Научная статья на тему 'Development of a predictive model for denial of home mortgage'

Development of a predictive model for denial of home mortgage Текст научной статьи по специальности «Медицинские технологии»

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Аннотация научной статьи по медицинским технологиям, автор научной работы — Wang Zongdi

Objective: This study aims to build a predictive model for the denial of home mortgage in Washington state using logistic regression model. Methods: A public database was used in this study. A logistic regression was used. Area under curve, optional cutoff point, mis-classification error, sensitivity and specificity were calculated. Results: A total of 49324(19.3%) home mortgage applications out of 255379 had were denied. According to the logistic regression, refinancing was 339.1% more likely to get denied. Home improvement was 291.8% more likely to get denied. Black were 77.8% more likely to get denied, Asian 33.5% more likely and Hispanic 36.3% more likely, and other race were 62.3% more likely to get denied. FHA, FSARHS and VA were more likely to get denied. People without co-applicants were 59.1% more likely to get a denial. The area under curve was 0.7052. The optional cutoff point is 0.459. The mis-classification error was 0.1905. the sensitivity rate is about 5.4% and the specificity is 99.0%. Conclusions: In this study, we identified several important predictors for the denial of home mortgage in Washington State in 2016, for example, race, mortgage type.

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Текст научной работы на тему «Development of a predictive model for denial of home mortgage»

Section 6. Economic security

https://doi.org/10.29013/EJEMS-19-4-61-65

Wang Zongdi, Hong Kong International School, China E-mail: [email protected]

DEVELOPMENT OF A PREDICTIVE MODEL FOR DENIAL OF HOME MORTGAGE

Abstract

Objective: This study aims to build a predictive model for the denial of home mortgage in Washington state using logistic regression model.

Methods: A public database was used in this study. A logistic regression was used. Area under curve, optional cutoff point, mis-classification error, sensitivity and specificity were calculated.

Results: A total of49324(19.3%) home mortgage applications out of 255379 had were denied. According to the logistic regression, refinancing was 339.1% more likely to get denied. Home improvement was 291.8% more likely to get denied. Black were 77.8% more likely to get denied, Asian 33.5% more likely and Hispanic 36.3% more likely, and other race were 62.3% more likely to get denied. FHA, FSARHS and VA were more likely to get denied. People without co-applicants were 59.1% more likely to get a denial.

The area under curve was 0.7052. The optional cutoff point is 0.459. The mis-classification error was 0.1905. the sensitivity rate is about 5.4% and the specificity is 99.0%.

Conclusions: In this study, we identified several important predictors for the denial of home mortgage in Washington State in 2016, for example, race, mortgage type.

Keywords:

1. Instruction

There are 5 most common reasons why a home mortgage loan application could be denied: Poor Credit History; Insufficient Income/Asset Documentation; Down Payment is Too Small; Problems With the Property; Inadequate Employment History.

Recent news articles suggest that the significantly higher mortgage denial rates for black and Hispanic

borrowers establish the presence of racial discrimination in mortgage lending.

This study aims to build a predictive model for the denial of home mortgage in Washington state using logistic regression model.

2. Data and Methods:

Data:

Inside this data set contains 466.566 observations of Washington State home loans - variables

include; demographic information, area specific data, loan status, property type, loan type, loan purpose and originating agency. The data is available at: https://www.kaggle.com/miker400/washington-state-home-mortgage-hdma2016.

Optimal Cutoff for Binary Classification maximizes the accuracy.

Mis-Classification Error is the proportion of all events that were incorrectly classified, for a given probability cutoff score.

Sensitivity: probability that a test result will be positive when the disease is present (true positive rate.

Specificity: probability that a test result will be negative when the disease is not present (true negative rate, expressed as a percentage). e, expressed as a percentage).

3. Results

A total of 49324(19.3%) home mortgage applications out of 255379 had were denied.

