Loan Prediction

Predict whether or not loans acquired by Fannie Mae will go into foreclosure. Fannie Mae acquires loans from other lenders as a way of inducing them to lend more. Fannie Mae releases data on the loans it has acquired and their performance afterwards here.

Installation

Download the data

• Clone this repo to your computer.
• Get into the folder using cd loan-prediction.
• Run mkdir data.
• Switch into the data directory using cd data.
• Download the data files from Fannie Mae into the data directory.
  • You can find the data here.
  • You'll need to register with Fannie Mae to download the data.
  • It's recommended to download all the data from 2012 Q1 to present.
• Extract all of the .zip files you downloaded.
  • On OSX, you can run find ./ -name \*.zip -exec unzip {} \;.
  • At the end, you should have a bunch of text files called Acquisition_YQX.txt, and Performance_YQX.txt, where Y is a year, and X is a number from 1 to 4.
• Remove all the zip files by running rm *.zip.
• Switch back into the loan-prediction directory using cd ...

Install the requirements

• Install the requirements using pip install -r requirements.txt.
  • Make sure you use Python 3.
  • You may want to use a virtual environment for this.

Usage

• Run mkdir processed to create a directory for our processed datasets.
• Run python assemble.py to combine the Acquisition and Performance datasets.
  • This will create Acquisition.txt and Performance.txt in the processed folder.
• Run python annotate.py.
  • This will create training data from Acquisition.txt and Performance.txt.
  • It will add a file called train.csv to the processed folder.
• Run python predict.py.
  • This will run cross validation across the training set, and print the accuracy score.

Extending this

If you want to extend this work, here are a few places to start:

• Generate more features in annotate.py.
• Switch algorithms in predict.py.
• Add in a way to make predictions on future data.
• Try seeing if you can predict if a bank should have issued the loan.
  • Remove any columns from train that the bank wouldn't have known at the time of issuing the loan.
    • Some columns are known when Fannie Mae bought the loan, but not before
  • Make predictions.
• Explore seeing if you can predict columns other than foreclosure_status.
  • Can you predict how much the property will be worth at sale time?
• Explore the nuances between performance updates.
  • Can you predict how many times the borrower will be late on payments?
  • Can you map out the typical loan lifecycle?