Practical Applications of Climate Risk and Real Estate Prices: What Do We Know?
Bibliographic record
Abstract
In Climate Risk and Real Estate Prices: What Do We Know? from the October 2021 Special Real Estate Issue of The Journal of Portfolio Management, Jim Clayton of York University and Steven Devaney, Sarah Sayce, and Jorn Van de Wetering, all of the University of Reading, find that growing awareness of climate risks to real estate has had a sustained, if imprecise, effect on decision-making, including property pricing and lending practices. The authors analyze the connections between property values and extreme-weather events and climate risk. They review existing, often ambiguous, studies on climate risk’s effects on (primarily residential) property values and on associated real estate market activities. They then derive conclusions about how these risks, and perceptions thereof, may affect commercial property markets and investors. Because much of the existing research concerns the residential market and the mostly short-term impacts on pricing after weather events, more research is needed. Still, there are conclusions that real estate stakeholders can draw from the existing research: Property prices often modestly decline after weather events; climate change might have longer-term impacts on values, often depending on stakeholder beliefs; and sustained risk-mitigating government involvement might limit price declines in areas subject to increased climate risk.
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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 | 0.010 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".