The influence of Beijing rail transfer stations on surrounding housing prices

Habitat International xxx (2016) 1e10

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The influence of Beijing rail transfer stations on surrounding housing prices Xuezhen Dai*, Xin Bai, Min Xu School of Management Science & Engineering, Central University of Finance & Economics, Beijing 100081, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 7 November 2015 Received in revised form 1 January 2016 Accepted 26 February 2016 Available online xxx

This paper focuses on the resale prices of housing around the 10 rail stations opened in the last four years in Beijing. The premium effect of rail transit on surrounding residences is compared and analyzed using the hedonic price model. The research shows that the scope of influence, the degree of influence, the range of influence of negative externality and negative impact of transfer stations are greater than that of non-transfer stations, and the difference between transfer stations and non-transfer stations is much greater in the suburbs than that in the city. This research can assist with policy making, subway station planning, housing investment and real estate development. © 2016 Elsevier Ltd. All rights reserved.

Keywords: Transfer station Non-transfer station Rail transit Housing price

1. Introduction Many cities are confronting problems e such as traffic congestion, air pollution and sprawling development patterns e brought about by rapid population growth. The construction or extension of a rapid rail transit system is one way to mitigate these problems, for it is fast and efficient. Pollution is minimized, growth is concentrated in a small area and energy is saved. These factors draw residents to live near rail stations and attract private and public sector transportation investment (Li, Luan, Yang, & Lin, 2013). Conventional urban economic theory e derived from utility maximization e suggests a relationship between improvements to transport systems and property values. Transit stations offer better accessibility to destinations such as central business districts, employment, health care, schools and colleges, and entertainment and recreation, thus reducing commuting time and offering tangible benefits such as a less stressful commute. In addition, the neighborhood commercial services in station areas, such as retail establishments, also may benefit nearby residents eregardless of whether or not they ride the train. The benefits of accessibility and convenience will be priced into nearby real estate, for those want to live, work or do business near transit stations will bid up home and land prices accordingly. * Corresponding author. E-mail addresses: [email protected] (X. Dai), [email protected] (X. Bai), [email protected] (M. Xu).

Previous research has empirically investigated the effect of rail station proximity on property values from the aspect of time or space and confirms that when the rail transit station's location or distance to the same orbital station is different, its impact on housing prices is quite different. And the influence on home prices changes depending on whether it is the planning period, the construction period, or whether it is early or late in the operational period. The research methods adopted include a single factor analysis using the travel cost model (TCM) (Weinstein et al., 2002), and a multi-factor analysis using the hedonic price model (AlMosaind, Dueker, & Strathman, 1993; Rosen, 1974; Gu & Guo, 2008; Debrezion, Pels, & Rietveld, 2011; Feng, Li, & Zhao, 2011; He & Jing, 2013; Su, Zhu, Zheng, Wang, & Chen, 2015). Some scholars introduced econometric analysis into the hedonic price
, model (Hui, Chau, Pun, & Law, 2007; Munoz-Raskin, 2010; Dube
riault, & Des Rosiers, 2013; Efthymiou & Antoniou, 2013). The Overall, the most commonly used model seems to be the hedonic price model which is based on the ordinary least square (OLS) method. Initially, the motivation of the scholars’ study was from the rail transportation. For instance, the fact that a subway station causes fluctuation of housing prices of the surrounding areas attracted remarkably interesting (Dewees, 1976; Webber, 1976; Bae, Jun, & Park, 2003), aiming to define the theories and analysis methods in order to understand the transport/land use system behavior (Henneberry, 1998; Hui et al., 2007; Feng et al., 2011). Afterwards, more studies tend to solve the funds shortage of rail transit

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construction and operation (Kim & Zhang, 2005; Zheng & Liu, 2005; Jiang, Ye, & Wang, 2007; Loomis, Santiago, & De Jesus, 2012; Wang, Yun, & Guo, 2015). In order to further investigate the law and mechanism of the influence of rail transportation on the housing prices, and propose a verifiable hypothesis in theory, depending on the case analysis, the scholars made studies on the
et al., 2013; significance and difference of this influence (Dube Efthymiou & Antoniou, 2013; Jiang et al., 2007; Liu, Wu, & Wang, 2015). Of course, there are studies from some scholars aiming for solving practical problems and providing references for the policy, such as helping the house developers and buyers solve their problems on investment and purchase decision (Su et al., 2015) and providing policy supports for the urban land planning, feasibility analysis of rail transportation and tax fairness problems etc (Gu & Guo, 2008; Pagliara and Papa, 2011; Wang, Feng, & Gan, 2014). The motivation of this paper comes from the interest on the contrastive study of transfer stations and non-transfer stations. This paper aims to support an integrated urban transport planning decision process. At present, much of the research has been limited to the analysis of the significance of the influence of rail transit in terms of studying objects without distinguishing between transfer stations and non-transfer stations. Because of rapid urbanization throughout China, the difference in the degree of influence, the realm of influence and the influence mechanism in different regions between transfer and non-transfer stations is of interest to government departments and the real estate industry. Compared with non-transfer stations, transfer stations formed by cross rail transit lines have higher transportation accessibility and more traffic. Transfer stations can better guide passenger flow and reduce congestion as well as promote the surrounding areas of commercial and residential construction, encourage the formation and development of a city sub-core district, and reasonably guide the urban structure development. Since transfer stations have higher construction and transportation security facilities costs derived from passenger transfer flow, it is particularly important to accurately evaluate and internalize the external benefits in order to compensate for construction and operating costs. In addition, the negative effects due to passenger transfers e such as congestion and noise e are more serious. And a mixed population also may lead to higher crime rates and safety concerns. Therefore, it is important to learn how transfer stations and non-transfer stations differ in influence. Are the influences of transfer stations on surrounding housing prices positive or negative? How large of an area is influenced? What are the differences between transfer stations and non-transfer stations? All these questions will be addressed in this paper. At the same time, the current research about the impact of rail transit on surrounding housing prices seems to be concentrated on one or several rail transit lines in particular. Few studies have tried to research the influence of a rail transit network on housing prices from the perspective of the whole urban environment. Compared with one rail transit line, the study of an entire urban rail transit network is more representative and may contribute to the comprehensive evaluation of the benefit of the rail traffic network. It also can provide a point of reference for rail transit construction planning and urban development. As the capital of China, Beijing has a subway system that has become the people's main form of transportation. Planning for the Beijing rail transit system began in 1953, and in 1971, it opened its first route: subway line 1. By January 2014, there were 17 operating routes in Beijing covering 11 municipal districts. In all, there were 273 operating stations (including 40 transfer stations) for a total length of 465 km. The Beijing subway is the oldest subway in China. It also has the highest passenger capacity, making it one of the

