Geographic Field Model based hedonic valuation of urban open spaces in Wuhan, China

Landscape and Urban Planning 98 (2010) 47–55

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Geographic Field Model based hedonic valuation of urban open spaces in Wuhan, China Limin Jiao a,b,∗ , Yaolin Liu a,b a b

School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan 430079, China

a r t i c l e

i n f o

Article history: Received 19 July 2009 Received in revised form 14 July 2010 Accepted 21 July 2010 Available online 21 August 2010 Keywords: Geographic Field Model Spatial hedonic modeling Urban open spaces Amenity value Housing price

a b s t r a c t Economic valuation of the amenities of urban open spaces will lend support to urban planning and development. In the absence of an explicit market for these amenities, the hedonic approach is often employed. Hitherto there have been a relatively limited number of studies on hedonic valuation of urban open spaces in China. This paper employs Geographic Field Model (GFM) to specify the externalities of urban open spaces with regard to their specific scale of influence, and builds a GFM-based spatial hedonic model to value the environmental amenities. The GFM quantification of open space variables overcomes the potential bias caused by traditional distance measures without influence scale limitations and the discontinuousness of the dichotomous index indicating the proximity to open space. A GFM-based spatial hedonic analysis was conducted in Wuhan, a metropolis in central China with various open spaces. Proximity to the Changjiang River recreation space and the East Lake were found to exert remarkable and positive impacts on apartment price. But proximity to other lakes and rivers were not significant in the result. The study showed that city level parks have significant amenity values, but district level parks do not. The amenity values of these urban open spaces were demonstrated by measuring the value they added to apartment prices. Some unexpected findings, such as the positive effect of noise and the powerful impact of floor height on housing price, may be common rules in densely populated cities in China. © 2010 Elsevier B.V. All rights reserved.

1. Introduction Urban open spaces, including green spaces, water bodies and civic squares, provide amenities and recreation services that contribute fundamentally to the quality of urban life (Miller, 1997; Shafer et al., 2000; Van Herzele and Wiedemann, 2003; Chiesura, 2004). Economic valuation of the external benefits of urban open spaces will lend support to urban planning, nature conservation and development (Jim and Chen, 2006). In the absence of an explicit market for the amenities of urban open spaces, the hedonic approach has become an established pricing model by formulating the relationships between housing price and various factors including environmental elements. Many studies on urban open space valuation have been conducted in Europe and US, but hitherto there have been a relatively limited number of such research projects in China.

∗ Corresponding author at: School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. Tel.: +86 27 8715 2613; fax: +86 27 6877 8893. E-mail address: [email protected] (L. Jiao). 0169-2046/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.landurbplan.2010.07.009

The external benefits of urban open spaces to society are a kind of environmental externalities. The hedonic pricing method could effectively value the environmental externalities (Garrod and Willis, 1999; Freeman, 2003; Jim and Chen, 2007; Boyle and Kiel, 2001; Neumann et al., 2009). Amenity values attached to urban open spaces are non-market price, since environmental benefits cannot be traded directly on an open market (Sengupta and Osgood, 2003). The fundamental assumption is that the house buyer is paying not only for the dwelling unit but also for its locational qualities and environmental amenities. The housing price is composed of a bundle of individual value components, such as floor area, structure, location and ambient environmental attributes (Cheshire and Sheppard, 1998; Freeman, 2003; Paggourtzi et al., 2003; Sirmans et al., 2005; Jim and Chen, 2007). The hedonic model uses the data set of property transacted price as a proxy, which includes the information of the value of environmental amenity implicitly traded. Hedonic modeling of environmental amenity originates with the classic studies of Ridker and Henning (1967) and Harrison and Rubinfeld (1978) and has generated a voluminous literature on valuation of various environmental and ecological services, such as landscape (Geoghegan et al., 1997; Anderson and Cordell, 1988; Benson et al., 1998; Luttik, 2000; Paterson and Boyle, 2002), agricultural land and forest (Le Goffe, 2000; Thorsnes, 2002; Price, 2003;

