A statistical analysis of insurance damage claims related to rainfall extremes

Abstract

Abstract. In this paper, a database of water-related insurance damage claims related to private properties and content was analysed. The aim was to investigate whether the probability of occurrence of rainfall-related damage was associated with the intensity of rainfall. Rainfall data were used for the period of [2003][2004][2005][2006][2007][2008][2009] in the Netherlands based on a network of 33 automatic rain gauges operated by the Royal Netherlands Meteorological Institute. Insurance damage data were aggregated to areas within 10-km range of the rain gauges. Through a logistic regression model, high claim numbers were linked to maximum rainfall intensities, with rainfall intensity based on 10-min to 4-h time windows. Rainfall intensity proved to be a significant damage predictor; however, the explained variance, approximated by a pseudo-R 2 statistic, was at most 34 % for property damage and at most 30 % for content damage. When directly comparing predicted and observed values, the model was able to predict 5-17 % more cases correctly compared to a random prediction. No important differences were found between relations with property and content damage data. A considerable fraction of the variance is left unexplained, which emphasizes the need to study damage generating mechanisms and additional explanatory variables.

The relationship between precipitation and insurance data for floods in a Mediterranean region (northeast Spain)

“…Therefore, we have been able to confirm that 30 min rainfall is a better predictor of the probability of large damages than daily rainfall in urban areas, and this result confirms previous studies such as that of Torgersen et al (2015), who found a significant relationship between insurance data and short-lasting rainfall when studying urban floods in Norway. In addition, Spekkers et al (2013) showed that high claim numbers associated with private property and content damage were significantly related to maximum rainfall intensity, based on a logistic regression, with rainfall intensity for 10 min to 4 h time windows.…”

“…Several recent works have used this kind of data to explore the causes and impacts of floods. For instance, in several European regions researchers have noted that precipitation has a significant influence on flood insurance data (see, for instance, Spekkers et al, 2013Spekkers et al, , 2015, for the Netherlands; Zhou et al, 2013, for Denmark;Sampson et al, 2014, for Ireland;Moncoulon et al, 2014, for France;Torgersen et al, 2015, for Norway). These data are very valuable for establishing causal relationships between the costs of flood damage and precipitation extremes, for developing risk maps and to use as a validation tool for damage models (Zhou et al, 2013).…”

Opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks

“…Studying the spatial distribution of possible explanatory variables may help to explain how a heavy rainfall event triggers occurrence of flooding impacts in a city. Recent works have analyzed whether occurrence of citizens' complaints and insurance claim reports on rainfall-related incidents could be explained by variability in urban topography (Gaitan et al, 2015) and rainfall intensity (Spekkers et al, 2013). Response and explaining variables in those studies were selected apriori.…”

“…Jacobs, 2012), are examples of such measures. However, their effectiveness depends on the available knowledge of the mechanisms leading to damage after heavy rains, which are still not completely understood (Gaitan et al, 2015;Spekkers et al, 2013Spekkers et al, , 2014ten Veldhuis et al, 2011).…”

Rapid mapping of urban development from historic Ordnance Survey maps: An application for pluvial flood risk in Worcester

“…Parker, Priest, & McCarthy, 2011;Spekkers, Kok, Clemens, & ten Veldhuis, 2013). Understanding of historic urban development in relation to this type of flooding could contribute to our understanding of these mechanisms.…”