Competing Risks of Mortgage Termination: Who Refinances, Who Moves, and Who Defaults?
Bibliographic record
Abstract
Why, when, and who terminates their mortgages? The primary reasons for mortgage termination are refinancing, selling of the property, and default. This article is the first to explicitly model these competing risks within a unified conceptual framework and provide a link between theoretical value-maximizing mortgage-termination models and empirical estimation. I find, for instance, that the refinancing risk is highly sensitive to interest-rate changes and other variables capturing the value of the mortgage. On the other hand, the necessity to relocate, either through sale of the property of default, is sensitive to the local economic conditions but largely independent of the value of the mortgage. Furthermore, I explicitly model the spatial distribution of the mortgage-termination risks. This approach captures striking spatial patterns of mortgage termination. It also mitigates, at least partially, one of the biggest obstacles to mortgage termination estimation: omitted variables. Copyright 2001 by Kluwer Academic Publishers
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".