Modeling Tenant's Credit Scoring Using Logistic Regression

This study implements the multivariable logistic regression to develop a credit scoring model based on tenants' characteristics. The credit history of tenant is not considered. Rental information of tenants was collected from a landlord company in Malaysia. Parameters of the multivariable logistic regression were estimated by using the penalized maximum likelihood estimation with ridge regression since separation in training data was detected. The initial factors considered that affect tenants' credit score were their gender, age, marital status, monthly income, household income, expense-to-income ratio, number of dependents, previous monthly rent, and number of months late payment. However, the marital status factor was then excluded from the logistic regression model due to its low significance to the model. Meanwhile, a tenant's credit scoring model was generated by calculating the tenant's probability of defaulting. The main factors of the tenant's credit score are the number of months late payment, the expense-to-income ratio, gender, previous monthly rent, and age. There is no underfitting or overfitting in the proposed credit scoring model which means the model's bias and variance are low.

Automated summary

This study develops a credit scoring model based on tenants' characteristics using multivariable logistic regression, without considering their credit history. Rental information of tenants was collected from a landlord company in Malaysia, and parameters were estimated using penalized maximum likelihood estimation. The main factors influencing tenants' credit score are the number of months late payment, expense-to-income ratio, gender, previous monthly rent, and age.