Impacts of urban environmental elements on residential housing prices in Guangzhou (China)

Landscape and Urban Planning 78 (2006) 422–434

Impacts of urban environmental elements on residential housing prices in Guangzhou (China) C.Y. Jim ∗ , Wendy Y. Chen Department of Geography, The University of Hong Kong, Pokfulam Road, Hong Kong Received 11 September 2005; received in revised form 23 December 2005; accepted 23 December 2005 Available online 20 March 2006

Abstract The amenity value provided by urban green spaces, water bodies and good environmental quality is difficult to assess and incorporate into urban planning and development. Developers and governments in China hitherto have seldom objectively factored these attributes into property pricing and associated decisions. The hedonic pricing method offers an appropriate approach to gauge such external benefits which contribute to real-estate transaction prices. This study explored the impacts of key environmental elements with a bearing on residential housing value in Guangzhou, including window orientation, green-space view, floor height, proximity to wooded areas and water bodies, and exposure to traffic noise. Four large private housing estates composed of multi-storied blocks with similar design and price bracket, catering to the mass property market, were sampled. Transaction price data and structural attributes of 652 dwelling units were acquired directly from developers. Data on environmental attributes were collected in the field. Two functional hedonic pricing method models, linear and semi-log, were constructed. The semi-log model offered comparatively stronger explanatory power and more reliable estimation. High floor on the multi-storey tenement blocks contributed implicitly 9.2% to the selling price. View of green spaces and proximity to water bodies raised housing price, contributing notably at 7.1% and 13.2%, respectively. Windows with a southern orientation with or without complementary eastern or northern views added 1% to the price. Proximity to nearby wooded area without public access was not significant, expressing the pragmatic mindset in the hedonic behavior. Exposure to traffic noise did not influence willingness-to-pay, implying tolerance of the chronic environmental nuisance in the compact city. The study demonstrates that hedonic pricing method could be applied in the Chinese context with an increasingly expanding and privatized property market. It could inform the decisions of policy makers and property developers concerning land selling and buying, land conversion, property development, urban nature conservation, and design of ecological green-space networks. © 2006 Elsevier B.V. All rights reserved. Keywords: Hedonic pricing method; Housing market; Property valuation; Urban green space; Urban natural area; Environmental benefits; Amenity value; Compact city; Guangzhou; China

1. Introduction Urban green spaces, water bodies and good environments provide amenities and services that contribute fundamentally to the quality of urban life (Shafer et al., 2000; Van Herzele and Wiedemann, 2003; Chiesura, 2004). Due to their noncommodity and unpriced nature, and largely intangible benefits, their contribution is usually difficult to assess and quantify. Their importance to the well-being of cities and citizens is often neglected in mainstream urban planning and policy making related to development (More et al., 1988; Luttik, 2000;

∗

Corresponding author. Tel.: + 852 2859 7020; fax: +852 2559 8994. E-mail address: [email protected] (C.Y. Jim).

0169-2046/$ – see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.landurbplan.2005.12.003

Tyrv¨ainen and Miettinen, 2000; Tajima, 2003; McConnell and Walls, 2005). In recent years, increasing concern about urban green space and environmental quality has grown in tandem with rapid urbanization. Natural areas located in and near residential areas in developing cities, closely related to the amenity and health of residents, are of particular concern due to their vulnerability to damage and usurpation. To make informed policies and decisions about green space and environmental improvement, assessment of their benefits and values is essential (Tyrv¨ainen, 1997; Luttik, 2000; Tajima, 2003; McConnell and Walls, 2005; Jim and Chen, 2006). Various approaches have been proposed and tested, amongst which the hedonic pricing method has been widely applied in western countries to estimate the value of nature associated with settlements. For instance, the impacts of green spaces situated

C.Y. Jim, W.Y. Chen / Landscape and Urban Planning 78 (2006) 422–434

within and near a development are examined for their impacts on housing price and to construct a housing price index (Anderson and Cordell, 1988; Willis and Garrod, 1993; Chattopadhyay, 1999; Bolitzer and Netusil, 2000; Luttik, 2000; Tyrv¨ainen and Miettinen, 2000; Geoghegan, 2002; Ouyang and Wang, 2003; Price, 2003). Similar studies have seldom been applied in developing countries, including the rapidly expanding real-estate market in China (Chen et al., 2002; Ma and Li, 2003). In cities of China, the government’s administrative allocation system of residential units dominated the sector from 1949 to the 1980s. Private housing then was non-existent. In the centrally controlled socialist system, pragmatism prevailed in the generation of decision, and hedonic features such as amenity and landscape were hardly considered in the design and construction of residential housing. Most vegetation in residential areas was planted by residents of their own accord in small scattered pockets. Lacking proper planning and management, such enclaves are often more grey than green, and are burdened with poor plant growth. Residents thus tend to shun them, despite their proximity to homes, and instead visit better parks and gardens situated father away (Jim and Chen, 2006). Thus such old-fashioned or relict residential green spaces denote a large pool of under-utilized resource that could perhaps be overhauled to realize their potentials. With the advent of open-door policy and economic reform from the late 1980s in China, the interest in residential green spaces and associated landscape elements has been rekindled. The consequent marketization of the economy brought fast economic and urban growth and a significant boom of the private property market in Chinese cities. To meet the increasingly affluent and discerning clientele, including overseas investors, developers are competing to offer good landscape and environment to lure buyers. Increasing attention has been devoted to beautifying residential grounds (Liu, 2003; Qin et al., 2004), bringing notable improvement in the quality and diversity of landscape designs. A green and pleasant outdoor environment has indeed become de rigueur in many residential estates, including both the mass and luxury markets. Such drastic changes in the community have not been matched by corresponding modifications in policies and practices. Municipal governments often found it difficult to link urban nature and environmental features to the quality of urban life. For instance, the common lack of official guidelines on green-space conservation and provision in residential areas fails to guarantee a minimum standard. With rapid urbanization in Chinese cities, the pressing demand for land has pushed up its value, and existing and potential green spaces are frequently subject to development pressures (Huang et al., 2003; Tan and Dong, 2004; Li et al., 2005). Governments are often urged or even obliged to facilitate development which could sacrifice the long-term welfare of the community. For many real-estate developers, the implicit value of environmental attributes is seldom incorporated into the design and valuation of properties (Shi, 1994), thus curtailing their competitive edge in the burgeoning market. This study aimed to clarify how and to what extent urban green spaces and environmental amenities affect residential

