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Modelling the drivers of data science techniques for real estate professionals in the fourth industrial revolution era

Temidayo Oluwasola Osunsanmi (School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh, UK)
Timothy O. Olawumi (School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh, UK)
Andrew Smith (School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh, UK)
Suha Jaradat (School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh, UK)
Clinton Aigbavboa (CIDB Centre of Excellence, University of Johannesburg, Johannesburg, South Africa)
John Aliu (CIDB Centre of Excellence, University of Johannesburg, Johannesburg, South Africa)
Ayodeji Oke (Department of Quantity Surveying, Federal University of Technology Akure, Akure, Nigeria) (School of Social Sciences, Universiti Sains Malaysia, Penang, Malaysia)
Oluwaseyi Ajayi (Department of Quantity Surveying, University of Lagos, Lagos, Nigeria)
Opeyemi Oyeyipo (Bells University of Technology, Ota, Nigeria)

Property Management

ISSN: 0263-7472

Article publication date: 6 January 2023

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Abstract

Purpose

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

Design/methodology/approach

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

Findings

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Originality/value

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.

Keywords

Citation

Osunsanmi, T.O., Olawumi, T.O., Smith, A., Jaradat, S., Aigbavboa, C., Aliu, J., Oke, A., Ajayi, O. and Oyeyipo, O. (2023), "Modelling the drivers of data science techniques for real estate professionals in the fourth industrial revolution era", Property Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/PM-05-2022-0034

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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