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The purpose of this paper is to study the user experiences of the futures wheel method to investigate its suitability to advance futures thinking in the real estate field.

The user experiences of the futures wheel method are investigated through questionnaire answers of 114 master’s level students and real estate experts taking part in future wheel workshops.

The futures wheel method could enhance future-oriented thinking and decision-making in the real estate field. The respondents see futures thinking as an important skill and recognize several advantages concerning the method.

The futures wheel method bears great potential to be used in the real estate sector and it could be a fruitful addition to the curriculums at different education levels in real estate studies.

Futures thinking is essential when aiming for sustainable decisions in the real estate field which again would benefit the whole surrounding society.

This paper is the first published paper concentrating on the user experiences of the future wheel method in the real estate sector. The benefits and the disadvantages of the method are investigated but also the attitudes indicating the potential of the method to be successfully adopted in the field are analyzed.

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.

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.

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.

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.