When Tourists Displace Tenants: Insights on Airbnb in New York City and Toronto
Bibliographic record
Abstract
Short-term rentals have increased dramatically in recent years thanks to peer-to-peer home sharing platforms like Airbnb which allow people to rent spare rooms or entire homes online. Critics contend that Airbnb is reducing housing supply for long-term residents, increasing housing costs, threatening quality of life, and unfairly copeting with hotels. Most of this concern has centred on the conversion of entire homes into dedicated short-term rentals, or de facto hotels. This research uses a combination of Airbnb listing data, census data, and GIS analysis to suggest that private-room Airbnb rentals are impacting the availability of rental housing for people live as roommates in New York Cit and Toronto. Further, this research uses spatial analysis of Airbnb listings to that entire homes and apartments are being converted into multiple, private-room listings on Airbnb exclusively for the purpose of short-term rentals, a phenomenon we term a "ghost hotel", which counter the ethos of "home sharing" and may pose threats to residents' quality of life. Drawing on interviews with six residents of Toronto's Kensington Market, a site of conflict between residents and shor-term rentals, this research suggests that while Airbnb benefits travelers and hosts, it can also have negative neighbourhood impacts such as public nuisances, commercial and residential gentrification, displacement, and loss of social ties, especially when entire properties are converted to short-term rentals. This research provides new insight into Airbnb activity in New York City and Toronto, and discusses possible implications for short-term rental regulations and enforcement.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.001 |
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".