Making Sense of Student Loan Bonds
Contributed by Zach Escalante. He is currently in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between April 11th to July 1st, 2016. This post is based on his second class project - R Shiny (due on the 4th week of the program).
Introduction:
Federal Family Education Loan
During my time working on an Asset Backed Securities desk, investors frequently asked for various reports and spreadsheets to explain the underlying characteristics and potential for cash shortfalls of the FFELP bonds they were looking to add or sell in their portfolio. This gave me the idea to create a comprehensive and easy to access web application that provides the user with both time series and static quarterly data on the underlying assets in each bond.
Shiny Web Application:
The dropdown menus underneath "FFELP Static Pool Data" allow the user to select both the bond and the quarter which they would like to evaluate (the list of bonds was obtained from the Navient website).
The first chart shows us what amount of each delinquency status is present in the underlying loans of each bond, ranging from "Current", to ">360 Days" delinquent.
This provides valuable insight into what amount of the pool an investor might reasonably expect will enter default, and hence recoup 97% of their principal and unpaid interest from the Federal Government. We can also see what amount of the pool is not contributing cash to the trust, another potential source of delinquencies. Finally, historical delinquency data can also provide insight into the characteristics of borrower in the student loan
The second chart on the page shows a repayment status snapshot for each bond. With this chart investors can see the amount of each loan trust that is still in school, forbearance, deferment, an IBR plan, and the total amount in repayment.
The next valuable aspect of this web application for analyzing bonds is time series data of prepayments (which can be found on the second tab). This is very important to decipher exactly how much of the loan pool has been paying off their student loans ahead of schedule. A significant drop in the Constant Prepayment Rate (CPR) could be another sign of potential trouble for the loan trust to adhere to its stated final maturity date
Future Improvements:
I believe this application is just the first step into financial education for investors without access to sophisticated software systems such as Bloomberg and Intex. Much of the data on the underlying assets of securitized products is obtainable, and with some re-formatting can be made easily readable as well.
The next stage of development of this application will include adding more tranches (such as consolidated student loan
To view the code for this project, please visit my Github account and click on the "ShinyApplication" link: https://github.com/zachescalante/Zach-Escalante-Code
Thank you for your interest in this web application. If you have questions comments, or additional suggestions for improvement, please feel free to reach me at [email protected]
(1) https://www.moodys.com/research/Moodys-reviews-for-downgrade-106-tranches-from-57-FFELP-student--PR_328361
(2) https://www.bloomberg.com/news/articles/2015-12-23/after-math-error-fitch-doubles-student-debt-downgrade-estimate
(3) https://www.whitehouse.gov/issues/education/higher-education/ensuring-that-student-loans-are-affordable