Multimodal Accessibility and the Capitalization of Realized Access by Car and Transit in Housing Prices Across Canada
Bibliographic record
Abstract
Although the transportation accessibility benefits afforded by multiple transportation modes play a fundamental role in determining transportation costs and land prices, there are challenges associated with isolating how multimodal accessibility benefits are capitalized into property values. In response, this research extends calibrated gravity-based measures of potential accessibility to employment by car and transit to include mode share proportions for commuting to reflect how this accessibility potential is realized. These realized accessibility measures capture not only the presence but also the performance of different modes for facilitating commuting trips. Spatial econometric models examine the extent to which access is capitalized into property values across Canada’s twelve largest metropolitan regions. Results suggest that car and transit access potential is generally valued across most cities, although there is evidence of moderate multicollinearity between the unweighted car and transit access potential scores. In contrast, the origin and destination mode use weighted measures of realized access offer advantages in terms of model performance and interpretation. These models show that premiums are generally linked with trade-offs between car and transit access, with the highest overall premiums found for properties in locations with the highest levels of transit access and transit use for commuting in most cities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".