The Impact of Mature Trees on House Values and on Residential Location Choices in Quebec City
Bibliographic record
Abstract
Abstract: When choosing their home, households are willing to maximize their satisfaction and utility while trying to avoid noise and inconvenience. Along with economic constraints, this decision process involves several types of criteria, including preferences and perception of environment in the neighbourhood. Previous research in spatial economy has addressed the impact of vegetation and environment quality on single-family house values, using hedonic price models. However, assessing the economic valuation of trees is not sufficient to fully understand the choice-setting mechanisms behind the conversion of environmental preferences into residential location choices. New modelling approaches integrating behaviour, attitudes, tradeoffs and motivations could certainly improve our understanding of people’s valuation of nature. This paper develops such a behavioural model considering a housing market which was firstly analysed using the hedonic modelling approach. Logistic regression was then used in order to model households ' propensity for buying a house on a wooded lot (with mature trees). Our purpose is to highlight the potential of combining economic and behavioural modelling to enhance understanding of landscaping in urban regions. Our research integrates various data sets collected in Quebec City from 1993 to 2001: an opinion poll of 640-home buyers; a summary of their transactions (sale price); in-site surveys of properties to assess vegetation status; socio-economic attributes of families; census data; accessibility to services modelled using GIS; finally, a full
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".