Automated Real Estate Portfolio Risk Assessment with Machine Learning

Develop a comprehensive Python-based risk assessment framework for a multi-million dollar real estate investment portfolio using pandas, scikit-learn, and advanced statistical modeling. Create a predictive model that integrates historical property transaction data, neighborhood economic indicators, geospatial risk factors, and market volatility metrics to generate a dynamic risk score for each property. The solution should include: automated data ingestion from multiple sources, machine learning classification of risk levels (low/medium/high), probability-weighted financial impact projections, and an interactive dashboard using Plotly/Dash that allows real-time risk monitoring. Include feature importance analysis to help investors understand key risk drivers, and implement a Monte Carlo simulation to stress-test potential market disruption scenarios. Ensure the model can handle both residential and commercial real estate assets with scalable, modular architecture.