THE RELATIONSHIP BETWEEN URBANIZATION AND REAL ESTATE INDUSTRY: Evidence from Chinese Regions

Int. Journal for Housing Science, Vol.33, No.4 pp. 195-206, 2009 Published in the United States THE RELATIONSHIP BETWEEN URBANIZATION AND REAL ESTATE INDUSTRY: Evidence from Chinese Regions Y. Zhao Faculty of Construction Management and Real Estate Chongqing University China S. Li Chongqing Foreign Trade& Economic Relations Commission China ABSTRACT Urbanization of China develop rapidly and influence Chinese economy more and more deeply, especially real estate industry currently. Establish a factor analysis model and compare the indices of urbanization with them of real estate industry, and classify the 31 sample regions to four grades in China; it is supposed that highly rapid development of real estate industry could make for the urbanization process in many regions, but the local governments adopt developing real estate industry as a path to raise the levels of urbanization generally, which may be a fearful bubble in real estate market. Key words: Urbanization; real estate industry; factor analysis. 0146-6518/04/ 195-206, 2009 Copyright 2009 IAHS

196 Zhao and Li Introduction Urbanization is an irreversible trend in economic and social development of China, and the precursor of sustained development. Joseph Stiglitz, a Nobel Prize winner said: the urbanization in China and the high-tech development in the United States will be the two keys to influence the human development in the 21st century deeply... China's urbanization will be a locomotive for the regional economic growth and produce the most important economic benefits. Urbanization is that the style of human s production and subsistence changes from rural economics to urban economics, which is denoted that rural population transform to urban population continually. UN-Habitat uses the proportion of urban population to total population to denote the rate of urbanization. Urbanization process has great influence on the development of real estate industry in China. As Table 1, the proportion of urban population in China is growing steadily from 33.35% in 1998 to 43.90% in 2006, and floor space and total sale of commercialized buildings sold were rising 5.08 times and 8.29 times respectively in China. We could analysis the correlativity between the rate of urbanization and sale of commercialized buildings by calculate the correlation, use data in China from 1998 to 2006 and the formula as below. COV ( Xm, Xn) ρ j = (j=1,2) (1) DX ( m) DX ( n) Where ρ j is correlation, X m denotes the proportion of urban population, X n (n=1,2) are floor space and total sale of commercialized buildings sold, and COV(X m,x n, ) is their covariance, D(X m ) and D(X n ) are the deviations, the outcome is ρ 1 =0.952 and ρ 2 =0.905. So there are some potential associated relation between urbanization and real estate industry. Table 1 Proportion of Urban Population and Sale of Commercialized Buildings in China (1998-2006) Year Proportion of Floor Space of Total Sale of Urban Population Commercialized Buildings Commercialized (%) Sold (10 000 sq.m) Buildings(10 000 yuan) 1998 33.35 12185.30 25133027 1999 34.78 14556.53 29878734 2000 36.22 18637.13 39354423 2001 37.66 22411.90 48627517 2002 39.09 26808.29 60323413 2003 40.53 33717.63 79556627 2004 41.76 38231.64 103757069 2005 43.00 55486.22 175761325 2006 43.90 61857.07 208259631

