The Economic Impact of Flood Zone Designations on Residential Property Valuation in Miami-Dade County
Bibliographic record
Abstract
In the United States, flood events are the most economically damaging type of natural disaster. Some of the most widely used tools for understanding property flood risk in the United States are the Flood Insurance Rate Maps (FIRMs) produced by the Federal Emergency Management Agency (FEMA). Numerous previous studies have attempted to estimate the impact on property valuation from a home’s being mapped into a Special Flood Hazard Area (SFHA) within FIRMs. However, as these maps have widely served as the source of data about true flood risk, there have been limits on the ability of researchers to disentangle these zone designation impacts as due to actual flood risk or as due to perceived flood risk. New advancements in flood modeling have allowed for the prediction of high-quality property-level flood inundation, both now and in the future. By integrating these flood modeling advancements, true flood risk may be controlled for in models looking to explore the avenues by which property valuation impacts occur. To this end, this study builds on insights from recent research looking at the valuation of single-family residential properties in Miami-Dade County (MDC), which utilizes a high-resolution floodplain model to estimate the impact of actual property inundation on sales prices. By controlling for actual property flood risk, impacts of SFHA designations are estimated in MDC through implementation of a difference in difference model which utilizes the release of updated FIRMS in 2009 and the 217,222 transactions and 120,693 property designation changes which occurred within the dataset.
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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 | 0.001 | 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 | 0.000 |
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".