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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.

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 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 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.

This study aims to identify determinants of single-family residential property values and fill the gap by analyzing respondents’ willingness to pay/receive data alongside real transaction data. Ordinal logistic regression and ordinal least square regression were used.

Ordinal logistic regression effectively analyzes willingness-to-pay/receive data, accommodating the ordered nature of property value responses while incorporating multiple influencing factors. Ordinal least square regression quantifies the impact of continuous and categorical predictors on real transaction data.

Findings revealed strong associations between property values and several variables. Analysis of willingness-to-pay/accept data from 232 respondents showed significant impacts of factors such as the number of rooms, site area, construction material, property orientation, property age and proximity to bus stations and the central business district (p < 0.05). Similarly, ordinal least square regression analysis of transaction data confirmed the significance of most of these factors, except for property orientation, which indicates the difference of preference in the local market or reporting inconsistencies, demand further investigation. Variables such as views, proximity to wetlands, roads, green areas, religious institutions and schools were statistically insignificant across both data sets (p > 0.05).

It provides a robust basis for housing and urban development strategies. The stakeholders such as real estate developers, urban planners and policymakers are encouraged to incorporate these findings into housing policies, land value capture initiatives and urban planning frameworks to enhance residential property value and align with sustainable urban development goals.

This study contributes original insights into single-family residential property valuation by integrating willingness-to-pay and transaction data, substantiating the determinants of property value.

This paper aims to investigate the effect of the COVID-19 pandemic market shock on house pricing, time-on-market (TOM) and probability-of-sale functions using local multiple listing service data from Richmond, Virginia, USA.

The empirical analyses use a two-stage residual inclusion model to simultaneously address endogeneity and nonlinearity in modeling sales price and TOM, and a Heckman two-stage procedure to account for sample selection bias in estimating the probability-of-sale.

The pandemic shock not only directly impacted average home prices, TOM and probability-of-sale, but it also caused the coefficients of some of the factors that influence these metrics to change while others were stable to the exogenous shock of the pandemic. The authors find that coefficients in the hedonic pricing, TOM and probability-of-sale models did not shift instantaneously; instead, the impact evolved over several months at the beginning of the pandemic until stabilization.

The results should be of interest to buyers and sellers of residential properties, agents specializing in residential properties and researchers looking to better capture the impact of exogenous events on housing prices and buyer preferences.

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.

The purpose of this study paper is to highlight certain related barriers and issues of housing affordability and examine the factors that influence housing affordability in Malaysia.

This study used panel data including several variables, namely, household expense, population, home financing, interest rate, inflation rate (IF) and rental rate (RR). The regression models of panel data, namely, the ordinary least square model, the fixed effects model and the random effects model, were evaluated for their suitability.

The findings revealed that RR and IF have a positive and significant impact towards housing affordability. The results provide strong evidence that RR as alternative in determining the home affordability as it helped in reducing the cost and the financing duration period of houses while at the same time increasing the level of capability of homeownership. Meanwhile, the level of IF has positive and significant impact towards housing affordability because it will cause a drop or increase in the purchasing power of households, as well as a decline or increase in the capability to own a house.

The most significant aspects to consider when analysing housing affordability in Malaysia are demand and supply. However, this study focuses on only five variables and only covers Malaysia. As a result, future researchers should analyse the study’s location, such as by region or district, and include additional variables from both the demand and supply sides. Homeownership of affordability requires a broader and more realistic definition in the current context of a more disruptive environment where technology such as fintech, blockchain and the internet of things acts as enablers for not only promoting homeownership but also ensuring homeownership sustainability. As a result, democratising Islamic home financing appears to be a viable option that requires rethinking, and further research is recommended.

The study proposes an end-to-end solution to promote homeownership levels by considering the level of RR as significant variables among stakeholders such as the house buyers/owners, sellers, investors as well the government agencies in influencing affordability in Malaysia.

This paper discusses the indicators of housing affordability index over the 21-year period of 2000–2020, covering all states in Malaysia. The comparison of affordability level can be seen through all states and by regions. Besides that, the findings revealed that RR and IF have a positive and significant impact towards housing affordability. RR is considered an essential variable in promoting homeownership in Malaysia and warrants further investigation towards policy implication. This paper also provides contribution on data on RR by states in Malaysia that can be used by policymakers to some extent.