Landscaping attributes and house prices : looking at interactions between property features and homeowners' profiles
Bibliographic record
Abstract
This paper investigates the effect of environmental features on house values while focusing on the interactions between landscaping attributes and homeowners' sociodemographic and economic profiles of Quebec City's residents. The originality of this paper rests on the availability of two distinct, although related data sets on the single-family segment of Quebec City's housing market, which includes bungalows (one-storey, detached), cottages (multi-storey, detached or semi-detached) and row houses. The first data set focuses on landscaping features obtained via an extensive field survey of some 1 650 houses sold at least once between 1993 and 2000. In addition, a detailed phone survey of related homeowners' family status, age and income profiles is being conducted since 2000. Previous residential status, incentives to move, reasons for choosing neighborhood as well as housing preferences with respect to environmental issues are also disclosed; changes and improvements to landscaping features since the house was purchased are also reported in the survey. While still preliminary, findings derived from this analysis suggests that interactions between landscaping features and household profile is worth investigating further and may bring useful insights into homeowners utility patterns.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".