Urbanization and Real Estate Industry 197 There is a large volume of literature that provides in depth analysis of real estate industry or housing price and the influence of urbanization in various countries. Only some of the most important and historic papers are mentioned the relationship between urbanization and real estate industry. Some of them support that the huge expansion space that urbanization brings to the development of real estate industry and urbanization of a city could be seen as a stimulus to real estate industry. Jianlin Jiang points out that the real estate industry should actively meet the challenge of urbanization(1). Guiwen Liu and Jianwei Yang drew a conclusion that investment in fixed assets makes larger contribution to urbanization improvement than consumption and net export, especially, the investment in social infrastructure makes the largest contribution rate(2). Hong Ren and Zhao Wen supposed that the house price are mainly determined by the city value, the city value to upgrade leads to the rising of the house prices(3). with feedback effects observed to affect the vacancy rate of new dwellings changes in CPI, and change in per capita disposable income of urban households, Hongyu Liu and Yue Shen indicate that there is a long-term equilibrium relationship between housing prices and market fundamentals in China and it is the identified fundamentals that drive housing price up, rather than a bubble(4). On the other hand, some studies argued that urbanization could not infect real estate industry and housing price essentially. Lei Zhu supported that the essential of housing price climbing on the basis of supply and demand, though the demonstration of potential over-urbanization (5). Min Zhao and Ying Zhang provided new insights as to the paradox of China s urbanization lag of economic development, the income contribution of urbanization process, and the patterns of development(6). Sunsheng Han and Kevin O Connor explore the links between urban consolidation policy and house prices for the period 1991-2004 in Melbourne, they found that there were very weak statistical connections, and concluded that the policy has not been associated(7). In part because of the index of urbanization are more intractability, and the connection with real estate is not conspicuous, virtually no carefully methods were applied to analysis the urbanization and real estate industry according to some mutual factors in China. This paper provides an analysis of the relationship between urbanization and real estate industry. Data Model Specifications The purpose of this research is to explore the causal relationship between urbanization and real estate industry. Although the proportion of urban population to total population has been used to denote the rate of urbanization, the level of urbanization should include development of society and economy, construction of infrastructure and public enterprise etc(8), the indices of the fore facets are listed as Table 2; some of them are also indices of real estate industry.

198 Zhao and Li All original data are selected from 31 sample provinces and cities in China statistical yearbook 2007. The inconformity of dimensions and the quantitative differences that can be standardized according to the formula, a progressing to the original data as follow. Vij Vi Xij = (2) si Where X ij is new standardized data to variable V i, V ij is original data for variable V i, Vi is the average of original data of variable V i, s i is standard deviation. Table 2 Listing of variables available to the model variables Connotation of the variables development of society factors V 1 the proportion of urban population V 2 employed persons in urban V 3 average wage of staff and workers V 4 Number of Students in Institutions of Higher Education V 5 Number of schools, Institutions and universities of Higher Education V 6 Number of health institutions V 7 Number of total beds development of economy factors V 8 GDP V 9 per capita GDP V 10 average selling price of commercialized buildings V 11 savings deposit of urban households V 12 per capita annual disposable income of urban households V 13 per capita annual consumption expenditure of urban households V 14 Volume of sulfur dioxide emission construction of infrastructure factors V 15 total investment in fixed assets by urban areas V 16 real estate investment in fixed assets V 17 newly real estate increased fixed assets in urban area V 18 total government expenditure V 19 expenditure for operating expenses of education V 20 expenditure for operating expenses of department of science V 21 expenditure for public health V 22 expenditure for city maintenance V 23 developed areas of city construction V 24 floor space of commercialized buildings sold V 25 total sale of commercialized buildings sold Public enterprise factors V 26 public vehicles under operation per capita area of urban green areas V 27

Urbanization and Real Estate Industry 199 Establishment of urbanization analysis model For reducing dimensions with a minimum loss of information, factor analysis method could be used to this research, the statistical approach that can be used to analyze interrelationships among large numbers of variables, and to explain these variables in terms of their common underlying factors. The variable X used in this research is more complicated which are summarized and listed above. The estimated equation is specified as follows: y=ax+e (3) Where y=(y 1,y 2,,y k ) T denote the levels of urbanization of 31 sample regions in China, x=(x 1,x 2,,x m ) T is standardized data of indices of urbanization, e=(e 1,e 2,,e k ) T is respective especial factors, a11 a1m A = is load matrix ak1 a km If use capital letters denote random variable, especial factor is denoted ε i, the model could express as: X 1 =a 11 F 1 +a 12 F 2 + +a 1m F m +ε 1 X k =a k1 F 1 +a k2 F 2 + +a km F m +ε k This can be donated as matrix follow: Y=AX+ε (4) Among the equation X 1, X 2,, X m without relativity each other, random variables ε 1, ε 2,, ε k and X are independent. Preliminary analysis Factors Analysis We can start the experiment on the foundation of standardized data. After Kaise- Meyer-Olkin and Bartlett s test, the value of KMO is 0.621>0.6 which means that Kaiser-Meyer-Olkin measure of sampling is adequacy, and, for this data Bartlett s test is highly significant 0.000<1%, therefore factor analysis is appropriate(9).

