estimating the willingness-to-pay for urban housing in chinese cities

TSINGHUA SCIENCE AND TECHNOLOGY ISSNll1007-0214ll12/18llpp360-366 Volume 14, Number 3, June 2009

Estimating the Willingness-to-Pay for Urban Housing in Chinese Cities*

LONG Fenjie ( )**, GUO Ming ( ), ZHENG Siqi ( )

Institute of Real Estate Studies, Tsinghua University, Beijing 100084, China

Abstract: The housing market, an important component of the urban economy, is closely integrated with ur-

ban development. Urban development attracts labor inflows which then increase the housing demand in the

cities. Urban dwellers’ willingness-to-pay (WTP) for housing, as part of their living costs, depends on their

incomes they can earn in the cities and the quality of life (QOL) they want to enjoy. Urban wage growth and

quality of life improvements are always accompanied by increased demand and increased WTP. This paper

uses the average wages of fully-employed employees and various city indicators to reflect the urban QOL to

explain the relationships among people’s WTP for housing, their urban wages and their urban QOL across 35

metropolitan cities in China. The empirical results illustrate that the urban QOL represented by city indicators

and the average wage level accounts for approximately 70% of the housing price variation. Although wages

still have significant impact on the WTP, the QOL in Chinese metropolitan cities tends to contribute more to

the residents’ WTP for housing, indicating that social and natural environments are valued by urban residents

more and more.

Key words: urban development; housing market; income; quality of life (QOL); willingness-to-pay (WTP) for

housing

Introduction

Among the factors that affect urban housing prices, studies initially focused on basic urban attributes, such as population and income. Later studies identified many other factors that also exert an impact on housing prices, such as the urban environment and education levels. Actually, the housing market, as an important component of the urban economy, is closely integrated with urban development. Housing supply and demand are constantly changing. From the demand side, urban development drives housing demand and urban devel-opment determines workers’ location choices among

cities and their willingness-to-pay (WTP) for urban housing. From the supply perspective, the urban hous-ing supply adjusts the actual housing price and affects urban population changes, which then affect urban labor supply and costs, which in turn impact urban development.

Roback[1] used Rosen’s theory to come up with an equilibrium model relating wages, land rents, and ur-ban quality of life (QOL). The Rosen-Roback model assumes that, after basic assumptions such as sufficient labor migration are satisfied, there is an equilibrium between incomes, rents, and urban attributes. These urban attributes are known as urban amenities. They not only include tangible facilities in cities, such as the temperature, green space, public schools, and hospitals, but also cover intangible social factors, such as crime rate, opportunities to acquire new knowledge and skills, and opportunities for children’s education. The

Received: 2008-08-14; revised: 2008-10-15

* Supported by the National Natural Science Foundation of China(No. 70573055)

** To whom correspondence should be addressed. E-mail: [email protected]; Tel: 86-10-62792808

LONG Fenjie ( ) et al Estimating the Willingness-to-Pay for Urban Housing in Chinese Cities

361

aggregate measurement of these urban amenities is defined as the QOL, which is the extent of comfort enjoyed by the residents in a city for a range of urban attributes.

Berger et al.[2] altered the basic model for a trans-forming economy and showed that it is also applicable to transforming economies like Russia Florida[3] showed that a creative class as important human capi-tal plays a decisive role in urban development. The creative class demands more and more urban amenities, such as a humane environment, a good climate, and green space. Therefore, good urban amenities attract highly skilled labors and gather ever-increasing human resources for more urban development. Florida[3] used a multi-variable regression analysis of diversity indi-cators and creative indicators to demonstrate the cor-rectness of the assumption that urban diversity can at-tract a creative class. This coincides with the Rosen-Roback model. Glaeser et al.[4] emphasized four urban amenities: (1) adequate commodities and ser-vices, (2) appealing urban appearance, (3) complete public services, and (4) a convenient transportation and communication infrastructure. He divided American cities into three categories based on these amenities in 2000. His research also suggests that urban amenities can appeal to a highly skilled workforce and can pro-mote urban development. Gabriel et al.[5] used this the-ory to analyze the changes in QOL in different regions of the Unite States during 1980-1990 and to analyze the inherent reasons along with a time and space comparison.

