The Exploration Of Algorithm About The Chinese Real Estate’s Valuation In Bulk

With the real estate tax policy has been introduced and perfected. China’s real estate tax has entered a substantive stage. Housing prices become the key of the tax, but using the artificial method to complete the entire city real estate appraisal is almost impossible, China needs the CAMA system that used by western to assess housing prices. Multiple regression analysis as the core algorithm of CAMA system was introduced in detail in this paper. Expounding the advantages and disadvantages of the arithmetic based on multiple regression analysis. Then, try to using PSO to solve this estimate problem. The PSO can avoid the defect that caused by lacking data effectively, and it has been proved that PSO can be used to solve China’s housing prices estimate problem before China’s CAMA has been set up. The PSO can also offer the data which was very important to setting up China’s CAMA system. In this paper, MATLAB has been used to build PSO estimate system and assess a house locate in LANZHOU city with this system. The result shows that PSO can been used to solve China’s housing estimate problem. Although there was some limitation in the system, but the limitation can be reduce by using time series analysis. In a word, PSO was useful to estimate China’s housing prices.