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This paper aims to explore the various sociotechnical dimensions of building information modelling (BIM)-induced changes associated with stakeholder management of projects.

This paper relies on grounded theory and data collection from two case studies – one in the public sector and one in the private sector – and is underpinned by Leavitt’s (1964) sociotechnical model.

Findings reveal four new dimensions of stakeholder management as being affected through BIM-induced changes: commitment; transparency; learning and experience; and stakeholder satisfaction, with these extending beyond the dimensions recognised in the existing literature. Another novelty lies in bringing to light the highly context-specific nature of BIM-induced changes pertinent to stakeholder management, with the two case studies demonstrating differences in these changes. Furthermore, a theoretical model of the causal impacts of various identified dimensions is presented, in which the sequence of changes and the causal associations between the identified dimensions are conceptualised.

Through Leavitt’s (1964) Diamond lens, the procedure of change and its evolutionary procedure for various components of the sociotechnical system of stakeholder management are theorised. The tentative conceptualisations presented offer a springboard from which to further investigate the episode of change pertinent to various dimensions of stakeholder management in BIM-enabled projects.

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.