Abstract: Commission rates for housing transactions are twice as high in the United States than in other countries. Policymakers have raised concerns that the practice of sellers offering buyers’ brokers commissions can lead to high commissions and harm consumers. This paper empirically examines the equilibrium impacts of a proposed policy called “decoupling,” which would require buyers and sellers to each pay their respective brokers. I develop a structural model integrating buyers, sellers, and brokers to characterize the equilibrium house prices, commissions, and welfare. I estimate the model with rich observed heterogeneity and credible sources of identifying variation using shifters of house prices and commissions. I find that decoupling reduces 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 experience a surplus gain of 4% of the total transaction value from having higher net proceeds than the status quo. I find notable surplus gains for buyers across income as sellers pass through part of their commission savings to house prices.

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.

Abstract: Using detailed purchase order data for a sample of US hospitals 2009-15, we document large price dispersion across hospital buyers for identical brands in a variety of important medical supply categories. Hospitals also vary dramatically in the size and composition of the set of suppliers they contract with, and on average contract with less than a third of the suppliers available in the market. We develop a model and identification strategy to determine the extent to which this dispersion is determined by brand preferences, search/contracting costs, and bargaining abilities. Estimates suggest that markups are primarily driven by a lack of price sensitivity among health care providers in their usage decisions. Hospital administrator bargaining ability varies widely across hospitals, driving most of the observed price dispersion. Reducing search/contracting costs impacts hospital surplus through putting higher value brands in the choice set.