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housing price through the hedonic pricing method in a Chinese city. The findings could throw light on the effect of environmental and landscape qualities on house selection and purchase behavior in the context of Chinese society. Guangzhou as the principal city in south China with a well-developed realestate market (Li, 2000; Wang et al., 2004) served as the study area. By the Chinese yardstick, the municipality is reasonably well endowed with green and natural areas. The urban planning policy, however, often neglects the socioeconomic value of environmental–ecological features. Valuating such benefits could enhance understanding of their contribution to housing price and general environmental welfare. The results could help developers to rationalize their investments and decision makers to realize environmental improvement goals. The study could also yield insights into theoretical modeling and applicability of hedonic pricing method in China. 2. Residential property development in Guangzhou From 1949 to the 1980s, the central allocation system dominated housing supply and demand in Guangzhou. The general tenet then considered housing as a social necessity and welfare to be assigned by the administration, rather than a commodity to be bought, sold or rented by citizens. Common market criteria such as location and quality were largely ignored under the more-or-less egalitarian and non-market planning system. The non-descript and standard building design widely adopted during the 1950–1980s created many residential compounds composed of a series of monotonous ferro-concrete blocks less than six storeys in height. Provision of pleasant green spaces in residential grounds was not deemed to be essential. These old residential areas usually are beset with meagre green spaces with vegetation planted haphazardly by residents. The price of housing, expressed as rents, was centrally determined at a level unrelated to the size, quality, location or environs of the properties (Malpezzi, 1999). Housing reform in China commenced in 1980 with the primary aim of abating the serious and aggravating housing shortage in urban areas, through major departure from past practices, such as localization of housing investment (Fang, 2004) and promotion of home ownership (Li, 2000). During the early period of real-estate marketization (1980–1994), party and government units and state-owned enterprises were both developers and consumers of housing (Wu, 1996; Fang, 2004), which was subsequently considered to be problematic (Wang and Murie, 1996). A new leaf was turned when commodity residential housing, dwellings built by development companies and sold at full market price in the primary market, appeared sporadically in Guangzhou by late 1994. Several major policy initiatives were instrumental in laying a foundation for the privatized housing market to flourish. Development companies that operated according to commercial principles were facilitated and fostered after 1994. Subsidized housing system (welfare allocation of housing by various working units) was eventually abolished in 1998. Accompanied institutional support such as the provision of individual mortgage loans was introduced in 1999. These measures extricated the

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Table 1 Indicators of real estate development in Guangzhou in 1998–2002 Attribute (RMBa )

Gross domestic product per capita Gross domestic product per capita (US$) Investment in real estate (billion RMBa ) Investment in real estate (billion US$) Floor area with commenced constructionb (ha) Finished floor area (ha) Sold floor area (ha) City-wide per capita floor areac (m2 /person) Population of Guangzhou (million)

1998

1999

2000

2001

2002

Total or average

27581 3323 26.8 3.2 972.8 756.3 578.0 11.6 6.74

30610 3688 29.6 3.6 824.9 857.9 630.0 12.4 6.85

33615 4050 33.5 4.0 1004.8 930.0 559.0 13.1 7.01

38114 4592 38.1 4.6 1131.9 729.8 535.0 13.9 7.12

41674 5021 41.0 4.9 1302.0 788.0 860.0 15.7 7.21

32480.0 3913.3 169.0 20.3 5236.4 4062.0 3162.0 12.7

Source: Guangzhou Yearbook Editorial Committee (1999–2003) and Zhao (2003). a Chinese currency Renminbi at about US$ 1.00 = RMB8.30. b This attribute gives the floor area with construction commenced in a given year. c The value refers to the average residential floor area occupied by a person in Guangzhou.

tial grounds has become a key concern. Well-appointed outdoor spaces have been earnestly marketed to attract buyers (Zhao, 2003). In addition, many new developments include rather elaborate water landscapes to echo the wealth connotation of water in traditional Chinese belief. Apartments with green space or water views are considered more appealing, prestigious and signify an elevated social status, hence they could command a higher premium. For instance, flats located on both banks of the Pearl River that traverses the city are earnestly demanded. Despite the importance of such landscape features to both developers and home-makers, these environmental factors do not receive sufficient weight in current house price assessment. The main bottleneck is the lack of a scientific basis to value the worth of these landscape elements. This knowledge gap has reduced the accuracy of property value assessment (Chen et al., 2002) and hindered continued rationalization of the market. Most developers intuitively believe that green spaces and a pleasant environment in residential developments are important and a garden view could lift the price of apartments. Compared with the investments in the apartment buildings themselves, the inputs into beautifying the grounds are still insufficient. This poor understanding of environmental quality as a component of housing value could restrict relevant investment and does not encourage further improvements. A recent survey enumerated 336 ha of green spaces accommodating 85,539 trees in residential grounds in the built-up areas of Guangzhou (Guangzhou Landscape Bureau, 2002). For the entire city, the green area is

industry from long-standing bondages and generated the necessary conditions for a massive boom (Li, 2000; Zhao, 2003; Fang, 2004). Real estate market development in Guangzhou is consistent with, if not a natural progression from, the general trend of urbanization and development (Wang et al., 2004). Fast local gross domestic product elevation has stimulated the housing demand, a phenomenon not dissimilar to other countries (Malpezzi, 1990). The investment in housing projects and completed floor areas has increased steadily since 1998 (Table 1; Guangzhou Yearbook Editorial Committee, 1999–2003). In 2000, new regulations on pre-selling conditions were instituted to protect consumers from non-completion of construction. Moreover, regulations protecting the rights of tenants were enacted. Such official controls to forestall abuses and align the industry with modern practices, however, have somewhat dampened market activities (Zhao, 2003). Recent continued increase in housing investment, production and consumption signify the progressive maturation of the industry in Guangzhou. Residential housing dominated the Guangzhou real-estate market, taking over 85% of annual total built and sold floor areas (Table 2). As the largest asset owned by most households (Malpezzi, 1999), individual purchasing behavior has evolved after 1998 from solving acute housing shortage to pursuing hedonic fulfillment. Besides location and transportation, home buyers increasingly expect high-quality green space and environment within and around residential precincts (Zhao, 2003). To meet the evolving consumer preference, the design of residenTable 2 The composition of sold real estate in Guangzhou in 1998–2002 (area unit: ha) Type

1998 Area

Residential Commercial Office Othersa

495 39 34 10

Total

578

1999 % 85.7 6.7 5.8 1.8 100

Area 553 37 31 8 629

2000 % 87.9 5.8 5.0 1.3 100

Area 495 36 17 14 562

Source: Zhao (2003). a Include luxury residential housing such as villas, garage, and workshops.

