Data and Variables Using Opening of Subway Line 1
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Data and Variables Using Opening of Subway Line 1
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 definitions 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, occasionally, 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 distribution 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 proximity 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. Temporal dynamics of the property value effects for all the samples (yuan/m2).
Table 3 Relation between property price and distance to the nearest Line1 station based on cross-sectional comparison.
Property price (1)before opening (2) after opening
Time phases coefficients Standard errors
coefficients Standard errors
Treat (distance to line 1 < 1 km)
¡504.8*** 88.4 317.7* 143.3
dwelling size 19.7*** 2.3 8.1* 3.4 building height 106.4*** 15.8 ¡56.7*** 13.4 housing height � 5.1 11.3 17.9 11.3 distance to CBD 270.2** 84.3 20.5 165 distance to
XIZILake ¡1172.5*** 91.6 2098.2*** 266.1
distance to Qianjiang Newtown
165.1 86.2 1573.4*** 241.8
distance to Qiantang River
26.3 81.9 1780.3*** 304.7
university ¡424.4** 156.9 � 257.4 145.9 Age 46.0*** 11.9 ¡105.2*** 13.8 property management quality ¼ 1 Referred Referred ¼ 2 1280.2*** 134.9 67.2 217.4 ¼ 3 1082.3*** 139 685.5** 245.7 ¼ 4 2782.1*** 231.8 1218.0*** 289.4 ¼ 5 3659.0*** 351.2 3036.0*** 426.9 environment quality ¼ 1 No obs No obs ¼ 2 Referred Referred ¼ 3 189.8 128.1 ¡1116.9*** 173.3 ¼ 4 243 153.2 236 219.6 ¼ 5 3943.2*** 428.5 ¡1773.5*** 422 neighborhood quality ¼ 1 Referred Referred ¼ 2 1121.4*** 141.1 No obs ¼ 3 1308.0*** 154.8 1052.0*** 212.9 ¼ 4 1066.9*** 210.5 1289.5*** 259.3 ¼ 5 443.2 308.7 1989.7*** 389.2 infrastructure quality ¼ 0 Referred Referred ¼ 1 ¡1655.0*** 407.1 ¡1482.3*** 370.1 ¼ 2 2342.0*** 465.9 2277.1*** 448.1 ¼ 3 3039.0*** 468.6 248.5 480.5 ¼ 4 954.4* 427.3 363.6 419.3 ¼ 5 1025.2* 440.1 � 31.7 423.5 educational quality ¼ 1 Referred Referred ¼ 2 2098.2*** 266.1 1441.8 1011.5 ¼ 3 1573.4*** 241.8 1451.1 1011.7 ¼ 4 1780.3*** 304.7 1708.5 1035.5
Month fixed effects YES YES District fixed
effects YES YES
N 12542 4818 R2 0.622 0.513
*p < 0.05, **p < 0.01, ***p < 0.001.
- Tian et al.
Research in Transportation Economics 90 (2021) 100844
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coefficients. The result of Model 2 demonstrates that the treatment group earns a 444 premium from the opening when other variables are controlled.
Considering that properties with a specific distance to subway station might experience various negative externalities (Lieske, Nouwelant, Han, & Pettit, 2019), this study introduces distance fixed effects into the present models. For example, properties located within 200 m to subway
line can be bothered by noise, and properties with a distance of 700 m to subway tend to experience congestion (Mohammad, Graham, & Melo, 2015; Wen et al., 2018). Numerous dummy variables (D200, D300, D400, D500, D600, D700, D800, D900, and D1000) that indicate the distance range are included to capture the non-linear impact of the proximity to subway line. Variable D200 equals 1 represents that the distance to Line 1 is less than 200 m. Variable D300 represents that the
Table 4 Baseline regression with a DID framework.
Property price (1) (2)
RUBRIC
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