Algorithmic Pricing in Multifamily Rentals: Efficiency Gains or Price Coordination?

Abstract

This paper empirically evaluates the impact of algorithmic pricing on the U.S. multifamily rental market. We hand-collect data on management company adoption decisions of algorithmic pricing and combine it with a comprehensive database of building-level rents and occupancy from 2005 to 2019. We find strong evidence that algorithmic pricing helps building managers set prices that are more responsive to market conditions, with adopters lowering rents more rapidly than non-adopters during economic downturns. We also find that average rents are higher and average occupancies are lower in markets with greater algorithmic penetration during periods of economic recovery. Then, we estimate a structural model of housing demand to test for "algorithmic coordination." Compared to a model of own profit maximization, our pair-wise tests favor a model of joint profit maximization among adopters of the same software. We estimate that the coordination channel results in an average markup increase of $25 per unit per month, impacting about 4.2 million units nationwide. Our findings have important implications for regulators and policymakers concerned about the potential risks and trade-offs of algorithmic pricing.

Keywords: pricing algorithms, real estate, competition policy JEL Code: L12, L41, L43, R21, R31, D83

JEL Classification: L85, L86, L41, L13, D83, R31