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This study aims to examine the impact of some real variables such as real effective exchange rates, real mortgage rates, real money supply, real construction cost index and housing sales on the real housing prices.

This study uses a nonlinear autoregressive distributed lag (NARDL) model in the monthly period of 2010:1–2021:10.

The real effective exchange rate has a positive and symmetric effect. The decreasing effect of negative changes in real money supply on real housing prices is higher than the increasing effect of positive changes. Only positive changes in the real construction cost index have an increasing and statistically significant effect on real house prices, while only negative changes in housing sales have a small negative sign and a small increasing effect on housing prices. The fact that the positive and negative changes in real mortgage rates are negative and positive, respectively, indicates that both have a reducing effect on real housing prices.

This study suggests the first NARDL model that investigates the asymmetric effects on real housing prices instead of nominal housing prices for Turkey. In addition, the study is the first, to the best of the authors’ knowledge, to examine the effects of the five real variables on real housing prices.

In most economies, there are rules from the market regulators or government to sell at an equal wholesale price (EWP). But when one upstream channel is facing a negative demand disruption and another positive, EWP can create extra pressure on the disadvantageous supply chain partner, which faces negative disruption. The purpose of this study is to analyse the impact of EWP and the scope of the discriminatory wholesale price (DWP) during disruptions.

For the study, the authors used a dual-channel supply chain consisting of a manufacturer, online retailer (OR) and traditional brick-and-mortar (BM) retailer. Stackelberg game is used to model the interaction between the upstream and downstream channel partners, and the horizontal Nash game to analyse the interaction within downstream channel partners. For modelling asymmetric disruption, the authors took instances from the lock-down and post-lock-down periods of the COVID-19 pandemic, where consumers flow from BM retailer to OR store.

By analysing the disruption period, the authors found that this asymmetric disruption is detrimental to the BM channel, favourable to OR and has no impact on the manufacturer. But with DWP, the authors found that the profit of the BM channel and manufacturer can be increased during disruption. Though the profit of the OR decreased, it was found to be higher than in the pre-disruption period. Under DWP, the consumer surplus increased during disruption, making it favourable for the customers also. Thus, DWP can aid in creating a win-win strategy for all the supply chain partners during asymmetric disruption. Later as an extension to the study, the authors analysed the impact of the consumer transfer factor and found that it plays a crucial role in the optimal decisions of the channel partner during DWP.

Very scant literature analyses the intersection of DWP and disruptions. To the best of the authors’ knowledge, this study, for the first time uses DWP as a tool to help the disadvantageous supply chain partner during asymmetric disruptions. The study findings will assist the government, market regulators and manufacturers in revamping the wholesale pricing policies and strategies to help the disadvantageous supply chain partner during asymmetric disruption.

This study aims to estimate the quality-adjusted changes in residential mobile consumer prices by controlling for the changes in the relevant service characteristics and quality, in a case study on Hungary between 2015 and 2021; compare the results with changes measured by the traditionally calculated official telecommunications price index of the Statistical Office; and discuss separating the hedonic price changes from the effect of a specific government intervention that occurred in Hungary, namely, the significant reduction in the value added tax rate (VAT) levied on internet services.

Since the price of commercial mobile offers does not directly reflect the continuous improvements in service characteristics and functionalities over time, the price changes need to be adjusted for changes in quality. The authors use hedonic regression analysis to address this issue.

The results show significant hedonic price changes over the observed seven-year period of over 30%, which turns out to be primarily driven by the significant developments in the comprising service characteristics and not the VAT policy change.

This paper contributes to the literature on hedonic price analyses on complex telecommunications service plans and enhances this methodology by using weights and analysing the content-related features of the mobile packages.

The purpose of this study is to analyse numerous aspects affecting residential property price in Malaysia against macroeconomics issues such as gross domestic product (GDP), exchange rate, unemployment and wage.

The hedonic pricing model has been adopted as econometric model for this research to investigate the relationship between residential property price against macroeconomics indicator. The data for residential property price and macroeconomic variables were collected from 1991 to 2019. Multiple linear regression had been adopted to find the relationship between the dependent and independent variables.

The result shows that the GDP has a significant positive impact on residential property price, while exchange rate has no significant impact although it was positive. In addition, the unemployment rate has a significant impact on the residential property price and has a negative relationship. Similar to the wage that shows the negative relationship with residential property prices. Moreover, during the pandemic COVID-19 in Malaysia, this research shows a more transparent view of the relationship between residential property price and the macroeconomic issues of GDP, exchange rate, unemployment and wage.

The findings of this research found that macroeconomics issue cannot be eliminated due to Malaysia is a developing country, and there will always be an issue that will happen, but the issues can be reduced to maximise the advantages, e.g. during COVID-19, the solution to fight against COVID-19 were crucial and weaken the macroeconomics issues.

This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable).

The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map.

Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities.

Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

The purpose of this study is to quantify the impact of electricity power outages on the local housing market in South Africa.

This study uses the autoregressive distributive lag (ARDL) and quantile autoregressive distributive lag (QARDL) models on annual time series data, for the period 1971–2014. The interest rate, real income and inflation were used as control variables to enable a multivariate framework.

The results from the ARDL model show that real income is the only factor influencing housing price over the long run, whereas other variables only have short-run effects. The estimates from the QARDL further reveal hidden cointegration relationship over the long run with higher quantile levels of distribution and transmission losses raising the residential price growth.

Overall, the findings of this study imply that the South African housing market is more vulnerable to property devaluation caused by power outages over the short run and yet remains resilient to loadshedding over the long run. Other macro-economic factors, such as real income and inflation, are more influential factors towards long-run developments in the residential market.

To the best of the authors’ knowledge, this is the first study to examine the empirical relationship between power outages and housing price growth.

The purpose of this study is to reveal the dynamics of house prices and sales in spatial and temporal dimensions across British regions.

This paper incorporates two empirical approaches to describe the behaviour of property prices across British regions. The models are applied to two different data sets. The first empirical approach is to apply the price diffusion model proposed by Holly et al. (2011) to the UK house price index data set. The second empirical approach is to apply a bivariate global vector autoregression model without a time trend to house prices and transaction volumes retrieved from the nationwide building society.

Identifying shocks to London house prices in the GVAR model, based on the generalized impulse response functions framework, I find some heterogeneity in responses to house price changes; for example, South East England responds stronger than the remaining provincial regions. The main pattern detected in responses and characteristic for each region is the fairly rapid fading of the shock. The spatial-temporal diffusion model demonstrates the presence of a ripple effect: a shock emanating from London is dispersed contemporaneously and spatially to other regions, affecting prices in nondominant regions with a delay.

The main contribution of this work is the betterment in understanding how house price changes move across regions and time within a UK context.

Understandings of house prices and their interrelationships have undoubtedly drawn a great amount of attention from various market participants. This study aims to investigate the monthly newly-built residential house price indices of seventy Chinese cities during a 10-year period spanning January 2011–December 2020 for understandings of issues related to their interdependence and synchronizations.

Analysis here is facilitated through network analysis together with topological and hierarchical characterizations of price comovements.

This study determines eight sectoral groups of cities whose house price indices are directly connected and the price synchronization within each group is higher than that at the national level, although each shows rather idiosyncratic patterns. Degrees of house price comovements are generally lower starting from 2018 at the national level and for the eight sectoral groups. Similarly, this study finds that the synchronization intensity associated with the house price index of each city generally switches to a lower level starting from early 2019.

Results here should be of use to policy design and analysis aiming at housing market evaluations and monitoring.

The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations.

Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present.

This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated.

This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.