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

Abstract

We 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.