Practical Considerations in Evaluating a Long-term Care Securitization
Bibliographic record
Abstract
In many countries seniors requiring long-term care (LTC) are required to pay much of the cost of care themselves. Private LTC insurance has had relatively low take-up rates. In countries such as Canada and England a significant amount of seniors’ accumulated savings is represented by the equity in their homes. Traditional equity release products also have relatively low take-up rates. At the IAA Colloquium in Hong Kong in 2012, Andrews presented an outline of a public-private-partnership that might be used to release home equity for the purpose of financing LTC expenses that would involve a securitization. For this paper, the authors obtained data from various sources to attempt to price this LTC equity release securitization. They encountered a number of practical problems, such as inconsistencies in the housing data, determination of suitable LTC incidence rates, how to apply the housing data to longer time periods, and they developed some practical solutions. This paper presents some of the problems encountered and the solutions developed.
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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 | 0.093 | 0.365 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".