THE HOUSE PRICE PREDICTION SYSTEM USING AI FOR SUSTAINABLE URBAN DEVELOPMENT

Abstract

The rapid growth of urban populations and real estate markets has increased the demand for accurate housing price prediction systems. Traditional valuation methods are often inefficient and fail to capture complex relationships between property characteristics and market dynamics. This research presents an Artificial Intelligence (AI)–based house price prediction system that utilizes machine learning algorithms to analyze housing datasets and estimate property values accurately. The proposed system integrates predictive analytics with sustainability indicators such as environmental quality, proximity to public transportation, and green infrastructure to support sustainable urban development. Multiple machine learning algorithms including Linear Regression, Decision Tree, Random Forest, and Gradient Boosting are evaluated to identify the most effective predictive model. Experimental results demonstrate that ensemble learning methods outperform traditional models in terms of accuracy and reliability. The proposed system enhances transparency in real estate markets and contributes to data-driven decision-making in smart cities and sustainable housing planning.