The Design And Implementation Of Information Statistical System For Regional Housing

The development of information technology breeds the emerging market.As a new method of housing transaction,choosing and buying housing online offers great conveniences for property buyers.Nevertheless,to a certain extent customer experience has been compromised by some open problems,for instance,part of the housing related information is scattered and incomplete.In order to better serve customers,it is necessary that online service providers are able to collect and manage the regional housing information.This article presents the design and implementation of information statistical system for regional housing.This project aims to develop an information statistical system for regional housing according to buyers' requirements of housing features,in which also contains the function of housing price forecast.In terms of regional housing information statistics,we take community as the unit,developing the system according to buyers' requirements of housing features.The system allows data-entry staff to realize following operations,such as structural entry of more than 80 housing features,video and picture transcoding and compression,price forecast,content preview,content approval,role and permission configuration,data visualized analysis,system maintenance,database backup,mailbox password recovery and complex overall system retrieval.On the part of housing price trend forecast,we take transaction prices in various cities into consideration,so that we can build the price forecast model based on BP(Back Propagation)neural network and tell the future price with a reference to historical prices,it provides references for data-entry staffs.All of the above functions form a complete whole.We separate front-end development and back-end development of web system,Spring Boot and Vue are used during the process.In addition,according to JWT(JSON Web Token)standard,we conduct API verification between front-end and back-end to improve system integrity.In terms of usage,the information is input by data-entry staff on front-end and is stored detailedly in MySQL database.The content status varies with actual operations.To forecast housing price,we choose following indexes as research variables,such as the aggregate amount of real estates in research area,sales area of residential commercial housing,gross domestic product,consumer price index,local population,central bank benchmark interest rate and economic policy uncertainty index,so that it can be achieved based on BP neural network by using MATLAB.