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The purpose of this paper is to use the concepts of the multi-attribute utility theory to develop a model to evaluate the design of low-density residential units to increase the profit of the company from a certain design, by assessing the changes in the market shares as a result of the built unit’s attributes.

The proposed platform consists of two stages: Stage I or relational model development and Stage II or design evaluation. Stage I is concerned with developing a mathematical model that links design variables (e.g. the R-Value of the building envelope and construction material) with the assessment attributes (e.g. price and carbon emissions). Stage II ensures the fulfillment of the corporation’s goals in maximizing profit and market shares using multi-attribute utility theory.

The application of the proposed model on a case study – a single-family house – shows that reducing the selling price of the unit is not always the best marketing strategy builders should pursue to increase their sales and accordingly their profit, as accounting for other attributes (e.g. performance, operational cost and environmental impact) leads to larger changes in the market shares and accordingly in profit.

The limitations of this research are manifested in the following points: it does not account for the impact of the marketing campaigns on the market shares; it considers the profit as a percentage of the construction cost; and it has not been validated on high-density residential buildings.

This research provides speculative builders with a platform that allows the objective evaluation of houses’ designs prior to introducing them to the market so builders can increase their market shares and consequently their profit. The proposed platform also contributes to increasing the sustainable performance of the housing industry, as it allows for the assessment of the design against economic, environmental and social attributes concurrently, which ensures a balanced consideration of the built houses on sustainability pillars.

The proposed platform for design evaluation extends the assessment attributes beyond the traditionally considered economic and environmental attributes. By doing so, it assists decision-makers in evaluating the potential social influence of the proposed design and, as a result, reduces the unwanted impact.

This research combines the concepts of multi-attribute utility with market studies to develop an objective decision support tool for evaluating the design of speculative houses to increase the sustainable performance of the builders without compromising on their profit.

Researchers have not widely explored design-based factors that govern buildings’ physical properties and human–building interactions. This paper aims to understand the influence of design-related factors on the time-on-market (TOM) of listed houses and, consequently, study the effect of design features on the desirability of a given house.

This research analyzes a dataset of listed houses, provided by the REALTORS^® Association of Edmonton (RAE) and covers a period extending from January 2009 to August 2019, using Cox proportional-hazards regression model to identify building features that influence people purchasing decisions.

The research findings affirm the statistical insignificance of the price on the TOM compared to other design features, such as the construction method, the installed mechanical systems and cladding materials.

The data used in the analysis comes from a single North American region, i.e. Edmonton, Alberta, Canada. Also, the data provided by the RAE includes only records that involve a realtor.

The observations of the research presented in this paper influence the housing market players’ decisions about housing designs, mainly those concerned with building new residential dwellings such as speculative builders and designers.

The research novelty stems from two aspects: the medium used for analysis, i.e. Cox proportional-hazards regression model, which allows considering the listed-but-not-sold units and helps to eliminate the survivorship bias that leads to over-optimistic outcomes; and the assessment of design-related features which allows to understand people’s preferences in design alternatives.

The purpose of this paper is to develop a clear understanding of the features that increase the probability of condos’ sale, with a focus on design-related features.

The present research uses survival analysis (SA) and the Cox proportional-hazards regression (CPHR) to analyze condo sales data provided by the REALTORS® Association of Edmonton (RAE) (Alberta, Canada).

The analysis of the provided data shows that the listed price, building age, appliances and condo fees have less effect on the time a condo spends on the market compared to the condo’s physical features, such as construction material, interior finishing and heating type and source.

The data used in the present research comes from one geographical area (i.e. Edmonton, Canada). Furthermore, the data provided by the RAE does not include any real estate transactions not involving a realtor. Additionally, the present research, owing to its focus on design-related features, does not control features related to the external environment, such as community and transportation proximity.

The findings of the present research help construction practitioners (e.g. architects, builders and realtors) better understand the features that influence condo buyers’ decisions. This knowledge helps to develop designs and marketing strategies that increase the likelihood of selling and decrease the time listed condos spend on the market.

The present research expands our knowledge of the drivers influencing the purchasers’ decisions concerning the building’s physical features that can be controlled during the design stage. Also, analyzing the provided data by using SA and CPHR, as followed in this paper, facilitates the inclusion of records that are listed but not sold, which helps to overcome the survivorship bias and avoid the over-optimism that exists in the present literature.

