Study on the Driving Forces of Housing Projects Distribution in Beijing
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Study on Determinants of Housing Demand for Community in Beijing
Longjuan HE1, Lihua ZHAO2
1School of Geography, Beijing Normal University, Beijing China, 100875 Email:[email protected]
2School of Marketing, University of New South Wales, Sydney Australia, 2052 Email: [email protected]
Abstract With economy development and urban expansion, Beijing’s housing industry developed extremely rapid during the last decades. Residents’ community selection in Beijing is coincided with the spatial tendency of urban planning and infrastructures development. The housing demand is affected more by community attributes than dwelling attributes. In order to illustrate the relationship of community attributes with housing demand and their determination, we structured out four types of variables of housing demand for community basing on previous researches and real situation in Beijing: (1) fiscal variables and local public services, (2) transportation and job opportunity, (3) socio-economic characteristics of the population, and (4) built environment features of the community. Based on the correlation analysis and comparison method, we drown conclusions that public services and work opportunity are two determinant factors for housing demand for community. In another words, citizens are keener to live in a convenient community with connection to schools, hospitals, public transportation, and job opportunity, etc. The result also hinted that people don’t weight too much on physical and economic environment features of community as housing demand in Beijing is inelastic and unsatisfied. This study will make future study on housing demand more meaningful.
Keywords: community choice, housing development, housing demand, Beijing
1. Introduction The acceleration of the Chinese economy development and housing system reform drive the real estate industry flourish rapidly in the last two decades. Large numbers of houses have been built by developers, but the housing demand is far satisfied in some areas. The inconsistency between housing supply and demand now is a hard nut to crack in Beijing. Regional inequality of housing price and housing demand also exist apparently. The Beijing Government has been encouraging people to live in extension districts of urban function and new districts of urban development, and developing new cities (xincheng) for residential communities. Urban planning strategies and land provision plans play dominant roles in housing supply. Policies have been primarily aimed at increasing the supply of land for housing where good
1 facilities are already planned in place. Under this development situation, Beijing’s housing distribution was characterized by five residential regions as the extensions of main roads (Zhang, 2002).
Housing demand has been estimated by modeling consumption of houses in a region as a function of environmental, structural, and neighborhood characteristics. Most of the factors that affect housing demand are spatially heterogeneous among regions. It was said that what matters real estate most is location. The majority of households choose a community before they choose a specific neighborhood (Rapaport, 1997; Cho, 2005), so the housing demand needs to be modeled in the context of community choice at first. Community, characterized by residents’ income, living preference and administrative circumstances, can provide the suite of infrastructure, service, and other characteristics desired by the decision makers. When households choose housing in different communities, they take characteristics of the communities, local public facilities, community fiscal variables and equilibrium house prices into account (Nechyba, 1997, 1998).
Single factor of residential location and community choice has been modeled to a deeper extent (McFadden, 1978), especially, the influence of public facilities (school, hospital) and transportation on residents’ housing location choice (Anas, 1982). By contrast, the existing housing demand literature has seldom modeled the choice of a specific community as part of the household’s optimization problem (Cho, 2005).
Beijing housing market has become increasingly complex and diverse. Latest housing demand survey conducted by Beijing Construction Research Center in 2006 reflected that, transportation, location and price are three determinant factors when people make decision on housing purchase. People compare housing location from the infrastructure aspect, including commercial, education and health facilities. Because of the unbalanced spatial distribution of housing supply, there are more houses available in eastern districts among the third and fourth ring-roads. Hence, people have more choice in these regions.
It was felt that a better understanding of housing demand in Beijing could assist in developing more effective market ways and help achieve a better coordination between housing demand and supply. In this study, following the approaches for housing demand and community choice by Rapaport (1997) and Cho (2005), we apply correlation and comparison methods to examine the influence and determination of four types of variables on housing demand for districts in Beijing. In another words, the community attraction for housing demand will be demonstrated in this paper.
