Housing Discrimination in Major US Cities

Last registered on May 01, 2020


Trial Information

General Information

Housing Discrimination in Major US Cities
Initial registration date
February 26, 2020

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
March 02, 2020, 4:02 PM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
May 01, 2020, 10:17 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.


Primary Investigator

University of Illinois at Urbana Champaign

Other Primary Investigator(s)

PI Affiliation
Duke University
PI Affiliation
University of Illinois at Urbana Champaign

Additional Trial Information

In development
Start date
End date
Secondary IDs
This paper uses a correspondence experiment to compare rates of housing discrimination in the fifty largest Core-Based Statistical Areas (CBSAs) in the US. In addition to comparing the incidence of discriminatory behavior across the nation's major housing markets, we will use the metropolitan sample to examine the strength of discriminatory constraints in central-city versus suburban markets. A long-standing literature has focused on the effects of housing discrimination and other exclusionary practices such as redlining behavior, which have limited the access of racial minorities to suburban neighborhoods (Lake, 2017, Ondrichet al., 2003, Yinger, 1995). However, initial evidence from our recent correspondence study in 5 major cities (Houston, TX, San Jose, CA, Atlanta, GA, Philadelphia, PA, and Cincinnati, OH) suggests that the rates of discrimination are higher in high amenity urban neighborhoods than in suburban neighborhoods. Researchers have documented a shift in the distribution of amenities, high-skilled jobs, and skilled populations toward urban neighborhoods over the past two decades (Baum-Snow and Hartley 2017, Edlund et al. 2015). Couture and Handbury (2017) demonstrate that this trend is driven by young, college-educated residents who increasingly locate in central districts of urban areas. van Vuuren (2018) shows that these factors may play an important role in determining the location decisions of workers and in driving the process of gentrification. We hypothesize that discriminatory constraints in the rental housing market may be highly correlated with neighborhood amenity levels and excess demand for housing in certain neighborhoods.
External Link(s)

Registration Citation

Christensen, Peter, Ignacio Sarmiento-Barbieri and Christopher Timmins. 2020. "Housing Discrimination in Major US Cities." AEA RCT Registry. May 01. https://doi.org/10.1257/rct.5338-1.2000000000000002
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
(1) Response rate by race.
(2) Heterogeneity in response rate by city and by proximity to the city center (defined below).
(3) Heterogeneity in response rate by time on the market (defined below).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
(1) Response time -- Duration of the lapse between inquiry and response.
(2) Length of response (message)
(3) Heterogeneity in response rate by applicant characteristics: gender, maternal education level (high, middle, low).
(4) Heterogeneity by location attributes: rent, racial composition, poverty rate, neighborhood amenities.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our experiment uses a paired (tripled) experimental design. Responses from property managers will be captured in email (gmail address associated with each name), phone messages (individual phone numbers associated with each name), and text messages. Responses that indicate housing availability will be coded, as well as the time stamp, message length, and sentiment of responses. The sampling will focus on rental listings from the fifty largest US cities, sampling from downtown and suburban areas.
Experimental Design Details
Randomization Method
The paired experimental design used in this study requires that inquiries for each listing are made from each of the three racial groups that we study. Immediately following compilation of the relevant listings in a given market, an inquiry will be sent for each using a randomly assigned name drawn from each of the 3 racial groups. Each rental apartment will, therefore, receive 3 separate inquiries in the course of an experimental trial. Listings are divided into 3 blocks to ensure that inquiries for the same listing will never be sent from two race groups on the same day. Inquiries will be sent at an interval of 2-10 minutes over the course of the 3 day period. The names and within each racial group are randomly assigned to a listing with equal probability and the sequence of inquiries is also randomly assigned.
Randomization Unit
Individual real estate listing (property manager)
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
50 largest cities in the US
Sample size: planned number of observations
3000-5000 property listings.
Sample size (or number of clusters) by treatment arms
Sample of listings in the fifty largest US cities and in downtown and suburban zip codes. Based on the matched design, inquiries will be sent out in equal numbers from racial groups (e.g. 4000 African American, 4000 LatinX/Hispanic, 4000 White)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We use existing apartment listing data from the same online platform in a pre-trial in five cities: Houston, TX, San Jose, CA, Atlanta, GA, Philadelphia, PA, and Cincinnati, OH, to identify the sample size requirements for statistical power. The pre-trial data contains 6,015 listings. The pre-trial yielded a 43.36% response rate to white names and 39.75% to minority, non-white, names (38.99% for African American and 40.52% for LatinX/Hispanic names). Moreover, approximately 10\% of the listings are in downtown areas. We estimate odds ratios of 0.66 (0.4883,0.9053) , 0.68 (0.6185, 0.7681) for downtown and suburban listings respectively. 95% confidence intervals are reported in parenthesis. We then simulate the effect of increasing the sample size in a conditional logit model with matched inquiries sampling an equal number from downtown and suburban areas. Simulation results indicate that effect sizes of 0.75 for downtown and 0.58 for suburbs can be detected with a sample size of 2,400 properties. Figures 2 and 3 in our supporting materials plot simulation results ( p-values and odds ratio) at different sample sizes. To detect significant effects across cities our simulations suggest that between 65 and 95 listings per city are needed, which would imply between 3,240 and 5,000 total listings in our sample. Figure 4 in the supporting materials plots the number of significant results at 10% and 5% significance levels by the number of listings in each city. The figure shows that at 65 listings per city we get can detect discrimination in 35 cities at 5% and 40 at 10% cities. Between 90 and 95 listings we are able to detect discrimination in 45 cities. Giving the significance level, some cities are not significant just by chance. The plot suggests that given our sampling this may be at least 5 cities. References - Baum-Snow, N. and Hartley, D. (2017). Accounting for Central Neighborhood Change. Federal Reserve Bank of Chicago. - Couture, V. and Handbury, J. (2017). Urban Revival in America, 2000 to 2010. National Bureau of Economic Research, Working Paper No. w24084. - Edlund, L., Machado, C., and Sviatschi, M. M. (2015). Bright Minds, Big Rent: Gentrification and the Rising Returns to Skill. National Bureau of Economic Research,Working Paper No. w21729. - Holian, M. J. and M. E. Kahn (2012). The Impact of Center City Economic and Cultural Vibrancy on Greenhouse Gas Emissions from Transportation. - Manning, A. and Petrongolo, B. (2017). How Local are Labor Markets? Evidence from a spatial job search model. American Economic Review, 107(10):2877-2907. - Marinescu, I. and Rathelot, R. (2018). Mismatch Unemployment and the Geography of Job Search.American Economic Journal: Macroeconomics, 10(3):42-70. - van Vuuren, A. (2018). City Structure and the Location of Young College Graduates. Journal of Urban Economics, 104:1-15.
Supporting Documents and Materials

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Institutional Review Boards (IRBs)

IRB Name
University of Illinois, Urbana-Champaign Review Board
IRB Approval Date
IRB Approval Number


Post Trial Information

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Is the intervention completed?
Data Collection Complete
Data Publication

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Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials