Prediction of Delay Occurrence in Disputed Real Estate Projects: An Artificial Neural Network Approach
Publication: Journal of Legal Affairs and Dispute Resolution in Engineering and Construction
Volume 16, Issue 2
Abstract
Real estate disputes are inevitable and regarded as being the most important factor influencing timely completion of projects. Indian real estate sector is largely affected due to different project characteristics and cultural practices adopted around the different regions of country resulting into disputes. Given various reasons for disputes in Indian real estate sector, the project’s completion may be delayed. To eliminate the discrepancy of an unorganized sector, Government of India (GOI) enforced the Real Estate (Regulation and Development) Act, 2016 by forming The Real Estate Regulatory Authority (RERA). The reform of regulatory framework leads to elimination of differential cultural practices but the real estate sector still encounters disputes due to varied project characteristics. Thus it becomes necessary to understand, analyze and forecast the pattern generating delay due to claims and disputes in Indian real estate sector. This study attempts to implement artificial neural network (ANN) to predict whether disputed project might be delayed or not based on the project characteristics. The soft computing model is developed utilizing around 180 past judgments from five Mahanagar Palika (municipal corporation) namely Ahmedabad, Surat, Vadodara, Gandhinagar and Rajkot under Gujarat RERA (GujRERA). The findings of the research will help the authority to take necessary steps at initial stage of project to eliminate the disputes and to avoid utilization of considerable amount of resources for resolving these disputes.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. The item includes (1) the analysis of delay model using Logistic Regression Analysis.
References
List of Statutes
The Real Estate (Regulation and Development) Act, 2016, Act Number 16 of 2016, Ministry of Law and Justice, Government of India.
Works Cited
AAA (American Arbitration Association). 2022. “AAA 2021 annual report and financial statements.” Accessed October 12, 2022. https://www.adr.org/annual-reports.
Alozn, A. E., and A. Galadari. 2018. “Evidence admissibility and evaluation models in commercial arbitration.” J. Leg. Aff. Dispute Resolut. Eng. Constr. 10 (3): 04518008. https://doi.org/10.1061/(ASCE)LA.1943-4170.0000258.
Alqershy, M. T., and R. Kishore. 2021. “Construction claims prediction using ANN models: A case study of the Indian construction industry.” Int. J. Constr. Manage. 23 (6): 1–12. https://doi.org/10.1080/15623599.2021.1955322.
Arditi, D., F. E. Oksay, and O. B. Tokdemir. 1998. “Predicting the outcome of construction litigation using neural networks.” Comput. Civ. Infrastruct. Eng. 13 (2): 75–81. https://doi.org/10.1111/0885-9507.00087.
Arditi, D., and T. Pulket. 2005. “Predicting the outcome of construction litigation using boosted decision trees.” J. Comput. Civ. Eng. 19 (4): 387–393. https://doi.org/10.1061/(ASCE)0887-3801(2005)19:4(387).
Arditi, D., and T. Pulket. 2010. “Predicting the outcome of construction litigation using an integrated artificial intelligence model.” J. Comput. Civ. Eng. 24 (1): 73–80. https://doi.org/10.1061/(asce)0887-3801(2010)24:1(73).
Berry, M. J. A., and G. S. Linoff. 2004. Data mining techniques: For marketing, sales, and customer relationship management. New York: Wiley.
Chaphalkar, N. B., K. C. Iyer, and S. K. Patil. 2015. “Prediction of outcome of construction dispute claims using multilayer perceptron neural network model.” Int. J. Project Manage. 33 (8): 1827–1835. https://doi.org/10.1016/j.ijproman.2015.09.002.
Chaphalkar, N. B., and S. S. Sandbhor. 2015. “Application of neural networks in resolution of disputes for escalation clause using neuro-solutions.” KSCE J. Civ. Eng. 19 (1): 10–16. https://doi.org/10.1007/s12205-014-1161-3.
Chau, K. W. 2007. “Application of a PSO-based neural network in analysis of outcomes of construction claims.” Autom. Constr. 16 (5): 642–646. https://doi.org/10.1016/j.autcon.2006.11.008.
Chen, J. H., and S. C. Hsu. 2007. “Hybrid ANN-CBR model for disputed change orders in construction projects.” Autom. Constr. 17 (1): 56–64. https://doi.org/10.1016/j.autcon.2007.03.003.
Chinyere, I. I. 2011. “Procedures and arrangement for dispute resolution management in international construction development projects.” Interdiscip. J. Res. Bus. 1 (9): 61–71.
Chou, J. S., C. F. Tsai, and Y. H. Lu. 2013. “Project dispute prediction by hybrid machine learning techniques.” J. Civ. Eng. Manage. 19 (4): 505–517. https://doi.org/10.3846/13923730.2013.768544.
Construction Industry Development Council. 2022. “Construction industry arbitration/litigation database-CIDC.” Accessed October 8, 2022. https://cidcdatabase.com.
Diekmann, J. E., and M. J. Girard. 1995. “Are contract disputes predictable?” J. Constr. Eng. Manage. 121 (4): 355–363. https://doi.org/10.1061/(ASCE)0733-9364(1995)121:4(355).
Fatima, A., T. Seshadri Sekhar, and B. S. K. Prasad,. 2019. “Prediction of construction dispute using artificial neural network testimonies from Indian construction projects” Int. J. Civ. Eng. Technol. 10 (1): 582–594.
Fernández, A., S. Garcia, M. Galar, R. C. Prati, B. Krawczyk, and F. Herrera. 2018. Learning from imbalanced data sets. New York: Springer.
