Flood impact on property value a UK and Canada a preliminary study
Bibliographic record
Abstract
Background: Whether due to climate change or due to bad urban planning, flooding is an important issue. Climate change impact on see level and precipitation amount is well known and will eventually increase the number of flooding areas. Another reason for change in flooding area is the urban development and bad water management planning. Flooding area maps are been updated to take into account new risk. Been in these new flooding risk area is likely to have an impact on real estate value.Purpose: This paper intend to bring a contribution about the value lost in flooding area. The paper present preliminary result using UK and Quebec (Canada) data.Methodology/Design: We use transaction databases, geographic information system (GIS) and building characteristics in order to analyze the impact of been in flooding area on the value of a residential building. Since houses near water usually has value added we control for the water shore distance. Literature shows that flooding area usually has impact on real estate value. Nevertheless some study show mitigated results. We suspect that it is due to value added to houses which are on water shore. UK database is UK has 3 flooding area maps definition, we compute the regression for all of them. Quebec city data are been gathered at this moment and descriptive statistics will be available at the moment of the conference.Results: As of now, results are preliminary but at the time of the conference, we will results for at least one of the two location. We expect the impact to be in line with the literature for UK using historical flooding maps while we expect mitigated for flood risk maps. For Quebec, we expect some area to have more impact due to the very strong media coverage. Limitations For UK, individual characteristic of the building are limited. We use postal code and building type to assume similarity of hedonic characteristics. If houses among the same postal code and type are very heterogeneous the results could be biased.Practical implications: The implication are relevant to compute the value of mitigation installations (dam) or to assess the compensation to be offered to owner impacted by new flood risk area.Originality: We use GIS data allowing to discriminate between water shore proximity and flooding area. We use original data from Quebec which include a high media profile area and several "normal" area.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".