TY - JOUR
T1 - Purchase Motivation, Landscape Preference, and Housing Prices
T2 - Quantile Hedonic Analysis in Guangzhou, China
AU - Wen, Haizhen
AU - Li, Shuyuan
AU - Hui, Eddie C.M.
AU - Jia, Shijun
AU - Cui, Wenjun
N1 - Funding Information:
The authors would like to thank the editor and anonymous referees for their excellent comments and suggestions. This study was supported by the National Natural Science Foundation of China (No. 71974169), and The Hong Kong Polytechnic University’s research funding (Project No. G-SB0D).
Publisher Copyright:
© 2021 American Society of Civil Engineers.
PY - 2021/9/1
Y1 - 2021/9/1
N2 - Urban landscapes are important factors that affect housing prices, and significant differences between landscape preferences of various homebuyers may be observed because of the different reasons for purchasing a house (consumption or investment). However, the hedonic price model widely applied in most existing studies only captures the average effects of landscapes as a whole sample, and may ignore the heterogeneity of landscape preferences. To fill this gap, this study constructed hedonic price models and quantile regression models with the housing data in Guangzhou, China from 2013 to 2016 and analyzed the landscape preferences of buyers with different purchase motivations. Empirical results showed that the landscape preferences of buyers were different in housing submarkets. The implicit value of landscapes was greater in consumption demand than in investment demand, whereas investment buyers were more vulnerable to the disamenity effect of unattractive landscapes. In addition, the quantile effect of landscapes was identified, in which the buyers of high-priced housing will pay more for high-quality landscapes. This study revealed the diversified housing demands and landscape preferences of homebuyers, which is important for urban planning and project development.
AB - Urban landscapes are important factors that affect housing prices, and significant differences between landscape preferences of various homebuyers may be observed because of the different reasons for purchasing a house (consumption or investment). However, the hedonic price model widely applied in most existing studies only captures the average effects of landscapes as a whole sample, and may ignore the heterogeneity of landscape preferences. To fill this gap, this study constructed hedonic price models and quantile regression models with the housing data in Guangzhou, China from 2013 to 2016 and analyzed the landscape preferences of buyers with different purchase motivations. Empirical results showed that the landscape preferences of buyers were different in housing submarkets. The implicit value of landscapes was greater in consumption demand than in investment demand, whereas investment buyers were more vulnerable to the disamenity effect of unattractive landscapes. In addition, the quantile effect of landscapes was identified, in which the buyers of high-priced housing will pay more for high-quality landscapes. This study revealed the diversified housing demands and landscape preferences of homebuyers, which is important for urban planning and project development.
KW - Housing price
KW - Housing sub-markets
KW - Landscape preference
KW - Purchase motivation
KW - Quantile regression
UR - http://www.scopus.com/inward/record.url?scp=85106879262&partnerID=8YFLogxK
U2 - 10.1061/(ASCE)UP.1943-5444.0000734
DO - 10.1061/(ASCE)UP.1943-5444.0000734
M3 - Journal article
AN - SCOPUS:85106879262
SN - 0733-9488
VL - 147
JO - Journal of Urban Planning and Development
JF - Journal of Urban Planning and Development
IS - 3
M1 - 04021033
ER -
