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Research in Transportation Economics 90 (2021) 100844

Available online 21 April 2020 0739-8859/© 2020 Elsevier Ltd. All rights reserved.

Subway boosts housing values, for whom: A quasi-experimental analysis

Chuanhao Tian a, b, Ying Peng a, Haizhen Wen b, c, *, Wenze Yue a, Li Fang d

a School of Public Administration, Zhejiang University, Hangzhou, 310058, China b Center for Real Estate Studying, Zhejiang University, Hangzhou, 310058, China c Department of Civil Engineering, Zhejiang University, Hangzhou, 310058, China d Department of Urban and Regional Planning, Florida State University, Tallahassee, 32306, USA

A R T I C L E I N F O

Keywords: Property price Subway Difference-in-differences Temporal dynamics Heterogeneous sub-market

A B S T R A C T

This study investigates the property price premium brought by the opening of a subway to illustrate temporal dynamics and heterogeneous mechanism of property value effects. The estimation of changes caused by subways on property value can aid in assessing the benefits of public transit investments well. On the basis of residential property transaction records in Hangzhou, China in 2009–2013, hedonic models in a difference-in-differences framework are applied to handle certain endogeneity problems of estimation by eliminating unobserved fac- tors. Results show that treatment groups located within 1000 m of a subway have an average price increase of 444 yuan per m2 from the opening of Line1. High-cost houses constantly gain significant increment and their price premium regress during the research period. However, for their counterparts in low-cost communities, the insignificant price effects are negative for a short time, and then become positive. The generalized results are robust when subway radius is adjusted.

1. Introduction

Subway lines play an important role in the process of urbanization. Traffic convenience determines the capacity and attractiveness of a city. Cities with a developed transportation system can cover additional re- sources, and citizens tend to have access to favorable opportunities. Therefore, city governments tend to build subway lines to boost the social and economic development.

However, not all of people in cities need subway lines to the same extent. In accordance with their features and affordability, they can have heterogeneous preference towards subway lines (Yang, Chen, & Le, 2016). Even during a short time period, the preference of a same group of people can be unstable and varies regularly (Agostini & Palmucci, 2010). An investigation of this heterogeneous preference can help policy makers to recognize people in urgent need for subway and optimize the allocation.

A way to study people’s preference towards subway lines is provided by Lancaster (1966). Since there is no explicit market for public infra- structure, the value of subway lines for citizens is embedded into the purchasing costs of housing. People should pay more for a recent subway line. This implied price shows people’s willingness to pay for subway

lines, as well as their deferential preference. A research on this hetero- geneous price premium can be a theoretical foundation for land value capture to keep a fiscally sustainable urbanization.

Previous studies utilized a cross-sectional comparison to probe the price effects (Wang. 2017, Teng, Yan, & Zhou, 2014; Cervero & Duncan, 2002). As an outcome of endogeneity, the estimation in those papers is subject to biases (Ko & Cao, 2013; Pagliara & Papa, 2011). Furthermore, the price effects reflecting people’s preference would vary over time and among sub-markets, which has not been investigated at the same time. The present study aims to fill those gaps and has two contributions to the present understanding of property value effects. On the one hand, this study applies a hedonic price model within a difference-in-differences (DID) framework to control unobserved factors and estimate property value effects precisely. On the other hand, this study elucidates the dynamics of a heterogeneous effect across different sub-markets, trun- cated based on the average property prices of the community. This improvement is helpful in determining the kind of property that has the highest price premium.

To achieve the mentioned goals, this study selects Hangzhou, China as the case study. Hangzhou is a regional central city in China, wit- nessing massive infrastructure construction in recent years. The subway

* Corresponding author. Center for Real Estate Studying, Zhejiang University, Hangzhou, 310058, China. E-mail addresses: [email protected] (C. Tian), [email protected] (Y. Peng), [email protected] (H. Wen), [email protected] (W. Yue), [email protected]

(L. Fang).

Contents lists available at ScienceDirect

Research in Transportation Economics

journal homepage: http://www.elsevier.com/locate/retrec

https://doi.org/10.1016/j.retrec.2020.100844 Received 2 April 2019; Received in revised form 9 February 2020; Accepted 6 April 2020

Research in Transportation Economics 90 (2021) 100844

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has sprung up from nothing in the city center as well as the suburb. The change in Hangzhou reveals patterns about the citywide relation be- tween property prices and local transportation operation in developing areas. Consequently, experience in Hangzhou can be a favorable example for urbanizing cities worldwide wherein large-scale subway constructions will occur.