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Zhu and Zhang, 2008; Fleischer and Tsur, 2009), urban green spaces (More et al., 1988; Powe et al., 1995; Bengochea-Morancho, 2003; Bolitzer and Netusil, 2000; Geoghegan, 2002; Tajima, 2003; Barbosa et al., 2007), water bodies (Qiu et al., 2006; Jim and Chen, 2006), wetlands (Bin, 2005; Tapsuwan et al., 2009), etc. In recent decades, geographic information systems (GIS), spatial statistics and spatial analysis techniques have developed rapidly, which made possible the development of accurate, consistent explanatory variables, such as influence scoring of urban open space, in a fast and efficient manner (Kong et al., 2007). These techniques have been incorporated in hedonic modeling (Theriault and Des Rosiers, 1995; Bastian et al., 2002; Lee and Li, 2009). House prices are often spatially autocorrelated since neighborhood properties tend to have similar structural characteristics and share location amenities. The parameters in hedonic house price equations are usually estimated using procedures that assume independent observations (e.g., ordinary least squares). If the house prices are spatially autocorrelated, the hedonic parameter estimates will produce incorrect confidence intervals for estimated parameters (Basu and Thibodeau, 1998). Spatial hedonic models were developed and applied in many hedonic analyses (Anselin, 1988, 2001; Anselin and Lozano-Gracia, 2008; Basu and Thibodeau, 1998; Brasington and Hite, 2005; Hui et al., 2007). To identify the implicit values of different urban open spaces, the open space variables are involved in hedonic models. Distancebased measures are often used as open space variables in hedonic studies, such as distance to open space (Bengochea-Morancho, 2003; Anderson and West, 2006; Barbosa et al., 2007; Troy and Grove, 2008; Lee and Li, 2009), logarithm distance (Irwin and Bockstael, 2001; Cho et al., 2008), etc. Time cost to open space is used as an alternative variable in some literature (Kong et al., 2007). Some researchers noticed the influence scale of open space and introduced dummy variables representing the effect of distance from an open space in their hedonic models (Bolitzer and Netusil, 2000; Lutzenhiser and Netusil, 2001; Irwin and Bockstael, 2004). Considering the influence scale of open space, area proportion or amount of open spaces within a specified neighborhood surrounding a parcel is involved in some hedonic analysis (Geoghegan et al., 1997; Irwin and Bockstael, 2001; Irwin, 2002; Geoghegan, 2002). Commonly, the dichotomous index, which indicates whether a property is adjacent to an open space, e.g., within a 500 m buffer, is used in hedonic models (Jim and Chen, 2006, 2010; Hui et al., 2007). Distance-based measures neglect the scale of influence of

open spaces. It might cause bias in the interpretation of amenity values according to hedonic estimates. A dichotomous index in a hedonic model, which takes the scale of influence of open space into account, can tell whether adjacency to open space has significant impact on housing price, but cannot demonstrate a continuous quantification of this effect. The aforementioned dummy variables, area percentage or amount of open spaces cannot be used to continuously describe the distance-decay effect in hedonic pricing of urban open spaces. A Geographic Field Model (GFM) is a data model used widely in geo-statistics, spatial analysis and spatial data mining (Longley et al., 2005). By employing a specific attenuation function, a field theoretically assigns a quantity to any point in an unlimited or limited geographical space. A limited field can be used to simulate the external effect of an urban open space. The field extent is assigned to the scale of influence of the open space. The attenuation function of the field, which is used to specify the distance decay of the open space’s external effect, generates a quantity that attenuates continuously from a given value at the source to zero at the border. When different GFMs are used with different influence distances to quantify different urban open spaces, the type of urban open space is implicitly involved in the hedonic model. The value given at the source, namely, the original score, reflects the quality of the open space, such as area and functionality. There are relatively limited studies regarding urban green space amenities based on hedonic pricing models in mainland China. This is mainly the result of the previous housing market system (Kong et al., 2007). The administrative allocation system of residential units (welfare housing) dominated the sector from 1949 to the 1980s. After the advent of an open-door policy and economic reform from 1978, housing reform in China commenced in 1980. Welfare allocation of housing was eventually abolished in 1998. Since then, a free housing market has emerged with consumers given the right to choose housing attributes depending on their preference. This provides an opportunity to conduct hedonic pricing estimation of the benefits deriving from urban open space amenities (Jim and Chen, 2006; Kong et al., 2007). Wuhan is a metropolis in the middle of China, and it is also a very typical city with beautiful landscapes. There are many lakes, hills and parks in the city. Especially the Changjiang River and Hanshui River, which is the largest branch of the Changjiang River, converge and cross right through the center of the city. Better understanding of urban open space amenity as a component of housing value could encourage relevant invest-

Fig. 1. A typical urban residential estate in China.