200 Zhao and Li Factor extraction The result of variance analysis by applying statistics software SPSS is expressed with Table 3, which displays the eigenvalue in terms of the percentage of variance explained. In the final part of the Table 3, the eigenvalue of factors after rotation are displayed, rotation has the effect of optimizing the factor structure and one consequence for these data is that the relative importance of the three factors is equalized. Table 3 Result of variance analysis Initial Eigen values Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings Total % of Variance Cum. % Total % of Variance Cum. % Total % of Variance Cum. % 1 17.401 64.447 64.447 17.401 64.447 64.447 13.933 51.603 51.603 2 5.355 19.835 84.282 5.355 19.835 84.282 8.597 31.841 83.444 3 1.159 4.294 88.576 1.159 4.294 88.576 1.386 5.133 88.576 Used varimax rotation method, Table 4 shows the rotated component matrix which is a matrix of the factor loadings for each variable onto each other, factor loadings less than 0.6 have not been displayed because we asked for these loadings to be suppressed. We can see from the table, the common themes among highly loading indices can help us identify what the construct might be. Factor1 seem to all relate to the level of social and economic development of the regions, which includes employed persons in urban(v 2 ), number of students in institutions of higher education(v 4 ), number of schools, institutions and universities of higher education(v 5 ), number of health institutions(v 6 ), number of total beds(v 7 ), GDP(V 8 ), savings deposit of urban households(v11) total investment in fixed assets by urban areas(v15), real estate investment in fixed newly real estate increased fixed assets in urban area(v 17 ), total government expenditure(v 18 ), expenditure for operating expenses of education(v 19 ), expenditure for public health(v 21 ), expenditure for city maintenance(v 22 ), developed areas of city construction(v 23 ), floor space of commercialized buildings sold( V 24 ), total sale of commercialized buildings sold(v 25 ), public vehicles under operation(v 26 ), we label this factor fear of regional comprehensive strength factor. The loading highly on factor2 also seem to relate to the economic force of the region, which includes the proportion of urban population(v 1 ), average wage of staff and workers(v 3 ), per capita GDP(V 9 ), average selling price of commercialized buildings(v 10 ), per capita annual disposable income of urban households(v 12 ), per capita annual consumption expenditure of urban households(v 13 ), real estate investment in fixed assets(v 16 ), expenditure for operating expenses of department of science(v 20 ), total sale of commercialized buildings sold(v 25 ), all of the indices above are also indices of real estate market, V 1,V 3,V 9,V 12,V 13 are demand indices of real estate market, V 10,V 16, V 20 are supply

Urbanization and Real Estate Industry 201 indices of real estate industry, V 25 are sale indices of real estate market, we label this factor fear of real estate industry factor(10). Factor3 all seem to relate to environmental protection, which includes volume of sulfur dioxide emission (V 14 ), per capita area of urban green areas(v 27 ), we can see from Table 4 that the influence of V 14 is negative, we label this factor fear of environmental protection factor. Table 4 Rotated component matrix Component 1 2 3 X 1 0.889 X 2 0.953 X 3 0.905 X 4 0.892 X 5 0.907 X 6 0.848 X 7 0.967 X 8 0.925 X 9 0.936 X 10 0.949 X 11 0.851 X 12 0.941 X 13 0.941 X 14-0.667 X 15 0.935 X 16 0.735 0.618 X 17 0.687 X 18 0.900 X 19 0.923 X 20 0.816 X 21 0.823 X 22 0.782 X 23 0.938 X 24 0.882 X 25 0.606 0.761 X 26 0.840 X 27 0.786 Factors value calculation We can calculate the value of the factors above use the formula(4)

