Impact of Short-Term Rentals on the Housing Market in Canadian Major Cities
Bibliographic record
Abstract
The rise of short-term rental (STR) platforms, such as Airbnb, has profoundly affected the housing markets in major Canadian cities, particularly Toronto and Vancouver. This paper explores the multifaceted impacts of STRs, including the reduction of long-term housing availability, increased rental prices, and disruptions to neighborhood stability. Through a mixed-methods approach, the study combines quantitative analyses of STR densities and housing trends (2019–2023) with qualitative evaluations of municipal regulatory frameworks. Findings reveal a direct correlation between the proliferation of STRs and housing affordability challenges, as properties are diverted from long-term leases to more lucrative short-term uses. This trend intensifies pressures on already constrained housing markets and disproportionately affects lower-income residents. The paper examines case studies of regulatory responses, highlighting Toronto’s Municipal Code Chapter 547 and Vancouver’s STR by-law, which attempt to mitigate these impacts through licensing and compliance measures. Despite these efforts, enforcement challenges persist due to resource limitations and regulatory loopholes. Comparatively, smaller municipalities like Victoria and Gibsons have achieved greater success by implementing stricter enforcement and zoning policies, effectively preserving affordable housing while accommodating tourism. This research underscores the need for enhanced enforcement, robust data collection, and more targeted policy designs to address the socio-economic tensions STRs create in urban housing markets. By drawing on successful practices from smaller jurisdictions, Canadian cities can better balance the dual objectives of fostering tourism and maintaining housing affordability.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".