Machine Learning Approaches for Forecasting Financial Market Volatility

Abstract

Forecasting real estate market volatility is essential for investors, developers, and policymakers in the dynamic real estate industry landscape, which can be considered a financial market. This paper extends the discussion of forecasting financial market volatility using machine learning techniques to the real estate market context. Drawing upon insights from relevant research studies, we delve into the diverse methodologies, performance evaluation metrics, and case studies specific to predicting real estate market volatility. Machine learning models, including regression analysis, time series models, ensemble methods, and deep learning networks, are applied to capture the intricate patterns and uncertainties in the real estate market. Economic indicators, investor sentiment, geospatial data, and housing market fundamentals enhance forecasting accuracy. Performance evaluation metrics like Intersection over Union (IoU) and Mean Squared Error (MSE), prove indispensable for evaluating the reliability of predictive models in this domain. The studies presented in this review demonstrate the practical applications of machine learning in forecasting real estate market volatility across diverse regions and property types. By adapting methodologies from the broader financial market context, we provide valuable insights for stakeholders seeking to make informed decisions in the ever-evolving real estate financial market.