Investigating the economic value of green roofs in Canada's leading cities (Vancouver and Toronto): A hedonic pricing analysis
Bibliographic record
Abstract
This report speaks to the urgent need for solid economic arguments to expand the implementation of green roofs in urban areas across Canada by investigating the property values added by such an infrastructure. Using the Hedonic Pricing Model, this report looks at how the existence of green roofs influences the listing prices of houses in Vancouver and Toronto. One thousand housing listing samples are collected for both datasets, totaling 2000 samples, mainly apartments. The study also includes other structural (information on room, floor, and living area) and socio-economic variables (household income) in the HPM model. The results of the HPM analysis demonstrate strong positive effects in both cities, with the establishment of green roofs in Vancouver estimated to bring a 708,117 CAD increase in the listing price. At the same time, 327.46 CAD of a house in Toronto comes with every additional area of green roof in Toronto. This research is crucial since it offers explicit place-based empirical evidence for implementing more robust policies and incentives regarding green roofs. Such conclusions are crucial in combating contrary views, skepticism, and misunderstandings of green roof performance across sectors. Calling for future studies in expanded scope to investigate diverse house types, this report appeals to efforts that reveal the economic performance of green roofs and bridge consensus for more robust policies and actions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".