The structure of this paper is as follows. Part 2 reviews previous literature about the property value effect caused by subways and the improvement that requires accurate dynamics. Part 3constructs the main model for estimating the price effects. Part 4 describes the quasi- experimental setting in Hangzhou and the corresponding variables. Part 5 presents the empirical findings on the dynamics of property value effects. The final part provides the discussions and conclusions drawn from this study.

2. Literature

A subway line offers significant advantages for most real estate. Savings in commuting costs and promotion of accessibility are the main advantages (Ahlfeldt, 2013; Alonso, 1964; Hess & Almeida, 2007; Mills, 1967). The superiority can also partly contribute to subways’ image effect (Pan, 2013), that is, a subway line makes neighborhoods appear modern and dynamic. Properties with such advantages have higher values and this relation can be called property value effects caused by the subway on houses.

The added value that a subway line can contribute is typically dis- cussed using the hedonic price model. According to the hedonic price model, the price of a property is derived from its feature rather than the goods itself (Lancaster, 1966). By calculating the price that can be contributed to the distance feature, the extent of property value effects can be estimated. Using this strategy, Bae, Jun, and Park (2003) inves- tigate the effect of a new subway line on property price in Seoul, Korea in four separate years. He finds that property earns a 0.3% price increase from being 100 m near the subway station in the opening year. Similar negative relations between the distance to subway and property value are found in Beijing, CN (Wang, 2017), Tianjin, CN (Teng et al., 2014), Washington DC, US, Atlanta, US (Cervero & Landis, 1993), Utah, US (Li et al., 2016) and San Diego, US (Cervero & Duncan, 2002). Several literature works adopt a binary variable to represent the houses’ prox- imity to the subway (Dai, Bai, & Xu, 2016; Pan, 2013). Properties adjacent to the subway line will exhibit lower values than comparable properties located slightly further away (Pagliara & Papa, 2011; Ko & Cao, 2013). Influence of environmental negative externalities is verified and accounted for certain insignificant results, such as expected con- gestions and crime (Gatzlaff & Smith, 1993; Landis, Guhathakurta, & Zhang, 1994).

A universal concern of these studies is that the cross-sectional esti- mation might be biased because of endogeneity (Bae et al., 2003; Riekkinen, Hiironen, & Tuominen, 2015; Ko & Cao, 2013). On the one hand, the project placement is an endogenous decision of a local gov- ernment (Donaldson, 2018; McDonald, 2010). It means, the local gov- ernment tends to build the subway in places of low property prices and small population to reduce the cost and develop regional economy (Knaap, Ding, & Hopkins, 2001; Quan, Liu, & Chen, 2006; Yang et al., 2016). On the other hand, omitted variables which should be included into hedonic model can result in false estimations, such as neighborhood features, including race, neighbors’ income, and educational back- ground (Bayer & Mcmillan, 2007). Additionally, the potential for reaching opportunities by subway (P�aez, Scott, & Morency, 2012), walking impedance (Higgins, 2019), direct distance (Wen, Gui, Tian, Xiao, & Fang, 2018; Xiao, Hui, & Wen, 2019) and commuting time (Alonso, 1964) have been used as the measurement of accessibility. The lack of a definite proxy for distance also leads to omitted variables (Higgins & Kanaroglou, 2016). Furthermore, an increase in property value can be contributed to economic development rather than a sub- way’s opening (Agostini & Palmucci, 2010; Zhang, Liu, & Hang, 2016).

An overlook of the above mentioned problems can lead to insignificant or opposite results.

Several researchers use the DID methods to address endogeneity problems and improve the estimation accuracy of property value effects (Rosenthal, 2003; Black, 1999; Ahlfeldt, 2013; Dub�e, Th�eriault, & Ros- iers, 2013). Heckman (1978) proposes a DID estimator to handle auto-correlation, whereas Ashenfelter and Card (1985) first suggest a DID framework and apply this framework to policy evaluation. The core of this method is the removal of unobserved factors through longitudinal and latitudinal differences. Out of a similar thinking, Li, Wei, et al. (2016) and Li, Yang, et al. (2016) develop a first-differenced hedonic price framework to compare the property price for move-in date and survey date. Furthermore, Gibbons and Machin (2005) show that the magnitude of quasi-experimental coefficients is significantly larger than those of cross-sectional estimates. Im and Hong (2018) compare a before–after estimate of property prices between homes within and beyond 500m from a subway in Daegu, South Korea. The results illus- trate that the treatment group could earn a premium of US$96.3 per square meter mainly when these homes are within 500 m of the new line and beyond 5 km of the existing lines.

