Exploring spillover effects of ecological lands: A spatial multilevel hedonic price model of the housing market in Wuhan, China

Ecological Economics 170 (2020) 106568

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Analysis

Exploring spillover effects of ecological lands: A spatial multilevel hedonic price model of the housing market in Wuhan, China

T

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Tian Liua,b, Weiyan Hua, , Yan Songb, Anlu Zhanga a b

College of Land Management, Huazhong Agricultural University, Hubei, Wuhan 430070, China Chinese Urban Programs, University of North Carolina at Chapel Hill, United States of America

A R T I C LE I N FO

A B S T R A C T

Keywords: Spillover effect Ecological compensation Ecological lands Spatial multilevel hedonic model Non-linear spatial spillover

This paper attempts to explore the spillover effects of ecological lands, including forest, grassland, wetland, and cultivated land, on housing prices. To this end, we test hypotheses from a spatial multilevel hedonic model in Wuhan, China. We find that forest size and wetland size has a linear positive spillover effect on urban housing prices, and a moderate grassland area and distance from wetland generates positive spillover effect on urban housing prices, while too much or poor grassland area and distance from wetland may not. Also, only cultivated land very proximity to urban residential areas may raise the housing prices, most of the cultivated land in our case may reduce the housing prices. This article contributes to the literature by integrating different ecological lands into the hedonic analysis based on spatial multilevel models and deepens the relationship between the accessibility and visibility of ecological lands and housing prices. This result implies that demand for the forest, grassland, and wetlands can be well reflected in the housing market, while demand for cultivated land is less reflected in the housing market. Our findings urge policymakers to increase the effective supply of ecological lands through urban development planning and maintain the continuous supply of existing ecological lands by implementing market, differential ecological protection mechanisms.

1. Introduction Ecological land is an important land space for maintaining ecological systems (Xie et al., 2016). Exploring the spatial spillover effects of ecological lands would contribute to improving urban development planning and differentiated ecological compensation policies (Liebelt et al., 2018; Wu, 2017). Ecological lands provide many ecological services, such as improving air quality, reducing stress, and stimulating physical activity (Ekkel and Vries, 2017), which improve the living standards and well-being of urban residents (Liebelt et al., 2018). However, exploring the spatial spillover effects of ecological lands is challenging due to the absence of an explicit market for ecological lands (Jiao and Liu, 2010). Because the ecological services provided by ecological lands cannot be traded directly on the open market, ecological lands do not have an explicit market price (Jiao and Liu, 2010; Liebelt et al., 2018). Several methods have been proposed for measuring the spatial spillover effects of ecological lands (Liebelt et al., 2018). The most commonly employed methods are the hedonic price model and the contingent valuation model (CVM) (Brander and Koetse, 2011; Czembrowski and Kronenberg, 2016; Liebelt et al., 2018). In the

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hedonic price model, the basic assumption is that the house buyer is paying not only for the unit but also for the services provided by the ecological lands surrounding the house, so the spatial spillover effects of the ecological lands are considered in terms of the value they add to housing prices (Jiao and Liu, 2010). Compared to the CVM, the hedonic price model can effectively avoid limitations such as subjectivity and high implementation costs when the sample number is very large (Yamagata et al., 2016). In recent years, spatial analysis based on spatial statistics and geographic information systems has been gradually incorporated into the hedonic price model to form the spatial hedonic model (Jiao and Liu, 2010), as a spatial correlation of housing prices due to similar locations and environments has been found (Liebelt et al., 2018). However, the existing spatial hedonic model is employed on a single scale and lacks the nest structure of the housing price data into consideration, and these features produce incorrect estimations (Yamagata et al., 2016; Liebelt et al., 2018). A spatial multilevel hedonic model that incorporates a nested structure and spatial correlation, combining the multilevel regression model and the spatial hedonic model, is widely applied in multilevel spatial data analysis (Yamagata et al., 2016), but is less involved in the hedonic analysis. Several quantitative analyses of ecological lands have been

Corresponding author at: College of Land Management, Huazhong Agricultural University, Wuhan 430070, China. E-mail address: [email protected] (W. Hu).

https://doi.org/10.1016/j.ecolecon.2019.106568 Received 7 June 2019; Received in revised form 15 November 2019; Accepted 4 December 2019 0921-8009/ © 2019 Elsevier B.V. All rights reserved.

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Offering products For example, food product, raw material, and aquatic product

Multi-functional ecological land - Production - Ecology - Landscape and --culture

Well-being Availability of ecological land - Accessibility - Visibility

Reducing harm For example, climate and flood regulation, air filtering, noise reduction

For example, environmental optimization, quality of life improvement

Housing price

Increasing Recreation For example, providing recreation area, beautiful landscape, cultural space Fig. 1. The spatial spillover of ecological lands on housing prices.

ecological lands on housing prices. Section 3 introduces the data and variables. Section 4 describes our improved hedonic price model. Section 5 shows the empirical results, and Section 6 concludes the paper.

