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Real estate industry is both capital-intensive, highly related industries and industries essential to provide the daily necessities. However, the real estate pricing models and methods of research rarely receive the critical attention and development it deserves. This paper utilizes the principal components analysis method of multi-dimensional statistical analysis and artificial neural networks to determine the price of real estate. By using principal component method to process a number of listed real estate pricing indices. Firstly, the index system of accident risk was established. Then principal component analysis was applied to eliminate the indexes having the relativities and overlap information. Finally, based on historical data and artificial neural networks, a new real estate pricing models was established. The experiment results show that this method is effective and precise.