202 Zhao and Li Y=AX+ε As values of ε denote especial factors, we can neglect and don't account. The value of matrix X is shown as Table 5. Table 5 Component score coefficient matrix Component 1 2 3 X 1-0.044 0.135-0.028 X 2 0.078-0.031 0.056 X 3-0.061 0.142 0.016 X 4 0.084-0.042-0.067 X 5 0.078-0.020 -.0106 X 6 0.096-0.074-0.123 X 7 0.096-0.059-0.079 X 8 0.073-0.030 0.104 X 9-0.040 0.142-0.051 X 10-0.047 0.142-0.016 X 11 0.056 0.011 0.014 X 12-0.032 0.142-0.085 X 13-0.034 0.148-0.114 X 14 0.031 0.036-0.532 X 15 0.080-0.045 0.093 X 16 0.033 0.048 0.022 X 17 0.031 0.035 0.088 X 18 0.062 0.018-0.081 X 19 0.069 0.000-0.055 X 20-0.003 0.109-0.084 X 21 0.050 0.032-0.055 X 22 0.048 0.013 0.063 X 23 0.081-0.052 0.131 X 24 0.064-0.007 0.035 X 25 0.012 0.079 0.009 X 26 0.053 0.017 0.011 X 27 0.031-0.104 0.648 Use formula(5) and contribution rate above, we can calculate the level of urbanization. V=0.51603F 1 +0.31841F 2 +0.05133F 3 (5)

Urbanization and Real Estate Industry 203 Where V donates the level of urbanization, and F i donate the value of factors, the factor sore functions are listed as below, values of F i and V are arrayed, which are shown as Table 6. F 1= -0.044 X 1 +0.078 X 2-0.061 X 3 + +0.031X 27 F 2= 0.135 X 1-0.031 X 2 +0.142 X 3 + -0.104X 27 F 3= -0.028 X 1 +0.056 X 2 +0.016 X 3 + +0.648X 27 For discussing the contribution of real estate industry and its relation to urbanization progress, we also array the sequence of F 2 and V in Table 6. Sample regions classification With studying data of Table 6 above, we classify the sample regions to four grades; grade1 is that the sequence of F 2 and V are both in the front, grade2 is that the sequences of F2 are in the front, but sequences of V are in the back, grade3 is that the sequences of V are in the front, but sequences of F 2 are in the back, grade4 is that the sequences of F2 and V are both in the back. The results are listed as Table 7. Conclusion This paper investigates the relationship between real estate industry and urbanization in Chinese 31 sample provinces and cities, and classifies four grades of them. The grade1 regions take Peking and Shanghai as a representative. Objectively, background of real estate industry of these regions is in the social and economic developing rapidly, draw on a great deal of domestic and international investment, especially the quality higher investment, the regions have more sturdy development of social and economy to prop up, and the urban radiant functions are stronger, highly fast development of real estate industry could make for the urbanization process in these regions, rationally. On the contrary, real estate industry quickly extends that maybe have bad reflections in grade2 regions like Hainan and Chongqing before long. Because speed of development of industry and business could not be correspond to highly rapid development of real estate industry. Although real estate industry became one of the main reasons to push urbanization process in these regions, the huge consumption of real estate industry is sustained by the residents who come from around urban regions; therefore many residences begin to live in suburban area. Generally, the real estate is fundamental development as a result, but shouldn't be the main reason which pushes urbanization process in these regions. The real estate industry can not match the level of urbanization in grade3 regions, for example Shangdong and Hebei. The levels of urbanization in these regions are higher than the development of real estate industry. As firmer social and economic foundations, local governments could draw up policies to activate real estate market, and there are huge potential consumptive spaces in grade3 regions.

