We present the La Mobiliere insurance customers dataset: a 12-year-long longitudinal collection of data on policies of customers of the Swiss insurance company La Mobiliere. To preserve the privacy of La Mobiliere customers, we propose the data aggregated at two geographical levels, based on the place of residence of the customer: postal areas and municipalities. For each geographical area, the data provides summary statistics on: (i) the demographic characteristics of the customer base, (ii) characteristics of vehicles insurance policies and (iii) characteristics of housing and building insurance policies. To assess the validity of the data, we investigate the temporal consistency of the data and the representativeness of La Mobiliere customer base along several dimensions (total population, percentage of foreigners, etc.). We also show how the insurance data can reliably model the spatial patterns of socio-economic indicators at a high geographical resolution. We believe that the reuse of this data provides an opportunity for researchers to broaden the socio-economic characterization of Swiss areas beyond the use of official data sources.

High spatial resolution dataset of La Mobilière insurance customers

Battiston, Alice
First
;
Schifanella, Rossano
Last
2022-01-01

Abstract

We present the La Mobiliere insurance customers dataset: a 12-year-long longitudinal collection of data on policies of customers of the Swiss insurance company La Mobiliere. To preserve the privacy of La Mobiliere customers, we propose the data aggregated at two geographical levels, based on the place of residence of the customer: postal areas and municipalities. For each geographical area, the data provides summary statistics on: (i) the demographic characteristics of the customer base, (ii) characteristics of vehicles insurance policies and (iii) characteristics of housing and building insurance policies. To assess the validity of the data, we investigate the temporal consistency of the data and the representativeness of La Mobiliere customer base along several dimensions (total population, percentage of foreigners, etc.). We also show how the insurance data can reliably model the spatial patterns of socio-economic indicators at a high geographical resolution. We believe that the reuse of this data provides an opportunity for researchers to broaden the socio-economic characterization of Swiss areas beyond the use of official data sources.
2022
9
1
81
N/A
Battiston, Alice; Massaro, Emanuele; Binder, Claudia R; Schifanella, Rossano
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1947831
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