Effects of Urban Expressways on Housing Prices: A Case Study of Qiushi Highway,Hangzhou,China

Abstract

This paper discusses the effects of the construction of urban expressway on accessibility and further investigates the relationship between changes in accessibility and house prices caused by expressway construction. Taking the Qiushi Highway Phase III project in China as an example, a hedonic price model and difference-in-difference method are used to analyze the impact. The main innovation of the study is how it considers the improvement of the overall traffic conditions brought about by the construction of the expressway. In addition, three distance impedance parameters—namely Euclidean distance, road network distance, and actual transit time—are used to estimate the value of traffic accessibility during the process. The main research conclusions are as follows: (1) The opening of the Qiushi Highway Phase III project can significantly improve the accessibility of the surrounding areas to the city center. Additionally, this improvement can be extended to further sites through the urban expressway network; (2) The results of the difference-in-difference method show that the premium effect of the city expressway on house prices will not be directly generated by the expressway’s geographical proximity to the project. In fact, the expressway will increase accessibility in some related areas and will also have a further positive impact on housing prices; (3) Among the three types of traffic impedance parameters, accessibility calculated on the basis of actual transit time has the strongest connection to house prices.

Keywords

sustainability and resilience transportation and society transportation in the developing countries transport economics and finance

Expressways are a vital part of the urban transportation system. The expressway not only performs basic functions, such as increasing the traffic efficiency of the existing road and connecting the expressway network, but also has a significant diversion effect on the surrounding roads; thus, the overall traffic capacity of the road network is improved. In China’s urbanization process, various industries and public services have gathered in the city center. Numerous employment opportunities, a comfortable work environment, and rich entertainment services have attracted a large population to the central areas. At the same time, the gathering center is causing problems, such as rising housing prices, traffic congestion, and a lack of interior space in the city. Therefore, transportation infrastructure projects, such as viaducts, tunnels, and expressways, which can connect the main urban area and the new urban area, have become an essential way to reduce traffic pressure and evacuate the population from the main urban area.

Improving urban traffic conditions will reduce the travel costs of residents and improve travel convenience, and thus increase the price of residences nearby. From international experience, the optimization of urban traffic operation capacity can bring significant premium effects on the surrounding real estate values. In addition, the construction of high-capacity transportation facilities (including subways and bus rapid transit) can greatly improve the efficiency of urban land use and the intensity of development. Compared with the subway, construction of expressways, tunnels, and viaducts is faster. The construction of these transportation facilities provides the possibility of automobile traffic; the rapid increase in the number of family cars is also helping to increase the demand for housing in the new urban area. In the urban house market, because of the uneven distribution of urban transportation facilities, home buyers are often willing to pay more to obtain more convenient transportation conditions. Therefore, consumers prefer housing that is close to transportation facilities. This externality of transportation facilities will have a different premium impact on the housing prices in the surrounding residential area. However, this impact is not necessarily positive. It is related to factors such as the type of transportation facilities and the distance people have to travel to access those transportation facilities. For example, some scholars have found that negative externalities, such as noise and pollution, will negatively affect house prices within a certain range ( 1 , 2 ).

Many studies have been conducted on the impact of rail transit and other public transportation on the housing market ( 3 – 5 ). However, little research has been conducted on road infrastructure such as viaducts, tunnels, and expressways within the city. Although public transportation plays an important role in the urban transportation system, private cars also play an important role in urban traffic flow. Construction of urban transportation infrastructure, such as roads, overpasses, and tunnels connecting the new urban area to the main urban area, will greatly reduce the travel time between urban areas. This study provides an empirical study of the impact of urban expressways on the housing market in Hangzhou, China.

The concept of transportation accessibility provides a new perspective for researching the relationship between the construction of transportation facilities and housing prices. The previous literature, however, still has limitations. On the one hand, previous studies on the impact of urban expressways on the housing market have mainly focused on the distance from residential areas to expressways ( 2 , 6 ). This method considers only the direct impact of a single transportation facility on housing prices and does not consider the actual traffic demand. Because of the continuous improvement of urban transportation systems, the construction of a certain road facility will affect the traffic accessibility of the entire city, and thus the housing market. On the other hand, when measuring accessibility, most studies define distance impedance as the Euclidean distance, the road network distance, or the travel time according to the projected road speed ( 7 , 8 ). However, in practice, the speed of road traffic is usually much lower than the projected speed. As such, these studies ignore the general phenomenon of road traffic congestion.

This research focuses on the accessibility of residences to expressways and urban centers, the impact of the construction of urban expressway on the overall urban traffic, and the impact of changes in urban traffic on the housing market. As such, the limitations of previous studies have been somewhat avoided. Besides, based on the traffic geographic information system (GIS), this study combines data on actual traffic speed to construct an accessibility index and introduces that index into the house price model, thus improving the accuracy of the experiment.

The structure of this article is as follows. The next section is a literature review of relevant research. The section after that builds the main model used to measure the impact of traffic accessibility on housing prices. The following section describes the variables in the model and the case study. The antepenultimate section presents the empirical results, and the penultimate section contains related discussions. The final section summarizes the study.

Literature Review

Influence of Road Traffic Facilities on Accessibility

The concept of accessibility was first proposed by Hansen ( 9 ). He defined accessibility as the chance of interaction between nodes in the transportation network. Since then, accessibility research has received much attention and has been applied in other fields. Ingram constructed a computational model of spatial impedance and proposed the concepts of relative accessibility and comprehensive accessibility ( 10 ). Some scholars have measured accessibility over time and considered that accessibility is the number of development opportunities that can be reached in a limited time ( 11 , 12 ). Some scholars also believe that accessibility has social and economic value, and researchers should consider the attractiveness of traffic nodes ( 13 , 14 ).