However an empirical gap is that most of them do not reveal varia- tion over time and among different sub-markets simultaneously. The price premium is an implication of the buyers’ willingness to pay for the subway and this preference could be temporally adjusted. See analysis in Dijon, FR (Dub�e, Legros, & Devaux, 2018), Laval, CA, Hangzhou, CN (Wen et al., 2018), and Warsaw, PL (Trojanek & Gluszak, 2017). Agostini and Palmucci (2010) claim that, if consumers have rational and fluctuant expectations, the future utility will be capitalized into the present price, and the effect of a subway on property price can vary during different times. When information is gradually released following the basic project, the excessive price premium will fall off. Based on this thought, they show that the magnitude of property value effects after opening tends to regress to a conservative status. Pilgram and West (2018) and Diao, Leonard, and Sing (2017) apply similar DID model and also demonstrate that significant price effects diminish over time. This temporal variation mentioned above can explain some insignificant generalized effects (Gatzlaff & Smith, 1993; Lawhon, Nilsson, Silver, Ernstson, & Lwasa, 2018). On the contrary, Dub�e, Des Rosiers, Th�eriault, and Dib (2011) show that the price premium keeps going up after over 12 years and explain that market needs time to react. Therefore, investigation of temporal variation is crucial to understand the supply of mass transit.

Furthermore, a potential buyer that purchases different kinds of properties may have varying preference or requirements for public goods (Pilgram & West, 2018). The housing market may be segmented into several sub-markets and property prices are not affected evenly by a subway (Devaux, Dub�e, & Apparicio, 2017; Gatzlaff & Smith, 1993; Lawhon et al., 2018). Therefore, property value effects can vary with features of houses. Because property price can be a perplexing symbol of the owners’ social status (Wen et al., 2018; Zhang & Yi, 2017), and residents tend to sort and live with people sharing similar preference (Bayer & Timmins, 2005), community level features are improved in- dicators for specifying sub-markets. Interestingly, while Gatzlaff and Smith (1993) show that the price premium in high-income communities is larger, Nelson and McCleskey (1990) find an opposite result that property price decrease in high income neighborhoods. One explanation for the mixed results is a failure to account for heterogeneous attributes of the residence (Bowes & Ihlanfeldt, 2001), including the distance to the city center and the efficiency of connection. Another explanation can be that a generalized estimation in the sub-markets do not consider the temporal variation (Debrezion, Pels, & Rietveld, 2007). So an estimation with combination of temporal variation in sub-markets is important to demonstrate the property value effects and the underlying mechanism.

In summary, previous studies identify price effects of subway con- struction with unobserved factors controlled. Some of them address price adjustment (Agostini & Palmucci, 2010; Diao et al., 2017) and

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some others show the differential effects (Pilgram & West, 2018). However, an estimation of this differential effects over time and across sub-markets simultaneously is still needed to explain the mixed results and investigate the heterogeneous preference. Therefore, this study will utilize a similar DID model, and address the differential impact among different communities, as a comparison to Wen et al.’s work (2018). Then, this study will show the price effects in sub-markets over time, as an improvement of Pilgram and West’s (2018) work. To achieve the combination of DID and investigation of heterogeneity, the empirical part of the present study presents the following objectives:(1) to esti- mate the average property value effects of subway lines within a quasi-experimental framework,(2) to investigate the variation of this price premium over time, (3) to present the differential price effects among sub-market truncated based on the average price on a commu- nity level (APC) index and show the temporal fluctuations of these price premium in different sub-markets, and (4) to summarize the dynamics of property value effects from the subway’s operation.

3. Methodology

The present study constructs regression models to specify property value effects caused by subway’s opening (Line 1 in Hangzhou). The application of the DID framework can be helpful in evaluating the effect of Line 1 when addressing endogeneity (Abadie, 2005; Grafova, Freedman, & Lurie, 2014; Jim�enez & Perdiguero, 2017). Properties around subway stations are divided into treatment and control groups on the basis of whether they are located in the subway catchment. Thus, the direct distance from community centroid to the nearest subway station is less than 1000 m (Chen, 2011; Zhang & Jiang, 2014), the properties in this community are viewed as the treatment group. Moreover, properties in communities outside 1000 m are treated as the control group, thereby indicating that the distance to a subway is large, so that they are not influenced by the subway’s opening. A comparison of the property price of the control and treatment groups before and after the opening of Line1 shows the extent of the subway’s opening affecting house prices.

