Predicting Housing Prices in China Towards Real Estate Recommendation System Framework

Abstract

The study has recommended an anticipatory system in which accurate prediction of housing prices would assist decision making by buyers, sellers, and investors within Shandong Province, China. There is an identified need for a data-driven and user-centered model integrating property parameters, locational attributes, economic indicators, and preferences of users, with a view of developing reliable recommendation features. A quantitative methodology was used to collect data with a widely diversified group of real estate professionals, covering agents, developers, and investors. Thereafter, all statistical analyses and machine learning algorithms were laid to establish the relationship among the variables and validate accuracy, reliability, and consistency of the model. Findings indicate that user preference, although significant in terms of statistics, had little effect on the predictive accuracy of the system. The strongest influence was found in location and economic indicators regarding housing prices. The model has shown very high accuracy across different urban contexts, and this therefore underscores its flexibility. By such studies, the argument lies in focusing more on the quality and kernel location and economic data inputs and less on user customizations. This will yield benefits regarding usability for wider audiences while maintaining the consistency of operation for consumers. Overall strong, then, is the research in offering recommendation frameworks that can be sufficiently varied to enhance transparency, lure investments, and optimize property choices within Shandong's dynamic housing market.