A Novel Data Analytics Methodology for Analyzing Real Estate Brokerage Markets with Case Study of Dubai
Author(s): |
Ahmed Saif Al Abdulsalam
Maged Mohammed Al-Baiti Al Hashemi Mohammed Zayed Sulaiman Aleissaee Abdelaziz Saleh Husain Almansoori Gurdal Ertek Thouraya Gherissi Labben |
---|---|
Medium: | journal article |
Language(s): | English |
Published in: | Buildings, 8 October 2024, n. 10, v. 14 |
Page(s): | 3068 |
DOI: | 10.3390/buildings14103068 |
Abstract: |
Despite the vast economic impact of real estate markets worldwide, research on real estate brokerage markets remains limited. Specifically, there are few studies that provide a systematic, integrated, and replicable analytical methodology to analyze and benchmark a given real estate brokerage market. To this end, this paper introduces a data analytics methodology for analyzing real estate brokerage markets, integrating various statistical and analytical methods to extract insights from market data, supporting real estate investment decisions. The applicability of the methodology is demonstrated with a case study analyzing data from the top 50 real estate brokerage firms in Dubai, UAE. As shown in the case study, applying this methodology to brokerage market data enables the visual benchmarking of firms, identification of similarities between them, profiling and comparison of clusters of firms, and exploration of the impacts of various categorical and numerical attributes on performance. A notable finding for the Dubai real estate brokerage market is that it takes a minimum of 700 days for a brokerage firm to mature and advance to the next level of business success. |
Copyright: | © 2024 by the authors; licensee MDPI, Basel, Switzerland. |
License: | This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met. |
3.39 MB
- About this
data sheet - Reference-ID
10804711 - Published on:
10/11/2024 - Last updated on:
10/11/2024