DO PEER EFFECTS SHAPE RESIDENTIAL VALUES? RECONCILING THE SALES COMPARISON APPROACH WITH HEDONIC PRICE MODELLING
Bibliographic record
Abstract
Although the hedonic framework can be said to vary substantially from the traditional sales comparison approach used in real estate appraisal in that the former rests on much stronger conceptual grounds than the latter while benefitting from large transaction samples that enable statistical inference, both are derived from a similar paradigm with respect to how prices, hence market values, are determined. While the hedonic approach is much more explicit about the determinants of property values and can provide reliable estimates of individual attributesí marginal contribution, it may ñ unlike the sales comparison approach underestimate the prominent influence that surrounding properties exert on any given nearby housing unit and sale price. In this paper, a simple method for reconciling the two approaches is developed within a rigorous conceptual and methodological framework. It is based on peer effect models, an analytical device developed, and mainly used, by labour economists, which we adapt to the hedonic price equation so as to incorporate nearby propertiesí influences, thereby controlling for non observable neighbourhood effects. The ensuing model accounts for four types of effects, namely endogenous interactions effects (comparable sales influences), exogenous, or neighbourhood, effects, fixed location effects and, finally, spatial autocorrelation effects. This research relies on a database provided by the former Quebec Urban Community (CUQ) Assessment Division on some 15,700 sales of single-family detached houses that took place on the former CUQ territory between January 1990 and December 1996, with prices ranging from $50,000 (Can.) to $250,000. Preliminary findings suggest that integrating peer effects in the hedonic equation allows bringing out the combined impacts of endogenous, exogenous and spatially correlated effects in the house price determination process, with spatial autocorrelation of model residuals being significantly reduced without resorting to a spatial autoregressive procedure. Further investigation is still needed though in order to find out which sub-market delineation should be used to obtain optimal model performances.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".