Developing a Robust Rental Price Prediction System: Insights from Linear Regression, Decision Trees, and Random Forest

Estimation of the rent rate is an important task in the entire real estate industry and can have a heavy consequence for landlords and future tenants. This paper presents a predictive modeling in rental pricing, using machine learning approaches in the Java programming paradigm to build a strong system that could forecast rental values given a set of several property characteristics. Three algorithms were therefore used in this study: Linear Regression, Decision Tree, and Random Forest. The Linear Regression model assumes that there is a basic linear relationship in the middle of the input variables and rental pricing. In contrast, the Decision Tree uses a nonlinear methodology because it segments the data into various decision paths, therefore enhancing its interpretability. The Random Forest approach uses ensemble learning by combining multiple decision trees in order to enhance accuracy and robustness of the prediction. This is accomplished through an integrated web-based platform where these models were integrated into the platform, and a user could insert specific characteristics of the property and would return a set of predicted rental prices. The current study applies these algorithms to a real dataset in order to demonstrate pragmatic usage of machine learning in real estate. Detailed examinations of all three algorithms each have their individual advantages, which give valuable understanding of their efficacy in various prediction situations, hence the creation of a comprehensive instrument for estimating rental prices.