The Design And Realization Of Second-hand Housing Data Analysis System

With the rapid development of social economy, the real estate market is more and more popular, in which the second-hand housing transaction accounts for a large proportion. In the points of view of consumer, real estate agent and government, it is necessary to help them complete their own decision-making by analysis of second-hand housing data. As a result, the second - hand housing data analysis system has an important significance. By a large number of researches, the thesis finds that there is few intelligent analysis systems for second-hand house at home and abroad. It is difficult to collect second-hand housing data and technology aspects in developing the intelligent analysis system of second-hand house. Moreover, it is still a challenge to efficiently manage and analyze these data.The thesis completes the second-hand housing management system based on SaaS platform and uses intelligent technology to analyze the platform data in the system. Firstly, the thesis analyzes the system requirement and determines the final function modules, which contains housing management module, customer management module, contract management module, business management module,business setting and system setting module. There is association among these modules management above. Secondly, the thesis adopts SaaS platform architecture and employ distribution design of database in the platform. Then, the thesis adopts ASP.NET, SQL SERVER 2008 for development based on the mixed model with C/S and B/S. Finally, the thesis simply tests function modules of the system.In analysis of data, the thesis conducts visualization analysis of housing data in the second-hand housing system, which contains housing price, the number of houses,the number of house agent and influence of transaction of second-hand caused by the policies. The visualization can be presented according to different conditions. Then,the thesis preprocesses housing data and removes repetitive house resource by comparing similarity among different house resource. Next, the thesis uses Bayes classification algorithm and SVM classification algorithm to classify the second-hand housing data preprocessed. Finally, the thesis uses linear regression algorithm and BP neural network to predict the house price in the future. The thesis designs and implements the intelligent processing function of second - hand housing data, so that users can have a deep understanding of second - hand housing data and help themself to make decisions.