Do peer effects shape property values?
Bibliographic record
Abstract
Purpose Both hedonics and the traditional sales comparison approach are derived from a similar paradigm with respect to how prices, hence market values, are determined. While the hedonic approach can provide reliable estimates of individual attributes' marginal contribution, it may – unlike the sales comparison approach – underestimate the prominent influence that surrounding properties exert on any given nearby housing unit and sale price. This paper seeks to develop a simple method for reconciling the two approaches within a rigorous conceptual and methodological framework. Design/methodology/approach Peer effect models, an analytical device developed, and mainly used, by labour economists, are adapted to the hedonic price equation so as to incorporate nearby properties' influences, thereby controlling for non‐observable neighbourhood effects. In addition to basic, intrinsic, building and land attributes, the ensuing model accounts for three types of effects, namely endogenous interactions effects (i.e. comparable sales influences, or peer effects), exogenous, or neighbourhood, effects and, finally, spatial autocorrelation effects. Findings 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, even without resorting to a spatial autoregressive procedure. Research limitations/implications Further investigation is still needed in order to find out which submarket delineation should be used to obtain optimal model performances. Originality/value The paper leads to the conclusion that the comparable sales approach, as used in traditional appraisal practice, is valid, although its application is typically flawed by the too small sample size generally used by appraisers. Further investigation is still needed, however, in order to find out which submarket delineation should be used to obtain optimal model performances. This raises the paramount question as to whether the peer effect variable is adequately measured and addresses the tricky issue of kernel determination in spatial statistics and related applications, such as GWR.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".