Personal characteristics, such as age, marital status, education level and gender, vary among elderly residents in residential apartments. These characteristics may influence the elderly residents’ satisfaction with the facilities provided in their residential apartments. To ensure appropriate facilities management (FM) items are provided for the different categories of elderly people, it is necessary to understand their basic needs. Therefore, this paper aims to compare the satisfaction with FM items among elderly people with different personal characteristics in private domestic (PD) buildings.

A questionnaire survey was conducted among elderly people with different personal characteristics in PD buildings to collect information about their levels of satisfaction with FM items. A total of 41 FM items and four characteristics of the elderly, namely, age, gender, marital status and education, were identified in this study.

The result shows that satisfaction with natural daylight was significantly different among elderly people of different genders, while the one-way between-groups ANOVA indicate that satisfaction with the size of bedrooms, turning spaces at doors, temperature in bathrooms and/or toilets, colour, accessibility and ease of closing or opening the doors were significantly different among elderly people belonging to different age groups and of different marital status and education level.

Designers and private developers are therefore recommended to increase the sizes of bedrooms, install windows on opposite sides of walls in the flats and ensure there is an adequate light reflection ratio for wall and floor colours, to accommodate elderly people’s special characteristics.

The aim of this paper is to synthesize knowledge related to performance evaluation of automated construction processes during the planning and execution phases through a theme-based literature classification. The primary research question that is addressed is “How to quantify the performance improvement in automated construction processes?”

A systematic literature review of papers on automated construction was conducted involving three stages-planning, conducting and reporting. In the planning stage, the purpose of the review is established through key research questions. Then, a four-step process is employed consisting of identification, screening, shortlisting and inclusion of papers. For reporting, observations were critically analysed and categorized according to themes.

The primary conclusion from this study is that the effectiveness of construction processes can only be benchmarked using realistic simulations. Simulations help to pinpoint the root causes of success or failure of projects that are either already completed or under execution. In automated construction, there are many complex interactions between humans and machines; therefore, detailed simulation models are needed for accurate predictions. One key requirement for simulation is the calibration of the models using real data from construction sites.

This study is based on a review of 169 papers from a database of peer-reviewed journals, within a time span of 50 years.

Gap in research in the area of performance evaluation of automated construction is brought out. The importance of simulation models calibrated with on-site data within a methodology for performance evaluation is highlighted.

This study employs situated learning theory to elucidate the mechanisms of interorganizational collaboration by analyzing the relationships among absorptive capacity, institutional compensation, task cognitive integration and interorganizational collaboration in BIM-enabled construction projects.

An online questionnaire survey was conducted with managers and professionals involved in building information modeling (BIM-) enabled construction projects, and 220 valid responses were received. Data were analyzed by means of the linear regression models and bootstrap method.

The results show that (1) absorptive capacity, institutional compensation and task cognitive integration have a positive impact on interorganizational collaboration; (2) institutional compensation partially mediates the effect of absorptive capacity on interorganizational collaboration; (3) task cognitive integration fully mediates the effect of absorptive capacity on interorganizational collaboration; (4) institutional compensation and task cognitive integration serially and fully mediate the relationship between absorptive capacity and interorganizational collaboration and (5) the serial mediating model has a greater indirect effect than the other two models considered in this study.

This study contributes to the body of knowledge by demonstrating the way to break through the three types of organizational boundaries (i.e. syntactic, semantic and pragmatic organizational boundaries) and provide an internal collaborative mechanism from the perspective of situated learning theory. This study presents the critical effects of absorptive capacity, institutional compensation and task cognitive integration on interorganizational collaboration, selects the enhanced mediating model for explaining the effects of absorptive capacity on interorganizational collaboration and enables managers to update the traditional collaborative model in BIM-enabled construction projects.

Construction is a risky industry. Therefore, organizations are seeking ways towards improving their safety performance. Among these, the integration of technology into health and safety leads to enhanced safety performance. Considering the benefits observed in using technology in safety, this study aims to explore digital technologies' use and potential benefits in construction health and safety.

An extensive bibliometrics analysis was conducted to reveal which technologies are at the forefront of others and how these technologies are used in safety operations. The study used two different databases, Web of Science (WoS) and Scopus, to scan the literature in a systemic way.

