Abstract:
Purpose – Traditionally, early-stage investment decisions on buildings purely based initial capital costs and
simply ignored running costs and total lifecycle cost. This was basically due to the absence of estimating
models that yield running costs at the early design stage. Often, when the design of a building, which is
responsible for 10–15% of its total cost, is completed, 80% of the total cost is committed. This study aims to
develop a building characteristic-based model, which is an early-stage determinant of running costs of
buildings, to predict the running costs of commercial buildings.
Design/methodology/approach – A desk study was carried out to collect running costs data and building
characteristics of 35 commercial buildings in Sri Lanka. A Pareto analysis, bivariate correlation analysis and
hedonic regression modelling were performed on collected data.
Findings – According to Pareto analysis, utilities, services, admin work and cleaning are four main cost
constituents, responsible for 80% of running costs, which can be represented by highly correlated building
characteristics of building height, number of floors and size. Approximately 94% of the variance in annual
running costs/sq. m is expressed by variables of number of floors, net floor area and working hours/day
together with a mean prediction accuracy of 2.89%.
Research limitations/implications – The study has utilised a sample of 35 commercial buildings due to
non-availability and difficulty in accessing running cost data.
Originality/value – Early-stage supportive running costs estimation model proposed by the study would
enable construction professionals to benchmark the running costs and thereby optimise the building design.
The developed hedonic model illustrated the variance of running costs concerning the changes in
characteristics of a building.
Citation:
Geekiyanage, D., & Ramachandra, T. (2020). Nexus between running costs and building characteristics of commercial buildings: Hedonic regression modelling. Built Environment Project and Asset Management, 10(3), 389–406. https://doi.org/10.1108/BEPAM-12-2018-0156