QUT ePrints

Data Pooling for Early-Stage Price Forecasts

Yeung, Daisy K.L. & Skitmore, Martin (2005) Data Pooling for Early-Stage Price Forecasts. In ARCOM Annual Conference.

Abstract

Many clients rely heavily on pretender price forecasts, provided by the Quantity Surveyor (QS), for their investment decisions. During the preliminary design stage, it is very common in practice for Q.S. to use historical building price data on which to base the forecast of the target project – typically basing the forecast on the known price of one most similar project to the target. However, as statistician Beeston (1974) points out, a potentially more powerful method is to use the mean price of a suitable sample of similar projects on the rationale that there the price of the target project is one of a "family of prices" for that project, and for which the prices of those in the sample are a proxy. The major difficulty with this is what Flanagan (1980) has termed the "homogeneity" problem, in that the bigger the sample size the less similar are the projects in the sample. In statistical parlance this means that, although increased sample size reduces the standard error of the mean at the same time the sample becomes less representative of the population from which the target project is conjectured to belong. In other words, making the best use of available data involves a trade off between the need to restrict the sample of data used to that most relevant to the forecasted project while at the same time maximizing the sample size involved.

In 2001, Skitmore offered an approach to solving this in the risk analysis contect by empirically examining the effects of all possible pooling (sampling) permutations on forecasting errors with a view to selecting the pooling arrangement that most minimises the spread of errors.. This paper develops this idea for early stage forecasting. Using out-of-sample mean square errors to measure the forecasting accuracy a method is presented for finding the best pooling arrangement of the available data according to the characteristics of the target project. This involves the use of crossvalidated multivariate regression analysis to pool data into subgroups – typically within the building floor area, project type, number of bidders, etc.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation countsare sourced monthly from Scopus and Web of Science® 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 the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

74 since deposited on 19 Jun 2006
1 in the past twelve months

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.

ID Code: 4511
Item Type: Conference Paper
Keywords: data group pooling, price family, homogeneity, forecasting accuracy, mean square error, cross validadation, multivariate regression analysis
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 2005 (please consult author)
Deposited On: 19 Jun 2006
Last Modified: 29 Feb 2012 23:11

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page