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Insurers’ Loss Portfolio Similarity and Climate Risk Insurance Cost: A Spatial Analysis of US Homeowners Insurance Market
by
Sun, Tao
in
Arbitrage
/ Bias
/ Climate change
/ Competition
/ Costs
/ Crop insurance
/ Diversification
/ Friction
/ Geography
/ Homeowners
/ Homeowners insurance
/ Households
/ Hypotheses
/ loss portfolio similarity
/ Regulation
/ spatial autoregressive model
/ spillover
2025
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Insurers’ Loss Portfolio Similarity and Climate Risk Insurance Cost: A Spatial Analysis of US Homeowners Insurance Market
by
Sun, Tao
in
Arbitrage
/ Bias
/ Climate change
/ Competition
/ Costs
/ Crop insurance
/ Diversification
/ Friction
/ Geography
/ Homeowners
/ Homeowners insurance
/ Households
/ Hypotheses
/ loss portfolio similarity
/ Regulation
/ spatial autoregressive model
/ spillover
2025
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Do you wish to request the book?
Insurers’ Loss Portfolio Similarity and Climate Risk Insurance Cost: A Spatial Analysis of US Homeowners Insurance Market
by
Sun, Tao
in
Arbitrage
/ Bias
/ Climate change
/ Competition
/ Costs
/ Crop insurance
/ Diversification
/ Friction
/ Geography
/ Homeowners
/ Homeowners insurance
/ Households
/ Hypotheses
/ loss portfolio similarity
/ Regulation
/ spatial autoregressive model
/ spillover
2025
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Insurers’ Loss Portfolio Similarity and Climate Risk Insurance Cost: A Spatial Analysis of US Homeowners Insurance Market
Journal Article
Insurers’ Loss Portfolio Similarity and Climate Risk Insurance Cost: A Spatial Analysis of US Homeowners Insurance Market
2025
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Overview
This study examines the geographical spillover of the state-level average homeowners insurance cost for 48 US contiguous states. We estimate a panel spatial Durbin model with state and year fixed effect for data between 2001 and 2018. We found a significant positive spillover of average homeowners insurance cost as indicated by a large spatial autoregressive coefficient in the baseline model. We also found a positive relationship between underwriters’ loss portfolio similarity and the average homeowners insurance cost. We conduct several robustness tests and show that the baseline results are robust if against potential biases due to heterogenous state-level insurance regulation, an alternatively defined spatial weighting matrix, and the usage of average homeowners cost for the dominant policy form (the HO3 policy). We also adopt the generalized spatial two-step least squares to mitigate the bias due to endogenous explanatory variables and find that the results are consistent with these reported for the baseline model.
Publisher
MDPI AG
Subject
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