Synergies strategies for cost contingency: uniting reference class forecasting and probabilistic risk analysis in infrastructure project delivery

Establishing robust contingency estimates is pivotal to managing unforeseen challenges and accurately assessing project risks in today’s complex project environments. This study investigates the integration of Reference Class Forecasting (RCF) and Estimating using Risk Analysis (ERA) to enhance cost estimation in large-scale infrastructure projects. Drawing upon 33 completed projects valued at over HK$36 billion in Hong Kong, the study compares both methods using analytical and statistical approaches. RCF (P70, P80) delivers the most accurate estimates overall (mean percentage errors = −1.25%, −7.53%), compared with ERA (−17.16%). It outperforms ERA in 18 (55%) of the 33 projects, particularly in Civil and Road Works, while ERA performs better in Drainage Works. The Wilcoxon Signed-Rank test confirms a statistically significant difference (p < 0.001). This paper provides unique empirical evidence on RCF–ERA performance, identifies project-type-specific estimation patterns, and conceptually outlines how this combined approach can improve contingency estimation in complex infrastructure projects.