largest city subway systems in the world. Beijing plans to build a “center city chessboard type þ new town radiation type”1 rail transit network with a total mileage of more than 1010 km by 2020. Therefore, this paper will focus on the impact of rail transit transfer stations and non-transfer stations in Beijing. 2. Literature review Since the 1970s, as a result of the construction of rail transit in Washington, D.C., Atlanta, San Francisco and other places, scholars in many countries have studied the relationship between public transportation and real estate prices using econometric methods (Dewees, 1976; Webber, 1976). In recent years, with the accelerated pace of rail transit construction throughout the world, this field has become an important topic of study. Research has concentrated mainly on whether or not rail transit has a value-added effect on surrounding real estate and the differences of a value-added effect on space and time. More in-depth research has explored the variation by local context of the value-added effect. In an effort to determine whether or not rail transit has a valueadded effect on surrounding real estate prices, empirical studies have been conducted. Data were analyzed from Portland, Ore. and Washington, D.C., in the United States; Toronto and Montreal in Canada; Amsterdam in the Netherlands; Seoul in South Korea and other cities. Researchers found that, except for some special reasons, rail transit had an obvious value-added effect on land values and real estate values around the stations, and the value-added effect decreased with the increase of distance (Bajic, 1983; AlMosaind et al., 1993; Benjamin, Chinloy, & Sirmans, 2000; Debre
et al., 2013). At the same time, some studies zion et al., 2011; Dube have demonstrated that rail transit has an obvious negative effect on surrounding housing prices mainly because of the attendant noise (Webber, 1976; Baldassare, Knight, & Swan, 1979). In recent years, research conducted by Efthymiou and Antoniou (2013) suggested that the positive or negative impact depends on the type of transportation system. They studied Athens, Greece using a hedonic price model based on the ordinary least squares (OLS) method and a spatial econometric model. They found that a subway, tram, suburban railway station and bus station all had positive effects on housing prices, while the influences of an electric urban railway (ISAP), a national railway station, an airport and a shipping port on housing prices were negative e which was attributed to the resulting noise. In researching Beijing, Shanghai, Shenzhen and other places, Chinese scholars mostly have agreed that the positive effects of rail transit on housing prices diminished with an increase of distance from the station (Nie, Wen, & Fan, 2010; Feng et al., 2011; He & Jing, 2013; Zhang, Li, & Duan, 2012). Further, an inverted “U” was used to illustrate that housing prices along the rail transit expanded quickly at first and then the rate of acceleration shrank in recent years (Su et al., 2015; Liu et al., 2015). Notably Wang et al. (2014) researched the Shenzhen Longgang Line Suburban County and found that suburbs along the Longgang Line experienced higher housing prices, while there were lower housing prices along that same line in the exurbs. In addition, some studies also suggested that rail transit had no significant effect on housing prices. Hui et al. (2007) adopted the hedonic price model with an additional spaceweighted matrix so they could study the impact of the Hong Kong subway on surrounding housing prices. However, they found that the effect was not obvious because Hong Kong residents seemed to care more about the surrounding environment.