2. Construction of variables Various researches have shown that the construction of variables for housing demand for community is treated as a socio-economic system. Hua (1996) structured out the
2 socio-economic indicators of demand for residential property in Singapore: disposable income, economic growth, level of employment, existing housing stock, inflation rate, construction cost, mortgage credit availability/supply, and household personal savings. In his community choice research, Cho (2005) considered the influence of individual-specific characteristics (education level, political view, etc.) and choice- specific attributes (population density, crime level, air pollution, etc.). Summarily, attributes of housing demand for community are mostly described as socio-economic and environmental variables.
The socio-economic variables can be classified into economic variables, demographic attributes, facilities, and accessibility. GDP per capita is a major index for community economic development and has close influence on other industries and socio and economic activities. Housing price reflects the supply price of housing and any capitalization of the community mix of public services and tax levels (Rapaport, 1997). The price caused by community choice affects households’ housing demand. Accessibility is hypothesized to increase the likelihood that households choose a particular community (Bayoh, 2006), including access to transportation, retail establishments (shopping facilities, etc.) and job opportunity, etc. Major factors contain travel time to work or shopping, distance to major road, road index, distance to major city, the number of enterprises established in community and so on (Nechyba, 1998; Cho, 2005; Bayoh, 2006). The high-quality public facilities and services and community quality have a stronger pull on potential homeowners. Specially, public schools and hospitals are proved to be important for community choice. Demographic characteristics in community including population education level, income level, security and others are social capital of local community can produce benefit for residents (Ioannides et al. 2003), thus population density, crimes rate, per capital income, household education level, number of children, unemployment rate are discussed in previous researches. In addition, environment related features are significant determinants when people make community choice, including air quality, area of open space, river area, park area or green land rate etc (Nechyba, 1998; Cho, 2005).
These attributes’ distribution in different communities determines the housing values, which is the value of housing quality (Kain, et.al, 1970). Meanwhile, these attributes affect households’ preference for communities and housing demand. Consequently, property value interacts with housing demand. Housing demand can be represented by the number or area of houses within a community. Cho (2005) employed number of houses within the given area as the aggregate housing demand. Considering data availability and real situation, we will take the sold housing area in 2006 by each district to represent housing demand for each district in this year.
Based on previous research results with the study purpose and data availability and real situation in Beijing property market, there are four types of variables included in this study (explained in table 1): (1) fiscal variables and local public services, (2)
3 transportation and job opportunity, (3) socio-economic characteristics of the population, and (4) built environment features of the community. As for the first type of variables, we expect GDP per capita and average housing price for fiscal variables. The average price by community will stand for the housing price attribute. To account for the public services provided by government in community, we incorporate variables that are proxies for main services. Even though community safety is important when household makes choice, the crime rates are not concerned in this study because of their unavailability. As the result from the latest housing demand survey, education and health facilities are important when people make decision among communities. Thus, we use the key school rate and middle school rate to measure education quality and quantity, and the percentage of the second and third class hospitals to measure health service quality. As to the second type of variables, we use main road length to define transportation accessibility as we cannot get more transportation related data. Percentage of enterprises and percentage of working population in each community are expected to present as job opportunity. Socio- economic characteristics of the population are the third type of variables. Households have preference over these socioeconomic attributes. Disposable income per capita is included as a primary feature of the existing population. The level of education in community is another factor to attract people. It is hypothesized to have positive influence on a household’s location choice. People prefer to live with well educated neighbors. Hence, the rate of high-school education, under-graduate education and post-graduate education will be included. It is true that the higher employment rate is in one community, the more attractive housing market is. Hence, unemployment rate in community is also a social variable in this study. Built environmental features of the community are the fourth type of variables. Households have been paying increasingly attention about communities’ environment. We expect air pollution index (API) to assess air quality released by Beijing Environment Protection Bureau. Population density has significant effect on housing choice for community. Universities’ distribution is another feature to affect housing demand differently among communities. There are 85 universities in Beijing and most of then are located in haidian district. The concentrated distribution brings up high demand for housing around universities.
