ESSAYS ON INDUSTRIAL ORGANIZATION AND REAL ESTATE

In the first chapter of this dissertation, I examine the U.S. residential real estate market, where the commission rates for housing transactions are more than double those in other developed countries. Policymakers have raised concerns that the practice of sellers offering commissions to buyers' brokers can inflate commissions and harm consumers. This paper empirically evaluates the equilibrium impacts of "decoupling," a proposed policy that would require buyers and sellers to pay their own brokers directly. I develop and estimate a structural model of the housing market with buyers, sellers, and brokers to quantify the policy's effect on equilibrium house prices, commissions, and welfare. I find that decoupling reduces total commissions paid by 53%, as sellers no longer have to offer high commissions to attract buyers, and brokers compete for price-sensitive buyers. Sellers and buyers gain combined surplus equal to 4% of total transaction value due to lower commissions and higher net proceeds. Buyers across all income groups benefit, especially lower-income buyers, due to lower equilibrium house prices. The second chapter 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.

Author dc:creator

• Kim, Gi Heung

Advisor dc:contributor.advisor

• Wong, Maisy

• Economics

Language dc:language.iso

en

Repository record dc:identifier.uri

https://repository.upenn.edu/handle/20.500.14332/62286

OAI identifier oai:identifier

oai:repository.upenn.edu:20.500.14332/62286