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Sample Selection Approaches to Estimating and Allocating House Transaction Funding Price Differentials

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Abstract

This study examines house transaction price differentials observed among funding type combinations; accounting for potential sample selection and spatial biases yields a better approximation of price differentials between group combinations. Consistent with expectations we detect, and correct for, selectivity and spatial biases. Transactions with conventional financing have superior characteristics compared to all-cash funded transactions, and Federal Housing Administration (FHA) and Veterans Affairs (VA) funded houses have inferior characteristics relative to all-cash characteristics. Price counterfactuals for (1) all-cash financed property, (2) conventional, (3) FHA, and (4) VA property transactions reveal, consistent with expectations, unexplained coefficient pricing premiums, i.e., a financing premium. However, total all-cash explained housing/neighborhood characteristics, are superior relative to FHA and VA financed properties. Results reinforce the notion that credit matters in the provision of financial services with regard to housing prices, while Blinder-Oaxaca price differential decompositions provide additional insights.

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Notes

  1. According to National Association of Realtors® (2017). Realtors® Confidence Index Report and Market Outlook. http://www.realtor.org/, in January 2017, 23% of sales were reported as all-cash. Realtor® also reports that most cash purchasers are investment, international, distressed, and/or second house buyers.

  2. See, for example, Case and Shiller (1988, 2003), Scheinkman and Xiong (2003), and Geanakoplos (2009).

  3. See, Sirmans et al. (2005) for a comprehensive review of the literature.

  4. Self-selection bias is likely to remain problematic if funding indicator variables are used in a traditional regression model.

  5. Filters are imposed based on transaction price; we eliminate transactions with sale prices greater than $3,000,000 and less than $75,000 from our study, a standard practice in the housing literature. The final data set is reduced to 35,336 for 2014–2015.

  6. US Census Bureau estimates for 2012, the population of San Diego county is 3177, 063; http://www.census.gov/quickfacts/table/PST040215/06073,00; http://www.city-data.com/county/San_Diego_County-CA.html

  7. For a detailed description of models with self-selectivity, refer to Maddala (1983, pp. 257). For a description of the estimation procedure refer to “The QLIM Procedure” in SAS.

  8. We selected a 10 nearest neighbor spatial weight matrix.

  9. Summary mean decompositions are presented in the Appendix, Table 17. Detailed decompositions are available upon request.

  10. Due to FHA maximum loan levels, for this comparison we truncate our data to only include all-cash and FHA funded property transactions with prices at or below $800,000. This amount provided a natural point of separation in our data.

  11. Due to FHA maximum loan levels, for this comparison we truncate our data to only include conventional and FHA funded property transactions with first mortgage loan levels at or below $572,200. We then compare conventional and FHA funded transactions grouped by LTV.

  12. The spatial lag coefficient (Rho) is generally not statistically significant, perhaps an artifact of suppression attributed to multi spatial variables included in models.

  13. Table 16 in the Appendix presents results for the first-step Heckman method. Probit regressions include structural, neighborhood characteristics, and transaction characteristics variables, as well as distance measures to various amenities that act as spatial controls. These estimates are used to calculate the inverse Mills ratios (IMR), included as a covariate in the respective second-step spatial hedonic price equations.

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Authors and Affiliations

  1. Gazarian Real Estate Center, Craig School of Business, Department of Finance and Business Law, California State University, Fresno, 5245 N. Backer Ave. M/S PB5, Fresno, CA, 93740-8001, USA

    Andres Jauregui

  2. Culverhouse College of Commerce, Department of Economics, Finance, and Legal Studies, University of Alabama, 245 Mary Alston Hall, Box 870224, Tuscaloosa, AL, 35487, USA

    Alan Tidwell

  3. LIED Institute for Real Estate Studies, LEE Business School, University of Nevada, Las Vegas, BEH 530A, 4505 S. Maryland Pkwy, Las Vegas, NV, 89154, USA

    Vivek Sah

Authors
  1. Andres Jauregui
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  2. Alan Tidwell
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  3. Vivek Sah
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Correspondence to Andres Jauregui.

Appendix

Appendix

Table 16 Heckman first stage probit models
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Table 17 Blinder-Oaxaca summary decompositions
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Jauregui, A., Tidwell, A. & Sah, V. Sample Selection Approaches to Estimating and Allocating House Transaction Funding Price Differentials. J Real Estate Finan Econ 58, 366–407 (2019). https://doi.org/10.1007/s11146-018-9661-4

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