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The Journal of Real Estate Finance and Economics

Erschienen in:

01.07.2007

Do Some People Work Harder than Others? Evidence from Real Estate Brokerage

verfasst von: J. D. Benjamin, P. Chinloy, G. D. Jud, D. T. Winkler

Erschienen in: The Journal of Real Estate Finance and Economics | Ausgabe 1/2007

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Abstract

The decision to work and its levels of intensity are estimated for the real estate brokerage industry where workers can set their own hours. A three-stage model of the brokerage labor market is presented with decisions made recursively between full- and part-time status, wage offers and hours worked. The application is to data from a cross-sectional survey of 6,842 real estate licensees in the United States for 1999. Conditional on self-selection, an expected wage for real estate licensees is estimated given skills and personal characteristics. That expected wage enters the supply-side equation for the number of hours worked. The findings indicate that skills such as education, experience and licensee status are related to higher wages, but there is a negative self-selection in wages: part-time workers have higher unmeasured skills. Schooling and experience decreases hours worked, consistent with increasing efficiency. The resulting labor supply elasticity with respect to the wage is 0.24.

Vorheriger Artikel Who You Going to Call? Performance of Realtors and Non-realtors in a MLS Setting

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Prior direct regressions of the logarithm of total earnings on various personal characteristics such as education along with hours worked. These traditional equations do not account for the self-selection decision to work part-time, the self-selection of wage offers or the endogeneity of hours worked.

Real estate has a number of occupations where workers can set their own hours. These occupations are ideal for testing full models of labor supply. In most jobs, workers cannot set their own hours. Further, the data for real estate licensees include information on protected or restricted personal variables not reported in government-sponsored surveys.

These data are from the National Association of Realtors (NAR) website http://www.realtor.org as well as National Association of Realtors (2004).

This large number of buyers and sellers and freedom of entry and exit has had a smaller impact on the actual commission charged for the sale of a single-family house. The United States Government Accountability Office (2005) has reported that the commission for single-family housing has remained in a range of between 5% and 7% of the sales price, regardless of the level of the price or market.

Workers in financial services such as banking and insurance earn a higher return to schooling. More educated licensees are more likely to adopt productivity-enhancing technology (Benjamin et al. (2002)).

Agency issues also can occur with the brokerage sales contract. Rutherford et al. (2005) examine the conflicts between principals and agents in the brokerage contract for residential houses using Dallas-Fort Worth area data. Munneke and Yavas (2001) find that contract structure does not affect productivity. Sales through conventional brokers and those that receive all the commission such as at RE/Max do not result in differential selling times.

For Texas licensees, Sirmans and Swicegood (2000) find that the use of personal assistants and the use of computer technology enhance earnings. Their results are similar to those in their 1997 study: male licensees earn more than females, but race is not statistically significant. A test for differences in the means conducted on a variety of variables including high political activity shows no statistical significance at the 5% level.

Munneke and Slade (2000) note self-selection issues in commercial real estate indices.

The Heckman (1979) two-step estimation procedure for sample selection bias is applied as follows: (1) the probit equation is estimated and used for finding constructing the inverse Mills ratio, (2) the sample selection regression is estimated using the regression parameters including the sample selection correction variable \( \widehat{m} \). Failure to correct for a statistically significant sample selection bias results in problem similar to an omitted variable bias. The estimated regression coefficients would be biased and inconsistent if a correction is not made. The sample of part-time real estate professionals may be systematically different from that of full-timers due to differences in unobservable characteristics.

A sample selection variable is not included in the hours worked equation because the fitted values for wages from the hours worked equation includes a selection bias correction, and the fitted values enter the hours worked regression. In addition, the selection mechanism is part-time versus full-time work which is defined by the number of hours worked.

Respondents were asked to choose intervals of average work week including less than 20, 20–39, 40–59 or more than 60 h. The categorization of full-time employment of at least a 20 h work week produced the most significant probit results.

