Development and Testing of a Method for Forecasting Prices of Multi-Storey Buildings during the Early Design Stage: the Storey Enclosure Method Revisited

Although design decisions that are made in the preliminary design stages of a building are more cost sensitive than those that are made at later stages, previous research suggests that they result in only a slight improvement in the accuracy of building price forecasts as the design develops. However, established conventional forecasting methods lack measures of their own performance, which has inhibited the development of simpler early-stage techniques. One early-stage price forecasting model, the Storey Enclosure Method, which was developed by James in 1954, uses the basic physical measurements of buildings to estimate building prices. Although James' Storey Enclosure Model (JSEM) is not widely used in practice, it has been proved empirically, if rather crudely, to be a better model than other commonly used models. This research aims firstly to advance JSEM by using regression techniques and secondly to develop an objective approach for the assessment of model performance. To accomplish the first research aim, this research uses data from 148 completed Hong Kong projects for four types of building: offices, private housing, nursing homes, and primary and secondary schools. Sophisticated features of the modelling exercise include the use of leave-one-out cross validation to simulate the way in which forecasts are produced in practice and a dual stepwise selection strategy that enhances the chance of identifying the best model. Two types ofregressed models from different candidate sets, the Regressed Model for James' Storey Enclosure Method (RJSEM) and Regressed Model for Advanced Storey Enclosure Method (RASEM), are developed accordingly. In considering the RJSEM, RASEM, and the most commonly used alternative early stage floor area and cube models, all of the models except JSEM are found to be unbiased. The RJSEM and RASEM models are also examined for their consistency using a structured approach that involves the use of both parametric and non-parametric inference tests. This shows that although the RASEMs for different building types are generally more consistent, they are not significantly better than the other models. Finally, the combination of the forecasts that are generated from different models to capture the different aspects of information from the models is suggested as an alternative strategy for improving forecasting performance.