evaluating the effects of landscape on housing ... - Wiley Online Library

EVALUATING THE EFFECTS OF LANDSCAPE ON HOUSING PRICES IN URBAN CHINA QINGYUN DU*, CHAO WU*, XINYUE YE**, FU REN* & YONGJUN LIN*** * School of Resources and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. E-mail: [email protected] ** (Corresponding Author) Department of Geography, Kent State University, Kent, OH 44242, United States. E-mail: [email protected] *** Shenzhen Urban Planning & Land Resource Research Centre, Shenzhen 518034, China. Email: [email protected] Received: July 2016; accepted December 2017 ABSTRACT The rapid urbanisation of China has received growing attention regarding its urban residential environments. In this article, we model the spatial heterogeneity of housing prices and explore the spatial discrepancy of landscape effects on property values in Shenzhen, a large Chinese city. In contrast to previous studies, this paper integrates the official housing transaction records and housing attributes from open data along with field surveys. Then, the results using the hedonic price model (HPM), geographically weighted regression (GWR) without landscape metrics and GWR with landscape metrics are compared. The results show that GWR with landscape metrics outperforms the other two models. In summary, this research provides new insights into landscape metrics in real estate studies and can guide decision-makers plan and design cities while also providing guidance to regulate and control urban property values based on local conditions. Key words: housing prices, landscape index, GWR, spatial heterogeneity, Shenzhen

INTRODUCTION The spatial non-stationarity of housing prices is an important issue in urban geography studies (Kestens et al. 2006), Over the last few decades, the hedonic price model (HPM) has identified significant factors related to housing prices. Rosen (1974) stated that there were relationships between observed prices and influential characteristics. Since then, the HPM has been widely used in the field of real estate (Garrod & Willis 1992; Visser et al. 2008; Gibbons et al. 2014; Seo et al. 2014), In China, residential housing reform since 1998 has facilitated the commercialisation and socialisation of housing; it is thus possible to quantify the monetary value of attributes based on the HPM (Choy et al. 2007; Jim &

Chen 2007; Jim & Chen 2009a, 2009b; Wen et al. 2014; Wu et al. 2014; Li et al. 2016). In the HPM, different factors have various margin prices for housing values; however, these factors have the same effect on a global scope represented by the same regression coefficients. The HPM is employed as a global regression model. Hence, it cannot capture the spatial variation of the estimated parameters. Recently, geographically weighted regression (GWR) has been used in many fields such as urban geography (Fotheringham et al. 2001; Su et al. 2012), social economics (Huang & Leung 2002; Malczewski & Poetz 2005; € Partridge et al. 2008; Ocal & Yildirim 2010), urban planning (Buyantuyev & Wu 2010), medical health (Comber et al. 2011; Gilbert & Chakraborty 2011; Nakaya et al. 2005; Tu et al.

Tijdschrift voor Economische en Sociale Geografie – 2018, DOI:10.1111/tesg.12308, Vol. 00, No. 00, pp. 1–17.

C 2018 Royal Dutch Geographical Society KNAG V

2 2012), meteorology (Brunsdon et al. 2001; Lloyd 2005) and environmental conservation (Hu et al. 2013) for investigating the spatial variation and heterogeneity exhibited by relationships among dependent variables and independent variables. It is reasonable that not all the individuals in different samples demand the same housing characteristics (Iturra & Paredes 2014), GWR applied to housing prices belongs under the framework of the HPM and is used to study the spatial heterogeneity of housing prices due to its merits. This paper applies GWR to explore spatial variations in housing prices and analyse the effects of attributes on housing prices. Recently, the process of urbanisation has accelerated, especially in large Chinese cities such as Shenzhen; such a trend has the potential to strongly affect the landscape and ecosystem functions of cities and surrounding areas (Li et al. 2010; Wu 2008), Furthermore, land use determination in regard to housing values is not limited to accessibility; externality characteristics of land produce their own effects (Geoghegan et al. 1997), Changes in ecological conditions result in greater attention given to the landscape. Rodenburg et al. (2008) proposed that analysis of the assessment of the benefits of multifunctional land use is important. Cho et al. (2009) analysed amenity values of spatial configurations of forest landscapes over space and time. An excellent landscape and environment can significantly enhance the value of residential quarters (Wang et al. 2015), There have been numerous hedonic studies both in China and abroad illustrating the value of landscapes as reflected in property sale prices. Mahan et al. (2000) estimated the effects of the distance to wetlands and the size of wetlands on housing values. Geoghegan et al. (2003) reported that preserved open spaces increase property values on adjacent residential parcels. Jim and Chen (2010) indicated that neighbourhood parks could increase housing prices, harbour views attracted a premium of 5.1 per cent, and mountains were not welcome. Zhang et al. (2012) found that urban green space significantly and positively influenced neighbouring housing values. Payton and Ottensmann (2015) suggested that the value of public C 2018 Royal Dutch Geographical Society KNAG V

