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Ashuri, B, Shahandashti, S M and Lu, J (2012) Empirical tests for identifying leading indicators of ENR Construction Cost Index. Construction Management and Economics, 30(11), 917-27.

Hui, E C-M, Liang, C, Wang, Z, Song, B-T and Gu, Q (2012) Real estate bubbles in China: a tale of two cities. Construction Management and Economics, 30(11), 951-61.

Ozbek, M E, de la Garza, J M and Triantis, K (2012) Efficiency measurement of the maintenance of paved lanes using data envelopment analysis. Construction Management and Economics, 30(11), 995-1009.

Ponnaluru, S S, Marsh, T L and Brady, M (2012) Spatial price analysis of used construction equipment: the case of excavators. Construction Management and Economics, 30(11), 981-94.

Sedighi, F and Loosemore, M (2012) Employer-of-choice characteristics in the construction industry. Construction Management and Economics, 30(11), 941-50.

Tucker, J R, Pearce, A R, Bruce, R D, McCoy, A P and Mills, T H (2012) The perceived value of green professional credentials to credential holders in the US building design and construction community. Construction Management and Economics, 30(11), 963-79.

Yeung, D and Skitmore, M (2012) A method for systematically pooling data in very early stage construction price forecasting. Construction Management and Economics, 30(11), 929-39.

  • Type: Journal Article
  • Keywords: closed form equations; cross-validation; data pooling; early stage estimating; homogeneity
  • ISBN/ISSN: 0144-6193
  • URL: https://doi.org/10.1080/01446193.2012.733402
  • Abstract:
    Client owners usually need an estimate or forecast of their likely building costs in advance of detailed design in order to confirm the financial feasibility of their projects. Because of their timing in the project life cycle, these early stage forecasts are characterized by the minimal amount of information available concerning the new (target) project to the point that often only its size and type are known. One approach is to use the mean contract sum of a sample, or base group, of previous projects of a similar type and size to the project for which the estimate is needed. Bernoulli's law of large numbers implies that this base group should be as large as possible. However, increasing the size of the base group inevitably involves including projects that are less and less similar to the target project. Deciding on the optimal number of base group projects is known as the homogeneity or pooling problem. A method of solving the homogeneity problem is described involving the use of closed form equations to compare three different sampling arrangements of previous projects for their simulated forecasting ability by a cross-validation method, where a series of targets are extracted, with replacement, from the groups and compared with the mean value of the projects in the base groups. The procedure is then demonstrated with 450 Hong Kong projects (with different project types: Residential, Commercial centre, Car parking, Social community centre, School, Office, Hotel, Industrial, University and Hospital) clustered into base groups according to their type and size.