Academic Journal of Computing & Information Science, 2019, 2(3); doi: 10.25236/AJCIS.020302.
Science & Technology College, North China Electric Power University, Baoding, Hebei, 071003, China
With the development of China's economy and the continuous enhancement of its comprehensive strength, China has gradually entered the middle and upper levels of the world.However, residential prices have continued to rise and the number of properties has gradually increased.The increase in residential prices has exceeded the rate of economic growth.This paper mainly uses the idea of partial least squares regression to study the relevant data of a certain city. Calculated Q_h^2=0.0566<0.0975, to meet the principle of cross-validation judgment. Finally, the fitting coefficient of 0.88 was calculated, which proved that the equation fitting performance was good.
Partial least squares regression analysis of commodity housing price forecast
Qimeng Tao. Analysis of Commodity Housing Price Based on Partial Least Squares Regression. Academic Journal of Computing & Information Science (2019), Vol. 2, Issue 3: 9-15. https://doi.org/10.25236/AJCIS.020302.
 Ding Feng. Study on influencing factors and prediction of real estate prices [D]. Anhui University of Finance and Economics, 2014.
 Liu Hui. Influencing factors and Forecasting Research on the price change of commercial housing in Wuhan [D]. Wuhan University of Science and Technology, 2010.
 Chen Senjun. Influencing factors analysis and price forecast of commodity house prices in China [D]. Huazhong University of Science and Technology, 2008.