This document summarizes three projects related to optimizing capital allocation for mortgage market loans: 1. A loan default classifier that predicts if a customer will miss a monthly loan payment using AdaBoost and achieving 98% recall. 2. A location recommender that suggests new office locations based on existing office profitability, GDP growth, and balancing clustering vs spreading using basin hopping optimization. 3. A business forecaster that predicts quarterly business using SARIMAX to handle a one-time event in the data. Future work is outlined to improve each model.

1. O P T I M I Z I N G C A P I T A L
A L L O C A T I O N F O R M O R T G A G E
M A R K E T L O A N S
N A M A N J A I N
G I T H U B . C O M / N A M A N J / M O R T G A G E - M A R K E T - T R I - A N A LY S I S

2. LOAN DEFAULT CLASSIFIER
LOCATION RECOMMENDER
FORECASTING BUSINESS

3. • Built a classifier that predicts whether a customer is going to miss their monthly loan repayment
• Major Challenge - Classes were very imbalanced, with only 3% people ever defaulting on a
payment
• Raw Data:
• ~15k data points of 40 dimensions
• 20 variables were categorical, 19 numerical and 1 temporal
LOAN DEFAULT CLASSIFIER:

4. • AdaBoost
• No Oversampling
• Used sample weights
Approach:
Results:
• Recall 98%

5. • Recommend new office locations that maximize growth opportunity
• Parameters Used:
• Distance from existing office locations
• Profitability of existing location over the past 5 years
• GDP growth of potential locations over the past 5 years
LOCATION RECOMMENDER:
Approach:
• Scipy Optimize Basin Hopping
• Basemap

6. The model automatically strikes a balance in the cost function
between clustering of office locations vs spreading them out

7. FORECASTING BUSINESS:
• Predict amount of business over the next quarter so that the firm can better manage
its resources
• Challenge - Data had a one-time event in the middle of 2016
Approach:
• SARIMAX

9. NEXT STEPS
• Loan Default Classifier :
– Extend model to allow a particular branch to determine the risk of
default per portfolio
• Location Recommender :
– Extend Cost Function to determine the tradeoff between clustering of
office locations vs having a larger spread
– Make Cost Function less sensitive to initializations
• Forecasting Business :
– Incorporate Exponential Smoothing (ETS) in the forecasting

10. THANK YOU
QUESTIONS?
Naman.jain020@gmail.com
NamanJ/Mortgage-Market-Tri-Analysis
NamanJain1