Mixed Results and Investigate the Heterogeneous Preference
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Mixed Results and Investigate the Heterogeneous Preference
Research in Transportation Economics 90 (2021) 100844
3
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.
- 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)
- Tian et al.
Research in Transportation Economics 90 (2021) 100844
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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 effects. 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 cheap 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.
- 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 construction 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 convenience, 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.
- Tian et al.
Research in Transportation Economics 90 (2021) 100844
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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.
- Empirical results
Table 3 displays the regression result based on cross-sectional comparison. 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
RUBRIC
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