Figure 1. Matrix of correlations between variables Table 2.- Logistic Regression for Mental Health

Estimate Std. Error z value Pr(>|z|)

1 2 3 4 5 6

(Intercept) -1.219 0.075 -16.257 < 2e-16 ***

tract to msamd income -0.003 0.000 -9.400 < 2e-16 ***

population 0.000 0.000 -6.016 0.000 ***

minority_population 0.002 0.001 3.593 0.000 ***

number of owner occupied units 0.000 0.000 -0.476 0.634

number of1to4 family units 0.000 0.000 5.633 0.000 ***

loan amount 000s 0.000 0.000 1.760 0.078

hud median family income 0.000 0.000 -20.314 < 2e-16 ***

applicant income 000s -0.001 0.000 -10.018 < 2e-16 ***

1 2 3 4 5 6

Type_FHA 0.523 0.024 22.182 < 2e-16 ***

Type_FSARHS 0.456 0.085 5.364 0.000 ***

Type_VA 0.137 0.027 5.115 0.000 ***

Hom imp 1.366 0.031 44.105 < 2e-16 ***

Refinancing 1.480 0.018 81.418 < 2e-16 ***

No co app 0.465 0.016 29.803 < 2e-16 ***

Male 0.001 0.017 0.032 0.975

Black 0.575 0.042 13.627 < 2e-16 ***

Asian 0.289 0.026 10.954 < 2e-16 ***

Other race 0.485 0.047 10.359 < 2e-16 ***

Hispanic 0.310 0.031 9.925 < 2e-16 ***

According to the logistic regression, refinancing was 339.1% more likely to get denied. Home improvement was 291.8% more likely to get denied. Black were 77.8% more likely to get denied, Asian 33.5% more

likely and Hispanic 36.3% more likely, and other race were 62.3% more likely to get denied. FHA, FSARHS and VA were more likely to get denied. People without co-applicants were 59.1% more likely to get a denial.

Table 2.- Odds Ratio According to Logistic Regression

Variable OR Risk Increase

Refinancing 4.391 3.391

Hom imp 3.918 2.918

Black 1.778 0.778

Type_FHA 1.687 0.687

Other race 1.623 0.623

No co app 1.591 0.591

Type_FSARHS 1.578 0.578

Hispanic 1.363 0.363

Asian 1.335 0.335

Type_VA 1.146 0.146

minority_population 1.002 0.002

Male 1.001 0.001

number of1to4 family units 1.000 0.000

loan amount 000s 1.000 0.000

hud median family income 1.000 0.000

number of owner occupied units 1.000 0.000

population 1.000 0.000

applicant income 000s 0.999 -0.001

tract to msamd income 0.997 -0.003

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Figure 2. Odds Ratio (blue) and Risk Increase (red) According to Logistic Regression

Figure 3. ROC in testing sample for Logistic Regression

The area under curve was 0.7052. The optional cutoff time is 0.459. The mis-classification error was 0.1905. the sensitivity rate is about 5.4% and the specificity is 99.0%.

4. Discussions

A total of49324(19.3%) home mortgage applications out of 255379 had were denied. According to the logistic regression, refinancing was 339.1% more likely to get denied. Home improvement was 291.8% more likely to get denied. Black were

77.8% more likely to get denied, Asian 33.5% more likely and Hispanic 36.3% more likely, and other race were 62.3% more likely to get denied. FHA, FSARHS and VA were more likely to get denied. People without co-applicants were 59.1% more likely to get a denial.

The area under curve was 0.7052. The optional cutoff point is 0.459. The mis-classification error was 0.1905. the sensitivity rate is about 5.4% and the specificity is 99.0%.

In this study, we identified several important predictors for the denial of home mortgage in Washington State in 2016, for example, race, mortgage type.

References:

1. Peng C.J., Lee K. L., Ingersoll G. M. An Introduction to Logistic Regression Analysis and Reporting. The Journal of Educational Research, 96(1),- P. 3-14.

2. Tabachnick B., and Fidell L. Using Multivariate Statistics (4th Ed.). Needham Heights, MA: Allyn & Bacon, 2001.

3. Stat Soft. Electronic Statistics Textbook. URL:http://www.statsoft.com/textbook/stathome.html. http://www.statsoft.com/textbook/stathome.html.

4. Stokes M., Davis C. S. Categorical Data Analysis Using the SAS System, SAS Institute Inc., 1995.

5. Mortgage risk assessment. URL:https://www.mortgagecompliancemagazine.com > Featured.

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