2001 % 88.0 6.4 3.1 2.5 100

Area 475 30 22 8 535

2002 % 88.8 5.5 4.2 1.5 100

Area 751 50 17 42 860

Total % 87.3 5.9 2.0 4.8 100

Area 2769 192 121 82 3164

% 87.5 6.1 3.8 2.6 100

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the smallest amongst all land use categories, suggesting much room for enhancement. 3. Hedonic pricing method Two major approaches could estimate the monetary value of environmental services, namely stated preference and revealed preference (Adamowicz et al., 1994; Ready et al., 1997; Garrod and Willis, 1992, 1999; Freeman, 2003; McConnell and Walls, 2005). In the former, individual preferences and values for the services are elicited through survey techniques (Bateman et al., 2002). Usually, respondents are asked how much they are willing to pay to preserve good features for recreation and amenity (Tyrv¨ainen and V¨aa¨ n¨anen, 1998; Kwak et al., 2003; Jim and Chen, 2006). The aggregate willingness-to-pay amount in conjunction with the population at issue forms the basis to estimate the value of the services. The latter approach (revealed preference) is usually labeled the hedonic pricing method. The value of the services is inferred by estimating the sale price or value of property as a function of environmental attributes (such as proximity to urban parks and a view of a garden) in association with other property and neighborhood characteristics. Portions of the property value attributed to green spaces and other landscape endowments could be assigned by statistical computations. The fundamental proposition of hedonic pricing method is that a residential property is composite goods composed of a complex bundle of multiple characteristics, each of which contributes to its selling price. The compound and heterogeneous goods embodies the commonly recognized tangible housing attributes, as well as the less tangible environmental, ecological and landscape elements which could be factored into the valuation regime (Lancaster, 1966; Palmquist, 1991; Freeman, 1995; Cheshire and Sheppard, 1998; Sheppard, 1999; Pagourtzi et al., 2003). People often pay more for a beautiful view if two houses are identical except for the views, and the extra payment can be estimated as the value of the aesthetic service. In practice, many attributes jointly contribute to the selling price. Statistical techniques have been developed to separate parts of transaction prices due to each contributory attribute. Thus the hedonic equation serves to assign an implicit price to each constituent attribute. Hedonic pricing method is believed to be the most convincing approach in these types of valuations mainly because the technique is based on actual transaction behaviors in the market (Hoevenagel, 1994; Ready et al., 1997; Hidano, 2002; Tajima, 2003). It is considered relatively less controversial and less subject to interference by extraneous influences. Many studies have been conducted using hedonic pricing method in Europe and the US to evaluate environmental externalities and marginal value of ecosystem services, such as air pollution (Smith and Huang, 1995; Chattopadhyay, 1999), vehicular traffic (Hughes and Sirmans, 1992), environmental risk (Simons et al., 1997; Gayer et al., 2000), landscape and water quality (Luttik, 2000), environmental protection (Dale et al., 1999), agricultural land value (Le Goffe, 2000), noise impact (Espey and Lopez, 2000), presence of open spaces (Bolitzer and Netusil, 2000; Geoghegan, 2002), amenity of

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urban green spaces (Anderson and Cordell, 1988; More et al., 1988; Powe et al., 1995; Tyrv¨ainen, 1997; Benson et al., 1998; Tyrv¨ainen and Miettinen, 2000; Bengochea-Morancho, 2003; Price, 2003; Tajima, 2003), and social factors (Garrod and Willis, 1999). The quantification and specification of these values would lend support to relevant policy debates and urban planning. Assigning dollar values to environmental benefits could help understanding the magnitude of their contributions to society, and putting them on a level playing field in the keen contest for funding with physical infrastructure and other community projects. Some hedonic pricing method studies focused on the benefits of urban green spaces. In Portland, Oregon, 193 public parks ranging in size from 0.2 to 567.8 acres (0.081–229.96 ha), as a group, have a significant positive impact on the value of properties within a straight-line distance of 1500 ft (456 m) (Bolitzer and Netusil, 2000). Tyrv¨ainen and Miettinen (2000) found that in the housing market of Salo (Finland), buyers have to pay 4.9% more to obtain a dwelling with a forest view. Proximity to the nearest forested park has a significant positive effect on house prices. An increase of 1 km distance from the forest is estimated to reduce the price of a dwelling by 5.9%. In the valuation of Boston’s Big Dig Projects, the distance to parks has a negative correlation with property price. When the distance to the nearest park doubles, the property price was expected to decrease by 6% (Tajima, 2003). In another study of urban green spaces in the city of Castell´on, Spain, the distance from a green area significantly affects housing price, but the size of the nearest green area or the views of a garden or a public park did not influence the prices (Bengochea-Morancho, 2003). Combined with water bodies, the effect of green spaces could be notably augmented. A garden bordering on water could attract a premium 28% higher than one without this attraction (Luttik, 2000). Hedonic pricing method is a powerful and appropriate research tool to assess the value of environmental benefits and resources, to estimate the worth of urban welfare and to explore factors accounting for household allocation. Despite its wide applications in western countries, its use in developing countries has been sparse (Garrod and Willis, 1999). Particularly, it has seldom been employed in empirical studies in China, partly due to the limited experience of buying and selling houses in the rather nascent market. Additionally, the functions and values of environmental–ecological elements in urban areas have rarely been studied in detail. It is worthwhile to explore the applicability of hedonic pricing method in Chinese cities and to investigate the value of ecological elements which could facilitate sound environmental management and real estate development. 4. Development of the hedonic pricing method models 4.1. Empirical hedonic pricing method model In empirical studies using hedonic pricing method, different forms of equations have been employed to combine all attributes that determine the price that a purchaser is willing to pay. In general, the purchase price of a heterogeneous housing goods

