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Real estate prices forecasting based on gray correlation analysis and neural network model

  • Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In order to find out the main causes of real estate price' changes, the gray correlation analysis was used to quantify the influence degree of each factor of real estate price. In order to modify the residuals of GM (1, 1) model, the gray neural network is established. First, the simulated values and residuals of real estate price sequence are produced by GM (1, 1) model, then, put the residuals which produced by model GM (1, 1) as inputs to the neural network. The effectiveness of our methodology was verified with an empirical study that compared GM (1,1) model with the hybrid approach. And the results show that this method can be an effective tool to predict the real estate price, the precision of this method is advantage to GM (1, 1) model, which is useful to provide a scientific basis for the Marco control of government, Investment decisions of real estate developers and purchase strategy of consumer.

Original languageEnglish
Title of host publication2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008
DOIs
StatePublished - 2008
Event2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008 - Dalian, China
Duration: 12 Oct 200814 Oct 2008

Publication series

Name2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008

Conference

Conference2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008
Country/TerritoryChina
CityDalian
Period12/10/0814/10/08

Keywords

  • Forecasting real estate prices
  • Gray correlation analysis
  • Gray-neural network

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