Remotely sensed urban environmental indices and their economic implications

Habitat International 67 (2017) 22e32

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Remotely sensed urban environmental indices and their economic implications Limin Jiao a, b, *, Gang Xu a, b, Jianfei Jin a, b, Ting Dong a, b, Jiafeng Liu a, b, Yanxi Wu a, b, Boen Zhang a, b a b

School of Resource and Environmental Sciences, 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

a b s t r a c t

Article history: Received 23 January 2017 Received in revised form 12 May 2017 Accepted 26 June 2017

Numerous studies reveal that urban favorable amenities potentially contribute to housing prices, but we still lack proper indices to quantify intangible urban disservices and lack the understanding of their economic effects. We attempt to develop remotely sensed indices to reflect these unfavorable and intangible urban disservices. Taking Wuhan in central China as an example, we propose the Thermal Environment Index (TEI) and the Vegetation Coverage Index (VCI) to characterize the urban environment based on Landsat images and examine their influences on housing prices using a hedonic price model. We build the hedonic price model using the spatial lag regression between housing prices and explanatory variables, including the proposed environmental indices, locational variables, and apartment structural variables. The spatial regression shows that the floor area ratio, floor height, proximity to business centers, and road accessibility exert significant and positive influences on housing prices whereas the TEI and the VCI have significant and negative influences on housing prices. A one-percent increase in the TEI will decrease housing prices by approximately 55 RMB/m2 in 2010. We further investigate the differences between housing prices inside heat islands of different levels or types and outside heat islands, confirming our findings with the results of hedonic modeling. This study shows the potential of developing a remote sensing index to measure intangible urban disservices and exploring their economic implications. © 2017 Elsevier Ltd. All rights reserved.

Keywords: Urban heat islands Housing prices Environmental indices Hedonic price model Urban disservices Remote sensing

1. Introduction A favorable urban ecological environment fundamentally contributes to the quality of life of urban residents, which can yield not only ecological effectiveness but also economic benefits (Hartig & Kahn, 2016; Xu, You, Li, & Yu, 2016b). Numerous studies have quantitatively demonstrated the positive and significant effects of various ecological and environmental elements, such as forests, parks, lakes, and landscape views, on housing prices (Conway, Li, Wolch, Kahle, & Jerrett, 2010; Hui & Liang, 2016; Hui, Zhong, & Yu, 2012; Jiao & Liu, 2010; Kong, Yin, & Nakagoshi, 2007; Sander, Polasky, & Haight, 2010; Tyrv€ ainen & Miettinen, 2000). However, urban disservices, for instance, the urban thermal environment, have typically been neglected in previous housing price studies.

* Corresponding author. School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. E-mail address: [email protected] (L. Jiao). http://dx.doi.org/10.1016/j.habitatint.2017.06.012 0197-3975/© 2017 Elsevier Ltd. All rights reserved.

The urban thermal environment can be represented by the land surface temperature (LST) or by urban heat islands (UHIs). UHIs are typically related to industrial land use, traffic congestion, unplanned building arrangements, and improperly designed urban structure (Taleb & Abu-Hijleh, 2013). These unpleasant and unmeasurable environmental factors certainly have negative impacts on the willingness-to-pay of homebuyers. Thus, our questions are as follows: Can we derive an index by quantifying the urban thermal environment to represent urban disservices, and how can we relate urban disservices to housing prices using the index as a bridge? Urban heat islands (UHIs) are phenomena in which surface temperatures are higher in urban areas than in surrounding rural areas, which are associated with urban expansion and intensive human activities in urban areas (Rizwan, Dennis, & Liu, 2008; Wang, Myint, Wang, & Song, 2016). UHIs influence energy consumption, air quality, and the health and welfare of urban residents (Tan et al., 2009; Wong, Peng, Zou, Shi, & Wilson, 2016). The factors