conducted by employing hedonic price models (Siân de Bella et al., 2017; Markevych et al., 2017). However, those studies aim to determine the economic value of urban green space and blue space in general, and few studies concern the difference of ecological lands (Liebelt et al., 2018; Siân de Bella et al., 2017; Markevych et al., 2017). Recent studies have shown that various types of ecological lands may have different spatial spillover effects on housing prices (Czembrowski and Kronenberg, 2016). For example, Larson and Perrings (2013) found a positive spatial spillover effect of large wetland and a negative spatial spillover effect of cultivated land in the Phoenix Metropolitan Area. Similarly, Czembrowski and Kronenberg (2016) found that proximity to the largest forest greatly increases housing prices, followed by proximity to a small forest, whereas the proximity to cemeteries decreases housing prices. Considering the spillover forms of ecological lands may be crucial in valuing the spatial spillover effect of different ecological lands on housing prices (Yamagata et al., 2016). Studies have reported the linear spatial spillover effect of ecological lands on housing prices, these ecological lands include cultivated land (Kestens et al., 2004), urban forest (Liebelt et al., 2018), and wetland (Jiao and Liu, 2010). However, recent research has shown that the possibility of non-linear spatial spillover effects of ecological lands is worth considering, as Bertram and Rehdanz (2015) found an inverted U-shaped nonlinear curve between urban green space and residential well-being in Berlin. Similarly, Yamagata et al. (2016) also found that a “moderate amount” of forest view may increase housing prices, but “poor” and “too much” forest view may decrease the housing prices in Yokahama, Japan. Here, we aim to explore the spatial spillover forms and effects of four different types of ecological lands, including forest, grassland, wetland, and cultivated land, by employing the spatial multilevel hedonic model, in which a possible non-linear relationship, spatial correlation, and nested structure data are considered. The above categorization is meant to reflect the different needs that these ecological lands satisfy. It should be noted that, in general, ecological lands are divided into three categories: wetland, forest, and grassland, cultivated land is excluded (Ferretti and Pomarico, 2013; Xie et al., 2016). However, with the development of multifunctional theory, cultivated land, as an important ecological land, has gained people's attention (Wang et al., 2017; Hu et al., 2017). Hence, cultivated land as a type of ecological Land is included in this article. The research questions of the paper are as follows: How do ecological lands affect housing prices? Are there any differences in the spatial spillover effects of various ecological lands on housing prices? To address these questions, we employ an availability indicator, which combines the distance to the nearest ecological lands and size of the nearest ecological lands, and develop the spatial multilevel hedonic model. The structure of this paper is as follows. Section 2 describes an analytical framework of the spatial spillover effects of different

2. An analytical framework for the effects of ecological lands on housing prices Ecological lands have multiple functions, including providing food, regulating climate, reducing noise, beautifying the environment, and increasing leisure and entertainment spaces, which can affect housing prices by influencing residential well-being (Larondelle and Lauf, 2016). Specifically, the multifunctional services of ecological lands are of significant relevance for resident well-being through their availability, which includes accessibility and visibility (Bertram and Rehdanz, 2015; Siân de Bella et al., 2017; Liebelt et al., 2018; AlaHulkko et al., 2019). In addition, when entering into a housing transaction, people consider many housing welfares, such as comfortable living environment, and are not willing to compromise due to the longlasting effects of the housing transaction (Czembrowski, 2016), so housing prices are like an envelope of resident welfare, and those prices will change when the residential welfare changes (Yamagata et al., 2016). Based on the above analysis, the effects of ecological lands on housing prices are shown in Fig. 1. In general, ecological lands have positive spatial spillover effects on housing prices (Siân de Bella et al., 2017; Markevych et al., 2017; Larondelle and Lauf, 2016; Ciftcioglu et al., 2019). However, ecological lands may also negatively affect housing prices (Bertram and Rehdanz, 2015). For example, some common trees and bushes produce volatile organic compounds such as isoprene, monoterpenes, ethane, acetic acid, and formic acid, which can indirectly contribute to urban smog and ozone problems (Chaparro and Terradas, 2009; Gómez-Baggethun and Barton, 2013); D'amato (2000) also found that wind-pollinated plants can cause negative spatial spillover effects on human health in the form of allergic reactions. Similarly, Ives et al. (2017) found that the negative spatial spillover effects of ecological lands may be related to the presence of scary animals or a nighttime environment that appears unsafe. The trade-offs of “positive spillover” and “negative spillover” may lead to non-linear spatial spillover effects of ecological lands on housing prices. Regarding ecological lands accessibility, which is commonly measured by the distance to the nearest ecological lands (Ekkel and Vries, 2017), classic studies of economics geography have demonstrated how housing prices vary with accessibility, and the theory has turned into an empirical reality based on the rational behavior of house buyers (Li and Brown, 1980). The research of Mingche M. Li and H. James Brown had proposed that both the positive and negative effects associated with accessibility decline with distance from the non-residential 2

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considered. The housing transaction data inluding 788 apartment samples and 2485 unit samples are selected in our study.