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could be expressed by a hedonic pricing model which embraces a bundle of housing characteristics: P = f (x1 , x2 , . . . , xn )

(1)

where P is the market price of house and x1 , x2 , . . ., xn are the characteristics the house embodies. The partial derivatives of the price with respect to the constituent variables provide information on the marginal willingness-to-pay for an additional unit of each characteristic (Palmquist, 1991; Geoghegan et al., 1997; Garrod and Willis, 1999; Sheppard, 1999; Malpezzi, 2003; Bengochea-Morancho, 2003). As a result, the implicit price of individual characteristics could be deduced. This hedonic equation is a reduced form in which it is implicit that the supply of each and all characteristics is perfectly elastic. In most real world situations, however, such an assumption is often untenable (Malpezzi, 2003). Therefore, a second stage model to reveal the demand of each characteristic has been proposed (Palmquist, 1991; Freeman, 2003; Malpezzi, 2003). The choice of functional form of hedonic pricing method has been a major concern because there is not enough guidance from present economic theory about the intricate relationship between housing price and its multiple attributes. To fit the available data to hedonic pricing method, different transformation forms of hedonic pricing method could be defined, including linear (parametric, semi-parametric, and non-parametric), semilogarithmic, double logarithmic, and Box–Cox. The advantages and shortcomings of these functional forms have been investigated in various studies. It is believed that the Box–Cox transformation could yield a better fit of the data than other transformations. Nevertheless, it requires complicated transformation processes which could introduce more random errors (Davidson and MacKinnon, 1993). Other functional forms are relatively less complicated to apply, and in the case of missing relevant explanatory variables these methods could in fact give better results (Cropper et al., 1988; Garrod and Willis, 1999). It is acknowledged that the selection of functional form in an analysis should fit the research purpose, the characteristics and amount of available data, and the selection of dependent and explanatory variables (Bender et al., 1980; Tyrv¨ainen and Miettinen, 2000; Haab and McConnell, 2002; Malpezzi, 2003). 4.2. Selection of model variables The definitions of the dependent variable (PRICE) and 10 explanatory variables included in this study are given in Table 3. Five variables related to housing structural characteristics were considered: floor area of the apartment (APARTSIZE), floor height (STOREY), number of bedrooms (NBEDROOM), number of bathrooms (NBATHROOM), and window orientation direction (WINDOW). In some empirical studies, the garage is considered as an important variable (Din et al., 2001; Bengochea-Morancho, 2003). In the study area, car parking spaces situated in the communal area of the development site are usually available for home buyers to purchase or rent, the transactions of which form a separate segment of the housing market. Thus this variable was not included in this study.

Table 3 Definitions of variables related to the quality of housing units and their environs Variable name

Definition

Unit

PRICE APARTSIZE STOREY NBEDROOM NBATHROOM WINDOW GREENVIEW TRAFFIC DISTANCE GREENVICINITY WATERVICINITY

Purchase price of apartment Floor area of apartment The floor on which the apartment is situated Number of bedrooms Number of bathrooms Direction of window orientation View of green spaces through windows Exposure to traffic noise Distance to the new town centre Proximity to woodsb Proximity to water bodiesc

RMB m2 Count Count Count 1–6a 0, 1 0, 1 km 0, 1 0, 1

a

The codes for window orientation direction are given in Table 4. The code 1 denotes that the apartment is located within 500 m to woods and 0 denotes otherwise. c The code 1 denotes that the apartment is located within 500 m to water bodies (the Pearl River in this study) and 0 denotes otherwise. b

Window orientation of apartments (WINDOW) is both a structural and an intrinsic environmental factor, for it might affect bioclimatic regime of the indoor space, human comfort, air-conditioning energy consumption, with repercussions on housing price. Traditionally in south China, apartments with both southward and northward windows are preferred due to enough but not excessive sunshine in the subtropical climate, brightly and naturally lit rooms, and good traversing ventilation. Apartments with westward windows are regarded undesirable because of too much incident sunshine making summer afternoons excessively hot. Based on empirical experience and in situ observation, the window direction has been coded according to the degree of preference (Table 4). It is hypothesized that housing price would increase with the ordinal sequence. Two explanatory variables were identified to represent the proximal environmental attributes of the apartments. A dichotomous coding (0 or 1) denoted whether the apartment has a view of green spaces (GREENVIEW) and whether it is exposed to traffic noise (TRAFFIC). Thus the influence of green spaces (positive) within the site and noise (negative) adjacent to the site on housing value could be gauged. It was surmised that at least a room in the apartment with a view of internal green spaces would contribute to the selling price, and a room facing a street lying outside the residential precinct would suppress the price. Small water bodies such as fountains and ponds were considered as part of the green spaces, and no variable is assigned to them. Table 4 Codes for direction of window orientation of the housing units Window direction

Code

North only North and east South and west South and east South only South and north

1 2 3 4 5 6

Note: other window directions such as west only, east only, etc. are not observed in the samples of this study.

