# RECORD 80c7a3882a0d8e30de9d0f2d3d4f1558e6df1c50
# TITLE: The Transitional Spatial Pattern of Housing Prices in Beijing: Factors and Implications
# YEAR: 2014.0  VENUE: nan  DOI: 10.14246/irspsd.2.3_46
# AUTHORS: Fang Wang; Xiao-Lu Gao
# PAGES: 17  CANDIDATE TABLE PAGES (1-based): [13]
# ABSTRACT: : The shift from the free-allocation housing welfare system to a market system was one of the most important parts of the economic system reform implemented in China from 1998 onwards. In recent years, however, soaring housing prices have become a serious public concern. In this study, we explored the spatial characteristics of housing prices, using spatial data on the average transaction price of Beijing’s housing blocks from 2005 and 2012, in order to reveal the factors influencing changes in housing price and address the way in which such changes in turn can affect land use, transportation, the living environment, and quality of life. The analysis revealed that the spatial autocorrelation effects associated with housing prices (and their increase) became significantly stronger between 2005 and 2012, especially in the central part of Beijing. Autocorrelation was, however, identified for both years. This finding explains the emergence of sub-area markets in recent years. It also provides evidence which suggests that developers or real estate agencies may have boosted housing prices purposely, which can lead to a biased market environment. Targeted policies are required in order to address, and avoid, this problem in the future. The spatial patterns of housing prices in different parts of Beijing were examined in detail. The differentiation of housing prices showed that Beijing has, in the past decade, gradually become a polynuclear city. By comparing the data for the two years using a multivariate linear regression model, the factors influencing housing price growth were analyzed. Besides location and the environment, it was found that housing policies related to property rights, the construction of the transportation network, and population change all played a key role in explaining changes in the spatial pattern of housing prices in Beijing in recent years.


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46 
International review for spatial planning and sustainable development, Vol.2 No.3 (2014), 46-62 
ISSN: 2187-3666 (online) 
DOI: http://dx.doi.org/10.14246/irspsd.2.3_47   
Copyright@SPSD Press from 2010, SPSD Press, Kanazawa 
The Transitional Spatial Pattern of Housing Prices in 
Beijing: Factors and Implications 
Fang Wang1, 2 and Xiaolu Gao1* 
1 Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic 
Sciences and Natural Resources Research, Chinese Academy of Sciences 
2 University of Chinese Academy of Sciences 
* Corresponding Author, Email: gaoxl@igsnrr.ac.cn
Received 23 August, 2013; Accepted 24 Feberuary 2014 
Keywords: 
Housing Price, Spatial Autocorrelation, Spatial Structure, Beijing 
Abstract: 
The shift from the free-allocation housing welfare system to a market system 
was one of the most important parts of the economic system reform 
implemented in China from 1998 onwards. In recent years, however, soaring 
housing prices have become a serious public concern. In this study, we explored 
the spatial characteristics of housing prices, using spatial data on the average 
transaction price of Beijing’s housing blocks from 2005 and 2012, in order to 
reveal the factors influencing changes in housing price and address the way in 
which such changes in turn can affect land use, transportation, the living 
environment, and quality of life.  
The analysis revealed that the spatial autocorrelation effects associated with 
housing prices (and their increase) became significantly stronger between 2005 
and 2012, especially in the central part of Beijing. Autocorrelation was, however, 
identified for both years. This finding explains the emergence of sub-area 
markets in recent years. It also provides evidence which suggests that 
developers or real estate agencies may have boosted housing prices purposely, 
which can lead to a biased market environment. Targeted policies are required in 
order to address, and avoid, this problem in the future. 
The spatial patterns of housing prices in different parts of Beijing were 
examined in detail. The differentiation of housing prices showed that Beijing 
has, in the past decade, gradually become a polynuclear city. By comparing the 
data for the two years using a multivariate linear regression model, the factors 
influencing housing price growth were analyzed. Besides location and the 
environment, it was found that housing policies related to property rights, the 
construction of the transportation network, and population change all played a 
key role in explaining changes in the spatial pattern of housing prices in Beijing 
in recent years. 
1.
INTRODUCTION
The top priority for every family, housing constitutes an issue that is also 
relevant to the welfare, social stability, and economic development of a nation. 
From 1998 onwards, China has undergone intense urban housing system 
@Licensee SPSD Press 
This open access article is published under a 
Creative Commons [Attribution-
NonCommercial-NoDerivatives 4.0 
International] license. 
https://creativecommons.org/licenses/by-nc-nd/4.0/ 


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58 
IRSPSD International, Vol.2 No.3 (2014), 46-62  
 
4.2 
Linear regression model 
This study used a semi-log linear regression model, which has a better 
imitative and interpretation quality. Regression analysis was conducted, and 
multicollinearity detected by a stepwise regression technique (significance 
level is 0.05) in the SPSS16.0 (Table 5 and Table 6) using the data from 2005 
and 2012, respectively. The results show that 13 variables entered the model 
in 2005 and 14 variables in 2012. Whilst both models have good explanatory 
power (adjusted R2=0.771 in 2005, adjusted R2=0.549 in 2012), the imitative 
quality was found to be better in 2005 than in 2012, probably because the 
influencing factors were more complicated in 2012. Not all the variables 
entered into the model in 2005 had strong multicollinearity. In 2012, the three 
property levels in the variables had good multicollinearity. However, through 
testing – by changing the variables randomly in the model – the three property 
types were all found to have strong significance and their regression 
coefficients were found to change little. As such, the models are considered 
reliable. 
Table 5. Regression results of housing price in 2005 
 
