Grp10 Proposal

Background introduction

With the rapid development of China's economy, people have become more and more advanced in their quality of life and pursuit, which can be reflected in the economic migration of the population, the large number of rural population changes to urbanization and so on. As these things happened, property price become the concern. We can know about the development of economy and population of a city an urban agglomeration for now or even for the future, from the property price change in them. In addition, China's economic and housing policies will also have a great impact on housing prices. According to the data of property price, it can explore the change of property price and if the price has relationship with economy change and polices, through time series analysis. Secondly, through space analysis, it can observe the impact of transportation, especially, the HSR on the property price. In the end, it can make a forecast analysis to predict the change of property price for cities in China in 2 years.

Analysis method

Time series analysis

The residential property market change drastically recent 10 years, so a data set of long historical time series of Chinese nominal residential property prices will reveal price trends, city development scale, population flow and so on. Other method to analyses this time series data is “factor analysis”, this data is considered as a common influence of many factors, through decomposition and analysis of these factors the rules of price fluctuation will be reveal, and base on these rules we can predict future marketing trends. Since purchase demand is sensitive with restriction policy, tax policy and urban plan, and policy effect on price will release from certain time point, we keep our eyes on policy influence.

Space analysis

Urban agglomeration has become the basic unit of Chinese regional economic organization. High-speed transportations such as expressway, HSR, railway, airplane are accelerates the urban agglomeration which strengthen the economic relation. Due to high speed railways are lower time-consuming, more punctuality and scheduled more frequently than normal railway station, more and more people love to travel by HSR instead of other high-speed transportations. HSR has become the most popular travel method between two cities within same urban agglomeration. In this case, we want to find the relationship between HSR and property price and how will HSR station impact on property price by analyse price time series data. We plan to select three benchmarks cities—Beijing, Shanghai, Guangzhou as our target cities and other secondary cities which near about 300 km to these benchmark cities. Since HSR usually run almost 300 km per hours, we assume that second level cities in “one-hour zone” will be impacted on HSR station mostly. After that, we pin these selected locations on map and analysis housing price trends of these cities.

Forecast Analysis

The exploratory and TS similarity analytics on the data can help to forecast the future housing situation for cities in China in the future 2 year, and then we can use the data to check the accuracy of prediction.

Challenges

1- Property price not just simply influence by supply and demand relationship, economic development of this city but also by purchase restriction and tax policy. So how to separate those price influencing factors from HSR factor become one problem we need to fix. 2- Some policy may affect delay on price which increase difficulty to analyse and predict.