Do Sales Prices Overstate Underlying House Prices in Market Downturns? Evidence from the Canadian House Price Crash of 1991
Bibliographic record
Abstract
Abstract Over the last decade there has been a mounting realization that the quality‐adjusted price of properties that sell may be quite different from the quality‐adjusted price of all properties (Gatzlaff & Haurin, 1998). The difference is apt to be especially marked during downturns. Dependence on price indexes based on transactions data could result in overoptimistic appraisals by mortgage lenders during the early quarters of downturns. This paper provides evidence on this issue by examining the residential real estate crash at the start of the 1990s in Canadian cities, especially Toronto. The plunge in prices estimated here for Toronto is 17%. We estimate the drop in the transactions price using MLS data, which are not quality adjusted, supplemented by Royal LePage data, which are. We estimate the drop in the price of houses in the stock using hedonic indexes based on home owners' valuations; outlier observations are cut from the sample on the basis of the DFFITS criterion. The hypothesis of equality of the drop shown by the two measures is strongly rejected, for Toronto, and is also rejected for several other cities. Résumé Au cours de la dernière décennie, on s'est progressivement rendu‐compte du fait que le prix, ajusté pour la qualité, des maisons vendues peut être différent du prix, ajusté pour la qualité, des maisons en général (Gatzlaff & Haurin, 1998). La différence entre ces prix est particulièrement marquée en période de marasme. La dépendance sur des indexes de prix basés sur des données transactionnelles peut amener les prêteurs hypothécaires à faire des évaluations trop optimistes durant les premiers trimestres d'une période de marasme. L'article qui suit s'interroge sur la justesse de cette hypothèse en étudiant l'effondrement du marché immobilier au début des années 90 dans des grandes villes canadiennes, en particulier, dans la ville de Toronto. Nous estimons que la chute des prix à Toronto s'élève à 17 %. Nous évaluons la chute des prix de transactions immobilières à partir d'une banque de données MLS, qui n'est pas ajustée pour la qualité, et nous ajoutons celles‐ci à d'autres données de Royal LePage, qui elles sont ajustées pour la qualité. Nous évaluons la chute des prix des maisons dans l'inventaire à l'aide d'indicateurs hédoniques basés sur les évaluations des propriétaires; les données excentrées sont enlevées de l'échantillon à partir du critère DFFITS. L'hypothèse de l'égalité des chutes de prix indiquée dans les deux mesures ne s'applique absolument pas à Toronto, ni à plusieurs autres villes.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".