Machine Learning Approaches for House Price Prediction: Methods and Comparative Analysis
TL;DR
This paper reviews and compares the development of major machine learning models used for housing price prediction, including traditional regression models, tree-based ensemble approaches, and neural network architectures.
Abstract
: In real estate analytics, accurately predicting housing prices is essential, as it directly influences market regulation, policy planning, and investment decisions. Machine learning has become an effective technique to improve prediction performance in this field as a result of the development of data-driven approaches. This paper reviews and compares the development of major machine learning models used for housing price prediction, including traditional regression models, tree-based ensemble approaches, and neural network architectures. Although the ease of use and interpretability of linear regression make it valuable, it struggles to capture complex non-linear interactions. Tree-based models, such as Random Forest, offer greater accuracy and flexibility, but often lack transparency and require careful hyperparameter tuning. Neural networks, although computationally intensive, excel at modeling intricate feature relationships and are well-suited for large-scale or unstructured data. By analyzing the strengths and limitations of each model type, this review provides a clearer understanding of their applications and offers insights for selecting appropriate methods in housing price forecasting.