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Recently some cities' investments on fix assets increase too fast that lead to a property bubble. In order to prevent the overheating of real estate investment, this paper presents a pre-warning system developed to monitor and provide pre-warning to the governmental decision makers in real estate market. In the overall structure plan, the warning classification system is the most important so that we make an innovation to it using the novel ACO-PSO-hybrid algorithm. The hybrid algorithm makes use of advantages of both ACO and PSO methods therefore it is of benefit in solving clustering problems. And the experiment results demonstrate that our algorithm is significantly better than K-means methods in terms of quality. It is adaptive, robust and efficient, achieving high autonomy, simplicity and efficiency. Therefore it can effectively provide early warning corresponding to reality so that the pre-warning system can provide useful information to regulate the property market.