Building transportation infrastructure is one of the important methods used to improve urban accessibility. Some scholars have studied the impact of building international roads and inter-city roads on macro-level accessibility. Gutiérrez and Urbano studied the impact of the trans-European road networks on accessibility, adopting GIS for accessibility analysis ( 15 ). The results show that the construction of a new road network has significantly improved the accessibility level of economic activity centers. The accessibility of the entire EU-wide region to the center of economic activities has improved to varying degrees, and countries with a geographical position on the periphery have experienced greater improvement than central countries. Li and Shum studied the impact of building the main road network in China on regional and urban accessibility ( 16 ). The study points out that, in the initial stage of highway development, the accessibility of nodes in eastern coastal cities improved more than in inland cities. Some scholars are paying attention to the impact of urban road network construction on accessibility from the micro level. Linneker and Spence’s study shows that the London M25 ring road affects the level of accessibility ( 17 ). However, this significant change in accessibility is not always in the expected direction; it largely depends on the impedance selected by the model parameters (such as function properties and travel mode). Gutiérrez and Gomez analyzed the influence of the Madrid M-40 ring road on accessibility within the city ( 18 ). The accessibility change graph shows that the accessibility of the traffic node closest to the M-40 is the most affected by the newly built ring road. The farther the node is from the M-40, the smaller the impact will be. The accessibility gradient is changing faster in the city center than on the periphery.

Impact of Urban Roads on Housing Prices

Recalling previous research on the impact of housing prices on road facilities, some scholars have divided studies of road facilities into two generations ( 6 , 19 ). The first generation of road facilities research was conducted in the 1950s and 1960s, while the second generation of road facility research was conducted in the 1970s and 1980s. This paper classifies its research on the impact of transportation facilities on house prices from the 1990s to the third generation.

Because hedonic price models did not become popular until the late 1960s, early studies on the impact of road infrastructure relied heavily on descriptive statistical methods and longitudinal data analysis over time ( 20 ). In addition, the method of measuring accessibility was primitive, and did not even use the distance variable of a specific house or plot to the road but divided them into several buffer zones (band/ring), according to the distance to the road ( 21 – 23 ). Some of the empirical results of this research show that road facilities have a significant effect on the surrounding residential prices ( 21 , 24 – 26 ). However, other studies show that road facilities have little or no impact on surrounding housing prices ( 22 , 23 , 27 ).

The second generation of research on road facilities mainly used multiple regression analysis with residential prices as a dependent variable. Cribbins et al., Langley, and Tomasik used the Euclidean distance from the house to the nearest road or road junction as an accessibility index ( 28 – 30 ). Pendleton and Palmquist used travel time and travel distance to the central business district (CBD) as accessibility indicators, which were used to measure the impact of road construction on nearby houses ( 1 , 31 ). Gamble et al. used the regional accessibility index calculated by the Washington Government Commission, which includes travel time and travel distance variables from residence to the employment center ( 32 ). Since the dependent variable is the house price, the multiple regression equation also includes the characteristics of the building and the neighborhood of the house. It can be found that the accessibility variable in the research during the second generation changed from the original classification of housing (according to the distance to the road) to the use of the continuous variable of the distance to the road. Some scholars have also adopted distance or time to CBD as more accurate accessibility indicators. Observing the empirical results of these scholars, only one did not find a significant relationship between housing prices and the proximity of road facilities ( 28 ). This latter study assumes that the influence range of a road is within 2.5 mi of the surrounding area, while the influence range of the road defined by other studies is within 1.5 mi. Given the empirical results of the first generation, the influence range of the delineated road facilities may be too large, leading to insignificant empirical results.

With the development of theoretical methods and the advancement of computer technology, subsequent research has shown more diverse development. To reduce the impact of other transportation facilities during the study period, Hoogendoorn et al. used the difference-in-difference (DID) method to test the impact of the construction of the Westerschelde Tunnel on the residential market ( 33 ). To reduce the impact of the house’s own architectural characteristics and neighborhood characteristics, Levkovich et al. used the repeated transaction method and the DID method to analyze many repeated transaction data in the residential market in five provinces of the Netherlands ( 8 ).

Calculating accessibility indicators is also becoming more accurate and the measurement accuracy now tends to be microscopic. With the help of GIS technology, many researchers now use the vector grid method to measure the accessibility index of community units. Voith uses three reachability indicators: (1) whether rail transit is convenient, (2) the average commuting time of the car, and (3) the time it takes the car to go to the CBD through the road network ( 34 ). Based on the gravity model, Adair et al. considered factors such as travel generation, process, and different modes of transportation to measure the accessibility index of 182 traffic communities in Belfast ( 35 ). Iacono and Levinson adopted: (1) whether the house is adjacent to the road (dummy variable), (2) the distance to the road junction, and (3) the number of accessible employment opportunities within 30 min of the house, based on a cumulative opportunity model as an accessibility index ( 2 ). Hoogendoorn et al. used a cumulative opportunity model to measure the accessibility index to job opportunities, based on travel time of the road network ( 33 ). Levkovich et al. used population data to calculate economic activity indicators, with GIS used to calculate road network travel time as spatial impedance ( 8 ). The study estimates the economic potential accessibility index based on the potential model.

In addition, previous research has not been limited to exploration of the spatial dimension. In some literature, the difference in the time dimension of the impact of various stages of road facility construction on housing prices is studied. Chernobai et al. analyzed the impact of the construction of the road extension project in Los Angeles on the surrounding residential prices and found that the premium effect of house prices mainly exists after the road is opened ( 7 ). A study by Yiu and Wong on the impact of a tunnel improvement project on the prices of surrounding housing in the Western District of Hong Kong shows that, before the completion of the tunnel, a significant premium effect had already been realized ( 36 ). Recent literature has examined the impact of highway construction on surrounding housing prices using the DID approach ( 37 , 38 ). Since both studies examine highways passing through the country, they use travel time to the highway exit to measure accessibility, focusing only on the new highway. A national highway that runs from south to north through the center of the country, or connects developed and underdeveloped regions from east to west, does not have the same level of connectivity and impact as an intra-city expressway ( 37 , 38 ).