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affecting the occurrence, distribution, and intensity of UHIs can be divided into two categories, meteorological factors and urban parameters (Che-Ani, Shahmohamadi, & Sairi, 2009). The urban parameters include the location of the city, the size of the city and population, the density of built-up areas, the urban geometry, and land uses, among others (Che-Ani et al., 2009). Built-up areas dominated by impervious surfaces and industrial land with massive energy consumption typically experience relatively high surface temperatures (Feizizadeh & Blaschke, 2013). Areas with a dense population and traffic are also possible heat islands (Deilami, Kamruzzaman, & Hayes, 2016). The temperature on disorderly and unsystematic streets is higher because of poor ventilation conditions, particularly in urban villages in old urban areas, because the building form and city morphology affect the movement of wind and impact temperature variations (Golany, 1996; Taleb & AbuHijleh, 2013). The urban parameters of UHIs noted above are also factors that influence housing prices. Thus, UHIs seem correlated with housing prices, but the specifics of their relationship remain unclear. There is a long history of monitoring, assessing and predicting the urban environment and ecosystem, such as land cover changes, landscape dynamics, air quality, and urban heat islands, by remote sensing (Fang, Zou, Liu, Sternberg, & Zhai, 2016; Jiao, 2015; Jiao, Mao, & Liu, 2015; Shen, Huang, Zhang, Wu, & Zeng, 2016; Zou et al., 2016). Remote sensing has also been employed to identify and detect urban socio-economic characteristics in urban settings, such as the population density, quality of life, urban slums, and  pez-Carr, & Clarke, 2016; house values (Benza, Weeks, Stow, Lo Kohli, Stein, & Sliuzas, 2016; Patino & Duque, 2013; Yu & Wu, 2006). The environment qualities around a house will be considered when people buy houses, and such elements are reflected in the price (Jim & Chen, 2007). Thus, we can identify the influence of environmental factors on housing values with an implicit price for these attributes. Many previous studies have used a hedonic price model (HPM) to investigate the marginal implicit price of environmental factors in housing prices (Geoghegan, 2002; Geoghegan, Wainger, & Bockstael, 1997; Hui, Chau, Pun, & Law, 2007; Shabana, Ali, Bashir, & Ali, 2015). In hedonic modeling, the housing price is defined by a basket of individual value components, such as the floor height, location, and outdoor environmental characteristics (Jim & Chen, 2010). To address location-specific factors, Geographical Information System (GIS), landscape metrics, spatial statistics and remote sensing have been adopted in conventional hedonic modeling (Anselin, 1998; Hui et al., 2007; Kong et al., 2007; Yu & Wu, 2006). For instance, Yu and Wu (2006) adopt remotely sensed vegetation, impervious surfaces, and soil information to enhance the hedonic modeling of house values. Mansfield, Pattanayak, McDow, McDonald, and Halpin (2005) use the remotely sensed Normalized Difference Vegetation Index (NDVI) to measure the “greenness” of the housing position and surrounding area and prove that greenness and forest cover add value to houses. Lu, Im, Quackenbush, and Yoo (2013) find that using remote sensing data, one is capable of acquiring accurate house price information. These studies suggest that the environmental characteristics generated from remote sensing imagery can improve the interpretability of the hedonic price model (Lu et al., 2013; Mansfield et al., 2005; Yu & Wu, 2006). Although many publications have investigated the relationship between environmental amenities and housing prices (Conway et al., 2010; Hui & Liang, 2016; Kong et al., 2007; Mansfield et al., 2005; Sander et al., 2010; Yu & Wu, 2006), the effect of the urban thermal environment, representing urban disservices, has received limited attention. Taking Wuhan city (China) as an example, we develop a Thermal Environment Index (TEI) and a Vegetation

