What Can We Learn from Past Mistakes? Lessons from Data Mining the Fannie Mae Mortgage Portfolio
Fannie Mae has been widely criticized for its role in the recent financial crisis, yet no detailed analysis of the systematic patterns of the mortgage defaults that occurred has been published. To address this knowledge gap, we perform data mining on the Fannie Mae mortgage portfolio of the fourth q...
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| Veröffentlicht in: | The Journal of real estate research 2017-04, Vol.39 (2), p.235-262 |
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| creator | Mamonov, Stanislav Benbunan-Fich, Raquel |
| description | Fannie Mae has been widely criticized for its role in the recent financial crisis, yet no detailed analysis of the systematic patterns of the mortgage defaults that occurred has been published. 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 a 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 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. |
| doi_str_mv | 10.1080/10835547.2017.12091471 |
| format | Article |
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| source | JSTOR Archive Collection A-Z Listing |
| subjects | Credit scoring Data Data mining Default Delinquency Economic crisis Economic statistics Equity Errors Financial institutions Foreclosure Government sponsored enterprises Home loans Housing prices Loan workouts Mortgage companies Mortgages Prepayments Subprime lending |
| title | What Can We Learn from Past Mistakes? Lessons from Data Mining the Fannie Mae Mortgage Portfolio |
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