Decision-making model supporting the process of planning expenditures for residential building renovation

    Robert Bucoń Affiliation
    ; Michał Tomczak Affiliation


The problem of multi-family building maintenance is complex and comprises numerous issues, one of which is the process of planning expenditures for residential building renovation. This task is important from the manager’s point of view as their responsibility is to maintain a building in a non-deteriorated condition. To fulfil this task, the authors of this paper suggest utilising a decision-making model aimed at defining renovation activities making it possible to retain the maintenance standard (as regards newly commissioned residential buildings) or improve it (as regards existing buildings). The suggested model is based on a multi-criteria building assessment including seven requirements. The calculations conducted using the suggested model enable us to define the costs and scope of renovation taken to ensure the assumed building condition or, by assuming various rates paid to the renovation reserve, to define the period in which the above-mentioned goals may be achieved.

Keyword : renovation planning, building maintenance, decision-making model, building condition assessment, building condition criteria, selection of renovation

How to Cite
Bucoń, R., & Tomczak, M. (2018). Decision-making model supporting the process of planning expenditures for residential building renovation. Technological and Economic Development of Economy, 24(3), 1200-1214.
Published in Issue
May 28, 2018
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Alchimoviene, J.; Raslanas, S. 2011. Sustinable renovation and evaluation of block of multi-apartment house, in The 8th International Conference “Environmental Engineering”, 19–20 May 2011, Vilnius, Lithuania.

Bahr, C.; Lennerts, K. 2010. Quantitative validation of budgeting methods and suggestion of a new calculation method for the determination of maintenance costs, Journal of Facilities Management 8(1): 47–63.

Borissova, D.; Mustakerov, I. 2012. An integrated framework of designing a decision support system for engineering predictive maintenance, International Journal “Information Technologies and Knowledge” 6(4): 366–376.

Bucoń, R.; Sobotka, A. 2015. Decision making model for choosing residential building repair variants, Journal of Civil Engineering and Management 21(7): 893–901.

Bucoń, R.; Tomczak, M. 2016. Supporting building administrator’s decisions in determining maintenance costs of residential buildings, Engineering Structures and Technologies 8(1): 15–22.

Caccavelli, D.; Gugerli, H. 2002. TOBUS – a European diagnosis and decision-making tool for office building upgrading, Energy and Buildings 34(2): 113–119.

Christen, M.; Schroeder, J.; Wallbaum, H. 2014. Evaluation of strategic building maintenance and refurbishment budgeting method Schroeder, International Journal of Strategic Property Management 18(4): 393–406.

Chua, S. J. L.; Ali, A. S.; Alias, A. B. 2015. Implementation of Analytic Hierarchy Process (AHP) decision making framework for building maintenance procurement selection: case study of Malaysian public universities, Maintenance and Reliability 17(5): 7–18.

Dascalaki, E.; Balaras, C. A. 2004. XENIOS – a methodology for assessing refurbishment scenarios and the potential of applications of RES and RUE in hotels, Energy and Buildings 36(11): 1091–1105.

Dukić, D.; Trivunić, M.; Starčev-Ćurčin, A. 2013. Computer-aided building maintenance with “BASEFM” program, Automation in Construction 30: 57–69.

Ho, D. C. W.; Chau, K. W.; Cheung, A. K. C.; Yau, Y.; Wong, S. K.; Leung, H. F.; Lau, S. S. Y.; Wong, W. S. 2008. A survey of the health and safety conditions of apartment buildings in Hong Kong, Building and Environment 43(5): 764–775.

Ho, D. C. W.; Yau, Y.; Poon, S. W.; Liusman, E. 2012. Achieving sustainable urban renewal in Hong Kong: a strategy for dilapidation assessment of high rises, Journal of Urban Planning and Development 138(2): 153–165.

Jaśkowski, P.; Biruk, S.; Bucoń, R. 2010. Assessing contractor selection criteria weights with fuzzy AHP method application in group decision environment, Automation in Construction 19(2): 120–126.

Journal of Laws. 1997. No. 115, Item. 741. The Real estate management act from 21 August 1997, Polish Standard.

