Predicting mortgage default: Lessons from data mining fannie mae mortgage portfolio

Stanislav Mamonov; Raquel Benbunan-Fich

Predicting mortgage default: Lessons from data mining fannie mae mortgage portfolio

Information Management and Business Analytics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Recent advances in information technology have made possible the analysis of vast amounts of data. One promising area for the application of the new analytical methods is finance. We perform data mining on the Fannie Mae mortgage portfolio from the fourth quarter of 2007 that includes 341,348 mortgages with the total principal value of more than $70 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, application of data mining techniques fails to accurately capture the mortgage delinquency patterns in the historical data. These results are consistent with an exogenous shock explanation and reveal a fundamental challenge that can arise in data mining large datasets.

Original language	English
Title of host publication	Proceedings of the International Conferences on ICT, Society, and Human Beings 2016, Web Based Communities and Social Media 2016, Big Data Analytics, Data Mining and Computational Intelligence 2016 and Theory and Practice in Modern Computing 2016 - Part of the Multi Conference on Computer Science and Information Systems 2016
Editors	Luis Rodrigues, Ajith P. Abraham, Jorg Roth, Piet Kommers
Publisher	IADIS
Pages	187-194
Number of pages	8
ISBN (Electronic)	9789898533548
State	Published - 2016
Event	2017 International Conferences on Information and Communication Technology, Society, and Human Beings, ICT 2016, Web Based Communities and Social Media, WBC 2016, Big Data Analytics, Data Mining and Computational Intelligence, BIGDACI 2016 and Theory and Practice in Modern Computing, TPMC 2016 - Madeira, Portugal Duration: 1 Jul 2016 → 4 Jul 2016

Publication series

Name	Proceedings of the International Conferences on ICT, Society, and Human Beings 2016, Web Based Communities and Social Media 2016, Big Data Analytics, Data Mining and Computational Intelligence 2016 and Theory and Practice in Modern Computing 2016 - Part of the Multi Conference on Computer Science and Information Systems 2016

Other

Other	2017 International Conferences on Information and Communication Technology, Society, and Human Beings, ICT 2016, Web Based Communities and Social Media, WBC 2016, Big Data Analytics, Data Mining and Computational Intelligence, BIGDACI 2016 and Theory and Practice in Modern Computing, TPMC 2016
Country/Territory	Portugal
City	Madeira
Period	1/07/16 → 4/07/16

Keywords

Credit score
Data mining
Debt-toincome
Delinquency
Government sponsored enterprises
Loan-to-value
Mortgage default

Cite this

Mamonov, S., & Benbunan-Fich, R. (2016). Predicting mortgage default: Lessons from data mining fannie mae mortgage portfolio. In L. Rodrigues, A. P. Abraham, J. Roth, & P. Kommers (Eds.), Proceedings of the International Conferences on ICT, Society, and Human Beings 2016, Web Based Communities and Social Media 2016, Big Data Analytics, Data Mining and Computational Intelligence 2016 and Theory and Practice in Modern Computing 2016 - Part of the Multi Conference on Computer Science and Information Systems 2016 (pp. 187-194). (Proceedings of the International Conferences on ICT, Society, and Human Beings 2016, Web Based Communities and Social Media 2016, Big Data Analytics, Data Mining and Computational Intelligence 2016 and Theory and Practice in Modern Computing 2016 - Part of the Multi Conference on Computer Science and Information Systems 2016). IADIS.

Mamonov, Stanislav ; Benbunan-Fich, Raquel. / Predicting mortgage default : Lessons from data mining fannie mae mortgage portfolio. Proceedings of the International Conferences on ICT, Society, and Human Beings 2016, Web Based Communities and Social Media 2016, Big Data Analytics, Data Mining and Computational Intelligence 2016 and Theory and Practice in Modern Computing 2016 - Part of the Multi Conference on Computer Science and Information Systems 2016. editor / Luis Rodrigues ; Ajith P. Abraham ; Jorg Roth ; Piet Kommers. IADIS, 2016. pp. 187-194 (Proceedings of the International Conferences on ICT, Society, and Human Beings 2016, Web Based Communities and Social Media 2016, Big Data Analytics, Data Mining and Computational Intelligence 2016 and Theory and Practice in Modern Computing 2016 - Part of the Multi Conference on Computer Science and Information Systems 2016).

