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Within the labor-intensive construction industry characterized by distinctly structural shortages in the labor force worldwide, efficient and effective migration of construction workers across regions is critical for the smooth operation of construction activities. This study aims to investigate how the interregional migration patterns of construction workers are impacted by the disparities in both employment opportunities and environment amenities between the origin and destination provinces.

Drawing on the push and pull theory and the archival data on 13,728 migrant construction workers in China, descriptive analyses are first performed to characterize the interregional migration patterns of the investigated construction workers. Combining regional data in the National Bureau of Statistics of China, this study uses hierarchical regression modeling techniques to empirically test the relative importance of the employment-related and environment-related factors in driving the interregional migration of construction workers after controlling for the effects of related economic and geographic factors.

The results provide evidence that the interregional migration of construction workers is principally driven by the disparities in employment opportunities while disparities in environment amenities (including climate comfort disparity, medical service disparity and educational service disparity) generally play much fewer substantive roles. With regard to the impacts of employment opportunities, the results provide evidence that compared with the disparity in job market size, the disparities in job income and industry development level are more significantly relevant factors, which positively pull and adversely push the interregional migration flows, respectively.

This study contributes to a deepened understanding of how workers specifically balance their employment and amenity needs to make temporary migration decisions in the “laggard” labor-intensive construction industry. This study also adds to the literature on population migration by characterizing the specific characteristics of construction workers and the temporary nature of the workers' migration activities. The findings hold important practical implications for construction organizations and policymakers for effectively managing the mobility of migrant construction workers.

The extant literature on migrant construction workers has primarily focused on the consequences of international migration and the generalization of empirical findings on population migration mechanisms in other domains to the construction industry is substantially limited by the specific characteristics of construction workers and the temporary nature of their migration activities. In addressing this gap, this study represents an exploratory effort to quantitatively characterize the interregional migration patterns of construction workers in the labor-intensive construction industry and examines the roles of employment opportunity and environmental amenity in driving interregional migration.

Micro-regeneration can effectively enhance a neighborhood’s commercial vitality and serve as a viable approach to boost economic benefits. However, the small scale of micro-regeneration efforts and the fragmented nature of information currently limit the availability of strong empirical evidence demonstrating its impact on neighborhood commercial vitality. The aim of the study was to examine the link between micro-regeneration and neighborhood commercial vitality, focusing on the average, time-lag, spatial spillover, and spatial heterogeneity effects.

Using the panel data set of 1,755 neighborhoods in Chongqing from 2016 to 2021 as the research sample, the difference-in-differences (DID) method was employed in this study to explore the impact micro-regeneration has on neighborhood commercial vitality.

The results illustrate that: (1) micro-regeneration can promote neighborhood commercial vitality in terms of the number and types of local consumption amenities by 27.76 and 5.89%, respectively, with no time-lag effect; (2) the positive spillovers can exist within the range of 5,000 meters–5,500 meters of regenerated neighborhoods; and (3) the effect of micro-regeneration on neighborhood commercial vitality can be greater in peripheral areas than in core areas of the city.

The findings fill the knowledge gap on the relationship between micro-regeneration and neighborhood commercial vitality. Additionally, the results on the time-lag effect, spatial spillover effects, and spatial heterogeneity provide practical implications that can support the government and private sector in developing temporal and spatial arrangements for micro-regeneration projects.

This study aims to examine the determinants that influence housing prices in Dammam metropolitan area (DMA), Saudi Arabia, by using the analytic hierarchy process (AHP) model. The study considers determinants such as building age (BLD AG), building size (BLD SZ), building condition (BLD CN), access to parking (ACC PK), proximity to transport infrastructure (PRX TRS), proximity to green areas (PRX GA) and proximity to amenities (PRX AM).

The AHP decision model was used to assess the determinants of housing prices in DMA, using a pair-wise comparison matrix to determine the influence of the investigated factors on housing prices.

The study’s results revealed that building size (BLD SZ) was the most critical determinant affecting housing prices in DMA, with a weight of 0.32, trailed by proximity to transport infrastructure (PRX TRS), with a weight of 0.24 as the second most influential housing price determinant in DMA. The third most important determinant was proximity to amenities (PRX AM), with a weight of 0.18.

This study addresses a research gap by using the AHP model to assess the spatial determinants of housing prices in DMA, Saudi Arabia. Few studies have used this model in examining housing price factors, particularly in the context of Saudi Arabia. Consequently, the findings of this study provide unique insights for policymakers, housing developers and other stakeholders in understanding the importance of building size, proximity to transport infrastructure and proximity to amenities in influencing housing prices in DMA. By considering these determinants, stakeholders can make informed decisions to improve housing quality and prices in the region.

