1. Using Net Lift Measure to Model the Effects of Changes in Interest Rates on Mortgage Prepayment
Alec Zhixiao Lin, Lin’s Analytics
Xiao Hu, Southern California University

2. Contents Impact of interest rate drop on mortgage prepayment
- On consumers/borrowers
- On investors
Net lift measure
- A prerequisite
- Concept
- Searching for a good measure
Modeling
- Data
- Sample
- Regression Method and Results

3. Impact of interest rate drop
On consumers/borrowers
Purchase
Many consumers will sell current properties before buying new ones.
Rate/Term
Refinance
Many borrowers will apply for refinance in order to reduce monthly mortgage payment or to shorten loan terms.
Cash-out
Some borrowers will have more incentive to cash out a portion of their home equity.

4. Impact of interest rate drop
On other parties
Investors
Shortened term reduces return from investment.
Forced to invest the newly released fund to the market, which promises lower return.
Mortgage
Servicers
Revenue streams dry up as prepaid loans are marked as Paid In Full.

5. Impact of interest rate drop
Change in interest rate will impact the valuation of MBS (mortgage-backed securities)
Need to find a useful measure to quantify the impact of interest rate change on mortgage prepayment.

6. Net lift measure A Prerequisite
- Control period: activities as usual - Experimental period: a sudden change – such as a big drop - in the market rate. A period of lower interest rate persists for at least a few months.
control period
Experimental period
Control period
Our paper chooses this period for modeling.

7. Net lift measure Concept
Net lift measure uses pool-level change a proxy for changes in prepayment.
All loans within the same pool share the same value for changes in prepayment. These values can be used as a dependent variable for a linear regression.
Loans with FICO all have Y=0.105.

8. Net lift measure Search for a good measure
A good measure should contain enough variability while ensuring valid statistical significance.
- Y ranges from 5.8% to 10.7%.
- Distribution is almost even.
- Y ranges from 1.6% to 16%.
- Distribution is almost even.

9. Modeling Data
Freddie Mac and Fannie Mae published single-family home performance data online
The data contain two parts
- Origination information (FICO, LTV, DTI, etc)
- Performance history by month

10. Modeling Sample Single-family home mortgages with 30-year fixed rate.
Three states
- Georgia: most representative of US homeowners
- Massachusetts: highest home prices
- Tennessee: lowest home prices
We want to see whether any commonality can be seen in variable behavior
in these diverse samples.
Control period: 2019/ /03, average rate 5.04
Experimental period: 2012/ /06, average rate 3.47

11. Modeling Results
LTV and burnout factor (2 degrees) are most important predictors.
FICO, DTI and original balance are much less important than previously thought.

12. Contact Information
Name: Alec Zhixiao Lin Company: Lin’s Analytics City/State: Los Angeles, CA Phone: