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Profitability and Growth in Motor Insurance Business: Empirical Evidence from Germany

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Abstract

Over recent years, the German motor insurance business has faced significant changes, including a growing importance of direct insurance offerings. Motor insurance products are offered by a wide range of insurers, with companies differing in terms of legal status, size, product portfolio, distribution strategy and operational efficiency. Furthermore, one distinguishes between two main products, namely motor third-party liability (MTPL) and motor own damage (OD). In our research, we analyse to what extent the characteristics of the companies can explain the premiums, the total claims costs and the operating expenses per contract in MTPL and OD. For our analysis, we use panel data of insurance companies, offering motor insurance products in Germany, for the years 2002–2014. The panel data provide almost full market coverage. In our study, we apply different statistical tests and multilinear regression models. We show that mutuals relate to lower premiums, lower total claims costs and lower operating expenses per contract when compared to listed companies. In addition, direct insurance companies get along with lower premiums and lower operating expenses per contract compared to traditional companies selling via agents or brokers. Furthermore, we find major differences related to the range of the product portfolio, the size of the motor business, the dominance of the motor business within the non-life business, and the calendar year. Our results are relevant to academics and practitioners alike and help to better understand the German motor insurance business.

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Notes

  1. Insurance Europe (2014, 2016).

  2. Insurance Europe (2010).

  3. GDV (2012, 2014b).

  4. Insurance Europe (2015).

  5. Hartmann et al. (2014).

  6. Erdönmez et al. (2007).

  7. Laas et al. (2016).

  8. Schmeiser et al. (2014).

  9. Yeo et al. (2001).

  10. Guelman and Guillén (2014).

  11. Weidner and Weidner (2014).

  12. Surminski (2014).

  13. Paefgen et al. (2013).

  14. Kremslehner and Muermann (2016).

  15. Swiss Re Sigma (2014).

  16. Hocking et al. (2014).

  17. Eling and Luhnen (2008).

  18. German Federal Ministry of Justice (2015b).

  19. German Federal Ministry of Justice (2015a).

  20. Details and source references of these deals can be found in Table 3 in the section on direct insurance.

  21. GDV (2014a).

  22. Newly established companies are included in the graph from the first year after foundation. WGV-Versicherung AG is included because they do not have an agents network and were one of the founders of the aggregator platform aspect online.

  23. Towers Watson (2014).

  24. http://www.verivox.de/company/impressum/.

  25. http://www.check24.de/unternehmen/impressum/.

  26. Ajne (1975).

  27. Thomas (2008).

  28. McKinsey and Company (2014).

  29. Matouschek and Stricker (2013).

  30. Hocking et al. (2014).

  31. Mascher (2016).

  32. Swiss Re Sigma (2012, 2014).

  33. Statistisches Bundesamt (2016).

  34. GDV (2015).

  35. Hoffmann (2011); Staudt and Wagner (2017).

  36. Mahlow et al. (2015).

  37. Capgemini (2011).

  38. Eling and Luhnen (2010).

  39. Schwarz et al. (2008).

  40. Mahlow et al. (2015); Mahlow and Wagner (2016).

  41. Eling and Luhnen (2010); Schwarz et al. (2008).

  42. GDV (2016).

  43. Farny (2011).

  44. Lorson and Wagner (2014).

  45. Nemson (2014).

  46. Meier and Outreville (2006).

  47. Marsh and McLennan (2015, 2016).

  48. For more details on the explanatory power of each single variable see Tables A1 and A2 in the Appendix, where we have included the single regressions for each variable. In the results, we see the relevance of each of the variables. However, as can be expected the \(R^2\) values of the single variable models are very low. In fact, one single variable will not be able to explain \(PR^p_{i,t}\), \(CL^p_{i,t},\) or \(EX^p_{i,t}\).

  49. V.E.R.S. Leipzig and zeb (2016).

  50. Also for OD, attention must be paid to multicollinearity. The explanatory power of each single variable is displayed in Tables A1 and A2 in the Appendix, where we have included the single regressions for each variable.

  51. A possible explanation could also result from the contract deductibles. Because of the smaller budgets of younger drivers, they could be more willing to sign a lower-priced own damage insurance policy with a relatively high deductible. This would have a weakening effect on premiums and on claims costs. Due to limitations in our data set we cannot include deductibles in our analysis. The effect of deductibles could also be a topic of future research.

  52. Handelsblatt (2016).

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Authors and Affiliations

  1. Department of Actuarial Science, University of Lausanne, Quartier Dorigny - Bâtiment Extranef, 1015, Lausanne, Switzerland

    Liselotte Maichel-Guggemoos

  2. Department of Actuarial Science and Swiss Finance Institute, University of Lausanne, Quartier Dorigny - Bâtiment Extranef, 1015, Lausanne, Switzerland

    Joël Wagner

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  1. Liselotte Maichel-Guggemoos
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Correspondence to Joël Wagner.

Appendix

Appendix

Single variable regressions

Single variable regression models show the influence of individual variables. We define single regressions as follows:

$$\begin{aligned} Y = \beta _0 + \beta _1 \cdot X +\epsilon _{i,t} \end{aligned}$$

Here Y stands for the dependent variables \(PR^p_{i,t}\), \(CL^p_{i,t},\) and \(EX^p_{i,t}\) and X stands for the explaining variables \(MU_{i,t}\), \(DI_{i,t}\), \(NL_{i,t}\), \(MS^p_{i,t}\), \(GR^p_{i,t}\), \(CL^p_{i,t-1}\), \(LG_{i,t},\) and \(YR_{i,t}\). Tables A1 and A2 show the results for MTPL and OD.

Table A1 Single variable regressions (part I)
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Table A2 Single variable regressions (part II)
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Companies included in the analysis

Table A3 List of companies included in the data panel
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Maichel-Guggemoos, L., Wagner, J. Profitability and Growth in Motor Insurance Business: Empirical Evidence from Germany. Geneva Pap Risk Insur Issues Pract 43, 126–157 (2018). https://doi.org/10.1057/s41288-017-0053-4

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