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

Yue Ying, Mila Koeva, Monika Kuffer, Kwabena Obeng Asiama, Xia Li and Jaap Zevenbergen
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Yue Ying: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands
Mila Koeva: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands
Monika Kuffer: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands
Kwabena Obeng Asiama: Geodetic Institute, Gottfried Wilhelm Leibniz University of Hannover, Nienburger Strasse 1, 30167 Hannover, Germany
Xia Li: School of Earth Science and Resources, Chang’an University, No. 126, Yanta Road, Xi’an 710064, China
Jaap Zevenbergen: Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Enschede, The Netherlands

Land, 2020, vol. 10, issue 1, 1-26

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.

Keywords: 3D modelling; property value; remote sensing; hedonic price model; China (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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