Time Series Analysis Of House Price In Shanghai

Housing price has always been focused by people as it is relevant with many aspects in society.After rapid increasing during early twenty-first century,China's housing price has been at a very high level which makes imbalance comparing resident income.And the appearance of the real estate bubble has made housing price become one of the most important issues nowadays.Although our government has operated many policies to solve it,housing price doesn't have a clear decline overall and in some metropolises,it even continues its tendency of increasing.So to explore the rules of its development has positive affect to solve this bubble problem.On the other hand,the rise of Big Data makes housing price have more information itself.Among them,LBDS is a very important aspect because of its great role in housing valuation,location analyze,portrait description,credit management etc.So research on housing price and its tendency has great meaning in many industries.This paper uses time series to analyse housing price in Shanghai.Data comes from a website company called FangJia Web.We use ARIMA model to fit and forecast housing price in each region.Then we analyze housing price between regions and find out there exists cointegration between every two regions except two pairs of regions.At last,we test Granger causality between regions and explain the result combining with their geographical position.