© 1983 Urban Studies The Effects of a New Subway Line on Housing Prices in Metropolitan Toronto
Bibliographic record
Abstract
This paper examines the impact of the Spadina Subway line in Toronto on housing values. The analysis is conducted by means of the linkage of a modal choice model and housing price functions model. The need to utilize two different models stems from the fact that the direct benefits from the improvement in transportation (if capitalized into housing values) are reflected in the premium paid for housing in the impact area. The change in transportation is treated as a change in locational attributes that serve as proxies for accessibility to employment centres in the hedonic price functions. The value of such accessibility is estimated directly by the modal choice model. Such direct estimates permit identification of the current quasi-rents attributable to improved accessibility by means of the hedonic price functions models. Recent empirical work on both modal choice and housing as a differentiated good is diverse and voluminous. In this study, we adopt the McFadden random utility model (McFadden (1974) and Domencich and McFadden (1975)) in order to identify the direct benefits from the opening of the subway. [First received, January 1982; in final form, August 1982] Summary. An analysis by means of using the estimation results of a modal choice model and the hedonic price regressions model is conducted in order to identify the effects of a subway line in Toronto on the values of housing units. The modal choice model is used for the estimation of the direct benefits from the improvement in transportation, and the hedonic price equations for the identification of subway effects on housing prices. Empirical results indicate that the direct savings in commuting costs have been capitalized into housing values. 1. Introduction The
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".