activity, while the negative effects will decline much more rapidly than the positive effects (Li and Brown, 1980). Similarly, we assume that the positive and negative spatial spillover effects of ecological lands on housing prices have similar evolution patterns, and their net spatial spillover effects are equal to the vertical sum of the two curves, as shown in Fig. 2a. The net spatial spillover effects of ecological lands on housing prices may be non-linear and show a trend of initially increasing and then decreasing as the increase of the accessibility indicator. Regarding the visibility of ecological lands, which is usually measured by the size of the nearest ecological lands (Bertram and Rehdanz, 2015; Liebelt et al., 2018).The law of diminishing marginal return to scale has shown how housing prices vary with visibility. The microneighborhood externalities analysis conducted by Mingche M. Li and H. James Brown showed that the negative effects may be smaller than the positive effects. Hence, we assume that both the positive and negative spatial spillover effects of ecological lands will increase as the increase of the nearest ecological lands size, but the negative spatial spillover curve will be flatter than the positive curve (Liebelt et al., 2018; Ives et al., 2017; Li and Brown, 1980). Then, we determine the net spatial spillover effects of ecological lands on housing prices with the visibility (Fig. 2b). The net spatial spillover effects of ecological lands on housing prices may be non-linear from the perspective of visibility. Therefore, we assume that the spatial spillover effects of ecological lands on housing prices may be non-linear.

3.3. Variables The variables are described in Table 1. The dependent variable is the transaction price per square meter at the unit level. The explanatory variables include unit-level variables and apartment-level variables. The sets of unit-level variables include the physical housing attributes of the house, such as the total number of floors (Floor), number of bedrooms (Bedroom), number of living rooms (Living), and housing area (Area). The sets of apartment-level variables include crucial ecological lands variables and control variables. A distance-based accessibility indicator is commonly used as ecological lands variable to measure the spatial spillover effects of ecological lands on urban housing prices, such as distance to the nearest green space (Ekkel and Vries, 2017; Czembrowski and Kronenberg, 2016). However, distance-based accessibility indicator tends to ignore the size problem (Jiao and Liu, 2010; Ekkel and Vries, 2017). For example, if the physical activity, such as flying a kite, has to take place in the ecological lands itself, it may require a minimum amount of ecological lands (Ekkel and Vries, 2017). A size-based visibility indicator, which usually measured by the size of the nearest ecological lands, takes the scale influence of ecological lands into account (Bertram and Rehdanz, 2015; Liebelt et al., 2018). Hence, both distances to the nearest ecological lands and size of the nearest ecological lands are employed in our case. Individual variables (Age, FlrArRatio, Household, and GreenRatio), neighborhood variables (Financial, Medical, and School), and location variables (Dist_Bus, Dist_CBD, Dist_AAA, Dist_Subway, Dist_Key, and Dist_Park) as control variables are considered at the apartment level to eliminate possible omitted variable bias (Song and Knaap, 2004). Note that there are multi-centers instead of a single center in Wuhan based on the urban master planning in 2010–2020 (source:http://gtghj. wuhan.gov.cn/pc-998-108001.html), which is different between Wuhan and other cities (Jiao and Liu, 2010). Considering the house located far away from the city center has a relatively lower price, we use the distance to the nearest central business district to control the factor that may have influence on urban housing prices.

3. Data and variables 3.1. Study area Our study is conducted in Wuhan, the capital city of Hubei province in Central China. Wuhan is located in the east of the Jianghan Plain, where the Yangtze River and Han River converge. Our hedonic analysis focuses on the main urban area. This area is 1243 km2 and had approximately 656.47 km2 of ecological lands in 2015, including 292.51 km2 of cultivated land (44.56%), 27.83 km2 of forest (4.24%), 15.30 km2 of grassland (2.33%), and 320.82 km2 of wetland (48.87%). A larger amount of ecological lands and urban parks are close to the urban residential areas (Fig. 3), which makes the ecological space easily available to the urban residents. In addition, Wuhan has experienced an approximate increase of 2.72 km2 in the amount of grassland during the period of 2000–2015. However, during the same period, the losses of cultivated land, forest, and wetland are 229.25 km2, 4.11 km2, and 57.70 km2, respectively. In general, ecological lands have been reduced by 288.33km2 during the period of 2000–2015. This reduction in the number of ecological lands will exert strong pressure on existing ecological lands to meet the ecological demand of the residents in Wuhan. Hence, how to protect and increase ecological lands has become a key issue in Wuhan.

4. Models The hedonic price model estimates the economic value of goods or services by isolating the effects of different factors (Rosen, 1974; Yamagata et al., 2016). And it is often used to measure the non-market components of housing prices (Jiao and Liu, 2010; Siân de Bella et al., 2017; Markevych et al., 2017). The spatial spillover of the non-market goods, such as ecological lands, can be measured by estimating their value-added on the house prices. Housing price was found to be a spatial correlation (Jiao and Liu, 2010) and multilevel structure, such as a unit nested in a building (Yamagata et al., 2016), by many researchers. In our case, housing price has two-level structures such as unit level and apartment level, and its global Moran' I is 0.16, which is well above than 0. These results indicated that the urban housing price has significant spatial correlation and multilevel structure. We discuss this issue further in Section 5.1. Ignoring the spatial correlation and multilevel structure could cause regression errors (Yamagata et al., 2016; Liebelt et al., 2018), as doing so violates the basic assumptions of multiple regressions. A spatial multilevel hedonic model considers the spatial correlation and multilevel structure of urban housing prices in the model (Yamagata et al., 2016).To explore the spatial spillover of ecological lands more accurately, a spatial multilevel hedonic model is employed in our case. However, the function form of the spatial multilevel hedonic model does not be identified by the hedonic price theory (Yamagata et al., 2016). In our study, we test many functional forms, log-log, semi-log

