Hostname: page-component-848d4c4894-m9kch Total loading time: 0 Render date: 2024-06-05T06:10:26.649Z Has data issue: false hasContentIssue false
  • English
  • Français

Determinants of Restructured Farm Loan Performance

Published online by Cambridge University Press:  05 September 2016

Glenn Pederson ,
Ananth Rao  and
Michael Boehlje
Glenn Pederson
Affiliation:
Department of Agricultural and Applied Economics at theUniversity of Minnesota
Ananth Rao
Affiliation:
Department of Agricultural and Applied Economics at theUniversity of Minnesota
Michael Boehlje
Affiliation:
Department of Agricultural and Applied Economics at theUniversity of Minnesota
Get access Rights & Permissions [Opens in a new window]

Abstract

A probabilistic model is applied to cross-sectional data to identify determinants of post-restructure performance of Federal Land Bank loans. The results indicate that restructured loans were sensitive to factors that determine the debt repayment burden and the repayment ability of the restructured farm operations. Loan performance is found to be relatively more sensitive to the levels of the post-restructure interest rate and cash farm income than to the financial structure and leverage position of the restructured farm. The relationships between the post-restructure interest rate, cash farm income level, and the probability of loan performance are illustrated.

Keywords

restructured farm loansperformanceFederal Land Bank
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 23 , Issue 2 , December 1991 , pp. 39 - 48
Copyright
Copyright © Southern Agricultural Economics Association 1991

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Belsley, D.A., Kuh, E., and Welsch, R.. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York: Wiley, 1980.Google Scholar
Fiske, J.R., Batte, M.T., and Richenbacker, S.L.. “Factors Influencing the Currentness of Debt Payments for Ohio Commercial Farmers.” Dept. of Agr. Econ. and Rural Soc. ESO No. 1291, Ohio State University, 1986.Google Scholar
Gessner, G., Malhotra, N.K., Kamakura, W.A., and Zmijewski, M.E.. “Estimating Models with Binary Dependent Variables: Some Theoretical and Empirical Observations.” J. Bus. Res., 16(1988): 49–65.Google Scholar
Kennedy, P. A Guide to Econometrics. 2nd Edition. Cambridge, MA: MIT Press, 1985.Google Scholar
Lufburrow, J., Barry, P.J., and Dixon, B.. “Credit Scoring for Farm Loan Pricing.” Agr. Fin. Rev., 44(1984): 8–14.Google Scholar
Maddala, G.S. Limited and Qualitative Variables in Econometrics. London: Cambridge University Press, 1983.CrossRefGoogle Scholar
Mazzocco, M. A. “The Debt Structure Index: An Approach to Evaluating Financial Structure.” Agr. Fin. Rev., 49(1989): 105–111.Google Scholar
Miller, L.H. and LaDue, E.L.. “Direct Assessment Models for Farm Borrowers: ALogit Analysis.” Agr. Fin. Rev., 49(1989): 22–36.Google Scholar
Mortensen, T., Watt, D.L., and Leistritz, F.L.. “Predicting Probability of Loan Default.” Agr. Fin. Rev., 48(1989): 60–67.Google Scholar
Schwab, A. and Raup, P.. The Minnesota Rural Real Estate Market in 1988. Econ. Rep. ER89-3, University of Minnesota, St. Paul, 1989.Google Scholar