In recent years, Chinese researchers have begun re-search on QOL and the relationship between QOL and housing prices. However, the QOL depends more on the subjective sense of the residents, so Chinese related research is mainly based on subjective surveys and subjective weighting to integrate the indicators. For instance, Beijing International Institute for Urban De-velopment[6] employed a methodology combining ob-jective and subjective indicators to describe the objec-tive aspects (objective conditions of facilities, envi-ronment, and services provided by the urban govern-ment to the residents) and subjective aspects (subjec-tive views of urban residents on their living conditions and the cities where they live) of QOL in Chinese cit-ies, and evaluated more than 200 cities above the pre-fecture level across China. Ke and Mei[7] established a

QOL indicator system for the Macau with many objec-tive indicators used 12 fields related to the economy, politics, culture, society, and environment. Gu and Luo[8] introduced the outcomes of the research con-ducted by the Chinese Society for Urban Studies using the Scientific Urban Amenities Evaluation Indicator System. The system is based on six indicators related to society, economic prosperity, environment, resource carrying capacity, living convenience, and public secu-rity, 29 Grade-II indicators, and nearly 90 Grade-III indicators. The Horizon Research Consultancy Group[9] used a subjective survey of average residents and in-vestors in a 2006 report of a urban amenities index for China with a preliminary comparison and analysis of urban amenities and housing prices. Zheng et al.[10] examined the influence of urban quality of living as indicated by social and environmental amenities on the evolution of rents and wage rate differentials in China. Their findings show a strong increase in urban resi-dents’ willingness to pay for local amenities between 1998 and 2004.

This paper uses an empirical model based on the equilibrium theory of labor and housing markets to relate urban development and housing demand in 35 Chinese metropolitan cities based on the urban produc-tivity and urban QOL. The results reveal the effect of urban development on urban housing prices in terms of the actual selection behavior of residents, which is used to estimate the WTP for urban housing.

1 Theoretical Foundation

This paper extends Rosen-Roback’s model for the equilibrium between wages, rents, and urban QOL to the concept of WTP for housing in cities. Assuming free labor mobility, the hypothesis is that housing price that residents are willing to pay for living in a city is determined by the urban development conditions. Ma-jor factors that affect residents’ choice of intercity housing are labor income and the QOL in a city. The relationship between urban development and WTP is shown in Fig. 1.

Assuming free labor mobility, the utility indiffer-ence among cities means the laborers can obtain the same utility U for their labor. The utility is then de-fined as

j j jU C W R (1)

Tsinghua Science and Technology, June 2009, 14(3): 360-366

362

Fig. 1 Relationships between urban development and WTP

where U represents the indifferent utility of urban la-borers, Cj is the implicit benefit collected by laborers from urban amenities in a specific consumer city j, Wj is the labor income level in city j, and Rj is the housing cost in city j.

The relationship between the urban development and WTP is given by

e ( , )P f W A (2) eP W A (3)

where Pe represents the equilibrium housing price as a function of the labor income and the QOL from the demand point of view, that is the WTP. W represents the income earned by urban laborers, which reflects urban labor productivity, estimated from the average wage earned by fully employed urban employees. A represents the aggregate measurement of urban ameni-ties, which can be reflected by indicators of city attrib-utes. Pe represents the growth of WTP, is the con-tribution of labor income to the growth of WTP, and is the contribution of improved QOL to WTP.

Equations (2) and (3) suggest that from a labor de-mand and labor migration point of view, urban housing prices depend on the income and the QOL of urban dwellers. Variations in housing price are compensated by the labor income and urban QOL. Areas with higher income levels have to be confronted by higher housing prices. If residents expect to move to areas where the QOL is better, they have to expect lower incomes for the same utility. Growth of urban housing prices mainly results from variations in the labor income and urban QOL. Thus, urban development attracts laborers, who impact the housing market in terms of demand. Income and urban QOL are combined to determine the WTP for urban housing as the theoretical foundation for this paper.

2 Model

The data analysis is based on the variable-selection and WTP-estimation models because inadequate data pre-vented fitting all of the independent variables. For this reason, the variable-selection model was used for pre-liminary screening and analysis of the variables. Then, after analysis of the significance and economic mean-ing of the variables, the WTP-estimation model was used for the most relevant independent variables.

