Enhanced Real Estate Forecasting using Decision Tree with Hyperparameters Tuning

This research study analyzes the efficacy of using decision trees enhanced by hyperparameter tuning for real estate forecasting, a domain marked by complexity and dynamic nature. This study explores the integration of decision tree algorithms within real estate analytics, highlighting its adaptability and depth in addressing the sector’s challenges. The decision tree’s ability to determine non-linear relationships and diverse data, helps to accurately predict real estate market trends and property values. Hyperparameter tuning emerges as a critical process that refines the decision tree model, optimising its parameters to align with the unique attributes of real estate data, thereby enhancing predictive accuracy and model generalizability. This study demonstrates various methodologies, from improving market trend analysis and property valuation to customizing models for commercial real estate insights. This study represents these insights, illustrating how decision trees with hyperparameter tuning offer a robust analytical tool for real estate forecasting, providing stakeholders with a understanding of market dynamics and informed decision-making.