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Record W4360615125 · doi:10.1111/1540-6229.12435

Climate change and commercial real estate: Evidence from Hurricane Sandy

2023· article· en· W4360615125 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReal Estate Economics · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsConcordia University
FundersNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsReal estateCapitalization rateFlood mythCapitalizationEconomicsFlood insuranceSalience (neuroscience)Real estate investment trustAsset (computer security)HerdingFinancial economicsFinanceBusinessGeographyPsychologyComputer science

Abstract

fetched live from OpenAlex

Abstract We study how professional investors capitalize flood risk in commercial real estate (CRE) markets after hurricane Sandy. We show that New York CRE exposed to flood risk trades at a large, persistent discount. CRE in Boston, which mostly escaped direct hurricane‐related damage, also exhibits persistent price penalties. These price effects are driven by asset‐level capitalization rates, not building occupancy. Results from a placebo test using real estate prices in Chicago show that our inferences are not driven by coincidental, unrelated price trends for waterfront real estate assets. Our results are consistent with professional investors responding to a persistent shift in the salience of flood risk post‐Sandy, even in locations spared by the disaster.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.099
GPT teacher head0.262
Teacher spread0.163 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it