1 Beijing urban rail transit network planning (2011e2020) (http://www.tranbbs. com/news/cnnews/news_68545.shtml).

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In regard to the time difference of the impact of rail transit on real estate prices, Pagliara and Papa (2011) used GIS technology to analyze the impact of the subway system in Naples, Italy, on housing prices based on time series data. The numbers showed that for different rail transit routes, the effects on housing prices in different periods varied. Moreover, even for the same rail transit route, the effects on housing prices in different periods also were different. Henneberry (1998) studied the Sheffield light rail built in 1995 and found that in 1988 when the light rail was in the planning stage, the surrounding land values rose by 4%. However, by 1993 the increase was very slight. Al-Mosaind et al. (1993) studied the MAX light rail system in Portland, Ore. and found that in the two years after the light rail opened, housing prices within 500 m of a station were 10.6% higher than that of other regions. Bae et al. (2003) studied South Korea and found the influence of subway line 5 in Seoul began losing its significance after three years. By studying North America, Loomis et al. (2012) found that the price premium was different between the construction period and the operation period. By researching the Beijing subway, Chinese scholars found that the influence of Beijing subway line 13 on suburban housing prices two years after opening was no longer significant (Gu & Guo, 2008), while the influence of subway line 4 lasted for four years (He & Jing, 2013). In addition, Nie et al. (2010) studied the first phase of the Shenzhen metro and found that during the construction of the subway, housing prices were negatively impacted while in the operating period, the impact on housing prices was positive. This was especially noticeable in the second year after opening to traffic when the surrounding housing prices appreciated sharply. However, Wang et al. (2015) found that the impact of the Tianjin rail transit on housing prices began when the planning was announced. In addition, the degree of influence in the operational period was bigger than that in the construction period, while the latter was bigger than that in the planning period. Further, they found that the area of influence was larger in the construction period than that in operational period which was larger than that in the planning period. In regard to the spatial scope of the impact of rail transit on real
et al. (2013) used the hedonic price model with estate prices, Dube the difference-in-differences (DID) technique to analyze the influence of rail transit on real estate prices in Montreal, Canada. They found a premium on real estate prices only in certain areas. Cervero and Duncan (2002) performed an empirical analysis on Diego County, California, and found that the range of influence was 500 me800 m. Kim (1995) studied South Korea and divided the affected area into three levels: the major area (within 200 m), the second major area (200 me500 m) and the indirect impact area (500 me1000 m). By researching the first phase of the Shenzhen metro, subway lines 1, 4 and 13 in Beijing,, metro line 1 in Shanghai, and subway lines 1 and 2 in Nanjing, Chinese scholars reported different scopes of influence such as 700 m (Nie et al., 2010), 1100 m (He & Jing, 2013), 500 m (He & Zheng, 2004), 500 m to 1000 m (Zhang et al., 2012), 1600 m (Jiang et al., 2007) and 1500 m (Liu et al., 2015). Wang et al. (2015) found that for the Tianjin metro lines 1, 3 and 6, the scope of influence in the planning period and operational period was 300 me500 m while in the construction period it was 600 me800 m. In regard to local variation of the impact of rail transit on real estate prices, Kim and Zhang (2005) found that the value-added benefits of Seoul's rail transit had a different impact depending on the area. Previously Won and Son (1993) found that the influence of Seoul's station on land values in the central business district were bigger than in other areas. Munoz-Raskin (2010) studied Bogota, Colombia's capital, and found the rail transit had little

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impact on low-income areas, but the impact on high-income areas was significant. The empirical results from Naples, Italy, reported by Pagliara and Papa (2011) also showed that the impact time, area and the degree of rail transit was different based on region. Chinese scholars studied Hong Kong, Taipei and Kaohsiung and found many differences in the time effects, impact area and degree of influence (Shyr, Andersson, Wang, Huang, & Liu, 2013). By studying the Batong line on the Beijing subway, researchers found that the influence of rail transit on housing prices in the suburbs was bigger than that in the central business district (Gu & Guo, 2008). Research in southern Beijing showed that in the short term before the rail transit opened, housing prices in urban areas rose more than in the suburbs. After the rail transit began operations, housing prices in urban areas rose more slowly than in the suburbs. Longer term, the increase of housing prices in urban areas was much less than that in the suburbs (Su et al., 2015). Developed countries started earlier in the urbanization process with rail transit construction. This laid the foundation for the related research fields, and the research contents have had a great influence on the breadth and depth of study. The research area contained North America, South America, Europe and Asia e specifically Portland, Ore. and Washington, D.C. in the U.S.; Toronto and Montreal in Canada; Amsterdam in the Netherlands; Bogota, Colombia; Athens, Greece; Naples, Italy; Sheffield, U.K.; and Seoul, South Korea. Research methods generally adopted the hedonic price model or combined the econometric methods with GIS technology on the basis of the hedonic price model for analysis. In China, study in this field started relatively late, so the breadth and depth of the research is relatively insufficient e especially in regard to the time differences and commercial real estate. But in recent years, as the pace of urbanization and the development of rail transit has quickened, relevant empirical analysis is available, mainly involving cities such as Hong Kong, Taipei, Kaohsiung, Beijing, Shanghai, Chongqing, Shenzhen, etc. The research methods mainly followed those used by researchers in other countries or by researchers in related fields. Overall, the relationship between the rail traffic facilities and surrounding housing values appears to be of great interest in China and abroad. While most studies focused on one or several lines, little research has been conducted from the perspective of an entire city in terms of the influence of rail transit on the housing prices along the line. And there does not seem to be a published journal article that discusses the impact of a transfer station e in comparison with a non-transfer station e on housing prices. Therefore, this study focuses on Beijing and its well-developed rail transportation and the spatial variation by local context in relationship to the influence of rail transfer stations and non-transfer stations on housing prices. 3. Model construction 3.1. Object of study As discussed, the influence of rail transit on housing prices is bigger within three to four years after a line opens (Henneberry, 1998; Bae et al., 2003; He & Jing, 2013). Some long operated subway lines, such as Beijing's line 1 which opened in 1971 and line 2 which opened in 1984 since the corresponding historical data are nearly impossible to obtain. We also exclude subway lines at the planning stage or in construction, because the impact on housing prices has not been fully revealed. Thus, for this study, 10 rail transit routes in Beijing open to traffic during the period of 2000e2014 were selected: line 6, line 8 (second phase), line 9, line 10 (second phase), line 14, line 15, the Daxing line, the Fangshan line, the Changping line and the Yizhuang line. These lines have a total of

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Fig. 1. The study area.

