House Price Prediction

House price prediction aims to accurately estimate property values using various factors, including physical attributes, location, and neighborhood characteristics. Recent research emphasizes incorporating diverse data modalities—such as textual descriptions, images, and geographic information—into sophisticated models, including deep learning architectures like graph neural networks and attention-based methods, to improve prediction accuracy. These advancements offer more robust tools for real estate valuation, benefiting stakeholders from individual homeowners to policymakers by providing more reliable and nuanced price estimations, and also highlighting the complex interplay between predictive modeling and causal understanding in the housing market. Furthermore, research is increasingly exploring the feedback loop between AI-generated price estimations and actual market values, acknowledging the potential impact of algorithmic predictions on market dynamics.