QINGYUN DU ET AL. parks and greenways varies across space. Land use is a dominant factor in determining a landscape pattern, which is also the basis for human perception (Opdam et al. 2003), The aforementioned studies evidence that many urban amenities can significantly influence peoples’ quality of life and stimulate us to assume that land use configurations (landscape metrics) do affect households’ willingness to pay for properties. Previous research has contributed to an understanding of the effects of landscape on housing prices. However, most studies have explored individual landscape effect on housing prices and have ignored the combined effects of different types of landscapes. In addition, few studies have explored the effects comprehensively and systemically in China. This paper specifically adopts landscape metrics, including the number of patches (NP), the patch cohesion index (COHESION), the connectedness index (CONNECT) and Shannon’s diversity index (SHDI), from the perspectives of fragmentation, physical connectedness, functional connectedness and diversity in GWR to analyse the influence of landscape on property values. Synthesising the aforementioned points, we confirm that few studies have focused on the effects of landscape metrics on housing prices considering spatial non-stationarity in a large and growing Chinese city. Thus, this study applies landscape ecology to investigate spatial externalities of different landscape metrics affecting housing prices to attain a more comprehensive understanding of economic amenity values derived from the land use configurations. The main objectives of this paper are as follows: (1) reveal the market value of landscapes systematically and comprehensively. Whereas most studies have focused on one or several landscape variables, minimal research has been conducted from the perspective of a growing Chinese city in terms of comprehensively investigating the influence of landscape indexes on housing prices. (2) Study the differential characteristics of influential factors with GWR considering spatial heterogeneity, and reflect the varying effects of variables on housing prices for different locations. (3) Guide the formulation and implementation of urban plans. The results obtained herein can be

LANDSCAPE AND HOUSING PRICES used to analyse the intrinsic mechanism of changing prices and provide a guide to develop land and plan cities rationally. The results can identify areas where conservation easement policies are most likely to provide the greatest value in housing markets. The estimates derived from this paper provide guidance for planners and developers for allowing new housing to achieve revenue maximisation and satisfy the demands of users mostly through the use of urban planning. The structure of this paper is as follows. The following section introduces the methods in detail. The methods include the calculation of landscape metrics and the fundamental theory of GWR. The third section describes the study area data and explanatory variables. In the fourth section, GWR is applied to explore the relationships between the various factors and housing prices to verify whether there are some suitable landscape indexes influencing prices. Finally, some conclusions are drawn in the final section. DATA DESCRIPTION Study area and data – Shenzhen is located in the South of Guangdong Province and immediately North of Hong Kong. The city is composed of the districts of Luohu, Futian, Nanshan, Longgang, Baoan, Yantai, Guangming, Longhua, Pingshan and Dapeng (Figure 1). Here, because there are no samples or representative effects, the islands such as Nei Lingding I and Dachan are not shown. The city covers an area of 1,996.85 km2 and had a residential population of 10.8 million as of 2014 (Shenzhen Statistics and Information Bureau 2014), In 1980, Shenzhen became China’s first special economic zone (SEZ), Its special geographic location and economic policy resulted in a rapid urbanisation process, and Shenzhen has completed the work of expansion and urban renewal in a relatively short period of time. Therefore, land use in Shenzhen is changing with time. Land use is a leading factor in determining the spatial pattern of a landscape, which is also the basis for human perception (Opdam et al. 2003), There is every reason to believe

3 that landscape indexes strongly influence the housing prices. The data sources of this paper are as follows: (1) land use data (types and locations); (2) new house transaction data from related institutions; (3) attribute data on houses and real estate from the Fang.com website (Fang 2014); (4) field-survey data of real estate properties; and (5) road data and POI data of Shenzhen. Land use data are acquired from Landsat 2014 (TM) in the form of 30 3 30 m raster data. New house transaction data (known from second-hand housing) of 56,899 dwelling units (178 real estate properties in total) from 2015 are collected from the Shenzhen Research Centre of Digital City Engineering. To avoid potential biases, the property type considered in this paper is limited to ordinary commercial housing; duplex apartments and cottages are excluded from the study. Considering the influence of trading time on housing prices, we modify all prices to December 2015 using a house price index, making all the prices comparable. The Fang.com website is a highly authoritative website on real estate in China. Attributes such as apartment size, floor level, number of bedrooms, number of bathrooms, property management fees, and floor area ratio are collected from the Fang.com website (Fang 2014), For transaction data and house attribute data, the common properties are name, address and parcel of real estates. Two types of data are matched by name and parcel of the real estates without problems. The housing type information including number of bedrooms and bathrooms is matched using the data on the building and room numbers. In addition, fieldwork is performed to confirm and supplement the structural attributes for data integrity and accuracy. After pre-processing the housing data to remove invalid records, 56,700 valid samples are obtained finally. Explanatory variables – The definitions, basic descriptive statistics and expected signs (‘1’, ‘2’ and ‘unknown/#’) of all variables are listed in Table 1. Explanatory variables in this paper can be divided into three types: structural variables, locational variables and landscape variables. The following six structural characteristics are considered: apartment size (AREA), floor level (FLOOR), number of C 2018 Royal Dutch Geographical Society KNAG V

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QINGYUN DU ET AL.