2.1 Variable-selection model

The variable-selection model is given by ln ln lni iP c W A (4)

This model was used to separately analyze influence of the city amenities. Poor indicators were removed and then the urban amenities more closely related to the urban housing price were returned for the subsequent analysis. At the same time, the coefficients of the labor income and urban amenities, and , were used to evaluate the variation of each variable with time and to reflect the preference of the urban residents to each variable.

2.2 WTP-estimation model

The WTP-estimation model was used to establish the theoretical price as a function of the labor income level and the QOL, referred to as WTP in this paper. The model is given by

1ln ln ln

n

i ii

P c W A (5)

3 Selection of Variables and Data Source

The variables in these empirical studies were (1) the selling price of urban houses built over the period of 2000-2005, (2) the average income of full-time em-ployees, and (3) the urban amenities. There is a huge amount of data from various sources for these ques-tions, but data having the same origins was selected for this study for consistency.

The housing price and income level are known to be the two essential variables. Careful analysis of various other factors led to the urban amenities categories shown in Fig. 2. Typical indicators were selected from each category, as shown in Table 1.


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Fig. 2 Urban amenity categories

Table 1 Urban amenities indicator selection

Category Variable Unit Source Average altitude m City government websites

Natural landscape Points given by

city officials Website of China National Tourism

Administration Annual sunshine time h China Weather Yearbook Average summer temperature (July) China Weather Yearbook

Natural conditions

Average winter temperature (January) China Weather Yearbook Per capita GDP RMB China City Statistical Yearbook Foreign direct investment (FDI) Standard index World Bank Report

Economic development

Engle coefficient China City Yearbook Ratio of industrial wastewater up to

discharge standards % China City Statistical Yearbook

Per capital green space m2/person China City Statistical Yearbook Green coverage % China City Statistical Yearbook

Environmental quality

SO2 emissions per km2 t/km2 China City Statistical Yearbook Public library holdings per 100

residents Volumes/100

people China City Statistical Yearbook

Number of hospitals per 100 residentsHospitals/100

people China City Statistical Yearbook

Per capital expenditures on education RMB/person China City Statistical Yearbook Number of universities China City Statistical Yearbook Number of theatres and cinemas per

10 000 residents /10 000 people China City Statistical Yearbook

Per capita road area m2/person China City Statistical Yearbook

Living convenience

University enrollment rate % China City Statistical Bulletin

(incomplete data)

Public security Crime rate % China City Statistical Bulletin or Yearbook

(incomplete data) Capital city or not? 1 for yes, 0 for no Population density downtown People/m2 China City Statistical Yearbook City population 10 000 people China City Statistical Yearbook

Others

Unemployment rate % China City Statistical Yearbook

4 Empirical Studies 4.1 Variable selection

The relevance of the housing price and urban amenities, Ai, on the city QOL was based on data on the urban

amenities and housing prices during 1999-2005. The coefficient and significance level of each variable in the variable-selection model were calculated for each year. The basic criterion for the preliminary screening of the variables was to judge the significance of the fit results for the variable. The probability had to have at

Tsinghua Science and Technology, June 2009, 14(3): 360-366 364

least a 90% significance level (P<0.1). The signifi-cance levels of the variables were evaluated to exclude variables with poor significance. Variables with most probabilities in the six-year period being significant were reserved for further analysis.

The variable-selection model then led to selection of the statistically significant. A relevance analysis was performed to avoid multi-colinearity in the WTP-es-timation model.

4.2 WTP-estimation model

After the low significance variables were removed by

the variable-selection model and the relevance analysis was performed, the WTP-estimation model was used to fit the data for each year for the remaining variables. However, because the yearly degrees of significance for the indicators varied somewhat and some data was not available for some years, the fit results were based on the green coverage, population, and per capital ex-penditure on education for 2000, 2002, and 2005 for the comparative analysis of different years. The fit re-sults are listed in Table 2.

Table 2 WTP-estimation model fit results

Year Constant Average labor income level

Green coverage PopulationPer capita expenditure

on education Adjusted R2 Sample size

2000 0.820

( 0.443) 0.733***

(2.854) 0.151

(1.125) 0.0836**

(2.282) 0.111

(0.783) 0.704 35

2002 0.369

( 0.274) 0.552***

(3.099) 0.204***

(3.000) 0.327**

(2.150) 0.0787***

(2.956) 0.837 35

2005 0.337

( 0.171) 0.427*

(1.839) 0.457*

(1.874) 0.167**

(2.568) 0.243**

(2.539) 0.695 34

Notes: ***denotes the 99% significance level; ** 95% level; and * 90% level.

