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The jumbo-conforming spread: a semiparametric approach

  • Shane M. Sherlund

This paper estimates the jumbo-conforming spread using data from the Federal Housing Finance Board’s Monthly Interest Rate Survey from January 1993 to June 2007. Importantly, this paper augments the typical parametric approach by adding state-level foreclosure laws and ZIP-level demographic variables to the model, estimating the effects of loan size and loan-to-value ratio on mortgage rates nonparametrically, and including geographic location as a control for some potentially unobserved borrower and market characteristics that might vary over geography, such as credit scores, debt-to-income ratios, and house price volatility. A partial locallinear regression approach is used to estimate the jumbo-conforming spread, on the premise that loans similar to each other in terms of loan size, loan-to-value ratio, or geographic location might also be similar in other, unobservable borrower and market characteristics. I find estimates of the jumbo-conforming spread of 13 to 24 basis points—50 to 24 percent smaller since about 1996, when credit scores became widely used in mortgage underwriting, than estimates from a commonly used parametric model. I therefore attribute the difference in estimates to credit quality and other unobserved characteristics, among other potential explanations, making these controls an important issue in estimating the jumbo-conforming spread.

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Paper provided by Board of Governors of the Federal Reserve System (U.S.) in its series Finance and Economics Discussion Series with number 2008-01.

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Date of creation: 2008
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Handle: RePEc:fip:fedgfe:2008-01
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  1. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-54, July.
  2. Brent W. Ambrose & Michael LaCour-Little & Anthony B. Sanders, 2004. "The Effect of Conforming Loan Status on Mortgage Yield Spreads: A Loan Level Analysis," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 32(4), pages 541-569, December.
  3. Hahn, Jinyong & Todd, Petra & Van der Klaauw, Wilbert, 2001. "Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design," Econometrica, Econometric Society, vol. 69(1), pages 201-09, January.
  4. Patric H. Hendershott & James D. Shilling, 1988. "The Impact of the Agencies on Conventional Fixed-Rate Mortgage Yields," NBER Working Papers 2646, National Bureau of Economic Research, Inc.
  5. Wayne Passmore & Shane M. Sherlund & Gillian Burgess, 2005. "The Effect of Housing Government-Sponsored Enterprises on Mortgage Rates," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 33(3), pages 427-463, 09.
  6. Naranjo, Andy & Toevs, Alden, 2002. "The Effects of Purchases of Mortgages and Securitization By Government Sponsored Enterprises on Mortgage Yield Spreads and Volatility," The Journal of Real Estate Finance and Economics, Springer, vol. 25(2-3), pages 173-95, Sept.-Dec.
  7. Ambrose, Brent W & Buttimer, Richard & Thibodeau, Thomas, 2001. "A New Spin on the Jumbo/Conforming Loan Rate Differential," The Journal of Real Estate Finance and Economics, Springer, vol. 23(3), pages 309-35, November.
  8. Karen M. Pence, 2006. "Foreclosing on Opportunity: State Laws and Mortgage Credit," The Review of Economics and Statistics, MIT Press, vol. 88(1), pages 177-182, February.
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