Exploring some spatially constrained delineation methods in segmenting the Malaysian commercial property market
Abstract
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.
Keyword : submarket, segmentation, delineation, commercial property market, spatial constraint, cluster analysis, Principal Component Analysis (PCA)
How to Cite
Usman, H., & Lizam, M. (2023). Exploring some spatially constrained delineation methods in segmenting the Malaysian commercial property market. International Journal of Strategic Property Management, 27(6), 379–390. https://doi.org/10.3846/ijspm.2023.20498
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Helbich, M., Brunauer, W., Hagenauer, J., & Leitner, M. (2013). Data-driven regionalization of housing markets. Annals of the Association of American Geographers, 103(4), 871–889. https://doi.org/10.1080/00045608.2012.707587
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Inoue, R., Ishiyama, R., & Sugiura, A. (2020). Identifying local differences with fused-MCP: an apartment rental market case study on geographical segmentation detection. Japanese Journal of Statistics and Data Science, 3, 183–214. https://doi.org/10.1007/s42081-019-00070-y
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Keskin, B., & Watkins, C. (2017). Defining spatial housing submarkets: exploring the case for expert delineated boundaries. Urban Studies, 54(6), 1446–1462. https://doi.org/10.1177/0042098015620351
Kopczewska, K., & Ćwiakowski, P. (2021). Spatio-temporal stability of housing submarkets. Tracking spatial location of clusters of geographically weighted regression estimates of price determinants. Land Use Policy, 103, 105292. https://doi.org/10.1016/j.landusepol.2021.105292
Le Gallo, J., López, F. A., & Chasco, C. (2020). Testing for spatial group-wise heteroskedasticity in spatial autocorrelation regression models: Lagrange multiplier scan tests. Annals of Regional Science, 64(2), 287–312. https://doi.org/10.1007/s00168-019-00919-w
Leishman, C., Costello, G., Rowley, S., & Watkins, C. (2013). The predictive performance of multilevel models of housing sub-markets: a comparative analysis. Urban Studies, 50, 1201–1220. https://doi.org/10.1177/0042098012466603
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Lisi, G. (2019). Property valuation: the hedonic pricing model – location and housing submarkets. Journal of Property Investment & Finance, 37(6), 589–596. https://doi.org/10.1108/JPIF-07-2019-0093
Małkowska, A., & Uhruska, M. (2019). Towards specialization or extension? Searching for valuation services models using cluster analysis. Real Estate Management and Valuation, 27(4), 27–38. https://doi.org/10.2478/remav-2019-0033
Mayer, M., Bourassa, S. C., Hoesli, M., & Scognamiglio, D. (2019). Estimation and updating methods for hedonic valuation. Journal of European Real Estate Research, 12(1), 134–150. https://doi.org/10.1108/JERER-08-2018-0035
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