Identifying customer behavioral factors and price premiums of green building purchasing

In recent years, global urbanization and overdevelopment have resulted in environmental degradation and an energy crisis. Promoting green buildings is among the most effective methods for achieving environmental sustainability. Although the initial costs of green buildings are higher than those of ordinary buildings, people perceive that the environmental benefits of green buildings justify their higher price premiums. From a developer's perspective, devising optimal pricing strategies according to customer-perceived prices and developers' expected profit is complex and difficult. Hence, in this study, we developed a framework based on the Howard-Sheth model of consumer behavior to identify behavioral factors that may affect consumer purchases of green buildings. An artificial neural network (ANN) was then used to develop a pricing model for predicting the price premiums of green buildings. The results revealed that the ANN model's overall prediction capability was 94%; the model's robustness was demonstrated by comparing the results produced using the model with those produced using a multiple regression analysis. In addition, the characteristics of consumers who were willing to accept higher price premiums for green buildings were identified and discussed. The proposed model can be applied as an effective decision-support tool for green building pricing and formulating marketing strategies.