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Contributions of geolocated weather and building related data for insurance assessment of flood risks

Mulah Moriah, Franck Vermet, Pierre Ailliot, Philippe Naveau, Juliette Legrand

Abstract

Floods rank among the costliest natural hazards, causing over USD 100 billion in insured losses between 2013 and 2023. In France, persistent deficits in the natural catastrophe scheme highlight the need for accurate, building-scale flood risk assessment. Insurers typically rely on frequency-severity models supported by hazard maps and regional climate indicators. However, previous studies show that such large-scale variables explain only a limited share of the variability in individual flood losses. This study evaluates the marginal contribution of multiple georeferenced data layers to modeling flood claim occurrence and severity in a large French home insurance portfolio. Starting from a baseline model based on standard underwriting information, we sequentially introduce climate-expert variables, extreme rainfall indicators, and fine-scale geolocated building and environmental attributes. The analysis focuses on a practical setting in which insurers cannot deploy full hydrological or hydraulic catastrophe models because of budgetary, licensing, or operational constraints. Results show that rainfall-based indicators, particularly a newly constructed metric capturing intense local precipitation, substantially improve claim modeling performance. Building and environmental variables further enhance occurrence prediction. Overall, the findings demonstrate how high-resolution geolocated data improve exposure and vulnerability assessment, complement official flood maps, and provide insurers with an operational framework for refining flood risk evaluation and pricing.

Contributions of geolocated weather and building related data for insurance assessment of flood risks

Abstract

Floods rank among the costliest natural hazards, causing over USD 100 billion in insured losses between 2013 and 2023. In France, persistent deficits in the natural catastrophe scheme highlight the need for accurate, building-scale flood risk assessment. Insurers typically rely on frequency-severity models supported by hazard maps and regional climate indicators. However, previous studies show that such large-scale variables explain only a limited share of the variability in individual flood losses. This study evaluates the marginal contribution of multiple georeferenced data layers to modeling flood claim occurrence and severity in a large French home insurance portfolio. Starting from a baseline model based on standard underwriting information, we sequentially introduce climate-expert variables, extreme rainfall indicators, and fine-scale geolocated building and environmental attributes. The analysis focuses on a practical setting in which insurers cannot deploy full hydrological or hydraulic catastrophe models because of budgetary, licensing, or operational constraints. Results show that rainfall-based indicators, particularly a newly constructed metric capturing intense local precipitation, substantially improve claim modeling performance. Building and environmental variables further enhance occurrence prediction. Overall, the findings demonstrate how high-resolution geolocated data improve exposure and vulnerability assessment, complement official flood maps, and provide insurers with an operational framework for refining flood risk evaluation and pricing.
Paper Structure (32 sections, 7 equations, 12 figures, 4 tables)

This paper contains 32 sections, 7 equations, 12 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Flood risk and the role of insurance
  3. Existing approaches and their limitations
  4. Agenda and objectives
  5. Data
  6. Insurance data
  7. Hydrological and climatic indicators
  8. Rainfall related variables
  9. Building and its surroundings
  10. Modelling flood claim cost and probability framework
  11. Modelling approach
  12. Fitting details and model evaluation
  13. Cost modelling results and discussions
  14. Insurer model, "GLM ins", in table \ref{['tab:data_overview']} insurance data only
  15. Insurer + Climate model, "GLM ins+c", in table \ref{['tab:data_overview']} insurance + hydrological and climatic variables
  16. ...and 17 more sections

Figures (12)

  • Figure 1: A summary of data sources used in this study. Building, hydrological and climatic data rely on the latest available version of the data source. The production years vary from a variable to another.
  • Figure 2: Estimated tail weights with the shape parameter of a generalised Pareto distribution, rescaled between $0$ and $100$, where $100$ corresponds to the heaviest tails. Selected interest zones for modelling results visualization are also delineated in black.
  • Figure 3: Schematic description of the process needed to obtain the empirical probability associated with a building, for a given time window and flood date.
  • Figure 4: ann_MILRE (annual Most Intense Local Relative Event) spatial distribution for 2016 and 2017. Higher values indicate regions that have undergone extreme events relative to their historical rainfall levels.
  • Figure 5: Box plots of ann_MILRE by flood occurrence for the GASPAR and claim datasets, illustrating distributional differences between periods with and without events.
  • ...and 7 more figures