The Cost Effectiveness of Community-Based Foreclosure Prevention
Bibliographic record
Abstract
Ford Foundation for support in undertaking this research. The authors would like to acknowledge Jon Toppen for his contribution to an earlier version of this paper. Abstract – In this paper, we examine the cost-effectiveness of community-based foreclosure prevention interventions using two proxy measures: time to resolution and the rate of recidivism. We examine these issues with data from over 4,200 borrowers who received intense case-management, post-purchase counseling and/or assistance loans through the Mortgage Foreclosure Prevention Program in Minneapolis/Saint Paul. Overall, our findings suggest that community-based foreclosure prevention services are cost effective. With regard to time to resolution, the time to outcome for borrowers served by the program was on average 11 months. With regard to the rate of recidivism, about one quarter of borrowers who avoided foreclosure reported being delinquent again 12 months after program intervention. The rate increased to about one third after 36 months. Households that did not receive an assistance loan as part of the intervention had a higher incidence of recidivism over time, about 45 percent. Both time to resolution and recidivism among program participants compared favorably with those reported elsewhere for the industry. Finally, our findings identify several borrower, loan and program factors to be associated with shorter time to resolution, lower recidivism, and an overall higher likelihood of avoiding foreclosure. Consistently, the receipt of pre-purchase counseling is found to be favorably associated with the measures of cost-effectiveness examined. 3
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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".