The statistic varies less as different subsets of houses are draw in the simple linear model (Kuminoff, Parmeter, & Pope, 2010) and this study aims to investigate the price premium rather than the price elasticity. So the form of price function in this paper is set linear. Another potential concern is that the spatial dependency of property price might result in estimation bias (Bae et al., 2003). To tackle this issue, apart from con- structing a spatial model (Dub�e, Legros, & Th�eriault, 2014; Higgins, 2019), Kuminoff et al. (2010) provide an alternative option to add spatial fixed effects in the non-spatial model. Therefore district fixed effects are included in the model. Hence, the basic DID model for eval- uating the effect is expressed below (Bogart & Cromwell, 2000; Dub�e et al., 2013):

priceit ¼αTreatit þ γTreatit Tit þ δTit þ βXit þ ut þ λi þ eit (1)

Legend i in the equation represents each observation. Legend t rep- resents each period. Tit equals 1 indicates that the observation has been performed after the opening of Line1.When Tit equals 0, the transaction has been performed before the opening of Line1. Other observable fea- tures, such as Xit, are included. ut represents time fixed effects, λi refers to district fixed effects, and eit is the error term. For the interaction item (Treatit*Tit), the magnitude of the coefficient (γ) equals the house price difference between the treatment group and control group. This price difference can be interpreted as the average added value brought by the subway’s opening.

When the time period after the subway’s operation is divided into several phases, temporal adjustment can be probed. Siq in equation (2) refers that this transaction happens during the qth season after the subway’s opening. Parameters γiq represent the price effects during this season. It also means, after Line 1 is operated for q season, the properties in treatment group gain a price premium of γq.

Table 1 Definitions of variables in this study.

Category Variable Definition Theoretical basis

transaction information

property price Average price of a house (yuan/m2 )

Variable of interest

time to the opening of Line1

How many months did the trade occur before (� ) or after(þ) the opening?

Variable of interest

dwelling size Total area of a house (m2 )

Witte, Sumka, and Erekson (1979)

dwelling feature

building height How many floors does the building have?

Conroy, Narwold, and Sandy (2013)

housing height On which floor is the house located?

Conroy et al. (2013)

Age Age of house (years) Xu, Zhang, and Zheng (2018).

location feature distance to Xizi Lake

Shortest walking distance from the community center to the Xizi Lake (km)

Gibbs, Halstead, and Boyle (2002)

distance to Qianjiang New Town

Shortest walking distance to the Qianjiang New Town (km)

Wen, Jin, & Zhang, 2017a,b

distance to Qiantang River

Shortest walking distance to the Qiantang River (km)

Gibbs et al. (2002)

distance to CBD Shortest walking distance to the CBD (km)

Alonso (1964)

distance to Line1 Shortest walking distance to the nearest subway station of Line1 (km)

Variable of interest

neighborhood feature

property management quality

Property management quality of a community divided into five degrees (scores): very good (5), good (4), common (3), poor (2), and very poor (1)

Wen et al., 2018,2019

environment quality

Quality of external environment divided into five degrees (scores): very good (5), good (4), common(3), poor (2), and very poor (1)

Wen et al., 2018,2019

neighborhood quality

Quality of internal environment divided into five degrees (scores): very good (5), good (4), common(3), poor (2), and very poor (1)

Wen et al., 2018,2019

infrastructure quality

Supermarkets, food markets, hospitals, banks, and post offices within 1 km of the community. Each item receives a score of 1 out of a total score of 5.

Li, Wei, et al. (2016) and Li, Yang, et al. (2016).

University University within 1 km of the community: yes (1) and no(0)

Wen et al., 2017a,2019

educational convenience

Kindergarten schools, primary schools, junior high schools, and senior high schools within 1 km of the community. Each item receives a score of 1 out of a total score of 4.

Oates (1969)

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priceit ¼αTreatit þ X4

q¼1 γqTreatit Siq þ

X4

q¼1 δq Siq þ βXit þ ut þ λi þ et (2)

Furthermore, this study claims that properties in communities with different average price can undergo heterogeneous property value ef- fects. Therefore, this study divides the properties into four sub-markets on the basis of the APC. The communities are quartered by the position of the APC (i.e., the cheapest quartile of the communities denoted as very cheap communities, the second cheapest quartile as cheap, the third cheapest quartile as expensive, and the least cheapest quartile as very expensive). When the analyzed sample of regression model (1) covers different sub-markets, then the differential price effects can be discovered. When the sample of temporal model (2) covers different sub- markets, the dynamics of how price effects vary over time and across sub-markets can be probed.

4. Data and variables

This study uses the opening of subway Line 1 in Hangzhou as the quasi-experimental setting. Hangzhou is the capital and political, eco- nomic, cultural, traffic, and educational center of Zhejiang Province, located in the southeast of Mainland China. This second-tier city in China has experienced a rapid development and infrastructure con- struction after the 2000s. The local subway Line 1 in Hangzhou consists of 27 stations with two parts, namely, the western part located in main districts and eastern part in suburb districts. The construction of Line1 started in March 2007, and the western part opened in November 2012. The eastern part opened much later and has not been explored.

