Comparative Analysis of Ensemble and Linear Machine Learning Models in the Task of House Price Prediction

In today's evolving real estate market, accurately predicting real estate prices is important given the crucial role real estate plays in any country's economy. With numerous methods and techniques available, navigating the complexities of data types and the multitude of factors influencing prices requires careful consideration. This research paper presents a comparative analysis of ensemble and linear machine learning models in predicting real estate prices using a wide range of predictive models, including linear regression, ridge regression, lasso regression, random forest, decision trees, XGBoost and LightGBM. The study evaluates their effectiveness using metrics such as MAE (mean absolute error), RMSE (RMS error), RMSLE (RMS logarithmic error) and R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (coefficient of determination) and evaluates their training time. The experimental results showed that ensemble models outperformed linear models with a higher training time. These results can be useful both for researchers in the field of real estate and finance, as well as for investors interested in predicting and optimizing real estate investment strategies in a volatile market.