23

Coverage Index (VCI) from the remotely sensed LST and NDVI, respectively. Based on the two indices, we investigate their environmental effects on and economic implications for housing prices using a spatial hedonic price model since housing prices are often spatially autocorrelated (Conway et al., 2010; Jiao & Liu, 2010; Sander et al., 2010). 2. Materials and methods 2.1. Study area and data Wuhan, located in the northeast of the Jianghan Plain in central China, is the capital city of Hubei province (Fig. 1). Water bodies cover a quarter of the entire territory of Wuhan, distinguishing it from other cities in China. The Yangtze River and Hanshui River join together in the urban areas of Wuhan, dividing the city into three parts: Hankou, Wuchang, and Hanyang. Wuhan has a subtropical monsoon climate, with the annual average temperature ranging from 15.8
C to 17.5
C and annual amount of precipitation varying in 1150e1450 mm. Wuhan is famous for its high temperature in summer and is regarded as one of the “stove cities” in China (Shen et al., 2016). Wuhan has been experiencing rapid urbanization in recent decades, accelerating the destruction of natural environments and the expansion of impervious surfaces (Jiao, Xu, Xiao, Liu, & Zhang, 2017; Xu et al., 2016a). As one of the heavy industrial bases in China, the urban heat island effect is particularly evident in the industrial zone in Wuhan, which has caused significant impacts on the everyday life of city dwellers and has detrimental effects on the human body and mental health (Wu, Ye, Shi, & Clarke, 2014; Zhang et al., 2016). The data used in this study include housing price samples and explanatory factors. There are three types of explanatory factors, namely, remotely sensed environmental variables, structural variables, and locational variables. We collected the housing price data of new residential apartments that were sold from February 22 to October 31, 2010, via the YiFang website (YiFang Web) and the SouFang website (SouFang Web). To avoid possible biases and variations, villas and duplex residences were excluded. All of the online transaction prices were confirmed by calling sales agents. We initially selected 400 sample apartments in Wuhan through a random sampling method and collected data on the transaction price, location, floor height, number of bedrooms, number of bathrooms, and floor area ratio of each sample. We excluded residential samples with incomplete or uncertain data. Ultimately, 291 samples were included in our study. The spatial distribution of the housing price samples is shown in Fig. 1. Since available Landsat ETM þ images over the study area in the summer season were all heavily clouded, two images at 10:48 (local time) on September 17 and October 13, 2010, were obtained from the Geospatial Data Cloud with less than 2% cloud cover on each image (Geospatial Cloud Data). The spatial resolution of ETM þ images is 30 m. The data processing of the remote sensing images included stripe removal, atmospheric correction, and resizing to the study area. The image taken on September 17, 2010, was classified to obtain a land-use map using the supervised classification method (Maximum Likelihood) (Xu et al., 2016a). The built-up areas in 2010 are shown in Fig. 1. The methods of remote sensing retrieval of the LST and NDVI from ETM þ images are presented in Section 2.3. We chose five variables to reflect the influences of location on housing prices, namely the proximity to business centers, road accessibility, public transportation accessibility, and proximity to water bodies and urban parks. Eleven business centers were selected according to the “Wuhan City Master Plan (2010e2020)” (Fig. 1) (Wuhan City Master Plan). We collected the information on

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Fig. 1. Study area: (a) location of Wuhan city in China; (b) the spatial distribution of the housing price samples.

the business centers, including location, sales volume, plot area, total floorage, and functionality. The road network data were obtained from the local government and updated with the support of Google Maps, including spatial data and attribute data (road width, number of lanes, and directions). We collected the bus station data via the Wuhan Public Transportation Company (Wuhan Public Transportation Company), and 339 bus stop samples were selected. The spatial data and attributes of forty-five urban parks were obtained from the Wuhan City Bureau of Park and Forestry, including name, area, and location (Wuhan City Bureau of Park and Forestry). Water bodies were derived from the classified land-use map, and those with an area larger than 1 km2 were chosen, including the Yangtze River, the Hanshui River, the East Lake, and the Tangsun Lake, among others (Fig. 1). The methods of defining and calculating the influences of locational variables on housing price are presented in Section 2.4. 2.2. Hedonic price model

P ¼ rWP þ aY þ ε

Hedonic price modeling has been widely used to estimate the contribution of diverse characteristics to housing prices (Conway et al., 2010; Jiao & Liu, 2010; Jim & Chen, 2007; Mansfield et al., 2005; Sander et al., 2010). A hedonic price model typically takes the following form (Mansfield et al., 2005):

P ¼ f ðS; N; Q Þ þ ε

(e.g., proximity to parks), and ε is the error term. Many previous studies have found that the housing price is spatially autocorrelated (Conway et al., 2010; Jiao & Liu, 2010; Sander et al., 2010). The global Moran's I of housing prices in the study area is 0.351 (p ¼ 0.005), indicating significant spatial autocorrelation. Ordinary least squares (OLS) regression fails to identify the spatial interaction between adjacent observations, which may lead to a biased, inefficient estimation of coefficients in OLS (Conway et al., 2010; Jiao & Liu, 2010; Sander et al., 2010). With the considerable growth of spatial econometrics, spatial regressions have been increasingly employed to estimate the impacts of environmental factors on housing prices (Anselin, 1988; Conway et al., 2010). Our main purpose in the hedonic modeling is to estimate the implicit prices of the characteristics, particularly the impacts of urban heat island effects on housing prices. In this study, we use a spatial lag model for hedonic modeling that takes the following form (Anselin, 1988):

(1)

where P is the price of houses, S is a vector of the structural characteristics of the house (e.g., floor height), N is a vector of the neighborhood (locational) attributes of the house (e.g., distance to business centers), Q is a vector of environmental characteristics

(2)

where P is an n  1 (n is the number of observations) vector of the housing price, r is the spatial autoregressive coefficient, W is an nn spatial weights matrix, WP is the spatially lagged dependent variable, Y is an nk matrix of the values of the explanatory variables (structural variables, locational variables, and remotely sensed environmental variables), a is a k  1 vector of the regression coefficients, and ε is an n  1 vector of independently and identically distributed (IID) error terms. We used the Maximum Likelihood method to estimate the spatial lag model in GeoDa 1.8 (Anselin, Phoenix, AZ, USA). Considering that the housing samples are points, we define the

