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This study systematically reviewed existing literature on the impact of economic uncertainty on property performance to highlight focus areas and spur future research amid unprecedented global uncertainty levels. Conceptually, uncertainty levels and environmental dynamism are related to investors' risk judgement and decision-making.

Peer-reviewed journal articles published from 2007 to 2022 were assembled and arranged through the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol. The initial search produced 2,028 results from the Web of Science and Scopus databases, which were rigorously purified for a final dataset of 70 articles. These records were subsequently assessed through content analysis, bibliographic modelling, topic modelling and thematic analysis. Recurring themes were visualised using the VOSviewer software.

The existing literature suggests that economic uncertainty negatively impacts investment volumes, returns and performance. Research has also increased since 2018, with a strong emphasis on the housing sector and developed property markets. Commercial property and emerging markets account for only 10 and 8% of previous research, respectively.

These findings highlight the negative impact of economic uncertainties on property performance and investment volumes, which necessitate careful risk assessment. Given the high susceptibility of emerging and commercial property markets to uncertainty, these markets warrant further research amid ongoing uncertainty concerns across the globe.

Given current unprecedented levels of global uncertainty, the effects of economic uncertainty have received renewed interest. This study synthesised the current understanding of how different property markets respond to increased uncertainty and outlined future research directions to enhance understanding. Themes and relationships were also integrated into a conceptual map summarising the reported effects of economic uncertainty on housing, commercial property, investment and behaviour in the property market.

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.

Global crises have become increasingly recurrent events that jeopardize public-private partnerships (PPPs). In this context, the purpose of this paper is to expose the PPP-crisis research agenda by combining bibliometric and network analyses.

The PPP literature associated with global crises between the 2008 global financial crisis and 2022 was analyzed in three stages: (1) paper selection and screening for the inclusion/exclusion of articles relevant to this research, (2) semantic network development for examining thematic relationships among selected papers by considering the co-occurrence of keywords within the chosen studies and (3) calculation of network metrics for analysis.

The paper identified six research avenues for the PPP-crisis agenda: public interest, relational governance, risk management, user-pay PPPs, crisis management and financial performance. The PPP-crisis literature has spread significantly in the last five years driven by the case study approaches on a national or regional basis. Conversely, non-crisis periods generate room to strengthen user-pay PPPs and relational governance. The pandemic and post-pandemic times shared the priorities of the 2008 financial crisis but also strengthened the management of the risks and the structural drivers of the global crisis.

This study demonstrates that during global crisis periods, the public interest and financial performance gain relevance in a detriment of structural solutions to social legitimacy erosion of PPPs because of the urgency of giving tools to the public and private sectors to tackle the financial issues, which steer future issues for PPPs.

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.

This study aims to examine the solvency and performance persistence of defined benefit private and public pension plans (DBPPs) in the Republic of Congo.

The authors use the 2 × 2 contingency table approach and the time product ratio (TPR)-based cross-product ratio (CPR) on data covering ten years from 2011 to 2020, with variable funded ratios and excess returns, to determine the solvency and performance persistence of defined benefit pension plans.

The authors document a lack of solvency and performance persistence in DBPP funds. They conclude that the solvency and performance of DBPP funds are not repetitive. The previous year's private and public defined benefit pension funds’ results do not repeat in the current year. Hence, the current solvency and performance of defined benefit pension funds are not good predictors of future funds' solvency and performance.

To the best of the authors’ knowledge, this study is the first to combine solvency and performance to examine the persistence of defined benefit pension plans in sub-Saharan Africa.

This paper aims to propose a new housing finance mechanism through gold price as an alternative to interest rate in Islamic home financing, especially on Bai’Bithaman Ajil (BBA) contract.

This study using simulation approach to calculate the monthly installments for home financing using gold price references. In simple terms, propose a financing formula in the BBA contract by converting the selling price of the house to the gold price, and then the monthly installments also follow the actual gold price. The authors provide an example by simulating this formula using historical data and cases of housing financing at Indonesian Islamic banks. The authors compare housing financing models based on gold prices and interest rates. Finally, The authors can compare the two housing financing models that are affordable for low-income people.

The results show that in the initial period, monthly installments of BBA based on gold price were lower than home financing based on interest rate. This result makes it possible for low-income people who cannot access financing based on interest rates to access financing based on gold price. However, the total installments of financing based on gold prices are higher than the financing model based on interest rates.

The paper confines one contract, namely, BBA, as it is claimed to be more Shariah-compliant than others.

These findings suggest an alternative model for Islamic banks and regulatory authorities in Indonesia to replace the interest rate reference with the gold price in BBA contract housing financing. This model can offer competitive advantages for Islamic banks, including lower initial installments and inflation-protected profits, serving as a means of differentiating them from conventional banks.

Gold price-based housing financing model in Islamic banks will increase the affordability of housing financing for low-income people.