3.2. Data Ecological lands data come from Landsat TM/ETM remote sensing image data is provided by the Resource and Environment Data Cloud Platform.1; the location data at the apartment level is obtained from the Baidu map; the housing price is real transaction price and obtained from a professional network platform called Fang.com (http://wuhan. fang.com; the website provides the real housing transaction prices information at the unit level and at the apartment level). The housing transaction information can be filtered by some criteria. To avoid problems with property type variations, only the common residential estates transacted from January 2015 to December 2015 are 1 http://www.resdc.cn. [Reasource and Environment Data Cloud Platform. [EB/OL]. http://www.resdc.cn.]

3

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Value

Value Positive spatial spillover

Positive spatial spillover Net spatial spillover Net spatial spillover

0

0

Accessibility

Visibility Negative spatial spillover

Negative spatial spillover

a. Spatial spillover on housing prices due to accessibility

b. Spatial spillover on housing prices due to visibility

Fig. 2. The forms of spatial spillover effects of ecological lands on housing prices.

Fig. 3. Geographical distribution of ecological lands in Wuhan.

coefficient vector of unit variables; εij is the error term at the unit level; γi0 is the intercept term at the apartment level; ρ is the spatial regression coefficient; crucial parameters βim is spatial spillover effects of ecological lands; βil is the regression coefficient of control variables at the apartment level;μij is the error term at the apartment level; K is the number of variables at the unit level; M and L are the numbers of ecological lands and control variables at the apartment level. In order to avoid the biases caused by uneven distribution, we use a two-step method to construct a spatial weight matrix. At first, the Thiessen polygon is constructed using GeoDA software. Next, based on the common boundary principle, we build a spatial weight matrix. If apartment a and apartment b have spatial adjacency, then Wab = 1; otherwise, Wab = 0. The spatial spillover of ecological lands on urban housing prices is assumed to be linear in the above equations. However, the spatial spillover effect may be non-liner between ecological lands and urban

and simple linear. The semi-log model is the most meaningful and appropriate form, where the dependent variable was transformed by a nature logarithm. Hence, a semi-log spatial multilevel hedonic model is employed. Level-1 (Unit-level):

ln Pij = β0j +

K

∑k =1

βkij xkij + εij

Level-2 (Apartment-level): M

L

M

L

β0j = γ00 + ρW ln P′ij + ∑m = 1 β0m Zm j + ∑l = 1 β0l xlj + μ0j βkij = γi0 + ρW ln P′ij + ∑m = 1 βim Zm j + ∑l = 1 βil xlj + μij

(1)

where Pij is the housing price of unit i located in apartment j; xkij is unitlevel variables; W is the spatial weight matrix; WlnPij' is the spatial lag variable; Zmj is ecological lands variables; xlj is apartment-level control variables; β0j is the intercept term at the unit level; βkij is the regression 4

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Table 1 Definitions of variables and basic descriptive statistics. Category Dependent variable Apartment-level variables

Unit-level variables

Variables P Cult_Dist Cult_Size Forest_Dist Forest_Size Grass_Dist Grass_Size Wet_Dist Wet_Size Age FlrArRatio Household GreenRatio Dist_Bus Dist_CBD Dist_AAA Dist_Subway Dist_Key Dist_Park Financial Medical Schools Floor Bedroom Living Area

Description 2

Transaction prices of units/m Logarithm of the distance to the nearest cultivated land Logarithm of the size of the nearest cultivated land Logarithm of the distance to the nearest forest Logarithm of the size of the nearest forest Logarithm of the distance to the nearest grassland Logarithm of the size of the nearest grassland Logarithm of the distance to the nearest wetland Logarithm of the size of the nearest wetland Age of apartment in years Floor area ratio of the apartment Logarithm of the number of householders in the apartment Green space ratio of the apartment Logarithm of the distance to the nearest bus stop Logarithm of the distance to the nearest central business district Logarithm of the distance to the nearest class 3A hospital Logarithm of the distance to the nearest subway station Logarithm of the distance to the nearest key school Logarithm of the distance to the nearest urban park Scores of banks and ATMs within 800 ma Scores of hospitals and clinics within 800 mb Number of junior and senior high schools within 800 m Total number of Floors in which unit is situated Number of bedrooms Number of living rooms Logarithm of the living area of the unit

Min

Mean

Max

Standard deviation

2807.33 0.69 6.42 1.61 6.36 4.39 6.42 0.69 6.42 0.00 0.40 3.04 0.83 2.77 4.15 4.55 4.40 3.40 3.48 0 0 0 1 1 1 3.09

9953.33 6.81 12.21 7.34 11.70 7.86 12.15 6.50 15.25 10.24 2.80 6.64 3.51 5.84 7.98 7.54 7.14 7.32 6.86 17.32 17.72 3.80 17.20 2.44 1.69 4.54

