A spatial hedonic analysis of the housing market around a large, failing desert lake: the case of the Salton Sea in California
Bibliographic record
Abstract
Many lakes around the world exhibit acute environmental stress due to water transfers, persistent droughts, and polluted runoff. In addition, falling water levels worsen air quality by exposing desiccated shores. To our knowledge, however, no published hedonic study has analyzed the costs of deteriorating water quality jointly with the air quality impacts of falling water levels for a large inland water body. We conduct such an analysis for the Salton Sea, the largest lake in California. Our spatial autoregressive models estimated on single-family properties located within 10 miles (16.1 km) of the Sea show that a 1 km reduction in distance to the Sea results in a $595 decrease in the price of a single-family residence. In addition, a 1% increase in annual particulate matter concentration reduces the value of the average family residence by $1,140. These results highlight the vulnerability of poor rural communities to deteriorating environmental conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".