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ment and further improvement of the urban landscape. But to date, there has been no hedonic valuation analysis on the amenity of these urban open spaces reported in Wuhan. China’s housing market has some specific characteristics that discriminate it from housing markets in western countries. Due to the dense population in cities, urban land resources are very limited. The Chinese government stopped authorizing the provision of land for villa development in 2003. Most Chinese urban families live in apartments, not individual houses. A typical urban residential estate in China is illustrated in Fig. 1. A residential estate is usually gated with a fenced border, and is composed of several apartment buildings, internal roads, a green area, playground and auxiliary facilities; it is developed by a single developer and is tended by a housing management company after it is occupied by residents. Each apartment building is divided into one to three housing units vertically, and apartments in each housing unit share the same entry to the building. There are often two to three apartments on each floor of each housing unit. The most popular layouts of the apartments aimed at the mass market are of 2-1-1 style (2 bedrooms, 1 bathroom and 1 living room), 3-1-1 style or 3-2-1 style, with a floor area of 90–150 m2 . This kind of apartment meets the needs of nuclear families that prevail in Chinese cities. Before and after the completion of the construction of the housing estate, apartments in the estate are presold and sold in the free market. The factors that influence housing price in Chinese cities can be categorized into structural characteristics, internal environment of the estate, locational factors and neighboring environmental elements. The hedonic modeling in the context of the Chinese housing market will contribute to this field. The rest of the paper is organized as follows. We discuss the GFM that is used to specify the externalities of urban open spaces, and give GFM-based spatial hedonic model in the next section. We next provide the data sources and variables included in the model. This is followed by a discussion of estimation results. We close with some concluding remarks. 2. Methods In this study, hedonic modeling is the core analysis method, and the Geographic Field Model is the main tool to quantify the environmental variables. 2.1. Geographic Field Model (GFM) A geographic field is a limited space around original spatial objects, including points, lines and polygons, which have external influence, usually abstract and virtual, on the space encircling them. The outward influence is usually a sort of attenuation from the original objects’ locations. For example, an urban open space

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seems to increase the prices of nearby houses; this kind of influence decreases with distance from the open space. The field of an open space assigns a quantity at any point in the field, namely, intensity, representing the external effect score of the open space. An intensity function is used to describe the changing rule of geographic field intensity, which is defined as follows. Given object o (source of the field) in space S, (x) is the field intensity of location x in S derived from o, then: (a) (x) should be a continuous, smooth and limited function, defined in space S; and (b) (x) should be a monotonic decreasing function of the distance from x to o. (x) is at its maximum when the distance is 0, and is 0 when the distance is equal to or larger than the influence threshold. There are many kinds of functions that satisfy these rules and can be used as intensity functions, such as linear function, logarithm function, exponential function, etc. Since the linear distance-decay assumption has been used in many hedonic pricing models (Lutzenhiser and Netusil, 2001; Anderson and West, 2006; Lee and Li, 2009) and is easy to understand, the linear intensity function is employed in this study. To take the scale of influence of externalities into consideration, the intensity function should be constrained by limiting the maximum range of influence distance. The linear intensity function with a range constraint is expressed as (x) =  F × (1 − r(x)) r(x) =

d(x)/R, 1,

d(x) ≤ R d(x) > R

where (x) is the field intensity at location x, F is the original effect score at the 0 distance to object o, which should be calculated according to the object’s attributes and reflect the quality of the object. d(x) is the distance from x to object o, R is the maximum influence distance of object o, and r(x) is the relative distance measure given by dividing d(x) by R. F and R need to be predetermined while computing the intensity of geographic fields. We describe the computation of these parameters and field intensity for different locational factors in Section 3.3. The influence distance for an effecting object can be obtained by spatial analysis, spatial statistics or a survey-based method. Two spatial analysis methods, which calculate the influence distances for point factors and linear factors, respectively, are presented in Section 3.3. These two methods are only applicable for estimating the scale of the facilities that provide basic and necessary services for the whole city, such as business centers, transportation, and schools. A questionnaire survey was conducted to identify the influences of urban open spaces.

Fig. 2. Changing of estimated premiums based on different measures to urban open spaces.

