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Weather Indicators for Insured Hailstorm Damage to Motor Vehicles and Potential Climate Change Impacts

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Abstract

Projections of the potential effects of climate change on damage caused by local extreme weather events are important for the design of appropriate policies for greenhouse gas emission reduction and insurers’ adaptation responses to changing risks. This study estimates the relationships between daily insured damage from hailstorms to motor vehicles and several weather indicators, using statistical models with a high spatial resolution. We account for temporal dynamics and changes in exposure to hailstorms. The best-fitting model includes indicators of local daily maximum temperatures and regional spatially averaged precipitation. The projected increase in hailstorm damage of up to 33 per cent during the hail season as a result of anthropogenic climate change by the year 2050 is smaller than the increase found in previous studies.

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Notes

  1. Tol (2012); Van den Bergh and Botzen (2014).

  2. Ackerman et al. (2009).

  3. IPCC (2012).

  4. Botzen and Van den Bergh (2014).

  5. Moss et al. (2013).

  6. Botzen and van den Bergh (2009).

  7. Dlugolecki (2000, 2008).

  8. Charpentier (2008).

  9. Kunreuther and Michel-Kerjan (2007).

  10. Mills (2012).

  11. Scheel and Hinnerichsen (2012).

  12. IPCC (2012, 2013).

  13. Dessens (1995); Willemse (1995); Piani et al. (2005); Kunz et al. (2009); Berthet et al. (2011).

  14. Changnon and Changnon (1992); Mills et al. (2002); Mills (2005); Trapp et al. (2009).

  15. Botzen et al. (2010).

  16. Niall and Walsh (2005).

  17. Saa Requejo et al. (2011).

  18. Dessens (1995); Botzen et al. (2010); Saa Requejo et al. (2011).

  19. Czajkowski and Simmons (2014).

  20. Verbond van Verzekeraars (2013).

  21. We use data from the Interpolis division of Achmea which has a market share of about 10 per cent in the motor vehicle insurance market.

  22. The logarithm of damage has been set equal to zero on days without hailstorm damage.

  23. For example, Dessens (1995); Willemse (1995); Kunz et al. (2009).

  24. Dessens (1995).

  25. Piani et al. (2005).

  26. The need to make this assumption can be overcome by estimating a model with an aggregated hailstorm damage variable for the Netherlands and country average temperature and precipitation indicators. We estimated such a model (not reported separately here), but this alternative specification did not result in an improvement of model fit compared with the model results presented in this paper.

  27. We estimated a variety of models in which the temperature variable is specified as a dummy variable of a threshold temperature that is exceeded (value=1) or not (value=0). In particular, this was done for 20 different variables with threshold levels of temperatures between 15 °C and 35 °C. The information criteria of these models indicated that none of these model specifications results in a better fit than the model specification that includes temperature as a continuous variable. Lags of the temperature variables are highly correlated (about 0.9 or higher) with the temperature variables and, therefore, not included in the model to prevent problems with multi-collinearity.

  28. We estimated models that include the difference in daily temperature instead of the daily value itself. The information criteria of these models showed that model fit is worse compared with our final models that include the daily values of temperature.

  29. For example, Nordhaus (2010); Bouwer and Botzen (2011).

  30. For example, Nordhaus (2013).

  31. This was tested by estimating models that include a dummy variable of a threshold precipitation that is exceeded (value=1) or not (value=0) instead of the continuous precipitation variables. As thresholds we created variables of precipitation exceeding the 10th, 20th, 30th, 40th, 50th, 60th, 70th, 80th and 90th percentile values (if>0). The information criteria of these models indicated that none of these model specifications results in a better fit than the model specification that includes precipitation as a continuous variable.

  32. We estimated models that include the difference in precipitation and its lag instead of the daily value itself, which resulted in a worse model fit compared with our final models that include the daily values.

  33. More lags of precipitation are statistically insignificant.

  34. For example, Pinto et al. (2007).

  35. Other variables related to the exposure, such as characteristics of the damaged cars, are not available. Nevertheless, the value of the insured portfolio per zip code provides a good indicator of the aggregate exposure in such an area.

  36. Tobin (1958).

  37. Similar results were obtained using standard ordinary least squares (OLS) regressions (not reported separately here). Moreover, we estimated panel OLS regressions with zip code-specific random effects. The results are the same as those for the standard OLS and Tobit models and are, therefore, not reported here. As an additional robustness check we estimated a two-step Heckman selection model which confirmed that our explanatory variables have a significant influence on the occurrence of hailstorm damage.

  38. Huber (1967); White (1982).

  39. Moreover, we examined whether model fit can be improved by including interaction variables between precipitation and temperature which did not turn out to be the case.

  40. Klein Tank and Lenderink (2009); Klein Tank et al. (2014).

  41. Van den Hurk et al. (2006).

  42. Klein Tank and Lenderink (2009).

  43. For this purpose the percentage changes in precipitation in Table 4 are translated into absolute changes in cm of precipitation, using the sample average over time of the amounts of average precipitation (2.38) and maximum precipitation (3.69).

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Acknowledgements

We thank our colleague Nils de Reus for preparing the GIS data, Martijn Bakkum from Achmea for providing the hail damage data, and KNMI for making available the daily rainfall data. This research was supported through the BSIK A9 project “Financial arrangements for disaster losses under climate change”, funded by the Climate Changes Spatial Planning Programme (www.climateresearchnetherlands.nl), and has been co-funded by the Netherlands Organisation for Scientific Research (NWO), and the EU FP7 project ENHANCE (Grant Agreement number 308438).

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Appendices

Appendix A

Computation of marginal effects

The marginal effects of the Tobit model are (∂E[y t ]/∂x t )=(Φ(x t β/σ)β), where Φ is the cumulative normal density function; x t are the explanatory variables with coefficients β; and σ is the model standard deviation. The MME of the jth explanatory variable is computed as (McDonald and Moffitt, 1980): β j (1/n)∑t=1nΦ(x t β/σ).

Appendix B

Results of models using only observations recorded during the hail season between the months May and August

SeeTables B1, B2 and B3.

Table B1 Results of Tobit models of hailstorm damage that include different combinations of temperature and precipitation indicators using only observations recorded during the hail season
Table B2 Mean marginal effects of the main variables of interest for the models in Table B1
Table B3 Projected changes in key weather indicators for the summer season in the Netherlands according to scenarios of climate change for the year 2050

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Botzen, W., Bouwer, L. Weather Indicators for Insured Hailstorm Damage to Motor Vehicles and Potential Climate Change Impacts. Geneva Pap Risk Insur Issues Pract 41, 512–527 (2016). https://doi.org/10.1057/gpp.2015.16

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