This research investigates the impact of the COVID-19 pandemic on the use of transitional spaces in a Korean academic setting, to assess the impact of the pandemic on users' utilization of transitional spaces and evaluate any changes in their usage patterns. The research explores whether transitional spaces can function as social interactive spaces, aligning with Ray Oldenburg's “third-place” theory. The focus is on South Korean academic settings, aiming to create neutral and safe zones for users.

The adopted methodology involves reviewing the literature and employing design charrette as a major data collection tool. The design charrette provided a platform for users to share insights on current transitional spaces during the pandemic and envision these spaces as future social and interactive spaces.

The design charrette participants advocated for modifying the current transitional space design to transform these spaces into shared spaces for both visitors and regular users in the future. Restricting access for external users to the main building area until necessary. The significance of site amenities in determining transitional spaces as “third-places” was emphasized. While the nature of the building, its location and transitional space amenities are crucial aspects to consider, designers may prioritize user opinions and preferences, as the success or failure of the design ultimately centers on user behaviors.

The research focused on a specific university, hindered by limited access to other institutions during the pandemic. Restrictions on external users discouraged entry without proper permission, which was challenging to obtain. The conventional design charrette outlined in the research method was impossible due to pandemic-related limitations on gathering participants in one location. Therefore, the researcher modified the design charrette method to align with strict social distancing measures.

The results of the research are not limited to academic settings, but they can be implied in other environments where social interaction spaces are required and where there is a constant flow of visitors and regular users. The design charrette can be used as a methodology for interior spaces along with large-scale projects of urban planning.

The research analyzed transitional spaces during the pandemic, suggesting redesign to serve and act as buffer zones between private and public areas and become a common social gathering place for visitors and regular users within the built environment.

This study aims to examine whether or not residential properties closer to landfill sites have lower offer values by the developers. That is, by analyzing real estate data and landfill site locations, the study seeks to provide insights into whether properties situated closer to landfill sites tend to have a lower offer values than those located farther away.

The study is exploratory in nature, and a case study approach is applied. A landfill site named “Uruli Devachi” is selected in the region of Pune district, and data is collected from 102 developers selling residential projects within a radius of 15 km (about 9.32 mi). The gathered data is analyzed by using basic descriptive statistics, one-way ANOVA and ordinary least squares (OLS) regression. The OLS regression helps to determine whether there is a relationship between the distance of a residential property from a landfill site and its offer value.

The findings suggest that landfill sites have a detrimental impact on residential property offer values, with the negative impact increasing with proximity to a landfill site. The negative effect seems to vanish after over 10 km (about 6.21 mi). The developers provide extra facilities including a clubhouse, a children’s play area, a gym and a swimming pool in an effort to mitigate the negative effects of the landfill site on residential properties.

The findings of this study could have implications for property developers, real estate professionals and policymakers in understanding how landfill proximity might impact property offer values.

This study presents many novelties for the Indian housing market: the landfill sites do have a negative effect on the offer value of residential property; the closer the residential property to a landfill site, the higher the negative effect. Further, the developers try and mitigate the negative effect of landfill sites on residential properties by providing additional amenities such as a clubhouse, children’s play park, gym and swimming pool.

This study aims to assess housing affordability and estimate demand using a hedonic regression model in the context of Lucknow city, India. This study assesses housing affordability by considering various housing and household-related variables. This study focuses on the impoverished urban population, as they experience the most severe housing scarcity. This study’s primary objective is to understand the demand dynamics within the market comprehensively. An understanding of housing demand can be achieved through an examination of its characteristics and components. Individuals consider the implicit values associated with various components when deciding to purchase or rent a home. The components and characteristics have been obtained from variables relating to housing and households.

A socioeconomic survey was conducted for 450 households from slums in Lucknow city. Two-stage regression models were developed for this research paper. A hedonic price index was prepared for the first model to understand the relationship between housing expenditure and various housing characteristics. The housing characteristics considered for the hedonic model are dwelling unit size, typology, condition, amenities and infrastructure. In the second stage, a regression model is created between household characteristics. The household characteristics considered for the demand estimation model are household size, age, education, social category, income, nonhousing expenditure, migration and overcrowding.

Based on the findings of regression model results, it is evident that the hedonic model is an effective tool for the estimation of housing affordability and housing demand for urban poor. Various housing and household-related variables affect housing expenditure positively or negatively. The two-stage hedonic regression model can define willingness to pay for a particular set of housing with various attributes of a particular household. The results show the significance of dwelling unit size, quality and amenities (R² > 0.9, p < 0.05) for rent/imputed rent. The demand function shows that income has a direct effect, whereas other variables have mixed effects.