100 non-transfer stations and 22 transfer stations (Fig. 1). Based on the finding that the maximum scope of rail transit's influence on real estate prices was 1600 m (Jiang et al., 2007) and considering that the influence of a transfer station may be greater than that of the non-transfer station, this study preliminarily define the area of interest to within a radius of 2000 m, allowing for the availability of real estate data for different uses (the lack of real estate data for commercial and office use). Also, only data from home resale e rather than from new home construction e were analyzed. Therefore, this study focuses on home resale around the stations located outside the 10 rail transit lines opened in the past four years in Beijing. Specifically, the research area involve 10 districts: Xicheng, Dongcheng, Haidian, Chaoyang, Fengtai, Shunyi, Changping, Daxing, Tongzhou and Fangshan in Beijing. The transfer stations are located in six districts: Haidian, Xicheng, Dongcheng, Chaoyang, Changping and Fengtai. Due to large differences in economic and cultural development, location conditions, construction level of infrastructure, and planning in different administrative regions, it is necessary to explore the influence of transfer stations and nontransfer stations on housing prices in various administrative regions.

3.2. Model and variable determination The hedonic price model is widely used when dealing with the relationship between heterogeneous product characteristics and prices. Thus hedonic regression analysis is used in this study since this method isolates the factors that might affect property values and estimates the influence on property value attributed to proximity to rail stations (Rosen, 1974; Can, 1990). The model has four main forms:

P ¼ a þ b1 X1 þ b2 X2 þ / þ bn Xn þ ε

(1)

LnP ¼ a þ b1 LnX1 þ b2 LnX2 þ / þ bn LnXn þ ε

(2)

P ¼ a þ b1 LnX1 þ b2 LnX2 þ / þ bn LnXn þ ε

(3)

LnP ¼ a þ b1 X1 þ b2 X2 þ / þ bn Xn þ ε

(4)

Here P represents housing prices, Xi represents the housing characteristic variable i (¼1,2,/) among all housing variables, a is the constant term, bi is the coefficient to be estimated, and ε is the error term. Eq. (1) is a linear model; the regression coefficients are the implicit price corresponding to the characteristics, but it cannot show the law of diminishing marginal utility. Eqs. (2) and (3) have the limitations that variables cannot be zero. Eq. (4) is a semilogarithmic model with the logarithm of the dependent variable which is marginally increasing. According to previous research (Rosen, 1974; Can, 1990; He & Jing, 2013; Zhang et al., 2012) and in light of the difficulty in obtaining data, the researchers chose the unit housing price (yuan/ m2) as the dependent variable P of the model because the unit price can reflect the implicit prices of the specific residential characteristics more precisely. In addition, four residential characteristics e traffic facilities, location, neighborhoods and construction characteristics e are used as independent variables. Table 1 provides a list of the 21 independent variables in the four vectors, along with the definition, unit of measurement and expected symbol for each variable. The four vectors of independent variables that influence property values are described in detail below: (1) In terms of traffic service facilities, the variables of interest are whether or not it is a transfer station (INTERCHANGE), the distance to the nearest subway station (D_METRO), whether there are other metro stations within the scope of 1 km (METRO), the distance to the nearest bus station (D_BUS) and the number of bus stations within the scope of

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Table 1 Summary of the meaning of residential characteristic variables and expectations. Type of variables

Variable name

Traffic service facilities

INTERCHANGE D_METRO METRO D_BUS N_BUS AREA DECORATION DIRECTION FLOOR ROOM VOLUME GREEN AGE D_CENTER RING3-RING6

Construction characteristic variable

Location characteristic variable

Neighborhood characteristic variable

Unit Meaning

Sign prediction

Whether or not it is transfer station (transfer station 1, otherwise 0) The distance to the nearest subway station Whether there is another metro station within 1 km (exist 1, otherwise 0) m The distance to the nearest bus station a The number of bus stations within 1 km 2 m Construction area The decoration situation (fine decoration 1, otherwise 0) Orientation (south and north are transparent 2, facing south 1, otherwise 0) % Floor proportion (floor/total floor) a The number of bedrooms Plot ratio % Greening rate year The age of the building: 2014 minus the actual built years (year) km The distance to Tiananmen located within m the 3rd ring, between the 3rd and 4th ring, between the 4th and 5th ring, between the 5th and 6th ring (yes 1, otherwise 0) NORTH located in the northern area of the city (yes 1, otherwise 0) D_HOSPITAL m Distance to the nearest hospital D_PARK m Distance to the nearest park D_RESTAURANT m Distance to the nearest restaurant D_SCHOOL m Distance to the nearest middle and primary school D_SHOPPING m Distance to the nearest mall or supermarket m

1 km (N_BUS). The first three variables highlight the relevant properties of the subway. D_METRO, D_BUS and N_BUS are three continuous variables and the rest are dummy variables. It is predicted that D_METRO will have a negative effect on housing prices, while the rest of the variables will have positive impact. (2) For the construction characteristic variables, the construction area (AREA), the decoration situation (DECORATION), orientation (DIRECTION), the number of bedrooms (ROOM), floor proportion (FLOOR), plot ratio (VOLUME), greening rate (GREEN)2 and the age of building (AGE) are selected. DECORATION and DIRECTION are dummy variables, and the rest are continuous variables. It is predicted that VOLUME and AGE will have a negative relationship with housing prices. No predictions are made for FLOOR or DIRECTION. The remaining variables are predicted to have a positive correlation. (3) Three location characteristic variables e the distance to Tiananmen (D_CENTER), the circle line (RING3-RING6), and whether the line is located in the northern part of the city (NORTH) e are studied. Note that economic development in the northern area of Beijing is much higher than that in southern city. D_CENTER is a continuous variable and the rest are dummy variables. It is predicted that D_CENTER will have a negative effect on housing prices, and the rest will have a positive effect. (4) For the neighborhood characteristic variables, the effects of municipal public service facilities on housing prices are considered. Thus the distance to the nearest hospital (D_HOSPITAL), park (D_PARK), restaurant (D_RESTAURANT), school (D_SCHOOL) and commercial facilities (D_SHOPPING) are accounted for. All of these variables are continuous, measured in meters. No prediction is made in regard to the

2 The floor proportion is the ratio between the floor of the residential unit and the total floor. The plot ratio is the ratio of the total construction area and the area of the ground in a residential area. The greening rate is the sum of all green spaces in residential areas. The lower the plot ratio, the higher the green rate which may indicate more comfortable lives. Many of the variables were developed because the housing units under study are high-rise apartments e the most common housing type in China.

