Contamination by the Israeli military industry and its impact on apartment sale prices in an adjacent Tel Aviv neighborhood: a hedonic pricing model study
Bibliographic record
Abstract
This study quantifies the effect of environmental degradation due to contamination from Taas Magen, an Israeli Defense Force industrial facility, on the nearby housing market. A model was constructed so as to isolate the impact of the contamination from that of the facility itself by incorporating information regarding public awareness of the contamination. The resulting regression analysis suggests that distance from the site of the facility affected home prices only after the general public became aware of the contamination. Therefore, it is specifically the environmental contamination, rather than the facility as of itself, that negatively impacted prices.As a result of the contamination, apartment prices fell by an average of $24,665 (2006 dollars), equal to approximately 14% of the sales price of an average apartment in the sample area. Total losses to the surrounding housing market are accordingly estimated at between $267 and $287 million. These loss estimates serve as a lower bound for the total social and economic costs incurred by the greater community due to the contamination, which are estimated to total at least $358 million.Assuming the government were to fund the estimated $33 million cleanup costs, a minimum gain of 1.5% in sale prices within this $2.2 billion housing market would create the necessary economic benefit to offset the cost of decontaminating the site. Similarly, a more technologically advanced, yet expensive, remediation process would require a gain of 10.1% in housing market prices to offset its costs. Ultimately, reclaiming a lost aquifer, reduction in human health risks, restoration of environmental integrity, and further increases in housing market value are all benefits of remediation that may greatly outweigh the required cleanup costs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".