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

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Listed:
  • Landerito, Aiko O.
  • Dixon, Bruce L.
  • Ahrendsen, Bruce L.
  • Hamm, Sandra J.
  • Danforth, Diana M.

Abstract

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

Suggested Citation

  • Landerito, Aiko O. & Dixon, Bruce L. & Ahrendsen, Bruce L. & Hamm, Sandra J. & Danforth, Diana M., 2009. "Analyzing FSA Direct Loan Borrower Payback Histories: Predictors of Financial Improvement and Loan Servicing Actions," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49340, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea09:49340
    DOI: 10.22004/ag.econ.49340
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    References listed on IDEAS

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    Agricultural Finance; Farm Management;

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