The Stud Of Data Mining For Customer Information Of Real Estate

With the fast development of the social economy and the enhancement of urbanization process, the importance of realty business in macro economy gets constantly prominent, while it also reveals certain problems at the same time. On one hand, the newly built areas are on the rise; on the other hand, the vacancy rate goes up too. According to the data of realty business blue paper,2009, in the year 2008 the vacancy rate of commercial residential buildings has a swift growth. Real estate develop companies lack sufficient market research. They don't know the actual demand of customers and just produce the wrong development projects which beyond the requirement of customers. All this makes the dealing of realty business in dispute all the time, thus affects its sound development.So in order to win in the violent competition, real estate develop companies have to make the most of all kinds of economic data and customer information, analyze the supply and demand of the realty business market, make the accurate prediction. Only by doing so can they remain invincible in the rat race. Yet to achieve the goal, it is quite important for us to do the data mining about customer information.Data mining is the most powerful data analysis method among the field of data warehouse. Simply speaking, data mining is to establish mathematic model by facing vast storage data, to find both the default business rule and the useful information and put it into practice. I conduct the data pick-up on part of the real estate developer S and the data from the questionnaire survey of Xi'an Residential Situation 2009 and store it in the database SQL Server 2000. Then these survey data are analyzed using the Clustering Algorithm, Decision Tree Calculus and Association Rules Algorithm of SPSS Clementine 11.0. Based on the entire algorithm, I set up several models whose precisions are carefully examined, and finally I choose the best mathematical model, cramp out some useful models, rules and information to the realty business according to the result. On one hand, both models and rules reflect the situation of the housing need in the city of Xi'an objectively. On the other hand, they enable the realty business to make the right decision, locate the segment market and discover the clients'value in an utmost way, thus improve the property boom.