Rental Market Segmentation with Clustering: A Case Study of the Dubai Real Estate Market

Mohamed Zoghbour (), Mohamed Yusuf Hassan () and Gurdal Ertek ()
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Mohamed Zoghbour: United Arab Emirates University
Mohamed Yusuf Hassan: United Arab Emirates University
Gurdal Ertek: United Arab Emirates University

A chapter in Advanced Data Analytics, Machine Learning and AI in Business, 2026, pp 390-409 from Springer

Abstract: Abstract Rental market segmentation is an essential analysis in the real estate sector because it enables the identification of consumer patterns and data-driven decision-making. This study investigates the problem of rental market segmentation by utilizing a portfolio of clustering analysis methods to comprehensively analyze multidimensional data on apartments and villas. K-means, DBSCAN, (Density-Based Spatial Clustering of Applications with Noise) and hierarchical clustering are chosen as the methods. These techniques are applied and interpreted through visual analytics to identify actionable patterns that can inform strategic decision making, such as pricing strategies, targeted marketing campaigns, and resource optimization. By concentrating exclusively on the integrated application of clustering techniques and visual analytics, this study provides a diverse range of insights that could possibly be obtained. The selected market for the analysis is the real estate rental market in Dubai, the United Arab Emirates (UAE), a dynamic and rapidly evolving real estate environment.

Keywords: K-Means Clustering; DBSCAN; Hierarchical Clustering; Visual Analytics; Real Estate; Dubai; United Arab Emirates (UAE) (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-23493-3_24

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