Predicting Mortgage Default: Lessons from Data Mining Fannie Mae Mortgage Portfolio
Document Type
Conference Proceeding
Publication Date
1-1-2016
Journal / Book Title
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
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.
Journal ISSN / Book ISBN
ISBN: 9789898533548
MSU Digital Commons Citation
Mamonov, Stanislav and Benbunan-Fich, Raquel, "Predicting Mortgage Default: Lessons from Data Mining Fannie Mae Mortgage Portfolio" (2016). Department of Information Management and Business Analytics Faculty Scholarship and Creative Works. 103.
https://digitalcommons.montclair.edu/infomgmt-busanalytics-facpubs/103
Published Citation
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.
