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Genetics, Homeownership, and Home Location Choice

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Abstract

We find that a significant proportion of the cross-sectional variation in the choice to own or rent is attributable to a genetic factor, while parental influence is not found to affect this choice. We also find evidence of gene-environment interactions: The environment moderates genetic effects on homeownership in that growing up in a wealthier family results in a stronger expression of genetic predispositions, while idiosyncratic life experiences appear to explain a larger portion of the variation in homeownership among those who grew up in a less wealthy family environment. Furthermore, we find that home location choices, for example, a familiar home location close to one’s birthplace and an urban versus a rural home location, are explained by both genetic factors and parental influence. Because we control for an extensive set of individual characteristics analyzed in existing research, an interpretation of our evidence is that an individual’s preferences with respect to homeownership and home location are partly genetic. The findings contribute to a deeper understanding of the factors that explain individual behavior with respect to the housing market, and add to an expanding literature on the biological and genetic factors that influence individuals’ economic and financial decisions.

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Notes

  1. The use of twin data has a very long tradition in applied microeconomic research (e.g., Behrman and Taubman 1976, 1989; Taubman 1976; Ashenfelter and Krueger 1994; Behrman et al. 1994; Ashenfelter and Rouse 1998).

  2. The potential importance of research in the intersection of economics and biology has long been recognized by economists (e.g., Becker 1976; Hirshleifer 1977). For an overview of research at the intersection of genetics, neurobiology, and economics, we refer to Camerer et al. (2005) and Benjamin et al. (2008).

  3. For models of individual housing tenure choices, see, e.g., Hendershott and Shilling (1982), Henderson and Ioannides (1983), Linneman (1985), and Mills (1990).

  4. Our review focuses on the tenure choice literature. In the empirical analysis we also examine the housing consumption choice, i.e., the amount of housing chosen, conditional on being a homeowner. Existing work has identified housing consumption factors that include the same variables that have been shown to also affect the own versus rent decision (e.g., Goodman 1988; Henderson and Ioannides 1989; Davidoff 2006).

  5. Most papers in the tenure choice literature do not have data on credit history, FICO scores, or similar measures, a notable exception being the pseudo credit scores constructed by Barakova et al. (2003). Sweden has a system with a betalningsanmärkning, or “payment default score” (i.e., the Swedish system is one with only two credit scores: good or bad credit). Fewer than 10% in the overall population have a payment default score indicating bad credit, so even if we had access to such data, we believe the usefulness would be limited in our study because of the very skewed distribution. Barakova et al. (2003) conclude that wealth is the most important constraint, though it has diminished in importance in recent years, while credit quality has increased in importance as a constraint affecting homeownership. See, e.g., Brueckner (1986) for a model of the downpayment constraint on housing tenure choice.

  6. Shore (2011) finds that income volatility is transmitted from one generation to the next, but without observing the channel of such transmission.

  7. Cesarini et al. (2009) and Zyphur et al. (2009) also report that an individual’s risk preferences have a significant genetic component. Using experiments involving lottery choices and questionnaires, they elicit subjects’ risk preferences and then use twin research methodology to estimate genetic effects.

  8. STR’s databases are organized by birth cohort. The Screening Across Lifespan Twin, or “SALT,” database contains data on twins born 1886–1958. The Swedish Twin Studies of Adults: Genes and Environment database, or “STAGE,” contains data on twins born 1959–1985. In addition to twin pairs, twin identifiers, and zygosity status, the databases contain variables based on STR’s telephone interviews (for SALT), completed 1998–2002, and combined telephone interviews and Internet surveys (for STAGE), completed 2005–2006. For further details about STR, we refer to Lichtenstein et al. (2006).

  9. Zygosity is based on questions about intrapair similarities in childhood. One of STR’s survey questions was: Were you and your twin partner during childhood “as alike as two peas in a pod” or were you “no more alike than siblings in general” with regard to appearance? This method has been validated with DNA as having 98 percent or higher accuracy. For twin pairs for which DNA sampling has been conducted, zygosity status based on DNA analysis is used.

