Enhancing House Price Prediction Using Ensemble Machine Learning Models: A Comparative Study with U.S. Housing Data
DOI:
https://doi.org/10.61799/2216-0388.1885Keywords:
Data pre-processing, Ensemble, Feature selection, House price forecasting, Machine learning.Abstract
Background: Proper prediction of house prices is one of the primary issues in the real estate market, especially under unstable political and economic conditions. Forecasting models must adapt to increasing market complexities, yet such adaptability is difficult to achieve using traditional prediction techniques.
Objective: This paper aims to compare the predictive accuracy of five machine learning models, including XGBoost, LightGBM, CatBoost, HistGradientBoosting, and Bayesian Ridge Regression, using a Kaggle U.S. housing dataset to determine which model performs best and which features contribute most to accurate predictions.
Methods: A dataset of 1,460 samples and 32 variables from 1991 to 2024 was used. Both regression and classification measures were applied to evaluate model effectiveness. Feature importance analyses were conducted to identify variables that consistently influenced predictions.
Results: CatBoost demonstrated the strongest performance, with a training accuracy of 0.94, a test R² of 0.86, and an MCC of 0.81. Variables such as OverallQual and GrLivArea were consistently identified as highly influential. Ensemble models, particularly CatBoost and XGBoost, outperformed Bayesian Ridge in handling non-linearities and categorical variables. Classification metrics, including specificity and Cohen’s Kappa, provided additional insights into model robustness.
Conclusions: The proposed models produced competitive or superior results compared to previous studies while improving interpretability. The findings underscore the importance of ensemble learning methods and multidimensional analysis in developing robust, evidence-based real estate prediction models. The U.S. housing dataset used in this project was obtained from Kaggle and contains essential features for accurate house price prediction, representing historical data from 1991 through 2024.
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