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Proceedings Paper

Spatial and temporal variations in residential housing prices in Beijing
Author(s): Yan Zhang; Meng Lv; Zhongdong Yin
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Paper Abstract

During the past 10 years, the real estate industry in Beijing has been manifesting a strongly growing trend. Researching on the distribution of house prices and their tendencies is helpful to grasp and predict the development of the real estate industry and could be used as reference to city planning. 120 records of housing price data in 2005 to 2006 and open prices in 38 developing projects from the first quarter of 2002 to the second quarter of 2008 were used in this study to analyze the spatial and temporal variations of house price with geostatistical methods and nonlinear regression. Results show that there was a very strong autocorrelation among the house prices in Beijing within the range of about 11 km in 2005 to 2006, which can be well fitted with the spherical model. The isogram of the house prices formed a group of homocentric ellipses, with their long axis extending NW-SE, and the house prices decreased from the center to the periphery. The spatial pattern of house prices in Beijing changed obviously from 2003 to 2006. Although both the spatial patterns for the two periods were homocentric ellipses, the shapes of the ellipses and the directions of the axes changed greatly. And there were more imbalances in 2005 to 2006. The house prices in the Huilongguan-Qinghe residential zone, an example of the typical real estate industry in Beijing, kept growing from 2002 to 2008 and could be fitted with exponential growth model.

Paper Details

Date Published: 16 October 2009
PDF: 8 pages
Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 74923D (16 October 2009); doi: 10.1117/12.837792
Show Author Affiliations
Yan Zhang, Beijing Forestry Univ. (China)
Meng Lv, Beijing Forestry Univ. (China)
Zhongdong Yin, Beijing Forestry Univ. (China)


Published in SPIE Proceedings Vol. 7492:
International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining

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