Short-term rentals in Canada: Uneven growth, uneven impacts
Bibliographic record
Abstract
In the last several years, Airbnb and other short-term rental services have grown precipitously across Canada, but very little is known about the scale and character of this activity or its impact on housing. Relying on spatial analysis of big data, this study presents the first comprehensive analysis of Airbnb in Canada, with an emphasis on the interaction between the short-term rental market and long-term housing. Airbnb activity is highly concentrated geographically—nearly half of all active listings are located in the Toronto, Montréal and Vancouver metropolitan areas—and highly concentrated among hosts, the top 10% of whom earn a majority of all revenue. Contrary to the rhetoric of “home sharing”, almost 50% of all Airbnb revenue last year was generated by commercial operators who manage multiple listings. Moreover, between 17,000 to 43,000 entire homes were rented frequently enough last year that they are unlikely to house a permanent resident. This housing pressure disproportionately affects West Coast cities: between 10% and 70% of Vancouver, Victoria, Kelowna, and Abbotsford-Mission residents live in neighbourhoods whose rental vacancy rate is exceeded by the proportion of housing units that are frequently rented on Airbnb. While current Airbnb activity is concentrated in major cities, active listings, total revenue, hosts with multiple listings, and frequently rented entire-home listings are all growing at substantially higher rates in small towns and rural areas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".