L. Jiao et al. / Habitat International 67 (2017) 22e32

spatial weight matrix based on the distance weight (Euclidean Distance) using formula (3):


Wij ¼

1; 0  dij  d0 0; dij  d0

(3)

where Wij is the spatial weight between samples i and j, dij is the distance between samples i and j, and d0 is the default distance able to promise that there is at least one neighbor point around each sample; in this study, d0 ¼ 6441 m. 2.3. Remotely sensed environmental indices 2.3.1. Thermal environment index According to previous studies, the at-satellite brightness temperature can be converted from the digital numbers (DNs) of the thermal infrared band (Band 6) of ETM þ images using the following equations (Deng & Wu, 2013; Irish, 2000; Markham, 1986):

T¼

K2 lnðK1 =Ll þ 1Þ

Ll ¼

Lmax  Lmin ðDN  Qmin Þ þ Lmin Qmax  Qmin

(4)

(5)

where T is the absolute brightness temperature; K1 ¼ 666.09 W/ (m2$sr$mm) and K2 ¼ 1282.71 K are constants for the ETM þ thermal infrared band; l is the wavelength, and Ll is the spectral radiance received by the Landsat sensor; Lmax is the maximum detected radiation intensity, and Lmax ¼ 17.04 W/ (m2$sr$mm); Lmin is the minimum detected radiation intensity, and Lmin ¼ 0 W/(m2$sr$mm); DN is the value of the thermal band; and Qmax and Qmin are the maximum and minimum DN values (for ETM þ imagery, Qmax ¼ 255, Qmin ¼ 1), respectively. These constants and values can be found in the Landsat 7 Science Data Users Handbook (Irish, 2000). The retrieved brightness temperature is based on the assumption that land surfaces are blackbodies; thus, it cannot represent the real LST due to different levels of emissivity of various land cover categories. The relationship between emissivity and the NDVI was found based on empirical approaches; for example, when NDVI >0.5, the pixel is regarded as fully vegetated, and the emissivity is typically assumed to be 0.99 (Deng & Wu, 2013; Ding & Shi, 2013; nez-Mun ~ oz, & Paolini, 2004). Detailed information for Sobrino, Jime 0.2  NDVI 0.5 and NDVI <0.2 can be found in previous studies (Deng & Wu, 2013; Ding & Shi, 2013; Sobrino et al., 2004). We adopted the NDVI threshold method to calibrate the brightness temperature to the LST. The LST in the study area on September 17, 2010, was in the range of [21.29, 39.56], and the average was 27.29
C. The LST on October 13, 2010, was in the range of [14.94, 34.62], with 19.23
C being the average. We found that the spatial distributions of the LST on the two days were almost the same because we chose two days with similar weather conditions that were clear with a light breeze. Finally, we chose the LST grid from September 17, 2010, for further analysis. To avoid the biases caused by temperature variation over time, we normalized the LST to calculate thermal environment index (TEI) using formula (6):

TEIi ¼

Ti  Tmin  100 Tmax  Tmin

(6)

where TEIi is the TEI at pixel i, Ti is the original temperature at pixel i, and Tmin and Tmax are the minimum and the maximum surface

25

temperature in the study area, respectively. The spatial distribution of TEI [0, 100] in the study area is shown in Fig. 2. We defined the highest 30% of the temperature range [29.79, 39.56] as the high temperature region and extracted the high temperature region using the “Raster Calculator” tool in ArcGIS10.2. We then identified the concentrated, gathered, and clustered the region as the heat island areas and drew their boundaries based on visual interpretation in GIS (Fig. 2). According to the land-use map in the “Wuhan City Master Plan (2010e2020)”, we identified industrial areas, new investment and development areas, and commercial areas, based on which we defined three types of heat islands (HI), industrial HI, development HI, and commercial HI (Fig. 2). The land surface temperature is sensitive to surface materials; for example, concrete rooftops and squares tend to be hot areas. It was found that rooftop spectral properties were the major factors in influencing rooftop surface temperatures. To avoid random errors of the TEI at sample locations, we used neighborhood analysis to acquire the averaged TEI within a certain buffer of the housing price samples. We first set the buffer distance to 200 m in the neighborhood analysis. A detailed discussion on the spatial scale (buffer radiuses) in neighborhood analysis is presented in Section 4.1.

