Heterogeneous flood zone effects on coastal housing prices - Risk signal and mandatory costs

Zhenshan Chen (), Charles Towe and Xi He

Journal of Environmental Economics and Management, 2025, vol. 131, issue C

Abstract: Using a high-quality dataset and addressing various empirical challenges, we estimate the heterogeneous impact of flood zone on single-family housing prices in coastal Connecticut. Causal forest estimates suggest that transactions without a mortgage loan, where flood insurance is voluntary, show an insignificantly positive average flood zone effect. Conversely, with mandatory and upfront insurance costs, with-loan transactions exhibit a statistically significant average discount of $12.2k. To detect the nuanced risk signals associated with the flood zone designation and mandatory costs, we conceptualize and empirically test differences in distributions of heterogeneous flood zone effects between transactions with and without mortgage. Bootstrap tests reveal that, compared with the no-loan counterpart, the with-loan heterogenous effect has a significantly lower average, is first-order stochastically dominated, has a significantly lower dispersion, and is significantly more concentrated below a reference point indicated by the insurance costs. Robust evidence suggests that most transactions feature minimal flood zone discounts, if any, suggesting neither the flood zone designation nor the mandatory flood insurance conveys a sufficiently strong message about rising flood risks. Despite heightened risk perceptions from recent hurricanes and policies, stronger flood risk signaling is needed in coastal Connecticut.

Keywords: Flood zone; Coastal housing market; Generalized random forests; Causal machine learning; Regression discontinuity design; Coastal flood risk (search for similar items in EconPapers)
JEL-codes: Q54 R31 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jeeman:v:131:y:2025:i:c:s0095069625000373

DOI: 10.1016/j.jeem.2025.103153

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