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Two explanatory variables described the environmental amenities available in the environs of the development. Whether the apartment is located close to wooded areas (GREENVICINITY) and water bodies (WATERVICINITY) were evaluated with dichotomous codes. Such amenities situated within a 500 m threshold of the development were considered. Research findings indicated that most people are unwilling to walk over 500 m or 10 min to reach a green space (Burgess et al., 1988; United Nations Environment Programme, 2005). Other variables about neighborhood characteristics were not included in this study, assuming that they have limited effect on housing price. One explanatory variable related to apartment location measured the Euclidean distance (DISTANCE) from the development to the nearest new town centre where main shopping facilities and government buildings are located. Euclidean distance is the straight-line distance between two points on a map. On a plane with point P1 at map coordinate (x1 , y1 ) and point P2 at (x2 , y2 ), the Euclidean distance between the two points is calculated by (x1 − x2 )2 + (y1 − y2 )2 . In this study, the location data were obtained from a GIS database provided by the Guangzhou Geo-information Centre. The impacts of this variable on apartment price should theoretically be negative, which reflects the trade-off between housing prices and commuting costs (Geoghegan, 2002). Property management (quality and cost) influenced housing price in Beijing (Ma and Li, 2003). The study did not include this factor in the hedonic pricing method model, because in Guangzhou property management forms its own market with charges varying mainly according to service quality and range. The performance of the company is often not known or made known to house buyers, and thus such knowledge is only experienced a posteriori after moving into the premises. Thus this consideration would hardly play a role in housing price. Households could make collective decisions to switch property management company if they were not satisfied with the services or prices. 4.3. Construction of functional equations In this study, two functional forms of hedonic pricing method were applied to test the relationship between selling prices and the chosen characteristics of the apartments, namely the linear (Eq. (2)) and semi-logarithmic (Eq. (3)). When the relationship is assumed linear, the estimated equation is expressed as PRICE = ε + b1 APARTSIZE + b2 STOREY + b3 NBEDROOM + b4 NBATHROOM + b5 WINDOW + b6 GREENVIEW +b7 TRAFFIC + b8 DISTANCE + b9 GREENVICINITY + b10 WATERVICINITY (2) where ε is the constant and b1 –b8 are the marginal willingnessto-pay for each housing attribute.

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When the relationship is considered nonlinear, the estimated equation in a semi-logarithmic form is expressed as ln PRICE = β0 + β1 ln APARTSIZE + β2 ln STOREY + β3 ln NBEDROOM + β4 ln NBATHROOM + β5 ln WINDOW + β6 GREENVIEW + β7 TRAFFIC + β8 ln DISTANCE + β9 GREENVICINITY + β10 WATERVICINITY (3) in which β0 is the constant, and β2 –β8 are the price elasticity with respect to each estimator. Dichotomous variables were not log-transformed. 4.4. Sample areas and data This study focused on four well-known residential precincts in Haizhu district in the core area of Guangzhou: Pleasant View Garden, Poly Lily Garden, Urban Oasis, and Cˆote d’Azur (Fig. 1). Their selection aimed at avoiding biases caused by regional market segmentation which could reduce the applicability of hedonic pricing method. General characteristics of the sample areas are given in Table 5. Choosing samples within one district could minimize variations in housing type and their environs. The developers are similar in terms of experience and reputation. These four precincts are dominated by multi-storied buildings of around 20 storeys for residential use, but some blocks use the ground floor for commercial purpose. The developments aim at the mass residential market. The four estates share a common trait of well-designed and generous provision of green spaces (27.50–30.36% of the land area) which have contributed to their attraction to buyers. They were all constructed recently, with building activities initiated from 2001 to 2003, at a relatively low plot ratio of 2.75–4.17. They also share similarly good building quality. That their average sale prices fall within a limited span, from RMB 5200 to 6080, indicates similarity in terms of quality and amenity, thus the biases due to market segmentation could be minimized. Fewer variations in structural and locational attributes of the residential units could permit a better understanding of environmental effects on housing price. The judicious selection of sample areas and commonality of the explanatory variables also help to improve data quality (Garrod and Willis, 1999; Luttik, 2000; Janssen et al., 2001; Haab and McConnell, 2002; Malpezzi, 2003). A total of 758 sets of transaction data in 2003–2004 were initially collected directly from the developers, including selling price, apartment size, number of bedrooms, number of bathrooms, and floor height. Window direction and environmental attributes for each apartment were assessed by field survey. The distance to the nearest town centre was measured on a Guangzhou digital map. To avoid potential biases, duplex apartments were excluded, as they belong to a different segment of the market. In addition, apartments with irregularly shaped outline and partitions were excluded as such configurations suppress the

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Fig. 1. Map of the central core area of Guangzhou city showing the locations of the four sampled residential housing developments. Table 5 General development parameters of the sampled residential precincts in Guangzhou Attribute

Pleasant View Garden

Poly Lily Garden

Urban Oasis

Cˆote d’Azur

Total or average

Initiation year Land area (ha) Total residential units (no.)a Total floor area (ha) Plot ratiob Green space area (ha) Green space rate (%)c Average price (RMB/m2 )d

2001 40.00 9000 110 2.75 11.00 27.50 6080

2003 5.60 1200 16 2.86 1.70 30.36 5200

2003 9.60 3500 40 4.17 2.80 29.17 6350

2002 3.37 800 11 3.26 1.00 29.67 6000

58.57 14500 177 3.02 16.50 28.17 5907.50

a b c d

The data refer to the final number of units when the projects are completed. Calculated as (total floor area/land area). Calculated as (green space area/land area) × 100. Average price refers to the gross floor area under cover.

utility ratio and selling price in an inconsistent manner. After screening the data, 652 transactions were included in data analysis. The multiple regression analyses were computed using the SPSSPC software. 5. Interpretation of the hedonic pricing method models The results of the estimation of the linear model are presented in Table 6 and semi-log model in Table 7. These two models have similarly high explanatory power (R2 = 0.928 for the linear model and R2 = 0.952 for the semi-log model), and they show similar effects of the explanatory variables. The models attributed the bulk of the selling price to floor area and floor height. The sign (positive or negative) of the effects of

Table 6 Results of regression of linear hedonic price model (dependent variable, PRICE) Explanatory variable

Coefficient

t-Ratio

p-Value

APARTSIZE STOREY NBEDROOM NBATHROOM WINDOW GREENVIEW TRAFFIC DISTANCE GREENVICINITY WATERVICINITY Constant

5398.17 7442.64 5606.91 26892.32 5237.77 48772.51 −6221.44 −37015.01 6423.64 1233.93 192034.90

9.417 22.321 0.399 1.916 4.303 4.897 −0.862 −6.337 0.434 5.837 3.834

0.000 0.000 0.690 0.056 0.000 0.000 0.389 0.000 0.665 0.000 0.000

R2 = 0.928, adjusted R2 = 0.927, F-ratio = 759.037, n = 652.

