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This article seeks to offer an answer. It explores the criteria on which commercial property market participants can develop strategies in hugely challenging circumstances. For this purpose, a survey-based approach was developed with work conducted with property-market professional in the United Kingdom (UK), France, Germany and Sweden to produce a criteria-based tool supporting adaption to changing market circumstances.

The data have been analyzed using statistical analysis. The data's statistical analysis included Cronbach's alpha's application to evaluate the respondents' replies' reliability. A entral tendency test was used to identify the means of relevance of the criteria. The Mann–Whitney U test was used to determine potential material differences between the UK and other countries with Bonferroni corrections applied to minimize type-I errors.

Thirty characteristics have been identified that impact the dynamics of the commercial property market. Their relevance to the commercial property market was determined using a survey. The literature analysis showed that the researchers paid more attention to quantitative criteria and their comparison. The survey showed that the relevance of criteria to the commercial property market dynamics is unequal. However, the survey results showed that it is most important to pay attention to emotional criteria to adapt to uncertainty changing conditions. The problem of the environment has been on the agenda for the last four decades. Therefore, the fact that the results of the study showed that the environmental criteria are the least significant is unexpected.

The study involved economically developed countries of Europe. Extending the study's geographical scope would be valuable in revealing whether the same differences exist in other geographical areas (such as Australia or the USA).

The practical implication of the analysis may be to facilitate the decision-making process of either selecting a country for commercial property investment or selecting the most sensitive and relevant criteria for the decision-making.

Criteria for commercial property market performance which promote successful property investment have been developed. Moreover, the criteria affecting the commercial property market have been weighted by their relevance to the market and their sequence of relevance has been established. And finally, the developed criteria have been placed into five groups that could serve as a foundation for a macro-level assessment of commercial property market dynamics.

This paper studies the dynamic effects of selected macroeconomic factors on the performance of the housing market in Kenya using Autoregressive Distributed Lag (ARDL) Models. This study aims to explain the dynamic effects of the macroeconomic factors on the three indicators of the housing market performance: housing prices growth, sales index and rent index.

This study used ARDL Models on time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited.

The results indicate that household income, gross domestic product (GDP), inflation rates and exchange rates have both short-run and long-run effects on housing prices while interest rates, diaspora remittance, construction output and urban population have no significant effects on housing prices both in the short and long run. However, only household income, interest rates, private capital inflows and exchange rates have a significant effect on housing sales both in the short and long run. Furthermore, household income, GDP, interest rates and exchange rates significantly affect housing rental growth in the short and long run. The findings are key for policymaking, especially at the appraisal stages of real estate investments by the developers.

The authors recommend the use of both the traditional hedonic models in conjunction with the dynamic models during real estate project appraisals as this would ensure that developers only invest in the right projects in the right economic situations.

The imbalance between housing demand and supply has prompted an investigation into the role of macroeconomic variables on the housing market in Kenya. Although the effects of the variables have been documented, there is a need to document the short-run and long-term effects of the factors to precisely understand the behavior of the housing market as a way of shielding developers from economic losses.

The purpose of this study is to examine the impact of the shift towards a circular economy and marketing strategies on the collaborative purchasing behaviour of consumers in India. The study uses the theory of planned behaviour (TPB) and the marketing mix to understand the factors affecting a consumer’s intention to participate in collaborative consumption (CC).

A Web-based survey was conducted, and 349 valid responses were analysed using AMOS (Analysis of Moment Structures) structural equation modelling. The study emphasised the impact of price, promotion and perceived behaviour control on CC and provided direction and advice for companies that rent and swap apparel.

According to the study, promotion and perceived behaviour control are the two key characteristics that significantly impact a consumer’s willingness to participate in CC in India. The study also found that perceived behaviour control plays a significant direct role in behavioural usage. These findings emphasise the impact of price, promotion and perceived behaviour control on CC and offer direction and advice for companies that rent and swap apparel.

This article can be used to evaluate the business in different countries and can be developed further. It does, however, have some restrictions. Because most respondents are from northern and central India, in addition, some respondents are from the southwestern and southern regions, especially in the Mumbai and Chennai locales. Hence, the geographical sample was not diverse in terms of demographics. Furthermore, the gender identity of the respondents might essentially affect how the authors interpret customer buying behaviour, but the study missed this. Researchers could enhance this by using various sampling techniques and ensuring that other demographic characteristics are considered in the future. Furthermore, the survey could not distinguish between online and in-person transactions.