21,375 8.74 16.69 8.84 14.83 8.89 15.11 8.36 20.14 30 16 9.09 4.09 9.17 9.75 10.04 9.78 9.56 9.19 61 64.5 28 67 6 3 5.67

2594.43 1.57 2.21 1.01 1.72 0.71 1.43 1.07 3.58 5.87 1.64 0.92 0.24 1.22 0.81 0.82 0.96 0.86 0.94 11.05 11.11 3.39 10.69 0.86 0.48 0.39

a Considering the tolerable walking time of residents in their daily lives is 10 min, we chose 800 m as a buffer to measure the spatial spillover effects of surrounding facilities on house price (Zhu et al., 2015). b We adopted graduation statistics to distinguish between hospitals and clinics, as well as banks and ATMs(hospitals and banks are 1; clinics and ATMs are 0.5).

Fig. 4. LISA plot of housing prices.

housing prices based on the theoretical analysis in Section 2. Therefore, in addition to the linear model described in Eq. (3), we also estimate the possible non-linear form by adding the linear and squared form of ecological land variables to the model. In the non-linear case, Eq. (1) changes to Level-1(Unit-level):

ln Pij = β0j +

K

∑k =1

βkij xkij + εij

Level-2(Apartment-level):

5

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L

M

L ∑l = 1

M ∑m = 1

the numbers of bedroom and living room have positive and significant influences on housing prices. One more bedroom and living room translated on average into the increase of housing prices by 0.025% and 0.021% per square meter. The total number of floor have no significant impact on housing prices, which is the same from the studies conducted in western countries (Liebelt et al., 2018). Housing area has a significant and positive influence on urban housing prices, and each percentage increase in housing size generates an about 0.342% rise in the urban housing prices. However, the housing age and proximity to AAA hospital will weaken the positive influence of housing size on housing prices. This finding indicates people tend to purchase a small-sized apartment with proximity to AAA hospital or newly constructed. Regarding the coefficient estimates of the apartment-level variables, new apartment, more households, higher greening rate, convenient financial services, and school exert positive impacts on housing prices, and these effects have been frequently reported in other hedonic analyses (Wen et al., 2012). Proximity to the subway station, key school, and AAA hospital are considered good amenities, whereas proximity to the central business district is seen as unwelcome. A 1% decline in the distance to the nearest central business district translated on average into a decline of housing prices by 0.046% per square meter. This result is contrary to the location economics theory but is not unprecedented: Czembrowski and Kronenberg (2016) found that shopping centers significantly decreased the housing prices in Lodz, a city in central Poland. The same finding was found by Troy and Grove (2008) in Baltimore. The negative impact of proximity to the central business district may be related to noise and the urban ecological environment. First, the closer the apartment is to the central business district, the louder the noise will be (Mehdi et al., 2018). Second, the better the location is, the worse the ecological environment will be, due to expensive land prices (Mehdi et al., 2018). However, the negative impact of proximity to the central business district also indicates that residents no longer pursue an apartment for its location status, but rather seek an apartment located in a beautiful ecological environment. Most of the variables of ecological lands were significant, including distances to the nearest cultivated land and wetland, as well as the sizes of the nearest forest, grassland, and wetland. Regarding the spatial spillover form, the non-linear spatial spillover model shows that the housing prices increase with distance from cultivated land in a Ushaped way. We can see that if the distance from cultivated land less than 3.79 (44.26 m), the housing prices decrease constantly, but after 3.79 (44.26 m), the housing prices increase rapidly with distance from cultivated land. This result means that only cultivated land very proximity to urban resident area may raise the housing prices, while other cultivated land may reduce the housing prices. The finding differs from the studies carried out in western countries. Most of them have not considered the non-linear spatial spillover between cultivated land and housing prices. Some of them find that distance from cultivated land has no significant spatial spillover on housing prices, such as the one carried out in Prague, the capital of the Czech Republic (Melichar and Katerina, 2013). The possible reason for this difference is the fact in Wuhan that cultivated land very proximity to urban resident area has been transformed into urban agriculture, which can improve the resident's well-being by providing beautiful agricultural landscapes and recreational space. However, other cultivated land is still used for agricultural production in our study area. In addition, cultivated land size does not generate statistically significant spatial spillover on housing prices in Wuhan. This finding, although differs from expectations, is in line with previous studies (Melichar and Katerina, 2013). Turning to the spatial spillover of forest on housing prices, we find that the linear spatial spillover of forest size on housing prices. This result means that additional forest size linearly increases housing prices. However, distance to the nearest forest has no significant spatial spillover effect on housing prices, which is contrary to findings from existing studies (Czembrowski and Kronenberg, 2016; Melichar and Katerina, 2013). The insignificance of proximity to forest is possibly

β0j = γ00 + ρW ln Pij′ + ∑l = 1 β0l xlj + ∑m = 1 β0m Zm j + ∑m = 1 θ0m Zm2 j + μ0j βkij = γi0 + ρW ln Pij′ +

βil xlj +

βim Zm j +

M ∑m = 1

θim Zm2 j

+ μij (2)

