What can we learn from past mistakes? Lessons from Data Mining the Fannie Mae Mortgage Portfolio
Stanislav Mamonov () and
Raquel Benbunan-Fich ()
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Stanislav Mamonov: Montclair State University
Raquel Benbunan-Fich: Baruch College, CUNY
Journal of Real Estate Research, 2017, vol. 39, issue 2, 235_262
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.
JEL-codes: L85 (search for similar items in EconPapers)
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