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

Google Street View image of a house predicts car accident risk of its resident