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JACIII Vol.21 No.6 pp. 1040-1047
doi: 10.20965/jaciii.2017.p1040
(2017)

Paper:

Housing Market Hedonic Price Study Based on Boosting Regression Tree

Guangtong Gu*,**,***,† and Bing Xu*

*Research Institute of Quantitative Economics, Zhejiang Gongshang University
Hangzhou, Zhejiang 310018, China

**School of Economics and Management, Zhejiang A & F University
Hangzhou, Zhejiang 311300, China

***Center for China Farmers’ Development of Zhejiang
Lin’an, Hangzhou, Zhejiang 311300, China

Corresponding author

Received:
December 25, 2016
Accepted:
May 2, 2017
Published:
October 20, 2017
Keywords:
machine learning, gradient boosting, residential hedonic price, regression tree
Abstract

Based on the purchase price data of new real estate markets three cities in China, Beijing, Shanghai, and Guangzhou, including architectural features, neighborhood property features, and location features, in this study a boosting regression tree model was built to study the factors and the influence path of housing prices from the microcosmic perspective. First, a classical hedonic price model was constructed to analyze and compare the significant effect factors on housing prices in the market segments of the three cities. Second, the gradient boosting regression tree method that is proposed in this paper was applied to the three markets in combination to analyze the influence paths and factors and the importance of the type of housing hedonic price. The influence paths of housing hedonic prices and decision tree rules are visualized. The significant housing features are effectively extracted. Finally, we present three main conclusions and several suggestions for policy makers to improve urban functions while stabilizing real estate prices.

References
  1. [1] L. Liang, “Macro control tools and the effectiveness on housing price in China,” HKU Theses Online (HKUTO), pp. 1-10, 2016.
  2. [2] Y. Deng, R. Morck, and J. Wu, “Monetary and fiscal stimuli, ownership structure, and China’s housing market,” National Bureau of Economic Research, pp. 1-15, 2011.
  3. [3] A. Ahuja, L. Cheung, and G. Han, “Are house prices rising too fast in China?,” IMF Working Papers, pp. 1-31, 2010.
  4. [4] Y. Hou, Q. Ren, and P. Zhang, “The Property Tax in China: History, Pilots, and Prospects,” Springer, pp. 50-71, 2014.
  5. [5] A. Court, “The Dynamics of Automobile Demand,” New York: General Motors Corporation, pp. 10-35, 1939.
  6. [6] Z. Griliches, “Price Indexes and Quality Change,” Indian Economic Review, Vol.40, Issue 157, pp. 111-119, 1971.
  7. [7] S. Rosen, “Hedonic Price and Implicit Market: Product Differentiation in pure Competition,” J. of Political Economy, Vol.82, Issue 1, pp. 34-55, 1974.
  8. [8] L. Yu, S. Zheng, and H. Liu, “The Spatial Variation and Affecting Factors of the Housing Price Gradients ? The Case of Beijing,” Economic Geography, Vol.28, Issue 3, pp. 406-410, 2008.
  9. [9] H. Z. Wen, B. U. Xiao-Qing, and Z. F. Qin, “The Spatial Effect of Urban Lakes on Housing Prices ? The Case of the West Lake in Hangzhou,” Economic Geography, Vol.32, Issue 11, pp. 58-64, 2012.
  10. [10] H. Wen and S. Jia, “Market Segment and Hedonic Price Analysis of Urban Housing,” J. of Zhejiang University (Humanities and Social Sciences), Vol.36, Issue 2, pp. 155-161, 2006.
  11. [11] Y. Shi and L. I. Muxiu, “The Analysis of the Housing Price Gradient and Its Impact Factors of Shanghai City,” Acta Geographica Sinica, Vol.61, Issue 6, pp. 604-612, 2006.
  12. [12] J. C. Weiche and H. Zerbst, “The externalities of neighborhood parksan empirical investigation,” Land Economics, Vol.49, Issue 1, pp. 99-105, 1973.
  13. [13] M. J. Potepan, “Explaining Intermetropolitan Variation in Housing Prices Rents and Land Prices,” Real Estate Economic, Vol.1, Issue 24, pp. 219-245, 1996.
  14. [14] Y. Wang, “The Research of Application of Semi Parameter Regression Model to Price Index of Housing for Sale,” Statistical Research, Issue 4, pp. 25-29, April 2005.
  15. [15] B. Hofner, A. Mayr, N. Robinzonov, et al., “Model-based boosting in R: a hands-on tutorial using the R package mboost,” Computational Statistics, Vol.29, Issue 12, pp. 3-35, 2014.
  16. [16] Y. Shin, “Application of Boosting Regression Trees to Preliminary Cost Estimation in Building Construction Projects,” Computational Intelligence and Neuroscience, Issue 7, pp. 1-9, July 2015.
  17. [17] J. H. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine,” Annals of Statistics, Vol.29, Issue 5, pp. 1189-1232, 2001.

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Last updated on Dec. 17, 2017