In 2005, the housing price data in Zhengzhou origi-nated from a different source than for previous years, so the city was not included in the fit results for 2005 and the number of observations was reduced to 34. The WTP-estimation model fit results indicate that the ex-planatory variables for 2000, 2002, and 2005 mainly include the average income of fully employed urban employees, the green coverage green( )R , the population

urban(POP ) , and the per capita expenditure on education

edu( )C : e

1 green 2 urban 3 eduln ln ln ln POPP c W R C

(6)

5 Analysis of Empirical Results

In either the variable-selection model or the WTP- estimation model, coefficients and represent the influence of the labor income and the urban amenities on the urban QOL as affected in the housing price. From the housing market point of view, they indicate the contributions of labor income and urban amenities to the housing price; but from the perspective of the labor demand, they reflect the residents’ WTP for the labor income and urban amenities. The residents’

preferences can differ over time. Larger coefficients in the correlations at different times indicate the residents more highly value that attribute at that time and the more it contributes to the housing price. Therefore, comparison of the coefficients of an indicator over time can reveal the evolving residents’ preferences.

Due to each of a wide range of variable data, the variable selection is based on the empirical results of the variable-selection model to judge the trends for all kinds of variables. The primary criterion for the selec-tion is to ensure that the variables are significant in the model. Since the housing market may feature time lags, and such trends may become more obvious over time, the preliminary qualitative analysis selected the indi-cators that were significant over the longest time span. Influence factors and change tendencies of housing choices are listed in Table 3.

5.1 Labor income

The empirical results for both the variable-selection model and the WTP-estimation model show that the labor income coefficient is positive. This indicates that when other variables are controlled, residents are will-ing to pay higher housing prices as their income rises, which agrees well with the theory. However, the labor


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Table 3 Influence factors and change tendencies of housing choices

No. Indicator Initial coefficient Coefficient after change Time span Trend

1 Average income of fully

employed employees 1.193 0.427 2000-2005

Decreased significantly

2 Natural landscaping 0.232 0.324 2000-2003 Increased

3 Per capita GDP 0.281 0.555 2000-2005 Increased significantly

4 FDI 0.089 0.144 2000-2005 Increased

5 Rate of industrial wastewater

up to discharge standards 0.153 1.479 2000-2004

Increased significantly

6 Green coverage 0.347 0.632 2001-2005 Increased significantly

7 Per capita expenditure on

education 0.227 0.279 2001-2005 Increased

8 Number of theatres and

cinemas per 10 000 persons 0.088 0.094 2002-2004 Increased

9 Population 0.097 0.195 2000-2005 Increased significantly

income coefficient shows an obvious gradually de-creasing trend, which reveals that over time, the con-tribution of income to the housing price slowly de-creases; thus, when people are considering moving to a new city, the importance of their income decreases over time. This trend also reflects that residents are attaching more importance to other urban amenities besides labor income.

5.2 Urban natural landscape

First, analyze the coefficients of the various urban natural landscape variables. The coefficients of the natural landscaping and the green coverage are both positive, which indicates that when other variables are controlled, residents are willing to pay higher housing prices when these variables are higher. Thus, people appreciate more natural landscaping and more green coverage. Thus, the two variables are positively corre-lated with housing prices as expected. Theoretically, the rate of industrial wastewater up to discharge stan-dards should also be positively correlated with housing prices. However, its coefficient was negative in 2000, but the significance was not very high as residents at-tached little importance to this factor when they chose houses among cities in 2000.

5.3 Economic development

The per capita GDP and the FDI significantly affect

urban economic development as indicated by these

positive coefficients. Thus, residents are willing to pay higher housing prices as these two variables increase. The economic development variables suggest that richer, more economically developed cities have more appeal to laborers. This also implies that the workers will expect a higher QOL.

5.4 Urban public services

Variables that measure urban public services mainly include the number of theatres and cinemas per 10 000 people and the per capita expenditure on education. The coefficients of these two variables were both posi-tive indicating that residents are willing to pay higher housing prices for better public services. Larger num-bers indicate that the residents are willing to pay more for these public services. Thus, the increase in the ur-ban public service coefficient relative to the housing price over time shows that people are placing more and more emphasis on public service when choosing a new city.

