Skip to main content

The Effect of Down Payment Assistance on Mortgage Choice

The Journal of Real Estate Finance and Economics volume 49, pages 329–351 (2014)Cite this article

Abstract

Lack of wealth for a down payment is one of the most recognized barriers to home ownership. In response to this barrier, state and federal government have implemented many programs that provide down payment assistance to potential home buyers. Numerous studies have shown that this assistance can increase homeownership rates, but few have measured how receiving assistance may alter borrowing behavior. Using data from a down payment assistance grant in the Midwest, this study compares the loan type and size of grant recipients to other borrowers that report similar income and buy homes in the same census tract. Results indicate grant recipients are more likely to use conventional loans, which are less expensive than other loan types that require a smaller down payment. Estimates also suggest that the grant may reduce loan size for borrowers who are on the margin of using a conventional loan.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Notes

  1. Previous research on home-ownership programs has primarily focused on whether one-time cash assistance increases home ownership among targeted groups (Feldman 2001; Listokin et al. 2001; Quercia et al. 2003; Herbert and Tsen 2005).

  2. Research on the effect of rental housing vouchers on demand for rental housing is more common. Both Cage (1994) and Leger and Kennedy (1990) find evidence that voucher recipients rent homes that are more expensive.

  3. Documentation are found on the FHLB of Cincinnati website and are updated annually.

  4. The price also depends on the loan-to-value ratio. Loans with smaller loan-to-value ratios face lower prices. This is omitted from the theoretical model to simplify characterization of the loan decision. Including the loan-to-value ratio does not alter the ambiguous prediction for the effect on loan size.

  5. This timing provides an opportunity to use an instrument for the probability that a borrower receives the grant. Unfortunately, the comparison data do not report the month of application, so it is not possible to utilize differences in timing.

  6. The income variable between the two data sets does not always match because of differences in measurement. The FHLB data include the income of all household members who will reside in the home purchased. The HMDA data include only the income of the loan applicant. In many cases, these measurements produce marginal differences. We use applicant income to manually match observations only when a match is not certain using the other variables.

  7. A preferred variable is whether or not a bank was a member of the FHLB in a given year. Because the FHLB does not publish this information for past years, the grant provider indicator is meant to proxy for FHLB-member banks. The results using this variable are not meaningfully different than results that are estimated using a bank fixed effect.

  8. We do not analyze this specification using census tract fixed effects due to the incidental parameters problem associated with non-linear fixed effects models (Greene 2002).

  9. Loan size estimates that use a cutoff of 0.40 and 0.60 are not meaningfully different from those that use the 0.50 cutoff.

  10. We do not use the natural log of loan amount because the effect of the grant is not expected to be larger for borrowers that have larger loan amounts. The maximum decrease for all loans is $5,000 and the maximum increase is a function of loan-to-value ratios used by the bank.

  11. An F-test indicates that grant recipients initiate loans that are significantly less than FHA borrowers at the 5 % level for columns (2) through (4) in Table 7.

  12. For the regressions that include census tract fixed effects, F-tests indicate that the difference between grant recipients and unassisted conventional loan borrowers is not significantly different in 2007 and 2009. The difference is statistically different at the 1 % level for 2008.

References

  • Ambrose, B., Pennington-Cross, A., & Yezer, A. (2002). Credit rationing in the U.S. mortgage market: evidence from variation in FHA market shares. Journal of Housing Economics, 51, 272–294.

    Google Scholar 

  • Brueckner, J. (1994). The demand for mortgage debt: some basic results. Journal of Housing Economics, 3, 251–262.

    Article  Google Scholar 

  • Cage, R. (1994). How does rental assistance influence spending behaviour? Monthly Labor Review, 117(5), 17–28.

    Google Scholar 

  • Case, K. E., Shiller, R. J., Thompson, A. (2012). What have they been thinking? Home buyer behavior in hot and cold markets. NBER Working Paper No. 18400.

  • Deng, Y., Quigley, J., & Van Order, R. (1996). Mortgage default and low down payment loans: the cost of public subsidy. Regional Science and Urban Economics, 26, 263–285.

    Article  Google Scholar 

  • Di, W., Ma, J., & Murdoch, J. C. (2007). The impact of the mortgage assistance program in Dallas, Texas. Williams Review, 2, 59–90.

    Google Scholar 

  • Durning, D. (1987). The efficiency and distribution of mortgage revenue bond subsidies: the effects of behavioral responses. Journal of Policy Analysis and Management, 7, 74–93.

