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
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.
See, Sirmans et al. (2005) for a comprehensive review of the literature.
Self-selection bias is likely to remain problematic if funding indicator variables are used in a traditional regression model.
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.
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
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.
We selected a 10 nearest neighbor spatial weight matrix.
Summary mean decompositions are presented in the Appendix, Table 17. Detailed decompositions are available upon request.
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.
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.
The spatial lag coefficient (Rho) is generally not statistically significant, perhaps an artifact of suppression attributed to multi spatial variables included in models.
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.
References
Agarwal, V. B., & Philips, R. A. (1985). The effects of assumption financing across housing price categories. Real Estate Economics, 13(1), 48–57.
Agarwal, S., Ben-David, I., & Yao, V. (2015). Collateral valuation and borrower financial constraints: Evidence from the residential real estate market. Management Science, 61(9), 2220–2240.
Anselin, L. (1988). Spatial econometrics: Methods and models (Vol. 4). Dordrecht: Springer Science & Business Media.
Aroul, R., & Hansz, J. A. (2011). The role of dual-pane windows and improvement age in explaining residential property values. Journal of Sustainable Real Estate, 3(1), 142–161.
Asabere, P., & Huffman, F. (2008). FHA/VA financing and price discounts. Journal of Real Estate Research, 30(2), 191–206.
Asabere, P. K., Huffman, F. E., & Mehdiany, S. (1992). The price effects of cash versus mortgage transactions. Real Estate Economics, 20(1), 141–154.
Ben-David, I. (2011). Financial constraints and inflated home prices during the real estate boom. American Economic Journal: Applied Economics, 3(3), 55–87.
Berkovec, J. A., Kogut, D. J., & Nothaft, F. E. (2001). Determinants of the ARM share of FHA and conventional lending. The Journal of Real Estate Finance and Economics, 22(1), 23–41.
Blinder, A. S. (1973). Wage discrimination: Reduced form and structural estimates. Journal of Human Resources, 8, 436–455.
Bokhari, S., Torous, W., & Wheaton, W. (2013). Why did household mortgage leverage rise from the mid-1980s until the great recession. In American Economic Association 2013 Annual Meeting. San Diego.
Case, K. E., & Shiller, R. J. (1988). The efficiency of the market for single-family homes.
Case, K. E., & Shiller, R. J. (2003). Is there a bubble in the housing market? Brookings Papers on Economic Activity, 2003(2), 299–342.
Chan, S. (2001). Spatial lock-in: Do falling house prices constrain residential mobility? Journal of Urban Economics, 49(3), 567–586.
Geanakoplos, J. (2009). The leverage cycle. In D. Acemoglu, K. Rogoff and M. Woodford (Eds.), NBER Macroeconomic Annual (pp. 1–65). University of Chicago Press 24 .
Heckman, J. (1979). Sample selection Bias as a specification error. Econometrica, 47(1), 153–161.
Hendershott, P. H., LaFayette, W. C., & Haurin, D. R. (1997). Debt usage and mortgage choice: The FHA-conventional decision. Journal of Urban Economics, 41(2), 202–217.
Hite, D. (2000). A random utility model of environmental equity. Growth and Change, 31(1), 40–58.
Jauregui, A., & Hite, D. (2010). The impact of real estate agents on house prices near environmental disamenities. Housing Policy Debate, 20(2), 295–316.
Jauregui, A., Tidwell, A., & Hite, D. (2017). Sample selection approaches to estimating house price cash differentials. The Journal of Real Estate Finance and Economics, 54(1), 117–137.
LeSage, J., & Pace, R. K. (2009). Introduction to spatial econometrics. Boca Raton: Chapman and Hall/CRC.
Lusht, K., & Hansz, A. (1994). Some further evidence on the price of mortgage contingency clauses. Journal of Real Estate Research, 9(2), 213–217.
Maddala, G. S. (1983). Limited-dependent and qualitative variables in econometrics (no. 3). Cambridge: Cambridge University Press, Econometric Society Monographs.
Neumark, D. (1988). Employers' discriminatory behavior and the estimation of wage discrimination. Journal of Human Resource, 23, 279–295.
Oaxaca, R. (1973). Male-female wage differentials in urban labor markets. International Economic Review, 14, 693–709.
Oaxaca, R. L., & Ransom, M. R. (1994). On discrimination and the decomposition of wage differentials. Journal of Econometrics, 61(1), 5–21.
Pennington-Cross, A., & Nichols, J. (2000). Credit history and the FHA–conventional choice. Real Estate Economics, 28(2), 307–336.
Rosen, S. (1974). Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy, 82(1), 34–55.
Rosenthal, S. S., Duca, J. V., & Gabriel, S. A. (1991). Credit rationing and the demand for owner-occupied housing. Journal of Urban Economics, 30(1), 48–63.
Sah, V., Conroy, S. J., & Narwold, A. (2016). Estimating school proximity effects on housing prices: The importance of robust spatial controls in hedonic estimations. The Journal of Real Estate Finance and Economics, 53(1), 50–76.
Scheinkman, J. A., & Xiong, W. (2003). Overconfidence and speculative bubbles. Journal of Political Economy, 111(6), 1183–1219.
Sirmans, G. S., Smith, S. D., & Sirmans, C. F. (1983). Assumption financing and selling price of single-family homes. Journal of Financial and Quantitative Analysis, 18(3), 307–317.
Sirmans, S., Macpherson, D., & Zietz, E. (2005). The composition of hedonic pricing models. Journal of Real Estate Literature, 13(1), 1–44.
Tidwell, A., Jauregui, A., Sah, V., & Narwold, A. (2018). Cash and distressed house sales price discounts: Dual sample selection spatial interdependence approaches. The Journal of Real Estate Finance and Economics, 56(1), 101–139.
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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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DOI: https://doi.org/10.1007/s11146-018-9661-4