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 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 | 0.153 | 0.377 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.030 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".