The Effects of Revealed Information on Catastrophe Loss Projection Models' Characterization of Risk: Damage Vulnerability Evidence from Florida

• Published in: Risk analysis Vol. 36; no. 6; pp. 1224 - 1250
• Main Authors: Karl, J. Bradley, Medders, Lorilee A., Maroney, Patrick F.
• Format: Journal Article
• Language: English
• Published: United States Blackwell Publishing Ltd 01-06-2016
• Subjects: Buildings; Catastrophe modeling; Colleges & universities; Damage; Estimates; hurricane risk; Hurricanes; Information industry; Knowledge management; Measurement techniques; mitigation; Projection model; Raw materials; revealed information; Risk; Risk analysis; Risk assessment; Storm damage; Studies; Florida; United States > US; ASW, USA, Florida; A, Atlantic; hurricane risk; mitigation; revealed information; Catastrophe modeling
• ISSN: 0272-4332; 1539-6924
• DOI: 10.1111/risa.12524

We examine whether the risk characterization estimated by catastrophic loss projection models is sensitive to the revelation of new information regarding risk type. We use commercial loss projection models from two widely employed modeling firms to estimate the expected hurricane losses of Florida Atlantic University's building stock, both including and excluding secondary information regarding hurricane mitigation features that influence damage vulnerability. We then compare the results of the models without and with this revealed information and find that the revelation of additional, secondary information influences modeled losses for the windstorm‐exposed university building stock, primarily evidenced by meaningful percent differences in the loss exceedance output indicated after secondary modifiers are incorporated in the analysis. Secondary risk characteristics for the data set studied appear to have substantially greater impact on probable maximum loss estimates than on average annual loss estimates. While it may be intuitively expected for catastrophe models to indicate that secondary risk characteristics hold value for reducing modeled losses, the finding that the primary value of secondary risk characteristics is in reduction of losses in the “tail” (low probability, high severity) events is less intuitive, and therefore especially interesting. Further, we address the benefit‐cost tradeoffs that commercial entities must consider when deciding whether to undergo the data collection necessary to include secondary information in modeling. Although we assert the long‐term benefit‐cost tradeoff is positive for virtually every entity, we acknowledge short‐term disincentives to such an effort.