2.3.2. Vegetation coverage index The NDVI derived from remote sensing images is well known as a vegetation index of vegetation abundance, and there is a strong linear correlation between the NDVI and the vegetation density (Weng, Lu, & Schubring, 2004; Yuan & Bauer, 2007). According to Chavez (1988) (Chavez, 1988), the NDVI was retrieved from ETM þ data by:

NDVI ¼ ðrNIR  rRED Þ=ðrNIR þ rRED Þ h

r ¼ p  D2  ðLsatI  LhazeI Þ

i.

½ESUNI  cosðSz Þ 

(7) (8)

where rNIR and rRED refer to the surface reflectance of the near infrared band (Band 4) and the infrared band (Band 3) of the ETM þ image, respectively. The NDVI value is normalized over the range [-1, 1]. In addition, r is the relative ground reflectance; D is the Sun-Earth astronomical unit distance; LsatI is the ETM þ sensor spectral radiation value, which is the radiation energy at the top of the atmosphere; LhazeI is the atmospheric radiation value; ESUNI is the average spectral solar radiation at the top of the atmosphere, i.e., the solar irradiance at the top of the atmosphere; and Sz refers to the solar zenith angle. The retrieved NDVI range was [-0.998, 0.996]. We erased the areas with NDVI values in the range of [-0.998, 0.056], which were identified as non-vegetation areas. The thresholds were determined by examining the sample pixels that were between vegetation and non-vegetation. Finally, we defined the vegetation coverage index (VCI) as the normalized NDVI values with a range of [0, 100] using formula (9):

VCI ¼

NDVIi  NDVImin  100 NDVImax  NDVImin

(9)

where NDVIi is the NDVI at pixel i and NDVImax and NDVImin are the maximum and minimum NDVI in the study area, equal to 0.996 and 0.057, respectively. The spatial distribution of the VCI at each pixel in the study area is presented in Fig. 3. To avoid random errors of the VCI at the housing price sample locations, we used the averaged NDVI over a 200-m buffer around the housing price sample to represent the local vegetation coverage.

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L. Jiao et al. / Habitat International 67 (2017) 22e32

Fig. 2. Spatial distribution of the thermal environment index (TEI) in the study area.

2.4. Locational and structural attributes The explanatory variables other than the TEI and the VCI include locational variables and structural variables. The structural variables were used in the hedonic model with their original values. We calculated the influence scores of the locational variables using the Geographic Field Model (GFM) (Jiao & Liu, 2010). A geographic field is a limited space around a spatial object, which has an external influence on the surrounding environment (Fig. 4). The influence intensity of one spatial object (e.g., business center) on other places decreases with the growing distance to itself. In this study, the influence score at the location of an object (e.g., business center) is the scale index (F) of the object, which is related to its attributions. The influence score at other locations is calculated by a linear distance decay method within the maximum influence range (R), and the influence score beyond the maximum influence range is defined as zero (Fig. 4a). In other words, the key to calculating the influence score is the scale index (F) and influence range (R). Taking the business center as an example, we collected the location, sales volume, plot area, total floorage, and functionality of each business center, and the values of these attributes were scored and normalized into [0, 100]. Then, the scale index (F) for one business center was calculated by the weighted mean method considering these attributes. Next, the influence range (R) of business centers was calculated by considering the number (n) of business centers and the total influence area (S) of the business centers (Fig. 4b). Further, the influence score of a certain business center at any place within R was calculated based on a linear distance decay method with its scale index (F) and influence range (R) (Fig. 4a). Finally, the influences score of business centers on one housing price sample was the summation of 11 single influence

scores of each business center. A more detailed description of the calculation method can be found in our previous study (Jiao & Liu, 2010). The scale index (F) of road and public transportation was defined as one. The scale index (F) of water bodies and parks was defined as their area. The influence range (R) of roads was computed according to the total influence area (S) and the total length (l) of roads (Fig. 4c). The influence range of public stops was set to 300 m, which was calculated as half of the average distance between stops (Jiao & Liu, 2010). According to the questionnaire survey in our previous study in the same area (Jiao & Liu, 2010), the influence ranges of parks and water bodies were set to 500 m, which is an accessible distance for people to walk to a park in less than 10 min (Burgess, Harrison, & Limb, 1988; Jim & Chen, 2007). Finally, all of the influence scores of five locational variables in the study area were normalized into [0, 100], with higher scores meaning a stronger positive influence on housing prices. In our price hedonic model, we derived 11 explanatory variables with the descriptive statistics shown in Table 1. 3. Results Previous studies have found the negative correlation between the land surface temperature and vegetation abundance (Tan, Lim, MatJafri, & Abdullah, 2012; Weng et al., 2004). In this study, the Pearson's r between the averaged VCI and TEI values in a 200-m buffer around each housing price sample is 0.320 (N ¼ 291, p ¼ 0.000). Considering that correlated variables may lead to multicollinearity in the regression, we used the tolerance and variance inflation factor (VIF) statistics to check the multicollinearity of the explanatory variables. The tolerance statistics are

L. Jiao et al. / Habitat International 67 (2017) 22e32

27

Fig. 3. Spatial distribution of the vegetation coverage index (VCI) in the study area.