þ e þ e þ þ þ þ uncertain uncertain e þ e e þ þ uncertain e e e e

influence of D_HOSPITAL, but the other influences are predicted to be negative. Many of the items of interest are coded as dummy variables, so the functional forms chosen are a linear model (Eq. (1)) and a semilogarithmic model (Eq. (4)). In theory, the regression coefficient in the linear model can reflect the absolute value of the implicit price of each variable more intuitively. The regression coefficients of the semi-logarithm represent the price elasticity of the corresponding characteristics, namely the percentage changes in a dependent variable caused by a one unit change in the characteristic variable. This shows the degree of contribution of the characteristic implicit in the housing prices. This model seems to be practical because it reduces the scale of the dependent variable. In comparison with a linear model, the semi-logarithm model can improve heteroscedasticity.

3.3. Data sources The data for this study come from records of existing homes listed on the Beijing real estate transaction management network sites3 from Jan. 10, 2014 to Jan. 31, 2014. The information regarding neighborhood and the construction characteristics of the dwelling unit e including residential unit price, floor and total floors, building area, direction, decoration, dwelling size, age, volume, green rate and so on e are examined. Data related to distance and infrastructure come from Google Maps, with the shortest walking or traveling distance noted. The sample contains 2964 housing units including 598 residential units around a transfer station and 2366 residential units around non-transfer stations. Table 2 provides descriptive statistics for the dependent variables, i.e., the mean, maximum, minimum and standard deviation. The mean value of the residential unit price is 39,223.21 yuan/m2, and the mean distance from the sample housing to the subway station is 876 m.

3 Network sites of data source: SouFuncom(http://bj.soufun.com/), The Beijing real estate transaction management network (http://www.bjjs.gov.cn/tabid/2207/ Default.aspx), Homelink (http://beijing.homelink.com.cn/ershoufang/).

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Table 2 Statistical summary of data description. Variable

N

Minimum

Maximum

Mean

Standard deviation

Residential unit price INTERCHANGE D_METRO METRO D_BUS N_BUS AREA DECORATION DIRECTION FLOOR ROOM VOLUME GREEN AGE D_CENTER RING3 RING3-RING4 RING4-RING5 RING5-RING6 NORTH D_HOSPITAL D_PARK D_RESTAURANT D_SCHOOL D_SHOPPING Effective N (list state)

2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964 2964

9302 0 75 0 21 2 18 0 0 0.0345 0 1.000 10.00 0 3.6000 0 0 0 0 0 48 47 9 45 10 e

95,486 1 1999 1 837 10 140 1 2 1.0000 5 10.0000 66.00 56 40.1000 1 1 1 1 1 2574 9800 2054 2292 2300 e

39,223.21 0.2 875.79 0.16 301.79 7.34 87.15 0.62 1.09 0.5674 2.11 2.477564 33.6315 12.52 16.3933 0.19 0.27 0.21 0.30 0.59 910.71 1129.89 665.93 720.80 556.65 e

13,600.686 0.401 464.827 0.366 143.513 1.821 29.045 0.489 0.910 0.2733 0.732 1.3058723 7.10535 7.142 8.9255261 0.389 0.444 0.407 0.460 0.492 486.529 820.845 383.935 425.766 329.139 e

4. The influence of a Beijing rail transit transfer station on surrounding housing prices

Table 4 Regression results after distance variable newly added. Variable

4.1. Calculation of the scope of influence In order to calculate the scope of influence of a rail transfer station on surrounding housing prices, the variable “INTERCHANGE” is removed and the dummy variables D2-D18 are joined to represent the different ranges of the distance from a residence to the subway station (1800e2000 m as a reference). The specific meaning of each variable is shown in Table 3. The semi-logarithm model is selected and variables D2-D18 are chosen instead of “INTERCHANGE”, and then the transfer station status and non-transfer station one is entered into Eq. (4) for a regression analysis, respectively. Table 4 shows the results of the regression after new distance variables are added (sig. <0.10). The second column shows the regression results for housing transactions near transfer stations, while the third column shows the regression results for housing transactions near non-transfer stations. The coefficients of D2-D8, D12 and D14 in the second column of Table 4 are statistically significant, so it seems that a transfer station have a significant impact on surrounding housing prices within ranges of less than 800 m and 1000e1400 m. Therefore, it seems that the scope of influence of a transfer station is within 1400 m.

Table 3 Distance variables newly added to study the scope of influence. Characteristic variable

Distance to the nearest subway station

D2 D4 D6 D8 D10 D12 D14 D16 D18

<200 m (yes 1, otherwise 0) 200 - 400 m (yes 1, otherwise 0) 400 - 600 m (yes 1, otherwise 0) 600 - 800 m (yes 1, otherwise 0) 800 - 1000 m (yes 1, otherwise 0) 1000e1200 m (yes 1, otherwise 0) 1200e1400 m (yes 1, otherwise 0) 1400e1600 m (yes 1, otherwise 0) 1600e1800 m (yes 1, otherwise 0)

D2 D4 D6 D8 D10 D12 D14 D16 D18

Transfer station

Non-transfer station

B

Sig.

B

Sig.

