Municipal Regulation of Short-term Rentals in Alberta
Bibliographic record
Abstract
From 2015 to 2018, revenue from short-term rentals in Alberta grew from around $8 million to $151 million (Canada 2019d). This increase in market activity has been facilitated by the popularity of platforms like Airbnb. As this marketplace grew, concerns mounted about the negative aspects of these transactions and policymakers have been called to regulate. In this capstone, the short-term rental regulations are examined for six Albertan municipalities: Banff, Calgary, Canmore, Edmonton, Fort McMurray and Jasper. These jurisdictions were chosen to provide a snapshot of regulatory approaches across the province and because they face unique challenges with respect to vacancy rates, tourism, geographic location and population. To simplify the analysis, the regulations are categorized by the issues they are targeted to resolve: Housing Prices and Supply, Neighbourhood Preservation, Competitive Fairness and Safety. It is important to note that despite this categorization, many of the regulations within categories overlap and compliment regulations from other categories. None of the jurisdictions examined in this analysis have banned short-term rentals, but Banff, Canmore and Jasper are considerably more restrictive than Calgary or Edmonton. Fort McMurray does not have any regulations that pertain to short-term rentals. Recommendations for improving municipal short-term rental regulations include not missing revenue generating opportunities from short-term rental platforms, proactively enforcing regulations and ensuring that regulations match the problems faced by the municipality. To stay relevant and effective, municipalities need to revisit their short-term regulations often.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".