Articles | Volume 13, issue 8
Special issue:
Research article
08 Aug 2013
Research article |  | 08 Aug 2013

Contribution of insurance data to cost assessment of coastal flood damage to residential buildings: insights gained from Johanna (2008) and Xynthia (2010) storm events

C. André, D. Monfort, M. Bouzit, and C. Vinchon

Abstract. There are a number of methodological issues involved in assessing damage caused by natural hazards. The first is the lack of data, due to the rarity of events and the widely different circumstances in which they occur. Thus, historical data, albeit scarce, should not be neglected when seeking to build ex-ante risk management models. This article analyses the input of insurance data for two recent severe coastal storm events, to examine what causal relationships may exist between hazard characteristics and the level of damage incurred by residential buildings. To do so, data was collected at two levels: from lists of about 4000 damage records, 358 loss adjustment reports were consulted, constituting a detailed damage database. The results show that for flooded residential buildings, over 75% of reconstruction costs are associated with interior elements, with damage to structural components remaining very localised and negligible. Further analysis revealed a high scatter between costs and water depth, suggesting that uncertainty remains high in drawing up damage functions with insurance data alone. Due to the paper format of the loss adjustment reports, and the lack of harmonisation between their contents, the collection stage called for a considerable amount of work. For future events, establishing a standardised process for archiving damage information could significantly contribute to the production of such empirical damage functions. Nevertheless, complementary sources of data on hazards and asset vulnerability parameters will definitely still be necessary for damage modelling; multivariate approaches, crossing insurance data with external material, should also be investigated more deeply.

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