Machine Learning for Rental Price Prediction: Regression Techniques and Random Forest Model

Abstract

In the rapidly evolving real estate market, accurate and data-driven methods for predicting house rental prices are essential. This research paper explores the use of machine learning to forecast rental prices with precision. Traditional approaches to determining rental prices often rely on local market knowledge and intuition, but they struggle to account for the complex interplay of factors influencing rental rates. Machine learning, employing regression analysis and historical rental data, offers a revolutionary paradigm shift in rental price prediction. This paper investigates the impact of variables such as house age, furnished status, flat area, room size, and the number of rooms on rental prices, demonstrating how machine learning models can uncover hidden correlations and provide accurate rental price estimates. The motivation behind this research stems from the lack of highly accurate existing models, the need to mitigate overpricing, adapt to market dynamics, optimize investment returns, facilitate data-driven decision-making, promote market transparency, and develop a user-friendly interface. The primary objective is to develop a robust and accurate house rental price prediction model, empowering both tenants and property owners with data-driven insights.

Keywords: House Rental Prices, Machine Learning, Real Estate Market, Predictive Modelling, Data-Driven Prediction, Rental Price Forecasting, Regression Analysis, Feature Engineering, House price index, Simple linear regression, Multiple linear regression, Neural networks, Mean square error