The Market Effects of Algorithms

While there is excitement about the potential of algorithms to optimize individual decision-making, changing individual behavior will, almost inevitably, impact markets. Yet little is known about these effects. In this paper, I study how the availability of algorithmic prediction changes entry, allocation, and prices in the U.S. residential real estate market, a key driver of household wealth. I identify a market-level natural experiment that generates variation in the cost of using algorithms to value houses: digitization, the transition from physical to digital housing records. I show that digitization leads to entry by investors using algorithms, but does not displace investors using human judgment. Instead, human investors shift towards houses that are difficult to predict algorithmically. Algorithm-using investors predominantly purchase minority-owned homes, an area where humans may be biased. Digitization increases the average sale price of minority-owned homes by 5% or $5,000 and nearly eliminates racial disparities in home prices. Algorithmic investors, via competition, affect the prices paid by humans, which drives most of the reduction in racial disparities.  This decrease in racial inequality underscores the potential of algorithms to mitigate human biases at the market level.