The impact of regional housing price changes on voluntary termination of reverse mortgages: Focusing on age-group differences controlling for mortality risk
Bibliographic record
Abstract
This study aims to overcome the limitations of previous research by distinguishing between terminations due to death and those due to voluntary choice and by addressing the insufficient reflection of micro-level price changes in regional housing markets. We construct quarterly panel data at the subscriber level, utilizing the complete dataset of reverse mortgage subscribers for apartments up to the end of June 2025 and incorporating sales price indices at the district level. Furthermore, we aim to identify the pure effect of economic factors on voluntary termination by controlling for the possibility of attrition due to death by setting age-specific mortality probabilities as an offset in the model. Using a discrete-time hazard model, first, the analysis confirmed that both long-term cumulative housing price changes in enrollment and short-term price changes from the immediately preceding quarter were key drivers that increased the risk of voluntary termination. Second, the impact of housing price appreciation on termination was heterogeneous across age groups, showing a tendency to be most sensitive among the younger group and weaken with increasing age. This suggests the need for customized risk management that considers both regional and age-specific characteristics to ensure the sustainability of the system.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".