The systemic analysis of several studies showed that the digital technologies use in construction are still a niche theme and need more assessment. The study provided that sensors and wireless technology are of utmost importance in terms of construction safety. Moreover, the study revealed that artificial intelligence, machine learning, building information modeling (BIM), sensors and wireless technologies are trending technologies compared to unmanned aerial vehicles, serious games and the Internet of things. On the other hand, the study provided that the technologies are even more effective with integrated use like in the case of BIM and sensors or unmanned aerial vehicles. It was observed that the use of these technologies varies with respect to studies conducted in different countries. The study further revealed that the studies conducted on this topic are mostly published in some selected journals and international collaboration efforts in terms of researching the topic have been observed.

This study provides an extensive analysis of WoS and Scopus databases and an in-depth review of the use of digital technologies in construction safety. The review consists of the most recent studies showing the benefits of using such technologies and showing the usage on a systemic level from which both scientists and practitioners can benefit to devise new strategies in technology usage.

The purpose of this study is to elicit the success factors from empirical evidence, as construction industry requires an improved understanding of factors for managing projects to positive outcomes. Increased stakeholder involvement, including the new technologies, achieving sustainability and safeguarding health and safety, whilst at the same time facing uncertainties, it is crucial to examine whether there are new factors that drive construction projects to succeed, especially from a value-driven perspective.

Using a systematic review approach, this research reviewed 172 studies published after 2004. When compared to a comprehensive project success factor framework presented by Chan et al. in 2004, 19 factors are considered new since 2004.

Though several scholarly outputs highlighted significant improvements to project operations and innovations in equipment and techniques, there has not been a comprehensive oversight since Chan’s et al. (2004) conceptual framework. This paper investigates 16 years of industry changes and identified two new success factors categories (innovation and sustainability) and 19 new factors that add to Chan’s et al. (2004) study. Consequently, a new framework of factors affecting project success was developed.

This paper was very specific in its attempt to find the new and additional success factors for managing construction projects. A new conceptual framework, which includes the newly identified factors, was then developed that will create a greater awareness of stakeholders’ concerns and ultimately contribute to significant improvement in developing project objectives and defining success measures.

This study aims to develop a database of existing academic information in house purchase decision (HPD) using systematic literature review (SLR), to facilitate worldwide advancement of research under HPD domain.

This research examined papers from two reputable databases – Scopus and Google Scholar – from 1992 to 2022 using a scoping review technique (Arksey and O’Malley, 2005) and a theme analysis method. Out of 374, 181 articles fit the inclusion parameters and were evaluated using the theme analysis approach.

Data from 181 articles was evaluated thematically to create a thematic map of HPD research. Five main themes and their sub-themes were identified: consumer behaviour, housing attributes, factors influencing purchasing decisions, investment analysis and demographics, which proved essential in understanding HPD and customer preferences for house purchase.

Data from 181 articles were evaluated thematically to create a thematic map of HPD research. This SLR intends to provide useful new insights on consumer concerns about home purchases in the rapidly developing residential real estate market and the issues that marketers, housing sector stakeholders, real estate industry and existing and future researchers should prioritize.

This research is unique such that it is the only 30-year-long SLR on the subject matter of HPD. This paper makes a significant contribution to residential real estate domain signifying the present state of research in HPD.

The present study aims to develop a risk-supported case-based reasoning (RS-CBR) approach for water-related projects by incorporating various uncertainties and risks in the revision step.

The cases were extracted by studying 68 water-related projects. This research employs earned value management (EVM) factors to consider time and cost features and economic, natural, technical, and project risks to account for uncertainties and supervised learning models to estimate cost overrun. Time-series algorithms were also used to predict construction cost indexes (CCI) and model improvements in future forecasts. Outliers were deleted by the pre-processing process. Next, datasets were split into testing and training sets, and algorithms were implemented. The accuracy of different models was measured with the mean absolute percentage error (MAPE) and the normalized root mean square error (NRSME) criteria.

The findings show an improvement in the accuracy of predictions using datasets that consider uncertainties, and ensemble algorithms such as Random Forest and AdaBoost had higher accuracy. Also, among the single algorithms, the support vector regressor (SVR) with the sigmoid kernel outperformed the others.

This research is the first attempt to develop a case-based reasoning model based on various risks and uncertainties. The developed model has provided an approving overlap with machine learning models to predict cost overruns. The model has been implemented in collected water-related projects and results have been reported.