GujRERA. 2022. “Gujarat real estate regulatory authority ‘home.’” Accessed December 24, 2022. https://gujrera.gujarat.gov.in.
Gupta, T., K. A. Patel, S. Siddique, R. K. Sharma, and S. Chaudhary. 2019. “Prediction of mechanical properties of rubberised concrete exposed to elevated temperature using ANN.” Measurement 147 (Jun): 106870. https://doi.org/10.1016/j.measurement.2019.106870.
Hosny, O. A., M. M. G. Elbarkouky, and A. Elhakeem. 2015. “Construction claims prediction and decision awareness framework using artificial neural networks and backward optimization.” J. Constr. Eng. Project Manage. 5 (1): 11–19. https://doi.org/10.6106/jcepm.2015.5.1.011.
India Blooms. 2018. “Online dispute resolution mechanism is a laudable initiative and saves time and cost: Vice president Naidu.” Accessed October 8, 2022. https://www.indiablooms.com/news-details/N/39414/online-dispute-resolution-mechanism-is-a-laudable-initiative-and-saves-time-and-cost-vice-president-naidu.html.
Indian Kanoon. 2022. “Construction cases judgments.” Accessed October 8, 2022. https://indiankanoon.org/search/?formInput=construction+case++++doctypes%3A+judgments+fromdate%3A+1-1-2022+todate%3A+31-12-2022.
International Construction. 2021. “Construction growth to ‘power global economy.’” Accessed October 8, 2022. https://www.international-construction.com/news/construction-growth-to-power-global-economy-/8015150.article.
Jain, A. K., J. Mao, and K. M. Mohiuddin. 1996. “Artificial neural networks: A tutorial.” Computer (Long. Beach. Calif.) 29 (3): 31–44. https://doi.org/10.1109/2.485891.
Mahfouz, T., and A. Kandil. 2012. “Litigation outcome prediction of differing site condition disputes through machine learning models.” J. Comput. Civ. Eng. 26 (3): 298–308. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000148.
Marzouk, M., L. El-Mesteckawi, and M. El-Said. 2011. “Dispute resolution aided tool for construction projects in Egypt.” J. Civ. Eng. Manage. 17 (1): 63–71. https://doi.org/10.3846/13923730.2011.554165.
Ministry of Housing and Urban Affairs. 2022. “Real estate Regulation and Development Act, 2016. Accessed October 25, 2022. https://mohua.gov.in/upload/uploadfiles/files/1Real_Estate_Act_2016.pdf.
Ministry of Information and Broadcasting. 2020. “Reforms in real estate sector RERA-2016.” Accessed December 10, 2022. https://static.pib.gov.in/WriteReadData/specificdocs/documents/2021/sep/doc202191751.pdf.
Miyamoto, A., J. Puttonen, and A. Yabe. 2017. “Long term application of a vehicle-based health monitoring system to short and medium span bridges and damage detection sensitivity.” Engineering 9 (2): 68–122. https://doi.org/10.4236/eng.2017.92005.
Parikh, D., G. J. Joshi, and D. A. Patel. 2019. “Development of prediction models for claim cause analyses in highway projects.” J. Leg. Aff. Dispute Resolut. Eng. Constr. 11 (4): 1–11. https://doi.org/10.1061/(ASCE)LA.1943-4170.0000303.
Patel, D. A., and K. N. Jha. 2015. “Neural network model for the prediction of safe work behavior in construction projects.” J. Constr. Eng. Manage. 141 (1): 1–13. https://doi.org/10.1061/(asce)co.1943-7862.0000922.
Patel, M. B., and D. A. Patel. 2023. “Empirical analysis of real estate disputes.” J. Leg. Aff. Dispute Resolut. Eng. Constr. 15 (1): 1–12. https://doi.org/10.1061/(asce)la.1943-4170.0000564.
Shin, K.-C., and K. Molenaar. 2000. “Prediction of construction disputes in change issues.” In Proc., Construction Congress VI, 534–542. Reston, VA: ASCE. https://doi.org/10.1061/40475(278)58.
Tanielian, A. 2013. “Arbitration still best road to binding dispute resolution.” J. Leg. Aff. Dispute Resolut. Eng. Constr. 5 (2): 90–96. https://doi.org/10.1061/(ASCE)LA.1943-4170.0000111.
Xu, M. 2022. “Effect evaluation of WTO dispute settlement mechanism based on artificial neural network.” In Wireless communications and mobile computing, edited by K. Lakshmanna. London: Hindawi. https://doi.org/10.1155/2022/5196698.
Yousefi, V., S. H. Yakhchali, M. Khanzadi, E. Mehrabanfar, and J. Šaparauskas. 2016. “Proposing a neural network model to predict time and cost claims in construction projects.” J. Civ. Eng. Manage. 22 (7): 967–978. https://doi.org/10.3846/13923730.2016.1205510.
Information & Authors
Information
Published In
Copyright
© 2023 American Society of Civil Engineers.
History
Received: May 15, 2023
Accepted: Sep 21, 2023
Published online: Dec 28, 2023
Published in print: May 1, 2024
Discussion open until: May 28, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Business management
- Computer models
- Computer programming
- Computing in civil engineering
- Construction engineering
- Construction management
- Developing countries
- Dispute resolution
- Engineering fundamentals
- Government
- Infrastructure
- Legal affairs
- Models (by type)
- Neural networks
- Organizations
- Practice and Profession
- Project delay
- Project management
- Real estate
- Urban and regional development
- Urban areas
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.