  10. Home values are based on assessed tax values from the Swedish Tax Authority, which are computed based on actual transaction values for similar properties in the area of the home.

  11. As we do not observe the exact location of an individual’s birthplace within a state, i.e., we do not have data on the municipality where an individual was born, we use the population-weighted average distance to all municipalities in the birth state. We set Distance to Birthplace to zero for individuals that live in the state of their birth.

  12. In quantitative genetics research, the model is known as the threshold model for liabilities. It was introduced by Wright (1934) and first applied to human genetics by Falconer (1965).

  13. All models are estimated using the Mplus software. While maximum likelihood estimation in principle is an alternative, it is computationally more costly as it requires numerical integration. Reported standard errors are bootstrapped with 1,000 resamples.

  14. For continuous variables, including censored outcomes, we report Pearson correlations; for the binary variable, we report tetrachoric correlations.

  15. For Distance to Urban Center, the randomly selected non-twins are only matched by age and gender, not by municipality.

  16. Specifically, we report χ 2 differences for the Satorra-Bentler scaled χ 2 (Satorra (2000)), i.e., we test for the difference in χ 2 for an E versus CE model, and a CE versus ACE model.

  17. The R 2 reported in Table 2 is calculated as the variance of \(\hat{y}^*_{ij} = \hat{\beta}\mathbf{X}_{ij}\) divided by \(\hat{y}^*_{ij} + 1\) (Hagle and Mitchell 1992).

  18. The occupation data are based on the International Standard Classification of Occupations (ISCO-88) by the International Labour Organization (ILO).

  19. We measure Housing Consumption only for homeowners. We therefore report results from using Heckman’s 1976 two-stage sample selection approach. In the first stage, we estimate a probit model for Tenure Choice. In addition to the controls used in the second stage, the probit specification also includes Log of Mean House Value in Municipality.

  20. We are not able to measure parents’ wealth when the twins grew up so we use parents’ net worth in the first year that we have data on them.

  21. See, e.g., Goldberger (1979), Taubman (1981) and Bouchard and McGue (2003) for further discussion of some of the common concerns with respect to analysis of data on twins.

  22. See Taubman (1976) for an application of this empirical methodology.

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Acknowledgements

We are thankful for comments from seminar participants at the ARES 2012 Annual Meeting, Claremont Graduate University (School of Politics & Economics), Claremont McKenna College, and University of Washington, and Steve Cauley, Jack Goldberg, Greg Hess, David Hirshleifer, Steven Laposa, Peter Linneman, Clara Mulder, Andrey Pavlov, Dustin Read, Ed Rice, Tony Sanders, Nancy Segal, and Stephen Shore. We acknowledge research funding from the Financial Economics Institute and the Lowe Institute of Political Economy at Claremont McKenna College, and the Global Business Center and the CFO Forum at the University of Washington. This project was pursued in part when Cronqvist was Olof Stenhammar Visiting Professor at the Institute for Financial Research (SIFR), which he thanks for its support, and while Siegel was visiting Arizona State University, which he thanks for their hospitality. Statistics Sweden and the Swedish Twin Registry (STR) provided the data for this study. STR is supported by grants from the Swedish Research Council, the Ministry of Higher Education, AstraZeneca, and the National Institute of Health (grants AG08724, DK066134, and CA085739). Any errors or omissions are our own.

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  1. Robert Day School of Economics and Finance, Claremont McKenna College, Bauer Center 319, 500 E. Ninth Street, Claremont, CA, 91711, USA

    Henrik Cronqvist

  2. Foster School of Business, University of Washington, Box 353226, Seattle, WA, 98195-3226, USA

    Florian Münkel & Stephan Siegel

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  1. Henrik Cronqvist
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Appendix

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Table 8 Variable definitions
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Table 9 Distance to Birthplace and Distance to Urban Center
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Cronqvist, H., Münkel, F. & Siegel, S. Genetics, Homeownership, and Home Location Choice. J Real Estate Finan Econ 48, 79–111 (2014). https://doi.org/10.1007/s11146-012-9373-0

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