0.142 0.105 0.094 0.102 0.065 0.190 0.001 0.027 0.231

0.027 0.006 0.089 0.006 0.249 0.006 0.001 0.680 0.984

0.075 0.067 0.034 0.098 0.078 0.042 0.060 0.060 0.019

0.026 0.002 0.014 0.000 0.000 0.170 0.114 0.109 0.464

2

R ¼ 0:625 F ¼ 34.135 Sig. ¼ 0.000

2

R ¼ 0:666 F ¼ 153.072 Sig. ¼ 0.000

The coefficients of D2-D10 in the third column of Table 4 are statistically significant, so a non-transfer station appears to have e to some extent e an effect on surrounding housing prices within 1000 m. Hence the scope of influence of a transfer station on surrounding housing prices reaches 1200e1400 m, while the scope of influence of the non-transfer station is within 1000 m. There are two main reasons for this. One is that the transfer stations have a greater influence on several subway lines, so the high degree of traffic accessibility improves its radiation scope on surrounding housing prices. The other is that since a large number of commercial housing units are located near the transfer stations, residential buildings have to spread outward, thereby expanding the area of influence. 4.2. Aggregate analyses In order to examine the effects of a transfer station and a nontransfer station in detail, the housing samples that are more than 1000 m away from a non-transfer station and more than 1400 m away from the transfer station are eliminated from consideration.

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Thus 2105 samples fall within the scope of influence, including 535 residential unit samples around transfer stations and 1570 housing unit samples around non-transfer stations. A linear model (Eq. (1)) is selected for regression analysis using the overall data and the residential unit sample data around a transfer station or that of a non-transfer station, respectively. The results are shown in Table 5 in columns 2, 3 and 4. The dummy variable “INTERCHANGE” (whether or not the station had an interchange) only seems to make sense when a regression analysis is performed on the overall data (column 2). Its value represents the difference in influence of a transfer station and a non-transfer station on surrounding housing prices without considering other factors. As shown in Table 5, three regression analyses explain the impact of rail transit on surrounding housing prices. The impact for the data overall is 70.1%; the impact for a transfer station is 59.4%; and the impact for a non-transfer station is 68.1%. Note that most of the variables are statistically significant at the 10%, 5% and 1% level. The first finding is that there is a difference between the influence of a transfer station and a non-transfer station on surrounding housing prices. In column 2 of Table 5, the regression coefficient “INTERCHANGE” in the overall data is 3368.160, showing that, on average and without considering other factors, the housing prices around the transfer station are 3368.16 yuan/m2 higher than around a non-transfer station. Second, both a transfer station and a non-transfer station have strong value-added effects on surrounding housing prices, with the transfer stations having a greater influence. The coefficients of “D_METRO” in columns 2, 3 and 4 are 0.274, 0.965 and 0.230, respectively. This seems to illustrate that when the distance to a subway station is reduced by 100 m, the housing prices around a transfer station increase 96.5 yuan/m2 on average e higher than the overall market which is 27.4 yuan/m2, and much higher than the

Table 5 Linear model regression results. Variable

Overall data B

Sig.

B

Sig.

B

Sig.

(Constant) INTERCHANGE D_METRO METRO D_BUS N_BUS AREA DECORATION DIRECTION FLOOR ROOM VOLUME GREEN AGE D_CENTER RING3 RING3-RING4 RING4-RING5 RING5-RING6 NORTH D_HOSPITAL D_PARK D_RESTAURANT D_SCHOOL D_SHOPPING

17,209.984 3368.160 0.274 1515.159 2.986 211.524 6.728 35.065 1420.739 101.385 654.406 617.445 121.085 114.214 186.348 29,390.893 18,554.588 12,192.280 3908.714 9338.599 0.826 0.912 0.353 0.068 0.729

0.000 0.000 0.038 0.000 0.003 0.006 0.363 0.904 0.000 0.814 0.019 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.009 0.000 0.333 0.043 0.106

35,309.79 e 0.965 15.590 8.502 187.052 10.236 280.356 1965.772 1624.562 1014.09 669.231 102.719 220.607 643.213 12,850.306 e 4106.731 24,809.87 11,189.598 2.043 1.039 1.883 2.150 0.272

0.000 e 0.028 0.988 0.008 0.458 0.615 0.747 0.000 0.258 0.186 0.014 0.045 0.002 0.000 0.000 e 0.071 0.000 0.000 0.034 0.013 0.095 0.072 0.860

18,152.268 e 0.230 1978.387 0.722 135.276 1.944 236.299 1333.445 59.690 614.884 215.157 98.772 117.663 13.464 28,786.699 17,936.346 11,447.455 3508.882 9246.327 0.405 1.091 0.147 0.040 0.605

0.000 e 0.002 0.000 0.008 0.093 0.805 0.434 0.000 0.891 0.038 0.091 0.000 0.000 0.080 0.000 0.000 0.000 0.000 0.000 0.225 0.000 0.701 0.009 0.197