The non-linear form presumes that the spatial spillover effects of ecological lands may also depend on their current allocation. Zm2 and θm denote the squared form of ecological lands variables and their regression coefficients. The estimated relationship can be used to derive the spatial spillover effects of ecological lands on housing prices. For the linear and non-linear forms, the spatial spillover effects can be expressed as SS1 and SS2, respectively:

∂ ln Pij

SS1 =

∂Zmj

SS2 =

∂ ln Pij ∂Zmj

= βim

(3)

= βim + 2θim Zmj

(4)

5. Results 5.1. The rationality of the spatial multilevel hedonic model To verify the rationality of the spatial multilevel hedonic model, we conduct a spatial autocorrelation analysis using ArcGIS10.2 and an intra-class correlation coefficient (ICC) test (Hox et al., 2003) using HLM6.08, respectively. Fig. 4 shows that houses with similar housing prices, such as High-High and Low-Low tend to be clustered in the same area, which means that housing transaction prices have a significant spatial correlation. This finding can be also confirmed by the significant of WlnP in Table 3. We also find that ICC value (the ICC value equals apartment variance divided by the total variance) is 0.847 in Table 2. That is, 84.7% of the variance of housing prices is at the apartment level, suggesting that it is crucially important to consider the multilevel structure of urban housing prices to avoid type-I error in the model. Hence, the spatial multilevel hedonic model is the most appropriate model in our hedonic analysis. 5.2. The relationship between ecological lands and housing prices Table 3 summarizes the parameter estimation results of linear and non-linear spatial spillover of four ecological lands. Note that multicollinearity may lead to estimation bias, and variance inflation factors (VIFs) analysis are used to test whether there is multicollinearity in the model (Melichar and Katerina, 2013). In our case, after omission of interrelated variables (interactions and squared items), which are expected to reach high values due to their definition, none of the VIF scores exceeds 6. This result indicates that there are no serious problems with multicollinearity in the linear and non-linear spatial spillover model. With regard to the coefficient estimates of variables at the unit level, Table 2 Regression results for a null model of housing prices. Null model Coefficient Intercept Variance Variance between apartments(τ) Variance between units(σ2) ICC Apartment sample size House sample size ⁎⁎⁎

Standard error

9674.890⁎⁎⁎

91.035

5,934,699.437⁎⁎⁎ 1,069,459.182⁎⁎⁎ 0.847 788 2485

2436.124 1034.147

Denotes significance at the 1% level. 6

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Table 3 Regression estimation results. Linear spatial spillover Coefficient Cult_Dist Cult_Size Cult_Dist squared Cult_Size squared Forest_Dist Forest_Size Forest_Dist squared Forest_Size squared Grass_Dist Grass_Size Grass_Distsquared Grass_Size squared Wet_Dist Wet_Size Wet_Dist squared Wet_Size squared Intercept WLNP Age FlrArRatio Household GreenRatio Dist_Bus Dist_CBD Dist_AAA Dist_Subway Dist_Key Dist_Park Financial Medical Schools Floor Bedroom Living Area Area×Age Area×AAA

0.022 0.004

Non-linear spatial spillover T-ratio 2.945 1.238

−0.003 0.017

−0.427 3.736

−0.010 0.007

−0.847 1.293

0.000 0.005

0.033 2.187

9.151 0.041 −0.011 −0.002 0.028 0.292 −0.008 0.050 −0.076 −0.060 −0.047 −0.009 0.006 −0.004 0.005 0.000 0.025 0.021 0.360 −0.010 −0.060

37.082 2.525 −8.535 −0.301 3.272 2.535 −1.115 4.029 −5.570 −5.468 −4.003 −0.953 5.607 −3.475 1.541 0.073 3.971 2.978 2.536 −3.717 −3.289

Significant ⁎⁎⁎

⁎⁎⁎

⁎⁎

⁎⁎⁎ ⁎⁎ ⁎⁎⁎

⁎⁎⁎ ⁎⁎

⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎

⁎⁎⁎ ⁎⁎⁎

⁎⁎⁎ ⁎⁎⁎ ⁎⁎ ⁎⁎⁎ ⁎⁎⁎

Coefficient −0.091 −0.001 0.012 0.000 −0.028 0.054 0.002 −0.002 0.062 0.293 −0.006 −0.012 0.062 0.012 −0.006 −0.000 7.109 0.035 −0.011 −0.002 0.031 0.254 −0.002 0.046 −0.073 −0.061 −0.029 −0.006 0.004 −0.003 0.003 0.000 0.025 0.021 0.342 −0.010 −0.057

T-ratio

Significant

−3.271 −0.042 4.743 0.116 −1.207 1.705 0.995 −1.236 0.540 5.590 −0.749 −5.206 1.828 0.589 −1.769 −0.338 11.920 2.705 −6.826 −0.279 3.316 2.186 −0.279 3.979 −5.270 −5.318 −2.541 −0.610 4.386 −2.928 1.114 0.070 3.009 3.206 1.856 −3.483 −2.471

