Constructs Regression Models to Specify Property Value
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Constructs Regression Models to Specify Property Value
- 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 constructing 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 features, such as Xit, are included. ut represents time fixed effects, λi refers to district fixed effects, and it 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
4
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.
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