This study is case-specific and uses a data set generated from a primary survey. Although household surveys for a large sample size are resource-intensive exercises, they provide an opportunity to exploit microdata for a better understanding of the complex housing situation in slums.

All the stakeholders can use the findings to create an effective housing policy. The variables that are statistically significant and have a positive relationship with housing costs should be deliberated upon to provide the basic standard of living for the urban poor. The formulation of policies should duly include the housing preferences of the economically disadvantaged population residing in slum areas.

This paper uses primary survey data (collected by the authors) to assess housing affordability for the urban poor of Lucknow city. It makes the results of the study credible and useful for further applications.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

This study aims to demonstrate how women's involvement in urban planning and design in Gulf cities improves urban space's inclusivity and strengthens identity through cultural heritage revitalisation. It also promotes the participation of women in architecture and city-making by showcasing how shaping urban spaces offers local communities opportunities for social interaction and a more inclusive environment.

The paper critically compares two case studies in the Gulf region—one in the United Arab Emirates and the other in Bahrain—according to four inclusion criteria: context connection, cultural sensitivity, community engagement and choices of amenities. These inclusion criteria are also applied to an experimental project by women architects' students in Saudi Arabia to inspire the future female architects of the Gulf region. From urban to architectural scales, the project offers a glance into the heritage design by women architects.

In light of this critical analysis, this study highlights the sensitivity to issues related to the revitalisation of urban areas by women architects. The case studies identified show the role of the female architect in making architecture and linking cultural heritage with contemporary themes. These projects stitch the past with the present and link cultural identity with aspects related to sustainable architecture. Therefore, valorising women's architectural experience is necessary to contribute to sustainable urban development in the Gulf region and beyond.

The present study addresses the importance of the role of women architects in the Gulf region. The research promotes the full and equal participation of women in the architecture and construction of the city to recognise their achievements by increasing their involvement in the work in a more integrated and balanced way.

The current paper aims at exploring negative aspects in reviews about Airbnb listings in Athens, Greece.

The aspect-based sentiment approach (ABSA), a subset of sentiment analysis, is used. The study analyzed 8,200 reviews, which had at least one negative aspect. Based on dependency parsing, noun phrases were extracted, and the underlying grammar relationships were used to identify aspect and sentiment terms.

The extracted aspect terms were classified into three broad categories, i.e. the location, the amenities and the host. To each of them the associated sentiment was assigned. Based on the results, Airbnb properties could focus on certain aspects related to negative sentiments in order to minimize negative reviews and increase customer satisfaction.

The study employs the ABSA, which offers more advantages in order to identify multiple conflicting sentiments in Airbnb comments, which is the limitation of the traditional sentiment analysis method.

The aggressive competition in the real estate market forces real estate developers to tackle the challenge of selecting the best project construction phasing alternative. The real estate industry is characterized by high costs, high profit and high risks. The schedules of real estate projects are also characterized by having large number of repetitive activities that are executed over a long duration. The repetitiveness, long duration of execution, the high amounts of money involved and the high risk made it desirable to leverage the impact of changes in phasing plans on net present value of amounts incurred and received over the long execution and selling duration. This also changes the project progress, and delivery time as well as their respective impact on customer degree of satisfaction. This research addresses the problem of selecting the best phasing alternative for real estate development projects while maximizing customer satisfaction and project profit.

The research proposes a model that generates all construction phasing alternatives and performs decision-making to rank all possible phasing alternatives. The proposed model consists of five modules: (1) Phasing Sequencing module, (2) Customer Satisfaction module, (3) Cash-In calculation module, (4) Cost Estimation module and (5) Decision-making module. A case study was presented to demonstrate the practicality of the model.

The proposed model satisfies the real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model generates all construction phasing alternatives and performs multi-criteria decision making to rank all possible phasing alternatives. It quantifies the score of the two previously mentioned criteria and ranks all solutions according to their overall score.

The research proposes a model that assist real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model can be used to conclude general guidelines and common successful practices to be used by real estate developers when deciding the construction phasing plan. In this study the model is based on business models where all the project units are sold, rental cases are not considered. Also, the budget limitations that might exist when phasing is not considered in the model computations.

The model can be used as a complete platform that can hold all real estate project data, process revenues and cost information for estimating profit, plotting cash flow profiles, quantifying the degree of customer satisfaction attributable to each phasing alternative and providing recommendation showing the best one. The model can be used to conclude general guidelines and common successful practices to be used by real estate developers when tackling the challenge of selecting construction phasing plans.