Open Access Open Access  Restricted Access Subscription or Fee Access

Estimated Construction Cost Model Based on Building Functions in Indonesia

(*) Corresponding author

Authors' affiliations



This study aims to examine the factors that could influence the estimated costs of a building based on the function of the building itself. The building functions reviewed in this study are business function, socio-cultural function, and residential function. The independent variables consist of building area, building height, floors number, and project implementation time. On the other hand, the dependent variable is the total costs of the project. The data used as samples are as many as 155 and have been taken by using a probability sampling. Subsequently, the analysis of this study is performed by using a multiple linear regression method. The result shows that there is a difference in the estimated costs in each function, either business function, socio-cultural function, or residential function. When referring to the number of business function costs estimation, there is a difference by 32.01% for residential function and 21.9% for socio-cultural function. This means that both costs estimation for residential function and socio-cultural function are smaller than business function. This paper proposes a novel concept in the context of building cost estimation in Indonesia as currently the estimation merely depends on the size of the building without considering its various function. That way, the proposed view in this study will potentially change the way building cost is estimated in Indonesia.
Copyright © 2019 Praise Worthy Prize - All rights reserved.


Function; Building; Cost; Multiple Linear Regression Method

Full Text:



Cheng. M.Y, Tsai. H.C, & Sudjono. E, Conceptual Cost Using Evaluationary Fuzzy Hybrid Neural Network for Projects In Construction Industry. Expert Systems with Applications, 37(6), 4224-4231, 2010.

Blocher. E, Stout. D, & Cokins. G, Strategic Cost Management Emphasis. Jakarta: Salemba Empat, 2013.

Inloox, How Much Money is Needed to Complete The Project,, 2017.

Halpin. W, Woodhead. W, Construction Management (2ndEd). New York:John Wiley & Sons, Inc,1998.

Challal. A, Tkiouat. M, The Design of Cost Estimating Model of Construction Project: Applicaton and Simulation. Open Journal of Accounting, 1(1), 15-26, 2012.

Ahmed. S, Dlask. P, Hasan. B, Deviation in The Cost of Project, 2014.
Retrieved from,

Sugiarto, Estimate Construction Cost. 2013.
Retrieved from

Indrawan. S, Estimation of Road Maintenance Costs with Cost Significant Model, Case Study of Maintaining Road District in Jembrana Regency, 2011.

Aureza. S, Amin. A, &B. Siamak, A Neural Network Based Model for Cost Estimation of Industrial Building at The Projects Definition Phase. International Construction Specialty Conference of The Canadian Society For Civil Engineering (ICSC), 5th: 2015.

Park. M, Ji. S.H, Lee. S.H,Cost Estimation Model for Building Project Using Case-Based Reasoning. Candian Journal of Civil Engineering, 38(5), 570-587, 2011.

Elfaki. A.O, Alatawi. S, Abushandi. E,Using Intelligent Techniques In Construction Project Cost Estimation:10-Year Survey. Advances In Civil Engineering, Volume 2014, Article ID 107926, 1-11,2014.

Yoonseok. S, Application Of Boosting Regression Trees To Preliminary Cost Estimation In Building Construction Projects. Computational Intelligence and Neuroscience, Volume 2015, January, 1-9, 2015.

Cho. H.G, Kim. K.G, Jim. J.Y, Kim. G.H, A Comparison of Construction Cost Estimation Using Multiple Regression Analysis and Neural Network In Elementary School Project. Journal of The Korea Institute Of Building Construction, 13(1): 66-74, 2013.

Lucchi. E, Tabak. M, Troi. A, The “Cost Optimality” Approach for The Internal Insulation of Historic Buildings. Energy Procedia, 133(october), 412 – 423, 2017.

Becchio. C, Corgnati. S.P, Orlietti. L, & Spigliantini. G, Proposal for a Modified Cost-Optimal Approach by Introducing Benefits Evaluation. Energy Procedia, 82 (December), 445-451, 2015.

Ascione. F, Blanco. N., De Stasio. C, Mauro. G.M, & Vanoli. G.P, A New Comprehensive Approach for Cost Optimal Building Desing Integrated With The Multi Objective Model Predictive Control Of HVAC Systems. Sustainable Cities and Society, 31(may):136-150, 2017.

