Migliaccio, G C; Guindani, M; D'Incognito, M and Zhang, L (2013) Empirical assessment of spatial prediction methods for location cost-adjustment factors. Journal of Construction Engineering and Management, 139(7), pp. 858-869. ISSN 0733-9364
Abstract
In the feasibility stage of a project, location cost-adjustment factors (LCAFs) are commonly used to perform quick order-of-magnitude estimates. Nowadays, numerous LCAF data sets are available in North America, but they do not include all locations. Hence, LCAFs for unsampled locations need to be inferred through spatial interpolation or prediction methods. Using a commonly used set of LCAFs, this paper aims to test the accuracy of various spatial prediction methods and spatial interpolation methods in estimating LCAF values for unsampled locations. Between the two regression-based prediction models selected for the study, geographically weighted regression analysis (GWR) resulted the most appropriate way to model the city cost index as a function of multiple covariates. As a direct consequence of its spatial nonstationarity, the influence of each single covariate differed from state to state. In addition, this paper includes a first attempt to determine if the observed variability in cost index values could be at least partially explained by independent socioeconomic variables.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | budgeting; construction costs; estimation; geostatistics; planning |
| Index terms: | geostatistics, prediction method, prediction model, accuracy, budgeting, estimating, construction cost, estimation, cost index, estimate, regression analysis, variability |
| Subjects: | data analysis and analytics, prediction and forecasting, economic analysis, financial and cost management, professional development, financial management, statistical analysis |
| Topics: | Cost Management, Business Strategy, Research Practice, Information Management |
| Descriptive scope: | 3 PCA |
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