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The study aims to examine the impact of cultural dependency stemming from exchange rate fluctuations (specifically the US dollar) on herding behavior in the housing market across 31 provinces of Iran from Q2 2011 to Q1 2022, using a spatial econometrics approach. After confirming the presence of spatial effects, the Dynamic Spatial Durbin Panel Model with Generalized Common Effects (SDM-DPD(GCE)) was selected from various spatial models for these provinces.

The study examines the impact of cultural dependency stemming from exchange rate fluctuations (specifically the US dollar) on herding behavior in the housing market across 31 provinces of Iran from Q2 2011 to Q1 2022, using a spatial econometrics approach. After confirming the presence of spatial effects, the Dynamic Spatial Durbin Panel Model with Generalized Common Effects (SDM-DPD(GCE)) was selected from various spatial models for these provinces.

The model estimation results indicate that fluctuations in the free market exchange rate of the dollar significantly and positively impact the housing market in both target and neighboring regions, fostering herding behavior characterized by cultural dependency within the specified timeframe. Additionally, the study found that variables such as the inflation rate, population density index and the logarithm of stock market trading volume have significant and positive impacts on the housing market. Conversely, the variable representing the logarithm of the distance from the provincial capital, Tehran, significantly and negatively impacts the housing market across Iranian provinces.

Given that housing is a fundamental need for households, the dramatic price increases in this sector (for instance, a more than 42-fold increase from 2011–2021) have significantly impacted the welfare of Iranian families. Currently, considering the average housing price in Tehran is around 50 million Tomans, and the average income of worker and employee groups is 8 million Tomans (as of 2021), the time required to purchase a 100-square-meter house, even with a 30% savings rate and stable housing prices, is approximately 180 years. Moreover, the share of housing and rent expenses in household budgets now constitutes about 70%. The speculative behavior in this market is so acute that, despite 25 million of Iran’s 87 million population being homeless or renting, over 2.5 million vacant homes (12% of the total housing stock) are not used. Therefore, various financial behaviors and decisions affect Iran’s housing market. Herd behavior is triggered by the signal of national currency devaluation (with currency exchange rates increasing more than 26-fold between 2011 and 2021) and transactions at higher prices in certain areas (particularly in northern Tehran) (Statistical Center of Iran, 2023). Given the origins of housing price surges, a price increase in one area quickly spreads to other regions, resulting in herd behavior in those areas (spillover effect). Consequently, housing market spikes in Iran tend to follow episodes of currency devaluation. Therefore, considering the presented discussions, one might question whether factors other than economic ones (such as herd behavior influenced by dependence culture) play a role in the rising housing prices. Or, if behavioral factors were indeed contributing to the increase in housing prices, what could be the cause of this herd movement? Has the exchange rate, particularly fluctuations in the free market dollar rate, triggered herd behavior in the housing market across Iran’s provinces? Or has the proximity and neighborhood effect been influential in the increase or decrease in housing prices in the market?

This study aims to explore the intricate relationship between housing prices and transaction volumes in Tehran, a city with diverse socioeconomic and regional characteristics. This research addresses a critical gap in understanding the role of local spatial factors, which previous studies have often overlooked, focusing instead on macroeconomic variables.

Using a data set of housing transactions of Metropolitan Tehran from 2010 to 2020 sourced from secondary data, this study uses generalized linear mixed models and spatial clustering techniques. These methods enable an examination of geographical clustering and the effects of local contextual variables on the dynamics between housing prices and transaction volumes.

Results indicate significant spatial heterogeneity within Tehran’s housing market. Higher prices and transaction volumes are concentrated in the northern and western regions, influenced by factors such as employment rates, rental housing supply and the physical attributes of the housing stock. The findings suggest that macroeconomic policies alone are insufficient to address housing challenges in Tehran; targeted, localized interventions are necessary.

This study’s reliance on secondary data and its focus on a single urban environment may limit the generalizability of the findings. Further research incorporating a wider range of local and macro variables could strengthen the applicability of the results across different contexts.

This study underscores the need for region-specific housing policies that consider local economic, social and spatial conditions. Policymakers could improve housing affordability and accessibility in Tehran by implementing tailored strategies to address the distinct needs of different districts.

This study offers a novel perspective by integrating spatial and contextual factors in housing market analysis, providing insights that challenge the traditional macroeconomic focus. The use of advanced statistical and spatial analysis techniques contributes to a deeper understanding of urban housing market dynamics.

This paper aims to examine the impact of Digital Financial Inclusion (DFI) on three key economic aspects: banking crises, economic expansion and economic downturns across 93 countries from 2004 to 2017.

Bayesian Logit regression models provide important insights into how DFI influences these economic factors.

The findings show that DFI has a modest positive effect on banking crises (coefficients: 0.002–0.027), but rapid growth could increase crisis risks if not regulated. DFI positively impacts economic expansion (coefficients: 0.003–0.012), supporting growth at reasonable levels. For economic downturns, DFI has a negative effect, potentially reducing recession risks, though the impact is small. Regionally, DFI helps mitigate banking crises and downturns in Africa, Latin America and Asia, but in Europe, it slightly increases risks, suggesting potential instability if not properly managed.

The study provides original insights into the nuanced effects of DFI on banking crises, economic expansion and economic downturns across different regions, offering valuable policy recommendations based on these findings.

This article aims to study the nonlinear effect of financial inclusion on tax revenue of 21 low financial development countries (LFDCs) and 22 high financial development countries (HFDCs) from 2004 to 2020.

