This case uses one of Fannie Mae’s credit-risk transfer instruments (CRT) to explore its data set platform and predict loan defaults through machine learning algorithms. The CRT, called Connecticut Avenue Securities (CAS), issued bonds valued on the performance of preselected pools of mortgages. The material works well to unfold natural language processing using Python. Through a three-class series, students will learn to wrangle data, experience Python, scale up to a full data set in a cloud computing environment, and use Tableau to report findings. In addition, the material allows for an analysis of the drivers of mortgage loan defaults.
