A model of building price forecasting accuracy

Gunner, JC

Abstract

The purpose of this research was to derive a statistical model comprising the
significant factors influencing the accuracy of a designer's price forecast and as an aid
to providing a theoretical framework for further study. To this end data, comprising 181
building contract details, was collected from the Singapore office of an international firm
of quantity surveyors over the period 1980 to 1991.
Bivariate analysis showed a number of independent variables having significant
effect on bias which was in general agreement with previous work in this domain. The
research also identified a number of independent variables having significant effect on
the consistency, or precision, of designers' building price forecasts. With information
gleaned from bivariate results attempts were made to build a multivariate model which
would explain a significant portion of the errors occurring in building price forecasts.
The results of the models built were inconclusive because they failed to satisfy the
assumptions inherent in ordinary least squares regression. The main failure in the
models was in satisfying the assumption of homoscedasticity, that is, the conditional
variances of the residuals are equal around the mean. Five recognised methodologies
were applied to the data in attempts to remove heteroscedasticity but none were
successful.
A different approach to model building was then adopted and a tenable model
was constructed which satisfied all of the regression assumptions and internal validity
checks. The statistically significant model also revealed that the variable of Price
Intensity was the sole underlying influence when tested against all other independentpage xiv
variables in the data of this work and after partialling out the effect of all other
independent variables. From this a Price Intensity theory of accuracy is developed and
a further review of the previous work in this field suggests that this may be of universal
application.

Citation

Gunner, J. A model of building price forecasting accuracy. (Thesis). Salford : University of Salford

• Thesis Type: Thesis
• Deposit Date: Oct 3, 2012
• Award Date: Jan 1, 1997