Climate risk and commercial mortgage delinquency
Bibliographic record
Abstract
Abstract Natural disasters such as hurricanes, floods, heatwaves, and wildfires are projected to become more prevalent in the foreseeable future. Climate risk is, therefore, increasingly recognized as an important factor by policy makers, the investment community, and financial markets. Due to the immobility of assets, the commercial real estate industry is especially vulnerable to climate risk, and there is an increasing interest to understand the impact of climate risk on the value of commercial real estate. For commercial real estate lenders, changes in collateral value are only of partial importance. The ability of borrowers to meet their payment obligations is equally, if not more important. By combining historical data on two major climate‐related disasters—Hurricanes Harvey and Sandy—with longitudinal information on commercial mortgage performance, this paper identifies the impact of climate risks on mortgage delinquency rates for commercial real estate mortgages. The results show that both Harvey and Sandy led to elevated levels of commercial mortgage delinquency, with significant heterogeneity based on the extent of damage in the Census block group. Information provided through FEMA 100‐year floodplain maps partially mitigates the effects, an indication that lenders incorporate flood risk information in the underwriting process. An analysis of potential mechanisms indicates a decrease in property income during the 2‐year period following the event for Hurricane Harvey, but no evidence of income effects for Sandy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".