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Is Dual Agency in Real Estate a Cause for Concern?

The Journal of Real Estate Finance and Economics volume 48, pages 164–195 (2014)Cite this article

Abstract

We examine the effects of the regulation of dual agency in residential real estate transactions, for 10,888 transactions in Long Island, New York in 2004–2007. We find that dual agency has an overall null effect on sale price, but includes two opposing forces where buyer and seller interests might be compromised. The link between dual agency and timing of sales is less clear. These findings are robust to endogeneity bias. Although it appears dual agency does cause some market distortions, our analysis yields little evidence that prohibiting dual agency in real estate will increase welfare.

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Notes

  1. An interesting question is why commission-based agency persists in real estate markets when there are known to be inefficiencies in this system. See Jares et al. (2000) for a discussion, and an alternative that has the seller’s agent buy the property and have the put option of returning the house back to the seller. See also Bernheim and Meer (2008) for whether brokers perform enough functions to justify their commissions.

  2. Laws in some states have attempted to ensure that agents representing buyers clearly owe fiduciary responsibility to the buyer rather than be an agent for the seller or only loosely represent the buyer’s interest. As expected, such laws have led to a drop in the sale price (see Curran and Schrag 2000).

  3. See Genesove and Mayer (2001) and Garmase and Moskowitz (2004) for examples of other (consumer behavior) suboptimalities in real estate markets.

  4. While steering, disclosure of confidential information, and pressuring can occur even when dual agency is not allowed, the fact that these behaviors can increase the likelihood of dual agency and that agents can benefit from dual agency suggests we should observe them more often when dual agency is allowed. We discuss this further in How and Why Does Dual Agency Arise? section.

  5. A recent working paper examining dual agency is Xie (2012). Using data from Indiana, he finds that dual agency has no effect on sale price, but speeds up sale transactions. Our paper differs from his paper in many of the same ways outlined below, where we benchmark our work with existing work.

  6. A parallel situation is found in financial services markets where brokers are divided into agency and non-agency brokers. Agency brokers are precluded from buying for themselves, and are only allowed to buy on their client’s behalf. Non-agency brokers usually offer lower transactions fees but also offer lower prices to sellers because of their incentive to buy low from sellers and sell high to their buyer clients (Harris 2003). When non-agency brokers are better informed than their own clients, they trade on the value of their information. This finding is parallel to Levitt and Syverson (2008).

  7. Conversations with real estate agents who have worked at multiple agencies indicate that agencies often hold regular meetings in which agents describe their available listings and search parameters of their buyer clients to see if any matches can be made internally. Beyond these formal meetings, agents share information with colleagues about their clients in an effort to make matches.

  8. Although there may be favor trading across agencies, it is easier to sustain such favor trading within an agency. Also, there do not appear to be any incentives provided by agencies to encourage more in-house deals of either type (within-branch or within-agency) over cross-agency. Therefore, the drivers of the choice between within-branch versus within-agency are likely to be based only on the ease of favor trading, rather than any incentives provided that might favor one over the other (or over cross-agency deals).

  9. A discussion on www.bloodhoundrealty.com is particularly succinct in its description of issues. One agent posted his objection to dual agency by likening it to the same attorney representing both parties in a divorce. To which another agent responded “Who do you think usually comes out ahead in a divorce, the divorcing couple or the attorneys? If the divorce is amicable, or the couple doesn’t really have any assets or children, do they really need the additional expense (attorney)? Isn’t divorce, by definition, costly enough?”

  10. Merlo and Ortalo-Magné (2004) have a unique dataset from England that includes all offers made on a house before the final sale and any changes in list price during this period. This allows them to analyze seller and buyer behavior within a transaction (rather than across). Our dataset only has the original list price and the final sales price, and the identity of the seller agent and the buyer agent. This suffices for the purposes of our study.

  11. This proportion of dual agency cases may seem high. A likely explanation is that some of the dual agency deals are instances of subagency. In subagency, the agent listed as the buyer’s agent is actually a subagent of the seller, with fiduciary responsibilities to the seller only (note that cross-agency deals could also have instances of subagency). Previous papers on dual agency also face the same issue of misclassification, because MLS data do not indicate whether the buyer is represented by a buyer’s agent or a subagent of the seller. Subagency is likely to increase the sale price and increase the time-to-sale relative to transactions where the buyer is represented by a buyer’s agent (Curran and Schrag 2000). We find that on average, dual agency has the opposite effect on sale price and time-to-sale. These findings cannot be explained by the misclassification of subagency.