Figure 1. The case study city: Shenzhen. [Colour figure can be viewed at wileyonlinelibrary.com]

bedrooms (NOBEDROOM), number of bathrooms (NOBATHROOM), real estate property management fees (FEE), and ratio of floor area of real estate (RPLOT), These details are all obtained from Fang.com, Apartments in the same real estate complex share the same locational variables and landscape variables. The main locational variables include the proximity to public facilities such as metro stations, bus stations, central business district (CBD), district centre, schools and hospitals. The network distance is used to calculate accessibility based on road network using GIS technology. The focus of this paper is landscape variables, which include traditional landscape variables and landscape indexes. The traditional landscape variables are the ratio of green space area of the real estate (RGREEN) and the distance to the nearest city park (DPARK), The landscape indexes include number of patch (NP), Patches of cohesion (COHESION), Functional connectedness (CONNECT) and Shannon’s diversity index (SHDI), The meanings and calculation methods of the landscape indexes are described in Table 2. METHODS Measurement of landscape indexes – In addition to traditional factors affecting housing C 2018 Royal Dutch Geographical Society KNAG V

prices, this paper uses landscape indexes to reflect the land use conditions surrounding housing properties. Landscape indexes are different from factors related to accessibility and can be used to intuitively express spatial patterns (Turner et al. 1989; Riitters et al. 1995; Wu et al. 2002), Landscape indexes are widely used in the field of ecological phenomena (Gustafson & Parker 1992; Hargis et al. 1998; Syrbe & Walz 2012), Moreover, landscape indexes are closely linked to economic and social development levels (Frohn et al. 1996; Seto & Fragkias 2005), Under the background of human living environments, urban ecological landscapes have unique features and values, which result in varying housing prices. Geoghegan et al. (1997) first explored the effects of spatial landscape indexes on housing prices based on the HPM using GIS. Later, Kong et al. (2007) continued to conduct related studies, and landscape metrics were used in a hedonic price framework. In line with previous studies, we explore the effects of landscapes on housing prices using GWR systematically and comprehensively. The calculation of landscape indexes in this study depends on the scale, patch type and the definition of the patch, in contrast to previous studies, which used distance as the moving window radius to calculate landscape indexes (Smeaton

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LANDSCAPE AND HOUSING PRICES Table 1. Measurement methods and housing characteristic variable signs. Explanatory Variable

Structural variables AREA FLOOR NOBEDROOM NOBATHROOM FEE RPLOT Locational variables DCBD DDC DMETRO DBUS DHOSPITAL PSCHOOL MSCHOOL SUPERMARKET Landscape variables RGREEN DPARK NP COHESION CONNECT SHDI Dummy variables QUARTER 1 QUARTER 2 QUARTER 3

Variable definition and measurement method

Mean

Std.

Expected sign

Total floor area of the apartment (m2) Floor level on which the apartment is situated Number of bedrooms in unit Number of bathrooms in unit Real estate property management fees (RMB) Ratio of floor area of real estate (%)

99.03 17.85 3.09 1.69 4.02 99.03

34.42 9.99 0.92 0.62 1.45 34.42

1 1 1 # 1 –

Distance Distance Distance Distance Distance Distance Distance Distance

23.63 4.95 2.89 0.21 0.75 1.07 0.69 23.63

10.64 2.43 3.51 0.23 0.45 0.67 0.40 10.64

– – – – # – – –

0.34 1.14 1959.06 94.76 96.11 1.93

0.07 0.86 1491.79 379.59 1.43 0.22

0.24 0.30 0.27

0.43 0.46 0.45

to to to to to to to to

CBD (km) district centre (km) nearest metro station (km) nearest bus station (km) nearest hospital (km) nearest primary school (km) nearest middle school (km) nearest supermarket (km)

Ratio of green space area of real estate (%) Distance to nearest city park The total number of patches in the landscape Patches of cohesion Functional connectedness Shannon’s diversity index 1 if the house was sold in months 1–3;0 otherwise 1 if the house was sold in months 4–6;0 otherwise 1 if the house was sold in months 7–9;0 otherwise

1 – # # # # # # #

Note : 1, 2 and # represent positive effects, negative effects and unknown effects, respectively.