In summary, most studies on the impact of traditional transportation facilities on the housing market consider only the direct impact of a single transportation facility on housing prices. These studies do not consider that the construction of a single road facility will affect the traffic accessibility of the entire city, and eventually the housing market. This is one of the focuses and innovations of this study. In relation to accessibility calculation, with the development of measurement methods, the Euclidean distances that have been mainly used in related research in the past have been gradually replaced by road network distance or travel time. Because of traffic jams, the actual speed of traffic will usually be much lower than the projected speed. Therefore, this paper constructs an accessibility index based on data on actual traffic speed of vehicles, which more accurately quantifies the degree of traffic accessibility. In relation to research objects, there are currently many studies on the impact of public transportation (such as rail transit) on the housing market. However, there is little research on road infrastructure such as viaducts, tunnels, and expressways within the city, and this article provides an empirical case of the impact of urban expressways on the housing market. The network of intra-city expressway is much denser compared with national highways and has a closer impact on the daily commute of residents. The construction of the longitudinal expressway will communicate with other transverse expressways and improve the efficiency of the entire expressway network. So, not only residences near the exit from the new expressway will be affected, but also residences near the exit from other expressways. This is something that has not been a focus in other studies and represents a concern in this innovative study.

Methodology

Traffic Accessibility Model

This article focuses on the change in the degree of accessibility of the residential community to the city center. The urban centers of the main city of Hangzhou are mainly the traditional Wulin CBD and the Qianjiang New CBD. In this paper, a gravity model is used to calculate the degree of accessibility. Referring to Song and Sohn, Celik, and Yue et al., the inverse power model form is adopted as follows ( 39 – 41 ):

A_{i} = \sum_{j = 1}^{n} \frac{D_{j}}{d_{ij}^{α}}

(1)

where

A_i = accessibility of the residence I to the gravity points,

D_j = attractiveness of gravity point j,

d_ij = traffic impedance from point i to point j, which is expressed by the real driving time from the residential area to the gravity point,

n = number of gravity points, and

α = distance attenuation parameter (α = 1 was taken according to previous empirical study) ( 42 ).

For the selection of the gravitation point, this paper considers the city center. On the one hand, many studies have shown that, in the Chinese urban housing market, including Hangzhou, city centers have a significant impact on housing prices, and the wealthy still prefer to live near city centers ( 42 – 45 ). On the other hand, because of the lack of origin–destination (O-D) survey data on travel in each traffic zone, it is impractical to determine the employment ratio of each traffic zone and the travel probability between traffic zones. Referring to Yue et al., and Yang, this paper uses GPS data on taxi trajectories to draw a heat map through kernel density analysis and observes hotspot areas with high kernel density in the morning and evening peak hours ( 41 , 46 ). The hottest area is the East Railway Station, which, as one of the largest transportation hubs in Asia, has many inbound and outbound passengers every day. The next hottest area is the area between the traditional city center and the new CBD. Another hot spot is Huanglong Business Center, which is 5 to 10 min away from the traditional Wulin CBD. Therefore, in this paper, the two city centers are considered as gravitational points, and the attraction Dj is expressed by the corresponding kernel density values of the taxi trajectories. To observe the diverse impact between these two old and new city centers, instead of using a weighted approach, they were placed in the model separately to observe the impact of changes in their accessibility. Therefore, in fact, n in Equation 1 takes the value 1. Traffic impedance, or road speed, comes from real-time vehicle monitoring data from the Gaode City Brain data center. Road speed is taken as an average value based on road speed in both directions during the morning and evening peak periods on weekdays.

The Hedonic Price Model

The hedonic price model is widely used to explain housing prices from the perspective of building, location, neighborhood, and other factors ( 47 – 50 ). Common function forms of the hedonic price model are logarithmic form, semi-logarithmic form, linear form, and logarithmic linear form. The result of the trial regression showed that the logarithmic form and the logarithmic linear form have a good fit. However, in relation to variable significance, the logarithmic form is significantly better than the logarithmic linear form, so this study chooses the logarithmic function form for the model fitting.

The formula in the form of a logarithmic function is as follows:

L n P = α_{1} + \sum α_{i} L n S_{i} + \sum α_{j} Ln L_{j} + \sum α_{k} N_{k} + ε_{1}

(2)

where

P = property price,

S_i = the i-th architectural characteristic,

L_j = the j-th location characteristic,

N_k = the k-th neighborhood characteristic,

α_i, α_j, α_k = coefficients to be estimated, and

ε1 = a random term.

Since the neighborhood variables are 0 to 1 variables, or score variables, no logarithm is taken for them.

DID Model

The DID method has a wide range of applications in evaluating the effectiveness of public policy, the impact of major events, and project implementation. The general form of the DID model is as follows:

Y_{it} = α_{0} + α_{1} \cdot D + α_{2} \cdot T + α_{3} \cdot T \times D + ε_{it}

(3)

where

Y_it = the dependent variable,

α₁, α₂, and α₃ = the regression coefficients,

ε_it = the random disturbance term,

T = time dummy variable,

D = grouping dummy variables,

T × D = the interaction term, and

α₃ = the main coefficient of interest, which represents the effect of the DID estimator on the outcome variables.

Because of the heterogeneity of housing prices, it is not enough to include the grouping dummy variables and time dummy variables in the model. It is necessary to add other factors that may affect the explained variables. Thus, the form is rewritten as follows:

Ln P_{it} = α_{0} + α_{1} \cdot D + α_{2} \cdot T + α_{3} \cdot T \times D + \sum_{j = 1}^{n} β_{j} \cdot Z_{ij} + ε_{it}

(4)

where

P_it = the price of the house of the i-th community in year t, and

Z_{i j} = the j-th characteristics of the i-th community, including architectural characteristics, location characteristics, and neighborhood characteristics.

Data and Variables

Study Area

This research takes the Qiushi Highway Phase III project (referred to as Qiushi III) as an example to explore the effects of urban expressway construction on housing prices in the main urban area of Hangzhou. With the implementation of the urban strategy “construction along the river and development across the river,” the original Qiutao Road, as the main north-south corridor, now has a huge traffic flow; the road simply cannot meet the serious traffic demand. Therefore, there is an urgent need to build an elevated expressway. The Qiushi Highway is therefore positioned as an important channel in the “four vertical and five horizontal” expressway network system in Hangzhou. The highway represents a major improvement in the surrounding traffic environment. This improvement will be transmitted to other expressways through the networks. The Qiushi Highway runs from the Qingjiang Road interchange in the south, to the toll gate of the National Highway 320 in the north, with a total length of approximately 17.7 km. Since 2011, the Qiushi Highway has been partially completed (or under construction) north of the Shide Interchange on the Qiushi Highway (including the Shide Interchange, about 11.5 km long). Construction has not yet begun on the southern section of the Shide Interchange (Qiushi III), which is approximately 6.2 km long. In June 2012, Qiushi III was officially launched. On December 31, 2014, the project was completed and opened to traffic. Figure 1 is a planning diagram of Qiushi III from 2011.