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The externalities of the same type of geographic objects, e.g., business centers, compose a composite geographic field. The potential describes the quantity of a composite field. The potential at any location is set to the maximum intensity at the point. The premiums attracted by urban open spaces can be estimated by hedonic modeling involving open space variables. Different quantification methods for the externalities of urban open spaces are based on different distance-decay assumptions, and thus lead to different estimated premiums, which change against distance in different forms (see Fig. 2). It is assumed that urban open spaces have influence on all apartments in the whole city when distance or logarithm distance is employed. Dichotomous measures take the influence of distance into consideration, but cannot continuously demonstrate the changing rule of the estimated premium within the influence distance buffer. A GFM quantified variable characterizes the premium more naturally, which attenuates from an original value at the boundary of open space to zero at a specific distance. 2.2. GFM-based spatial hedonic modeling The implicit prices of spillover benefits from accessibility to urban open spaces can be inferred based on the hypothesis that a house buyer purchases both a dwelling and a set of site characteristics (Rosen, 1974). Hedonic methods relate the transaction price of a real estate with the characteristics that define it. Housing price was found to be spatially autocorrelated by many researchers (Anselin, 1988, 2001; Basu and Thibodeau, 1998; Brasington and Hite, 2005; Hui et al., 2007; Anselin and LozanoGracia, 2008). In the case of this study, the Moran’I (Moran, 1948) of housing price is 0.44, and the spatial autocorrelation analysis result indicates that the housing price is really correlated. Ordinary least squares does not account for the interplay between spatially close observations, which may lead to biased, inefficient and inconsistent parameter estimates (Anselin, 1988; Anselin and Bera, 1998). To estimate the values of urban open spaces more accurately, a spatial lag model (Anselin, 1988) is employed in this study. Estimation of the spatial lag model is supported by means of the Maximum Likelihood method (Anselin, 1988; Smirnov and Anselin, 2001). GFM is used to quantify the external effect of urban open spaces. Furthermore, GFM is also employed to simulate the outward influence of other locational factors, such as business centers, schools, roads, etc. The GFM-based spatial hedonic model takes the following form: P = WP + ˛X + ˇY +  where P is a n × 1 (n is the number of observations) vector of the transacted apartment price, W is a n × n spatial weights matrix, the term WP is the spatially lagged dependent variable, X is a n × k matrix of field potentials of the factors that exert effects outward, Y is a n × l matrix of the values of the other variables,  is the spatial autoregressive coefficient, ˛ and ˇ are k × 1 and l × 1 vectors of regression coefficients, and  is a n × 1 vector of independently and identically distributed (IID) error terms. 3. Data and variables 3.1. Study area Our study was conducted in the Wuhan metropolis, the capital of Hubei province. It is the largest city in middle China, and the seventh largest city in the whole of China. Our study focuses on the main urban area, including the four districts of Wuchang, Hankou, Hanyang and Zhuankou. This area is 1557 km2 and had a population of 6.6 million in 2008. Changjiang River, the longest

river in Asia and the third longest in the world, and the Hanshui River, the biggest branch of Changjiang, converge in the center of Wuhan. There are dozens of lakes in the built-up area of Wuhan metropolis, and East Lake is the largest, with an area of 33 km2 . The specific water resources in Wuhan distinguish it from other cities in China, even in the world. Water areas make up about one quarter of the whole city. It has aliases of “River City” and “City of a hundred lakes”. There are also dozens of small hills in Wuhan. 3.2. Data Two steps were employed to collect housing transactions. Firstly, we queried the residential estates via the Wuhan real estate market website (http://scxx.whfcj.gov.cn/xmqk.asp, official website containing real estate market information) in March 2009, and 910 residential estates in the urban district of Wuhan were retrieved. To avoid problems with time variations, only the estates that were put on sale from June 2007 to June 2008 were taken into consideration. The scope was further restricted to the common residential estates, which were aimed at the mass residential market, and embracing at least five apartment buildings. A total of 313 residential estates were selected according to the above conditions. Secondly, we directly collected apartment transactions within these residential estates from the developers. One apartment transaction per ordinary estate was collected. We collected two apartment transactions in each large estate, which embraced more than 20 buildings. We initially collected 375 transactions with the data of location, selling price, and structural characteristics such as floor height, number of bedrooms and number of bathrooms per apartment, and the precinct attributes, such as floor area ratio and green space ratio of each estate. To avoid possible biases, duplex apartments were excluded since they belong to a different market segment. In addition, apartments with an irregular layout and partitions were excluded as they may suppress the utility ratio and transaction price. Finally, 304 apartment transactions were included in the data analysis in this study. All market prices of apartments (in Yuan/m2 ) were revised to January 2008 according to Wuhan housing price indices, issued quarterly by the Bureau of Land Resource and Real Estate Management, Wuhan. We collected the geospatial data for various factors that may have influence on housing prices, including business centers, urban roads, public transportation stops, nursery schools, primary schools, population density, greenery coverage rate, air quality, noise level and urban open spaces. The selected open spaces in our study include Changjiang River, Hanshui River, 24 lakes including East Lake, 26 parks, nine civic squares, and 70 hills. The study area, sample residential estates, and urban open spaces are shown in Fig. 3. 3.3. Variables The dependent variable and explanatory variables in our hedonic model are described in Table 1. The dependent variable is the transacted apartment price per square meter. The apartments in the same estate are likely to exhibit similar structure, layout and interior and exterior design features, and are sold at almost the same price per square meter, only differing slightly from building to building, while excluding the influence of floor and structural attributes. The total price of an apartment is calculated by multiplying the price per square meter by floor area of the apartment when purchasing. Consumers can choose the size, floor and model of apartment, if available, in the same residential estate. Housing price in per square meter, not the total price of the apartment, is often used as the dependent variable in many Chinese and other Asian hedonic analyses, such as Hui et al. (2007) in Hong Kong, Kong et al. (2007) in Jinan, China, Gao

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Fig. 3. Study area, housing sample, and urban open spaces.