2

R ¼ 0:701 F ¼ 290.528 Sig. ¼ 0.000

Transfer station

2

R ¼ 0:594 F ¼ 40.639 Sig. ¼ 0.000

Non-transfer station

2

R ¼ 0:681 F ¼ 221.090 Sig. ¼ 0.000

7

residential housing around non-transfer stations which is 23.0 yuan/m2. The reason is that transfer stations have higher transportation accessibility than non-transfer ones, which can provide better travel convenience and efficiency for surrounding residents. Also, they can improve the quality of the surrounding residential area, and entice buyers to pay higher prices. At the same time, the optimization of a transfer station's surrounding living environment resulting from higher commercial enterprise concentrations may have exacerbated the trend. Third, the influence of “METRO” on housing prices around a transfer station is not statistically significant, while it seems to have a marked effect on housing prices around a non-transfer station. The regression coefficient in the fourth column shows that a residential unit price is higher by 1978.39 yuan/m2 when it is near a non-transfer station while the coefficient of the variable in the third column is not statistically significant, showing that a transfer station has less of an effect. The reason may be that the transfer function and efficiency improve the residential traffic accessibility to a greater degree, causing area residents pay less attention to travelling by other transit lines. Fourth, the coefficients of “D_BUS” in the second, third and fourth columns in Table 5 are 2.986, 8.502 and 0.722, respectively. This appears to illustrate that when the distance to a subway station is reduced by 100 m, the housing prices around the transfer station increase by an average of 850.2 yuan/m2. This is higher than the overall market which is 298.6 yuan/m2 and much higher than the increase around non-transfer stations which is 72.2 yuan/m2. The results seem to show the complementarity between the rail transit system and bus system: rail transport attracts longdistance passengers in the city, while the buses attract shortdistance passengers. Thus a rail transit station with or without transfers can't replace the bus system. Finally, the regression result of “N_BUS” in relationship to transfer stations is not statistically significant (the third column). But in relationship to non-transfer stations, the regression coefficient is 135.276 (the fourth column), showing that when the number of bus stations within 1 km increases by one, the housing prices around a non-transfer rail station will rise 135.28 yuan/m2. It may be that the residents living around a non-transfer station pay the same attention to other modes of transportation. In addition, some significant construction characteristics variables can be explained as follows: DIRECTION, VOLUME, GREEN and AGE are consistent with expectation which suggests that residents surrounding rail stations highly value environmental and residential quality. But the regression coefficients of AREA, DECORATION, FLOOR and ROOM are not significant showing that buyers do not pay attention to these different characteristics of the building. FLOOR has small impact on housing price. This may be because it does not reflect perfectly the residential floor location. Also it may be due to a complex relationship between floor and price. 4.3. Spatial differences According to the regression results of the second column in Table 4, a transfer station has a negative impact on surrounding housing prices within 200 m, contrary to the predicted outcome. In compare with the housing price in the range of 1800e2000 m, the housing price within the ranges of 200e400 m, 400e600 m, 600e800 m, 1000e1200 m and 1200e1400 m rises by 10.5%, 9.4%, 10.2%, 19% and 0.1%, respectively. However, the results are not significant within the range of 800e1000 m. This stands in contrast to the previous finding that “when the distance to a station increases, the housing prices will decrease gradually” (Alonso, 1964, pp. 50e55). Within a range of 200 m around a transfer station, the negative externality effect reduces the surrounding residential

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X. Dai et al. / Habitat International xxx (2016) 1e10

Thus within the range of 600 m around a non-transfer station, the influence on housing prices decreases as the distance increases. But within the range of 600e800 m, it rises and the coefficient reaches its maximum. This seems to illustrate the negative impact on residential values caused by noise, pollution, crowding, public security and other issues which almost disappear after 600 m. Compared with a non-transfer station, the negative externalities and negative impacts of transfer stations seem more obvious. The negative externality of a transfer station has a range of 800 m, while a non-transfer station has a range of only 600 m. After synthesizing the positive and negative effects, the impact of a transfer station on housing prices within 200 m is negative, while the non-transfer station still is positive. There appear to be two main reasons for this: One is that in comparison with a non-transfer station, a transfer station has more people flowing through the area, causing negative externalities such as traffic congestion, public security, noise and pollution. The other is that the transfer station can attract more office buildings, hotels, shopping malls and other commercial property near the station which further exacerbate the number of people flowing through the area and associated the negative externalities.

Fengtai). Table 6 lists the coefficients of variables related to rail transit which are statistically significant (<0.01). The results seem to show that the difference of influence of a transfer station and a non-transfer station on surrounding housing prices located in the suburbs is greater than that in the city. According to the variable “INTERCHANGE” all else being equal, compared with a non-transfer station, if the nearest station to a residence is a transfer station, then the housing prices within its scope of influence in the suburbs increase 6575.95 yuan/m2 on average. This is higher than the overall average of 3368.16 yuan/m2 (Table 5) and much higher than that in the Fengtai (3034.89 yuan/ m2) and the Haidian districts (2752.93 yuan/m2). However, the coefficients are not significant in the Xicheng, Dongcheng or Chaoyang districts. It should be noted that the geographical conditions and the economic and cultural development vary across administrative regions in Beijing. Thus the distribution of rail stations also is different. Therefore, the transfer stations in different areas have varying degrees of impact on surrounding housing prices. Nevertheless, the overall degree of influence of rail transit on surrounding residential prices located in a suburb is greater than that in an urban area. According to the variable “D_METRO” in the suburbs, when the distance to the station is reduced by 100 m, the housing prices on average increase 120.3 yuan/m2. This is higher than that in the Fengtai district (89.6 yuan/m2), the Haidian district (46.0 yuan/m2) and the Chaoyang district (48.3 yuan/m2), but there is no significant influence in the Xicheng or the Dongcheng districts. The reason may be that the urban rail transit is relatively advanced in these areas, and the density of the road network is higher, having less impact on the housing prices. But in the suburbs, the density of the rail transit road network is lower; therefore, improving the conditions of rail transit might dramatically change the location conditions which could have a greater positive impact on housing prices. And finally, the variable “METRO” is significant except in the Haidian district. This seems to suggest that although the transfer stations’ uplift function of traffic efficiency in some districts is limited, people still tend to focus on whether or not there are other subway stations nearby when buying a house. A dense traffic network increases the number of travel routes for residents, making travelling more convenient.

