Dance with Algorithms: Impact of Algorithmic Buyers on Housing Affordability

Abstract

Algorithms penetrate housing markets and have far-reaching effects on household well-being. We study algorithmic buyers (iBuyers) - through which platforms use algorithms to quickly value, buy, and sell homes - and their impact on housing affordability. Using millions of Zillow transaction data on a national scale, we investigate how iBuyers shaped the market equilibrium price. Leveraging a quasi-experiment of the staggered algorithmic transaction rollout, we find that iBuyers increased the home price by 4.1% in local markets, in which sellers tended to experience an increase in home transactions by nearly 16 and a longer median time-on-market by around 2.7 days. Yet, in non-disclosure states where home sales prices are not publicly available, iBuyers resulted in a decrease in home transactions and a minor increase in time-on-market. Our findings speak to the housing affordability issue attributed to algorithmic buyers and raise the caveat of platform intermediation in housing markets.

Keywords: Algorithmic buyer, iBuying, housing affordability, home price, housing markets, real estate transactions