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Analyzing FSA Direct Loan Borrower Payback Histories: Predictors of Financial Improvement and Loan Servicing Actions

  • Landerito, Aiko O.
  • Dixon, Bruce L.
  • Ahrendsen, Bruce L.
  • Hamm, Sandra J.
  • Danforth, Diana M.
Registered author(s):

    Classical and count data regression models are estimated to predict improvement in three key financial indicators—net worth, debt-to-asset ratio and current ratio—as well as the number of loan restructurings and delinquencies. Data consist of Farm Service Agency direct loans originated in fiscal years 1994-1996. Models to predict outcomes vary by loan type. Models explaining variation in the financial measures have modest explanatory power but initial levels of debt-to-asset ratio and current ratio are significant in explaining changes in debt-to-asset ratios and current ratios, respectively. Models explaining number of restructurings and delinquencies for operating loans have satisfactory explanatory power. Increasing crop revenues to total farm revenues and increasing farm size lead to increased loan servicing actions

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    File URL: http://purl.umn.edu/49340
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    Paper provided by Agricultural and Applied Economics Association in its series 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin with number 49340.

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    Date of creation: 2009
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    Handle: RePEc:ags:aaea09:49340
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    1. Allen M. Featherstone & Christine A. Wilson & Terry L. Kastens & John D. Jones, 2007. "Factors affecting the agricultural loan decision-making process," Agricultural Finance Review, Emerald Group Publishing, vol. 67(1), pages 13-33, May.
    2. Ani L. Katchova, 2005. "Factors affecting farm credit use," Agricultural Finance Review, Emerald Group Publishing, vol. 65(2), pages 17-29, July.
    3. Escalante, Cesar L. & Brooks, Rodney L. & Epperson, James E. & Stegelin, Forrest E., 2006. "Credit Risk Assessment and Racial Minority Lending at the Farm Service Agency," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 38(01), April.
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