Description of Variables and Summary Statistics

As mentioned in Mothodology,

the hedonic price model usually uses the housing price as a dependent variable. The independent variables are divided into three categories: architectural characteristic variable, location characteristic variable, and neighborhood characteristic variable. The variables used in this study are shown in Table 1.

Table 1.

Definitions of Variables

Category	Variable	Definition
Transaction information	Property price	Average price of a house ( $¥ / m^{2}$ )
Transaction information	Transaction time (T)	The trading time of house: in 2012 (0), in 2015 (1)
Architectural characteristics	Age	Age of house (years)
Location characteristics	Distance to West Lake	Euclidean distance from the community center to West Lake (km)
	Distance to Wulin central business district (CBD)	Euclidean distance from the community center to Wulin CBD (km)
	Distance to Qianjiang CBD	Euclidean distance from the community center to Qianjiang CBD (km)
	Wulin CBD road network accessibility	The traffic accessibility index calculated using the road network distance to Wulin CBD as the traffic impedance
	Qianjiang CBD road network accessibility	The traffic accessibility index calculated using the road network distance to Wulin CBD as the traffic impedance
	Wulin CBD transit time accessibility	The traffic accessibility index calculated using the real transit time to Wulin CBD as the traffic impedance
	Qianjiang CBD transit time accessibility	The traffic accessibility index calculated using the real transit time to Qianjiang CBD as the traffic impedance
Neighborhood characteristics	Environment quality	Quality of internal environment divided into five degrees (scores): very good (5), good (4), common (3), poor (2), and very poor (1)
	University	University within 1 km of the community: yes (1); no (0)
	Educational convenience	Kindergarten, 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.
	Sports convenience	Swimming pools, fitness facilities, basketball courts, tennis courts, badminton courts, table tennis courts, children’s amusement parks, elderly activity rooms, within 1 km of the community. Each item receives a score of 1 out of a total score of 8.
	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)
	Noise	Whether the community is affected by the noise pollution of the city’s expressway network—affected (1): the community is within the 0.5 km buffer zone of the expressway network; unaffected (0): the community is outside the 0.5 km buffer zone of the expressway network.

For housing transaction price data, this study applied a total of 503 communities of average second-hand housing transactions prices for residential communities in Hangzhou in 2012 and 2015. The data comes from the Transparent House Sales Research Institute (http://www.tmsf.com/), a real estate agency operating within the Hangzhou Housing Administration Bureau. All transactions in the main urban area of Hangzhou must be registered in the Transparent House Sales platform system. The geographical distribution of communities is shown in Figure 2. Other characteristic variables were obtained through a questionnaire-based survey of the community within the main urban area. The research center where the authors work conducted a long-term follow-up survey that examined the neighborhood characteristics and the environmental quality of the housing in Hangzhou. Relevant variables have been used in several previous studies ( 42 , 44 , 51 ). The descriptive statistics of the variables are shown in Table 2. The descriptive statistics, separated by groups, are shown in Supplemental Material, Tables A1–A3.

Table 2.

Descriptive Statistics of Variables

Variable	Obs.	Min.	Max.	Mean	Median	Standard errors
Property price (¥/m²)	1,006	9,252	78,978	21,084	20,186	5,716
Distance to Wulin central business district (CBD) (km)	1,006	0.350	12.790	4.655	4.1	2.685
Distance to West Lake (km)	1,006	0.300	13.100	4.103	3.75	2.274
Distance to Qianjiang CBD (km)	1,006	0.940	16.457	7.164	7.201	3.499
Age	1,006	2	33	14.630	14	7.011
Environment quality	1,006	1	5	3.100	3	0.792
University	1,006	0	1	0.611	1	0.489
Educational convenience	1,006	1	4	3.142	3	0.765
Sports convenience	1,006	0	6	1.812	2	1.269
Property management quality	1,006	1	5	2.523	4	1.277
Noise	1,006	0	1	0.187	0	0.387
Access change to Qianjiang CBD	503	0.071	1.401	0.755	0.768	0.197
Access change to Wulin CBD	503	0.021	0.981	0.467	0.477	0.214

Note: Obs. = number of observations; Min. = minimum; Max. = maximum.

Selection of Research Period

Concerning the selection of the research period in the DID model, this article focuses on the impact on housing prices before and after the construction of the expressway. At the beginning of 2012, the Qiushi Highway Phase II project was completed and opened to traffic. If the research scope before the experiment expands to the years before 2012, it will be affected by the opening of the Phase II project, which will lead to deviations in the research results. Considering that the city was able to host the G20 summit in 2016, Hangzhou has comprehensively improved the urban transportation environment and infrastructure level. Six expressways were completed or opened in 2016, of which the Zizhi Tunnel and the Donghu Expressway project are considered important parts of the “four vertical and five horizontal” expressway network system. These expressways will have a greater diversion effect on the north-south traffic flow, which in turn will affect the operation of the Qiushi III project, thus interfering with the results of this study. In addition, other expressways constructed between 2012 and 2015 run mainly in an east-west direction, with short road sections, or are located at the fringe of the study area. This area has a limited impact on the overall road network. Taking into account the actual construction of the Hangzhou Expressway, to specifically study the impact of the Qiushi III elevated project on housing prices and to try to avoid interference caused by the construction of other expressways, this study will distinguish between the start of the construction of the Qiushi III elevated project (June 2012) and its opening to traffic (December 2014) as being “ex-ante and ex-post.” In fact, the media publicly announced that construction began in June 2012, but the construction department registered that the start date was in December 2012. The period from June to December 2012 was mainly used for greening relocation, construction bidding, and other preparatory works. During this period, the market was not yet sensitive to the positive impact of the new project, and the negative impact of construction had not yet materialized. The sample of house price transactions from 2012 was selected as “ex-ante” data, and the sample of house price transactions from 2015 was used as “ex-post” data.

