Attenuating Racial Price Differentials in the Housing Market: Evidence from iBuyers

We study the implications of algorithmic buyers, specifically iBuyers, on racial price differentials in the U.S. housing market. iBuyers leverage algorithms and data analytics to automate the home buying and selling process, serving as a new type of U.S. housing market intermediary. With millions of housing transactions and mortgage data from markets with significant iBuyer presence, our analysis reveals that iBuyers significantly attenuate the price gap between Black and White homebuyers for comparable housing. To address potential selection bias, we employ coarsened exact matching to ensure comparable housing and neighborhood characteristics between iBuyer and non-iBuyer transactions, confirming the robustness of our findings. By separating iBuyers’ market-level and transaction-level effects, we uncover iBuyers’ role in correcting the information imbalance among racial groups. Notably, when comparing iBuyers to traditional housing market intermediaries, known as flippers, we find no evidence that flippers serve a similar function in addressing racial price disparities. In addition, our heterogeneity analysis explores how iBuyers’ impacts on racial price differentials vary by neighborhood racial composition and buyer income. We find that iBuyers’ mitigating effects remain strong across different neighborhood racial compositions and income levels.