The wage is annual income divided by the average work week. Respondents were not asked to provide the number of weeks worked per year. The variable used in the regression is the natural logarithm of wages which enables an interpretation of the coefficients in terms of percentage changes.

To determine the estimated percentage change, the following transformation is used: exp(D)-1, where D is the dummy variable coefficient.

Abelson, M. A., Kacmar, K. M., & Jackofsky, E. F. (1990). Factors influencing real estate brokerage sales staff performance. Journal of Real Estate Research, 5(2), 265–275.

Benjamin, J. D., Jud, G. D., Roth, K. A., & Winkler, D. T. (2002). Technology and realtor income. Journal of Real Estate Finance and Economics, 25(1), 51–65.CrossRef

Carroll, T. M., & Clauretie, T. M. (2000). A note on the earnings of real estate salespersons and others in the financial services industry. Journal of Real Estate Finance and Economics, 21(2), 315–323.CrossRef

Crellin, G., Frew, J. R., & Jud, G. D. (1988). The earnings of realtors: Some empirical evidence. Journal of Real Estate Research, 3(2), 69–78.

Follain, J. R., Lutes, T., & Meier, D. S. (1987). Why do some real estate salespeople earn more than others? Journal of Real Estate Research, 2(3), 73–81.

Glower, M., & Hendershott, P. H. (1988). The determinants of realtor income. Journal of Real Estate Research, 3(2), 53–68.

Heckman, J. (1979). Sample selection bias as a specification error. Econometrica, 47(1), 153–161.CrossRef

Johnson, H. M., Nourse, H. O., & Day, E. (1988). Factors related to the selection of a real estate agency of agent. Journal of Real Estate Research, 3(2), 109–118.

Jud, G. D., & Winkler, D. T. (1998). The earnings of real estate salesperson and others in the financial services industry. Journal of Real Estate Finance and Economics, 17(3), 279–291.CrossRef

Kennan, J. (2001). Repeated bargaining with persistent private information. Review of Economic Studies, 68(237), 729–755.

Larsen, J. (1991). Leading real estate sales agents and market performance. Journal of Real Estate Research, 6(2), 241–250.

Munneke, H., & Slade, B. A. (2000). An empirical study of sample selection bias in indices of commercial real estate. Journal of Real Estate Finance and Economics, 21(1), 45–64.CrossRef

Munneke, H., & Yavas, A. (2001). Incentives and performance in real estate brokerage. Journal of Real Estate Finance and Economics, 22(1), 5–22.CrossRef

National Association of Realtors (2004). Structure, conduct and performance of the real estate brokerage industry. Working Paper, Washington, DC.

Rutherford, R., Springer, T. M., & Yavas, A. (2005). Conflicts between principals and agents: Evidence from residential brokerage. Journal of Financial Economics, 76(3), 627–665.CrossRef

Sirmans, G. S., & Swicegood, P. G. (1997). Determinants of real estate licensee income. Journal of Real Estate Research, 14(1/2), 137–159.

Sirmans, G. S., & Swicegood, P. G. (2000). Determining real estate licensee income. Journal of Real Estate Research, 20(1/2), 189–204.

United States Government Accountability Office (2005). Real estate brokerage: Factors that may affect competition. Washington, DC: US GAO.

Yavas, A., Miceli, T. J., & Sirmans, C. F. (2001). An experimental analysis of the impact of intermediation on the outcome of bargaining games. Real Estate Economics, 29(2), 251–276.CrossRef

Titel: Do Some People Work Harder than Others? Evidence from Real Estate Brokerage
verfasst von: J. D. Benjamin
P. Chinloy
G. D. Jud
D. T. Winkler
Publikationsdatum: 01.07.2007
Verlag: Kluwer Academic Publishers-Plenum Publishers
Erschienen in: The Journal of Real Estate Finance and Economics / Ausgabe 1/2007
Print ISSN: 0895-5638
Elektronische ISSN: 1573-045X
DOI: https://doi.org/10.1007/s11146-007-9031-0

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