The effect of new metro stations on local land use and housing prices: The case of Wuhan, China

Tan, Ronghui; He, Qingsong; Zhou, Kehao; Xie, Peng

doi:10.1016/j.jtrangeo.2019.102488

Cited by 57 publications

(38 citation statements)

References 48 publications

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“…Taking distance as an example, we applied Euclidean distance to different locational attributes as the calculation method for the following two reasons: (1) this calculation is theoretically simple and straightforward; (2) according to the local contexts, due to the fast city expansion, the current road networks of Xi'an have not finalised and are subjective to changes in the future. The distance can also be calculated in network distance (using the primary/secondary roads) [72], or travel time [73,74]. Therefore, we advise that in future studies, the proxies for 2D factors such as the distance should be selected carefully according to the research purpose and scale.…”

Section: Model Performance Assessmentmentioning

confidence: 99%

Making the Third Dimension (3D) Explicit in Hedonic Price Modelling: A Case Study of Xi’an, China

Ying

Koeva

Kuffer

et al. 2020

Land

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Recent rapid population growth and increasing urbanisation have led to fast vertical developments in urban areas. Therefore, in the context of the dynamic property market, factors related to the third dimension (3D) need to be considered. Current hedonic price modelling (HPM) studies have little explicit consideration for the third dimension, which may have a significant influence on modelling property values in complex urban environments. Therefore, our research aims to narrow the cognitive gap of the missing third dimension by assessing both 2D and 3D HPM and identifying important 3D factors for spatial analysis and visualisation in the selected study area, Xi’an, China. The statistical methods we used for 2D HPM are ordinary least squares (OLS) and geographically weighted regression (GWR). In 2D HPM, they both have very low R2 (0.111 in OLS and 0.217 in GWR), showing a very limited generalisation potential. However, a significant improvement is observed when adding 3D factors, namely view quality, sky view factor (SVF), sunlight and property orientation. The obtained higher R2 (0.414) shows the importance of the third dimension or—3D factors for HPM. Our findings demonstrate the necessity to include such factors into HPM and to develop 3D models with a higher level of details (LoD) to serve more purposes such as fair property taxation.

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Section: Model Performance Assessmentmentioning

confidence: 99%

Making the Third Dimension (3D) Explicit in Hedonic Price Modelling: A Case Study of Xi’an, China

Ying

Koeva

Kuffer

et al. 2020

Land

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“…Trong lĩnh vực bất động sản, nó được ví như "chiếc đòn bẩy" cho giá trị bất động sản. Ronghui Tan và cộng sự (2019) đã chỉ ra trong phạm vi 1600m từ các ga tàu điện ngầm mới tại Vũ Hán, Trung Quốc, giá nhà đất có thể có giá trị cao hơn 7% -14% so với các bất động sản ở xa hơn [1]. Ở một phân tích tổng hợp theo các mức khoảng cách và loại bất động sản khác nhau, nghiên cứu của Debrezion và các cộng sự (2007) cho thấy các nhà ga đường sắt có ảnh hưởng làm tăng giá của bất động sản sử dụng cho mục đích thương mại chỉ thấy rõ ở khoảng cách ngắn.…”

Section: Mở đầUunclassified

“…Nhìn chung, các nghiên cứu cho thấy tác động của dự án phát triển hạ tầng giao thông có cả mặt tích cực và tiêu cực. Phương pháp để đánh giá chủ yếu dựa trên phân tích hồi quy Hedonic [4,6,7], hồi quy trọng số địa lý GWR [3], hồi quy đa biến [2,8], kết hợp với các công cụ hỗ trợ phân tích không gian trong GIS [1,9]. Hầu hết các nghiên cứu đều sử dụng bộ dữ liệu liên quan đến giá bất động sản (bao gồm cả đất và nhà) để xem xét mối quan hệ với hạ tầng giao thông.…”

Section: Mở đầUunclassified

Effects of Urban Transportation Infrastructure Development Projects on Residential Land Price in Cau Giay District, Hanoi City

Thuy

Tu²,

Tuan³

et al. 2020

EES

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Urban transportation infrastructure development projects usually act as a lever for real estate values. This research aims to assess the effects of some projects on urban land prices in three wards of Cau Giay District, namely Quan Hoa, Dich Vong, and Dich Vong Hau wards. With 161 samples surveyed, we established a multivariate linear regression model initially having 11 variables. As the result, six variables are identified as having significant effect on land prices in the study area. Among those, three variables are related to the local transportation infrastructure, namely: location of land parcels; distance to urban railway lines; and distance to public transport stations. Location of land parcels and distance to public transport stations are factors that have a significant influence in reality. Besides, the distance to the urban railway lines is the factor that has a positive impact on land prices, but it is only still a potential and should be extensively investigated in the future because the urban railway project is still in the implementation phase.

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“…The spatiotemporal characteristics of land price or land use intensity are studies separately. The research on land price mainly focuses on spatial heterogeneity [15,16], spatiotemporal evolution [17][18][19] and influencing factors [20][21][22][23][24]. The study of land use intensity chiefly concentrates on the calculation of use intensity level [25][26][27][28], influencing factors [29,30] and the utilization measures [31].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal pattern and coordination relationship between urban residential land price and land use intensity in 31 provinces and cities in China

et al. 2021

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The trend towards efficient and intensive use of land resources is an inevitable outcome of current social development. The rational matching of urban land prices and land use intensity has become an important factor under accelerating urbanization, and promotes the healthy development of the social economy. Using data on residential land price and on land use intensity for 31 provinces and cities in China, we employ the E-G cointegration test and quadrant map classification to determine the coordination relationship between land price and land use intensity. We then employ HR coordination to calculate the coordination degree of land price and land use intensity, and classify the coordination type accordingly. Our results are as follows. (1) The spatio-temporal distribution of urban land price shows high variability with multiple maxima, and follows a decreasing trend from the southeast coastal area to the northwest inland area and the northeast. (2) The overall land use intensity is at or above the middle level, and shows large spatial differences between provinces, but the agglomeration between provinces is increasing. (3) From the perspective of the relationship between urban land price and land use intensity at the inter-provincial scale, we find that the land price and land use intensity are well coordinated, and the number of provinces has been dynamically changing during different development periods. There is an east-west difference in the spatial distribution of land price and land use intensity coordination level. Different provinces and cities with the same coordination stage show differences in their land price and land use intensity level.

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The effect of new metro stations on local land use and housing prices: The case of Wuhan, China

Cited by 57 publications

References 48 publications

Making the Third Dimension (3D) Explicit in Hedonic Price Modelling: A Case Study of Xi’an, China

Making the Third Dimension (3D) Explicit in Hedonic Price Modelling: A Case Study of Xi’an, China

Effects of Urban Transportation Infrastructure Development Projects on Residential Land Price in Cau Giay District, Hanoi City

Spatiotemporal pattern and coordination relationship between urban residential land price and land use intensity in 31 provinces and cities in China

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