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Is there a Principal-Agency Problem with Real Estate Agents in Rental Markets?

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Abstract

This paper examines the principle-agency problem between landlords and real estate agents using novel data on rental contracts. Real estate agents are found to obtain higher contract rents by approximately 1% more for themselves (and family members) than for other landlords, which is economically small. The results suggest that the principle-agency program with real estate agents is less of a concern in the rental market than the ownership market. The reason potentially relates to the commission structure, the relatively low effort associated with finding a tenant, the landlord’s ability to evaluate an agent’s performance, and reputation concerns from repeated interactions.

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Notes

  1. Early theoretical work on the agency problem with real estate agents include: Yinger (1981), Geltner et al. (1991), Miceli (1991), Anglin and Arnott (1991), Arnold (1992), Yavaş (1994, 1995), and Williams (1998).

  2. For instance, Lopez and Yoshida (2021) point out that about one out of every five non-commercial residential units in Las Vegas show up in the rental MLS platform, suggesting that many homeowners lease property without using a real estate agent. On the other hand, the National Association of Realtors, 2016 reports that more than 88% of buyers purchased their homes using a real estate agent.

  3. Private platforms such as Redfin do not send data on rental listings to other competing platforms like Zillow. Moreover, rental platforms may require landlords to sign exclusive rental listing agreements that bar landlords from using multiple rental platforms simultaneously, and therefore, constrain the number of potential tenants viewing the property.

  4. The few studies that examine residential rental transactions examine the allocation of broker costs (Ben-Shahar, 2001; Bar-Isaac & Gavazza, 2015) or trade-off between the rental rate and search costs (Benjamin & Lusht, 1993; Allen et al., 2009).

  5. Exceptions may include landlords who are temporarily relocating or are in other situations in which they may be less concerned absolute getting the absolute highest rent possible and more interested in shielding against homeownership costs. However, over 94% of the rentals in the sample are vacant or tenant occupied and not owner occupied, suggesting that most rentals were placed on the market for investment purposes.

  6. The Las Vegas Realtors were previously known as the Greater Las Vegas Association of Realtors (GLVAR). The rental MLS dataset contains about 150,000 unique rental properties, representing about 22% of all the unique non-commercial residential properties recorded in the Clark County Assessor Office as of March 2019. Lopez and Yoshida (2021) also examine these data on rental contracts from the MLS.

  7. The reason that vacancy is at 92% is an empirical fact whose determinants will be left for future research.

  8. Note that time-on-market and the cap rate are winsorized at the 1% tails.

  9. Unfortunately, I do not observe operating expenditures or revenue ex-post lease, which makes it difficult to estimate the actual capitalization rate without using ad hoc assumptions.

  10. See https://addictedrealty.com/wp-content/uploads/2018/01/GLVAR-MLS-Policies-August-2016.pdf. An individual may look up a Nevada real estate license using the following website: https://red.prod.secure.nv.gov/Lookup/LicenseLookup.aspx

  11. Landlords who are unrelated to the real estate agent but personally affiliated to another real estate licensee perhaps under-report the said affiliation in which case fewer than the true number of agent-related rental properties would be identified and bias the analysis towards finding little differences in the market outcomes between agent-related and arm’s-length rental properties.

  12. Later in the analysis, agent-owned or agent-related properties are matched to arm’s-length properties using propensity score matching to reduce concerns about examining unbalanced groups of properties.

  13. Sections 4 and 5 discuss strategies to reduce plausible concerns about the underlying mechanism driving variation in the share of agent-owned and agent-related properties.

  14. If the agent-owned or agent-related status is under-reported, then coefficient estimates of \(\delta _{1}\) and \(\delta _{2}\) would be biased towards zero since the control sample would include rentals that are truly agent-owned and agent-related. However, this bias is unlikely as discussed in Section 3.

  15. See http://red.nv.gov/Content/Licensing/Initial_Requirements/.

  16. Note that for categorical variables, the largest class is set as the base category.

  17. Year-quarter-zip fixed effects do not materially affect the principal results. Subdivisions are more granular delineation of neighborhoods than census tracts and zip codes, including condominiums.

  18. For example, as discussed and examined in Section 5, rental properties that generate multiple rental contracts in the sample tend to be held by large or corporate landlords, while properties with one rental contract in the sample tend to be owned by small or individual landlords.

  19. I use the “reghdfe” package in Stata, which iteratively identifies and removes singleton observations (see Correia, 2014, 2016). An observation that uniquely makes up a class in a categorical variable is a singleton observation (e.g., an observation of a single property in a subdivision).