and Asami (2007) in two cities in Japan, Kim et al. (2007) in Seoul, Korea. The locational factors with external effects were quantified by GFM, including business centers, roads, public transportation stops, and schools. There are several business centers instead of a CBD in Wuhan due to the particular terrain. The attributes of each business center,

including sales volume, plot area, total floorage, and functionality, were surveyed. Each attribute was normalized into [0,100] by

v = 100 ·

v − vmin vmax − vmin

where v is the normalized value, v is the original value of the attribute, and vmax and vmin are the maximum and minimum values

Table 1 Variable descriptions. Variable Price PBizCen PRoad PPubStp PNurSch PPriSch PMidSch SGreenery SAirQua SAcousEnv Storey Number of bedrooms Number of bathrooms FlrArRatio GreenRatio PChjReaSpa PEastLake PRivers PLakes PCityParks PDistriParks PHills

Description 2

Transaction price for the apartment in Yuan/m (dependent variable) Potential of the geographic field of business centers Potential of the geographic field of urban roads Potential of the geographic field of public transportation stops Potential of the geographic field of nursery schools Potential of the geographic field of primary schools Potential of the geographic field of middle schools Score of greenery coverage rate of the block embracing sample estate Score of air quality, negatively correlated with Air Pollution Index Score of acoustical environment, negatively correlated with noise level (in decibel) The floor on which the apartment is situated Number of bedrooms in the sample apartment Number of bathrooms in the sample apartment Floor area ratio of the residential estate in which a sample is located Green space ratio of the residential estate in which a sample is located Potential of the geographic field for the core area of Changjiang River recreation space Potential of the geographic field of East Lake scenic area Potential of the geographic field of ordinary rivers other than the core recreation space of Changjiang River Potential of the geographic field of ordinary lakes other than East Lake Potential of the geographic field for city level parks (squares) Potential of the geographic field for district level parks (squares) Potential of the geographic field of hills. All hills are covered by forest.

Max

Min

Mean

15,000 99.98 99.52 79.95 96.87 97.83 97.54 100.00 90.00 100.00 40 3 2 18.5 0.6 77.50 99.00 92.47 98.56 99.01 87.84 100.00

3550 0.89 72.50 0.00 0.00 0.00 0.00 20.00 60.00 30.02 4 2 1 0.38 0.06 0.00 0.00 0.00 0.00 0.00 0.00 0.00

5918.47 56.26 89.81 8.77 47.49 42.90 44.48 71.73 80.82 57.82 15.88 2.84 1.70 2.99 0.36 2.94 2.64 1.97 12.36 17.4 2.12 2.91

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L. Jiao, Y. Liu / Landscape and Urban Planning 98 (2010) 47–55 Table 2 Influence distances of urban open spaces in Wuhan. No. Urban open spaces

Max. (m) Min. (m) Mean (m) Std. Dev.

1

1000

600

805.6

118.4

900 600 650 650 600 350

600 300 350 400 400 200

788.9 500.0 511.1 516.7 505.6 288.9

89.4 93.5 85.8 79.1 58.3 41.7

2 3 4 5 6 7

Fig. 4. Influence distance of the spatial objects with external effects.

of the attribute, respectively. A scale index was calculated for each business center, which is equal to the weighted mean of the normalized values of the center’s attributes. The scale index of each business center was used as the original influence score in GFM. According to the scale index, the business centers in Wuhan were classified into four levels: city level, section level, community level, and block level. According to Yan and Lin (1999), the influence distance of each level was estimated by



Ri =

s n

where Ri is the influence distance of business centers of level i, s is the area of city, n is the number of business centers of this level and upper levels,  ≈ 3.14. The mechanism of this function is illustrated in Fig. 4(a). There are two business centers that provide commercial services for the assumed urban area, the rectangle. Each center owns half of the total area, and the service radius of the center is calculated when the serving region is transformed into a circle with the same area. The simple example can be generalized to an ordinary situation. After determining the original score and influence distance of a business center, we can calculate its field intensity at any sample location by intensity function. The potential at any location of the composite field of business centers is set to the maximum intensity at that point. The attributes of urban roads, including width, type and surface condition, were surveyed. In a similar way to business centers, the values of these attributes were scored and normalized into [0,100], and a scale index for each road was calculated by the weighted mean method. The scale index of each road was used as the original score in the intensity function of GFM. According the scale index, the roads in Wuhan were classified into three grades: main roads, secondary roads and minor roads. The influence distance of each grade of road was estimated (Yan and Lin, 1999; P.R. China, 2002) using Ri =

s 2l

where Ri is the influence distance of grade i, s is the built-up area of the city, and l is the total length of the roads of this grade and upper grades. This equation can be intuitively understood by a simplified example. Fig. 4(b) shows a rectangular area, and it is assumed that the total area is served only by the road crossing the center.

