Protecting Permanent Rental Housing Supply: Formalizing Short-Term Rentals Via “Airbnb” Regulations
Bibliographic record
Abstract
The advent of short-term rental platforms such as Airbnb, Flipkey, and HomeAway have generated new challenges for urban planners and policy makers in various jurisdictions throughout Canada and the world at large.Airbnb and other short-term rental companies are rapidly expanding and reshaping urban and rural housing markets.What started out as a way for residents to earn some "extra cash" has become commercialized by hosts who own, lease, or otherwise acquire dozens of homes for the purpose of turning them into short-term tourist accommodations.Seen as disruptive by the traditional hotel/accommodation sector, a nuisance by community residents, and a factor complicating an affordable housing crisis, steps are being taken to formalize the informal shortterm rental business in many Canadian municipalities.In conjunction with Fairbnb Canada, this project examined the current policies and policy recommendations to regulate short-term rentals in the following ten (10) Canadian jurisdictions: Calgary, Edmonton, Ottawa, Kelowna, Oshawa, Hamilton, Banff, Whistler, St. John's, and Montreal.An analysis of a selection of current academic literature was completed to identify effective methodologies for implementation of policy tools and their effectiveness in regulating short-term rental tourist accommodations.Interviews with key informants within the examined ten jurisdictions provided insight into the challenges facing policy makers in the development of regulations to govern short-term rentals in their respective jurisdictions.This project concluded that short-term rentals have had a significant impact on housing rental markets in the examined jurisdictions.The regulatory environment in the analyzed ten Canadian jurisdictions is fluid, and the regulatory policies implemented share the following characteristics: Licensing, registration, maximum allowable rental days, primary residence, taxation, safety standards, and enforcement regulations, which remains an ongoing challenge.
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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 | 0.018 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".