Price Impact of Climate Risk on Commercial Real Estate
Bibliographic record
Abstract
We study how hurricane-related climate risk affects the commercial real estate (CRE) market using hurricane Sandy as an example. We introduce an ex-post climate risk measurement for CRE assets by supplementing a sample of CRE transactions in the New York Metropolitan Area before and post Sandy with a dataset containing detailed assessments of the severity of the storm-related damage and flooding. Among the four major types of CRE assets including offices, retail stores, warehouses, and apartments, we only document a significant and negative price impact by Sandy to offices, whereas no significant effect on the value of other CRE types. Specifically, we find robust evidence that unaffected office assets surrounded by severely damaged properties within proximity experienced a significant price penalty for four years following the storm. Meanwhile, we did not find any significant price impact on unaffected office assets with moderately damaged properties or flooded properties nearby. Additionally, it seems that the documented price penalty to unaffected office assets located in most severely damaged neighborhoods is mainly driven by a decline in building occupancy. We attribute the differential impact of Sandy on office and other CRE to the increase in remote work for office employees triggered by Sandy. We also note that the absence of any impact of Sandy on other CRE, including apartments, compared to significant and negative impact on single family homes reported in earlier studies (Fang et al. 2023) is an indication of more rational expectations and pricing in the commercial space.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".