Modelling the running costs of buildings

Assem Al-Hajj and Malcolm Horner

Construction Management and Economics, 1998, vol. 16, issue 4, 459-470

Abstract: The Building Maintenance Cost Information Service (BMCIS) offers a comprehensive and rigorous framework for collecting data about the running costs of buildings. Nevertheless, it is pitched at such a level of detail that the amount of data collected and analysed may be constrained. This paper describes the deveopment and testing of a novel technique which reduces the amount of data to be collected without any unacceptable reduction in utility. It draws on the principle of cost-significance to create a simple model of maintenance and operating costs (together called running costs) from a rare and consistent set of data for 20 buildings at York University. The model contains only 11 elements, yet can predict the total running costs of each of four categories of building to an accuracy of about 21 2%. It can also predict annual costs to about 7%, despite variations in the periodicity of costs such as painting and insurance. The model was tested using the jacknife method and on virgin data. It proved to be extremely robust, predicting the running costs of 12 new buildings to within 5%. The model offers a simple framework for collecting and analysing reliable and consistent data on running costs.

Keywords: Life Cycle Costs; Maintenance Costs; Operating Costs; Cost-significance (search for similar items in EconPapers)
Date: 1998
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/014461998372231 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:conmgt:v:16:y:1998:i:4:p:459-470

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RCME20

DOI: 10.1080/014461998372231

Access Statistics for this article

Construction Management and Economics is currently edited by Will Hughes

More articles in Construction Management and Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().