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What can we learn from past mistakes? Lessons from Data Mining the Fannie Mae Mortgage Portfolio

Author

Listed:
  • Stanislav Mamonov

    (Montclair State University)

  • Raquel Benbunan-Fich

    (Baruch College, CUNY)

Abstract

Fannie Mae, the largest government sponsored enterprise, has been widely criticized for its role in the financial crisis of 2008, yet no detailed analysis of the agency mortgage portfolio has been published to date to evaluate the systematic patterns of mortgage defaults that occurred. To address this knowledge gap, we perform data mining on the Fannie Mae mortgage portfolio of the fourth quarter of 2007 which includes 340,537 mortgages with the total principal value of $69.8 billion. This portfolio had the highest delinquency rate in the agency's history - 19.4% versus the historical average of 1.7%. We find that although a number of information variables that were available at the time of mortgage acquisition in Q4, 2007 are correlated with the subsequent delinquencies, building an accurate model proves challenging. Identification of the majority of delinquencies in the historical data comes at a cost of low precision. We discuss the implications of the results for practice and policy.

Suggested Citation

  • Stanislav Mamonov & Raquel Benbunan-Fich, 2017. "What can we learn from past mistakes? Lessons from Data Mining the Fannie Mae Mortgage Portfolio," Journal of Real Estate Research, American Real Estate Society, vol. 39(2), pages 235-262.
    • Handle: RePEc:jre:issued:v:39:n:2:2017:p:235_262
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    Cited by:

    1. Hwang, Ruey-Ching & Chu, Chih-Kang & Yu, Kaizhi, 2020. "Predicting LGD distributions with mixed continuous and discrete ordinal outcomes," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1003-1022.

    More about this item

    JEL classification:

    • L85 - Industrial Organization - - Industry Studies: Services - - - Real Estate Services

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