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Anastasopoulos, P C, Haddock, J E and Peeta, S (2014) Cost Overrun in Public-Private Partnerships: Toward Sustainable Highway Maintenance and Rehabilitation. Journal of Construction Engineering and Management, 140(06).
Becker, T C, Jaselskis, E J and El-Gafy, M (2014) Improving Predictability of Construction Project Outcomes through Intentional Management of Indirect Construction Costs. Journal of Construction Engineering and Management, 140(06).
Chen, Y Q, Zhang, Y B and Zhang, S J (2014) Impacts of Different Types of Owner-Contractor Conflict on Cost Performance in Construction Projects. Journal of Construction Engineering and Management, 140(06).
El-Abbasy, M S, Senouci, A, Zayed, T, Mirahadi, F and Parvizsedghy, L (2014) Condition Prediction Models for Oil and Gas Pipelines Using Regression Analysis. Journal of Construction Engineering and Management, 140(06).
Hawas, F and Cifuentes, A (2014) Valuation of Projects with Stochastic Cash Flows and Intertemporal Correlations: Practical Modeling Guidelines. Journal of Construction Engineering and Management, 140(06).
Ko, C and Chung, N (2014) Lean Design Process. Journal of Construction Engineering and Management, 140(06).
Lee, H W, Choi, K and Gambatese, J A (2014) Real Options Valuation of Phased Investments in Commercial Energy Retrofits under Building Performance Risks. Journal of Construction Engineering and Management, 140(06).
Maghrebi, M, Travis Waller, S and Sammut, C (2014) Assessing the Accuracy of Expert-Based Decisions in Dispatching Ready Mixed Concrete. Journal of Construction Engineering and Management, 140(06).
O’Connor, J T, O’Brien, W J and Choi, J O (2014) Critical Success Factors and Enablers for Optimum and Maximum Industrial Modularization. Journal of Construction Engineering and Management, 140(06).
Russell, M M, Hsiang, S M, Liu, M and Wambeke, B (2014) Causes of Time Buffer and Duration Variation in Construction Project Tasks: Comparison of Perception to Reality. Journal of Construction Engineering and Management, 140(06).
West, J (2014) Collaborative Patterns and Power Imbalance in Strategic Alliance Networks. Journal of Construction Engineering and Management, 140(06).
Zhang, S, Migliaccio, G C, Zandbergen, P A and Guindani, M (2014) Empirical Assessment of Geographically Based Surface Interpolation Methods for Adjusting Construction Cost Estimates by Project Location. Journal of Construction Engineering and Management, 140(06).
- Type: Journal Article
- Keywords: Construction costs; Project management; Geographic information systems; Construction cost estimates; Project location adjustment; Surface interpolation methods; Cost and schedule;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0000850
- Abstract:
The accuracy of cost estimates is crucial to the success of construction projects with knowledge of project location being fundamental to the outcome of cost estimating. Location factors are commonly used to adjust construction cost estimates by project location, but these factors are not available for all locations. To date, the nearest neighbor (NN) method, based on simple, proximity-based interpolation has been used when a city lacks a sampled location factor even if this method was not empirically validated. This paper provides several contributions. First, the validity of the proximity-based method was substantially supported through the analysis of the global spatial autocorrelation of the changes in location factors from 2005 to 2009. Using two established cost index databases, it was found that there is spatial correlation between cost indexes for specific cities. Therefore, cost estimators can spatially interpolate values to understand cost indexes at places that are not listed in the database. Second, comparing different spatial interpolation techniques, including NN for how well they can interpolate cost indexes across space revealed that two alternative methods, the conditional nearest neighbor (CNN) and the inverse distance weighted (IDW) methods, lead to more accurate estimates. These findings are relevant to industry practitioners because they justify the use of interpolation as well as identify how to obtain improvements in preliminary estimates by simply replacing an interpolation method with another. Last, this study also provided preliminary evidence supporting the development of spatial prediction models for construction costs, which is an additional point of departure from the existing body of knowledge.