The core area of Changjiang Recreation Space East Lake Rivers (Excluding No.1) Lakes (Excluding No.2) City level parks District level parks Hills

The influence distance of the road can be estimated to be a half of the width of the rectangle, as given by the above equation. The example can be generalized to an ordinary situation and is used to calculate the influence distance of each grade of urban roads. The intensity of the geographic field of each road at every sample location was calculated by intensity function after determining the original influence score and influence distance. The potential at any point in the composite field of urban roads was set to the maximum intensity. The potential of the fields of nursery schools, primary schools, and middle schools were computed in the same way as for business centers. The influence distance of public stops was set to 300 m, which is calculated as half of the average distance between stops and is an accessible distance for people to move to a public stop on foot. The variables related to urban open spaces were quantified by GFM. As mentioned before, there are several kinds of urban open space in Wuhan, including rivers, lakes, parks, squares and hills. There are two outstanding open spaces that are famous in the city. One is the core area of the Changjiang River recreation space, marked by diagonals in Fig. 3, which was built between 2001 and 2004, and is now a symbol of tourism and recreation in the city. The other is the East Lake, which is the biggest urban lake in China, and is a famous national scenic area. There are many estates at high price surrounding these two open spaces that are considered so outstanding that we treat them separately in our hedonic model. In addition, there are a total of 35 parks and squares in our study area, but they are diverse in area and functionality. They were divided into two groups: city level parks (squares) and district level parks (squares). The city level parks (squares) have much bigger areas and more recreational functions; there are nine such parks, namely, Yellow Crane Tower park, Jiefang park, Zhongshan park, Wuhan zoo, Sheshan park, Guishan park, Hongshan civic square, Shuiguohu Park and Nanganqu park. Some research findings indicate that people are unwilling to walk over 500 m or 10 min to reach a green space (Burgess et al., 1988; Jim and Chen, 2006). Hui et al. (2007) employed 300 m as the distance threshold of adjacency to green belt areas in Hong Kong. A questionnaire survey was conducted to identify the influence distance of urban open spaces in Wuhan. Using random sampling, 300 residential sites in Wuhan were chosen out of 910 estates (retrieved from Wuhan real estate market website, http://scxx.whfcj.gov.cn/xmqk.asp, in March 2009). A face-to-face interview was conducted with a randomly selected resident at each site. The response rate was 77%, and 1.7% of the questionnaires with incomplete answers were eliminated. The statistics of a total of 227 valid respondents are shown in Table 2. The influence distances of urban open spaces are set to integral hundred digits based on the statistics of the survey. The influence distance of the core area of Changjiang Recreation Space and East Lake is set to 800 m. The influence distance of rivers and lakes, city level parks and district level parks is set to 500 m, and 300 m for hills. The original influence scores of the core area of Changjiang Recreation Space and East Lake are set to 100. As for other categories, including rivers, lakes, city level parks, district

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level parks, and hills, a scale index for each open space was calculated according to the area and functionality by implementing the attribute normalization and weighted sum method in the same category. The scale indices are used as original influence scores of the open spaces in GFM. After determining the influence distance and scale index of each open space, its field intensity at each apartment sample location can be calculated by intensity function. When a location is influenced by two or more open spaces of the same type (e.g., city level parks), the potential of the composite field of this type of open spaces at this location was set to the maximum intensity. Block greenery coverage (%) was calculated for each block based on the urban land use map, and was normalized into [0,100] by the normalization method presented before. We collected the data of air pollution and noise pollution from Wuhan environment protection bureau. The study area was divided into nine sub-areas according to air pollution, and the average API (Air Pollution Index) of each sub-area from June 2007 to June 2008 was calculated. According to noise level, the study area was divided into 89 subareas, including 15 block areas and 74 belt areas along urban main roads. The average noise level (in decibels) of each sub-area from June 2007 to June 2008 was calculated. Air quality and acoustic quality of each sub-area were scored by



fi = 100 · 1 −

vi − vmin vmax − vmin



where fi is the quality score, vi represents the original value, API or noise level, vmax and vmin are the maximum and minimum values of API or noise level, respectively. Block greenery coverage, air quality, and acoustic quality are the factors that cover the study area, and overlay analysis was utilized to obtain the scores at sample locations. The inner characteristics of estates and structural variables of apartments are involved in the hedonic equation with original values, including floor area ratio and green ratio of the estate where an apartment is located, floor height, number of bedrooms, and number of bathrooms in an apartment. 4. Results The results of regression of our GFM-based spatial hedonic model are shown in Table 3. The explanatory variables account for 61.7% of the housing price variance. It is observed that proximity to the core area of Changjiang River recreation space and proximity to East Lake have significant and positive effects on apartment price. More exactly, proximity to the two open spaces has positive influence on the selling price of the apartments located in the 800 m buffer around the two open spaces referring to the GFM model employed. According to the result, proximity to the core area of Changjiang River recreation space and proximity to the East Lake scenic area raise the housing value

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Table 3 Regression result of GFM-based spatial hedonic model. Variable

