Flood-damage prediction models are essential building blocks in flood risk assessments. So far, little research has been dedicated to damage from small-scale urban floods caused by heavy rainfall, while there is a need for reliable damage models for this flood type among insurers and water authorities. The aim of this paper is to investigate a wide range of damage-influencing factors and their relationships with rainfall-related damage, using decision-tree analysis. For this, district-aggregated claim data from private property insurance companies in the Netherlands were analysed, for the period 1998–2011. The databases include claims of water-related damage (for example, damages related to rainwater intrusion through roofs and pluvial flood water entering buildings at ground floor). Response variables being modelled are average claim size and claim frequency, per district, per day. The set of predictors include rainfall-related variables derived from weather radar images, topographic variables from a digital terrain model, building-related variables and socioeconomic indicators of households. Analyses were made separately for property and content damage claim data. Results of decision-tree analysis show that claim frequency is most strongly associated with maximum hourly rainfall intensity, followed by real estate value, ground floor area, household income, season (property data only), buildings age (property data only), a fraction of homeowners (content data only), a and fraction of low-rise buildings (content data only). It was not possible to develop statistically acceptable trees for average claim size. It is recommended to investigate explanations for the failure to derive models. These require the inclusion of other explanatory factors that were not used in the present study, an investigation of the variability in average claim size at different spatial scales, and the collection of more detailed insurance data that allows one to distinguish between the effects of various damage mechanisms to claim size. Cross-validation results show that decision trees were able to predict 22–26% of variance in claim frequency, which is considerably better compared to results from global multiple regression models (11–18% of variance explained). Still, a large part of the variance in claim frequency is left unexplained, which is likely to be caused by variations in data at subdistrict scale and missing explanatory variables.
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