Scholarly Papers
Dec 28, 2023

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.
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

Go to Journal of Legal Affairs and Dispute Resolution in Engineering and Construction
Journal of Legal Affairs and Dispute Resolution in Engineering and Construction
Volume 16Issue 2May 2024

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

Permissions

Request permissions for this article.

ASCE Technical Topics:

Authors

Affiliations

Rutvik A. Patel [email protected]
Formerly, Postgraduate Student, Dept. of Civil Engineering, Sardar Vallabhbhai National Institute of Technology Surat, Surat, Gujarat 395007, India. Email: [email protected]
Mukul B. Patel [email protected]
Ph.D. Research Scholar, Dept. of Civil Engineering, Sardar Vallabhbhai National Institute of Technology Surat, Surat, Gujarat 395007, India (corresponding author). Email: [email protected]
K. A. Patel [email protected]
Assistant Professor, Dept. of Civil Engineering, Sardar Vallabhbhai National Institute of Technology Surat, Surat, Gujarat 395007, India. Email: [email protected]
D. A. Patel [email protected]
Associate Professor, Dept. of Civil Engineering, Sardar Vallabhbhai National Institute of Technology Surat, Surat, Gujarat 395007, India. Email: [email protected]

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.

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share