The Market Effects of Algorithms

TL;DR

This paper investigates how the digitization of housing records enables algorithmic investors to influence market entry, prices, and racial disparities, showing that algorithms can reduce racial inequality in the housing market.

Contribution

It provides empirical evidence on how digitization facilitates algorithmic investment and reduces racial disparities in home prices, highlighting market-level effects of algorithms.

Findings

Digitization leads to increased algorithmic investor entry.

Algorithmic investors mainly purchase minority-owned homes.

Racial disparities in home prices decrease by 45% due to algorithms.

Abstract

While there is excitement about the potential for algorithms to optimize individual decision-making, changes in individual behavior will, almost inevitably, impact markets. Yet little is known about such effects. In this paper, I study how the availability of algorithmic prediction changes entry, allocation, and prices in the US single-family housing 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 push out investors using human judgment. Instead, human investors shift toward houses that are difficult to predict algorithmically. Algorithmic investors predominantly purchase minority-owned homes, a segment of the market where humans may be biased. Digitization increases the average sale price of minority-owned homes by 5% and reduces racial disparities in home prices by 45%. Algorithmic investors, via competition, affect the prices paid by owner-occupiers and human investors for minority homes; such changes drive the majority of the reduction in racial disparities. The decrease in racial inequality underscores the potential for algorithms to mitigate human biases at the market level.