Empirical Results

Primary Test Based on Hedonic Price Model

The regression results of the hedonic price model are shown in Table 3. The main location characteristic variables—West Lake distance, Qianjiang CBD distance, and Wulin CBD distance—all explain the average price of housing to a large extent. Also, the coefficient signs are in line with expectations. That is, the farther the house is from West Lake, Wulin CBD, or Qianjiang CBD, the lower the house price. In relation to coefficient size, the coefficient of the West Lake distance variable is the largest, followed by the Wulin CBD and the Qianjiang CBD. The West Lake scenic area and the Wulin CBD are still the urban center of the entire Hangzhou City. The coefficient of Qianjiang CBD is also closer to Wulin CBD, indicating that Qianjiang CBD also has a high impact on house prices. All other control variables, except age and environmental quality, were significant at the significance level of 1%, and the coefficient sign was in line with expectations. The age variable is not significant, which may be caused by the hedonic price model itself. One study on second-hand housing prices in Hangzhou found that, when the spatial expansion model is used, the age of the community is significant, at a significance level of 10%, while natural environmental variables are not significant. This may be because the values of the natural environmental variables for each sample are close ( 52 ).

Table 3.

Influence of Accessibility on Housing Prices Under Different Distance Impedances

Dependent variable: ln price		Euclidean	Accessibility (by road distance)	Accessibility (by transit time)
Transaction time	na	−.035***	−.034	−.108***
ln age	na	−.002	−.004***	−.006
Distance to West Lake	na	−.208***	−.195***	−.206***
ln Wulin central business district (CBD) accessibility	Euclidean	−.071***	na	na
	Network distance	na	.089***	na
	Transit time	na	na	.089***
ln Qianjiang CBD accessibility	Euclidean	−.052***	na	na
	Network distance	na	.062***	na
	Transit time	na	na	.073***
Environment quality	na	.011	.012*	.009
University	na	.058***	.054***	.053***
Educational convenience	na	.029***	.029***	.029***
Sports convenience	na	.023***	.024***	.025***
Property management quality	na	.061***	.060***	.061***
Noise	na	−.060***	−.061***	−.072***
Adjusted R²	na	0.577	0.585	0.586
Obs.	na	1006	1006	1006

Note: Obs. = number of observations; ln = log_e(N); na = not applicable.

p < 0.05. ***p < 0.001.

In addition to using the Euclidean distance from the city center to characterize location accessibility, this paper also uses the urban center accessibility value calculated based on the road distance (or transit time) as the influence of location characteristic variables on residential prices.

Analyzing the regression results of the three methods of calculating the distance impedance, it can be concluded that, apart from the age and environmental quality variables, other variables are significant at 1% level. In addition, comparing the goodness-of-fit of the three regression results, it can be found that the adjusted R² values for the three models are 0.577, 0.585, and 0.586, respectively, all of which have a good interpretation of the model. However, these three do not differ much. When the transit time is used as the impedance parameter of the accessibility index, the goodness of fit of the model is somewhat better.

Tentative Test Based on Grouping DID Model

This section uses the DID model to study the impact of the construction of Qiushi III on property prices from multiple perspectives. In the DID model, the time variable T = 0 represents the time before the opening of Qiushi III, and T = 1 represents the time of opening the project. The grouping variable D = 1 indicates that the house was affected by the project, as a treatment group, and D = 0 means that the house was not affected by the project and is set as a control group. This study focuses on the regression results of the D*T interaction term coefficients. Additionally, for grouping variable D, the following research will be conducted from three perspectives: taking into account the road distance to the entrance and exit of the project, the road distance to the entrance and exit of the city expressway network, and the extent of changes in traffic accessibility. This is taken as a basis for grouping, to discuss the impact of Qiushi III on property prices.

Grouping by the Distance to the City Expressway Network

The most common grouping method is to divide the residential communities according to the shortest straight-line distance to the road, and to group the samples at equal intervals. First, the road distances to the entrance and exit of Qiushi III are used to group community samples. The treated group (variable D = 1) will be set as the distance to the nearest exit of the project within X_i km. Residential communities in the treated group were affected by the improved accessibility brought about by the opening of the project. The sample control group (variable D = 0) was set to be X_i km outside the nearest entrance and exit of the project, which means this group of residential areas was not affected by the opening of the project. In previous studies examining the impact of road facilities on house prices, the distance interval was mostly set to 0.5 km or 0.5 mi, and the maximum impact range was mainly concentrated between 2 and 3 km (7, 26, 30). In this section, X = 1, 1.5, 2, 2.5 km is taken. The geographical distribution of communities near the project is shown in Figure 3. The regression results are shown in Supplemental Material, Table A4.

For interaction term T*D, the regression results show that only 0–2.5 km is significantly negative. This finding may be because the residential areas in the range of 0–2.5 km are less affected by positive externalities of improved traffic accessibility than by the impact of negative externalities. For residences 0–1, 0–1.5, and 0–2 km from entrance and exit, the above-mentioned positive externalities and negative externalities do not differ much, showing statistically insignificant results.

The construction of road infrastructure not only affects the accessibility of the surrounding houses but can also affect the overall operation of the urban road network system. Qiushi III is an important part of the “four vertical and five horizontal” expressway network system in Hangzhou, which in turn has led to more conversion plans between horizontal expressway networks. In addition, in relation to vertical commuting, the completion of this project may help to divide the traffic flow of the originally congested Shangtang-Zhonghe elevated project. Therefore, the opening of the project could affect the expressway network throughout Hangzhou. This section divides the sample of communities according to the distance to the nearest entrance to the expressway network. Since the distance between the adjacent expressways in Hangzhou is only 3–5 km, the sample interval is divided into 0–0.5 and 0–1 km from the entrance and exit, according to the Euclidean distance from one community to the nearest expressway entrance and exit. That is, the residential areas located within 0–0.5 or 0–1 km from the expressway network entrance are the treatment group (D = 1). The residential areas located 0.5 or 1 km away from the entrance to the expressway network are used as the control group (D = 0). The geographical distribution of communities near the entrance and exit of the expressway network in the main urban area of Hangzhou is shown in Figure 4. The regression results are shown in Table 4.

