Road traffic injuries are a leading cause of death worldwide. Proper estimation of car accident risk is critical for appropriate allocation of resources in healthcare, insurance, civil engineering, and other industries. We show how images of houses are predictive of car accidents. We analyze 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, public availability of house images raises legal and social concerns, as they can be a proxy of ethnicity, religion and other sensitive data.

https://arxiv.org/abs/1904.05270?utm_source=Benedict%27s+newsletter&utm_campaign=80ec9639df-Benedict%27s+Newsletter_COPY_01&utm_medium=email&utm_term=0_4999ca107f-80ec9639df-70280797