B 
Std. 
Error 
Beta 
t 
sig 
Tolerance VIF 
 
(Constant) 
8.530 .057 
 
149.613 .000  
 
Sub-center 
distance 
-.034 .003 
-.360 -12.189 .000 .338 
2.955 
Property costs 
.079 
.006 
.261 
12.265 .000 .653 
1.530 
Apartment 
.416 
.052 
.373 
8.004 
.000 .136 
7.371 
Third-level 
hospital 
.107 
.023 
.089 
4.577 
.000 .781 
1.281 
Urban 
center 
distance 
-.011 .002 
-.140 -4.588 
.000 .315 
3.175 
Building age 
-.025 .004 
-.114 
-6.429 
.000 .944 
1.060 
Key high school 
.071 
.025 
.054 
2.801 
.005 .780 
1.281 
Plot ratio 
.013 
.004 
.068 
3.678 
.000 .864 
1.158 
Number 
of 
bus 
station 
.006 
.002 
.058 
3.141 
.002 .854 
1.171 
Park 
.060 
.020 
.056 
3.000 
.003 .854 
1.171 
Cottage 
.373 
.086 
.091 
4.331 
.000 .661 
1.513 
Common residence .169 
.048 
.160 
3.545 
.000 .144 
6.939 
Subway station 
.039 
.019 
.038 
2.085 
.037 .897 
1.115 
Dependent Variable: ln (2005 housing price) 
Table 6. Regression results of housing price in 2012 
 
B 
Std. 
Error 
Beta t 
sig 
Tolerance VIF 
 (Constant) 
9.927 .097 
 
102.196 .000 
 
 
Sub-center 
distance 
-.033 .001 
-.399 -24.193 
.000 .330 
3.031 
Urban 
center 
distance 
-.016 .001 
-.244 -12.482 
.000 .235 
4.247 


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Fang Wang and Xiaolu Gao 
59 
 
Property costs 
.041 
.002 
.184 16.388 
.000 .715 
1.399 
Cottage 
.518 
.092 
.248 5.613 
.000 .046 
21.834 
Building age 
.005 
.001 
.077 7.174 
.000 .787 
1.271 
Third-level 
hospital 
.075 
.011 
.073 6.911 
.000 .796 
1.256 
Common 
residence 
.333 
.091 
.303 3.666 
.000 .013 
76.013 
Greening ratio 
.002 
.000 
.041 4.043 
.000 .884 
1.131 
Population density .001 
.000 
.029 2.230 
.026 .546 
1.830 
Subway station 
.038 
.011 
.035 3.599 
.000 .977 
1.024 
Apartment 
.282 
.091 
.229 3.089 
.002 .016 
60.963 
Number of bus 
station 
.002 
.001 
.047 2.833 
.005 .327 
3.054 
Plot ratio 
-.005 .002 
-.026 -2.676 
.007 .916 
1.091 
Key high school 
.031 
.012 
.027 2.652 
.008 .851 
1.175 
Dependent Variable: ln (2012 housing price) 
4.3 
Analysis of influencing factors 
4.3.1 
Location and traffic 
Location characteristics play a very important role in determining housing 
prices. This study found that the housing price decreased as distance to the 
urban center increased; as such, prices obeyed the rent distance attenuation 
law. A 1-km increase in the distance to the urban center was found to lead to a 
1.1% decrease in price per square meter in 2005 and a 1.6% decrease in 2012, 
respectively. At the same time, the variable describing the distance to the 
nearest sub-center also exerted a powerful, negative influence on housing 
prices in both of the years studied. Furthermore, the influence coefficients for 
the sub-center variable were greater than those generated by the urban center 
variable, indicating that sub-centers exert greater influence than the urban 
center, and thus that the polycentric pattern of Beijing has, at least initially, 
now been established. 
In addition, variables relating to transport convenience within the location 
characteristics were also found to have a significant and positive impact on 
housing prices. The variables relating to the proximity of a subway station and 
to the number of bus stations were both approved in a significance test for 
both of the years studied. Holding all other conditions the same, the unit 
housing prices of housing blocks that have a subway station within 1500 m 
were found to be about 3.9% and 3.8% higher per square meter in 2005 and 
2012, respectively, than those without a subway station (we used the subway 
station plan to 2015 for the 2012 subway station data). Further, each additional 
bus station was found to raise the housing price by 0.6% per square meter in 
2005 and 0.2% in 2012. Convenient transport access is good for trade 
development and population centralization; as such, it will stimulate the 
development of commercial housing. Transport will also directly influence 
the level of convenience experienced in people’s daily life – for instance, 
commuting and shopping, constituting a significant factor to consider when 
residents purchase houses.