Gwang. H, Jae. M, Sangyong. M, & Yoonseok. S, Comparison of School Building Construction Costs Estimation Methods Using Regression Analysis, Neural Network, and Support Vector Machine.Journal of Building Construction And Planning Research, 1(1),1-7, 2013.

Falcao, V., Ferreira Nobre Júnior, E., de Athayde Prata, B., Optimization Techniques Applied to Earthmoving and Highway Construction: a Survey, (2016) International Review of Civil Engineering (IRECE), 7 (5), pp. 137-147.

Cheddadi, Y., Diouri, O., Gaga, A., Errahimi, F., Es-Sbai, N., Design and Simulation of an Accurate Neural Network State-of-Charge Estimator for Lithium Ion Battery Pack, (2017) International Review of Automatic Control (IREACO), 10 (2), pp. 186-192.

Skitmore. R.M, Patchell. B.R.T, Development in Contract Price Forecasting and Bidding Technique. London: E&FN Spon, 1990.

Gunaydin. M.H, Dogan. S.Z, A Neural Network Approach for Early Cost Estimation of Structural Systems Of Building. International Journal Project Manage, 22(7), 595-602,2004.

Alshamrani, O.S, Construction Cost Prediction Model for Conventional and Sustainable College Buildingin North America. Journal Of Taibah University for Science, 11(2): 315-323, 2017.

Alshemosi, A.M.B., &Alsaad, H. S.H. Cost Estimation Process for Construction Residential Projects by Using Multifactor Linear Regression Technique. International Journal of Science and Research, 6(6): 1-6, 2017.

Hence. S, Bonny. F, Robert. J, Conceptual Cost Model of Building Construction Conceptual Phase with Parametric Methods. Jurnal Ilmiah Media Engineering, 4(2), 103-108, 2014.

Mohsin. M, Nuaimi. A, Modeling of Construction Cost of Villas in Oman. TJER, 11(1), 34-43, 2013.

Wibowo. A, Wuryanti. W, Capacity Factor Based Cost Models for Building of Various Functions. Civil Engineering Dimension, 9(2), 70-76, 2007.

Lowe. J, Emsley. W, Harding. A, Predicting Construction Cost Using Multiple Regression Techniques. Journal of Construction Engineering and Management, 132(7), 750-758, 2006.

Li. H, Shen. Q.P, Love. P.E.D, Cost Modeling of Office Building in Hongkong an Exploratory Study, Facilities Journal, 23(9/10),438 – 452, 2016.

Law No. 28 of 2002 on Buildings.

Semarang City Regulation No. 5 of 2009 concerning Buildings.

Sekaran. U, Bougie. R, Research Methods for Business. New York: John Wiley & Sons, 2010.

Cecic. I, & Musson. R, Macroseismic surveys in the theory and practice. Natural Hazards, 31(1), 39-61, 2004.

Wibisono. D, Guide to Thesis, Thesis and Dissertation Development. Yogyakarta: Andi, 2013.

Oza, M. P., Srivastava, V. K., Pariswad, B. S., &Setty, K. R. V. Relationship Between Landsat MSS Data and Forest Tree Parameters. International Journal of Remote Sensing, 10(11): 1813-1819,1989.

Ross. M, Introductory Statistics. Sandiago: Elsevier. Inc, 2010.

Bluman. G, Elementary Statistics a Step by Step Approach. Boston: Mc Graw Hill, 2007.

Anwar. H, Uji F and Uji T, 2012.

Retrieved from

Trijono. R, Quantitative Research Methods. Jakarta:Papas Sinar Sinanti, 2015.

Lin, O., Miyauchi, H., Reliability Forecasting in Distribution System Considering Variable Failure Rate: Combination of Equipment Inspection Method and Weibull Analysis, (2017) International Review of Electrical Engineering (IREE), 12 (1), pp. 67-72.

Sheboniea, M., Darwish, M., Janbey, A., Investigation and Regression Analysis of Weekly Household Appliances in the UK, (2017) International Journal on Energy Conversion (IRECON), 5 (3), pp. 79-87.

Isabona, J., Srivastava, V., Radio Channel Propagation Characterization and Link Reliability Estimation in Shadowed Suburban Macrocells, (2017) International Journal on Communications Antenna and Propagation (IRECAP), 7 (1), pp. 57-63.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2022 Praise Worthy Prize