Table 4.

Results of the Impact of the City Expressway Network on Property Prices

Dependent variable: ln price	Distance interval to the entrance and exit of the expressway network system
	0–0.5 km	0–1 km
Constant	10.273***	10.304***
Log of distance to West Lake	−.220***	−.210***
Log of distance to Wulin central business district (CBD)	−.073***	−.079***
Log of distance to Qianjiang CBD	−.042***	−.055***
Log of age	−.084***	−.088***
Environment quality	.025***	.027***
University	.020***	.022***
Educational convenience	.059**	.056**
Sports convenience	.018***	.018***
Property management quality	.045***	.041***
Noise	−.002	−.016
Time variable (T)	−.003	.019
Grouping variable (D)	−.073***	−.047***
TD*	−.049*	−.074***
Adjusted R²	0.582	0.578
Obs.	1006	1006

Note: Obs. = number of observations; ln = log_e(N).

p < 0.05. **p < 0.01. ***p < 0.001.

Here, all that is analyzed is whether prices are affected by elevated road noise and the coefficient of the T*D interaction term. The noise variable is defined as the residential area within 500 m (straight-line) from the expressway. The value is 1; otherwise, it is 0. This variable is not significant in the regression results; it may be affected by the grouping variable D. There is an intersection between the two variables. Therefore, the noise effect is not significant. The coefficient of the interaction term D*T is significantly negative. One of the possible reasons is that from 2012 to 2015, the load on the expressway network increased, and the absolute value of the traffic flow around the entrance and exit of the elevated road network also increased. The result was an increase in negative traffic externalities. The prices of houses near the entrances and exits of the network dropped significantly from 2012 to 2015. The coefficients of the interaction terms for 0–0.5 km and 0–1 km from the expressway network entrance and exit are −0.049 and −0.074, respectively. That is, the residential areas that are far from the network entrance and exit experienced a larger decline in house prices during the period 2012 to 2015. One possible reason for this finding is that the communities further away from the entrances and exits of a network will experience a positive increase in accessibility that is less than a negative increase in traffic externalities.

Grouping According to Changes in Traffic Accessibility

As mentioned in the above two sections, the opening of Qiushi III did not significantly increase residential prices, either near the entrance or exit of the project or the city’s expressway network. The negative impact may be because of the failure to separate the impact of the traffic accessibility change caused by the project. The result is a combination of the impact of changing accessibility and the negative externalities of traffic. Therefore, this section will explore changes in traffic accessibility of residential areas both before and after the opening of the project, as well as whether this change in traffic accessibility will further affect residential prices.

In this section, the D variable of the regression model represents the magnitude of the change in the accessibility of the residential community to the Wulin or Qianjiang CBD. Based on the traffic accessibility model, the changes in accessibility of each community in Hangzhou to Wulin CBD and Qianjiang CBD are shown in the Appendix. Among them, the rate of change in CBD accessibility is in the top 50% of all rankings as the “experimental group.” For this group, the D value is 1. The CBD accessibility change rate is in the bottom 50% of all rankings as the “control group,” and D value is 0; other variables remain unchanged.

Table 5 shows the regression results of the impact of accessibility changes to the Wulin CBD and Qianjiang CBD on residential prices. Unlike the results of the previous two models, most of the variables in this regression are significant. The coefficients of the variable D are 0.075 and 0.029, respectively. That is, the area with the highest increase in traffic accessibility to the Wulin CBD has a higher housing price than the area with lowest increase in accessibility (about 7.8%) Also, the residential area with higher change in accessibility to the Qianjiang CBD has a higher house price than the residential area with lower change in accessibility (2.9%). The coefficient of the T*D interaction term to the Wulin CBD is significantly positive at 0.053. That is, for a place where the accessibility to the Wulin CBD changes greatly, the opening of the project will increase the residential price by 5.4%. This means that the improvement of traffic accessibility to Wulin CBD, which comes with the opening of the project, will significantly increase housing prices in areas where the increase in traffic accessibility is higher. This is because increased accessibility to the Wulin CBD can reduce the time cost factor for those areas further away. However, the T*D coefficient to Qianjiang CBD is not significant, which means that the increased accessibility to the Qianjiang CBD brought about by the project does not have a significant impact on residential prices.

Table 5.

Results of the Impact of Changes in Traffic Accessibility on Property Prices

Dependent variable: ln price	Wulin central business district (CBD)				Qianjiang CBD
Variable	Coefficient	P-value	Elastic coefficient	Semi-elastic coefficient	Coefficient	P-value	Elastic coefficient	Semi-elastic coefficient
ln age	−.042***	.002	−.042***	na	−.033**	.022	−.033***	na
ln distance to West Lake	−.233***	.000	−.233***	na	−.225***	.000	−.225***	na
ln distance to Wulin CBD	−.050***	.000	−.050***	na	−.030***	.009	−.030***	na
ln distance to Qianjiang CBD	−.033***	.000	−.033***	na	−.047***	.000	−.047***	na
Environment quality	.018***	.004	na	.018***	.013*	.055	na	.013*
University	.033***	.002	na	.034***	.058***	.000	na	.060***
Educational convenience	.019***	.009	na	.019***	.028***	.000	na	.028***
Sports convenience	.027***	.000	na	.027***	.026***	.000	na	.026***
Property management	.047***	.000	na	.048***	.053***	.000	na	.054***
Noise	−.036***	.001	na	−.035***	−.067***	.000	na	−.065***
Time variable (T)	−.061***	.000	na	−.059***	−.034**	.027	na	−.033***
Grouping variable (D)	.075***	.000	na	.078***	.029**	.045	na	.029***
TD*	.053***	.005	na	.054***	−4.781E−5	.998	na	−4.8E−5
Adjusted R²	0.637	na	na	na	0.585	na	na	na
Obs.	1006	na	na	na	1006	na	na	na

Note: Obs. = number of observations; ln = log_e(N); na = not applicable.

p < 0.05. **p < 0.01. ***p < 0.001.