204 Zhao and Li Table 6 Value and its sequence of Chinese 31 sample regions regions F 1 F 2 Seq. F 3 V Seq. Beijin -0.23212 2.92564 2 0.61141 0.84 4 Tianjin -1.11934 1.36078 4-0.11769-0.15 16 Hebei 0.81616-0.68283 28-0.78701 0.16 10 Shanxi -0.22099-0.31876 12-1.62286-0.30 21 Inner Mongolia -0.44887-0.31484 11 0.51709-0.31 24 Liaoning 0.80825-0.04237 9-0.53072 0.38 7 Jilin -0.39281-0.34747 15 0.06207-0.31 23 Heilongjiang -0.00799-0.41508 19 0.03716-0.13 15 Shanghai -0.39955 3.27054 1-1.03866 0.78 6 Jiangsu 2.14051 0.39826 6 1.30781 1.30 2 Zhejiang 0.74453 1.36582 3-0.29647 0.80 5 Anhui 0.13259-0.53069 23 0.01816-0.10 14 Fujian -0.32283 0.33939 7 0.15974-0.05 13 Jiangxi -0.15829-0.70206 30 0.25991-0.29 19 Shandong 2.21528-0.61278 26 1.35927 1.02 3 Henan 1.04263-0.82663 31-0.30611 0.26 8 Hubei 0.46329-0.59311 25 0.10951 0.06 11 Hunan 0.41022-0.54928 24-0.96831-0.01 12 Guangdong 2.21006 1.01510 5 1.00616 1.52 1 Guangxi -0.29387-0.51491 22 0.22786-0.30 22 Hainan -1.30702-0.40774 18 2.17518-0.69 28 Chongqing -0.55548 0.06903 8-0.58590-0.29 20 Sichuan 0.95032-0.69583 29-0.62494 0.24 9 Guizhou -0.59591-0.37337 17-2.56882-0.56 26 Yunnan -0.26328-0.34251 14-0.59355-0.28 18 Tibet -1.50498-0.35365 16 1.65896-0.80 31 Shaanxi -0.11217-0.41764 20-1.15274-0.25 17 Gansu -0.66322-0.64622 27-0.18675-0.56 27 Qinghai -1.40033-0.32323 13 0.94417-0.78 30 Ningxia -1.34419-0.31000 10 1.17957-0.73 29 Xinjiang -0.59059-0.42353 21-0.25347-0.45 25 The real estate industry and level of urbanization are both lag in grade4 regions comparatively. These regions are most undeveloped regions in west of China. Investment of fixed assets should be enlarged to improve construction of infrastructure, and the real estate industry could be developed with the process of urbanization together in grade4 regions at the same time.

Urbanization and Real Estate Industry 205 Table 7 Classification of Chinese 31 sample regions classification regions grade1 Shanghai, Beijin, Zhejiang, Guangdong, Jiangsu, Liaoning grade2 Chongqing, Hainan, Tianjin,Qinghai, Ningxia, Tibet, Jilin, Inner Mongolia grade3 Shandong, Hebei, Anhui, Hunan, Hubei, Sichuan, Jiangxi grade4 Yunnan, Shanxi, Gansu, Guizhou, Guangxi, Shaanxi, Henan Heilongjiang, Fujian, Xinjiang If the real estate is a kind of result of the urbanization, regional government adopted real estate industry as a path to develop currently, real estate industry act as important reason of urbanization, which disobey the basic economic regulation and may exist a fearful bubble. As the analysis above, economic or political policies of urbanization are drew up should be conformance with the regulation as to the relationship of real estate and urbanization. References 1. Jianlin Jiang, Weixing Jin & Yunfeng He, (Dec 2003)Urbanization and the real estate industry of China[J], Xi an Univ. of Arch.& Tech, p.367-371, Xi an, China 2. Guiwen Liu, Jianwei Yang & Xun Deng, (May, 2006)Study on economic factors affecting urbanization in China[J], Urbanization, p.9-12, China 3. Hong Ren, Zhao Wen & Guangming Lin, (Aug 2007) Demonstration and analysis of value of cities decides housing price and advice of policy[j], Construction Economy, p.22-26, Chongqing, China 4. Hongyu Liu & Yue Shen, (Oct 2005)Housing prices and general economic conditions: an analysis of Chinese new dwelling market[j], Tsinghua Science and Technology, p.334-343, Beijing, China 5. Lei Zhu, (Jul 2007)Analysis of the climbing price of real estate under potential over urbanization [J], Economy and Management, p.5-9, Tianjing, China 6. Min Zhao & Ying Zhang, (May 2008)Development and urbanization: a revisit of Chenery-Syrquin s patterns of development[j], The Annals of Regional Science, Springer Berlin, Germany 7. Sunsheng Han & Kevin O Connor, Urban consolidation and house prices: a case study of Melbourne 1990-2004[J], Geo Journal, Springer Science-Business Media B.V.2008, Melbourne, Australian 8. Jianzhong Xu & Lin Bi, (Feb 2006)Evaluation of urbanization development level based on factor analysis[j], Journal of Harbin Engineering University, p.313-318, Harbin, China

206 Zhao and Li 9. Qingguo Ma, (2002)Management Statistics[M], Science publishing company, p.320, Beijing, China 10. Hongyu Liu, (2005)Exploitation and management of real estate[m], China construction industry publishing company, p.35-38, Beijing, China