Access to Primary Schools and House Values: Testing the Spatial Homogeneity of Hedonic Prices
Bibliographic record
Abstract
Since the mid 1970s, the impact of nearby schools on property values has been a major theme in the North-American literature, with most studies focusing on the influence of school quality. To a lesser extent, the impact proximity to school exerts on house prices has also been investigated together with the way the urban environment and distances to school affect student modal choices. Using the hedonic approach, this paper aims at assessing the price effect ensuing from proximity of, and accessibility to, the nearest primary school based on a sample of 8,285 single-family houses sold over the 1993-1996 period in Quebec City, Canada. While home-to-school walking distances are computed for properties located within one kilometre ñ a distance beyond which a school bus service is provided, car travel times measured on the 1994 GIS-operated topological street network are used for houses located further away from school. Main property features as well as neighbourhood and household descriptors are used as control variables in the model, with four urbanization patterns being distinguished on the grounds of residential densities. A major objective of this study is to test the spatial homogeneity of hedonic prices with respect to primary schools. In order to do so, the OLS method, Cassettiís expansion method and the geographically weighted regression (GWR) method are compared. Each method is assessed in regard to its ability to account for, and deal with, the presence of spatial autocorrelation in the residuals while bringing out spatial shifts in household preferences.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".