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Google Street View image predicts car accident risk

Author

Listed:
  • Kita-Wojciechowska Kinga

    (Faculty of Economic Sciences, University of Warsaw, Poland)

  • Kidziński Łukasz

    (Department of Bioengineering, Stanford University, Stanford, CA, USA)

Abstract

Road traffic injuries are a leading cause of death worldwide. Proper estimation of car accident risk is critical for the appropriate allocation of resources in healthcare, insurance, civil engineering and other industries. We show how images of houses are predictive of car accidents. We analyse 20,000 addresses of insurance company clients, collect a corresponding house image using Google Street View and annotate house features such as age, type and condition. We find that this information substantially improves car accident risk prediction compared to the state-of-the-art risk model of the insurance company and could be used for price discrimination. From this perspective, the public availability of house images raises legal and social concerns, as they can be a proxy of ethnicity, religion and other sensitive data.

Suggested Citation

  • Kita-Wojciechowska Kinga & Kidziński Łukasz, 2019. "Google Street View image predicts car accident risk," Central European Economic Journal, Sciendo, vol. 6(53), pages 151-163, January.
    • Handle: RePEc:vrs:ceuecj:v:6:y:2019:i:53:p:151-163:n:9
    DOI: 10.2478/ceej-2019-0011
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Generalized Linear Model; risk modelling; insurance pricing; satellite imagery; Google Street View;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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