability of a response may affect the eventual housing outcome adds more evidence to the claim that racial bias exists in the rental housing market. The study puts forward a framework that could be used to continue studying racial biases in the future, in housing and otherwise.