Table 2. Details of landscape indexes. Landscape index NP COHESION

Meaning and calculation The total number ofP patches in the landscape. .h n i pij 12 p1ffiffiZffi 100, where COHESION5 12 Pn j51 pffiffiffiffi j51

CONNECT

SHDI

Use Fragmentation Physical connectedness

pij aij

pij and aij are the perimeter and area of the jth patch of patch P type i, respectively. CONNECT5 nj6¼k cijk = ni ðn2i 21Þ 100. ni is the number of patches of type i, and cijk is the connectedness between the jth and kth patch of patch type i. This value is determined based on both the number of different patch types and the proportional distribution P of areas among patch types: SHDI52 m i51 Pi ln ðPi Þ, where m is the total number of patch types and Pi is the proportion of the landscape area occupied by patch type i (unitless).

Functional connectedness

Diversity

Note: The details of these landscape parameters can be found in McGarigal et al. (2002). C 2018 Royal Dutch Geographical Society KNAG V

6 1957), Land use and landscape may influence residential property values not only at the neighbourhood level (300 m–500 m buffer) but also at larger scales that may be important for daily activities (Schl€apfer et al. 2015), Based on the administrative levels of ‘City-district-subdistrict-community’ in Shenzhen, each community is a landscape unit. The paper studies spatial patterns according to the boundary of a community. It is helpful to distinguish the differences in landscape indexes between communities for moderate areas; in addition, the results do not have overlap as a buffer. It is easy to avoid the effects of scale in the analysis and provide results that are substantially closer to reality (Turner et al. 1989; Li & Wu 2004; Wu 2004). Geoghegan et al. (1997) showed that landscape fragmentation and diversity have negative effects on real estate values, except very close to and very far from Washington DC. Lee et al. (2008) noted that human perception is very powerful and that humans can perceive various aspects of landscape diversity and fragmentation in their neighbourhood environments. Diversity and fragmentation are highly valued by residents, as they may allow easy access to convenient amenities. Connectivity is an important attribute of housing that reflects accessibility and can affect convenience (Matthews & Turnbull, 2007), Therefore, combining previous research results and conclusions, we choose NP, COHESION, CONNECT and SHDI from the perspectives of fragmentation, physical connectedness, functional connectedness and diversity as variables at the landscape level. All indexes in this paper are selected according to their presumed economic relevance. The fragmentation of landscape addresses the atomisation of land use activity and can result from natural processes and human activity. Trzcinski et al. (1999) and McGarigal et al. (2002) noted that fragmentation continues as the NP increases; the NP can thus be used to represent fragmentation. The COHESION of landscape is the result of the dispersal stream across the landscape, which affects neighbourhood environments. CONNECT is the degree of spatial connectivity among landscape elements and affects flows of energy and materials in daily life. SHDI is a C 2018 Royal Dutch Geographical Society KNAG V

QINGYUN DU ET AL. measurement for calculating the diversity of landscape and addresses the variegation of land use activities within a given scale. The details of the landscape indexes are provided in Table 2. In this paper, the land use dataset is classified into 12 types (farmland, garden plot, woodland, grassland, commercial land, industry, residential, public facility, specially designated land, traffic and road, water and others) according to the Chinese ‘Current Land Utilisation Classification’ (Standardization Administration of the People’s Republic of China, 2007), The impact of land use patterns on housing prices is determined by how people perceive a particular location, and the effects of these landscape indexes remain unknown (details in Table 1) before GWR is performed. The spatial distributions of different landscape indexes are shown in Figure 2. NP is used to represent the fragmentation of landscape. The degrees of fragmentation for Baoan, Guangming, Dapeng and Pinshan are high, with larger numbers of patches. The landscape fragmentation of Futian is the least obvious, with relatively small numbers of patches for all streets. The discrepancy of COHESION is relatively small, as per Figure 2 (b), COHESION can be used to distinguish the geomorphic types. The high-altitude areas of Shenzhen, such as the Southeast of Shenzhen, have high COHESION values. The CONNECTs over Shenzhen are completely different. From Figure 2(c), it is apparent that Futian and Luohu have the highest CONNECT values. The streets of Fuyong and Henggang are exceptions because of the presence of Shenzhen Bao’an International Airport in Fuyong and Shenzhendong Railway Station in Henggang. The differences in SHDI between the North and South in Shenzhen are obvious. The SHDI for overall Shenzhen is 2.092. The spatial heterogeneity of the landscape is also obvious, and the degree of landscape diversity is high. Geographically weighted regression – The GWR model is a spatial extension of the linear regression model based on ordinary least squares (OLS) (Fotheringham et al. 1998), This model is a simple but effective model for exploring spatial heterogeneity. The

Figure 2. The spatial distribution of landscape indexes: (a) NP (b) COHESION (c) CONNECT (d) SHDI. [Colour figure can be viewed at wileyonlinelibrary.com]

LANDSCAPE AND HOUSING PRICES 7


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QINGYUN DU ET AL.