Further, the authors performed a parallel trend test for the grouping, according to the accessibility of Wulin CBD. Because of the limited number of undisturbed years “before treatment,” quarterly data are used. In this test, the first quarter of 2013 is set as the current period and the fourth quarter of 2012 is set as “pre_1,” while “pre_12” is set as the first quarter of 2010. The post-treatment observation period lasts until the fourth quarter of 2015, that is, post_11. The test results are shown in Table 6 and Figure 5; the coefficients of the interaction terms between the pre-treatment periods and the experimental group are mostly insignificant, indicating that the results are reliable.

Table 6.

Parallel Trend Test from the First Quarter of 2010 to the Fourth Quarter of 2015

Dependent variable: ln price	Coefficients
pre_12*D	−0.064 (−1.459)	post_1*D	0.024* (1.877)
pre_11*D	−0.018 (−0.900)	post_2*D	0.027 (1.461)
pre_10*D	−0.051 (−1.413)	post_3*D	0.024** (2.001)
pre_9*D	−0.034** (−2.466)	post_4*D	0.023 (0.565)
pre_8*D	−0.032 (−1.026)	post_5*D	0.047*** (3.168)
pre_7*D	−0.037** (−2.380)	post_6*D	0.043** (2.444)
pre_6*D	−0.052*** (−2.834)	post_7*D	0.032** (2.389)
pre_5*D	−0.013 (−0.705)	post_8*D	0.091 (1.323)
pre_4*D	−0.000 (−0.000)	post_9*D	0.027** (1.995)
pre_3*D	−0.001 (−0.050)	post_10*D	0.054*** (2.783)
pre_2*D	0.005 (0.274)	post_11*D	0.033** (2.440)
Current*D	−0.035 (−0.964)	na	na
Time fix	Yes	na	na
Group fix	Yes	na	na
Control variables	Yes	na	na
Obs.	4112	na	na
R²	0.587	na	na

Note: Obs. = number of observations; D = grouping variable; na = not applicable.

Main Specification Based on a Continuous DID Model

In the traditional DID model, the sample of communities is divided according to whether the rate of change of accessibility of their urban center is in the top 50% of all residential ranks. In fact, since the change rate in accessibility of urban centers is continuous, artificially dividing the “experimental group” and the “control group” by 50% before and after the change in the rate of accessibility can lead to erroneous settings ( 53 ). Therefore, a continuous DID model will be used in this section, with the rate of change of urban center accessibility as a continuous grouping variable. The sample of residences will be divided into “experimental groups,” according to the continuous value of the accessibility change rate (large increase in accessibility) and “control group” (small increase in accessibility), so that the impact of the accessibility change rate on housing prices can be explored. This generalized DID method differs from the traditional DID form, replacing the “treatment” dummy variable with a continuous “intensity” variable, that is, the accessibility index in this paper. This method has been applied to many natural experimental problems ( 54 – 56 ). The basic idea of this approach is that, although all individuals are simultaneously exposed to policy shocks, policy does not affect everyone with the same intensity. Therefore, the interaction term of intensity and time may well explain the effect of policy ( 57 , 58 ).

In this section, the D variable of the regression model represents the change rate of the accessibility to the Wulin or Qianjiang CBD, from 2012 to 2015. The other variables remain unchanged.

Table 7 shows the regression results of the continuous DID model. In the regression results, all variables—except the age and environmental quality variables—are significant. The Wulin CBD results show that the coefficient of the T*D interaction term is significantly positive, which is in line with the results of the traditional DID model. The regression coefficient of T*D is 0.314, which means that for every 1% increase in accessibility to the Wulin CBD, the residential price will increase by 0.369%. This improvement in traffic accessibility to the Wulin CBD that came with the opening of Qiushi III will significantly increase housing prices in areas where the rate of change in traffic accessibility is higher. The Qianjiang CBD results show that, in contrast to the traditional DID model, the coefficient of the T*D interaction term (0.142) is significantly positive. That is, for every 1% increase in traffic accessibility to Qianjiang CBD, the housing price will increase by 0.153%. The improvement in traffic accessibility to the Qianjiang CBD brought about by the opening of the project will significantly increase the housing prices in this area. The different results for Qianjiang CBD (between the grouping DID method and the continuous DID method) may be because, from 2012 to 2015, the Qianjiang CBD, as a new urban center, had lower real impact on the residential market than Wulin CBD, as a traditional city center. Moreover, this weak effect is revealed only in the continuous DID model. The results of the parallel trend test in measuring the accessibility of Wulin CBD are shown in Figure 6. Before treatment, the coefficients of the interaction term between the quarterly time variable and the accessibility change variable are mostly insignificant; after the treatment, that is, after the project is completed and opened to traffic, the coefficients of the accessibility change, and the time interaction term are almost all significantly positive. The significant impact of accessibility changes on housing prices is further confirmed. In contrast, the parallel trend test of the accessibility measure of Qianjiang CBD shows that the coefficients of the interaction terms are mostly insignificant before and after the treatment, which indicates that the impact of changing Qianjiang CBD accessibility is not very robust.

Table 7.

