Modelling Residential Housing Rent Price Using Machine Learning Models

Abstract

Background: Providing shelter and security through housing is a basic need for people. However, the housing market is frequently troubled by issues such as exorbitant rent pricing and a lack of transparency, posing challenges for both landlords and home renters. Objective: This study examines the factors affecting residential housing rent in Ibadan City to thoroughly investigate the cost-effectiveness of rents. We looked into a number of variables, including neighborhood characteristics, property size, amenities, and location, aiming to pinpoint the essential elements significantly impacting rental costs. Methods: Data were collected from two well-known real estate websites, privateproperty.com and propertypro.com. We built a rent prediction model and determined key features using regression analysis utilizing Ordinary Least Squares (OLS), and the Extreme Gradient Boosting (XGBoost) algorithm. The analysis revealed several factors with notable relationships to rent prices. Results: The five variables with the highest correlation coefficients were identified as duplex (0.54), toilet (0.34), detached (0.31), flat (-0.3), and bedroom (0.29), indicating their substantial influence on housing rental rates. Furthermore, the study highlights specific neighborhoods as either relatively expensive or budget-friendly in terms of median rent prices. Notably, Agodi and Kolapo Ishola emerged as the most expensive areas, surpassing the overall median rent by significant margins (454.55% and 450%, respectively). Conversely, Sanyo, Moniya, and Academy recorded the lowest median rent prices, being 50% lower than the overall median. Conclusion: This study provides valuable insights into the intricate factors governing residential housing rent in Ibadan City, offering a foundation for further research and practical applications in the real estate domain.