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MLS Information Sharing Intensity and Housing Market Outcomes


The primary function of Multiple Listing Services (MLS) in housing markets is to disseminate information from listing brokers to other member brokers about houses listed for sale. This study examines the impacts of MLS-member information sharing intensity on housing market outcomes, with information sharing intensity measured as the average daily number of times MLS members view an individual house’s listing during its marketing period. We develop a theoretical model and derive the equilibrium. The model predicts that increased information sharing intensity leads to greater probability of sale, reduced time on market, and higher house prices. Analysis of data from 32,102 listing records validates the model’s propositions. We find that a one-unit increase in the average daily number of views of a house’s listing increases the probability of a successful transaction by 5.7%, increases selling price by 0.2%, and reduces marketing time by 1.6 days.

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  1. “Broker,” as used here, refers to both brokers and sales associates, regardless of state license type.

  2. MLS also serve as data repositories for historical information about expired, withdrawn, and closed listings.

  3. MLS also distribute selected information about available houses to the general public, though full information, including, but not limited to historical information, is restricted to members. This study focuses exclusively on information sharing between MLS-member brokers.

  4. Not all house transactions, of course, involve brokers as intermediaries between buyers and sellers. See Yavas and Colwell (1995) and Hendel et al. (2009) for comparisons of house sales in brokered and non-brokered transactions.

  5. As noted by an anonymous reviewer, the current study does not address the simultaneity could be a concern if price is a determinant of number of MLS views. Also, the current study does not consider the number of buyers searching for a particular type of house and the number of such houses available in the market. In thin markets, omitted variable bias may be a concern.

  6. For more insights about this evolution, see Federal Trade Commission Report (1983).

  7. Numerous other studies debate the degree of competition in the market for real estate brokerage services. A representative, but incomplete, list of examples includes Sirmans and Turnbull (1997), Hsieh and Moretti (2003), Schnare and Kulick (2009), and Han and Hong (2011).

  8. For details of this litigation, see the case filings available at (accessed on 5/24/2016).

  9. MLS report offers of compensation to cooperating brokers, but do not report total actual commissions paid to brokers. The commission rate data used by Jia and Pathak (2010) are limited to the compensation offered in the MLS by listing brokers to cooperating brokers, which Jia and Pathak assume is exactly half of the actual total commission paid by house sellers to brokers. This assumption may result in a biased measure of commission rates if listing brokers accept different commission rates than those offered to cooperating brokers or if the commission rate and/or splits are renegotiated between the listing and closing dates. Actual commission is typically recorded on HUD-1 Settlement Statements, but these are proprietary documents that are not easily obtainable.

  10. See Han and Strange (2015) for useful discussions of this and other related issues within the larger context of the microstructure of housing markets.

  11. This assumption eliminates the possibility that the broker can affect the bargaining between buyer and seller: Yavas (1992) and Rubinstein and Wolinsky (1987) studied such issues. We also assume that both brokers and sellers perceive the same distribution of offers, F(.), as in Anglin (1994).

  12. Arnott (1989) provided a formal model with this attribute.

  13. Our model does not analyze the listing agent’s effort choices. Previous literature (Rutherford et al. 2005 and Bian et al. 2015) shows that many factors affect listing agent’s optimal effort choice. We assume the listing agent takes into consideration the relevant factors and determines the expected effort level before choosing information sharing intensity. Thus, we take listing agent’s effort level as given when modeling the choice of information sharing intensity.

  14. We follow the standard assumption that the listing agent has unlimited time to arrange a sale.

  15. This footnote shows the proof of the statement: since EP(x) =\( {\int}_x^{\infty}\frac{Pf(P) dP}{\left(1- F(x)\right)} \), we could derive\( \frac{\partial EP(x)}{\partial x}=-\frac{ x f(x)}{\left(1- F(x)\right)}+\frac{\int_x^{\infty } Pf(P) dP}{\left(1- F(x)\right)2} \). So that \( \frac{\partial EP(x)}{\partial x}<1 \) is the same as, \( {\left(1- F(x)\right)}^2>- xf(x)\left(1- F(x)\right)+ f(x)\left[ x\left(1- F(x)\right)+{\int}_x^{\infty}\left(1- F(P)\right) dP\right]= f(x){\int}_x^{\infty}\left(1- F(P)\right) dP \). Since, through integration by parts, \( {\int}_x^{\infty } Pf(P) dP=-\left[ P\left(1- F(P)\right)\right]+{\int}_x^{\infty}\left(1- F(P)\right) dP \). This inequality is satisfied if and only if F(P) is a logconcave function. This condition arises in many contexts if probability theory.

  16. As pointed out by an anonymous reviewer, when the search is set up to return only partial information about the house a member may easily click on any house she is particularly interested in to view its full information.

  17. Johnson et al. (2007), Benefield and Sirmans (2009), and Brastow et al. (2012) use logit specifications to analysis probability of sale effects. Jia and Pathak (2010) use ordinary least squares and probit specifications. Rutherford and Yavas (2012) use probit and bivariate probit specifications.

  18. Carrillo (2008), Benefield et al. (2011), and Allen et al. (2015) document housing market outcome effects from visual information such as photographs and virtual tours of houses included in MLS listings. Anglin et al. (2003) demonstrate that the degree of overpricing (the residuals from a hedonic list price regression, or DOP) is positively related to marketing time. Johnson et al. (2007) show that DOP is negatively related to probability of sale.

  19. Haurin (1988) and Haurin et al. (2010) demonstrate that atypical houses experience longer marketing times.

  20. For comparison purposes, the OLS estimate (not shown) of the MLSVIEWS variable coefficient is −0.038, with a t-statistic of −4.839.

  21. Because the regression is in log form, the coefficients on regular variables are elasticities, while those on dummy variables are multiplicative constants.


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Correspondence to Marcus T. Allen.

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Allen, M.T., Dare, W.H. & Li, L. MLS Information Sharing Intensity and Housing Market Outcomes. J Real Estate Finan Econ 57, 297–313 (2018).

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  • Information sharing
  • Multiple listing service
  • House prices
  • Time on market
  • Probability of sale
  • Brokerage