Real Estate

Real estate research focuses on accurately predicting property values and understanding the factors influencing them, aiming to improve market efficiency and inform policy decisions. Current studies employ diverse machine learning models, including deep learning architectures, tree-based methods (like Random Forests and XGBoost), and novel approaches like covariate-distance weighted regression, often incorporating geospatial data, satellite imagery, and even mobile location data to enhance predictive accuracy and causal inference. This research is significant for its potential to improve real estate valuation, optimize resource allocation, and address issues of bias and fairness in algorithmic pricing models, ultimately impacting both the financial industry and urban planning.