The study calculates the world average financial development index (FD̅) for all countries using data from the IMF. The average FD of HFDCs is higher than (FD̅). On the other hand, the average FD of LFDCs is lower than (FD̅). Data of 21 LFDCs and 22 HFDCs cover the period 2004–2020. With the small sample problem, we applied the Bayesian method to examine the nonlinear effect of financial inclusion on the tax revenue of the two groups of countries.

Using the Bayesian method, the results show that financial inclusion negatively impacts tax revenue with an absolute probability of 100% in LFDCs and a lower probability of 92.45% in HFDCs. Additionally, the financial inclusion threshold at LFDCs is 18.90. Below this threshold, financial inclusion promotes tax revenue with a 100% probability. On the contrary, when financial inclusion exceeds the threshold, it will have a negative effect on tax revenue. Similarly, the financial inclusion threshold at HFDCs is 20.14, with a probability of 92.45%.

To the best of the authors’ knowledge, this is the first paper to examine the nonlinear impact of financial inclusion on tax revenue in high and low financial development countries.

The purpose of this study was to examine how two socio-historical contexts within the United States, the Movement for Black Lives and the COVID-19 pandemic, informed approaches to improving racial climate in science, technology, mathematics and engineering (STEM) graduate education.

The authors used a general qualitative inquiry research study design to conduct focus groups (n = 121) with graduate students, postdoctoral fellows and faculty members from across STEM disciplines as well as administrators whose work involves STEM graduate students. Participants were from two US institutions involved in a National Science Foundation networked improvement community seeking to create inclusive environments for STEM graduate students.

This study demonstrates how these socio-historical contexts illuminated and amplified on-going efforts to address racial climate for graduate students in US-based graduate education. In response to these events, STEM faculty devoted time that otherwise might have gone to purely technical or scientific endeavors to addressing racial climate. However, some faculty members remain hesitant to address racial climate and efforts appear to have further waned over time. While diversity, inclusion and equity efforts came to the forefront of the collective consciousness during this time, participants worry that these efforts are not sustainable, particularly without support from faculty and administrators.

The findings from this study will inform efforts to improve racial climate in STEM graduate programs.

This study fills an identified need to capture how socio-historical contexts, like the US Movement for Black Lives and the COVID-19 pandemic, have influenced approaches to improving racial climate in STEM graduate programs.

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.

The purpose of this study is to use vector error-correction modeling together with directed acyclic graphs (DAG) for analyzing dynamic relations among monthly retail property price indices of 10 major cities in China from 2005–2021.

This paper apply both the PC and Linear Non-Gaussian Acyclic Model (LiNGAM) algorithms for inference of the DAG, with the former leading to the causal pattern and the latter leading to the causal path. This paper carry out innovation accounting analysis based on the causal path according to the LiNGAM algorithm.

Their results show sophisticated dynamics among processes of price adjustments following shocks. The results do not reveal clear evidence that supports dominance of the price series of the top-tier cities.

These results suggest that it could be beneficial to design policies at granular levels regarding regional retail property prices in China.

This study aims to seek, on the basis of Hofstede's culture consequences, to explore the notion that regional characteristics may influence the prioritisation of certain types of public-private partnerships (PPP) contract governance skills over others. It further sets out to determine which skills are considered the most critical between the groups of respondents surveyed.

To bring this important and neglected perspective into the mainstream of PPP discussions, the study, being of an exploratory nature, relied on a survey of 340 respondents from around the globe. The respondents are a rich mix of public policy experts, economists, construction professionals, project finance experts, lawyers and academic researchers in PPP.s.

Analysis revealed that, regional characteristics was an important factor influencing skills prioritisation. Furthermore, exploratory factor analysis with Monte Carlo principal component analysis (PCA) confirmation revealed that project management, contract design, negotiations, performance management and stakeholder management skills were very critical for successful contract management of PPP projects.

The findings indicate that the design and implementation of regulatory governance for infrastructure PPPs should be context-specific rather than the current one-size-fits all model. Training should be tailored to reflect regional specific characteristics.

Studies are increasingly pointing to the absence of critical PPP skills among institutions responsible for managing PPP contracts. This lack of capacity has resulted in poor oversight of private companies providing public services resulting in poor services, and financial recklessness, which threaten the sustainability of service provision.

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.

Profitability is crucial for a company’s sustainability. This study aims to examine the influence of profitability and specific variables on the value of real estate companies in Indonesia.

The study uses a sample of 42 real estate companies listed on the Indonesia Stock Exchange from 2017 to 2023. A static panel regression approach was adopted, with the best model being the fixed effect model, verified through a robustness test.

The results indicate that the fixed effect model is the most effective in explaining firm value. Profitability, proxied by return on assets (ROAs), does not significantly impact firm value. This finding is confirmed by robustness tests using another profitability measure, return on equity (ROE). Additionally, company size negatively and significantly impacts firm value, while activity ratio and leverage have a positive and significant effect. Liquidity and company growth do not significantly affect firm value.

The research is limited to Indonesian real estate firms, cautioning against broad generalization to other countries or industries. The study could not demonstrate the influence of profitability on the value of real estate companies. Instead, firm value is influenced by company size, activity ratio and leverage.

Real estate firms should increase their activity, optimize funding and consider company size to enhance firm value.

This study contributes to the Indonesian real estate sector by revealing that profitability does not enhance firm value. Indonesian real estate companies generally have low profitability and firm value.