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J Constr Eng Manag. 2013 Jul 1;139(7):858-869.

Empirical Assessment of Spatial Prediction Methods for Location Cost Adjustment Factors.

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University of Washington, Department of Construction Management, Box 351610, Seattle, WA 98195-1610, phone: +1 (206) 685-1676, fax: +1 (206) 685-1976.
The University of Texas MD Anderson Cancer Center, Department of Biostatistics - Unit 1411, P. O. Box 301402, Houston, TX 77230-1402, phone: +1 (713) 563-4285.
Politecnico di Bari, DICATECh - Department of Civil, Environmental, Building Engineering, and Chemistry, Bari, Italy.
Rice University, Department of Statistics, Houston, TX.


In the feasibility stage, the correct prediction of construction costs ensures that budget requirements are met from the start of a project's lifecycle. A very common approach for performing quick-order-of-magnitude estimates is based on using Location Cost Adjustment Factors (LCAFs) that compute historically based costs by project location. Nowadays, numerous LCAF datasets are commercially available in North America, but, obviously, they do not include all locations. Hence, LCAFs for un-sampled locations need to be inferred through spatial interpolation or prediction methods. Currently, practitioners tend to select the value for a location using only one variable, namely the nearest linear-distance between two sites. However, construction costs could be affected by socio-economic variables as suggested by macroeconomic theories. Using a commonly used set of LCAFs, the City Cost Indexes (CCI) by RSMeans, and the socio-economic variables included in the ESRI Community Sourcebook, this article provides several contributions to the body of knowledge. First, the accuracy of various spatial prediction methods in estimating LCAF values for un-sampled locations was evaluated and assessed in respect to spatial interpolation methods. Two Regression-based prediction models were selected, a Global Regression Analysis and a Geographically-weighted regression analysis (GWR). Once these models were compared against interpolation methods, the results showed that GWR is the most appropriate way to model CCI as a function of multiple covariates. The outcome of GWR, for each covariate, was studied for all the 48 states in the contiguous US. As a direct consequence of spatial non-stationarity, it was possible to discuss the influence of each single covariate differently from state to state. In addition, the article includes a first attempt to determine if the observed variability in cost index values could be, at least partially explained by independent socio-economic variables.


Budgeting; Construction costs; Estimation; Geostatistics; Planning

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