Coefficient

Z-Value

Z-Probability

Constant PBizCen PRoad** PPubStp PNurSch PPriSch PMidSch SGreenery** SAirQua SAcousEnv** Storey** NRoom NBath* FlrArRatio GreenRatio PChjReaSpa** PEastLake** PRiver PLake PCityParks* PDistriParks PHill

349.495 −2.695 36.179 −0.644 1.505 −0.658 −2.013 6.238 −24.201 −4.578 71.120 83.899 212.347 12.892 546.203 41.092 21.261 −2.094 −2.338 4.271 −5.255 4.888

5.385 0.193 −0.599 2.882 −0.121 0.699 −0.293 −0.811 2.065 −1.322 −0.992 8.324 0.426 1.349 0.375 0.631 7.452 4.832 −0.413 −0.983 1.869 −1.014

0.847 0.549 0.004 0.904 0.485 0.770 0.417 0.039 0.186 0.032 <0.001 0.670 0.077 0.708 0.528 <0.001 <0.001 0.680 0.325 0.062 0.311 0.239

Dependent variable: apartment price, Yuan/m2 . R2 = 0.617. * Significant at the 90% confidence level. ** Significant at the 95% confidence level.

by 41.092 and 21.261 Yuan/m2 for each percentage increase of the indices, respectively. Proximity to these two open spaces generates a bigger marginal value compared with other environmental variables. The amenity values of the two open spaces can be reflected by the housing value added with the proximity to the open space, which can be inferred by referring to the GFM, see Table 4. The result demonstrates that proximity to city level parks has a positive effect on housing price, but district level parks do not. This shows that bigger and multifunctional parks are more attractive to house buyers and have higher amenity values. Each percentage increase in the intensity of the influence field of city level parks increases the housing value by 4.271 Yuan/m2 . The housing value added by the proximity to city level parks is shown in Table 4. Proximity to lakes and rivers other than East Lake and the segment in Changjiang River recreation space, and proximity to hills are not observed to exert significant impact on housing price in this study. We find that floor height, number of bathrooms, road infrastructure, and block greenery coverage have positive influence on housing value. The variable storey has a high explanatory power for the selling price of apartments, and it increases the housing price by 71.120 Yuan/m2 for each higher floor. This finding differs from the studies conducted in western countries. Most of them do not involve this variable in their hedonic analysis. Some of them find that floor height has no significant influence on housing price, such as the one conducted in Spain (Bengochea-Morancho, 2003). The

Table 4 Housing value added by increasing proximity to open spaces. Distance (m)

Added value 1 (Yuan/m2 )

Added value 2 (Yuan/m2 )

Added value 3 (Yuan/m2 )

0 100 200 300 400 500 600 700 800

4109.2 3595.6 3081.9 2568.3 2054.6 1541.0 1027.3 513.7 0.0

2126.1 1860.3 1594.6 1328.8 1063.1 797.3 531.5 265.8 0.0

427.10 341.68 256.26 170.84 85.42 0.0 0.0 0.0 0.0

Housing value added 1: housing value added by increasing proximity to the core area of Changjiang River recreation space. Housing value added 2: housing value added by increasing proximity to the East Lake scenic area. Housing value added 3: housing value added by increasing proximity to city level parks.

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possible reason for this discrepancy is the fact that high residential buildings are often crowded in our study area. The apartments at higher floors can have a better and wider view, more sunshine and better ventilation. They also offer a distance-decay-feeling that street noise and air pollution are less serious. The view from lower flats is often dominated by other buildings and artificial structures. Actually, this is true not only in Wuhan; this urban residential style is common in the whole of China. It is also found that floor height incurs the most significant effect on housing price in the study conducted in Guangzhou (China) by Jim and Chen (2006). Hui et al. (2007) obtain a similar result in Hong Kong. The number of bathrooms incurs significant and positive effects on apartment price, while the number of bedrooms does not. Consumers often have the chance to choose 2-bedroom or 3-bedroom apartments in a residential estate according to their needs, while the apartments with different sizes and structures in the same estate are often labeled with a similar price per square meter. Compared with the old style apartment with only one bathroom, the availability of a second bathroom in the master bedroom is a common desire and will upgrade the living standard. Thus, owning two bathrooms in a flat could be construed as a significant advancement. According to the result, one more bathroom will raise the price by 212.347 Yuan/m2 . Urban road infrastructure has a significant and positive influence on housing price, and each percentage increase in influence potential of the roads field generates a 36.179 Yuan rise in the transacted price of apartments. The positive price impact of the greenery coverage rate at block scale indicates people tend to purchase apartments in the estates with a high green ratio area. On average, a 1% increase in the score of greenery coverage results in a 6.238 Yuan/m2 rise in apartment price. The result indicates unexpectedly that the acoustical environment is negatively correlated with the dependent variable. According to our regression result, the sale price of apartments decreases by 4.578 Yuan/m2 for each percentage increase in the score of acoustic environment. That is, a 4.578 Yuan/m2 increase in housing price for a 1% increase in noise level. This finding does not seem in line with our common understanding and other studies. The discrepancy may be attributed to the substantial differences in types of residential environment and styles of going out between most cities in China and overseas cities. The majority of people in Wuhan live in high-rise buildings and go to work, to school, shopping, etc., by public transport. Most people prefer to live in the estates that are convenient for accessing public transport and other public services, such as shopping, dining, daily needs, etc. So people tend to sacrifice serenity for convenience and live in the area with a dense population and proximity to roads where noise sources of residents are associated mainly with neighborhood activities and road traffic. Another unexpected result of the hedonic analysis is that proximity to business centers is not a significant factor with a positive impact on the housing price in Wuhan. But it is usually reported that accessibility to the CBD is positively correlated with housing price in many cities, such as reported by Bengochea-Morancho (2003), Jim and Chen (2006), Kong et al. (2007), Hui et al. (2007) and Troy and Grove (2008). There should be a difference between Wuhan and other cities according to our findings. There are several business centers in the Wuhan urban area instead of a single CBD due to the particular urban terrain, and this kind of multi-center urban pattern will lower the intent to reside near to the “center” of the city. Instead, places with a better landscape and convenient transportation will be favored by housing buyers.

