Dynamic modeling of public and private decision-making for hurricane risk management including insurance, acquisition, and mitigation policy

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dc.contributor.authorGuo, Cen
dc.contributor.authorNozick, Linda
dc.contributor.authorKruse, Jamie
dc.contributor.authorMillea, Meghan
dc.contributor.authorDavidson, Rachel
dc.contributor.authorTrainor, Joseph
dc.date.accessioned2022-08-04T16:28:33Z
dc.date.available2022-08-04T16:28:33Z
dc.date.issued2022-06-08
dc.descriptionThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.en_US
dc.description.abstractWe develop a computational framework for the stochastic and dynamic modeling of regional natural catastrophe losses with an insurance industry to support government decision-making for hurricane risk management. The analysis captures the temporal changes in the building inventory due to the acquisition (buyouts) of high-risk properties and the vulnerability of the building stock due to retrofit mitigation decisions. The system is comprised of a set of interacting models to (1) simulate hazard events; (2) estimate regional hurricane-induced losses from each hazard event based on an evolving building inventory; (3) capture acquisition offer acceptance, retrofit implementation, and insurance purchase behaviors of homeowners; and (4) represent an insurance market sensitive to demand with strategically interrelated primary insurers. This framework is linked to a simulation-optimization model to optimize decision-making by a government entity whose objective is to minimize region-wide hurricane losses. We examine the effect of different policies on homeowner mitigation, insurance take-up rate, insurer profit, and solvency in a case study using data for eastern North Carolina. Our findings indicate that an approach that coordinates insurance, retrofits, and acquisition of high-risk properties effectively reduces total (uninsured and insured) losses.en_US
dc.description.sponsorshipWiley Open Access Accounten_US
dc.identifier.citationGuo, C., Nozick, L., Kruse, J., Millea, M., Davidson, R., &Trainor, J. (2022). Dynamic modeling of public and private decision‐making forhurricane risk management including insurance, acquisition, and mitigation policy.RiskManagement and Insurance Review, 25, 173–199.https://doi.org/10.1111/rmir.12215GUOET AL.|199en_US
dc.identifier.doi10.1111/rmir.12215
dc.identifier.urihttp://hdl.handle.net/10342/10976
dc.language.isoenen_US
dc.relation.urihttps://onlinelibrary.wiley.com/doi/10.1111/rmir.12215en_US
dc.titleDynamic modeling of public and private decision-making for hurricane risk management including insurance, acquisition, and mitigation policyen_US
dc.typeArticleen_US
ecu.journal.issue2en_US
ecu.journal.nameRisk Management and Insurance Reviewen_US
ecu.journal.pages173-199en_US
ecu.journal.volume25en_US

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