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Sample Selection Approaches to Estimating House Price Cash Differentials

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Abstract

We extend the literature on house price cash differentials in important ways. First, our paper is the first to employ methods to correct for sample selection bias, using both switching regression and propensity score matching of cash vs. non-cash transactions. We use selection models to produce price counterfactuals for cash and noncash buyers. We also include both average treatment effect and a propensity score weighted selection models. From the selection models, we find that previous studies likely overstate the cash discount. Results from counterfactual tests examining cash discounts suggest amplified cash discounts in areas with close proximity to an environmental hazard; and also a pricing differential based on CBG level income, with purchasers in high income areas more likely to pay a cash premium compared to market participants in areas with comparably lower income, where a cash discount is detected. These results provide useful insights for market participants including real estate appraisers, brokers, and buyers and sellers of real estate.

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Notes

  1. Realtor® also reports that most international buyers purchase properties with cash, given their limited access to conventional financing options.

  2. In a recent interview with USA Today, Guy Cecala, publisher of Inside Mortgage Finance suggested that the main catalyst for the increasing closing costs are origination fees having increased by 8 % in the past year. Additionally, he remarked that rising mortgage rates often result in lenders making less profit on their loans, often they mitigate this potential loss with higher closing fees.” Source: “Mortgage closing costs are on the way up,” USA Today (Aug. 5, 2013).

  3. For a detailed description of models with self-selectivity, refer to Maddala (1983, pp. 257).

  4. For recent uses of PSM in the real estate domain, see, for example, Liu et al. (2013), Reichardt (2013); Nanda and Ross (2012); McMillen (2012); Holupka and Newman (2012); and Ding et al. (2011).

  5. See Becker and Ichino (2002) and Liu and Lynch (2011) for a summary of PSM.

  6. The propensity score is generally the predicted probability of an observation being either cash vs. noncash.

  7. PCC|NC represents the price a noncash buyer would pay if they had the same return to characteristics as a cash buyer.

  8. The propensity score results for cash and non-cash transactions, were based on the probit results.

  9. For comparative purposes of the treatment effect, covariates in the probit model remain the same across the parceled sample analyses. All subsamples examined are not completely balanced, however the comprehensive model is balanced, and the potential trade-off resulting in differing sub-sample probit models does not exceed the benefit of model consistency.

  10. See Literature Review for discussion of previous findings.

  11. We performed hypothesis test on the differences in coefficients between our coefficient estimates and estimates from three previous studies mentioned in the literature review section. Tests statistics reject the null hypothesis in favor of the alternative hypothesis at a 10 % significance level, confirming that our coefficients estimates are statistically smaller than the estimates from previous studies. Calculated z-score and p-values correspond to a one-tailed test.

  12. Income at the level of individual homeowners is unavailable to us.

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Acknowledgments

We would like to gratefully thank participants at the 2014 American Real Estate Society Annual Conference, and journal reviewers for insightful comments.

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Authors and Affiliations

  1. Department of Accounting and Finance, Turner College of Business, Columbus State University, 4225 University Avenue, Columbus, GA, 31907, USA

    Andres Jauregui & Alan Tidwell

  2. Department of Agricultural Economics and Rural Sociology, College of Agriculture, Auburn University, 209B Comer Hall, Auburn, AL, 36849, USA

    Diane Hite

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  1. Andres Jauregui
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  2. Alan Tidwell
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  3. Diane Hite
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Correspondence to Diane Hite.

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Jauregui, A., Tidwell, A. & Hite, D. Sample Selection Approaches to Estimating House Price Cash Differentials. J Real Estate Finan Econ 54, 117–137 (2017). https://doi.org/10.1007/s11146-015-9529-9

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