The study provides practical advice for companies that rent and swap apparel, emphasising the impact of price, promotion and perceived behaviour control on consumer willingness to participate in CC. The findings suggest that companies can improve consumer participation by focusing on promotion and perceived behaviour control. In addition, the significance of perceived behaviour control on behavioural usage highlights the importance of empowering consumers to control their decisions to participate in CC.

To the best of the authors’ knowledge, this study is one of the first to examine the factors influencing consumer willingness to participate in CC in the context of the shift towards a circular economy in India. By examining the impact of the TPB and the marketing mix on consumer intention, the study provides valuable insights for companies that rent and swap apparel. The findings highlight the importance of promotion and perceived behaviour control in shaping consumer behaviour and provide practical direction for companies to promote and market their products effectively. The study adds to the existing knowledge on the circular economy and the role of CC in reducing waste and promoting sustainability.

The development of the digital age has made data and information more transparent, enhancing the strategic perspectives of both buyers (strategic waiting) and sellers (price fluctuations) in their decision-making. This research investigates the optimal dynamic pricing strategy of the content product developer in relation to their consideration of consumer fairness concerns to elucidate the impact of consumer fairness concerns on the dynamic pricing strategy of the developer.

This paper assumes that monopolistic content developers implement a dynamic pricing strategy for the content product. Through constructing a two-period dynamic pricing game model, this research investigates the optimal decisions of the content developer, contingent upon their consideration or disregard of consumer fairness concerns. In the extension section, the authors additionally account for the influence of myopic consumers on these optimal decisions.

Our findings reveal that the degree of consumer fairness concerns significantly influences the developer’s optimal dynamic pricing decision. When a developer offers content products with lower depth, there is a propensity for the developer to refrain from incorporating consumer fairness concerns into a dynamic pricing strategy. Conversely, in cases where the developer offers a high-depth content product, consumer fairness concerns benefit the developer. Furthermore, our analysis reveals a consistent benefit for the developer from the inclusion of myopic consumers.

Few studies have delved into the conjoined influence of consumer fairness concerns and strategic behavior on dynamic pricing strategy. Our findings indicate that consumer fairness concerns can enhance the efficiency of the value chain for content products under specific conditions. This paper not only enriches the existing literature on dynamic pricing by incorporating consumer fairness concerns theoretically but also offers practical insights. The outcomes of this research can guide content product developers in devising optimal dynamic pricing strategies.

With the rapid development of the sharing economy in manufacturing industries, manufacturers and the equipment suppliers frequently share capacity through the third-party platform. This paper aims to study influences of manufacturers sharing capacity on the supplier and to analyze whether the supplier shares capacity as well as its influences.

This paper deals with conditions that the supplier and manufacturers share capacity through the third-party platform, and the third-party platform competes with the supplier in equipment sales. Considering the heterogeneity of the manufacturer's earning of unit capacity usage and the production efficiency of manufacturer's usage strategies, this paper constructs capacity sharing game models. Then, model equilibrium results under different sharing scenarios are compared.

The results show that when the production or maintenance cost is high, manufacturers sharing capacity simultaneously benefits the supplier, the third-party platform and manufacturers with high earnings of unit capacity usage. When both the rental efficiency and the production cost are low, or both the rental efficiency and the production cost are high, the supplier simultaneously sells equipment and shares capacity. The supplier only sells equipment in other cases. When both the rental efficiency and the production cost are low, the supplier’s sharing capacity realizes the win-win-win situation for the supplier, the third-party platform and manufacturers with moderate earnings of unit capacity usage.

This paper innovatively examines supplier's selling and sharing decisions considering manufacturers sharing capacity. It extends the research on capacity sharing and is important to supplier's operational decisions.

The current literature has not investigated the perceived value of energy efficiency by households, regardless of financial benefits. Furthermore, there is a severe lack of research that investigates the effectiveness of the current format of EPC-labels. Therefore, the purpose of this paper is twofold: to study how households value energy efficiency in the housing market, regardless of price effects.

This study uses multiple hedonic regression models to analyse 706,778 Flemish properties for sale or rent between 2019 and 2023. The data is provided by Immoweb – the largest online real estate platform in Belgium. Given that the selling market is driven by different mechanisms than the rental market, the data set was divided in sold (522,164 listings) and rented properties (184,614 listings).

