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Survival analysis and mortgage termination at AgChoice ACA

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Author Info

  • Jonathan B. Dressler
  • Jeffrey R. Stokes

Abstract

Purpose – This paper aims to identify factors that affect agricultural mortgage default and prepayment. Design/methodology/approach – Using a sample of farm credit system loans, prepayment and default are modeled as competing risks with potentially non-stationary covariates using a statistical/econometric technique called survival snalysis (SA). Findings – The analysis suggests that the primary drivers of prepayment and default are the rate of interest charged by the lender at origination and the borrower's current ratio at origination. Tests of the existence of a geographic effect indicate that despite bank management belief to the contrary, branches may not be homogeneous. Research limitations/implications – This analysis would be improved if more data were available in an easily obtainable manner to control for unobserved heterogeneity. Unobserved heterogeneity or incomplete specification within a model can be problematic. Inferences among regression coefficients can be problematic in that the estimates have inflated variances and unreliable test statistics. In addition, more frequent measures of the time-varying covariates could be obtained to improve upon the SA models presented above. Future analyses could also incorporate other sections of the agricultural credit association portfolio, as well as a comparison to variable rate notes. One other logical next step would be to obtain loan collateral values to obtain estimates of the exposure at default, and the loss given default, or the estimates needed for the advanced internal ratings based approach described in the Basel Accords. Originality/value – This paper provides a method for lenders to measure and model mortgage termination, an important consideration for risk managers when determining capital adequacy described in the Basel Accords.

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Bibliographic Info

Article provided by Emerald Group Publishing in its journal Agricultural Finance Review.

Volume (Year): 70 (2010)
Issue (Month): 1 (May)
Pages: 21-36

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Handle: RePEc:eme:afrpps:v:70:y:2010:i:1:p:21-36

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Web page: http://www.emeraldinsight.com

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Postal: Emerald Group Publishing, Howard House, Wagon Lane, Bingley, BD16 1WA, UK
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Web: http://www.emeraldinsight.com/afr.htm

Related research

Keywords: Agriculture; Banks; Interest rates; Loans; Mortgage default;

References

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  1. Kerry D. Vandell & Walter Barnes & David Hartzell & Dennis Kraft & William Wendt, 1993. "Commercial Mortgage Defaults: Proportional Hazards Estimation Using Individual Loan Histories," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 21(4), pages 451-480.
  2. Jerry Green & John B. Shoven, 1983. "The Effects of Interest Rates on Mortgage Prepayments," NBER Working Papers 1246, National Bureau of Economic Research, Inc.
  3. Yongheng Deng & John M. Quigley & Robert Van Order, 2000. "Mortgage Terminations, Heterogeneity and the Exercise of Mortgage Options," Econometrica, Econometric Society, vol. 68(2), pages 275-308, March.
  4. Ambrose, Brent W & Sanders, Anthony B, 2003. "Commercial Mortgage-Backed Securities: Prepayment and Default," The Journal of Real Estate Finance and Economics, Springer, vol. 26(2-3), pages 179-96, March-May.
  5. Brent A. Gloy & Eddy L. LaDue & Michael A. Gunderson, 2005. "Credit risk migration and downgrades experienced by agricultural lenders," Agricultural Finance Review, Emerald Group Publishing, vol. 65(1), pages 1-16, May.
  6. Stokes, Jeffrey R. & Brinch, Brian M., 2001. "Valuing Agricultural Mortgage-Backed Securities," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 33(03), December.
  7. Dunn, Kenneth B & McConnell, John J, 1981. "Valuation of GNMA Mortgage-Backed Securities," Journal of Finance, American Finance Association, vol. 36(3), pages 599-616, June.
  8. Katchova, Ani L. & Barry, Peter J., 2005. "The New Basel Capital Accord: Implications for US Agricultural Lenders," Choices, Agricultural and Applied Economics Association, vol. 20(1).
  9. Jeffrey R. Stokes & Brent A. Gloy, 2007. "Estimating delinquency migration and the probability of default from aggregate data," Agricultural Finance Review, Emerald Group Publishing, vol. 67(1), pages 75-85, May.
  10. Deng, Yongheng, 1997. "Mortgage Termination: An Empirical Hazard Model with a Stochastic Term Structure," The Journal of Real Estate Finance and Economics, Springer, vol. 14(3), pages 309-31, May.
  11. Brian A. Ciochetti & Yongheng Deng & Bin Gao & Rui Yao, 2002. "The Termination of Commercial Mortgage Contracts through Prepayment and Default: A Proportional Hazard Approach with Competing Risks," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 30(4), pages 595-633.
  12. Ciochetti, Brian A, et al, 2003. "A Proportional Hazards Model of Commercial Mortgage Default with Originator Bias," The Journal of Real Estate Finance and Economics, Springer, vol. 27(1), pages 5-23, July.
  13. Eric S. Rosengren, 2007. "Market and risk management innovations: implications for safe and sound banking," Economic Review, Federal Reserve Bank of Atlanta, issue Q1-2, pages 36 - 39.
  14. Bruce J. Sherrick & Peter J. Barry & Paul N. Ellinger, 2000. "Valuation of Credit Risk in Agricultural Mortgages," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 82(1), pages 71-81.
  15. Allen M. Featherstone & Laura M. Roessler & Peter J. Barry, 2006. "Determining the Probability of Default and Risk-Rating Class for Loans in the Seventh Farm Credit District Portfolio," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 28(1), pages 4-23.
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Cited by:
  1. Bruce L. Dixon & Bruce L. Ahrendsen & Brandon R. McFadden & Diana M. Danforth & Monica Foianini & Sandra J. Hamm, 2011. "Competing risks models of Farm Service Agency seven-year direct operating loans," Agricultural Finance Review, Emerald Group Publishing, vol. 71(1), pages 5-24, May.

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