RESIDENTIAL RENT PRICE PREDICTION BASED ON MACHINE LEARNING

The dynamic and competitive real estate sector of Lagos, Nigeria, has become a growing area concern, particularly, the issue regarding fluctuations in the cost of rent. This research conducts comprehensive data analysis about factors that are influencing residential rent prices in Lagos, with the aim to develop a predictive model. The model seeks, not only to enrich comprehension but also foster making informed decisions by stakeholders, renters, landlords, policymakers, and investors. This research utilized varied datasets and advanced machine learning techniques to analyze the complex interplay of socioeconomic, urban development, and market-driven variables influencing prices of house rent in Lagos. Having the understanding of these relationships is of utmost importance for accurate predictive modelling in such a dynamic environment. Through examination of feature exhaustively and rigorous analysis of model performance, five machine learning technologies were tested in order to identify the most accurate predictive model. The study also investigates the implications of rent price prediction models for various stakeholders including landlords, policy makers, investors, and renters, offering insights for informed decision-making. Summarily, this thesis contributes to the field of house rent prediction in Lagos by leveraging advanced methodologies and comprehensive datasets to enhance understanding of the housing market dynamics and empower stakeholders with actionable insights.