Parametric Cost Estimating

Parametric cost estimating model for conceptual cost estimating of building construction projects, Kan Phaobunjong, 2002 Conceptual Cost Estimation Cost engineer Historical data Actual data Project scope Four essential inputs to conceptual cost estimating are project scope, information, estimating methodology, and an estimator. Cost Methods (in order of complexity, information required, effort required, estimate accuracy) Analogous Unit method/Parametric Assembly methodExpertise means possessing the ability, competency, experience, familiarity, knowledge, skills, and mastery in a specific field. The estimator’s ability to visualize the scope of work from incomplete scope definition is one of the most important factors in conceptual cost estimating.

History of Parametric Estimating Before the development of parametric estimating, the quantitative method in conceptual cost estimating had been limited to performing the detailed quantity takeoff and pricing [Orczyk 1990].Quantity takeoff involved a lot of data identification, computation, and documentation. The development of the parametric cost estimating model generally involves the following six steps. [Black, 1984] 1)Problem definition It encompasses the determination of the objectives and scope of the research study.

2)Data collection Parametric estimating requires an extensive database, where historical records are extremely important. The design or engineering parameters that rive parametric cost estimates are developed from historical cost databases. Data collection can be regarded as the most crucial step. Without sufficient relevant data, the parametric estimating cannot be successfully implemented. 3)Data normalization This process ensures that every individual record in the database is on the same base. 4)Parametric cost estimating model development It involves the determination of interdependencies of the variables to be used in the model and the derivation of the cost estimating relationships.

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The basis for selecting the parameters for use in the model should be more than just the statistical validity, but the inclusion of the parameters should also be based on the logical and theoretical considerations. 5)Establishment of limitations The model is generally developed from a limited data set, is therefore valid only for the ranges of variables used in the model. 6)Documentation The assumptions and limitations of the model must be properly documented to facilitate the successful model implementations.