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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.

Despite the proven evidence of ever-growing productivity gains in the manufacturing industry as a result of years of research and investment in advanced technologies, such as robotics, the adoption of robots in construction is still lagging. The existing literature lacks technical frameworks and guidelines that account for the one-of-a-kind nature of construction projects and the myriad of materials and dimensional components in construction activities. This study seeks to address existing technical uncertainty and productivity issues associated with the application of robotics in the assembly-type manufacturing of industrialized construction.

To facilitate the selection of suitable robotic arms for industrialized construction activities, primarily assembly-type manufacturing tasks of offsite production processes, an activity-based ranking system based on axiomatic design principles is proposed. The proposed ranking system utilizes five functional requirements derived from robot characteristics—speed, payload, reach, degrees of freedom and position repeatability—to evaluate robot performance in an industrialized construction task using simulations of a framing station.

Based on design parameters obtained from activity-based simulations, seventy six robotic arms suitable for the framing task were scored and ranked. According to the sensitivity analysis of proposed functional requirements, speed is the key functional requirement that has a notable effect on productivity of a framing station and is thus the determinant in robot performance assessment for framing tasks.

The proposed ranking system is expected to augment automation in construction and serve as a preliminary guideline to help construction professionals in making informed decisions regarding the adoption of robotic arms.

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.