Racial and Ethnic Biases in Rental Housing: An Audit Study of Online Apartment Listings
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".