Accurate prediction of real estate prices is a complex task influenced by multiple dynamic factors such as location, market trends, property features, and economic conditions. Traditional estimation methods often rely on manual analysis or static models, which may fail to capture evolving patterns in the housing market. This paper presents NestIQ, an intelligent real estate forecasting system that leverages machine learning techniques to generate reliable and data-driven predictions. The proposed system integrates key property attributes, in-cluding area, location, number of rooms, and historical pric-ing data, to train predictive models capable of identifying hidden relationships within the dataset. By applying efficient preprocessing techniques and selecting suitable machine learning algorithms, the system produces accurate and consistent price estimations. NestIQ is designed to be scalable, user-friendly, and computationally efficient, making it suitable for real-world applications. Experimental evaluation demonstrates that the system pro-vides meaningful predictions across diverse property profiles while maintaining performance stability. The model’s ability to generalize across varying inputs highlights its practical usability in assisting buyers, sellers, and real estate professionals in making informed decisions. The proposed approach offers a balance between accuracy, simplicity, and adaptability, contributing to the development of intelligent decision-support systems in the real estate domain.
Sara et al. (Fri,) studied this question.