The ambiguous results of the A-label in the selling market indicate that the “class evaluation effect” found in related markets which use labels (e.g. household appliances) is also present in the housing market. However, the results of the other (lower) labels clearly show that owners do value energy improvements within labels, and this effect becomes stronger as the EPC-label becomes better. The rental market shows the opposite results. Energy improvements are only valued if they translate into a financial benefit. Taking these findings into account, the second part of this research shows that rescaling the EPC-label creates an incentive for improvements within labels.

This paper provides novel insights by studying the perceived value of energy efficiency in the absence of financial benefits and critically studying the effectiveness of the EPC-labels in their current shape. By investigating both the sales and rental market, the authors are able to make a comparison which creates valuable insights for academia, governments and real estate professionals.

This is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.

The random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.

Results show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.

The findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.

Although machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.

This study aims to investigate the impact of housing knowledge, housing challenges and housing policy on the renting intention and satisfaction of young people.

A questionnaire survey helped collect data from young people in the study area, which were then analysed using the Statistical Package for the Social Sciences (SPSS) 27 software. A descriptive analysis and the Cronbach’s alpha test were adopted to analyse the data. The confirmatory factor analysis confirmed a significant relationship between housing knowledge, housing challenges and housing policy and renting intention and satisfaction.

The overall findings revealed that most young people intend to own a home one day, and a minority of them decided to continue renting. The findings suggest that there is a significant relationship between housing knowledge and housing intention. However, housing challenges and housing policies do not appear to impact renting intentions. On the other hand, housing knowledge and housing challenges were found to be associated with housing satisfaction, while housing policy does not show a significant relationship.

This study, however, poses limitations as it uses a limited model and location and involves only a cross-sectional study. Future studies can use the methodology used in this study to conduct further investigations on housing intention and satisfaction in other regions of the country, thereby validating the findings of this study.

In terms of practical implications, this study has made a valuable contribution to the field of housing literature by shedding light on two crucial elements, namely, housing intention and satisfaction, which have been understudied. Understanding the determinants of housing intention and satisfaction is vital in efforts to implement appropriate policy reforms.

Findings from this study offer valuable insight related to managerial and practical implications, with the former implicating a need to prioritise initiatives that enhance renters’ housing knowledge. Implementing educational programmes and providing accessible resources can empower renters with a better understanding of the rental process and other important housing information.

This paper is relevant because it provides a guideline for policymakers to initiate regulations concerning housing and implement appropriate policy reforms. This study can also help housing providers develop more affordable housing that meets the needs of young people currently renting because most have expressed their housing intentions. Understanding housing intention and satisfaction determinants is vital to implementing appropriate policy reforms.

The purpose of this paper is to explore the significance impact of air quality as a contributing factor on residential property rents by applying geo-informatics to economic issues. Since air pollution poses a severe health threat, city residents should have a right to know about the (invisible) hazards they are exposed to.

Within spatial-temporal modeling of air pollutants in Berlin, Germany, three interpolation techniques are tested. The most suitable one is selected to create seasonal maps for 2018 and 2021 with pollution concentrations for particulate matter values and nitrogen dioxide for each 1,000 m² cell within the administrative boundaries. Based on the evaluated pollution particulate matter values, which are used as additional variables for semi-parametric regressions the impact of the air quality on rents is estimated.

The findings reveal a compelling association between air quality and the economic aspect of the residential real estate market, with noteworthy implications for both tenants and property investors. The relationship between air pollution variables and rents is statistically significant. However, there is only a “willingness-to- pay” for low particulate matter values, but not for nitrogen dioxide concentrations. With good air quality, residents in Berlin are willing to pay a higher rent (3%).

These results suggest that a “marginal willingness-to-pay” occurs in a German city. The research underscores the multifaceted impact of air quality on the residential rental market in Berlin. The evidence supports the notion that a cleaner environment not only benefits human health and the planet but also contributes significantly to the economic bottom line of property investors.

The paper has a unique data engineering approach. It collects spatiotemporal data from network of state-certified measuring sites to create an index of air pollution. This spatial information is merged with residential listings. Afterward non-linear regression models are estimated.

The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.

The model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.

Total cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.

The study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.

The issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.