AI Algorithms in Real Estate: A Roadmap to Precision Housing Price Predictions

This article examines the application of artificial intelligence (AI) in real estate, with a particular focus on its role in housing price forecasting. The study starts with the hedonic pricing model and conducts a comprehensive review of existing models. These models are divided into several groups, including traditional statistical methods such as regression and decision trees, as well as advanced machine learning methods such as random forests, support vector machines, K-nearest neighbors, gradient boosting machines, neural systems, networks and recurrent neural networks, convolutional neural networks, finite periodic units, generative adversarial networks, transformer model, XGBoost, fuzzy models, operation research and analytic hierarchy process. The analysis evaluates these models based on various criteria, including ability to learn online, sensitivity, handling of missing values, robustness, scalability, prediction speed, and linearity. Understanding the strengths and limitations of each model for predicting housing prices can provide real estate practitioners and researchers with valuable insight into selecting appropriate AI models for specific applications.