Has Falling Crime Driven New York City’s Real Estate Boom?
Bibliographic record
Abstract
We investigate whether falling crime has driven New York City’s post-1994 real estate boom, as media reports suggest. We address this by decomposing trends in the city’s property values from 1988 to 1998 into components due to crime, the city’s investment in subsidized low-income housing, the quality of public schools, and other factors. We use rich data and employ both hedonic and repeat-sales house price models, which allow us to control for unobservable neighborhood and building-specific effects. We find that the popular story touting the overwhelming importance of crime rates has some truth to it. Falling crime rates are responsible for about a third of the post-1994 boom in property values. However, this story is incomplete because it ignores the revitalization of New York City’s poorer communities and the large role that housing subsidies played in mitigating the earlier bust.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".