Growing Value: Describing the Nature of the Relationship Between Street-Level Greenery and Housing Prices in Toronto
Bibliographic record
Abstract
The role of urban forestry has become increasingly important in the context of sustainability, both from an environmental context, and from a developmental context. Greenery in an urban environment has demonstrable implications for health, air quality, aesthetics, and land value, as described broadly across the literature. Until recently, studies on green urban canopies and housing prices have been limited in their methodology by using aerial-perspective data. The MIT Senseable City Lab in 2015 developed the Treepedia project, which uses Google Street View images to quantify greenery levels in urban environments. Using the green view index (GVI) data from the Treepedia project, street-level greenery densities were compared against housing prices across Toronto. Models for different property types, accounting for characteristic, locational, and demographic variables, were estimated. It was determined that a statistically significant relationship between street-level greenery and housing prices exists in Toronto for detached homes, semi-detached homes, row/townhouse units, condo apartments, and condo townhouses.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".