SUPPORT VECTOR MACHINE VS. LOGISTIC REGRESSION FOR PREDICTING MORTGAGE DEFAULTS
(2021) In Bachelor's Theses in Mathematicas Sciences FMSL01 20212Mathematical Statistics
- Abstract
- Mortgage loan providers estimate the credit risks it caries when approving a
mortgage loan to their clients. Further, defaulting a mortgage loan is a risk
that has been calculated through decades using statistical models. By using
entries at the time of a mortgage application, the goal of the thesis is to com-
pare the accuracy between logistic regression and Support Vector Machine
in predicting a mortgage loan default. For this purpose, Fannie Mae 30-year-
fixed-rate single-family mortgage loans are used for the years; 2000, 2005 and
2010. The models aim is to predict probability of default during five years
period from the loan acquiring date. While the result showed that logistic re-
gression was both faster and less... (More) - Mortgage loan providers estimate the credit risks it caries when approving a
mortgage loan to their clients. Further, defaulting a mortgage loan is a risk
that has been calculated through decades using statistical models. By using
entries at the time of a mortgage application, the goal of the thesis is to com-
pare the accuracy between logistic regression and Support Vector Machine
in predicting a mortgage loan default. For this purpose, Fannie Mae 30-year-
fixed-rate single-family mortgage loans are used for the years; 2000, 2005 and
2010. The models aim is to predict probability of default during five years
period from the loan acquiring date. While the result showed that logistic re-
gression was both faster and less complex to implement, SVM proved to have
a marginally better prediction with the drawback of a longer computational
time. The forecast accuracy to compare the two models at hand was ROC
and Precision-recall, although precision-recall was favored due to the unbal-
anced data. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9068605
- author
- Olvbo, Aram LU
- supervisor
- organization
- course
- FMSL01 20212
- year
- 2021
- type
- M2 - Bachelor Degree
- subject
- publication/series
- Bachelor's Theses in Mathematicas Sciences
- report number
- LUTFMS-4012-2021
- ISSN
- 1654-6229
- other publication id
- 2021:K43
- language
- English
- id
- 9068605
- date added to LUP
- 2022-02-02 10:58:43
- date last changed
- 2022-02-02 14:19:14
@misc{9068605,
abstract = {{Mortgage loan providers estimate the credit risks it caries when approving a
mortgage loan to their clients. Further, defaulting a mortgage loan is a risk
that has been calculated through decades using statistical models. By using
entries at the time of a mortgage application, the goal of the thesis is to com-
pare the accuracy between logistic regression and Support Vector Machine
in predicting a mortgage loan default. For this purpose, Fannie Mae 30-year-
fixed-rate single-family mortgage loans are used for the years; 2000, 2005 and
2010. The models aim is to predict probability of default during five years
period from the loan acquiring date. While the result showed that logistic re-
gression was both faster and less complex to implement, SVM proved to have
a marginally better prediction with the drawback of a longer computational
time. The forecast accuracy to compare the two models at hand was ROC
and Precision-recall, although precision-recall was favored due to the unbal-
anced data.}},
author = {{Olvbo, Aram}},
issn = {{1654-6229}},
language = {{eng}},
note = {{Student Paper}},
series = {{Bachelor's Theses in Mathematicas Sciences}},
title = {{SUPPORT VECTOR MACHINE VS. LOGISTIC REGRESSION FOR PREDICTING MORTGAGE DEFAULTS}},
year = {{2021}},
}