Regional transport accessibility and residential property values: The case study of the Greater Toronto and Hamilton area
Bibliographic record
Abstract
There has been a growing interest in land value capture as a means of funding investments in transport infrastructure (TI), as reported in a vast literature analyzing the relationship between property values and accessibility provided by TI in general and transit specifically. There has, however, been limited research on the role of network-level regional transport accessibility and the intra-regional spatial heterogeneity of the price effects. Furthermore, studies usually focus on one transport mode, disregarding the multi-modal competition, and are mostly (pooled) cross-sectional analyses which do not reflect the dynamic nature of developments in TI and housing markets. To address these gaps, this paper empirically investigates the roles of local and regional transport accessibility by car and transit on the evolution of sales prices of single-family homes from 2001 to 2016, across different geographical contexts while controlling for various determinants in the Greater Toronto and Hamilton Area (GTHA). The spatial panel models’ results confirm that regional transport accessibility does indeed play a significant role in property values over and above the local proximity to TI, with variations between transit and car and over the spectrum of low–high density areas, which needs to be accounted for in land value capture policies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".