A TAXONOMY OF CONCEPTUAL COST ESTIMATION INACCURACY DRIVERS IN CONSTRUCTION DEVELOPMENT

Conceptual cost estimation is a critical input to early construction development decisions, yet it is frequently prepared under conditions of limited scope definition, incomplete technical information, and high market uncertainty. Decisions made during the concept design stage can significantly influence final project costs, while early-stage estimates often remain subject to substantial accuracy limitations. This creates a strategic risk for project developers, cost managers, and clients who rely on conceptual estimates to assess feasibility, funding requirements, procurement routes, and design direction. This paper investigates the principal drivers of conceptual cost estimation inaccuracy in construction development and develops a taxonomy for categorising these drivers. The study adopts an inductive qualitative research design based on a literature review and semi-structured interviews with construction development, project management, and cost management professionals in Germany and the United Kingdom. The original study identified conceptual estimation inaccuracy as a result of interrelated issues, including scope-related uncertainty, design adaptation limitations, future change orders, missing building mass information, cost breakdown structure uncertainty, and supply chain uncertainty. The findings suggest that conceptual cost estimation inaccuracy is not caused by a single technical weakness, but by a combination of five interdependent driver groups: scope and design maturity, estimation methodology and cost-code structure, technical and operational systems, supply chain and resource conditions, and contingency and risk planning. The proposed taxonomy provides a structured basis for understanding how these drivers influence early-stage cost reliability and how they can be addressed through improved data structures, clearer scope definition, better contingency logic, and digital cost-management processes. The paper contributes to construction cost management research by reframing conceptual estimation inaccuracy as a systemic development-stage problem rather than only an estimating problem. It also provides a foundation for future research into digital cost intelligence, AI-assisted estimation, BIM-based cost planning, and cross-standard cost data integration.