Doctoral Dissertation Research: The Geography of Insurance Litigation and Post-hurricane Recovery

About This Grant

This project examines legal actions involving insurance companies that have denied claims following natural disasters, because geographical variation in litigation may help to explain why communities vary in their pace of recovery. The research examines demographic and economic factors that potentially underlie variation in litigation rates and the legal outcomes. The study also investigates correlations between litigation and recovery rates as indicated by construction that is evident over time in satellite images, which are processed with deep-learning algorithms. The findings and recommendations are made available to a broad range of stakeholders, including regional planners. The project also provides opportunities for the training and education of multiple students. As a contribution to the geographical sciences, this study examines the dynamic processes that lead to spatial variation in recovery rates after natural disasters. Communities may vary in their litigiousness, and this study uses qualitative methods to elucidate the reasons for considering legal recourse among a sample of impacted residents. Complementary methods are used to construct a quantitative parcel-level dataset in communities that have been negatively impacted by recent disasters. This dataset combines property records, court records, and satellite images of the study area. Deep learning algorithms are used with the satellite images to detect evidence of damaged homes and subsequent recovery. The dataset facilitates multilevel hazard modeling of parcels’ time to recovery as a function of litigation participation and related outcomes. The study refines understandings of the role of litigation as an adaptive strategy for homeowners who have been affected by natural disasters. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.