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Commission Splits in Real Estate Transactions

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Abstract

We examine commission splits between listing and selling agents in real estate transactions. We construct a theoretical model to show that agency problems arise when a listing agent attempts to maximize his or her payoff while setting the commission split. Mitigation to these agency problems can be achieved through the imposition of a limited duration on listing contracts. Our model produces several testable hypotheses, which are supported by empirical evidence. We find property listings with higher list prices and quick sales are associated with lower commission splits. Commission split is more likely to be higher when the listed property has a high degree of atypicality and/or is overpriced. Additionally, agent-owned properties pay higher commission splits.

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Notes

  1. Because real estate agents usually possess superior information about market values of properties, sellers often consult their agents on setting list prices and on accepting offers. Although real estate agents may not set prices directly, they can persuade sellers to underpriced a property when listing the property and/or attempt to convince sellers to accept low-price offers.

  2. Miceli (1991) suggests that commission splits are unlikely determined solely by sellers. If it were the case, Miceli (1991) shows it would be optimal to give the entire commission to whoever finds a buyer. This solution apparently deviates from current industry practices.

  3. Previous studies adopting a similar bargaining structure include Miceli (1989, 1991) and Rutherford et al. (2005).

  4. We assume that β offered is high enough such that at least one cooperating agent’s participation constraint is satisfied.

  5. To keep the analysis tractable, when modeling the choice of commission split, we treat listing agent’s effort level as given. We acknowledge that the listing agent’s optimal effort choice, in reality, can vary by property, and the chosen effort level may depend on many factors. For example, Rutherford et al. (2005) shows that agents contribute more effort to sell their own properties. Bian et al. (2015) find that greater agent listing inventory dilutes efforts contributed to each listing. Our view is that the listing agent takes into account all relevant factors and first determines the expected effort level, and the expected effort level then serves as the basis for the agent to further choose the level of commission split. In other words, the determination of the optimal effort level is outside the scope of our model.

  6. Through this paper, we use a subscript to denote the derivative with respect to the subscript variable.

  7. Commission splits are certainly affected by many factors other than what we included in our model. For example, if cooperative sale is a repeated game and a listing agent expects to serve as a selling agent in the future, reciprocity may motivate the listing agent to follow the industry norm and offer a commission split that everyone else offers. We have no doubt that reciprocity is influential in determining commission splits. However, reciprocity tends to push commission splits to be homogeneous. Motivated by the observed variation of commission splits observed in our data, we choose to construct a theoretical framework to explain this variation. This is why we omit reciprocity in our model and focus on factors that can explain the variation of commission splits.

  8. We do not explicitly model competition among listing agents in terms of strategically setting the commission split to attract cooperating agents’ attention. We assume effects from such competition are embedded in λ S(β). When the commission split offered is not competitive, λ S is reduced as a result of the lack of interest from cooperating agents.

  9. We do not have exact acreage of lot size in the data. However, when entering data on the MLS, brokers indicate whether or not the lot size is less than or equal to one acre or greater than one acre. Given our sample includes a significant number of properties on farmland, presumably with abnormally large lot sizes, we only include observations with a lot size less than or equal to one acre. Over 65 % of our observations have a lot size less than or equal to one acre.

  10. Commission splits at levels other than 2.5 or 3 % may be associated with non-conventional brokerage arrangements (e.g., discount brokerage). As a robustness check, we estimate an ordinary least square (OLS) model using observations with commission rates at all levels. Our OLS results tell a story very similar to the logit results.

  11. As a robustness check, we also computed the over-pricing measure introduced in Yavas and Yang (1995). Overpricing is captured by the logarithm of the ratio of the predicted sale price to the listing price. This measure is highly correlated with our DOP measure. Our empirical results show similar patterns when it is used instead of DOP.

  12. The Consumer Sentiment Index is developed from the Survey of Consumers, a national survey based on telephonic household interviews. We obtain our CSI data series from the Federal Reserve Bank of St. Louis: http://research.stlouisfed.org/fred2/series/UMCSENT.

  13. As a robustness check, we substitute broker fixed effect with agent fixed effect and re-estimate the OLS model. Our results remain qualitatively unchanged.

  14. One possible explanation is that it is harder to sell a property when the owner is a male, and this induces listing agents to offer higher commission splits for male-owned properties.

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Acknowledgments

We thank Richard Buttimer, Ken Johnson and participants at the American Real Estate Society (ARES) 2012 annual meeting and at the American Real Estate and Urban Economics Association (AREUEA) 2013 annual meeting for their helpful comments. Special thanks to the anonymous reviewer who provided constructive and insightful comments that led to significant improvements of our work.

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Authors and Affiliations

  1. Department of Accounting, Economics, Finance and Real Estate, College of Business & Economics, Longwood University, 201 High Street, Farmville, VA, 23909, USA

    Xun Bian & Bennie D. Waller

  2. Department of Real Estate and Urban Land Economics, Wisconsin School of Business, University of Wisconsin - Madison, Madison, WI, 53706, USA

    Abdullah Yavas

Authors
  1. Xun Bian
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  2. Bennie D. Waller
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  3. Abdullah Yavas
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Correspondence to Xun Bian.

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Bian, X., Waller, B.D. & Yavas, A. Commission Splits in Real Estate Transactions. J Real Estate Finan Econ 54, 165–187 (2017). https://doi.org/10.1007/s11146-015-9541-0

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