Automated Property Valuation Machine Learning Pipeline

Design a comprehensive Python-based machine learning pipeline using scikit-learn and pandas that can predict real estate property values with 95% accuracy. The system must integrate multiple data sources including historical sales data, neighborhood demographics, economic indicators, and geospatial features. Implement cross-validation techniques, handle feature engineering for categorical and continuous variables, and create a Flask API endpoint that allows real-time valuation predictions. Include robust error handling and a mechanism to continuously retrain the model as new data becomes available.