general form of GWR can be characterised as follows: yi 5b0 ðui ; vi Þ1

X

bk ðui ; vi ÞXik 1Ei (1)

k

i51; . . . . . . ; n

the weighting function. It is a non-negative parameter that provides a balance between the mean and variance of distances. dij represents the Euclidean distance between i ðui ; vi Þ and j uj ; vj calculated in x-y coordinates. The distance between points i and j can be calculated using Equation (5):

where ðui ; vi Þ are the x-y coordinates of observation i in space, b0 ðui ; vi Þ denotes the intercept value, and bk ðui ; vi Þ represents a set of values of parameters of observation i. The most striking feature of GWR is that the values of bk ðui ; vi Þ vary with location. Ei is the error at location i and represents the effects of omitted variables. Equation (1) will work with the HPM if the values of bk ðu; v Þ are constant for all points ðu; v Þ in the study area. This also suggests that the essence of GWR differs from that of the HPM, namely, that GWR is capable of capturing the spatial variability of geographical data. Parameter estimation in GWR is based on OLS, and the estimated parameters can be expressed by Equation (2):

According to Equation (3) and Equation (5), if i and j have the same coordinate, dij will be zero, and at that moment, Wij 51. This is in accordance with reality and is of practical significance; it also means that the Gaussian kernel function can be applied widely. Without loss of generality, the Gaussian kernel function is used in this paper. It is important to decide the bandwidth that determines the weighting matrix. The optimal bandwidth can be obtained based on the minimum Akaike information criterion (AIC).

^ ðui ; vi Þ5 X T W ðui ; vi ÞX 21 X T W ðui ; vi Þy; (2) b

RESULTS AND ANALYSIS

where the weighting matrix W ðui ; vi Þ is an n 3 n diagonal matrix, and the diagonal elements represent the geographical weighting of observation data for observation i, namely; W ðui ; vi Þ5diag ðWi1 ; Wi2 ; . . . . . . Win Þ: Hence, each observation point has a corresponding weighting matrix W ðu; v Þ. To estimate the parameters in GWR Equation (1), it is essential to choose proper criteria for calculating the weighting matrix. There is a unique weighting matrix for each location. The weighting matrixes are obtained using a spatial kernel function. The most commonly used kernel functions are the Gaussian distance decaybased function and the exponential function, as shown in Equation (3) and Equation (4): dij2

! ;

(3)

dij Wij 5exp 2 ; h

(4)

Wij 5exp 2

h2

where h denotes the bandwidth, which characterises the effect of distance decay in C 2018 Royal Dutch Geographical Society KNAG V

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 2ffi dij 5 ui 2uj 1 vi 2vj :

(5)

Model estimation – Using a housing price dataset in Shenzhen, semi-log HPM (price transformed into the form of a natural logarithm) based on OLS regression (model 1) and GWR with and without landscape indexes (GWR-based models: model 2 and model 3) are conducted; the results are reported in Table 3. The coefficients of the semi-log function can be used to determine the marginal price of corresponding attributes. A positive regression coefficient means that attributes increasing by 1 unit will drive increasing housing prices and vice versa. We attempt to choose effective influencing factors and judge multicollinearity between variables. First, a correlation analysis is performed between pairs of dependent variables. All the Pearson’s correlation coefficients are less than 0.276 and are significant at the 0.01 level. There are no highly correlated relationships between the variables. Second, scores of the variance inflation factor (VIF) for variables do not exceed a value of 10, indicating that multicollinearity is at a level that would not raise concern. According to

0.000*** 0.000*** 0.000*** 0.740 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.256 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000***

p-value 221.360 20.283 20.382 20.423 20.222 28.086 23.369 260.977 210.459 230.080 236.040 22.520 226.391 224.158 26.943 22.270 235.743 – – – – 20.487 20.403 20.298

Min 20.859 20.035 0.029 20.056 20.024 0.072 20.312 20.580 20.560 20.827 20.143 20.379 20.127 20.036 20.210 20.056 20.205 – – – – 20.335 20.288 20.129

Max

Min

Lower Upper quartile Median quartile

0.956 0.436 0.3163

0.967 0.403 0.3168

11.677 0.699 0.198 0.386 0.251 6.119 18.005 9.506 3.178 26.866 13.321 1.254 1.416 4.082 3.612 1.858 3.614 29.547 1.223 1.886 8.417 0.026 0.038 0.109

Max

0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.781 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.027** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000***

p-value

Model 3 (GWR with landscape indexes)

