<![CDATA[Objective: The primary aim of the study is to create and apply an intelligent model that would correctly forecast prices of houses based on different factors that affect the prices, including location, size of the property, number of rooms, infrastructure and market trends. Method: The information employed in this research is presumed to be gathered on popular online platforms, which offer real-time and a wide variety of data. The predictive performance of several machine learning algorithms, such as Linear Regression, Random Forest, and Gradient Boosting, is taken into consideration. Results: The suggested system not only increases the accuracy of predictions, but it also increases the process of making decisions among buyers, sellers and real estate professionals. The experimental findings indicate that the ensemble-based models are more accurate and robust than the traditional statistical methods. Novelty: The present paper is devoted to the creation of an intelligent algorithm for price forecasting of houses depending on the data obtained from Internet platforms. The paper can be seen as an addition to the increasing body of research in the area of intelligent real estate analytics since it provides an efficient and scalable method of housing price forecasting.]]>
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