  12. Our data are only a random sample of the completed transactions during this time period. We could not obtain all transactions because of limits on downloads from the MLS website. This limits our ability to accurately measure the distribution of firm size, and especially its variation over time, since some of the variation that we observe results from the sampling.

  13. In principle, ε should be uncorrelated with AB as well. However, in our final model to be estimated (Eq. (3) below), AB will be part of the error term and not a regressor. Consequently, we do not need to assume zero correlation between ε and AB.

  14. We prove this in the Appendix.

  15. Note that we abstract from general equilibrium implications of this assumption. For example, dual agency on some houses might lower (or raise) sale price and increase (-or decrease) time to sale for cross-agency deals as part of overall market clearing dynamics. It is hard to speculate what direction and magnitude these general equilibrium effects might take. Therefore, for simplicity, we abstract from these effects.

  16. The data also contain a variable measuring square footage; however, in many cases, lot size was recorded in the data instead of interior square footage. Moreover, this variable is missing in many cases. Nonetheless, our results are generally robust to inclusion of this variable.

  17. Taylor (1999) suggests that a house with a low list price sitting on the market for a long time can be viewed as a lemon. Genesove and Mayer (1997) show that sellers with lower equity positions built in the house set higher list prices and receive higher sale prices. Our inclusion of list price captures these effects too.

  18. Note that agency and agent fixed effects are not collinear because agents change agencies within the sample.

  19. Hsieh and Moretti (2003) show that the low cost of entry in to the residential real estate agent market causes the number of agents to be positively related to the cost of land (and hence the size of the commission in any transaction) in the market. Our zip code-year fixed effects control for both market-specific land price differences, and competitive structure differences.

  20. If the hypothesized effect of DA is δ, we need only regress Y-δ*DA on X to get consistent estimates for β and the ε’s.

  21. Our set of controls is comparable in breadth to theirs, and for their analysis they claim “the ratio of selection on unobservables relative to selection on observables is likely to be less than one.”

  22. When we break dual agency into three components, we effectively have three possible “treatments” rather than one. In assessing the bias for any one of these three treatments (say DA1), if the hypothesized effect of DA1 is δ, we regress Y-δ*DA1 on X only and on X, DA2, DA3 to get estimates for β and the ε’s. In all cases, our results are substantively unchanged.

  23. Using experimental analysis, Yavas et al. (2001) show that the price outcome of a bargaining game is increasing in the initial ask price of the seller.

  24. Note that the marginal effects that we report are approximations. The marginal effect in a semilog specification, like ours, equals exp(β)-1 (Thornton and Innes 1989). Nonetheless, in our results, the estimated coefficients and the marginal effects on the dual agency variables have negligible differences. Therefore, we simply report the estimated coefficients.

  25. We thank two anonymous referees for their suggestions on these robustness analyses.

  26. To obtain the predicted selling price, we regress ln(Sale price) on all of the house characteristics, as well as the zip code-year fixed effects.

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Acknowledgments

The authors thank Ellie Cohn, Aaron Moss, David Simon, and Michael Waldman for very helpful discussions, the Johnson School Dean’s Research Lunch and International Industrial Organization Conference participants for comments, Norman Mendolsohn for data, and Lars Backstrom for research assistance.

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Authors and Affiliations

  1. Johnson Graduate School of Management, Cornell University, 385 Sage Hall, S.C., Ithaca, NY, 14853, USA

    Vrinda Kadiyali

  2. Kelley School of Business, Indiana University, BU 456, 1309 E. Tenth St., Bloomington, IN, 47405, USA

    Jeffrey Prince

  3. School of Environmental and Public Affairs, Indiana University, SPEA Room 231, 1315 E. Tenth St., Bloomington, IN, 47405, USA

    Daniel H. Simon

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  1. Vrinda Kadiyali
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Correspondence to Jeffrey Prince.

