Research on Real Estate Market Prediction Models Based on Big Data and Artificial Intelligence

The intersection of big data and artificial intelligence has significantly transformed computational techniques for predicting real estate market trends, aligning closely with the thematic scope of “Frontiers in Computer Science.” Traditional real estate forecasting models often fail to capture the intricate spatial, temporal, and economic dependencies essential for robust prediction. Challenges such as data sparsity, nonlinearity, and market volatility remain inadequately addressed, limiting their scalability and adaptability in dynamic market conditions. To address these gaps, we propose a novel framework comprising the Dynamic Relational Price Network (DRPN) and the Market Adaptive Optimization Strategy (MAOS). DRPN integrates graph-based reasoning for spatial dependencies, temporal forecasting with recurrent networks, and hierarchical feature learning to improve interpretability and predictive accuracy. Meanwhile, MAOS dynamically adjusts model parameters and regularization strategies based on real-time market conditions, ensuring robust generalization across diverse scenarios. Experimental results demonstrate the superior performance of our approach in predictive accuracy, stability, and scalability compared to conventional methods, providing actionable insights into market dynamics. This research offers a scalable and adaptive solution to real estate forecasting, contributing to the broader applications of AI in computational market analysis.