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abstract = "Recent advances in information technology have made possible the analysis of vast amounts of data. One promising area for the application of the new analytical methods is finance. We perform data mining on the Fannie Mae mortgage portfolio from the fourth quarter of 2007 that includes 341,348 mortgages with the total principal value of more than $70 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, application of data mining techniques fails to accurately capture the mortgage delinquency patterns in the historical data. These results are consistent with an exogenous shock explanation and reveal a fundamental challenge that can arise in data mining large datasets.",

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Mamonov, S & Benbunan-Fich, R 2016, Predicting mortgage default: Lessons from data mining fannie mae mortgage portfolio. in L Rodrigues, AP Abraham, J Roth & P Kommers (eds), Proceedings of the International Conferences on ICT, Society, and Human Beings 2016, Web Based Communities and Social Media 2016, Big Data Analytics, Data Mining and Computational Intelligence 2016 and Theory and Practice in Modern Computing 2016 - Part of the Multi Conference on Computer Science and Information Systems 2016. Proceedings of the International Conferences on ICT, Society, and Human Beings 2016, Web Based Communities and Social Media 2016, Big Data Analytics, Data Mining and Computational Intelligence 2016 and Theory and Practice in Modern Computing 2016 - Part of the Multi Conference on Computer Science and Information Systems 2016, IADIS, pp. 187-194, 2017 International Conferences on Information and Communication Technology, Society, and Human Beings, ICT 2016, Web Based Communities and Social Media, WBC 2016, Big Data Analytics, Data Mining and Computational Intelligence, BIGDACI 2016 and Theory and Practice in Modern Computing, TPMC 2016, Madeira, Portugal, 1/07/16.

Predicting mortgage default: Lessons from data mining fannie mae mortgage portfolio. / Mamonov, Stanislav; Benbunan-Fich, Raquel.
Proceedings of the International Conferences on ICT, Society, and Human Beings 2016, Web Based Communities and Social Media 2016, Big Data Analytics, Data Mining and Computational Intelligence 2016 and Theory and Practice in Modern Computing 2016 - Part of the Multi Conference on Computer Science and Information Systems 2016. ed. / Luis Rodrigues; Ajith P. Abraham; Jorg Roth; Piet Kommers. IADIS, 2016. p. 187-194 (Proceedings of the International Conferences on ICT, Society, and Human Beings 2016, Web Based Communities and Social Media 2016, Big Data Analytics, Data Mining and Computational Intelligence 2016 and Theory and Practice in Modern Computing 2016 - Part of the Multi Conference on Computer Science and Information Systems 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Mamonov S, Benbunan-Fich R. Predicting mortgage default: Lessons from data mining fannie mae mortgage portfolio. In Rodrigues L, Abraham AP, Roth J, Kommers P, editors, Proceedings of the International Conferences on ICT, Society, and Human Beings 2016, Web Based Communities and Social Media 2016, Big Data Analytics, Data Mining and Computational Intelligence 2016 and Theory and Practice in Modern Computing 2016 - Part of the Multi Conference on Computer Science and Information Systems 2016. IADIS. 2016. p. 187-194. (Proceedings of the International Conferences on ICT, Society, and Human Beings 2016, Web Based Communities and Social Media 2016, Big Data Analytics, Data Mining and Computational Intelligence 2016 and Theory and Practice in Modern Computing 2016 - Part of the Multi Conference on Computer Science and Information Systems 2016).

Predicting mortgage default: Lessons from data mining fannie mae mortgage portfolio

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