The Analysis And Forecast Of Chinese Real Estate Price

We have a comprehensive reform of policy on housing in 1998. The real estate market are extremely active and rapidly developed, which made an outstanding contribution to the growth of the national economy. So the real estate industry has become a pillar industry of our country. Because of China’s real estate started relatively late, and its development is not perfect, so it has some obvious characteristics of volatility and typical primary market. The state will select the appropriate measures to guide estate companies to make appropriate and reasonable investment.As a barometer of the national economy and macroeconomic vane, operation of the real estate status not only affect the level and pace of national economic development, but also related to the country’s economic and financial security. It also have an effect on people’s living level. The most important and direct reflection for the real estate market and is the price of real estate. As price increases is a very complex economic issues, and the social, economic and other natural ingredients have a great impact on the development of real estate, real estate prices, thus changes affecting the quantitative relationship between economic factors can not be used to describe the precise mathematical language. In addition, more importantly, with respect to the foreign real estate market, our residential real estate, especially commercial development is not developed for a long time, and relevant statistics data is relatively small. Therefore, this paper mainly based on gray system theory and artificial neural network, multi-disciplinary knowledge of real estate economics, statistics and numerical analysis, using Matlab software to analysis and predict.Grey system theory is one of the topics at the forefront of Systems Science, is to study the "little data", "poor information" uncertainty, and as an important part of the gray theory. Gray prediction theory is the main content of gray system. Therefore the study of gray prediction model is very important. Therefore, this article is subject to gray system, mainly studied the gray prediction model. Initially talked about the development status of gray prediction model and its development trend, then tells the modeling process and residual test of gray GM (1,1) model, and applied the research into the forecasting of real estate price.BP neural network is an important branch of artificial neural networks, which promotes the continuous development of machine intelligence learning. In 1985, Rumelhart and some other well-known scholars put forward back propagation learning mechanisms, they continued to carry out the error back-propagation analysis and research in the theory and made some achievements in applications and practice, then through continuous improvement, the current BP neural network are gradually developed. BP neural network has a complete theoretical system, clear algorithm processes, as well as powerful data identification and simulation capabilities. In this paper, we give the prediction of China’s commercial real estate price by means of BP neural network.This article introduced related forecasting model, gray prediction model and artificial neural network model at first. Including principles, algorithms and testing of these two models, as well as commonly used GM (1,1) model and neural network model commonly used in BP model introduced in details. Then based on the gray GM (1,1) forecasting model and BP model in predicting and mature use pattern recognition to China’s real estate market, empirical research as an object, and the use of Matlab and other statistical data mining tools, China’s real estate market relevant data to establish the appropriate model, and make predictions. Finally, introduce the comparison of two methods to give the more apposite models, and form a comprehensive predictive analysis system.