Juan, Y. K.; Kim, J. H.; Roper, K.; Lacouture, D. C. 2009. GA – based decision support system for housing condition assessment and refurbishment strategies, Automation in Construction 18(4): 394–401.

Kaklauskas, A.; Tupenaite, L.; Kanapeckiene, L. 2008. Automated selection of value efficient buildings refurbishment alternatives, in The 25th International Symposium on Automation and Robotics in Construction ISARC-2008, 26–29 June 2008, Vilnius, Lithuania.

Kaklauskas, A.; Zavadskas, E. K.; Raslanas, S. 2005. Multivariant design and multiple criteria analysis of building refurbishments, Energy and Buildings 37(4): 361–372.

Kasprowicz, T. 2005. Modelling of complex small buildings operation and maintenance, in The 9th International Conference on “Inspection, Appraisal – Repairs and Maintenance of Structures”, 19–21 October 2005, Fuzhou, China.

Ko, C. H. 2009. RFID-based building maintenance system, Automation in Construction 18(3): 275–284.

Kolokotsa, D.; Diakaki, C.; Grigoroudis, E.; Stavrakakis, G.; Kalaitzakis, K. 2009. Decision support methodologies on the energy efficiency and energy management in buildings, Advances in Building Energy Research 9(3): 121–146.

Langevine, R.; Allouche, M.; Abourizk, S. 2006. Decision support tool for the maintenance management of buildings, in Joint International Conference on Computing and Decision Making in Civil and Building Engineering, 14–16 June 2006, Montréal, Canada.

Lounis, Z.; Vanier, D. J. A. 2000. Multiobjective and stochastic system for building maintenance management, Journal of Computer-Aided Civil and Infrastructure Engineering 15(5): 320–329.

Medineckiene, M.; Zavadskas, E. K.; Björk, F.; Turskis, Z. 2015. Multi-criteria decision-making system for sustainable building assessment/certification, Archives of Civil and Mechanical Engineering 15(1): 11–18.

Mickaityte, A.; Zavadskas, E. K.; Kaklauskas, A.; Tupenaite, L. 2008. The concept model of sustainable buildings refurbishment, International Journal of Strategic Property Management 12(1): 53–68.

Mohd-Noor, N.; Hamid, M. Y.; Abdul-Ghani, A. A.; Haron, S. N. 2011. Building maintenance budget determination: an exploration study in the Malaysia government practice, Procedia Engineering 20: 435–444.

Perng, Y. H.; Juan, Y. K.; Hsu, H. S. 2007. Genetic algorithm-based decision support for the restoration budget allocation of historical buildings, Building and Environment 42(2): 770–778.

Rasiulis, R.; Ustinovichius, L.; Vilutiene, T.; Popov, V. 2016. Decision model for selection of modernization measures: public building case, Journal of Civil Engineering and Management 22(1): 124–133.

Raslanas, S.; Alchimoviene, J.; Banaitienė, N. 2011. Residential areas with apartment houses: analysis of the condition of buildings, planning issues, retrofit strategies and scenarios, International Journal of Strategic Property Management 15(2): 152–172.

Reed, R.; Wilkinson, S.; Bilos, A.; Schulte, K. W. 2011. A comparison of international sustainable building tools – an update, in The 17th Annual Pacific Rim Real Estate Society Conference, 16–19 January 2011, Gold Coast, Australia.

Straub, A. 2009. Dutch standard for condition assessment of buildings, Structural Survey 27(1): 23–35.

Yau, Y.; Ho, D. C. W.; Chau, K. W. 2008. Determinants of the safety performance of private multi-storey residential buildings in Hong Kong, Social Indicators Research 89(3): 501–521.

Yin, H.; Stack, P.; Menzel, K. 2011. Decision support model for building renovation strategies, Computing in Civil Engineering (2011): 834–841.

Zavadskas, E. K.; Kaklauskas, A.; Tupenaite, L.; Mickaityte, M. 2008. Decision-making model for sustainable building refurbishment. Energy efficiency aspect, in The 7th International Conference of Environmental Engineering, 22–23 May 2008, Vilnius, Lithuania, 894–901.