Racial and Ethnic Biases in Rental Housing: An Audit Study of Online Apartment Listings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As rental markets move online, techniques to assess racial/ethnic rental housing discrimination should keep pace. We demonstrate an audit method for assessing discrimination in Toronto's online rental market. As a multicultural city with less segregation and more diverse visible minorities than most US cities, Toronto lends itself to multiname audit studies. We sent 5,620 fictitious email inquiries to landlords offering apartments on Craigslist, a popular Internet classifieds service. Each landlord received one inquiry each from five racialized groups—Caucasian, Black, E/SE Asian, Muslim/Arabic, and Jewish. In our experiments, “opportunity denying” discrimination (exclusion through nonresponse) was 10 times as common as “opportunity diminishing” discrimination (e.g., additional rental conditions). We estimate Muslim/Arabic–racialized men face the greatest resistance, with discrimination occurring in 12 percent of experiments. The level of discrimination is modest but significant for Asian men (7 percent), Blacks (5 percent), and Muslim/Arabic women (5 percent). Discrimination was evenly spread throughout the city.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it