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Can the Ag Credit Survey predict national credit conditions?

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  • Brian C. Briggeman
  • Christopher Zakrzewicz

Abstract

With the farm boom ending in 2009, many farmers have become less able to repay short-term loans. As farm profit margins erode and farm loan delinquencies rise, some in the agricultural industry worry that lending standards will tighten?as they did in the farm debt crisis of the 1980s. ; One barometer of future agricultural credit conditions is agricultural bankers. Experience and access to information give these bankers a unique perspective on agricultural credit conditions. In fact, several Federal Reserve banks survey agricultural bankers in their district to tap this source of information. But how reliable are regional Federal Reserve agricultural credit surveys? And can a regional survey shed light on future loan delinquencies and credit standards nationwide? ; Briggeman examines the Federal Reserve Bank of Kansas City?s Survey of Agricultural Credit Conditions to explore these questions. He concludes that the Survey of Agricultural Credit Conditions reliably predicts farm loan repayment rates in the district and provides valuable insight into future farm loan delinquencies and credit standards nationwide. The most recent Survey data suggest that the nation?s farm loan delinquencies will continue to rise in the year ahead, which may cause collateral requirements to stay elevated heading into 2010.

Suggested Citation

  • Brian C. Briggeman & Christopher Zakrzewicz, 2009. "Can the Ag Credit Survey predict national credit conditions?," Economic Review, Federal Reserve Bank of Kansas City, vol. 94(Q IV), pages 93-110.
    • Handle: RePEc:fip:fedker:y:2009:i:qiv:p:93-110:n:v.94no.4
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    File URL: https://www.kansascityfed.org/documents/948/2009-Can%20the%20Ag%20Credit%20Survey%20Predict%20National%20Credit%20Conditions%3F.pdf
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    References listed on IDEAS

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    1. Charles A. Towe & Mitchell J. Morehart, 2009. "Credit Constraints: Their Existence, Determinants, and Implications for U.S. Farm and Nonfarm Sole Proprietorships," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(1), pages 275-289.
    2. Olman Hee, 1966. "Tests for Predictability of Statistical Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 48(5), pages 1479-1484.
    3. Raymond M. Leuthold, 1969. "An Analysis of Daily Fluctuations in the Hog Economy," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 51(4), pages 849-865.
    4. William R. Keeton & Michael Verba, 2004. "What can regional manufacturing surveys tell us? lessons from the Tenth District," Economic Review, Federal Reserve Bank of Kansas City, vol. 89(Q III), pages 39-70.
    5. Brian C. Briggeman, 2009. "Monitoring credit conditions in rural America," Main Street Economist, Federal Reserve Bank of Kansas City, issue 3.
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    Cited by:

    1. Hartarska, Valentina M. & Nadolnyak, Denis A., 2012. "Financing Constraints and Access to Credit in Post Crisis Environment: Evidence from New Farmers in Alabama," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124882, Agricultural and Applied Economics Association.
    2. Hartarska, Valentina M. & Nadolnyak, Denis A., 2012. "Financing Constraints and Access to Credit in Post Crisis Environment: Evidence from New Farmers in Alabama," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 44(4), pages 1-15, November.
    3. Hartarska, Valentina & Nadolnyak, Dennis, 2012. "Financing Constraints and Access to Credit in a Postcrisis Environment: Evidence from New Farmers in Alabama," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 44(4), pages 607-621, November.

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