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Subsidence and household insurances in France : geolocated data and insurability

Author

Listed:
  • Pierre Chatelain

    (LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

  • Stéphane Loisel

    (LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

Abstract

The insurability of natural disasters has always been an issue faced by the insurers, states, and insured persons. In France, the insurer and the legislator are concerned about the subsidence risks due to several consecutive dry years. More and more open data are provided in France, which allows insurers by geolocating their portfolio to have better knowledge. This knowledge plus the increase in subsidence risks query the insurability of the subsidence risk. Using mostly GLMs, the most common models used in France, this paper shows the improvement of the knowledge subsidence risks. The results bring to the fore the importance of legislative control and the recently enforced new CatNat program, leading authors to question the CatNat fee stagnation.

Suggested Citation

  • Pierre Chatelain & Stéphane Loisel, 2021. "Subsidence and household insurances in France : geolocated data and insurability," Working Papers hal-03791154, HAL.
    • Handle: RePEc:hal:wpaper:hal-03791154
    Note: View the original document on HAL open archive server: https://hal.science/hal-03791154v1
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    References listed on IDEAS

    as
    1. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    2. repec:cup:astinb:v:21:y:1991:i:01:p:93-109_00 is not listed on IDEAS
    3. Arthur Charpentier & Molly James & Hani Ali, 2021. "Predicting Drought and Subsidence Risks in France," Papers 2107.07668, arXiv.org.
    4. Renshaw, A.E. & Verrall, R.J., 1998. "A Stochastic Model Underlying the Chain-Ladder Technique," British Actuarial Journal, Cambridge University Press, vol. 4(4), pages 903-923, October.
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    Cited by:

    1. Geoffrey Ecoto & Aurélien F. Bibaut & Antoine Chambaz, 2025. "Forecasting the cost of drought events in France by Super Learning from a short time series of many slightly dependent data," Computational Statistics, Springer, vol. 40(5), pages 2277-2321, June.

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