University of California Transportation Center

Auto insurance rates can vary dramatically, with much higher premiums in poor and minority areas than elsewhere, even after accounting for individual characteristics, driving history and coverage. This project used a unique data set to examine the relative influence of place-based socioeconomic characteristics (or redlining) and place-based risk factors on the place-based component of automobile insurance premiums. We used a novel approach of combining tract-level census data and car insurance rate quotes from multiple companies for sub-areas within the city of Los Angeles. The quotes are for a hypothetical individual with identical demographic and auto characteristics, driving records and insurance coverage. This method allowed the individual demographic and driving record to be fixed. Multivariate models are then used to estimate the independent contributions of these risk and redlining factors to the place-based component of the car insurance premium. We find that both risk and redlining factors are associated with variations in insurance costs in the place-based component, with black and poor neighborhoods being adversely affected, although risk factors are stronger predictors. However, even after risk factors are taken into account in the model specification, SES factors remain statistically significant. Moreover, simulations show that redlining factors explain more of the gap in auto insurance premiums between black (and Latino) and white neighborhoods and between poor and nonpoor neighborhoods. The findings do not appear sensitive to the individual characteristics of the hypothetical driver.