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Abstract：
The effective prediction of housing prices is of great significance to buyers and real estate developers. However, most existing traditional approaches are inefficient and low accuracy. In this paper, we forecast the growth rate of house prices in different levels of cities of China by using Dynamic Model Averaging and Dynamic Model Selection, which allows both parameters as well as forecasting model rapid change. In addition, some evidence show that Internet search indexes could help model to improve prediction accuracy, so we have introduce Internet search indexes by using some key words under the background of big data in recent years. Under the cirteria of MAFE, MSFE and MLPL, the results of model forecast performance indicate that DMA and DMS model achieve significantly forecasting accuracy especificlly forgetting factors α=λ=0.95. © 2018 IEEE.
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Year： 2018
Language： English
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ESI Highly Cited Papers on the List： 0 Unfold All
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30 Days PV： 2
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