“Racial laundering?”: The Neighborhood Consequences of Large-Scale Investor Activity in Single-Family Real Estate

• In Event: Racial inequality in housing market processes

Abstract

The rapid expansion of institutional investors in single family housing has intensified public concern about affordability, displacement, and racial equity. Yet despite widespread attention, the mechanisms linking investor activity to racial disparities in housing markets remain poorly understood. This paper provides new evidence on how institutional and algorithmic intermediaries interact with longstanding patterns of discrimination, and how their entry reshapes neighborhood trajectories.

Using comprehensive administrative data on all single-family transactions in North Carolina linked to voter registration records, I identify the race of both buyers and sellers and track properties as they pass through different types of owners. I leverage the emergence of iBuyers, which are algorithmic and rapid turnover intermediaries, as a quasi-experimental lens to isolate price effects tied to seller identity. Because iBuyers purchase and resell the same home within short windows, their transactions allow me to decompose price components attributable to observable characteristics, unobserved home quality, and bargaining dynamics.

The results reveal two key mechanisms. First, Black sellers systematically receive lower prices than observationally similar white sellers, consistent with reduced bargaining power in thin housing markets. Second, because these lower prices mask higher underlying home quality, algorithmic intermediaries can arbitrage this gap. I refer to this dynamic as racial laundering. iBuyers and institutional investors disproportionately purchase from Black households, not because they target race per se, but because discriminatory pricing creates profitable opportunities. This dynamic helps explain the strong correlation between investor activity and minority neighborhoods that has been highlighted in public discourse.

To assess broader implications, I develop a dynamic structural model of household location choice and investor entry. Simulations show that investor activity can generate more demographically mixed neighborhoods, not through displacement, but by expanding liquidity for historically disadvantaged sellers. However, these gains coexist with distributional concerns. The financial upside of correcting discriminatory underpricing accrues primarily to institutional actors rather than to the households who bore the original penalty.

Taken together, the findings highlight a nuanced policy landscape. Algorithmic intermediaries may reduce some forms of discrimination through arbitrage, yet they also capitalize on inequities that public policy has failed to address. The paper concludes by discussing regulatory options, including transparency mandates, appraisal reforms, and interventions that target bargaining disparities, to ensure that efficiency gains do not come at the expense of racial equity.

Author

• Motoyuki Yoshihara, Cornell University
  Presenting Author