Abstract
Accurately predicting building cost is of a great importance to house building companies. In this study, a machine learning (ML) framework with several regression approaches is developed to model and estimate the building costs accurately in both actual and inverse ways. A dataset of 10,000 samples, the real data computed by different services of our partner construction company, is used to train and to validate ML models. The ML-based estimated results indicate that linear regression model and decision tree model provide the most accurate results for construction cost and maintenance cost, respectively. Furthermore, an artificial neural network framework is considered in the inverse way to get the highest regression accuracy in order to identify the best available features of to-be-built house that a buyer can have for a given budget.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Mirmirani S, Li HC (2004) A comparison of VAR and neural networks with genetic algorithm in forecasting price of oil. Adv Econometrics 19:203–223
Guresen E, Kayakutlu G, Daim TU (2011) Using artificial neural network models in stock market index prediction. Expert Syst Appl 38(8):10389–97
Siripurapu A (2014) Convolutional networks for stock trading. Stanford Univ Dep Comput Sci 1–6
Banerjee D, Dutta S (2017) Predicting the housing price direction using machine learning techniques. In: 2017 IEEE international conference on power, control, signals and instrumentation engineering (ICPCSI), Chennai, pp 2998–3000
Mu J, Wu F, Zhang A (2014) Housing value forecasting based on machine learning methods. Abstr Appl Anal 2014:1–7. https://doi.org/10.1155/2014/648047
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Acknowledgements
The support of Thu Dau Mot University for this work within the “Modelling and Simulation in the Digital Age—MaSDA” research program is greatly appreciated.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pham, T.Q.D., Quang, N.H., Vo, N.D., Bui, V.S., Tran, V.X. (2021). Fast and Accurate Estimation of Building Cost Using Machine Learning. In: Kumar, R., Quang, N.H., Kumar Solanki, V., Cardona, M., Pattnaik, P.K. (eds) Research in Intelligent and Computing in Engineering. Advances in Intelligent Systems and Computing, vol 1254. Springer, Singapore. https://doi.org/10.1007/978-981-15-7527-3_49
Download citation
DOI: https://doi.org/10.1007/978-981-15-7527-3_49
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7526-6
Online ISBN: 978-981-15-7527-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)