Results of the Impact of Changes in Traffic Accessibility on Property Prices (Continuous Difference-in-Difference [DID])

Central business district (CBD)	Wulin CBD				Qianjiang CBD
Variable	Coefficient	P-value	Elastic coefficient	Semi-elastic coefficient	Coefficient	P-value	Elastic coefficient	Semi-elastic coefficient
ln age	−.010	.444	−.010	na	−.007	.624	−.007	na
ln distance to West Lake	−.223***	.000	−.223***	na	−.208***	.000	−.208***	na
ln distance to Wulin CBD	−.067***	.000	−.067***	na	−.066***	.000	−.066***	na
ln distance to Qianjiang CBD	−.039***	.000	−.039***	na	−.047***	.000	−.047***	na
Environment quality	.012*	.056	na	.012*	.010	.141	na	.010
University	.040***	.000	na	.041***	.059***	.000	na	.061***
Educational convenience	.022***	.003	na	.022***	.029***	.000	na	.029***
Sports convenience	.025***	.000	na	.025***	.023***	.000	na	.023***
Property management quality	.055***	.000	na	.057***	.059***	.000	na	.061***
Noise	−.045***	.000	na	−.044***	−.061***	.000	na	−.059***
Time variable (T)	−.179***	.000	na	−.164***	−.141***	.000	na	−.132***
T*grouping variable (D)	.314***	.000	na	.369***	.142***	.000	na	.153***
Adjusted R²	0.615	na	na	na	0.590	na	na	na
Obs.	1006	na	na	na	1006	na	na	na

Note: Obs. = number of observations; ln = log_e(N); na = not applicable.

p < 0.05; ***p < 0.001.

In summary, in the empirical part, this study compares the strength of accessibility interpretation calculated by the three types of traffic impedance parameters: Euclidean distance, road distance, and actual transit time. The results reveal that the actual transit time has the strongest connection with house prices, and the fitting effect of this model is the best. Then, this paper uses the DID method to analyze the impact of Qiushi III on housing prices. The regression results show that, for houses near the project, the opening of the project will not lead to a significant price premium. Also, the communities within the range of 0–2.5 km from the project entrance are likely to see decrease in residential prices because of the opening of the highway when compared with residential buildings that are located 2.5 km outside the project. Additionally, the prices of houses adjacent to both the entrance and exit of the Hangzhou Expressway Network will fall because of the opening of the project. However, while the impact of the opening of the project on the accessibility of the city center is still being considered, it must be acknowledged that the opening has improved the accessibility of residences to Wulin or Qianjiang CBD. Residential prices with a greater increase in CBD accessibility will rise significantly, directly because of increased accessibility. The impact of increasing accessibility to Wulin CBD on residential prices is greater than the effect of increasing accessibility to Qianjiang CBD.

Conclusions

The impact of the urban expressway construction on residential prices has always been a hot topic in the field of real estate economy. However, previous studies in this area still have limitations. On the one hand, the factors considered in the urban expressway’s impact on residential prices are generally limited to the distance from the residential area to the expressway. The division between the treatment and the control groups in the DID method is often based on distance. This cannot fully reflect actual traffic demand, for several reasons. On the other hand, in the accessibility calculation, most studies define the distance impedance as the Euclidean distance from one point to another point, or the road distance, ignoring the general congestion phenomenon found in road traffic conditions. This paper attempts to solve these two problems by making more accurate calculations of traffic accessibility, separating the impact of the expressway on traffic accessibility, and then evaluating the impact of the urban expressway construction on house prices.

First, this study systematically reviews the methods used in existing research, as well as the results of accessibility theory and the impact of expressways on the real estate market. Second, this study measures the impact of expressway construction on traffic accessibility of residential communities, using real transit time as a cost parameter, to construct a more accurate accessibility index. Then, the level of transportation accessibility from Hangzhou residential areas to two city centers, Wulin CBD and Qianjiang CBD, is systematically expressed. Third, this study explores the impact of the opening of Qiushi III on residential prices from three perspectives: close to the project entrance and exit, the overall operational status of the city’s expressway network, and changes in accessibility.

The study reveals that, because of the potential negative externalities of the expressway, those residential communities adjacent to the entrance and exit of the highways will not have a significant increase in housing prices with the opening of Qiushi III. When the impact of accessibility is considered separately, the housing prices of those residential communities with a greater increase in accessibility to the CBDs will increase significantly, because of the opening of the project. This illustrates that improving the accessibility of road infrastructure will have a significant premium effect on housing prices. The results of this study provide a basis for the city governments to accelerate the establishment of the expressway systems and to improve their urban road networks. This research also gives ideas on how to guide the development and construction of new urban areas and how the real estate market should be developed. Empirical results in this paper show that the use of continuous type DID is a better approach to testing the impact of road improvements, using the accessibility index as a basis for observation. As this method can better reflect the network effect generated by speed change, there is no need to strictly differentiate between treatment and control groups based on distance or other variables, thus breaking the spatial boundaries of treatment groups. This method can be a reference for future studies. It should also be noted that this study was able to observe the effect of the important elevated road using the advantages of a relatively undisturbed time window. In fact, this expressway will still have long-term impacts at a later stage, but the effects will be difficult to distinguish from that of other new roads. Therefore, if there is frequent road construction that leads to constant changes in the accessibility index, the use of panel data to analyze the impact of traffic network accessibility may be more appropriate.

Supplemental Material

sj-docx-1-trr-10.1177_03611981221084700 – Supplemental material for Effects of Urban Expressways on Housing Prices: A Case Study of Qiushi Highway, Hangzhou, China

Supplemental material, sj-docx-1-trr-10.1177_03611981221084700 for Effects of Urban Expressways on Housing Prices: A Case Study of Qiushi Highway, Hangzhou, China by Ling Zhang, Rui Shen, Tianqi Li and Qingfeng Zhou in Transportation Research Record

Footnotes

Acknowledgements

The authors would like to thank the editor and anonymous reviewers for the constructive comments and suggestions.

Author Contributions

The authors confirm the contribution to the paper as follows: study concept and design: L. Zhang; data collection: T. Li; analysis and interpretation of results: T. Li, R. Shen, Q. Zhou; draft manuscript preparation: L. Zhang, R. Shen. All authors reviewed the results and approved the final version of the manuscript.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This paper is supported by the National Natural Science Foundation of China No. 72174178.

ORCID iDs

Ling Zhang

Qingfeng Zhou

Data Accessibility Statement

The data of housing price comes from the Transparent House Sales Research Institute (), a real estate agency operating within the Hangzhou Housing Administration Bureau. The data of road speed comes from real-time vehicle monitoring data from the Gaode City Brain data center. Requests for access to these data should be made to Qingfeng Zhou (tianwen610031@163.com).

Supplemental Material

Supplemental material for this article is available online.

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