The Impact of Surrounding Land Use and Vegetation on Single-Family House Prices
Bibliographic record
Abstract
The aim of this paper is to assess the marginal effect of land-use locational externalities on the sale price of single-family houses, considering various spatial scales—in accordance with perception theories—and trade-off with accessibility to the city centre. From land-use and vegetation data derived from aerial photographs and Landsat TM satellite images, two sets of hedonic models, using OLS regression, are built from two samples of single-family properties sold in Quebec City. A standard model integrates property-specific factors, census factors, accessibility, and location attributes. In a second model, land-use and vegetation variables are considered on various spatial scales; a third step introduces the interaction effect of the surrounding land use with location, with car-time distance to the main activity centres being used as the main indicator. This allows for an analysis of the spatial variation of the environmental impact throughout the city considering relative proximity to the centre. The successful integration of environmental variables concerning location enhances our understanding of the local land-use and vegetation effects. It also improves the overall performance of the model while virtually removing spatial autocorrelation among residuals. Such models could be used in order to assess the fiscal impacts of various land zoning by law policies, thereby providing planning administrations with a useful decisionmaking tool.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".