The variance of elemental estimates
Skitmore, Martin & Ng, S. Thomas (2000) The variance of elemental estimates. In Serpell, Alfredo (Ed.) Information and Communication in Construction Procurement, The International Council for Building Research Studies and Documentation (CIB) W-92 Procurement System Symposium, 2000, Department of Construction Engineering and Management, Pontificia Universidad Catolica de Chile, Santiago, Chile.
One of the factors affecting the success or otherwise of the procurement process is the quality of its financial management. In many cases, particularly in the early stages of a project, this is dependent on the accuracy of forecasts of future costs. For risk management purposes, what is most useful is the representation of future costs in the form of a probability distribution. In the case of early stage building contract price forecasts the approximate distribution of the likely error is usually obtained by the stochastic simulation of values from the distribution of known elemental rates under the simplifying assumption that the elemental rates are from independent random variables. This paper describes an empirical study aimed at investigating the accuracy of this method in comparison with one which incorporates the intervariable correlations. This confirms previous work in showing that the estimated second moment (variance) is considerably different when using the mean element rates as estimators, with the approximate method producing much lower estimates. It is then shown that, under the empirically supported assumption that element rates are normally distributed, the total project is also lognormally distributed and that this is completely specified. It is also shown that the coefficient of variation is unaffected by the size (floor area) of the project. The analysis then continues to examine the role of professional judgement and, with the simulated data used, the independent approximation is shown to be reasonably accurate – the professional judgement absorbing most of the intercorrelations involved. A final example is given involving the use of elemental unit quantities and rates. This shows how a unique project rate variance can be calculated for an individual future project, based on relevant data from previous projects and taking into account interelemental rate correlations. The results again suggests that the approximating method may be quite accurate.
Citation countsare sourced monthly fromand citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|Keywords:||Errors, forecasting, covariance, price, stochastic simulation, variance|
|Subjects:||Australian and New Zealand Standard Research Classification > BUILT ENVIRONMENT AND DESIGN (120000) > BUILDING (120200) > Quantity Surveying (120203)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering|
|Copyright Owner:||Copyright 2000 (please consult author)|
|Deposited On:||13 Sep 2007|
|Last Modified:||03 Mar 2011 15:42|
Repository Staff Only: item control page