The Forecast of Housing Price in Xi’an Based on Big Data Analysis
Abstract
Based on the statistical data, the forecast for housing price of Xi’an city is made by identifying 13 different kinds of indicators. The multi-variable regression model and SPSS are used to analyze the data in linear and non-linear way respectively. R2 for both methods are over than 0.9. So we can get the similar conclusion that housing price in Xi’an will not increase dramatically recently and keep stable.
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References
Ding J. The impact factor of commodity housing’s price analysis in Xi’an and the price prediction [MSc thesis]. Xi’an: Xi’an University of Science and Technology; 2009. p. 8–18.
Peek J, Wilcox JA. The baby boom, “pent-up” demand, and future house prices. Journal of Housing Economics 1991; 1: 347–367.
Shi Q, Fu J. Analyze the current housing price trends in our city from the cost composition: A survey of housing prices in Nantong city (in Chinese). Marketing Week 2007; (3): 35–36, 47.
Wang L. From the composition of the real estate price to discuss the cause of excessively high housing price (in Chinese). Contemporary Manager 2006; (11): 228.
Sheng G, Li X. Commercial house: Looking at house prices from the cost structure (in Chinese). Economic Forum 2004; (5): 136–137.
Statistical Yearbook of Xi’an. Available from: http://navi.cnki.net/KNavi/YearbookDetail?pcode=CYFD&pykm=YXATJ&bh=.
Mi H, Zhang W. Practical modern statistical analysis methods and application of SPSS (in Chinese). Beijing: Contemporary China Publishing House; 2004. p. 121–150.
