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Why Do Buyers Pay Different Prices for Comparable Products? A Structural Approach on the Housing Market

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Abstract

We focus on the housing market and examine why nonlocal home buyers pay 12% more for houses than local home buyers. We established a database on the residential housing market for Lafayette and West Lafayette, Indiana, that includes house transactions from 2000 to 2020. The dataset contains highly detailed information on individual buyers and house characteristics. We explain the price differential controlling for arguments such as imperfect information on prices, wealth effects, heterogeneous buyer preferences, and differential search and travel costs across buyers, among others. We estimate a housing demand model that returns heterogeneous marginal willingness to pay parameters for housing attributes. Our results show that nonlocal home buyers are willing to pay more for specific housing attributes, especially for house size, school quality, and house age. We also find that arguments such as gratification, reward, and imperfect price information explain the price differential to a large extent. Search and travel cost arguments have an adverse effect on nonlocal buyers’ house spending.

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Notes

  1. For contributions in the health care markets, see Brown, 2019; Cooper et al., 2019; Grennan, 2013; in the automobile markets, see Goldberg & Verboven, 2001; in the retail markets, see Hitsch et al., 2019; DellaVigna & Gentzkow, 2019.

  2. For example, a house in Beverly Hills, California, is valued and priced differently than a comparable house in Indiana.

  3. Turnbull and Sirmans (1993) and Elder et al. (1999) have shown that NLBs face higher search and travel costs.

  4. We use this information from various sources, such as the Multiple Listing Services, the county assessor, and mortgage documents.

  5. Bartik (1987) and Epple (1987) point out that the endogeneity of amenities can be caused by unobserved household preferences having an effect on the quantity of a characteristic consumed and the hedonic price of that characteristic.

  6. We use the approach by Bishop and Timmins (2019) and we would also like to thank Kelly Bishop for providing valuable insights.

  7. Local buyers pay an estimated average of \(\mathrm{\$}187,524\) for houses, while NLBs pay \(\mathrm{\$}210,070\).

  8. Due to data limitations, our study is not able to include the effect of agencies on house prices. This is certainly an interesting and important topic that will be discussed further at the end of the paper.

  9. See, for example, Salop and Stiglitz (1977), Varian (1980), and Janssen et al. (2005); see also Baye et al. (2006) for an overview.

  10. For further information on search frictions arising from deadlines, see Coey et al. (2019).

  11. For information on the evolution of housing prices and appreciation rates in different states, see the U.S. Census Bureau at http://www.census.gov/const/www/quarterly starts completions.pdf and OFHEO at http://www.ofheo.gov/media/hpi/2q07hpi.pdf. All monetary values in this study are expressed in 2020 U.S. dollars using the Consumer Price Index.

  12. In comparing our database with the Census of Population and Housing database, the latter database contains self-reported or estimated home values, which are less reliable than the house prices in our database. Moreover, the prices are partitioned into 23 mutually exclusive categories, and this represents a loss of information compared to our pricing data.

  13. The identities and some other information about the home buyers are kept anonymous in the study.

  14. Most apartments in Lafayette and West Lafayette are rental properties, so we would not expect any crucial concerns from removing these.

  15. This includes most of the variables that have been provided to us.

  16. We dropped house purchases by non-U.S. residents.

  17. We would like to thank a referee for the suggestion to separately control for education effects of nonlocal home buyers that purchase a home in West Lafayette. This separation allows us to test whether nonlocal buyers that work in academia (as captured by the dummy \(WLNLB\)) are willing to spend a premium on school quality and the education for their children.

  18. For further information on list price strategies in the housing market, see Beracha and Seiler (2014) and Cardella and Seiler (2016).

  19. We thank a referee for the suggestion to adopt this preliminary regression.

  20. We would like to thank an anonymous referee for valuable feedback on this section. We also thank Kelly Bishop for providing support on the estimation algorithm.

  21. For notational simplicity, we suppress time subscripts.

  22. The \({i}^{*}\) indicates that the \(\beta\) coefficients hold locally for each household-level observation in \(Z\).

  23. For further information on choosing the optimal bandwidth and the associated rule of thumb, see Silverman (1986) and Haerdle et al. (2004).

  24. It should be noted that the estimated coefficients reflect the implicit prices averaged across all buyers. We turn to the estimation of individual-specific marginal willingness to pay parameters in the second stage of our estimation procedure.

  25. Remember that this prediction is evaluated at the average implicit price.

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Acknowledgements

We would like to thank Stephen Martin, Kelly Bishop, seminar participants, and two anonymous referees for valuable feedback and support. We also thank the Tippecanoe County Assessor's Office, the Board of Realtors in Indiana, the Real Estate Agents Association in Indiana, and Real Estate Agent Amy Junius for support.

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Authors and Affiliations

  1. Purdue University, Department of Economics, Krannert School of Management, 47906, West Lafayette, IN, USA

    Ralph B. Siebert

  2. College of William & Mary, Raymond A. Mason College of Business, 23187, Williamsburg, VA, USA

    Michael J. Seiler

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Correspondence to Michael J. Seiler.

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Siebert, R.B., Seiler, M.J. Why Do Buyers Pay Different Prices for Comparable Products? A Structural Approach on the Housing Market. J Real Estate Finan Econ 65, 261–292 (2022). https://doi.org/10.1007/s11146-021-09841-5

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