Is something better than nothing? The impact of foreclosed and lease-purchase properties on residential property values
Bibliographic record
Abstract
Lease-purchase (L-P) programmes that rehabilitate foreclosed property for sale as affordable housing may provide a way to reduce foreclosure externalities on nearby property values. This paper investigates the feasibility of such a strategy by estimating the effects of foreclosed properties on nearby residential property values compared with those of an L-P programme operated by the Cleveland Housing Network, Cleveland, Ohio. The findings indicate that although both L-P and foreclosed properties have a negative effect on the value of nearby non-distressed homes, the negative effect of foreclosure is larger. At the same time, the scope of the foreclosure externality is greater in low- and moderate-income neighbourhoods, while the foreclosure externality is generally smaller in high income neighbourhoods. Such results imply that an L-P strategy is likely to be more effective in offsetting foreclosure externalities in low- and moderate-income neighbourhoods than in high income neighbourhoods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".