  20. In the appendix, Table A.1 controls for the rental contract term length non-linearly; the results are identical to the baseline estimates. Table A.2 shows that the main results hold within property type subsamples. Consistent results also arise when using the monthly rent per square foot as the dependent variable (see Table A.3). Furthermore, the results remain unchanged when using listing year-month fixed effects instead of listing year-quarter fixed effects, suggesting that the baseline estimates of the agent-owned and agent-related premiums are robust to possible within-quarter seasonal effects.

  21. Sant’Anna and Zhao (2020) argue that a doubly robust approach for causal inference is useful since the propensity score model or outcome regression could be misspecified. King and Nielsen (2019) point out potential pitfalls of using propensity score matching.

  22. I use PSMATCH2 command in STATA to compute Eq. 2. King and Nielsen (2019)

  23. Cohen’s d-statistic is measured as the mean difference between the treatment group and control group divided by the pooled standard deviation. Generally, a d-statistic is considered small and economically meaningless if its absolute value is (or less than) 0.2 (see Cohen, 1977).

  24. \(\$499 = 0.003 \times \$1,290 \times 12 / 9.3\%\); \(\$1,115 = 0.0067 \times \$1,290 \times 12 / 9.3\%\)

  25. Lopez (2021) employs a similar strategy.

  26. The small sample size of agent-related listings introduces volatility to the point estimates of the agent-related premium.

  27. I define a household as an individual or group of individuals that is not a fictitious entity such as a trust or corporation using the “grantee” variable and by flagging observations that do not have abbreviations or key words such as “LLC”, “Inc”, and “Trust”.

  28. See for example Agarwal et al. (2019) who use the sales price to listing price ratio as a proxy for bargaining effort.

  29. For expired/withdrawn listings, I set a similar set of filters to those reported in Section 3 for leased listings.

  30. Following concerns that TOM may be constructed differently depending on whether withdrawn or expired listings are in the sample (Benefield & Hardin, 2015), I find similar results when measuring TOM as the number of days between the “off-the-market” date and listing date and including withdrawn or expired listings in the sample.

References

  • Agarwal, S., He, J., Sing, T. F., & Song, C. (2019). Do real estate agents have information advantages in housing markets? Journal of Financial Economics, 134, 715–735.

    Article  Google Scholar 

  • Allen, M. T., Rutherford, J., Rutherford R., & Yavas, A. (2016). Conflicts of interest in residential real estate transactions: New evidence, Unpublished Working Paper.

  • Allen, M. T., Rutherford, R. C., & Thomson, T. A. (2009). Residential asking rents and time on the market. The Journal of Real Estate Finance and Economics, 38, 351–365.

    Article  Google Scholar 

  • Anglin, P., Rutherford, R., & Springer, T. (2003). The trade-off between the selling price of residential properties and time-on-the-market: The impact of price setting. The Journal of Real Estate Finance and Economics, 26, 95–111.

    Article  Google Scholar 

  • Anglin, P. M., & Arnott, R. (1991). Residential real estate brokerage as a principal-agent problem. The Journal of Real Estate Finance and Economics, 4, 99–125.

    Article  Google Scholar 

  • Arnold, M. A. (1992). The principal-agent relationship in real estate brokerage services. Real Estate Economics, 20, 89–106.

    Article  Google Scholar 

  • Bar-Isaac, H., & Gavazza, A. (2015). Brokers’ contractual arrangements in the manhattan residential rental market. Journal of Urban Economics, 86, 73–82.

    Article  Google Scholar 

  • Barwick, P. J., Pathak, P. A., & Wong, M. (2017). Conflicts of interest and steering in residential brokerage. American Economic Journal: Applied Economics, 9, 191–222.

    Google Scholar 

  • Ben-David, I. (2011). Financial constraints and inflated home prices during the real estate boom. American Economic Journal: Applied Economics, 3, 55–87.

    Google Scholar 

  • Ben-Shahar, D. (2001). A study of the brokerage cost allocation in a rental housing market with asymmetric information. The Journal of Real Estate Finance and Economics, 23, 77–94.

    Article  Google Scholar 

  • Benefield, J. D., & Hardin, W. G. (2015). Does time-on-market measurement matter? The Journal of Real Estate Finance and Economics, 50, 52–73.

    Article  Google Scholar 

  • Benjamin, J., & Lusht, K. (1993). Search Costs and Apartment Rents, The. Journal of Real Estate Finance and Economics, 6, 189–197.

    Article  Google Scholar 

  • Beracha, E., & Hardin, W. G., III. (2018). The capitalization of school quality into renter and owner housing. Real Estate Economics, 46, 85–119.