20.157 0.138 38.615 223.097 20.104 0.046 0.356 0.082 0.145 0.699 20.283 20.032 0.076 0.142 0.041 0.061 0.199 20.382 0.029 0.040 0.061 0.021 0.093 0.386 20.423 20.056 0.007 0.094 0.023 0.061 0.251 20.222 20.022 0.021 0.054 0.206 0.552 133.548 21.847 0.054 0.213 0.521 20.031 0.110 13.101 24.072 20.167 20.011 0.148 0.095 20.188 0.140 131.236 26.149 20.299 20.098 20.078 0.375 3.178 210.459 20.316 20.067 0.476 20.171 0.022 17.147 221.824 20.240 20.077 0.041 0.048 0.347 8.978 24.868 20.209 20.014 0.277 20.120 0.072 20.548 223.453 20.424 20.046 0.048 0.038 0.217 5.109 23.008 20.136 0.022 0.190 0.052 0.284 27.843 26.038 20.045 0.038 0.143 0.129 20.051 0.195 11.874 213.240 20.112 20.029 0.134 0.300 20.592 20.711 20.027 0.061 0.448 0.019 0.227 3.589 226.135 20.084 20.009 0.162 – – – 226.851 20.385 20.148 20.029 – – – 24.670 20.082 20.001 0.029 – – – 23.344 20.061 0.046 0.124 – – – 228.807 20.254 20.088 0.099 20.256 20.171 0.050 20.485 20.386 20.278 20.197 20.189 20.120 0.040 20.474 20.300 20.208 20.127 20.069 20.021 0.109 20.467 20.138 20.071 20.022

Lower Upper quartile Median quartile

Model 2 (GWR without landscape indexes)

Note: *** and ** denote zero coefficient statistically significant at the 1% and 5% level (two-tailed test).

0.789 0.458 –

0.799 17.302 18.851 20.320 26.205 148.153 52.993 287.127 239.854 28.903 27.506 250.877 10.760 21.136 15.580 31.863 27.892 2104.241 17.343 35.748 62.513 2135.949 2108.273 251.028

t-ratio

Model 1 (HPM)

Coefficient

INTERCEPT 0.002 AREA 0.048 FLOOR 0.037 NOBEDROOM 20.01 NOBATHROOM 0.071 FEE 0.339 RPLOT 0.126 DCBD 20.299 DDC 20.085 DMETRO 20.023 DBUS 20.017 DHOSPITAL 20.113 PSCHOOL 0.025 MSCHOOL 20.002 SUPERMARKET 0.033 RGREEN 0.074 DPARK 20.017 NP 20.321 COHESION 0.037 CONNECT 0.079 SHDI 0.172 QUARTER 1 20.350 QUARTER 2 20.285 QUARTER3 20.132 Diagnostic information R2 SE Bandwidth

Explanatory variable

Table 3. The results of the models.

LANDSCAPE AND HOUSING PRICES 9


10 the correlation analysis and VIF, the dataset can be used to construct regress models. For model 1, a total of 78.9 per cent of the variation in housing prices can be explained by the model according to the R-square value. The landscape variables of NP, SHDI, COHESION, CONNECT, DPARK and RGREEN are all significant at the 1 per cent level. Compared with the HPM, GWR-based models have higher fitting degrees and stronger interpretability. The highest R-square and lowest standard error (SE) values of the regression analysis suggest that model 3 has the strongest performance. The results with respect to landscape metrics are essentially based on the observations. Therefore, we test the clustering of SEs, which obeys a normal and independent distribution. Moreover, the results of model 3 are used to analyse the details. As mentioned in the Geographically weighted regression subsection, the Gaussian kernel function is used in this paper, which has been widely used in previous studies on GWR. Through iterative optimisation, the optimal bandwidth is 0.3168 according to AIC. GWR with landscape indexes can explain 96.7 per cent of the variation according to the value of R-square. This is mainly attributed to the ability to exploit the spatial heterogeneity of GWR and landscape attributes affecting the willingness of the public to buy real estate. To ensure the validity of the GWR-based model, we applied spatial autocorrelation analysis to the residual errors. Moran’s I of residual errors is 0.044 at a 95 per cent confidence interval. These values reveal the randomness of the residuals, which is in accordance with the basic theory of statistics. In real economic studies, the assumption of spatial homogeneity is invalid. GWR is a more robust model compared to HPM. Ertur et al. (2006) highlighted that the robustness test of spatial econometrics models can be conducted using different types of spatial weight matrixes. The spatial weight matrix is the key to describing the spatial interaction and determines the fitting result of the model. Therefore, we use different types of spatial weight matrixes to test robustness. The Gaussian distance decay-based function C 2018 Royal Dutch Geographical Society KNAG V

