Published October 3, 2018 | Version 1
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HouCo Flood Loss Model

  • 1. Oeschger Centre for Climate Change Research, Institute of Geography, University of Bern, Bern CH-3012, Switzerland

Description

Beside the flood hazard analysis, a comprehensive flood risk assessment requires the analysis of the exposure of values at risk and their vulnerability. Currently, the main focus of such analysis is on losses on building structure. However, loss on household contents accounts for up to 30% of the total losses on buildings due to floods. Here, we present two functions for estimating flood losses on household contents based on flood losses on building structure. The models are constructed from and validated for insurance claim records. 

One model is based on a regression of the degree of loss for household content on the degree of loss for building structure. The second model is based on the same regression structure between the absolute losses of both types. 
Moreover, we tested our models for robustness, predictive power and transferability. Both models generate appropriate results with a comparative advantage of the relative over the absolute loss model. 

A detailed examination of the model residuals, shows that the Box-Cox transformation works well to accurately fit a standard regression model to general right-skewed loss data as the transformed data meet the assumptions of a regression model.

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References

  • Box, G.E.P.; Cox, D.R. An Analysis of Transformations. Journal of the Royal Statistical Society. Series B (Methodological) 1964, 26, 211–252.
  • Breusch, T.S.; Pagan, A.R. A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica 1979, 47, 1287. doi:10.2307/1911963.
  • Carroll, R.J.; Ruppert, D. Power Transformations when Fitting Theoretical Models to Data. Journal of the American Statistical Association 1984, 79, 321. doi:10.2307/2288271.
  • Good, P.I.; Hardin, J.W. Univariate Regression. In Common Errors in Statistics (and How to Avoid Them); John Wiley & Sons, Inc, 2003; pp. 127–143. doi:10.1002/0471463760.ch9.
  • Greene,W.H. Econometric analysis, 7th ed.; Pearson AddisonWesley: Harlow and New York, 2012.
  • Royston, J.P. An Extension of Shapiro and Wilk's W Test for Normality to Large Samples. Applied Statistics 1982, 31, 115. doi:10.2307/2347973.
  • Sakia, R.M. Retransformation bias: A look at the box-cox transformation to linear balanced mixed ANOVA models. Metrika 1990, 37, 345–351. doi:10.1007/BF02613542.
  • Sokal, R.; Rohlf, F. Biometry; the principles and practice of statistics in biological research; Series of books in biology,W. H. Freeman, 1969.
  • Taylor, J.M.G. The Retransformed Mean after a Fitted Power Transformation. Journal of the American Statistical Association 1986, 81, 114–118. doi:10.1080/01621459.1986.10478246.
  • Weisberg, S. Nonlinear Regression. In Applied Linear Regression; John Wiley & Sons, Inc, 2005; pp. 233–250. doi:10.1002/0471704091.ch11.
  • Weisberg, S. Simple Linear Regression. In Applied Linear Regression; JohnWiley & Sons, Inc, 2005; pp. 19–46. doi:10.1002/0471704091.ch2.