Predictive Analytics of Post-Purchase Consumer Dynamics in Real Estate Cancellation Prediction Model

Abstract

The real estate sector, known for its complex customer dynamics, often struggles with high post- purchase cancellations, which negatively affect revenue and overall project success. This study presents a predictive analytics model for forecasting customer cancellations in real estate transactions. By leveraging advanced machine learning techniques and using data from past projects, the model aims to assist sales and collection teams in identifying high-risk customers, thus enabling proactive intervention strategies. The research integrates consumer behavior patterns, financial data, and project-specific variables, offering a comprehensive understanding of post- purchase decision-making in real estate. The results demonstrate the potential of predictive analytics to improve retention rates and optimize customer relationship management (CRM) in the real estate industry.