Abstract
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.
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Data availability
The datasets generated and analysed during the current study are not publicly available due to privacy issues and the subjection to a confidential agreement but are available from the corresponding author on reasonable request.
Notes
The code for SIP algorithm, PCA and K-means clustering, and hedonic price modeling can be found online in this link https://github.com/Tingting202/Swarm-intelligence-Housing-Market-Segmentation.
It is noteworthy that the recorded transactions in the database may be smaller than the actual transactions happened in Taipei city during this period. Transaction records from 2008 to 2010 were obtained mainly because this set of data were agreed to be disclosed to the authors, while more updated transaction records have not been disclosed yet.
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Chen, JH., Ji, T., Su, MC. et al. Swarm-inspired data-driven approach for housing market segmentation: a case study of Taipei city. J Hous and the Built Environ 36, 1787–1811 (2021). https://doi.org/10.1007/s10901-021-09824-1
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DOI: https://doi.org/10.1007/s10901-021-09824-1