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Sustainability has been the subject of several scientific investigations. Many researchers in the construction industry have also examined a range of sustainability-related studies. However, few studies have thoroughly reviewed implementing sustainability concepts in high-rise residential buildings (HRRBs).

By adopting scientometrics and systematic review (SR), this study seeks to map out recent sustainability trends and concepts in the design, development and operation of HRRBs worldwide and in Hong Kong. With a focus on bibliographic records from the Web of Science (WoS) database, 1,395 journal articles from 2013 to 2022 were analysed. Furthermore, thirteen studies were systematically reviewed.

The SR indicated that sustainable practices in developing Hong Kong's HRRBs emphasised zero-carbon buildings, reduced energy usage and energy-efficient retrofitting. Likewise, terms such as BIM, urban density, life cycle assessment and system dynamics are strongly connected with clusters that include “residential buildings”, “high-rise buildings” and “high-rise residential buildings”. The study identified significant themes in establishing HRRBs by combining sustainable practices, emphasising urban governance and policy management, building performance and thermal comfort, energy and design optimisation, occupant behaviour and sensitivity analysis. Core sustainability ideas have improved resource management, air quality management and knowledge of user behaviour in HRRBs.

The study allows researchers and practitioners to explore future research directions in the built environment per the application of sustainable concepts in the development of HRRBs from design, construction and post-construction phases.

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.