Data Study on Student Loan Bonds

Posted on May 15, 2016
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
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).


Data shows Federal Family Education Loan Program (FFELP) backed securities have long been viewed as a safe and steady fixed income cashflow with a AAA rating, appropriate for investors seeking principal preservation and predictable returns.

This changed on June 22, 2015 when Moody's rating agency placed $34 billion of these securities on downgrade watch (1). The rating agency Fitch soon followed suit, and by the fall of 2015 $71.2 billion FFELP backed loans were being reviewed for a potential ratings downgrade (2). The reason ratings agencies began to call into question the creditworthiness of these bonds (whose underlying assets are backed by the full faith and credit of the US Government) had to do with an expansion of Income Based Repayment (IBR) options to student loan borrowers by the White House in 2010 (3).

This provision enabled borrowers to maintain a 'current' status on their student loans by only paying a certain percentage of their disposable income towards their loan payment. In some cases, this amount does not cover the interest due on the loan (according to a fixed-rate-prepayment plan) causing potential cash shortfalls to the trust from which the securities are issued.

Since the borrower is not technically defaulting, the Federal Government is not obligated to fund the previously agreed-upon 97% of the unpaid principal and interest to the student loan trust in event of a default by the borrower, creating the possibility that certain FFELP backed bonds will not receive enough cashflow to adhere to their stated maturity date.


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 Data:

Data Study on Student Loan Bonds

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.Screen Shot 2016-05-09 at 10.05.44 PM

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 trust and how likely they are to re-finance their student loans into an Interest Based Repayment (IBR) plan.

Repayment Status

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.

Data Study on Student Loan BondsLoans with high IBR balances may have problems with generating the cashflow necessary to the student loan trust in order to pay off the trusts' liabilities (these are CUSIP'd securities) before their stated final maturity date.


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.
Data Study on Student Loan Bonds

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 pools) to the list of potential bonds for the investor to view. Also, I would like to add more time series data options for the user to be able to analyze on the second tab.

To view the code for this project, please visit my Github account and click on the "ShinyApplication" link:

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]



About Author

Zachary Escalante

Zach Escalante's path to the field of Data Analysis has not been a conventional one. Born and raised in South Florida, Zach did his first bachelor's degree in Finance at Florida Atlantic University (FAU). Following the completion of...
View all posts by Zachary Escalante >

Related Articles

Leave a Comment

cialis 20mg for sale April 28, 2021
cialis 20mg for sale Generic for sale
Google January 8, 2021
Google Although websites we backlink to below are considerably not related to ours, we feel they are really really worth a go by way of, so possess a look.
Google December 26, 2020
Google Every after in a though we select blogs that we read. Listed below would be the latest web pages that we select.
Google July 16, 2020
Google Here is a superb Weblog You might Discover Interesting that we encourage you to visit.
Google July 15, 2020
Google The details talked about in the post are a few of the most beneficial readily available.
Jeffry July 7, 2017
Twerking is a movement that benefits from manipulating the fat and muscle tissues in your butt.
Zachary Escalante May 20, 2016
That's fine with me - if you could give me the links to some of your previous along posts, that'd be great!
Homepage May 20, 2016
... [Trackback] [...] Informations on that Topic: [...]

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI