EXPLORING SOME SPATIALLY CONSTRAINED DELINEATION METHODS IN SEGMENTING THE MALAYSIAN COMMERCIAL PROPERTY MARKET

This study delves into the property submarket in Kuala Lumpur and Selangor, Malaysia. The submarket is anticipated to be simple, uniform, and dense, making it highly influenced by neighbouring properties. However, traditional data-driven methods that overlook spatial contiguity disregard this densit...

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Bibliographic Details
Published in:International journal of strategic property management 2023-12, Vol.27 (6), p.379-390
Main Authors: Usman, Hamza, Lizam, Mohd
Format: Article
Language:English
Subjects:
Cluster analysis
commercial property market
Commercial real estate
delineation
Market equilibrium
Market segmentation
Marketing
Methods
Prices
Principal components analysis
segmentation
spatial constraint
Spatial data
submarket
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Summary:This study delves into the property submarket in Kuala Lumpur and Selangor, Malaysia. The submarket is anticipated to be simple, uniform, and dense, making it highly influenced by neighbouring properties. However, traditional data-driven methods that overlook spatial contiguity disregard this density condition. To tackle this problem, the study investigates spatially constrained data-driven methods utilizing Principal Component Analysis (PCA) and cluster analysis. The findings reveal that spatially constrained methods outperform traditional methods by minimizing errors and enhancing model fit. Specifically, the two-step cluster method and k-means cluster method reduce errors by 6.96% and 7.22%, respectively, but at the cost of model fit by 11.23% and 13.94%. Conversely, the spatial k-means and spatial agglomerative hierarchical cluster methods reduce errors by 8.68% and 8.17%, respectively, while improving model fit by 7.1% and 6.35%. Hence, the study concludes that spatially constrained data-driven methods are more effective in differentiating commercial property submarkets than traditional methods.
ISSN:1648-715X
1648-9179
DOI:10.3846/ijspm.2023.20498