Evaluating the effects of supervised consumption sites on housing prices in Montreal, Canada using controlled interrupted time series and hedonic price models
Bibliographic record
Abstract
Abstract Background In 2017, three brick and mortar supervised consumption sites (SCS) began operating in Montreal, Canada. Opponents argued the sites would attract people who use drugs to the respective neighbourhoods, contributing to reductions in residential real estate values. Methods We used controlled interrupted time series and hedonic price models to evaluate the effects of Montreal’s SCSs on residential real estate. We linked the Quebec Professional Association of Real Estate Brokers’ housing sales data provided by Centris Inc. with Statistics Canada’s census tract data, neighbourhood proximity measures, and Canadian Urban Environmental Health Research Consortium’s gentrification measures. We restricted analysis to sales between 1 January 2014 and 31 December 2021, and within 1,000m of a SCS (treated) or a men’s homeless shelter (control). We controlled for internal (e.g., number of bed/bathrooms, unit size) and external attributes (e.g., neighbourhood demographics; proximity to amenities), and included a spatio-temporal lag to account for correlation between sales. Results When controlling for census tract data and gentrification measures, the price of homes sold immediately after SCSs were implemented was 5.2% lower (95% CI: -1.4%, -8.8%) compared with control sales (level effect). However, the monthly value increased 0.6% faster (95% CI: 0.4%, 0.7%) in treated neighbourhoods (trend effect). Compared with the counterfactual (i.e., SCS never implemented), sales in treated neighbourhoods observed an absolute increase of $37,931.86 (95% CI: $12,223.35, $138,088.50) by December 2021. When we also controlled for proximity scores, the immediate level effect post-implementation disappeared (−3.3%, 95% CI: -0.7%, 1.1%), but monthly trend gains persisted (0.9%, 95% CI: 0.7%, 1.0%). Conclusion We observed a modest negative effect on prices immediately following SCS implementation. However, controlling for proximity to neighbourhood amenities eliminated the level effect. Positive month-on-month price gains were consistently observed, suggesting community wide benefits of SCS implementation.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".