Appendix

Appendix

Correlation between DA it and α(AB it   − E(AB it ))*DA it :

$$ Corr\left( {D{A_{it}},\alpha \left( {A{B_{it}} - E\left( {A{B_{it}}} \right)} \right)*D{A_{it}}} \right) \propto E\left( {D{A_{it}}*\alpha \left( {A{B_{it}} - E\left( {A{B_{it}}} \right)} \right)*D{A_{it}}} \right) - E\left( {D{A_{it}}} \right)*E\left( {\alpha \left( {A{B_{it}} - E\left( {A{B_{it}}} \right)} \right)*D{A_{it}}} \right) $$
$$ \matrix{ {E\left( {D{A_{it}}*\alpha \left( {A{B_{it}} - E\left( {A{B_{it}}} \right)} \right)*D{A_{it}}} \right) - E\left( {D{A_{it}}} \right)*E\left( {\alpha \left( {A{B_{it}} - E\left( {A{B_{it}}} \right)} \right)*D{A_{it}}} \right) = } \hfill \\ {\alpha \left( {E\left( {D{A_{it}}*A{B_{it}}} \right) - E\left( {A{B_{it}}} \right)*E\left( {D{A_{it}}} \right)} \right) - \alpha \left( {E\left( {D{A_{it}}} \right)*\left( {E\left( {D{A_{it}}*A{B_{it}}} \right) - E\left( {A{B_{it}}} \right)*E\left( {D{A_{it}}} \right)} \right)} \right) = } \hfill \\ {\alpha *\left( {1 - E\left( {D{A_{it}}} \right)} \right)*Corr(D{A_{it}},A{B_{it}})} \hfill \\ }<!end array> $$

The last term above is non-zero since α ≠ 0, E(DAit) < 1, and Corr(DAit,ABit) ≠ 0.

Correlation between DA it and \( \left( {A{B_{it}} - E\left( {A{B_{it}}\left| {D{A_{it}} = 1} \right.} \right)} \right)*D{A_{it}} \):

$$ Corr\left( {D{A_{it}},\alpha \left( {A{B_{it}} - E\left( {A{B_{it}}\left| {D{A_{it}} = 1} \right.} \right)} \right)*D{A_{it}}} \right) \propto E\left( {D{A_{it}}*\alpha \left( {A{B_{it}} - E\left( {A{B_{it}}\left| {D{A_{it}} = 1} \right.} \right)} \right)*D{A_{it}}} \right) - E\left( {D{A_{it}}} \right)*E\left( {\alpha \left( {A{B_{it}} - E\left( {A{B_{it}}\left| {D{A_{it}} = 1} \right.} \right)} \right)*D{A_{it}}} \right) $$
$$ \matrix{ {E\left( {D{A_{it}}*\alpha \left( {A{B_{it}} - E\left( {A{B_{it}}\left| {D{A_{it}} = 1} \right.} \right)} \right)*D{A_{it}}} \right) - E\left( {D{A_{it}}} \right)*E\left( {\alpha \left( {A{B_{it}} - E\left( {A{B_{it}}\left| {D{A_{it}} = 1} \right.} \right)} \right)*D{A_{it}}} \right) = } \hfill \\ {\alpha \left( {E\left( {D{A_{it}}*A{B_{it}}} \right) - E\left( {A{B_{it}}\left| {D{A_{it}} = 1} \right.} \right)*E\left( {D{A_{it}}} \right)} \right)} \hfill \\ { - \alpha \left( {E\left( {D{A_{it}}} \right)*\left( {E\left( {D{A_{it}}*A{B_{it}}} \right) - E{{\left( {D{A_{it}}} \right)}^2}*E\left( {A{B_{it}}\left| {D{A_{it}} = 1} \right.} \right)} \right)} \right) = 0} \hfill \\ }<!end array> $$

The last equality relies on the fact that DA and AB are binary variables, so Pr(DA = 1) = E(DA) and Pr(DA = 1 and AB = 1) = E(DA*AB).

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Kadiyali, V., Prince, J. & Simon, D.H. Is Dual Agency in Real Estate a Cause for Concern?. J Real Estate Finan Econ 48, 164–195 (2014). https://doi.org/10.1007/s11146-012-9385-9

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Keywords

  • Conflict of interest
  • Real estate
  • Strategic pricing
  • Leaning on the seller
  • Time-to-sale