Title insurance and the “race to the bottom”
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to use a stylised multi‐period model to describe the economic dynamics related to title risks and the implications of title insurance in the risk management decision process. Some fear that insofar as the quality of public records is concerned, the purchase of title insurance might induce a race to the bottom that would reduce the possibility to identify and correct title defects. This phenomenon is illustrated and the impact of different title risk management strategies and conditions under which recourse to lawyers is preferable to the purchase of title insurance are examined. Design/methodology/approach The main source of risk is the disparity between the characteristics of a property and what is written on its title. The choice of a title risk management strategy is modeled as the probability of hiring a lawyer versus that of buying insurance. Consistent with the literature, the deterioration of public records is made a function of the risk management decisions. Findings Results suggest that when public records are sound, the race to the bottom can be avoided and high title values maintained by adopting a risk management strategy based on recourse to lawyers; when public records are flawed, insurance companies may be in a better position than lawyers to maintain title values; switching back and forth from insurers to lawyers does not benefit title values. Originality/value The originality and the value of this paper is the study of title insurance which, while an important component of title risk management process, has received limited attention in the academic literature.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.004 |
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 it