Housing Consumption And Customer Information Data Mining Research

Along with town inhabitants' standard of living gradually arrives at a whole relatively comfortable life in our country; housing consumption has already been becoming a new consumption hotspot during the subsequent years. And because of placing an important role in improving inhabitants' living standard, pulling the national economy growth, absorbing labor, real estate industry will take up a more important role in the Gross national product. In the same time, with the constant improvement and more critical inhabitants' consumption standard, the housing market's grown up and the more intensive market competition environment, all these pose a new challenge toward housing developer: They must think much more of the market and customers' needs and need more sufficient information and judgments toward all kinds of changing. The developers have accumulated abundant data that includes: industry information data, the whole economy data and customers' data. Through Data Mining method, this various data can be valuable and means a lot. So, data mining in housing consumption is very necessary.Data mining has already been used in bank, telecom and insurance etc. industry. This thesis introduces this technique in housing consumption industry. And it comprehensively uses data mining method, customer research theories and real estate industry background knowledge, establishing housing product's customers' satisfaction evaluation guideline and idiographic flow. In this thesis, it gives a model for housing consumption customer information analysis system based on data mining. Through empirical approach and related enterprises investigation, it designs a questionnaire system to collect data. In empirical part, it uses decision tree, clustering, neural network, association, regression, and factor analysis etc. data mining methods. Several model including crowd segmentation that is about to buy a house; clustering analysis buying which kind of house; purchasing power analysis; housing product satisfaction factor analysis etc. are given in this part. It finds some valuable results.It hopes that this thesis can help our house developers to carry more precise quantitative analysis and scientific forecast so that improving their competition power.