AI-DRIVEN PREDICTIVE ANALYTICS FOR CUSTOMER BEHAVIOUR IN REAL ESTATE: A MACHINE LEARNING APPROACH

The real estate industry faces significant challenges in predicting customer behavior due to the complex, multifaceted nature of property purchasing decisions.Traditional forecasting methods often fall short in capturing the intricate relationships between property attributes, market conditions, and customer preferences.This paper explores the application of machine learning (ML) techniques, specifically predictive analytics, to forecast customer behavior in the real estate market.By leveraging algorithms such as Linear Regression, Decision Trees, Random Forests, K-means clustering, and Neural Networks, we develop a predictive framework that analyses customer data to identify buying patterns, predict future demand, and optimize pricing strategies.The models were evaluated using real-world real estate data, with a focus on customer segmentation and property price predictions.Our results demonstrate the effectiveness of machine learning models in accurately predicting customer preferences, improving the accuracy of property valuations, and enhancing targeted marketing strategies.This paper contributes to the growing body of knowledge on the integration of artificial intelligence in real estate, providing valuable insights for real estate professionals seeking data-driven solutions to enhance decision-making and competitive advantage.