Entry and Inefficiency in the Real Estate Brokerage Industry: Empirical Evidence and Policy Implications∗

This paper studies entry in local real estate brokerage to investigate sources of potential cost inefficiency under free entry. Building upon recent empirical work on games of incomplete information, we construct a structural entry model and estimate the model using nested pseudo likelihood algorithms. Using data from 5% PUMS in 2000, we find direct evidence for two sources of cost inefficiency – wasteful non-price competition and loss of economies of scale. Using our estimates, we further investigate welfare implications of prohibiting rebates on commissions and the diffusion of the Internet. We find that rebate bans are welfare-reducing, not only because they discourage price competition from discount brokers, but also because they encourage excessive entry by traditional full-commission brokers. Removing these rebate bans would decrease the equilibrium number of realtors by 5.8% and reduce total variable costs by 3.9%. Welfare implications of the Internet diffusion are mixed, however. An increase in the number of Internet adopters alone could encourage traditional brokers’ entry by helping them reach and match potential clients. However, a commensurate increase in online search intensity among the Internet adopters may discourage such entry by facilitating the development of alternative brokerage models, such as discount brokerage and FSBOs. ∗We thank seminar participants at the Minneapolis Fed, University of California at Berkeley, University of Toronto, University of Wisconsin, 2008 IIOC conference and summer real estate symposium. All errors are our own. Max Rempel provided excellent research assistance in data processing. Contact information: Lu Han: Rotman School of Management, University of Toronto, 105 St. George St., Toronto, Ontario, Canada M5S 3E6. Seung-Hyun Hong: Department of Economics, University of Illinois at Urbana-Champaign, 428 David Kinley Hall, 1407 West Gregory Drive, Urbana, IL 61821.