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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.

This paper investigates the relationship between the sustainable finance disclosure regulation (SFDR) and the performance of unlisted real estate funds.

While existing literature has primarily focused on the impact of voluntary sustainability disclosure, such as certifications or reporting standards, this study addresses a significant research gap by constructing and analyzing the financial J-Curve of 40 funds under the SFDR. The authors employ a panel regression analysis to examine the effects of different SFDR categories on fund performance.

The findings reveal that funds categorized under Article 8 of the SFDR do not exhibit significantly poorer performance compared to funds categorized under Article 6 during the initial phase after launch. On average, Article 8 funds even demonstrate positive returns earlier than their peers. However, the panel regression analysis suggests that Article 8 funds slightly underperform when compared to Article 6 funds over time.

While investors may not anticipate lower initial returns when opting for higher SFDR categories, they should nevertheless be aware of the limitations inherent in the existing SFDR labeling system within the unlisted real estate sector.

To the best of our knowledge, this study represents the first quantitative examination of unlisted real estate fund performance under the SFDR. By providing unique insights into the J-Curves of funds, our research contributes to the existing body of knowledge on the impact of sustainability regulations in the financial sector.

The delay in real estate projects in India is pervasive. Organization and management (O&M) and project management (PM)-related challenges are argued to contribute to project delays. This study examined the O&M and PM factors that cause delays, the level of implementation of various O&M and PM aspects in real estate projects and how the challenges can be alleviated.

Perception surveys among the consumers and relevant stakeholders engaged in real estate projects in the Bhubaneswar and Cuttack regions of India were conducted to collect data on the factors of delay and implementation of the O&M and PM aspects. Relevant statistical methods and structural equation modeling (SEM) were used for data analysis.

Findings suggest that from the O&M point of view, poor decision-making, mishandling of finance, concurrent execution of many projects, diversion and misuse of finance for unrelated activities, lack of PM personnel and poor management contribute to the delay. Further, although the project initiation is satisfactorily done, most of the PM principles are not largely used, thus leading to delay.

The study does have limitations, including its reliance on a perception survey of consumers and stakeholders, a limited sample size and a restricted number of projects. Nevertheless, the study highlights the need to address poor O&M and the insufficient application of PM principles to combat project delays in the Indian real estate sector.

Proper O&M and adequate application of PM will enable professional management of the projects and avoid delay.

Proper O&M and the application of adequate PM would reduce delays in real estate projects. Consequently, conflicts between the companies and consumers might be reduced and housing and infrastructure demands might be met.

The study manifested that the lack of adequate implementation of O&M and PM aspects leads to delays. So, it is theorized that O&M and PM play critical roles in the success of real estate projects. Appropriate implementation of the principles and best practices linked to these aspects might alleviate the challenges of delay in real estate projects in India.

ESG (Environment, Social, Governance) has taken on increased importance in recent years for all stakeholders, with the S dimension now taking on a stronger focus in the real estate space. This paper proposes a new metric to be used in the S space to assess improvements in aspects such as gender equality and cultural diversity in real estate. It adds to the S metrics currently available to see the more effective delivery of the S dimension into real estate investment decision-making.

A new S metric in ESG is proposed and validated. Using this metric, examples regarding gender equality and cultural diversity are assessed among leading real estate players in Australia. This S metric is assessed over a number of time periods to demonstrate the improvements in gender equality and cultural diversity in these major real estate players.

This new S metric is seen to be highly effective and robust in capturing the changes in various aspects of the S dimension in ESG in the real estate space today; particularly concerning gender equality and cultural diversity. It is clearly able to demonstrate the significant changes in increased participation of women at the more senior leadership levels by leading players in the real estate space.

With ESG becoming a critical issue in the real estate sector, issues involved in the S space will take on increased significance going forward. This is critical, as the elements of the S dimension such as gender equality and cultural diversity are important aspects for an effectively functioning real estate industry. The S metric developed in this paper can be used for benchmarking purposes over time, as well as between real estate players, between sub-sections within a real estate organisation, and comparing against other industry sectors. It is also relevant in all organisations, and is not just limited to the real estate sector. Additional metrics in the S space are an important development to further empirically assess the effective delivery of the S dimension of ESG in the real estate sector and more broadly.

This paper specifically proposes this new S metric in ESG in the real estate industry. This is a key issue for the real estate industry going forward at all levels, as it will facilitate a more diverse real estate industry and more effective real estate investment decision-making. This S metric is applicable in all organisational sectors where the S dimension of ESG is important.

The purpose of this study is to develop a financial performance measurement model for real estate business.

The study uses balanced scorecard (BSC) proposed by Kaplan and Norton (1996) as a theoretical support. The study, being exploratory in nature, uses survey method to collect data on several dimensions of BSC as well as on other performance measures used by real estate businesses in India. The survey data collected is analyzed using exploratory factor analysis (EFA) to explore the model constructs. This is followed by building an integrated conceptual model for measuring the financial performance of a real estate business. The model is tested using partial least squares structural equation modeling (PLS-SEM).