Fig. 4. Geographic Field Model (GFM) with a linear distance decay method (a) and the definition of the influence ranges (R) of point features (b) and line features (c).

all greater than 0.1, and the VIF statistics are all less than 10, which indicates that there is no significant multicollinearity between explanatory variables. The results of the spatial lag regression of the hedonic modeling of housing prices are shown in Table 2. The currency unit of housing prices is Renminbi Yuan (hereinafter RMB). Overall, the explanatory variables account for 56.96% of the housing price variance (Pseudo-R2 ¼ 0.5696). According to the regression results, there are six explanatory variables that significantly (p < 0.10) influence housing prices. For the structural variables, the floor area ratio and floor height have significant impacts

on housing prices. Each one-unit increase in the floor area ratio will increase the housing price by 345.15 RMB/m2. The region with a high rate of volume is generally located in the city center because of government policy and planning in China. Meanwhile, a high floor area ratio leads to high building costs, which have a positive and significant influence on the selling price of an apartment. The floor height is a suitable index for implicit value analysis in the Chinese real estate market, and it increases the housing price by 53.38 RMB/ m2 for each higher floor. High-rise buildings can provide good views, increasing natural ventilation and sunlight, which makes the apartment more attractive to homebuyers.

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L. Jiao et al. / Habitat International 67 (2017) 22e32

Table 1 Descriptive statistics of 11 explanatory variables. Variables

Description

Structural Variables FRatio Floor area ratio of the residential estate where a sample is located FHeight The floor height on which an apartment is situated NBedroom Number of bedrooms in sample apartment NBathroom Number of bathrooms in sample apartment Locational Variables BusiCent Influence score of business centers PubTran Influence score of public transport stops Road Influence score of urban roads Water Influence score of water bodies Park Influence score of urban parks Environmental Variables VCI Vegetation coverage index, the average of the VCI values of the pixels around the sample in a 200-m buffer TEI Thermal environment index, the average of the TEI values of the pixels around the sample in a 200-m buffer

Table 2 Regression results of the spatial hedonic model of housing prices. Variables

Coefficient

Constant 346.00 Structural Variables FRatio 345.15*** FHeight 53.39* NBedroom 206.72 NBathroom 43.81 Locational Variables BusiCent 16.80** PubTran 7.21 Road 70.52** Water 6.18 Park 11.36 Environmental Variables VCI 33.75* TEI 54.54**

Std.Error

z-value

p

5034.44

0.07

0.945

108.50 37.08 359.14 393.49

3.18 1.80 0.58 0.11

0.001 0.071 0.565 0.911

7.36 8.34 30.30 8.05 10.31

2.28 0.86 2.33 0.77 1.10

0.023 0.387 0.020 0.442 0.271

20.47 22.40

1.65 2.43

0.099 0.015

*p < 0.10, **p < 0.05, ***p < 0.01. p-values in bold indicate the values are less than 0.1.

In terms of locational variables, the road conditions and business centers significantly increase the housing price by 70.52 RMB/ m2 and 16.80 RMB/m2, respectively, for each percentage increase in the variables. In general, the road conditions and business centers have a strong positive appeal to homebuyers in choosing their home address due to the convenient transportation, favorable infrastructure, better traffic conditions, and systematic support facilities surrounding business centers. Both remotely sensed environmental variables have significant impacts on the housing price. The regression results indicate that the thermal environment index (TEI) has a significant and negative influence on housing prices. Each percentage increase in the TEI decreases the housing value by 54.54 RMB/m2. We find that the VCI decreases the housing price by 33.75 RMB/m2 for each percentage increase in the index scores. In general, housing prices are positively related to the vegetation coverage (NDVI) in the central urban area, which has been demonstrated in previous studies (Mansfield et al., 2005; Tyrv€ ainen & Miettinen, 2000). However, they are negatively correlated from the perspective of the entire city because there is much more vegetation (NDVI) in the urban fringe and suburban area than in the central urban area. A higher VCI commonly indicates an area far from the central urban area, where the population density, building density, and road density are relatively low and the afforestation rate is higher; thus, in our study, the vegetation coverage index has a negative effect on housing prices. It is logical to infer that housing prices and the LST are positively correlated from the perspective of the entire city because the LST and housing prices are both higher

N

Min.

