Explaining the Effects of Credit Score on Mortgage Rates
Nathan Hephner & Clifton Miller, Fort Hays State University
Abstract
In this research project, we are trying to reject the null
hypothesis that credit score does not affect the interest rate on
a mortgage loan. This is important because the majority of
homeowners have financed their home using a mortgage loan,
and the interest rate they receive depends on many different
factors, including credit scores. If homeowners are aware that
a higher credit score will effectively help them lower the
interest rate they pay on their mortgage, they can work to
improve it and save money. Our main findings indicate that
there is a significant relationship between mortgage interest
rates, combined loan to value, unpaid principal balance,
original loan term, occupancy, property type, and whether
they are a first-time home buyer. The data also suggests that
credit score does indeed have a statistically significant effect
on what kind of interest rate borrowers receive on their loans.
A higher credit score will tend to lower the interest payment
homeowners must pay on their mortgage.
Introduction
The topic of this research paper is whether or not there is
evidence that the credit scores of borrowers affect the interest
rate they receive on their mortgage loan. This paper uses data
from FreddieMac, released “at the direction of its regulator, the
Federal Housing Finance Agency, as part of an effort to increase
transparency and help investors build more accurate credit
performance models.” The null hypothesis is that a borrower's
credit score has no effect on the interest rate given to them for a
mortgage loan. The dependent variable was the original interest
rate as indicated on the mortgage note for quarter four of 2021.
The independent variables we chose were combined loan to
value (cltv), unpaid principal balance (upb), original loan term
(term), occupance (occ), property type (propertytype), and first-
time home buyer (firsttimehomebuyer).
Regression Results
The regression results are shown in table 3. Regression (1) shows the coefficient is
negative and significantly significant at the 1% level, which is consistent with the notion
that credit score is associated with mortgage interest rates. The coefficient -0.00261
implies if a borrower's credit score at the time of negotiation increases by one point, then
their mortgage interest rate is predicted to decrease by 0.00261%. To put this into a more
reasonable scale, a 50-point increase in credit score, which is a very attainable increase,
would lead to the prediction of a 0.13% decrease in the mortgage interest rate the
borrower receives.
Regressions (2) through (7) reveal how the estimated relationship between CS and IR
changes as more control variables are added to the model. Regression (7) contains the full
set of controls used in this study. Approximately 57% of the total variation in IR is
explained by this model. The estimated coefficient for IR shown in regression 7 is -
0.00234. Thus, an individual's interest rate on their mortgage is predicted to decrease by
around 0.12 percentage points if their credit score increases by 50 points after controlling
for combined loan-to-value, unpaid balance, term, occupancy status, property type, and
whether or not this is their first home.
Conclusion
In conclusion, our results reaffirmed our intuition that an
increase in credit score corresponds to a decrease in
mortgage interest rate. While I don't believe our findings
and intuitions would be of much surprise to anyone, after
running various tests on more than 850,000 observations,
we trust that we can have confidence in our results. We
believe our findings are important because of the rise in
prices, leading to a rise in the need for credit. According to
the consumer financial protection bureau, from 2020 to
2021, closed-end loans increased by 528,000,
approximately 4% (2022). The results from our research
could be a great resource for individuals desiring credit.
Analyzing the effect of critical deciding factors for one's
mortgage rate could in turn help them financially. We
believe our model could be improved by adding other
explanatory variables and analyzing the interaction
between the variables as well.
