 Modeling Tenant’s Credit Scoring Using Logistic RegressionKim Sia Ling, Siti Suhana Jamaian, Syahira Mansur and Alwyn Kwan Hoong Liew SAGE Open, 2023, vol. 13, issue 3, 21582440231189693 Abstract: 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. Keywords: credit scoring; logistic regression; penalized maximum likelihood (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:13:y:2023:i:3:p:21582440231189693 DOI: 10.1177/21582440231189693 Access Statistics for this article More articles in SAGE Open |
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