⁎⁎⁎

⁎⁎⁎

⁎

⁎⁎⁎

⁎⁎⁎ ⁎

⁎

⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎

⁎⁎⁎ ⁎⁎

⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎

⁎⁎⁎ ⁎⁎⁎

⁎⁎⁎ ⁎⁎⁎ ⁎ ⁎⁎⁎ ⁎⁎

⁎

Denotes significance at the 10% level. Denotes significance at the 5% level. ⁎⁎⁎ Denotes significance at the 1% level. ⁎⁎

addition, proximity to the grassland has not significant spatial spillover on housing prices, despite the fact that its importance to the citizens has been confirmed by other studies (Saphores and Li, 2012). This result may relate to the spatial distribution of the grassland in Wuhan, most of them are scattered around the wetland. Compared to the stronger spatial spillover of the wetland on housing prices, the spatial spillover of the grassland distance is negligible, and even may not be noticed at all. Comparing the regression results of the linear spatial spillover model with the non-linear spatial spillover model, we find a linear relationship between the size of the nearest wetland and housing prices. It is because wetland size had a significant and linear spatial spillover on housing prices in the linear spatial spillover model, while the significance disappeared in the non-linear spatial spillover model where square items were added. In addition, the non-linear spatial spillover model also provides evidence of the inverted U-shaped spatial spillover between distance from wetland and housing prices. That is, for values under 5.17 (176 m), the housing price increases, but when values reach and exceed 5.17 (176 m), it decreases with the distance from wetland. It means that proximity to wetland when the distance from wetland above than 176 m may raise housing prices, while proximity to wetland when the distance from wetland less than 176 m may reduce housing prices. The negative of very proximity to wetland might be because of the particular feature of Wuhan. According to statistics in 2010, the wetland area makes up about one-quarter of the whole city, so Wuhan has

related to the fact that the forest is mixed instead of classified in Wuhan. The large forest is often widely recognized in terms of its size and renown in the city, proximity to the large forest may have positive spatial spillover on housing prices, while proximity to the small forest may not have the positive spatial spillover, it is because that smaller forest cannot provide similar services as large forest due to their size constraints. If exploring the spatial spillover of mixed instead of classified forest on housing prices, which will weaken the spatial spillover effect of forest distance. In fact, not in Wuhan, this phenomenon is also found in other cities. Melichar and Katerina (2013) found proximity to the large forest may raise the housing prices in Prague, while proximity to the larger and small forest may not. Considering the spatial spillover of grassland on housing prices, there is significant, inverted U-shaped spatial spillover between grassland size and housing prices. This seems plausible because the spatial spillover effect of grassland depends on its current allocation (Bertram and Rehdanz, 2015). The inverted U-shaped spatial spillover of grasslands is no surprise, despite the early studies aimed to determine the linear spatial spillover effect of grasslands on housing prices (Sander and Haight, 2012). The possible reasons for this inverted U-shaped spatial spillover may be related to the competitive relationships among different lands (Bertram and Rehdanz, 2015; Liu et al., 2018). In Wuhan, urban land is constant and scarce, if the grassland is excessive, it will hinder the development of other lands, such as transportation land or commercial land, which reduces the resident's convenience. In 7

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cultivated land, One possible explanation is that the cultivated land in our study area is mainly used for highly intensive agricultural production (Tan, 2011), characterized by high input and high output, which cause negative spatial spillover effects, such as water, air, and soil pollution (Glebe and Thilo, 2007; Tan, 2011). However, were these cultivated land more transformation into ecological agriculture, which can provide leisure opportunities and scenic vistas to urban residents and effectively reduce the emissions of pollutants such as waste gas and wastewater (Sanders, 2010), perhaps the negative spatial spillover of these cultivated land would be lower.

Table 4 The spatial spillover effects of different ecological lands.

Distance

Size

Low Mean High Low Mean High

Cultivated land

Forest

Grassland

Wetland

−0.074 0.072 0.119 – – –

– – – 0.054 0.054 0.054

– – – 0.139 0.001 −0.070

0.054 −0.016 −0.038 0.005 0.005 0.005

aliases of “River City” (Jiao and Liu, 2010). Besides, Wuhan has so abundant rainfall well above 1100mmthat it may bring floods. The risk of flooding is highest where the house closest to the wetland. Hence, The negative spatial spillover of proximity to wetland might be attributable to such flood risk of Wuhan.

