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Predicting Failure of Savings & Loan Associations

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  • Coleen C. Pantalone
  • Marjorie B. Platt

Abstract

Early‐warning systems are intended to provide regulators with identification of problem institutions sooner than is possible with the present system of call reports and periodic on‐site examinations. Given an earlier indication of potential problems, resources can be focused on those institutions most in need of monitoring, thus potentially reducing both the number of failures and FSLIC financial assistance in the remaining failures. While some early‐warning systems were developed in the mid‐1970s for use by commercial bank regulators, very little attention has been focused on developing similar systems for the thrift industry. To address this issue, multiple discriminant analysis was used to develop an early‐warning system for savings and loan associations in the Boston district of the Federal Home Loan Bank system. The results suggest that use of this model would have given signals of impending trouble well before the actual failure occurred. Consequently, an early‐warning system could provide information to regulators sooner, permitting scarce resources to be allocated more effectively. In addition, earlier intervention could reduce the amount of FSLIC financial assistance required. By intervening earlier, FSLIC may be able to arrest the failure‐promoting activities in which the association is engaged.

Suggested Citation

  • Coleen C. Pantalone & Marjorie B. Platt, 1987. "Predicting Failure of Savings & Loan Associations," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 15(2), pages 46-64, June.
    • Handle: RePEc:bla:reesec:v:15:y:1987:i:2:p:46-64
    DOI: 10.1111/1540-6229.00418
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    Cited by:

    1. P. K. Viswanathan & Sandeep Srivathsan & Wayne L. Winston, 2022. "Multiclass Discriminant Analysis using Ensemble Technique: Case Illustration from the Banking Industry," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 21(1), pages 92-115, March.
    2. Steven Ongena, 1995. "Monetary policy and credit conditions: new evidence," Macroeconomics 9503001, University Library of Munich, Germany.
    3. Malhotra, Manoj K. & Sharma, Subhash & Nair, Satish S., 1999. "Decision making using multiple models," European Journal of Operational Research, Elsevier, vol. 114(1), pages 1-14, April.
    4. Colburn, Christopher B. & Hudgins, Sylvia C., 1996. "The influence on Congress by the thrift industry," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 473-494, April.
    5. Patricia M. Rudolph, 1989. "The Insolvent Thrifts of 1982: Where Are They Now?," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 17(4), pages 450-462, December.
    6. Andrzej Geise & Magdalena Kuczmarska & Jarosław Pawlowski, 2021. "Corporate Failure Prediction of Construction Companies in Poland: Evidence from Logit Model," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 99-116.
    7. Harrison, Patricia & Ragas, Wade R., 1995. "Financial variables contributing to savings and loan failures from 1980-1989," Review of Financial Economics, Elsevier, vol. 4(2), pages 197-210.
    8. William C. Handorf & J. Minor Sachlis, 1990. "A Note on the Accounting Model for Problem Real Estate Loans," Journal of Real Estate Research, American Real Estate Society, vol. 5(3), pages 381-392.
    9. Patricia Harrison & Wade R. Ragas, 1995. "Financial variables contributing to savings and loan failures from 1980–1989," Review of Financial Economics, John Wiley & Sons, vol. 4(2), pages 197-210, March.

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