Identifying Market Segment for the Assessment of a Price Premium for Green Certified Housing: A Cluster Analysis Approach

Abstract

While the literature confirms the existence of a green premium, various researchers have acknowledged the importance of homogeneity in the dataset used for premium assessment. This study developed a systematic approach to extract a sales subsample containing transactions from green-certified apartments and their most similar counterparts based on cluster analysis. The study applied k-means and PAM clustering algorithms to split an extensive sales sample of 81,605 transactions into sales subsamples. A sales subsample containing transactions from green-certified apartments and their peers was extracted and used for green premium estimation. The results indicate that through cluster analysis, a market segment containing above 80% of the total housing transactions from green-certified apartments could be identified. Through the hedonic model, a green premium of 26.2% was identified from the entire sales sample (no market segmentation). However, this value was reduced to 12.2 and 17.8% when estimated from a sales subsample extracted through k-means and PAM clustering, respectively. These findings have implications for the close assessment of green certification impact on the sale prices. In addition, they can also serve as an indicator of housing categories that need extra effort to promote green practices in that particular market segment.