Table 1. Theoretical Definition of Variables
Groups Variables Initial Definition Real Housing Demand RHD floor area of sold commercial housing in each district in 2006 Fiscal Variables Per GDP Per GDP Gross Domestic Product per Capita and Public Housing Price HP Price of housing per km2 Facilities and Middle schools Rate Rsch Percentage of middle schools in Services district to total amount in Beijing Key school Rate Rk-sch Percentage of key schools in district
4 to total amount in Beijing class 2-3 hospitals Rate Rhspt Percentage of hospitals classed 2 and 3 in district to total amount in Beijing Crime rate Rcrim Percentage of crimes in district to total amount in Beijing ( not available) Transportation Main road length MRI length of main roads in district in and Job km within 1 km2 of area ( not Opportunity available) Enterprises Rate Rent Percentage of enterprises in district to total amount in Beijing Working population Rate Rwp Percentage of workers employed in district to total amount in Beijing Universities Rate Runi Percentage of universities in district to total amount in Beijing Socio-economic Disposable Income per DI Disposable income per person attributes of Capita population Unemployment Rate ER Rate of unemployed population to total labor force Population Density PD Permanent population within 1 km2 of area 12-year Educated Redu12 Percentage of 12-year educated Population Rate population to total population in Beijing 16-year Educated Redu16 Percentage of 16-year educated Population Rate population to total population in Beijing 19-year Educated Redu19 Percentage of 19-year educated Population Rate population to total population in Beijing Built Air Pollution Index API Air Pollution Index Environmental Parks Pk Area of parks in district ( not features available)
3. Data and methodology 3.1 study area Community is a broader regional conception and can be defined with different study purposes. For example, Rapaport (1997), Nechyba(1998) and Bayoh (2006) et.al defined school district as community because of the importance of local public education to the residential choice process. Cho (2005) grouped census blocks into community types based on the degree of urbanization.
5 In China, census blocks are conterminous with administrative units. The local administrative districts exhibit variation in the variables of economy, public facilities, social attributes, demography and others. The housing distribution spatially varies in different districts. Thus, we choose the 18 administrative districts in Beijing as study communities, as shown in Figure 1. According to definition of different development function definition by the latest Beijing General Urban Plan (2004-2010), the 18 districts can be classified into four types of function regions: core districts of capital function (dongcheng, xicheng, xuanwu, chongwen), extension districts of urban function (haidian, chaoyang, fengtai and shijinshan), new districts of urban development (changping, tongzhou, shunyi, daxing, fangshan), and development districts of ecological preservation (mentougou,pinggu, miyun, huairou and yanqing). The study area has 16.4 thousand square kilometers of land area and over 15.8 million of permanent population in 2006. In Beijing, core districts of capital function and extension districts of urban function, are the major market for the commercial housing (Zhang, 2003; Wang et,al. 2004).
Figure 1. Study Area
3.2 Data Three principle data sources were used in this study. SOHO China Limited Company published the Beijing property market report of 2006, and it released information on sold commercial housing in each district, which was based on the data from authorized Beijing Construction Committee. Thus, we employ floor area of sold commercial housing in 2006 to be housing demand in 2006. Annual Beijing Yearbook from Beijing Statistic Bureau is an authorized data source. We can collect economic, demographic, educational data from 2006 Beijing Yearbook. Moreover, we cannot collect transportation related data officially, such as road length in each district, so we should employ calculation methods to calculate the main road length in each district
6 by use of GIS data, but with the limitation of full information on road GIS data in Beijing, we can’t work out the road length in this study.