4.4. Variation by local context

5. Conclusions

Although 10 districts are included in the study area, five of the areas e Daxing, Fangshan, Tongzhou, Changping and Shunyi e are located in the suburbs. Since residential density is lower in the suburbs, the number of valid samples collected is limited, and the differences of neighborhood environmental characteristics are relatively small. Thus the data from the five aforementioned districts are combined into a category called “Suburbs”. A linear model (Eq. (1)) is selected for regression with Suburbs and each of the other five districts (i.e., Xicheng, Dongcheng, Chaoyang, Haidan,

In the past few years, with the development and improvement of the Beijing rail transit system and the prosperity of the housing market, home buyers seem to be concerned with the location of rail transit. Besides considering the convenience of rail transit stations, they also focus on the effects of unfavorable factors such as a crowded environment and noise. In recent years, real estate transaction information availability has improved significantly which makes it much simpler to study the impact of public transportation options on home prices.

quality. When the distance increases continuously, the transfer station has a statistically significant, positive influence on housing prices e even within the range of 1200e1400 m. Perhaps the results are not significant within the range of 800e1000 m because the negative external effect of a transfer station still exists in this distance which devalues residential home prices. According to the regression coefficients of a non-transfer station (D2-D10 in the third column of Table 4), the logarithm ratio of housing prices located in different distances within the range of 1000 m around the non-transfer station are:

LnP2 : LnP4 : LnP6 : LnP8 : LnP10 ¼ 0:075 : 0:067 : 0:034 : 0:098 : 0:078 Based on this, the price ratios of housing prices located in different distances around the non-transfer station become:

P2 : P4 : P6 : P8 : P10 ¼ e0:075 : e0:067 : e0:034 : e0:098 : e0:078 ¼ 1:077 : 1:069 : 1:035 : 1:102 : 1:081

Table 6 Regression results for each district by linear model. Variable

Xicheng

INTERCHANGE D_METRO METRO Other variable

e e e e 2294.162 2962.176 (The influence law is similar to the overall data) 2

R ¼ 0:360 F ¼ 6.406 Sig. ¼ 0.000

Dongcheng

2

R ¼ 0:549 F ¼ 7.763 Sig. ¼ 0.000

Chaoyang

Haidian

Fengtai

Suburbs

e 0.483 1253.416

2752.932 0.460 e

3034.886 0.896 483.832

6575.953 1.203 2877.989

2

R ¼ 0:580 F ¼ 55.046 Sig. ¼ 0.000

2

R ¼ 0:167 F ¼ 36.220 Sig. ¼ 0.000

2

R ¼ 0:661 F ¼ 73.791 Sig. ¼ 0.000

2

R ¼ 0:307 F ¼ 6.929 Sig. ¼ 0.000

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X. Dai et al. / Habitat International xxx (2016) 1e10

The present research shows that there is a clear difference in the effect of a transfer station and a non-transfer station on the resale prices for homes in the surrounding area. First of all, the sphere of influence of a transfer station is bigger, reaching from 1200 m to 1400 m, whereas a non-transfer station has an area of influence area of up to 1000 m. Second, both a transfer station and a nontransfer station have a value-added effect on surrounding residential prices, with transfer stations’ rate having a larger increase. When the distance to a subway station is reduced by 100 m, the residential unit price around a transfer station will rise 96.5 yuan/ m2 on average, which is higher than that of the overall market level (27.4 yuan/m2) and much higher than that around a non-transfer station (23.0 yuan/m2). Without considering other factors, the average housing prices are 3368.16 yuan/m2 higher near a transfer station in comparison to those near a non-transfer station. Third, both the range of influence of negative externality and negative impact of a transfer station are greater than that of a non-transfer station. The negative externalities range up to 800 m for a transfer station. After synthesizing the positive and negative influences, the impact on housing prices within 200 m is negative, while the negative impact area of a non-transfer station is only 600 m. After synthesizing the positive and negative influences, its impact on housing prices still is positive. Fourth, from the aspect of local variation, the impact of a transfer station and a non-transfer station on surrounding residential prices in the suburbs is greater than that in the urban core, and the value-added effect of rail transit on surrounding residential prices in the suburbs is greater than that in the urbanized areas. The results of this study can provide some new ideas for the comprehensive evaluation of urban rail transit cost-effectiveness. Specifically, in respect to policy making, there is a big difference in construction operation costs and external real estate benefits between a transfer station and a non-transfer station. Also, the external benefits are different in urban and suburban areas. Secondly, in respect to subway planning, government departments can make rational use of the externality effects of various types of rail transport stations and local variation, through reasonably locating transfer stations, and balancing the differences in housing prices between areas of a city with disparate home values, in the hope of sensibly developing an urban area. Finally, the results can provide a reference for housing buyers when they purchase homes and need to consider factors such as traffic, convenience, noise, crowding and local variation. It's complicated to quantitatively analyze the influence of rail transit on surrounding real estate prices. Because of the limitation of sample size, this study does not perform comparative analyses on the influence of a transfer station versus a non-transfer station on surrounding residential prices in different municipal districts. Leading the analysis of local variation is neither comprehensive nor specific, which is a limitation of this paper. At the same time, because of the diversity of the spatial distribution and dynamics of change of urban housing prices, as well as the limitation by data acquisition difficulty, a follow-up study will focus on: (1) comprehensively and objectively evaluating the influence of a transfer station and a non-transfer station on surrounding residential prices in the planning, construction, and various operational stages and (2) probing the impact of traffic on different uses of real property (such as office buildings, commercial real estate, etc.).

Acknowledgments This work was supported by “the Fundamental Research Funds for the Central Universities”.

9

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