  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.
