META-ANALYSIS OF AIRPORT NOISE AND HEDONIC PROPERTY VALUES : PROBLEMS AND PROSPECTS
Bibliographic record
Abstract
Meta-analysis is applied to the negative relationship between airport noise exposure and residential property values. The effect size in the analysis is the percent depreciation per decibel increase in airport noise, or the noise discount. Twenty hedonic property value studies are analyzed, covering 33 estimates of the noise discount for 23 airports in Canada and the United States. About one-third of the estimates have not been previously reported in the literature or were not included in previous meta-analyses. The weighted-mean noise discount is 0.58 % per decibel. A meta-regression analysis examines the variability in the noise discounts that might be due to country, year, sample size, model specification, mean property value, data aggregation, or accessibility to airport employment and travel opportunities. The analysis indicates that country and model specification have some effect on the measured noise discount, but the other variables have little systematic effect. The cumulative noise discount in the U.S. is about 0.5 % to 0.6 % per decibel at noise exposure levels of 75 dB or less, while in Canada the discount is 0.8 % to 0.9 % per decibel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".