The Spatial and Temporal Decomposition of the Effect of Floods on Single-Family House Prices: A Laval, Canada Case Study
Bibliographic record
Abstract
This paper aims to estimate and decompose the spatial and temporal effect of a flood event occurring in the city of Laval in 1998 using a hedonic pricing model (HPM) based on a difference-in-differences (DID) estimator. The empirical investigation of the impact of flood as a natural disaster must take into account the fact that the negotiation process between buyers and sellers may well occur before the event. It is argued that the evaluation procedure needs to be adjusted to account for this reality because the estimation of the effects may otherwise be biased and isolate other effects. To test this hypothesis, the study focuses on transactions occurring between (1995 and 2001) and within designated floodplains to adequately isolate and decompose the impact of flood. The original database contains information on 252 single-family houses transactions. The results suggest that the estimation of the impact is time dependent, with a measured negative effect appearing several months after the flood, suggesting that the impact is hard to establish right after the event since transactions, and the final sale price, could have been fixed by negotiations well before the event. The statistical methodological framework of flood research should be adapted to account for the negotiation process occurring prior to the flood event to be able to correctly isolate the impact for the after event. The flooded area also needs to be precisely identified to be able to correctly estimate the flood impact on houses that have faced flood.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| 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) | 0.002 | 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".