The Web Application of Tenant Credit Scoring using Python

Abstract

Having a poor credit score or no credit history can restrict options for both housing and employment. This study seeks to mitigate the issue of credit invisibility among the low-income demographic with limited credit history by using tenant credit scoring web application. In this study, a credit scoring model is created using tenants’ attributes, monthly rent and rental payment history through the implementation of graphical user interface. A simple logistics regression is applied to compute the credit score of tenants based on their characteristics. Based on the findings of this study, the primary determinants of the tenant's credit score include gender, age, the number of months with late payments, the expense-to-income ratio, and the previous monthly rent. The development of the web application involves the utilization of HTML, CSS and Python. Finally, the web application is developed and the creditworthiness of a tenant is calculated in a credit scoring model.