high-rise and densely populated city in the context of the Chinese urban housing market. The major contributions of this paper are described as follows. First, this paper proposes a GFM-based hedonic analysis method. GFM was used to quantify the factors that have external effects on housing price, such as the influence of urban open spaces and other locational factors. Generally, an urban open space cannot exert influence on the market price of all residential properties in the whole city. Using GFM, we can mathematically describe the continuous change of the influence of urban open spaces under the limitations of specific influence distances. The GFM quantification of open space variables overcomes the potential bias caused by traditional distance-based measures without influence scale limitations and the discontinuousness of the dichotomous index indicating the proximity to open space. Secondly, our hedonic analysis in Wuhan achieved several noteworthy findings. Proximity to the Changjiang recreation space was found to exert significant and powerful impact on apartment price. This tremendous open space has a specific scarcity and is seen as very attractive by property buyers. It is also deemed to be a consequence of the continuous investment for environmental improvement in these bank areas from 2001 to 2004. Another major urban open space, East Lake, also has significant and positive impact on housing market price. The city level parks other than district level ones were observed to have significant amenity values. The economic values of these urban open spaces’ amenities were demonstrated by computing the added values on apartments by increasing proximity to these open spaces. Proximity to lakes and rivers other than East Lake and the segment in the Changjiang River recreation space were not observed to have significant impact on housing price. The findings concerning these precious God-given natural water bodies urge local government, developers, and the general public to take action to protect and improve their amenities. In addition, proximity to business centers was found to have no significant influence on housing value as an accidental finding, which is very different from common knowledge in the China housing market. But it is reasonable due to the fact that Wuhan is a typical polycentric city. Floor height was observed to have a high explanatory power for apartment price. The result also indicated that consumers would prefer to pay for the apartments with two bathrooms. Acoustic environment is unexpectedly observed as negative. It is probably common in densely populated cities in China. In addition, urban road infrastructure, greenery coverage rate, and number of bathrooms were also found to have significant and positive impacts on housing price. Our findings help improve our understanding of the amenity values of urban open spaces and the housing price determination in a high-rise and densely populated environment, and are useful for academics, local policy makers, developers and the general public. Some aspects of the proposed method deserve future research. This study employed a questionnaire survey to determine the scale of influence of urban open spaces. It is challenging to establish some spatial statistics or data mining methods to reveal the influence distance. In addition to linear intensity functions of geographic fields, the use of nonlinear intensity functions needs to be investigated in the future. Additional hedonic applications in the Chinese housing market or other similar high-rise and densely populated environment need to be conducted and to verify whether the specific rules observed in this study can be generalized in similar contexts, such as the effect of noise level, the degree of influence of floor height, and the significance of number of bathrooms in an apartment.

5. Conclusions

Acknowledgments

This study introduces a GFM-based spatial hedonic method for the valuation of urban open spaces, and conducts a case study in a

This research was funded by National Natural Science Foundation of China (40901188), Key Laboratory of Geo-informatics of

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Yaolin Liu received the BS and MS degrees in remote sensing application from China University of Geo-Science, Wuhan, China, in 1982 and 1988, respectively. He received a Postgraduate Diploma in soil surveying, with a specialisation in remote sensing, from ITC in 1989. He received PhD degree in GIS from Wageningen University, The Netherlands. His research interests include land evaluation, land use and Geographic Information Science.