5.5 Population

The coefficient of population is positive, which implies that as population expands, residents are willing to pay higher housing prices because of the increased oppor-tunities of meeting other workers and the knowledge spillover resulting from urban growth. The increase in the population coefficient over time suggests that residents are paying more and more attention to the

Tsinghua Science and Technology, June 2009, 14(3): 360-366 366

population when choosing a new city.

6 Conclusions and Policy Implications

The average income of fully employed urban employ-ees and the QOL satisfactorily explain the variation of housing prices among cities. According to the labor and housing market equilibrium model, the urban income and QOL are the main factors that attract peo-ple to migration and the formation of the WTP for housing. These empirical results show that the average income of fully employed employees and the QOL reflected by the urban amenities indicators can explain about 70% of the variation of housing prices among cities.

With the social and economic development of Chinese cities, the QOL in the cities tends to contribute more to the increasing WTP for housing. Analysis of the WTP for housing shows that over time as society develops, the significance of wage levels continuously declines when choosing where to live, while the sig-nificance of urban amenities (e.g., environment, land-scaping, entertainment, education, medical treatment, and schooling opportunities) are generally enhanced. This illustrates that the economic and social develop-ment of China is causing Chinese to place more em-phasis on the quality of life. The residents’ rising WTP for the QOL will push market forces and non-market forces (governments and non-government organiza-tions) to improve the urban environment.

Improved urban amenities and a higher QOL are keys to enhancing a city’s competitive strength. The ability to attract high quality labor and enhance the city’s competitiveness is an important issue currently confronting major Chinese cities. These empirical re-sults reveal that residents choosing homes in various cities are paying less attention to their current income and more attention to urban amenities, such as a natu-ral environment, economic development, environ-mental quality, and public services. Therefore, local governments should improve urban amenities and the QOL as the key factor to enhance a city’s competitive

strength. Regarding the natural conditions, the urban environment can be improved by reducing pollution, increasing green space, and improving urban land-scaping. Regarding the urban environment and public services, the urban QOL can be enhanced by providing more educational, medical, and entertainment opportu-nities. City governments should also improve the in-vestment environment to accelerate their economic development.

References

[1] Roback J. Wages, rents and the quality of life. The Journal of Political Economy, 1982, 90(6): 1257-1278.

[2] Berger M C, Blomquist G C, Peter K S. Compensating differentials in emerging labor and housing markets: Estimates of quality of life in Russian cities. Journal of Urban Economics, 2008, 63(1): 25-55.

[3] Florida R. The rise of the creative class. http://www. washingtonmonthly.com/features/2001/0205.florida.html. 2005-08-07.

[4] Glaeser E L, Kolko J, Saiz A. Consumer city. National Bureau of Economic Research, Working Paper 7790. http://www.nber.org/papers/w7790. 2007-09-20.

[5] Gabriel S A, Mattey J P, Wascher W L. Compensating differentials and evolution of quality of life among U.S. states. Regional Science and Urban Economics, 2003, 33(5): 619-649.

[6] Beijing International Institute for Urban Development. Ranks of QOL of Chinese cities of 2006. http://www. ccgov.net.cn/asp/search/view.asp?id=436. (in Chinese)

[7] Ke Yan, Mei Zhigang. Establishment of quality of life index system of macau SAR. Statistics and Decision Making (Theoretical Version), 2007, 6: 65-67. (in Chinese)

[8] Gu Wenxuan, Luo Yameng. Scientific evaluation of livable cities. Beijing Planning and Construction, 2007, 1: 7-10. (in Chinese)

[9] Horizon Research Consultancy Group. 2006 Report on Urban Amenities Index of China. http://www.horizonkey. com/showsoft.asp?soft_id=267. (in Chinese)

[10] Zheng Siqi, Fu Yuming, Liu Hongyu. Demand for urban quality of living in China: Evolution in compensating land-rent and wage-rate differentials. Journal of Real Es-tate Finance and Economics, 2009, 38(3). DOI: 10.1007/s11146-008-9152-0.

estimating the willingness-to-pay for urban housing in chinese cities

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