C.Y. Jim, W.Y. Chen / Landscape and Urban Planning 78 (2006) 422–434 Table 7 Results of regression of semi-logarithmic hedonic price model (dependent variable, ln PRICE) Explanatory variable

Coefficient

t-Ratio

p-Value

APARTSIZE STOREY NBEDROOM NBATHROOM WINDOW GREENVIEW TRAFFIC DISTANCE GREENVICINITY WATERVICINITY Constant

0.955 0.093 0.010 0.058 0.009 0.069 0.010 −0.581 0.010 0.027 9.793

13.357 23.104 0.180 2.106 1.944 5.253 −0.975 −8.799 0.477 8.043 35.391

0.000 0.000 0.857 0.028 0.052 0.000 0.330 0.000 0.634 0.000 0.000

R2 = 0.952, adjusted R2 = 0.951, F-ratio = 1166.66, n = 652.

the variables are consistent with expectations. In both models, the same three variables, including number of bedrooms, exposure to traffic noise, and proximity to wooded areas, are statistically non-significant. Four structural attributes, namely larger floor area, higher floor, more bathrooms, possession of both southward and northward windows, contribute positively to apartment selling price. The effect of window direction is positive and statistically significant (Tables 6 and 7), which verifies the hypothesized coding sequence given in Table 4. Proximity to water bodies would increases the price. Green view of internal garden also raises the selling price. Distance from the town centre lowers the selling price, reflecting the frictional effect of time and monetary travel cost. Floor height incurs significant effects, with higher floors commanding better prices. Both models assign much importance to this attribute (t-ratio at 23.1 and 22.3, respectively). This finding differs from overseas studies, such as the one in The Netherlands, where floor height does not condition selling price (Bengochea-Morancho, 2003). This discrepancy might be explained by the fact that in the study area residential buildings are crowded together often with less than 50 m separation between individual high-rise blocks. Sunlight and the view of the sky are more obstructed at lower floors. Thus higher floors enjoy multiple benefits of better and wider view, more sunshine access and brighter rooms, and better ventilation. Living higher above the ground also gives the distance-decay feeling that street-level noise and traffic air pollution are less serious. The view out of low-floor windows is less desirably dominated by other buildings and artificial structures. In contrast, in overseas cities apartment buildings are usually situated wide apart from each other, hence the floor height might be less important than other building traits. The number of bedrooms is the only insignificant structural variable, apparently indicating that buyers have excluded it in determining willingness-to-pay for an apartment. The number of bathrooms, however, is given notably more weight than bedrooms, echoing a common desire to have a second bathroom in the master bedroom. The small nuclear families that prevail in Chinese cities do not normally require more than two bedrooms, and most flats in any case have at least two bedrooms to

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satisfy their needs. However, the availability of a second bathroom would signify a notable lifting of living standard to most families, and thus fulfilling a popular yearning. Comparing with many old tenement units in the city, some of which have poor or communal sanitary facilities, having two bathrooms in a flat could be construed as a pronounced advancement. Besides these fundamental and mainly intrinsic considerations, buyers are willing to pay for other attributes that could improve the living environment and quality of life. They consider the extrinsic milieu in the grounds of the development and in the hinterland farther afield, and explore how they could bring benefits or reduce disadvantages. Contrasting overseas findings that traffic noise might decrease house price by 5% (Luttik, 2000), traffic noise in the study area was not deemed important in house transaction price. This apparently aberrant response might be partially attributed to the low sensitivity towards noise in a congested Chinese city with a high ambient noise level. Additionally, the almost ubiquity of traffic noise in old city areas suggests the futility of attempts to escape from this nuisance. People have a tendency to develop a fatigue syndrome to a chronic and prevalent environmental nuisance, learning to adapt to it and intuitively dampening the sense towards it. If they cannot do anything to abate the traffic noise, treating it with resignation could be a palliative means to escape from the rather immutable annoyance. Habituation to a long-standing negative impact has subconsciously and sublimally tamed the mind and trained the senses to ignore it. For noise generally considered as shorter term or episodic, people’s tolerance threshold tends to be lower. Thus other kinds of noise, such as those emanated from residential neighbors or from nearby construction activities, could take precedence in people’s perception amongst the plethora of urban noise sources. In comparison with other factors, this disadvantage has been suppressed in making home buying decisions. Contrary to expectation, a positive effect of locating close to wooded areas has not been demonstrated. This somewhat unexpected result could be explained by the fact that in this study the main wooded area situated near the sampled precincts, designated as a protected area where development is not permitted, is out of bounds to the public for recreational activities. The rather natural land, embedded within the built-up matrix, is occupied by an old lychee orchard at present kept productive by local farmers. The home buyers have exercised their pragmatic instinct and would opt not to pay for a green space that they do not have access and cannot use. The indirect or less tangible environmental services of the wooded land, such as generation of cleaner and fresher air (Schabel, 1983), have not reflected in home purchase behavior. The buyers are either unaware of such ex situ benefits, or are unwilling to pay for them. Even if a natural green space with high ecological value is situated in the vicinity of homes, if it is off limit to residents, it could not whet their appetite to pay more for the apartments. In other studies, accessible green spaces near homes could raise house price by 5–6% in European (Tyrv¨ainen and Miettinen, 2000) and US cities (Tajima, 2003). This finding suggests that protected areas occluded within or situated on the fringe should explore co-use for nature conserva-