Max.

Mean

S.D.

291 291 291 291

0.40 3 1 1

12.52 20 4 3

3.22 10.38 2.48 1.35

1.68 4.22 0.60 0.49

11 339 5152 18 45

0 0 0 0 0

99.98 83.03 98.77 98.36 97.68

59.42 17.83 64.02 10.29 6.04

29.92 18.69 9.11 20.91 13.40

291 291

0 51.6

30.46 71.58

5.36 61.89

5.05 6.25

in the central urban area. However, in our hedonic modeling, the LST (TEI) is significantly and negatively correlated with housing prices, which indicates that the urban thermal environment can represent urban disservices to some extent. An urban heat island is a comprehensive reflection of the local ecological and environmental disservices. These unpleasant environments and lower amenity values decrease the willingness-to-buy of homebuyers. Landsat images have been widely used to retrieve the surface temperature (Shen et al., 2016). We employed two representative images to generate the spatial distribution of thermal environment in the case area. There is less than 2% cloud cover on the two images, with no rain on both days. According to the data from China Meteorological Data Service Center, the daily means of wind speed on September 17, and October 13, 2010 in Wuhan are 2.2 m/s and 1.0 m/s, respectively, which indicates stable atmospheric conditions (CMDC, 2017). The stable atmospheric conditions promise the representativeness of spatial pattern of the remotely sensed LST (Mohan & Siddiqui, 1998). We found that the spatial distributions of the LST on the two days were almost the same, which further indicated that the spatial pattern of the LST was representative.

4. Discussion 4.1. Spatial scale in neighborhood analysis The noise in spectral information, such as the strong reflective surfaces on rooftops, may bring accidental error to the regression results. To address these problems, we adopted neighborhood analysis to derive the thermal environment index (TEI) and the vegetation coverage index (VCI) around the housing price samples (Shimizu, 2014; Zhai et al., 2017). Specifically, we replaced the original pixel value with the mean value of pixels surrounding the original pixel to a certain distance. We took a series of buffer radiuses (50 m, 100 m, 150 m, 200 m, 250 m, 300 m, 350 m, and 400 m) to investigate the spatial scale in neighborhood analysis. We developed series spatial lag regressions of housing prices with TEI variables in different buffer zones, with the other explanatory variables being the same as those in Table 2. The coefficients and levels of significance of the TEI variables and the model performance are shown in Table 3. According to the significance and regression coefficient, the varying TEI variables calculated over different buffer zones have different effects on housing prices. In the 250-m buffer, the TEI affects housing prices more significantly than at other scales, reaching 57.84 RMB/m2. However, a change in the buffer distance does not cause significant variation in the p and R2 values of the spatial regression models when the radiuses of the buffers are

L. Jiao et al. / Habitat International 67 (2017) 22e32 Table 3 Coefficient and levels of significance of the TEI in different buffer zones in the spatial lag regression of housing prices. Variable

Coefficient

Std. Error

z-value

p

Pseudo-R2

TEI TEI-50 m TEI-100 m TEI-150 m TEI-200 m TEI-250 m TEI-300 m TEI-350 m TEI-400 m

71.99 47.43 45.27 47.95** 54.54** ¡57.84** 56.66** 55.60** 55.54**

46.10 29.97 27.88 22.99 22.40 23.04 23.58 23.50 23.24

1.56 1.58 1.62 2.36 2.43 ¡2.51 2.40 2.37 2.39

0.12 0.11 0.10 0.02 0.01 0.01 0.02 0.02 0.02

0.5557 0.5514 0.5518 0.5602 0.5696 0.5735 0.5756 0.5773 0.5794

*p < 0.10, **p < 0.05, ***p < 0.01. Bold values indicate the highest coefficient of TEI at 5% significance level.

equal to or larger than 150 m. 4.2. Quantization methods of heat island effects We adopted neighborhood analysis to quantify the effects of heat islands in our hedonic modeling (Table 2). To compare different quantization methods, we also used binary quantization and grading quantization to reflect the impacts of the thermal environment index (TEI) on housing prices. Binary quantization is a method for setting the TEI value to one when the housing price sample is located inside the heat island area and to zero when the sample is located outside. Grading quantization classifies the TEI into five levels through a natural break dividing method, and the high to low levels correspond to the temperatures from high to low. The results of the neighborhood quantization of the TEI are presented in Table 2, and the spatial lag regression results of the binary and grading quantization methods of the TEI are shown in Table 4. As Table 2 shows, the housing price significantly decreases 54.54 RMB/m2 for each percentage increase in the TEI in the neighborhood quantization (p ¼ 0.099). However, neither of the TEI variables using binary or grading quantization methods is significant in the regression models (p > 0.1, Table 4). The housing price remarkably decreases 389.17 RMB/m2 when the TEI index increases from zero to one in the binary quantization method. It is much simpler to quantify the influences of heat islands without considering the spatial extent of their influences. After dividing the TEI