3.3 methods Many forecasting techniques and approaches are applied to analyze and estimate housing demand for community. Conventional regression techniques have been used very often, including multiple regression models for quantity of housing demand (Hua, 1996), multinomial logit model and hybrid conditional logit model for community demand and location choice (Nechyba, 1998; Isaac, 2006). Artificial Neural Networks forecasting techniques has been proved to be more efficient for housing demand estimation in recent research (Hua, 1996). Housing demand from community choice has robustly spatial aspects, so GIS serves as the research platform both to manage spatial data and to imply spatial analysis methods (Can, 1998), can provide the optimal framework for investigating both types of locational factors in housing demand (Case, 1991).
In this study, we can only expect to use correlation analysis and comparison method to explore the degree of influence of determinants on housing demand for community, to find out the important factors that influent housing demand for community in Beijing and explain the relationship.
4. Results 4.1 Relationship between fiscal variables and housing demand for community As stated, GDP per capita is a major index for community economic development. In 2006, Beijing’s GDP per capita was up to 48.8 thousand Chinese Yuan (Beijing Statistic Bureau). The property investment was 171 billion Yuan, took 51 percent of the total fixed gross investment, the total floor area of sold commercial building in this year was 22.88 million square m2. However, GDP per capita in each district has not too much influence on housing demand for it. The correlation coefficient is only 0.040 (p=0.874). Xicheng (core district) has the highest GDP per capita (102.6 thousand Chinese Yuan) in Beijing, but the floor area of sold commercial building was 0.41 million square m2. Inversely, Chaoyang (extension district) has 47.4 thousand Chinese Yuan but the floor area of sold commercial building was ranked the first 8.74 million square m2. Housing price and housing demand interact with each other. The price has lagged affect on demand, namely, last-period price determines the demand at this period in a certain region, and inversely, the demand can reflect price at the same period. However, the correlation coefficient between the floor area and price of sold commercial housing in 2006 is not significant(r=0.251, p=0.315). The figure 2 double illustrated their non-relationship.
7 Figure 2. Comparison of floor area of sold commercial housing and fiscal variables (2006)
4.2 Relationship between local public services and housing demand for community As the result from the latest housing demand survey showed, education and health facilities are important when people make decision among communities. Our analysis result also proved this true. Quantity of education facility has more significant effect on housing demand for community than education quality in Beijing. The correlation coefficient of middle school rate in each district is positively significant (r=0.735, p=0.001) but the coefficient for key school rate is weak and not significant (r=0.277, p=0.265). This result is different from the situation happened in western countries, the education quality has strong influence on household community choice. Health facility is another key factor for household housing decision making. The percentage of the second and third-class hospitals in each district has apparently positive influence on housing demand (r=0.522, p=0.026). In extension districts of Chaoyang and Haidian, the floor area of sold commercial housing takes 49.6 percent of total floor area sold in 2006, and around 23% and 36% of middle schools and high-quality hospitals (third and second class hospitals) are allocated in the two districts.
4.3 Relationship between accessibility and housing demand for community Transportation and job opportunity mainly determine that whether one community can attract more people to live there. Close to public transportation, trains, and main roads, it is convenient for residents to commute and to travel for daily life. Hence, Beijing’s ring-road networks divide residential regions into five parts, and the area between the northern and eastern third and fourth ring-roads is more crowed by residential buildings. It was proved that people prefer to living near to workplaces because of the commuting time saving. The community with more enterprises will be more competitive for housing demand. The correlation coefficient of enterprises proportion with housing demand is 0.763. Chaoyang and Haidian, are two concentration districts for enterprises, with the percentage of 19% and 23%, and the real housing demand (floor area of sold commercial housing) in the two districts took up to 50 percent in the whole market. Proportion of universities is another significant determinant for housing demand (r=0.634, p=0.005). People who work in universities
8 are most likely to buy housing nearby.
