ABSTRACT:

This paper evaluates the impact of algorithmic pricing on the U.S. multifamily rental market using hand-collected data on management company adoption and building-level rents and occupancies from 2005 to 2019. We find algorithmic pricing allows managers to set prices that are more responsive to changing market conditions, resulting in lower rents during downturns and higher rents during recoveries. Based on a structural model of rental demand, our pair-wise conduct test favors a model of joint profit maximization among adopters of the same software, leading to an average markup increase of $25 per unit monthly, affecting 4.2 million units nationwide.

For further information: giorgia.bosco@unibocconi.it