The study finds that the financial performance of the real estate business revolves around customer satisfaction, employee satisfaction and external networks. The right alignment of these components lead to superior financial performance. It also provides a competitive advantage to the real estate business. These three components (customer satisfaction, employee satisfaction and external networks) have direct and indirect influences on the financial performance of real estate business.

A small sample size (78 respondents), as well as the respondent’s geographical concentration in India, are the limitations of the study. Hence, generalization of findings may be difficult until the findings are validated across the globe.

The conceptual performance measurement model suggested in this research provides an effective tool to plan and strategize to achieve superior financial performance, particularly for stakeholders in the real estate business.

To the best of the authors’ knowledge and belief, this is the first attempt to develop a comprehensive financial performance measurement model for real estate business and test it using EFA and PLS-SEM.

The real estate markets may be significantly influenced by the uncertainty in global economic policy. This paper aims to evaluate the time-varying connectedness between global economic policy uncertainty and regional real estate markets to understand how regional real estate markets and uncertainty in global economic policy are related throughout time.

The current study includes the monthly data from April 2007 to August 2022 of major regions (i.e. Asia Pacific, Europe, Africa, North America and Latin America). Moreover, the authors use the time-varying parameter vector auto-regression (TVP-VAR) approach for the analysis.

The finding revealed a significant level of connectedness among global economic policy uncertainty and selected regional real estate markets. The result highlights more than 80% connectivity between the two variables, which makes the current study valuable. Furthermore, results determine Africa and North America are the shock transmitters; thus, they are considered safe-haven for investors to invest in these markets.

The main novelty is that this research highlights the time-varying connectedness between global economic policy uncertainty and five regional real estate markets (Africa, Asian Pacific, Europe, Latin America and North America) using TVP-VAR. Furthermore, the authors used the standard and poor daily real estate investment trust (REIT) indices for the selected REIT markets. Finally, this research suggests practical implications for real estate investors, property developers, stakeholders, policymakers and managers to revise their current policies to maintain the real estate market stability during economic and political uncertainty or in other uncertain situations.

This paper aims to examine the influence of Covid-19, current war and other factors on the dynamics of real estate prices in Ukraine from 2019Q2 to 2022Q4. More specifically, the authors examine the extent of the influence of Covid-19 and war on the real estate market in Ukraine.

The authors monitor and accumulate information flows from the existing real estate market with their subsequent in-depth math-stat processing to examine dynamics and drivers of Ukrainian real estate prices evolution.

The study finds that the Ukrainian residential property market has experienced an average growing trend from June 2019 to December 2022, despite the strong influence of pandemic and war. The analysis shows that the impact of these factors varies across different regions and property types, with some areas and property types being more affected than others. The study also identifies the main drivers of the market evolution, including cost-sensitive factors such as floor level, overall area, housing conditions and geographical location.

This research is oriented to analyze evolution of residential property market in Ukraine in 2019–2022 years characterized by influence of such disturbing factors as pandemic and military actions.

Results gained are essential for any type of Ukrainian residential market analytics implementation including but not limited to investment analysis, valuation services, collateral, insurance and taxation purposes, etc. In broader sense, it can be also useful for comparison with same type market development in other geographical arears.

Initial data base collected and constantly monitored covers all different regions of the country that gives a broad view on the overall market development influenced by pandemic and war.

The lack of a reliable database of the purchase and sale of residential properties remains one of the biggest obstacles in obtaining reliable data on their market value. This considerably complicates the process of carrying out a valuation and reduces the accuracy and reliability of the results of such work. This is especially important for market which evolves in times of unrest being influenced by such strongly disturbing factors as pandemic and military actions. The originality of the study lies in the development of a complete probabilistic processing of the initial database, which provides a reliable and accurate assessment of the market evolution. The results achieved could be used by various stakeholders, such as property owners, investors, valuers, insurers, regulators and other interested customers, to make informed decisions and mitigate risks in the turbulent Ukrainian real estate market.

Building information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.

This paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.

Four domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.

This paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.

In periods of economic distress, expectations for businesses change and there is a heightened need for reporting quality. This study investigates the impact of crises on earnings management in the real estate sector.

The data consisted of financial statements from 2005 to 2021 from real estate firms listed on 10 European stock exchanges. Estimated discretionary accruals from four standard accruals models were used as a proxy for earnings management, using cross-sectional industry and firm fixed effects models. The authors examined earnings management during three crises: the financial crisis (2008–2009), the debt crisis (2011–2012) and the COVID-19 pandemic (2020–2021).

The results showed less earnings management during the COVID-19 crisis and more earnings management during the financial crisis, though with slightly weaker evidence. The authors did not find significant evidence of earnings management related to the debt crisis. These results suggest that stakeholders in the real estate sector should be extra vigilant in crisis periods.

This study is the first to investigate earnings management in European real estate firms, focusing on the impact of crises.