International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Volume XLII-3/W10
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 1281–1287, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-1281-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W10, 1281–1287, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W10-1281-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  08 Feb 2020

08 Feb 2020

EXPLORING THE IMPACT OF REAL ESTATE POLICY ON REAL ESTATE TRADING USING THE TIME SERIES ANALYSIS

S. W. Shao, X. Huang, L. X. Xiao, and H. Liu S. W. Shao et al.
  • Wuhan Land Resource and Urban Planning Information Center, Wuhan, China

Keywords: Time series analysis, Real estate, Policy analysis, Turning point detection

Abstract. Housing price is a major issue affecting people's lives, but also closely related to the interests of the people themselves. Housing prices are affected by various factors, such as economic factors, population size factors, social factors, national policy factors, the internal factors of real estate and environmental factors. With the deepening of urbanization and the agglomeration of urban population in China, housing prices have been further accelerated. The Chinese government has also introduced a series of policies to limit real estate transactions and affect property prices. This paper also aims to explore a time series analysis method to analyse the impact of real estate policies on real estate prices. Firstly, the article searches for policy factors related to real estate through government official channels such as state, Prefecture and city, and analyses key words related to policy by means of natural language processing. Then, the real estate registration volume, transaction volume and transaction house price data which are arranged into time series are modelled using ARIMA time series model, and the data are processed according to scatter plot, autocorrelation function and partial autocorrelation function graph of the model to identify its stationarity. Finally, the LPPL (logarithmic periodic power) model and MPGA (multi-population genetic algorithm) are used to fit and detect turning points of real estate registration data, and the time series detection algorithm is used to obtain the inflection time nodes of the sequence, and then the relationship between real estate policy and real estate transactions is analysed. Taking the real estate registration data in Wuhan as an example, this paper validates the above time series analysis method. The results show that some real estate policies (such as purchase restriction policy, public rental policy, etc.) have a certain impact on real estate transactions in a short time. Part of the real estate policy (such as graduate security, settlement policy, etc.) does not have a significant impact on real estate transactions. To sum up, the government's brutal blockade of macro-control of the housing market cannot fundamentally solve the housing difficulties of the people, but also standardize the real estate market trading mechanism, innovate the market trading mode, so as to promote the long-term development of the housing market.