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tion, passive recreation and outdoor education in a country park system to satisfy the huge innate demand from citizens (Jim, 1986; Williams et al., 2005). In the present case, the orchard could well be transformed into an agro-recreational site to welcome nearby residents. The beneficial effects of proximity to water bodies are clearly expressed. Buyers are willing to pay a premium to have convenient access to water bodies that lie within 500 m of their residences. Both models assigned significant importance to the positive effect of nearness to this key landscape feature. For a city traversed by a major river (the Pearl River) and its tributaries and distributaries, with plenty of chances to be close to water, the citizens have not taken for granted the advantages of water bodies. Instead, they have reinforced their preference and fondness for this attribute. They desire to fulfil an innate urge to approach water bodies near their homes and would be willing to pay for propinquity to this amenity. More importantly, water bodies as a key landscape element of Guangzhou are readily accessible and liberally equipped with promenades and other recreational grounds. Many stretches of the riversides have been designed to satisfy human affinity for the land–water interface. The fact that a good proportion of the river banks is open to the public is playing a key role in encouraging this mode of outdoor recreation and in nurturing demands. In a compact city, water bodies often serve as precious breathing spaces where air circulation and solar access are less impeded. The water also serves to ameliorate air temperature extremes and improve human comfort. The fringes of water bodies are therefore well suited for public green spaces. The linearity of the river courses together with the linear green spaces on its banks could serve as ready-made greenways (Flink and Searns, 1993) to connect other open-space pockets into a green-space network according to landscape ecology principles (Dramstad et al., 1996; Jim and Chen, 2003). The disparate parts could be linked together into a linear park that can be easily accessed by a large number of residents in its 500 m wide catchment belt (M¨uller-Perband, 1979). A linear park also has a long interface with adjacent land uses to bring microclimatic amelioration and other benefits to the areas beyond. The city has made good use of its natural endowment to promote sustainable use of its river edges, and landscape planning could further augment this advantage and to satisfy increasing community demand. In future redevelopment and urban renewal projects, it is pertinent that such access rights should not be eroded by enclosure in gated properties or usurpation by new riverside roads. Privatization of public space which has beset some cities (Cybriwsky, 1999) could be effectively forestalled in Guangzhou. If feasible, more of the river banks should be opened up to expand the scope of this popular recreational opportunity. Adjacent land use could be adjusted such that uses that are non-compatible or conflict with recreation should be avoided. The green view out of windows is strongly preferred by buyers, as indicated by both models. This view refers to the garden embedded within the development that can be reached within minutes on foot by residents. This finding implies that green

spaces located within the residential grounds, with higher quality landscape design and management, with a higher degree of privacy and exclusiveness, and better security and safety, are more treasured by users (Van Herzele and Wiedemann, 2003). Green spaces which children can reach without crossing vehicular roads and can play safely with little supervision are particularly welcomed (Burgess et al., 1988). Thus the internal green spaces serve dual purposes, as recreational venues and as green scenery to provide pleasant view out of windows which could have a bearing on human physical and mental health (Wilson, 1984; Ulrich, 1986). Compared with the external woods that cannot be used, the internal quasi-public green spaces within the neighborhood (Gobster, 2001) obviously have a competitive edge to contribute to housing price. The innate urge to be cloistered and shielded in semi-private in situ green spaces has been implicitly conveyed. By eliminating collinear variables (with variance inflation factors ≥5) and insignificant variables, including number of bedrooms, exposure to traffic noise, and proximity to wooded areas, the previous linear and semi-log models were improved and the results are given in Table 8. The coefficients of all explanatory variables are statistically significant at p < 0.05, and the residuals of both models are homoscedastic, which indicates the residual of the model does not vary with changes in explanatory variables, thus confirming the validity of the regression analysis. The collinearity diagnosis shows no significant correlation between the explanatory variables. The modified tests have improved the explanatory power of both models. The semi-log model (R2 = 0.973) has slightly higher explanatory power than the linear (R2 = 0.923). In the semi-log model, the t-ratio for apartment floor area has been enhanced (Table 8), making it the strongest explanatory variable for housing price. It is followed by floor height, proximity to water bodies, green area view, number of bathrooms, and window orientation. Distance to new town centre carries a negative effect. In the linear model, the floor height has more explanatory power than floor area, otherwise the sequence of other variables is similar to the semi-log model. In a recent investigation, floor area was the main concern of home buyers in Guangzhou (Zhao, 2003). Therefore, the semi-log model is more Table 8 Linear and semi-log hedonic pricing models estimated after eliminating collinear and insignificant variables Explanatory variable

APARTSIZE STOREY NBATHROOM WINDOW GREENVIEW DISTANCE WATERVICINITY Constant

Linear model

Semi-log model

Coefficient

t-Ratioa

Coefficient

t-Ratioa

5014.143 7367.619 35617.295 5421.342 61672.069 −5894.387 78367.061 −89026.300

17.490 21.896 4.025 4.415 7.498 −2.361 11.379 −4.996

0.936 0.092 0.059 0.010 0.071 −0.088 0.132 8.798

30.792 21.963 3.818 2.253 7.040 −3.433 13.984 16.898

In linear model, R2 = 0.923, adjusted R2 = 0.923, F-ratio = 1020.31; in semi-log model R2 = 0.973, adjusted R2 = 0.947, F-ratio = 1497.25. a All coefficients are statistically significant at p < 0.05.

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accurate than the linear in reflecting consumer attitudes and behaviors. The implicit price estimates offered by the semi-log coefficients assigned 7.1% of the selling price to green space view. Proximity to water bodies is more important by contributing 13.2% to the price. High floor contributes 9.2% of the price, and desirable window orientation 1.0%. The findings match the reality of the real-estate market in Guangzhou, where both banks of the Pearl River and Ersha Island, which is situated in the middle of the river course, are dominated by expensive luxury residences. 6. General discussion and conclusions The multivariate scope of the hedonic pricing method could convey reasonably accurate information about the apportionment of house transaction price to different overt or latent factors. The roles played by green spaces and other environmental amenities on property price could be isolated and quantified. In the present real-estate market of China, rather conventional cost–benefit analysis and market comparison, both focusing on the relationship between supply and demand, are commonly employed to estimate residential sale price (Chen et al., 2002; Ma and Li, 2003). Hedonic pricing could provide an alternative and more holistic approach to analyze housing price structure and property value and to encompass environmental externalities. The method probes and deciphers consumer utility, the maximization of which signifies the ultimate human aspiration in a market milieu. Developers could better understand a fundamental market determinant: how a home buyer decides on whether a dwelling is worthy of acquiring. It can literally bring the mindset of the consumer closer to the developer, build a bridge between the two parties, and use the feedback to rationalize key investment and pricing decisions. Hedonic pricing method has been demonstrated to be an advanced technique for real estate valuation in western housing markets (Boyle and Kiel, 2001; Pagourtzi et al., 2003). The present study extends its applicability to China’s evolving realestate market. A developed and open regional real-estate market is a crucial pre-condition for successful application of hedonic pricing method, which can supply enough dwelling units of different kinds to meet the diversified needs of home buyers, and which people could select from the bundle of preferred attributes under the overriding control of affordability (Garrod and Willis, 1999). Substantial amount of transactions and avoidance of explicit asymmetries within the regional market could elicit reliable estimations. Moreover, carefully selected sample areas and explanatory variables could help to minimize potential biases and to enhance the explanatory power of the resulting models. This study indicates that the semi-log hedonic pricing model provides more accurate housing price estimates than the linear one. Housing price in the study area is a vector of structural, locational and environmental characteristics. Whereas most findings concur with overseas experience, some unique features have emerged. Both green space view and the proximity to water bodies have notably enhanced residential housing price. Access