    Article  Google Scholar 

  • Bhattacharya, U., Huang, D., & Nielsen, K. M. (2021). Spillovers in prices: The curious case of haunted houses. Review of Finance, 25, 903–935.

    Article  Google Scholar 

  • Cannaday, R. E., Munneke, H. J., & Yang, T. T. (2005). A multivariate repeat-sales model for estimating house price indices. Journal of Urban Economics, 57, 320–342.

    Article  Google Scholar 

  • Cohen, J. (1977). Statistical Power Analysis for the Behavioral Sciences, Academic Press, INC: London, 2 edition.

  • Correia, S. (2014). REGHDFE: Stata module to perform linear or instrumental-variable regression absorbing any number of high-dimensional fixed effects, Statistical Software Components, Boston College Department of Economics. https://ideas.repec.org/c/boc/bocode/s457874.html. Accessed 3 Nov 2022. 

  • Correia, S. (2016). Estimating multi-way fixed effect models with reghdfe, 2016 Stata Conference. http://scorreia.com/research/reghdfe-slides.pdf. Accessed 3 Nov 2022 

  • D’Lima, W., & Schultz, P. (2020). Residential real estate investments and investor characteristics, The Journal of Real Estate Finance and Economics, 1–40.

  • Emmerling, T., Yavaş, A., & Yildirim Y. (2020). To accept or not accept: Optimal strategy for sellers in real estate, Real Estate Economics, 1–29.

  • Favilukis, J., Ludvigson, S. C., & Van Nieuwerburgh, S. (2017). The macroeconomic effects of housing wealth, housing finance, and limited risk sharing in general equilibrium. Journal of Political Economy, 125, 140–223.

    Article  Google Scholar 

  • Geltner, D., Kluger, B. D., & Miller, N. G. (1991). Optimal price and selling effort from the perspectives of the broker and seller. Real Estate Economics, 19, 1–24.

    Article  Google Scholar 

  • Han, L., & Hong, S.-H. (2016). Understanding in-house transactions in the real estate brokerage industry. The RAND Journal of Economics, 47, 1057–1086.

    Article  Google Scholar 

  • Hayunga, D. K., & Munneke, H. J. (2021). Examining both sides of the transaction: Bargaining in the housing market. Real Estate Economics, 49, 663–691.

    Article  Google Scholar 

  • Heathcote, J., & Perri, F. (2018). Wealth and volatility. The Review of Economic Studies, 85, 2173–2213.

    Article  Google Scholar 

  • Hendel, I., Nevo, A., & Ortalo-Magne, F. (2009). The relative performance of real estate marketing platforms: MLS versus FSBOMadison.com. American Economic Review, 99, 1878–1898.

    Article  Google Scholar 

  • Holmström, B. (1979). Moral hazard and observability, The Bell Journal of Economics, 74–91.

  • Holmström, B. (2017). Pay for performance and beyond. American Economic Review, 107, 1753–77.

    Article  Google Scholar 

  • Jia, P., & Pathak, P. A. (2010). The impact of commissions on home sales in greater boston, American Economic Review, 100, 475–479, 122nd Annual Meeting of the American-Economics-Association (p. 2010). GA: Atlanta.

    Google Scholar 

  • King, G., & Nielsen, R. (2019). Why propensity scores should not be used for matching. Political Analysis, 27, 435–454.

    Article  Google Scholar 

  • Kurlat, P., & Stroebel, J. (2015). Testing for information asymmetries in real estate markets. The Review of Financial Studies, 28, 2429–2461.

    Article  Google Scholar 

  • Levitt, S. D., & Syverson, C. (2008). Market distortions when agents are better informed: The value of information in real estate transactions. The Review of Economics and Statistics, 90, 599–611.

    Article  Google Scholar 

  • Liu, C. H., Nowak, A. D., & Smith, P. S. (2019). Asymmetric or incomplete information about asset values? The Review of Financial Studies.

  • Lopez, L. A. (2021). Asymmetric information and personal affiliations in brokered housing transactions. Real Estate Economics, 49, 459–492. https://doi.org/10.1111/1540-6229.12325

    Article  Google Scholar 

  • Lopez, L. A., & Yoshida, J. (2021). Estimating housing rent depreciation for inflation adjustments. Regional Science and Urban Economics, 103733.

  • McMillen, D. (2012a). Quantile Regression for Spatial Data, Springer Science & Business Media.

  • McMillen, D. P. (2003). The return of centralization to chicago: Using repeat sales to identify changes in house price distance gradients. Regional Science and Urban Economics, 33, 287–304.

    Article  Google Scholar 

  • McMillen, D. P. (2012). Repeat sales as a matching estimator. Real Estate Economics, 40, 745–773.

