Statistical model development to identify the best data pooling for early stage construction price forecasts

Abstract

In the early feasibility study stage, the information concerning the target project is very limited. It is very common in practice for a Quantity Surveyor (Q.S.) to use the mean value of the historical building price data (with similar characteristics to the target project) to forecast the early construction cost for a target project. Most clients rely heavily on this early cost forecast, provided by the Q.S., and use it to make their investment decision and advance financial arrangement. The primary aim of this research is to develop a statistical model and demonstrate through this developed model how to measure the accuracy of mean value forecast. A secondary aim is to review the homogeneity of construction project cost. The third aim is to identify the best data pooling for mean value cost forecast in early construction stages by making the best use of the data available. Three types of mean value forecasts are considered: (1) the use of the target base group (relating to a source with similar characteristics to the target project), (2) the use of a non-target base group (relating to sources with less or dissimilar characteristics to the target project) and (3) the use of a combined target and non-target base group. A formulation of mean square error is derived for each to measure the forecasting accuracy. To accomplish the above research aims, this research uses cost data from 450 completed Hong Kong projects. The collected data is clustered into two levels as: (1) Level one - by project nature (i.e. Residential, Commercial centre, Car parking, Social community centre, School, Office, Hotel, Industrial, University and Hospital), (2) Level two -by project specification and construction floor area. In this research, the accuracy of mean value forecast (i.e. mean square error) for a total number of 10,539 of combined data groups is measured. From their performance, it may reasonably be concluded that (1) the use of a non-target base group (relating to sources with less or dissimilar characteristics to the target project) never improves the forecasting performance, (2) the use of a target base group (relating to a source with similar characteristics to the target project) cannot always provide the best forecasting performance, (3) the use of a combined target and non-target base group in some cases can furnish a better forecasting performance, and (4) when the cost data groups are clustered into a more detailed level, it can improve the forecasting performance