Coastal Real Estate Vibes: An Analysis of the Association Between Coastal Residential Ownership and the Resident Occupant’s Risk Tolerance
Bibliographic record
Abstract
This study examines the association between the location, relative to the coast, of an individual’s primary residence and the homeowners’ risk tolerance. Utilizing data from the 2021 National Financial Capability Study and employing a probit model, we analyzed how varying risk tolerance levels affect the likelihood of owning a home in a coastal ZIP code. The respondent’s risk tolerance was classified as high, medium, or low according to their self-reported willingness to take financial risks. Our results suggest that individuals with lower risk tolerances are less likely to own a home within a coastal ZIP code. Specifically, homeowners with medium-risk tolerance are 2.91% less likely, and those with low-risk tolerance are 3.17% less likely to own a primary residence in a coastal ZIP code when compared to those with high-risk tolerance. These results are statistically and economically similar when using a logit model. These findings are both statistically significant and align with economic theory. The analysis also included various demographic and socioeconomic factors, finding that age, income, and certain employment statuses influence coastal homeownership. This research contributes to the understanding of home ownership location choices and risk tolerance. Our results provide policymakers with insights into the risk characteristics of individuals who prefer coastal areas as their primary residences. This information can inform future policy decisions by highlighting the societal and economic implications of regulations related to residential coastal development.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".