Climate Change and Asset Prices: Hedonic Estimates for North American Ski Resorts Preliminary draft
Bibliographic record
Abstract
Delaware for providing selected data on ski resort characteristics, and Michael Dettinger from the Scripps Institute of Oceanography for helpful discussions on the use of climate data. They also thank Monica Ortiz and Abigail Urtz for helpful research assistance, along with seminar participants at the Public Policy Institute of California for comments. The views expressed in this paper are those of the authors and should not be attributed to the Federal Reserve Bank of San Francisco, the Federal Reserve System, or the staff, officers, or Board of Directors of the Public Policy Institute of California. Climate Change and Asset Prices: Hedonic Estimates for North American Ski Resorts We use a hedonic framework to estimate and simulate the impact of global warming on real estate prices at North American ski resorts. To do so, we combine data on resort-area housing prices from two sources—data on average prices for U.S. Census tracts across a broad swath of the western U.S. and detailed data on individual house prices for four markets in the western U.S. and Canada—with detailed weather data and characteristics of ski resorts in those areas. Our hedonic regression models of changes in house prices with respect to medium-run
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".