Each house is taken as the basic analysis unit. The relevant data comes from three main sources. The housing price data are gathered from the Hangzhou Real Estate Administration. The neighborhood characteristic data, such as environment quality, educational conve- nience, infrastructure quality, etc., are obtained through a field survey involving 660 housing communities. Then, an electronic map is used to measure location features. Using the measuring distance function, we can obtain the distance from the housing community to the nearest subway station, Xizi Lake, Qianjiang New Town, and Qiantang River.

The proximity variable is also coded with the electronic map. We use a dummy variable to evaluate a university within 1 km from the com- munity as 1 and that outside 1 km as 0. Table 1 summarizes the defi- nitions of the above variables mentioned.

This study emphasizes the property value effect of subway Line 1. Only observations within 2000 m (twice the effect radius) of Line1 (Pagliara & Papa, 2011) are included in the empirical analysis. When the observed property is too far from Line1, factors other than the subway line may have a significant effect on the housing price (Cervero & Duncan, 2002). Furthermore, properties affected by subway lines other than Line 1 are disregarded. The object of previous studies is frequently one subway line. This choice of object’s quantity mainly results from two reasons. First, the subway system has not been developed well during the time. Second, the specification can be easy and precise when one subway line is focused on, and the disturbing effect from other subway lines is becoming constant. In addition, the effects from the different subway lines cannot be added literally (Jiao, 2016), and, oc- casionally, the effect is significant only when no interference exists (Im & Hong, 2018). In 2009–2013, two subway lines, namely, Lines 1 and 2, exist in Hangzhou. Therefore, observations influenced by Line2 are

Fig. 1. Location of subway Line 1 and distribution of observations.

Fig. 2. Average property price over time.

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removed. Moreover, only 17360 records are retained, and their distri- bution in the city is demonstrated in Fig. 1. Table 2 summarizes the statistical description of those observations (see Fig. 2).

The average property price of the observations in each month is presented in Fig. 3. The treatment group and control group share a common trend. An apparent decline has been observed for all properties prior to the opening given the policy to limit property purchasing in 2011. The price steadily increases after the subway opening. After a comparison with the control group, whether the treatment group significantly earns a price premium after the opening remains unclear. Whether this effect is significant and whether this gap originates from the subway can be observed through the succeeding regression.

5. Empirical results

Table 3 displays the regression result based on cross-sectional com- parison. These models investigate the relation between the house price and distance to Line 1 before and after the subway’s opening separately. This cross-sectional comparison is universally adopted by previous

studies. The result shows that, before and after opening, the property price is lower in the treatment group than in the control group. It means that the property price is low when the property is close to a subway station. This relationship illustrates the existence of self-selection that subway station is tend to be built at places with lower property price (Yang et al., 2016), which causes endogeneity that leads to estimation bias and false causality. When compared horizontally, the coefficients changes from negative to positive, thereby the price gap is reversed. This comparison confirms that the opening of a subway line thus the prox- imity to the subway line increases the price of the treatment group. Whether the change in price is statistically significant or not remains to be investigated.

Modification to address endogeneity is implemented by applying the DID framework. The results of Equation (1) are summarized in Table 4. A robust standard error is applied to calculate the significance of

Table 2 Summary statistics of variables.

Variable Obs Mean Std. Dev.

Min Max

property price 17360 18963.18 5314.29 694.93 82788.89 time to the opening of

Line 1 17360 � 17.69 18.99 � 46 13

Treat 17360 0.63 0.48 0 1 Dwelling size 17360 76.01 36.00 15.55 378.01 distance to Line1 17360 899.38 455.77 14.99 1999.52 building height 17360 11.06 8.07 3 35 housing height 17360 5.99 5.15 1 33 distance to CBD 17360 4.08 2.62 0.54 12.14 distance to Xizi Lake 17360 4.11 2.25 0.16 13.1 distance to Qianjiang

New Town 17360 5.15 1.97 0.72 11.03

distance to Qiantang River

17360 4.22 2.43 0.1 11.22

property management quality

17360 2.62 1.32 1 5

environment quality 17360 3.25 0.70 2 5 neighborhood quality 17360 3.27 0.91 1 5 infrastructure quality 17360 4.24 1.13 0 5 university 17360 0.20 0.40 0 1 educational

convenience 17360 2.92 0.56 1 4

Age 17360 13.55 7.09 1 50

Fig. 3. Tempo

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