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to in situ quasi-public green spaces has secured an implicit value. Nearness to a wooded area which cannot be used by residents has not contributed to residential price, vividly portraying the consumers’ pragmatic bent. It implies limited appreciation of the holistic and spill-over environmental benefits of natural areas. Thus green-space usability (direct and tangible benefit) could be more attractive than propinquity (indirect and less tangible benefits). Exposure to traffic noise has little impact on residential price, suggesting the relatively high tolerance, or possible sensory fatigue of people, or even a resigned attitude in the compact city to the chronic environmental nuisance, to the extent that it has been virtually shunned from the buying decision. Our research findings could provide useful insights and hints for both real-estate developers and the government. Hedonic pricing studies could help to refine the art and science of property valuation. It adds a new dimension to the study of related issues in China, including property purchase and sale, transfer, tax assessment, investment and financing. Developers could apply the method to make an informed weighing of alternatives in land acquisition for residential development. The analysis could help judgment on marketability and potential profit margin. It could enlighten decision on whether to purchase a piece of expensive land situated in a densely urbanized area with limited room for green spaces, or a piece of cheaper rural land with similar green space rooms plus good external environment. The determination of sale prices of individual units could more accurately take into account people’s preference and desire to have view or access to green spaces and other environmental amenities. Architectural and landscape design could be molded to improve the quantity and quality of such supplies to meet consumer demands and to raise the value of the development. Governments could use the method to inform land use zoning, especially to find optimal sites for residential use. It could provide objective data to develop a nature conservation plan (van Langevelde et al., 2002) and green space plan for the city to preserve natural areas of high ecological value (Mazzotti and Morgenstern, 1997), to upgrade existing ones, and to provide new ones based on landscape ecology principles (Cook, 2002; Leit˜ao and Ahern, 2002; Jim and Chen, 2003). For local governments, the budget for nature conservation work in urban areas is often insufficient. At present, land rent is collected by local governments when developers use land for buildings, and often land price determination is inordinately affected by political and administrative factors (Becker and Morrison, 1999). Local government revenue from land rent could be properly estimated by the hedonic pricing method approach, especially when a piece of land is attached close to green spaces and areas of high ecological or scenic quality. The design and distribution of new green spaces could aim at maximizing the amenities and the associated hedonic component of property prices. The increased public revenue could be ploughed back to develop and protect more green spaces and other amenities. In the drive towards a healthy and sustainable urban environment (Jackson, 2003) and smart growth (Stone and Rodgers, 2001), it is high time that such tasks should be elevated to the high-priority platform.

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The research agenda for the future could aim at expanding studies in different categories of housing with varied environmental qualities and green space provision to further enhance our understanding of the intricate people-environment-behavior confluence. In China and many developing countries, similar researches are scanty, and awareness and understanding of this pertinent segment of consumer behavior is inadequate. A fertile research direction could be found in monitoring people’s comprehension of and reaction to the contribution of environmental amenities to house prices in China in the course of its transformation into a full-fledged market economy. Residents of cities at different stages of development and sizes could be probed for the importance ascribed to pleasant endowments in the environs of housing areas. How people of different socioeconomic profile will exercise their judgment in attributing environmental value in home purchase decision deserves to be more deeply explored. For advocates of urban greening and naturalistic design of urban green spaces, the hedonic pricing could yield research findings that lend support to both their idea and funding for their realization. They could well extend their research into this cognate realm to generate reliable if not credible monetary valuation of green spaces in cities. Developing cities, in particular, need such scientific data to add a new dimension to the quest for clean, green and sustainable urban living. Acknowledgements The authors would like to express gratitude to the research grant support kindly provided by the Hui Oi Chow Trust Fund, the Committee on Research and Conference Grants of the University of Hong Kong, and the John Z. Duling Research Grant of the International Society of Arboriculture. References Adamowicz, W., Louviere, J., Williams, M., 1994. Combining revealed and stated preference methods for valuing environmental amenities. J. Environ. Econ. Manage. 26, 291–292. Anderson, L.M., Cordell, H.K., 1988. Influence of trees on residential property values in Athens, Georgia (USA): a survey based on actual sales price. Landscape Urban Plann. 15, 153–164. Bateman, I.J., Carson, R.T., Day, B., Hanemann, M., Hanley, N., Hett, ˝ T., Jones-Lee, M., Loomes, G., Mourato, S., Ozdemiro˘ glu, E., Pearce, D.W., Sugden, R., Swanson, J., 2002. Economic Valuation with Stated Preference Techniques: A Manual. Edward Elgar, Cheltenham, UK, 458 pp. Becker, C.M., Morrison, A.R., 1999. Urbanization in transformation economies. In: Cheshire, P., Mills, E.S. (Eds.), Handbook of Regional and Urban Economics, vol. 3: Applied Urban Economics. Elsevier, Amsterdam, pp. 1673–1790. Bender, B., Gronberg, T.J., Hwang, H., 1980. Choice of function form and the demand for air quality. Rev. Econ. Statist. 62, 638–643. Bengochea-Morancho, A., 2003. A hedonic valuation of urban green spaces. Landscape Urban Plann. 66, 35–41. Benson, E.D., Hansen, J.L., Schwartz, A.L., Smersh, G.T., 1998. Pricing residential amenities: the value of a view. J. Real Estate Finance Econ. 16, 55–73. Bolitzer, B., Netusil, N.R., 2000. The impact of open spaces on property values in Portland, Oregon. J. Environ. Manage. 59, 185–193.

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C.Y. Jim is a Chair Professor at the Department of Geography, University of Hong Kong. His research interests cover urban ecology, urban forestry, urban greening, arboriculture and soil science. Wendy Y. Chen has recently obtained her Ph.D. under C.Y. Jim’s supervision, with thesis title “Assessing the services and value of green spaces in urban ecosystem: A case of Guangzhou city”. She is conducting postdoctoral research at the same department.