Machine Learning-Based House Rent Prediction Using Stacking Integration Method

Abstract

With the advancement of urbanization and the gradual increase of the rental population, the housing rental market is growing rapidly. It is important to achieve accurate housing rent prediction in order to stabilize the rental housing market. The influence of spatial and temporal factors has led to the complexity of house rent prediction, so it has always been difficult to find an appropriate method. In recent years, machine learning models have been widely studied and applied in various fields, which may provide a promising solution to it. In this paper, a stacking-based ensemble learning model is proposed to solve the problem of house rent prediction. First, the raw data are preprocessed, including decomposing hybrid features, removing rent outliers using scatterplot, removing uncorrelated features, and applying one-hot encoding to transform categorical features into numerical features. Second, the pre-processed data is normalized to unify the magnitudes. Then, the competent base predictive models are selected from all the trained base predictive models and integrated into a comprehensive ensemble model using the stacking integration method to make the final prediction. Finally, the various models are evaluated by some metrics. The experimental results show that the proposed stacking integration-based machine learning method outperforms the individual machine learning methods in solving the house rent prediction problem.