6. Conclusion and discussion Employing availability indicators, including accessibility(i.e., distance from the nearest ecological land) and visibility (size of the nearest ecological land), and a spatial multilevel hedonic model, in which nonlinearity, spatial correlation, and nested structure were considered, we determined and quantified the spatial spillover effects of different ecological lands, including forest, grassland, wetland, and cultivated land, on urban housing prices in Wuhan, China. The combined availability indicators overcome the possible deviations caused by traditional accessibility indicators, which do not consider scale limitations, and visibility indicators, which do not consider distance effects. The first basic problem we focused on was the rationality of the spatial multilevel hedonic model. The results of spatial autocorrelation analysis and an ICC test indicate that the spatial correlation and nest structure of housing prices are very significant, so it is necessary to explore the spatial spillover effects of different ecological lands by considering the multilevel structure and spatial correlation of housing prices data. That is, the spatial multilevel hedonic model is the most effective model. This result indicates that the regression analyses conducted on a single scale and that ignore spatial correlation are not appropriate, especially when nested geographic data is employed. On this basic test, our results suggest that the spatial spillover effects of different ecological lands on housing prices are non-linear, except for the forest size and wetland size. We find that cultivated land very proximity to urban may bring positive spatial spillover on housing prices, while other cultivated land may not. It is further found that moderate grassland size and proximity to wetland may raise the urban housing prices, while too much or too poor grassland size and proximity to wetland may reduce the urban housing prices. Such spatial spillover could not have been detected if we had used linear spatial spillover models. To effectively improve the well-being of urban residents and housing prices, our results imply that urban planners should aim to increase the amount of ecological land in Wuhan, particularly the area where ecological lands are scarce, to meet the ecological demands of the area's residents. Further, our results suggest that different ecological lands exert different spatial spillover effects on housing prices. More specifically, the greatest positive spatial spillover effect is exerted by forest size based on the current average value of ecological lands. The distance from wetland, followed by wetland size, and grassland size turned out to be had a positive spatial spillover effect on housing prices. In addition, only very proximity to cultivated land is seen as welcome features by house buyers. This result implies that the value of forest, wetland, and grassland is well reflected in the urban housing market, while the value of cultivated lands less reflected in the urban housing market. Our findings also urge the Chinese government to establish differentiated ecological lands protection policies mixed with market-oriented mechanisms for ecological lands and government-oriented compensation for cultivated land to ensure a continuous supply of ecological lands. However, this study has several limitations. First, the spatial spillover effects of ecological lands are directly related to the ecological environment in which it is located (Du and Yuan, 2015). For example, in eco-cities where the ecological supply is relatively sufficient, the spatial spillover effects of ecological lands on housing prices will be

5.3. Valuing spatial spillover effects of different ecological lands Following the methods described in Eqs. (3) and (4), we calculated the spatial spillover effects of different ecological lands using their minimum, mean, and maximum values. The spatial spillover effects are summarized in Table 4. As expected, different ecological lands exerted different spatial spillover effects on housing prices. The ecological lands variable with the strongest positive spatial spillover effect was the forest. The housing prices will constantly rise by 0.054% of the average square meter price for the 1% increase in a forest size. This result indicates the value of forest can be captured in the housing prices and increasing the forest size may continuously improve the resident's well-being and housing prices. Reverse thinking, it is more direct approach to improving residential welfare by ensuring the continued supply of existing forest through implementing market-oriented forest protection policy. Wetland plays an important role in explaining the differences in housing prices. In fact, the positive spatial spillover effect of proximity to wetland is estimated to be one of the strongest. The positive spatial spillover effect of proximity to wetland was 0.016% of the average square meter price based on the current average in the study area. Its estimated range was from −0.054% for high wetland accessibility to 0.038% for low wetland accessibility. That finding indicates that proximity to wetland where low wetland accessibility may raise the housing prices, while proximity to wetland where high wetland accessibility may not. Regarding wetland size, the positive spatial spillover effect of wetland size was constant 0.005% of the average square meter price. This result means that increasing the wetland size may bring a continuous increase in housing prices. Such information could be useful for urban planners and land designers. Compared to forest and wetland, the positive spatial spillover effect of grassland is relatively weak, but it can be also captured by the urban housing market. Based on the average grassland size, we found that an additional percent of grassland size translated on average into an increase in housing prices by 0.001%. However, the spatial spillover effect of grassland size becomes negative for high grassland visibility. That means that residents tend to consider more increments in the size of the smaller grassland than in lager grassland spaces, where an additional increase in grassland size may not be noticed at all. For city planning, our result imply that policies should aim at improving grassland visibility in areas where they are especially scarce. Regarding cultivated land, proximity to cultivated land may raise the housing prices by 0.074% per square meter based on the minimum of distance from cultivated land. However, for the average or maximum of distance from cultivated land. An additional percent decrease in distance from cultivated land translated into a decrease in housing prices by 0.072% and 0.119%. In the case of Wuhan, 97.73% of the residents still suffer the negative spatial spillover of proximity to cultivated land. For the negative spatial spillover of relatively proximity to 8

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weakened. However, in industrial cities where the ecological supply is insufficient, the spatial spillover effects of ecological lands on housing prices will be strengthened. Therefore, the conclusions of this study apply only to the cities with an ecological environment similar to that of Wuhan. Second, only cultivated land very proximity to urban residential area has a significant, positive spatial spillover effect on housing prices, while other cultivated land may not. Many possible factors can explain this finding. For instance, a likely explanation is related to the agricultural farming method. Specifically, the cultivated land in our study area is mainly used for highly intensive agricultural production, which can cause many negative spatial spillover effects, such as water and soil pollution (Glebe and Thilo, 2007; Tan, 2011). Fortunately, ecological agriculture, which can provide angling opportunities and scenic vistas to urban residents and effectively reduce the emissions of pollutants such as waste gas and wastewater (Sanders, 2010), has successfully attracted the attention of the Chinese government (Overall plan of ecological civilization system reform in China, 2015). Hence, we expect that the negative spatial spillover effect may disappear with the practice of ecological agriculture and aesthetic rural construction.

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