A Value-Cost Integration Framework for Construction Project Estimation

Thesis Abstract

In construction project estimation, traditional cost-driven methods frequently overlook the intrinsic value delivery dimensions that drive long-term project performance, such as lifecycle value, sustainability benefits, and client satisfaction, leading to suboptimal decision-making and higher total cost of ownership. This study addresses the gap by developing a Value-Cost Integration Framework (VCIF) that explicitly links value-focused drivers with cost estimation processes to enhance decision support for project estimators. The aim is to operationalize a framework that reconciles value creation with cost constraints across early-design to procurement stages, thereby improving estimation accuracy and project outcomes. Specific objectives are (i) to identify value drivers relevant to construction projects from client and stakeholder perspectives; (ii) to translate these value drivers into measurable estimation parameters aligned with cost components; (iii) to integrate value-cost metrics into a unified estimation model; (iv) to validate the framework through empirical data from real-world projects; and (v) to assess the framework’s impact on estimation accuracy and decision quality. The methodological approach combines a mixed-methods design grounded in the Resource-Based View and Value-Based Management theory. The population comprises 60 completed commercial and healthcare construction projects across three metropolitan regions. A stratified random sample of 30 projects is selected, with 20 from public-sector tenders and 10 from private-sector developments, ensuring representation of varying procurement routes and project scales. Data collection triangulates (a) archival project documents, including initial feasibility studies, cost plans, and post-completion value assessments; (b) semi-structured interviews with 18 estimators, project managers, and value-engineering consultants; and (c) a survey administered to 60 clients and end-users to capture perceived value outcomes. Instrument validity and reliability are established through expert review, pilot testing, Cronbach’s alpha (target >0.7), and inter-rater reliability checks for qualitative coding. Analytical techniques encompass both quantitative and qualitative analyses. Descriptive statistics summarize value and cost indicators; regression analysis and structural equation modeling (SEM) assess the relationships between identified value drivers, estimated costs, and realized value outcomes, testing the VCIF’s hypothesized pathways. Thematic analysis of interview transcripts identifies emergent value-cost interaction patterns, complemented by cross-case synthesis to reveal contextual nuances. Model specification includes a valuation-adjusted cost-estimation equation, integrating a value-adjustment factor (VAF) derived from client-perceived value scores and lifecycle benefit indices. Sensitivity analyses examine robustness to variations in discount rates, project duration, and market conditions. Expected findings indicate that incorporating a validated set of value drivers (e.g., lifecycle cost savings, occupant productivity, sustainability credits, and risk-adjusted performance) into estimation models significantly improves predictive accuracy of total project value, reducing estimation error by 12–18% across cases. The SEM is anticipated to reveal that value-driven adjustments mediate cost components such that high-value design choices yield favorable net present value despite higher upfront costs. Qualitative insights are expected to show that estimators with explicit value-cost frameworks demonstrate greater alignment with client expectations and better adoption of value-engineering opportunities. The study contributes to knowledge by (i) introducing the VCIF as a transferable framework that formalizes value integration within construction cost estimation, (ii) operationalizing value drivers into measurable estimation parameters with an accompanying valuation-adjustment factor, (iii) validating the framework across diverse project types and procurement settings, and (iv) providing methodological guidance for integrating value considerations into standard estimation practice. The practical implications include improved estimation accuracy, enhanced client satisfaction, and more deliberate decision-making that balances short-term costs with long-term value realizations. The study concludes that value-cost integration yields superior project performance indicators, advocating for policy and professional practice changes that embed value-driven estimation into standard industry workflows, with recommendations for software tool development to automate value parameter incorporation and for further research into sector-specific value drivers and long-term performance tracking.