Links between Residential Choice Criteria and Property Values. Some Evidence Using Correspondance Analysis and Hedonic Modelling
Bibliographic record
Abstract
With the hedonic modelling approach, real estate values are traditionally explained using property specific descriptors and locational attributes. Until yet, few attention has been given to the impact of the buyerís or the sellerís profile or motivations. However, it appears that the residential choice criteria may vary depending on the buyerís characteristics ñ e.g, socio-economic status, life-cycle factors. Also, these differences in choice criteria may indicate the presence of submarkets, which may lead to spatial ñ or social ñ heterogeneity in implicit prices. Following a large phone survey held in Quebec City, Canada, the choice criteria of 750 single-family property buyers are first analyzed using Correspondence Analysis. The resulting eight residential choice factors are then introduced within a hedonic model, indicating significant links between the dimensions of choice, the property values, and the implicit prices. By explaining the heterogeneity of implicit prices using the residential choice factors, the understanding of the complex mechanism of value determination is enhanced. Furthermore, by doing so, the local spatial autocorrelation of the residuals becomes close to null, showing that the introduction of the buyerís choice criteria contributes to explaining the spatial complexity of property markets.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".