Table 4 Spatial lag regression results of the TEI quantified by binary and grading methods. Variables

Quantitative Method Binary

Constant Structural Variables FRatio FHeight Bedroom Bathroom Locational Variables BusiCent PubTran Road Water Park Environmental Variables VCI TEI Model Performance (Pseudo-R2)

Grading

Coefficient

p

Coefficient

p

5710.65

0.1601

4697.69

0.3149

351.89*** 51.43 190.45 88.31

0.0015 0.1767 0.6069 0.8267

344.82*** 58.86 178.67 54.98

0.0020 0.1221 0.6251 0.8911

17.95** 8.50 80.74*** 2.19 13.81

0.0212 0.3116 0.0074 0.8323 0.1900

19.64*** 7.96 84.14*** 3.46 14.14

0.0098 0.3374 0.0043 0.6629 0.1784

442.82 389.17 0.5470

0.3590 0.4954

74.81 166.80 0.5534

0.5785 0.2050

*p < 0.10, **p < 0.05, ***p < 0.01.

29

into five levels, each increase of the level of the TEI will lead to a 166.80 RMB/m2 decrease in the housing price. Among the three quantization methods of urban thermal environment effects, only the neighborhood quantization of the TEI shows significant influences on housing prices in spatial lag regressions (Table 2).

4.3. Distance from heat islands We developed three buffers (500 m, 1000 m, and 1500 m) around the identified heat islands. The housing prices of the samples located inside and with different distances from the heat islands are shown in Fig. 5. The boxes are drawn with widths proportional to the square roots of the number of observations in the groups. As shown in Fig. 5, the median (black thick line) of housing prices inside heat islands is clearly lower than others, demonstrating the negative influences of heat islands on housing prices. The average housing price inside the heat island is 7226 RMB/m2, and the average housing prices in the 500-m, 1000-m, and 1500-m buffers are 8458, 8595, and 9099 RMB/m2, respectively, and the average housing price 1500 m away from the heat islands is 8972 RMB/m2. The average housing price increases as the buffer zone increases, and the price is stable when the distance is greater than 1000 m. Homebuyers are less willing to pay for an unfavorable thermal environment, with the minor amenity values and uncomfortable inhabitant experiences. This aspect leads causes the transaction price to vary inversely with the distance to the heat islands. The average housing price of the 241 samples located outside the urban heat islands is 8912 RMB/m2, and the gap in the housing prices between the interior and exterior areas of the heat islands is 1686 RMB/m2. However, in the spatial lag regression using the binary thermal environment index (TEI), the variation in housing prices is 467.97 RMB/m2 (Table 4), which also indicates that the binary quantization method does not adequately reflect the impacts of the urban thermal environment on housing prices.

4.4. Impacts of heat island types The housing prices of the samples located in different types of heat islands are shown in Fig. 6. The boxes are drawn with widths proportional to the square roots of the number of observations in the groups. The median (black thick line) of housing prices inside the industrial and development heat islands is clearly lower than that in commercial heat islands and lower than that outside heat islands. The average housing prices in industrial, development, commercial heat islands are 6644, 6668, and 8070 RMB/m2, respectively, and non-heat island areas have a mean value of 8912 RMB/m2. As a consequence of the fully equipped infrastructures, convenient living conditions, and prosperous business in the commercial areas, the housing prices in the commercial heat islands are 1426 and 1402 RMB/m2 higher than the prices in the industrial and development heat islands, respectively. In the industrial and development heat island areas, residents suffer more from factory noise, air pollution, water contamination, and other negative environmental effects, resulting in a lower willingness-to-pay for apartments in these areas. The housing price in non-heat island areas is higher than the transaction price in all types of heat islands, with prices 842, 2268, and 2244 RMB/m2 higher than in commercial, industrial, and development heat island areas, which means that the more amenable ecological environment in non-heat island areas plays an important role in the real estate market.

Fig. 5. Housing prices of the samples located inside and with different distances from the heat islands.

Fig. 6. Housing prices of samples located in different types of heat islands.

L. Jiao et al. / Habitat International 67 (2017) 22e32

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