4.4 Relationship between demographic features and housing demand for community Population density can influence residents’ living condition in communities, as bigger population can result in lots of socio-economic problems, such as resource shortage, heavy traffic, job competition, crowed shopping, low air quality and so on. However, the result of correlation analysis shows that even though there is a negative association between population density in district and housing demand in this area, it is very weak and not significant (r=0.037, p=0.883). Population education level in certain community has significant relationship with housing demand in this region. The proportion of 12-year-educated population (high-school education), the proportion of 16-year-educated population (under-graduate education), and the proportion of 19-year-educated population (post-graduate education) have significantly positive relationship with real housing demand in each district (r=0.890, 0.732, 0.464, p=0.000, 0.001, 0.054 separately). Figure 3 demonstrates this correlation in detail.
As the correlation coefficients of them (r=0.266, -0.217, p=0.287, 0.388 separately) showed, disposable income per person and unemployment rate don’t have much relationship with housing demand. Even though, we can also get implication that residents’ income condition has a positive effect on community’s attraction of housing choice. Namely, people would like to sort themselves into homogeneous groups within locations. The unemployment rate has the converse influence on housing demand.
Figure 3. Relationship between housing demand and education level (2006)
4.5 Relationship between built environmental features and housing demand for community Air quality and entertainment (parks, etc) are major components of built environmental features of community. Good environment can bring high housing demand for community. Those districts with waste plants and factories are not ideal
9 destinations for housing choice. Air Pollution Index (API) is an efficient index to measure air quality. In Beijing, Mentougou and shijingshan are two districts with worst air quality, and the APIs are up to 113 (light pollution), the floor area of sold commercial housing in the two districts are less than other districts. However, the air quality among districts doesn’t have too much difference: the air pollution is the third level (light pollution) within core districts, extension districts and development districts. The biological conservation districts have better air quality but are too far away from core districts and not convenient to commute. Based on these reasons, the air quality doesn’t have too much effect on housing demand for districts except for the apparently worst situation. The correlation coefficient of API and housing demand is 0.237 (p=0.344), which is unreal to literatures stated.
Figure 4. Relationship between air quality and housing demand in Beijing (2006)
5. Conclusions Different from these cities in developed countries, real estate market in Beijing is relevantly immature, and residents’ satisfaction level is lower than people in western countries, thus housing demand for community is highly influenced by transportation and public facilities. From macro viewpoints to analyze housing demand for community, we can draw the conclusion the education facility has strongest effect on housing demand for community. Children education is the important considered factor when people choose location for residence. Hospital quality is another important public facility to influent households’ housing demand for community. Job accessibility is a vital factor people concern about during their housing decision. People tend to live where is near from workplaces. Hence, chaoyang and haidian are two crowed residential districts because of their high density of enterprises and universities. The result confirmed that in Beijing, residents’ housing demand is inelastic and far to be satisfied, so people can’t weight too much on physical and economic environment features of community.
However, there are a number of limitations in our study. Primary among these is the community definition. Here we set the administrative district as the community, the features of which are complicated and reduced the influence on housing demand. Land provision and housing supplying strategies significantly determined housing
10 distribution among districts, so the influence of many driving forces on housing demand for community was undiscovered and the degree of relationship was reduced greatly, for example, the relationship of housing price with housing demand is weak in Beijing, which reflects that price is not the important factor when residents select housing between districts. Another limitation is data and variables. In Beijing, as for the confidential and sensitive reason, data about detailed air quality, safety and transportation is difficult to be captured. The variables about entertainments including parks, open areas, green lands were not concerned in this study because of the difficulty of data collection. Simple analysis method is the third limitation. We only employed correlation analysis to explore the relationship between the factors and housing demand for community separately, but their aggregative influence on the housing demand and the projection of housing demand were not conducted. The fourth limitation is that we only observe a snapshot in time and therefore cannot account for changes over time. Analysis from one year data cannot reflect or bring out the deep characteristics of relationship of these variables with housing demand.
According to the consideration above, in future research, more specific criteria should be approached for community identification and classification, and housing market sub-districts level will be considered as the communities. With comprehensive structure of variables, we will apply panel data with time-series feature to and predict housing demand in different community.
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