    Article  Google Scholar 

  • McMillen, D. P., & Thorsnes, P. (2006). Housing renovations and the quantile repeat-sales price index. Real Estate Economics, 34, 567–584.

    Article  Google Scholar 

  • Miceli, T. J. (1991). The multiple listing service, commission splits, and broker effort. Real Estate Economics, 19, 548–566.

    Article  Google Scholar 

  • Mills, J., Molloy, R., & Zarutskie, R. (2019). Large-Scale Buy-to-Rent Investors in the Single-Family Housing Market: The Emergence of a New Asset Class. Real Estate Economics., 47, 399–430.

    Article  Google Scholar 

  • National Association of Realtors. (2016). Profile of home buyers and sellers. Available at https://www.nar.realtor/sites/default/files/reports/2016/2016-profile-of-home-buyers-and-sellers-10-31-2016.pdf. Accessed 3 Nov 2022

  • Ondrich, J., Ross, S., & Yinger, J. (2003). Now you see it, now you don’t: Why do real estate agents withhold available houses from black customers? The Review of Economics and Statistics, 85, 854–873.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Rutherford, R. C., Springer, T. M., & Yavas, A. (2007). Evidence of information asymmetries in the market for residential condominiums. The Journal of Real Estate Finance and Economics, 35, 23–38.

    Article  Google Scholar 

  • Saez, E., & Zucman, G. (2016). Wealth inequality in the united states since 1913: Evidence from capitalized income tax data. The Quarterly Journal of Economics, 131, 519–578.

    Article  Google Scholar 

  • Sant’Anna, P. H., & Zhao, J. (2020). Doubly robust difference-in-differences estimators. Journal of Econometrics, 219, 101–122.

    Article  Google Scholar 

  • Shi, L., & Tapia, C. (2016). The disciplining effect of concern for referrals: evidence from real estate agents. Real Estate Economics, 44, 411–461.

    Article  Google Scholar 

  • Goldsmith-Pinkham, P., & Shue, K. (2022). The gender gap in housing returns, Unpublished Working Paper, 00, 1–70. http://dx.doi.org/10.2139/ssrn.3559892. Accessed 3 Nov 2022

  • Smith, M., Zidar, O. M., & Zwick, E. (2021). Top wealth in america: New estimates and implications for taxing the rich. National Bureau of Economic Research: Technical report.

    Book  Google Scholar 

  • Williams, J. (1998). Agency and brokerage of real assets in competitive equilibrium. The Review of Financial Studies, 11, 239–280.

    Article  Google Scholar 

  • **e, J. (2018). Who is ‘misleading’ whom in real estate transactions? Real Estate Economics, 46, 527–558.

    Article  Google Scholar 

  • Yavaş, A. (1994). Middlemen in bilateral search markets. Journal of Labor Economics, 12, 406–429.

    Article  Google Scholar 

  • Yavaş, A. (1995). Can brokerage have an equilibrium selection role? Journal of Urban Economics, 37, 17–37.

    Article  Google Scholar 

  • Yinger, J. (1981). A search model of real estate broker behavior. American Economic Review, 71, 591–605.

    Google Scholar 

  • Zorn, T. S., & Larsen, J. E. (1986). The incentive effects of flat-fee and percentage commissions for real estate brokers. Real Estate Economics, 14, 24–47.

    Article  Google Scholar 

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  1. College of Business Administration, University of Illinois at Chicago, Illinois, USA

    Luis A. Lopez

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I thank Sumit Agrawal, Dominique Badoer, Itzhak Ben-David, Walter D’Lima, Yulia Demyanyk, Joan Ferre-Mensa, Erasmo Giambona, John Graham, Jacob Sagi, Ruchi Singh, Rohan Williamson and seminar participants at the Financial Management Association International 2020 Diversity Emerging Scholars Initiative for helpful comments and suggestions. I also thank the Nevada Real Estate Division, Clark County Assessor Office, and LIED Institute for Real Estate Studies at the University of Nevada Las Vegas for providing data.

Appendix

Appendix

Tables 9, 10, 11, 12, 13 and 14.

Table A.1 Controlling for Contract Term Non-linearly
Full size table
Table A.2 Property Type
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Table A.3 Price per Square Foot
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Table A.4 Capital Expenditures
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Table A.5 Propensity Score Probit Regression
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Table A.6 Post Matching Summary Statistics
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Lopez, L.A. Is there a Principal-Agency Problem with Real Estate Agents in Rental Markets?. J Real Estate Finan Econ (2022). https://doi.org/10.1007/s11146-022-09927-8

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