QINGYUN DU ET AL. and the exponential function have been introduced in the GWR section. We use the Gaussian distance decay-based function, which can provide lower biases and have greater surface overlap, to calculate the spatial weight matrix and model the relationships between housing prices and the most commonly used variables. Therefore, the exponential function is used to test the robustness of our model. The GWR results for the exponential function are shown in Table 4. According to the results, the overall tendency of estimated coefficients based on the Gaussian distance decay-based function and the exponential function are similar. The effects of explanatory variables on housing prices do not show structural changes and are significant when compared with the results of model 3. It can be concluded that the empirical results of model 3 are credible and that model 3 is robust. Effect analysis of landscapes based on HPM – The impact of NP on housing prices is positive based on the results of the HPM in Table 3. In a community, a larger NP means greater fragmentation. Fragmented landscapes serve to decrease the value of neighbouring properties. Reducing 1 unit of NP increases housing values by 0.321 per cent. The effect of NP on housing prices is consistent with results from a previous study (Kong et al. 2007), High landscape fragmentation indicates that a neighbourhood’s environment is suffering from interference by people. Buyers tend to be willing to buy houses where the landscape of the neighbourhood is relatively complete. COHESION, CONNECT and SHDI all have positive effects on housing values according to Table 3. Specifically, adding 1 unit of SHDI, COHESION or CONNECT increases housing prices by 0.172 per cent, 0.037 per cent and 0.079 per cent, respectively. SHDI is used to measure the diversity of landscapes. Diverse landscapes are highly valued for their accessibility to public facilities such as schools, hospitals and shopping areas. COHESION and CONNECT are represented as physical connectivity and functional connectedness, respectively. Landscape

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LANDSCAPE AND HOUSING PRICES Table 4. The results of GWR with the exponential function GWR (Exponential)

Explanatory variable INTERCEPT AREA FLOOR NOBEDROOM NOBATHROOM FEE RPLOT DCBD DDC DMETRO DBUS DHOSPITAL PSCHOOL MSCHOOL SUPERMARKET RGREEN DPARK NP COHESION CONNECT SHDI QUARTER 1 QUARTER 2 QUARTER 3 Diagnostic Information R2 SE Bandwidth

Min

Lower quartile

Median

Upper quartile

Max

p-value

221.288 27.293 20.545 20.604 20.851 21.280 24.310 21.244 210.191 221.288 24.215 223.369 23.323 26.296 210.249 20.189 221.152 220.334 24.074 23.395 224.682 20.560 20.439 20.311

20.035 20.084 0.022 20.109 20.053 20.003 20.017 20.085 20.029 20.043 20.017 20.037 20.042 20.037 20.019 20.019 20.021 20.060 20.017 20.025 20.024 20.266 20.223 20.091

0.017 0.113 0.040 20.003 0.008 0.005 00.000 20.022 0.001 20.015 20.001 20.006 20.001 20.002 0.001 0.004 20.002 20.019 20.001 20.001 20.001 20.061 20.092 20.029

0.067 0.296 0.069 0.112 0.115 0.041 0.044 0.000 0.036 0.046 0.210 0.017 0.029 0.020 0.029 0.038 0.116 0.001 0.012 0.023 0.021 20.012 20.007 0.004

10.296 22.331 0.392 0717 1.989 6.718 10.274 0.383 3.017 26.262 13.663 1.208 1.581 4.618 1.610 1.754 3.379 22.649 1.571 1.221 7.237 0.724 0.253 0.340

0.000*** 0.000*** 0.000*** 0.000*** 0.000*** .436 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** .024** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000***

0.961 0.421 4.472

Note: *** and ** denote zero coefficient statistically significant at the 1% and 5% level (two-tailed test).

connectivity is a vital element of landscape structure (Taylor et al. 1993), Dunning et al. (1992) reported that landscape connectivity is assumed to be homogenous over a landscape. High landscape connectivity can reflect good traffic conditions and harmonious neighbourhood environments with reasonable city planning. RGREEN and DPARK are significant at the 1 per cent level. Their effects are consistent with our predictions. DPARK has a negative effect on housing price. When the distance to a park increases by 1 km, the price decreases by 0.017 per cent. City parks with large areas and good views can provide leisure and entertainment. Accessibility to city parks has increasingly attracted buyers. Moreover, the

marginal price of RGREEN is 0.074, indicating that housing prices decrease by 0.074 per cent for every 1-unit decline of RGREEN. RGREEN is an important factor of a housing estate. A high ratio of green space area of a real estate complex can improve the residential environment and meet the demands of residents for good environments. Effect analysis of landscapes based on GWR – The local coefficients of the GWRbased model vary between locations. GWR can be used to visualise the spatial distribution of local parameters. In Figure 3, a fiveclass natural breaks (Jenks) classification method is applied to represent local coefficients of landscape variables, which are the C 2018 Royal Dutch Geographical Society KNAG V

Figure 3. The local estimated coefficients of factors: (a) NP (b) COHESION (c) CONNECT and (d) SHDI (e) DPARK (f) RGREEN. [Colour figure can be viewed at wileyonlinelibrary.com]

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QINGYUN DU ET AL.

