How to attract customers to buy green housing? Their heterogeneous willingness to pay for different attributes. (1st September 2019)

• Record Type: Journal Article
• Authors: He, Chenchen; Yu, Shiwei; Han, Qi; de Vries, Bauke
• Is Part Of: Journal of cleaner production. Volume 230(2019)
• Page Start: 709
• Page End: 719
• Publication Date: 2019-09-01
• Subjects: Green housing -- Willingness to pay -- Customer clusters -- Discrete choice model -- Developing strategy -- Stimulating policy; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2019.05.160
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: To promote the green housing (GH) diffusion, the paper analyzes the customers' willingness to pay (WTP) for GH. Especially, it divides them into clusters by two-step cluster to understand the heterogeneity in WTP. Moreover, instead of concentrating on the whole GH, it calculates the WTP for different attributes of GH by discrete choice model. 313 respondents are involved and divided into three clusters. And then, their heterogeneous WTP for attributes are calculated. It finds that: (i) The customers could be classified according to their socio-demographic and knowledge of GH, whereas the environmental concern could not be used to divide them; (ii) In general, the most attractive attributes are accessibility within 400 m to the service facilities and the comfortable indoor air quality, which could earn extra 381.63 and 375.5 RMB/m 2 respectively; (iii) Recently, people in the upper-middle class are the target customers for GH, since they are attracted by most of the green-related attributes and have the largest WTP(1233.60 RMB/m 2 ). They most want to pay for indoor air quality and higher green space ratio. People in the middle class are the potential customers for GH, while the lower-middle class does not prefer to GH. Accordingly, it further proposes suggestions for developers and government in terms of which cluster is the target group and what attributes are the most important to different customers.