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Efficiency and Incentives in Residential Brokerage

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Abstract

The literature on broker intermediation in residential real estate has shown positive pricing effects associated with the use of a broker and mixed results as far as the pricing effects of nonstandard commission structures. On the premise that real estate broker incentives emanate from two primary sources, factors that increase broker operating efficiency and negotiable features arising from the relationship between the listing broker and the seller, this study assesses the degree to which these incentives affect the marketing time, probability of sale, and selling price of single-family houses. Of particular interest, this study investigates efficiency and broker intermediation effects on residential property associated with a broker concentrating his listings into a service area. Empirical results show that properties within an individual broker’s GIS-determined service area are more likely to sell, sell faster, and sell with an associated price premium. These effects are more concentrated in the market for higher priced homes. Also, additional compensation favorably motivates the broker with higher-priced properties, but has no effect on the sale of lower-priced properties.

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Notes

  1. Historically, the National Association of Realtors has encouraged the concept of “developing a farm,” akin to the concept of service area.

  2. Because the data only include exclusive right to sell contracts, we will not be testing for differences in contract type. Also, we do not test for any differences between properties because of atypicality or liquidity. However, numerous variables describing property characteristics control for these effects.

  3. For clarification, the listing broker sets up the contractual relationship with the seller and has the primary marketing responsibilities. The selling broker, through the MLS arrangement, brings prospective buyers to listed properties.

  4. The listing price model is not reported. The predicted prices from this model become inputs to the calculation of Degree of Overpricing.

  5. Thus the omitted category consists of larger brokers (40 or more listings over the study period) for whom no service area can be identified.

  6. Right censoring occurs when a property remains unsold at the end of the study period or because the listing either expired or was withdrawn. Right-truncation occurs when right-censored cases are either not observed or ignored. Excluding right-censored cases not only leads to a loss of information and efficiency, but also can generate biases that can lead to erroneous conclusions. Intuitively, the omission of right-censored observations generates a downward bias in the expected duration. See Van den Bulte and Iyengar (2011) for an applied study on this topic.

  7. The Weibull hazard model is widely used in studies of marketing time. As noted previously, while this approach does not account for potential simultaneity between sales price and TOM, it does have the advantage of correcting for the inherent censoring issue.

  8. Among other explanatory variables, age and square footage of the property are specified in the logarithmic form. The quarterly time trend variable, Listtime, is expressed in the quadratic form to model real estate cycle effects over the sample period. Remaining variables are expressed in linear form.

  9. The results for the time coefficients suggest an expanding, then contracting, market across all of the models. This particular market did not experience the extreme appreciation observed in many “coastal” markets in the “real estate bubble” running from approximately 2003–2007. Thus this market also did not experience as consequential a downturn as of the end of the study. Also, the pricing peak in this market seems to have occurred later in time than the peak for most other markets. The study period is dominated by the upcycle. The downmarket seems to be a “flat” period rather than a steep decline and it is relatively short on observations.

  10. Derivation of marginal effects for logistic regression and Weibull hazard functions are not included here but are available from the authors. For the semi-log sales price regression, the effect of a dummy variable is approximated by eb−1, where b is the estimated dummy variable coefficient.

  11. Degree of Overpricing was not included in the pricing model because it is the result of an estimated hedonic list price regression and is highly collinear with explanatory variables in a hedonic sales price equation.

  12. The results for these models are available from the authors.

  13. One advantage of our approach is to avoid possible bias of testing incentive effects separately. That is, the various broker incentives may all exist in actual transactions so estimating their effects separately could result in biased estimates, especially if their covariances are non-zero.

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Acknowledgements

The authors gratefully acknowledge the contributions of Professor Ed Kinman of Longwood University, who processed the GIS algorithm critical to the content of this paper, and the session participants at the American Real Estate Society annual conference in 2010.

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Authors and Affiliations

  1. Longwood University, 201 High Street, Farmville, 3909, VA, USA

    Raymond T. Brastow

  2. Richard H. Pennell Center for Real Estate Development, Clemson University, 301 Sirrine Hall, Clemson, 29634, SC, USA

    Thomas M. Springer

  3. Longwood University, 201 High Street, Farmville, 23909, VA, USA

    Bennie D. Waller

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  1. Raymond T. Brastow
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  2. Thomas M. Springer
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  3. Bennie D. Waller
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Correspondence to Thomas M. Springer.

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Brastow, R.T., Springer, T.M. & Waller, B.D. Efficiency and Incentives in Residential Brokerage. J Real Estate Finan Econ 45, 1041–1061 (2012). https://doi.org/10.1007/s11146-011-9308-1

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