13

LANDSCAPE AND HOUSING PRICES focus of this paper. We set a zero value as a class limit to highlight the difference in estimated coefficients, to distinguish the positive and negative effects, and to obtain more effective information (Wu et al. 2016), An increasing NP indicates greater heterogeneity and fragmentation, as well as lower coherence. Coherence is generally believed to positively affect scenic value (Kaplan & Kaplan 1989), A larger NP results in a poor experience for people. NP is inversely proportional to the price in most local places. Of course, there are some exceptions. In parts of communities in Longgang and Baoan, such as Bantian, Xixiang and Buji, that are near point numbers 2, 3 and 5 (Figure 1), an increasing NP can, in contrast, increase prices. The above areas are mainly industrial sites. Industrial production requires many raw materials, and land uses vary in these areas. In addition, buyers take the distance to their workplace as their primary consideration. SHID positively affects housing prices for most real estate areas of Shenzhen. Yue et al. (2016) noted that mixed land use can promote neighbourhood vibrancy and environments. SHID denotes the diversity of a landscape and degree to which land use is mixed, which represents accessibility to facilities such as convenience stores, schools, bus stations and subway stations. Kong et al. (2007) noted that a diverse landscape has a negative effect on housing prices because of the preference by people to live in a quiet and comfortable environment. For COHESION and CONNECT, local coefficients have similar distributions. Most negative effects are found in Futian, Luohu Nanshan and Yantian. Those four districts are earlier development areas in Shenzhen with complete facilities and especially good traffic conditions. The regional condition is superior, with high connectedness, and the residents in those areas care about the perception of privacy and other facilities. However, in other districts of Shenzhen, these developing areas need to improve their connectedness through rational planning. Undoubtedly, city parks are important to buyers. City parks have a positive effect on housing prices in most areas of Shenzhen. Parks promote housing prices to varying degrees. However, the opposite result for

some real estates that are close to city parks is observed. City parks can have a negative effect on housing values at a given location and scale. The neighbourhoods of Songgang Park, Longhua Hill Park and Longcheng Park are obvious examples. The shorter distance to parks is found to be correlated with lower housing prices. The observed traffic jams, crowding, noisy environments and crime rates near city parks may be the reasons for this trend. The above issues can reduce nearby residents’ quality of life. Morancho (2003) suggested that having many small green spaces distributed throughout an urban area might be more beneficial than having a few large parks. The coefficients for RGREEN also vary across the study area. Real estates with high ratios of green see increased housing prices in most areas. Certain real estates in Longgang and Baoan, however, show a high ratio of green spaces corresponding to reduced housing prices. According to field investigations, it is difficult to address the contradiction between green spaces and other public facilities for these real estates. The presence of green space landscapes may result in increased prevalence of diseases and insects. CONCLUSION In this study, landscape metrics and GWR are used to evaluate housing prices in Shenzhen, China. This research attempts to improve the understanding of value and spatial variations of landscape effects, especially landscape indexes, on housing prices in a large and growing Chinese city. The results show that applying landscape indexes to landscape factors increases the accuracy of a housing price model and reveal that landscape indexes have varying degrees of effects on housing prices. It is vital to address housing data to understand the value of ecological environments on properties. From a policy perspective, the GWR results can reveal local premiums of variables on housing prices and provide information on residents’ expectations and willingness to pay for influential factors. Our results suggest that new housing can achieve revenue C 2018 Royal Dutch Geographical Society KNAG V

14 maximisation and satisfy the demands of users mainly through the administration of urban planning. To meet community expectations, the planning and design of landscapes require accurate information on people’s preferences, considering the significant effects of landscape on housing prices. This study can provide guidance on investigating residents’ expectations, perceptions and interests about the landscape in a neighbourhood. Moreover, the results can help decision-makers plan and design cities, in addition to providing guidance for regulating and controlling urban property values. Most residents desire compact, connected and diverse communities, which has implications for land use and conservation efforts. Overall, the results can facilitate the achievement of providing an accessible park or green space within 500 m of every resident in Shenzhen. Some limitations remain in this line of research, and follow-up work is needed. (1) This paper only takes spatial heterogeneity into consideration, and does not consider the effects of temporal heterogeneity. (2) Importantly, because of the availability of data, this paper does not consider the effects of landscape metrics that depend on the covariate variables including crowding, traffic jams, noisy environments, high crime rates and other variables believed to influence the value of landscape metrics. Thus, an integrated and comprehensive approach, such as geographically and temporally weighted regression (GTWR), will be applied to explore the amenity value that residents attach to views and other building-specific controls. Additionally, local fixed effect models will be used to control for effects of omitted variables according to previous studies (Anderson and West, 2006; von Graevenitz and Panduro, 2015). Acknowledgements This study was supported by the National Natural Science Foundation of China (Project No. 41571438), the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation at Ministry of Land and Resources (No. KF-201602-028), and National Science Foundation (No. 1637242). C 2018 Royal Dutch Geographical Society KNAG V

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