Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 24 |
| Seitenumfang | 26 |
| Fachzeitschrift | Land Use Policy |
| Jahrgang | 10 |
| Ausgabenummer | 1 |
| Frühes Online-Datum | 30 Dez. 2020 |
| Publikationsstatus | Veröffentlicht - Jan. 2021 |
Abstract
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 R 2 (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 R 2 (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.
ASJC Scopus Sachgebiete
- Umweltwissenschaften (insg.)
- Globaler Wandel
- Umweltwissenschaften (insg.)
- Ökologie
- Umweltwissenschaften (insg.)
- Natur- und Landschaftsschutz
Ziele für nachhaltige Entwicklung
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in: Land Use Policy, Jahrgang 10, Nr. 1, 24, 01.2021.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Making the Third Dimension (3D) Explicit in Hedonic Price Modelling: A Case Study of Xi’an, China
AU - Ying, Yue
AU - Koeva, Mila
AU - Kuffer, Monika
AU - Asiama, Kwabena
AU - Li, X
AU - Zevenbergen, Jaap
N1 - Funding Information: Y.Y. is funded by China Scholarship Council (CSC) under the grant number [201906560015].
PY - 2021/1
Y1 - 2021/1
N2 - 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 R 2 (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 R 2 (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.
AB - 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 R 2 (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 R 2 (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.
KW - 3D modelling
KW - China
KW - Hedonic price model
KW - Property value
KW - Remote sensing
UR - http://www.scopus.com/inward/record.url?scp=85098854149&partnerID=8YFLogxK
U2 - 10.3390/land10010024
DO - 10.3390/land10010024
M3 - Article
VL - 10
JO - Land Use Policy
JF - Land Use Policy
SN - 0264-8377
IS - 1
M1 - 24
ER -