Swarm-inspired data-driven approach for housing market segmentation: a case study of Taipei city

Data-driven housing-market segmentation has been given increasing prominence for its objectiveness in identifying submarkets based on the housing data’s underlying structures. However, when handling high-dimensionality housing dataset, traditional statistical-clustering methods have been found to tend to lose low-variance information of the dataset and be deficient in deriving the globally optimal number of submarkets. Accordingly, with the intention of achieving more rigorous high-dimensionality housing market segmentation, a swarm-inspired projection (SIP) algorithm is introduced by this study. Using a high-dimensionality Taipei city’s housing dataset in a case study, a comparison of the proposed SIP algorithm and a statistical-clustering method using the combination of principal component analysis (PCA) and K-means clustering is conducted in evaluating the predictive accuracy of hedonic price models of the housing submarkets. The results show that, as compared to the original single market, the segmented submarkets resulting from SIP algorithm are more homogenous and distinctive, where the resulted hedonic price models have high-level statistical explanation and disparate sets of hedonic prices for different submarkets. In addition, as compared to the use of a statistical-clustering method, SIP algorithm is found to obtain a more optimal number of submarkets, where the resulted hedonic price models are found to achieve greater improvement of statistical explanation and more stable reduction of prediction error. These findings highlight the advantages of our proposed SIP algorithm in high-dimensionality housing market segmentation, and thus it is hoped that the present research will serve as a practical tool to better inform further studies aimed at market-segmentation-related problems.