This paper tries to solve two problems, namely, first, the problem of assuming that Islamic and conventional banks are the same, and second, the problem of housing finance affordability. This study needs to be explored.

The purpose of the study is to examine the efficiency of linear, nonlinear and artificial neural networks (ANNs), in predicting property prices.

The present study uses a dataset of 1,468 real estate transactions from 2020 to 2022, obtained from the Department of Property Taxes of Republic of Kosovo. Beginning with a fundamental linear regression model, the study tackles the question of overlooked nonlinearity, employing a similar strategy like Peterson and Flanagan (2009) and McCluskey et al. (2012), whereby ANN's predictions are incorporated as an additional regressor within the ordinary least squares (OLS) model.

The research findings underscore the superior fit of semi-log and double-log models over the OLS model, while the ANN model shows moderate performance, contrary to the conventional conviction of ANN's superior predictive power. This is notably divergent from the prevailing belief about ANN's superior predictive power, shedding light on the potential overestimation of ANN's efficacy.

The study accentuates the importance of embracing diverse models in property price prediction, debunking the notion of the ubiquitous applicability of ANN models. The research outcomes carry substantial ramifications for both scholars and professionals engaged in property valuation.

Distinctively, this research pioneers the comparative analysis of diverse models, including ANN, in the setting of a developing country's capital, hence providing a fresh perspective to their effectiveness in property price prediction.

The purpose is twofold. First, this study aims to establish that black box tree-based machine learning (ML) models have better predictive performance than a standard linear regression (LR) hedonic model for rent prediction. Second, it shows the added value of analyzing tree-based ML models with interpretable machine learning (IML) techniques.

Data on Belgian residential rental properties were collected. Tree-based ML models, random forest regression and eXtreme gradient boosting regression were applied to derive rent prediction models to compare predictive performance with a LR model. Interpretations of the tree-based models regarding important factors in predicting rent were made using SHapley Additive exPlanations (SHAP) feature importance (FI) plots and SHAP summary plots.

Results indicate that tree-based models perform better than a LR model for Belgian residential rent prediction. The SHAP FI plots agree that asking price, cadastral income, surface livable, number of bedrooms, number of bathrooms and variables measuring the proximity to points of interest are dominant predictors. The direction of relationships between rent and its factors is determined with SHAP summary plots. In addition to linear relationships, it emerges that nonlinear relationships exist.

Rent prediction using ML is relatively less studied than house price prediction. In addition, studying prediction models using IML techniques is relatively new in real estate economics. Moreover, to the best of the authors’ knowledge, this study is the first to derive insights of driving determinants of predicted rents from SHAP FI and SHAP summary plots.

Land value varies at a micro level depending on the location’s economic, geographical and political determinants. The purpose of this study is to present a comprehensive assessment of the determinants affecting land value in the Indian city of Thiruvananthapuram in the state of Kerala.

The global influence of the identified 20 explanatory variables on land value is measured using the traditional hedonic price modeling approach. The localized spatial variations of the influencing parameters are examined using the non-parametric regression method, geographically weighted regression. This study used advertised land value prices collected from Web sources and screened through field surveys.

Global regression results indicate that access to transportation facilities, commercial establishments, crime sources, wetland classification and disaster history has the strongest influence on land value in the study area. Local regression results demonstrate that the factors influencing land value are not stationary in the study area. Most variables have a different influence in Kazhakootam and the residential areas than in the central business district region.

This study confirms findings from previous studies and provides additional evidence in the spatial dynamics of land value creation. It is to be noted that advanced modeling approaches used in the research have not received much attention in Indian property valuation studies. The outcomes of this study have important implications for the property value fixation of urban Kerala. The regional variation of land value within an urban agglomeration shows the need for a localized method for land value calculation.

This study aims to investigate the effect of monetary policy on housing prices for US economy. It specifically examines whether nominal or real interest rates are the key drivers behind fluctuations in housing prices in US.

Monthly data from January 1991 to July 2023 and various appropriate analytical tools such as unit root tests, Johansen’s cointegration test, vector error correction model (VECM), impulse response function and Granger causality test were applied for the data analysis.

The Johansen cointegration findings reveal the presence of a long-term relationship among the variables. VECM results indicate a negative correlation between nominal and real interest rates and housing prices in both the short and long terms, suggesting that a strict monetary policy can help in controlling the housing price increase in the USA. However, housing prices are more responsive to changes in nominal interest rates than to real interest rates. Additionally, the study reveals that the COVID-19 pandemic contributed to the upsurge in housing prices in the USA.

This study contributes by examining the role that nominal or real interest rates play in shaping housing prices in the USA. Moreover, given the recent significant upsurge in housing prices, this study presents a unique opportunity to investigate whether these price increases are influenced by the Federal Reserve's monetary policy decisions regarding nominal or real interest rates. Additionally, using monthly data, this study provides a deeper understanding of the fluctuations in housing prices and their connection to monetary policy tools.