NHESSD Natural Hazards and Earth System Sciences Discussions NHESSD Nat. Hazards Earth Syst. Sci. Discuss. 2195-9269 Göttingen, Germany 10.5194/nhessd-1-3449-2013 Predicting the hurricane damage ratio of commercial buildings by claim payout from Hurricane Ike Kim J. M. 1 Woods P. K. 1 Park Y. J. 2 Kim T. H. 3 Choi J. S. 4 Son K. https://orcid.org/0000-0002-5592-7458 5 Texas A&M University, Department of Construction Science, College Station, Texas, USA Korea Military Academy, Department of Construction Engineering and Environmental Sciences, Seoul, Republic of Korea Mokpo National University, Department of Architectural Engineering, Mokpo, Republic of Korea Dongguk University – Seoul Campus, Department of Architectural Engineering, Seoul, Republic of Korea University of Ulsan, School of Architecture, Ulsan, Republic of Korea 23 07 2013 1 4 3449 3483 Copyright: © 2013 J. M. Kim et al. 2013 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://nhess.copernicus.org/preprints/1/3449/2013/nhessd-1-3449-2013.html The full text article is available as a PDF file from https://nhess.copernicus.org/preprints/1/3449/2013/nhessd-1-3449-2013.pdf

The increasing occurrence of natural disaster events and related damages have led to a growing demand for models that predict financial loss. Although considerable research has studied the financial losses related to natural disaster events, and has found significant predictors, there has not yet been a comprehensive study that addresses the relationship among the vulnerabilities, natural disasters, and economic losses of the individual buildings. This study identified hurricanes and their vulnerability indicators in order to establish a metric to predict the related financial loss. We identify hurricane-prone areas by imaging the spatial distribution of the losses and vulnerabilities. This study utilized a Geographical Information System (GIS) to combine and produce spatial data, as well as a multiple linear regression method, to establish a hurricane damage prediction model. As the dependent variable, we utilized the following ratio to predict the real pecuniary loss: the value of the Texas Windstorm Insurance Association (TWIA) claim payout divided by the appraised values of the buildings. As independent variables, we selected the hurricane indicators and vulnerability indicators of the built environment and the geographical features. The developed statistical model and results can be used as important guidelines by insurance companies, government agencies, and emergency planners for predicting hurricane damage.

 References Berg, R.: Tropical Cyclone Report: Hurricane Ike (al092008), National Hurricane, Center, 1–14 September 2008, Miami, FL, USA, 2009. Boissonnade, A. and Ulrich, P.: How to Best Use Engineering Risk Analysis Models and Geographic Information Systems to Assess Financial Risk from Hurricanes, Casualty Actuarial Society Discussion Paper Program, Arlington, VA, USA, 179–206, 1995. Brody, S. D., Zahran, S., Highfield, W. E., Grover, H., and Vedlitz, A.: Identifying the impact of the built environment on flood damage in Texas, Disasters, 32, 1–18, 2008. Burton, C. G.: Social vulnerability and hurricane impact modeling, Natural Hazards Review, 11, 58–68, 2010. Chock, G. Y. K.: Modeling of hurricane damage for Hawaii residential construction, J. Wind Eng. Ind. Aerod., 93, 603–622, 2005. Cutter, S. L.: Vulnerability to environmental hazards, Prog. Hum. Geog., 20, 529–539, <a href="http://dx.doi.org/10.1177/030913259602000407">https://doi.org/10.1177/030913259602000407</a>, 1996. Cutter, S. L. and Emrich, C. T.: Are natural hazards and disaster losses in the US increasing?, National Emergency Training Center, Emmitsburg, MD, USA, 2005. Dehring, C. and Halek, M.: Do Coastal Building Codes Mitigate Hurricane Damage to Residential Property, Property, Land Use & Real Estate Law eJournal 09/2006, 40 pp., <a href="http://dx.doi.org/10.2139/ssrn.928009">https://doi.org/10.2139/ssrn.928009</a>, 2006. Dunion, J. P., Landsea, C. W., Houston, S. H., and Powell, M. D.: A reanalysis of the surface winds for Hurricane Donna of 1960, Mon. Weather Rev., 131, 1992–2011, 2003. Fulton County, G.: Flood Risk and Homeowners' Flood Risk Perceptions: Evidence from Property Prices in Georgia, Universtiy of Georgia, Atlanta, USA, 2012. Highfield, W. E., Peacock, W. G., and Van Zandt, S.: Determinants & Characteristics of Damage in Single-Family Island Households from Hurricane Ike1, The Association of collegiate schools of planning conference, Minneapolis, Minnesota, 7–10 October, Abstract Index # 282010, 2010. Howard, J. and Scott, D. M.: Risk management method, Google Patents, US 20050177529 A1, 2005. Keim, B. D., Muller, R. A., and Stone, G. W.: Spatiotemporal patterns and return periods of tropical storm and hurricane strikes from Texas to Maine, J. Climate, 20, 3498–3509, 2007. Kennedy, A., Rogers, S., Sallenger, A., Gravois, U., Zachry, B., Dosa, M., and Zarama, F.: Building destruction from waves and surge on the Bolivar Peninsula during Hurricane Ike, Journal of Waterway, 137, 132–141, 2010. Khanduri, A. and Morrow, G.: Vulnerability of buildings to windstorms and insurance loss estimation, J. Wind Eng. Ind. Aerod., 91, 455–467, 2003. Division of Emergency Management: Hurricane Preparedness Planning, Texas Department of Public Safety, Austin, Texas, USA, p. 18., 2003. Noel, J. M., Maxwell, A., Platt, W. J., and Pace, L.: Effects of Hurricane Andrew on cypress (Taxodium distichum var. nutans) in south Florida, J. Coastal Res., Special Issue No. 21, 184–196, 1995. Pielke Jr., R. A. and Landsea, C. W.: Normalized hurricane damages in the United States: 1925–95, Weather Forecast., 13, 621–631, 1998. Powell, M. D. and Houston, S. H.: Surface wind fields of 1995 Hurricanes Erin, Opal, Luis, Marilyn, and Roxanne at landfall, Mon. Weather Rev., 126, 1259–1273, 1998. Powell, M. D., Houston, S. H., Amat, L. R., and Morisseau-Leroy, N.: The HRD real-time hurricane wind analysis system, J. Wind Eng. Ind. Aerod., 77, 53–64, 1998. Powell, M. D., Murillo, S., Dodge, P., Uhlhorn, E., Gamache, J., Cardone, V., Cox, A., Otero, S., Carrasco, N., and Annane, B.: Reconstruction of Hurricane Katrina's wind fields for storm surge and wave hindcasting, Ocean Eng., 37, 26–36, 2010.