    Article  Google Scholar 

  • Federal Home Loan Bank of Cincinnati Unaudited Financial Highlights. (2011). https://web.fhlbcin.com/Bank/NewsandEvents/NewsReleases/Documents/unaudited%20financial%20results.pdf. Accessed 1 May 2013.

  • Feldman, R. J. (2001). Mortgage rates, homeownership rates, and government-sponsored enterprises. The Region. Annual Report, 5–23.

  • Goodman, J., & Nichols, J. (1997). Does FHA increase homeownership or just accelerate it? Journal of Housing Economics, 6, 184–202.

    Article  Google Scholar 

  • Goodwin, K. R., & Zumpano, L. V. (2011). The home buyer tax credit of 2009 and the transition to homeownership. Journal of Housing Research, 20(2), 211–224.

    Google Scholar 

  • Government Accountability Office. (2010). Tax administration: Usage and selected analyses of the first-time homebuyer credit.

  • Greene, W. H. (2002). Econometric analysis (5th ed.). Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • Haurin,D., Hendershott, P., Wacther, S. (1996). Borrowing constraints and the tenure choice of young households. NBER Working Paper No. 5630.

  • Hendershott, P., Haurin, D., & Layfayette, W. (1997). Debt usage and mortgage choice: the FHA conventional decision. Journal of Urban Economics, 41, 202–217.

    Article  Google Scholar 

  • Herbert, C. E., & Tsen, W. (2005). The potential of down payment assistance for increasing homeownership among minority and low-income households. Washington, DC: U.S. Department of Housing and Urban Development, Office of Policy Development and Research.

    Google Scholar 

  • Kelly, A. (2008). ‘Skin in the game’: zero down payment mortgage default. Journal of Housing Research, 17, 75–98.

    Google Scholar 

  • Leger, M.L., & Kennedy, S.D. (1990). Final comprehensive report of the freestanding housing voucher demonstration. U.S. Department of Housing and Urban Development, Office of Policy Development and Research.

  • Linneman, P., & Wachter, S. (1989). The impacts of borrowing constraints on homeownership. AREUEA Journal, 17(4), 389–402.

    Article  Google Scholar 

  • Listokin, D., Wyly, E. K., Schmitt, B., & Voicu, I. (2001). The potential and limitations of mortgage innovation in fostering homeownership in the United States. Housing Policy Debate, 12(3), 465–513.

    Article  Google Scholar 

  • Pennington-Cross, A., & Nichols, J. (2000). Credit history and the FHA conventional choice. Real Estate Economics, 28(2), 307–336.

    Article  Google Scholar 

  • Quercia, R. G., McCarthy, G. W., & Wachter, S. M. (2003). The impacts of affordable lending on homeownership rates. Journal of Housing Economics, 12(1), 29–59.

    Article  Google Scholar 

  • Quercia, R. G., & Stegman, M. A. (1992). Residential mortgage default: a review of the literature. Journal of Housing Research, 3(2), 341–379.

    Google Scholar 

  • Rosen, H. S. (1985). Housing subsidies: Effects on housing decisions, efficiency, and equity. In A. J. Auerbach & M. Feldstein (Eds.), Handbook of public economics, Vol. 1 (pp. 375–420). Amsterdam: Elsevier Science Publishers B.V.

    Chapter  Google Scholar 

  • Zhang, Y. (2010). High Default risk on down payment assistance program: Adverse selection vs. program characteristics? Ohio Housing Finance Agency Research.

Download references

Acknowledgments

The authors are very grateful to David Hehman and Jeff Reynolds at the Federal Home Loan Bank of Cincinnati for motivating and facilitating this research. The authors also would like to thank an anonymous referee for invaluable feedback and guidance.

Author information

Authors and Affiliations

  1. Department of Economics, Xavier University, 3800 Victory Parkway, Cincinnati, OH, 45207, USA

    Bree J. Lang & Ellen H. Hurst

Authors
  1. Bree J. Lang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ellen H. Hurst
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bree J. Lang.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Lang, B.J., Hurst, E.H. The Effect of Down Payment Assistance on Mortgage Choice. J Real Estate Finan Econ 49, 329–351 (2014). https://doi.org/10.1007/s11146-013-9432-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11146-013-9432-1

Keywords

  • Mortgage subsidies
  • Down payment assistance
  • Mortgage size
  • Borrowing behavior