A risk-based cost estimation model for optimizing construction projects in Palestine
DOI:
https://doi.org/10.15282/ijim.20.1.2026.11952Keywords:
Risk management, Monte Carlo simulation , Analytic Hierarchy Process (AHP), Construction cost estimation, Palestinian construction industryAbstract
This study investigates the critical risk factors affecting cost estimation in the Palestinian construction industry and develops a risk-based cost estimation model. Given the complexity of construction projects, this research employed a mixed-methods approach that combined qualitative interviews with industry professionals and a quantitative survey. A total of 39 risk factors were identified, with the most influential being project location, material availability, and market fluctuations. The study emphasizes the importance of these risks in the context of Palestine, where challenges such as geopolitical instability, resource limitations, and market volatility are common. A risk-based model incorporating Monte Carlo simulations and the Analytic Hierarchy Process was proposed to improve the accuracy and reliability of cost estimation. The model allows for a more systematic approach to managing risk by quantifying its impact on project costs. The results indicate that the top five risk factors account for 68.56% of the total cost of risk, underscoring the need for targeted risk management strategies. This research provides valuable insights and actionable strategies for construction professionals in enhancing cost estimation accuracy, improving decision-making processes, and contributing to more resilient and sustainable construction projects in Palestine.
References
Abdel-Monem, M., Alshaer, K. T., & El-Dash, K. (2022). Assessing risk factors affecting the accuracy of conceptual cost estimation in the Middle East. Building, 12(7), 950.
Ahmad, M., Ahmed, Z., Alvarado, R., Hussain, N., & Khan, S. A. (2024). Financial development, resource richness, eco-innovation, and sustainable development: Does geopolitical risk matter? Journal of Environmental Management, 351, 119824.
Akinshin, A. (2023). Weighted quantile estimators. arXiv preprint. Retrieved from http://arxiv.org/abs/2304.07265
Alazemi, M. K. T. E., & Mohiuddin, A. K. M. (2019). Conflict management of construction projects – A research. International Journal of Engineering and Advanced Technology, 8(6), 3809-3817.
Alhammadi, S. A., Tayeh, B. A., Alaloul, W. S., & Salem, T. J. (2021). Risk management strategies in construction organizations. The Open Civil Engineering Journal, 15, 406–413.
Alhammadi, Y., Al-Mohammad, M. S., & Rahman, R. A. (2024). Modeling the causes and mitigation measures for cost overruns in building construction: The case of higher education projects. Buildings, 14(2), 487.
Alkhuadhan, A., & Naimi, S. (2023). A new model for cost estimation construction project using hybrid importance regression ensemble method. Acta Logistica, 10(2), 191–198.
Alqudah, H., Amran, N. A., Hassan, H., Lutfi, A., Alessa, N., alrawad, M., & Almaiah, M. A. (2023). Examining the critical factors of internal audit effectiveness from internal auditors’ perspective: Moderating role of extrinsic rewards. Heliyon, 9(10), e2049.
Alshihri, S., Al-Gahtani, K., & Almohsen, A. (2022). Risk factors that lead to time and cost overruns of building projects in Saudi Arabia. Buildings, 12(7), 902.
Arif, A., Wibowo, K., Sumirin, S., & Ni’am, M. F. (2024). Factors causing time delays completion of construction projects. Journal of Green Science and Technology, 8(1), 203-212.
Castro Miranda, S. L., Del Rey Castillo, E., Gonzalez, V., & Adafin, J. (2022). Predictive analytics for early-stage construction costs estimation. Buildings, 12(7), 1043.
Çavuşoğlu, D., Zorba, Y., & Esmer, S. (2022). A set of criteria for logistics center development: A fuzzy analytic hierarchy process. Journal of ETA Maritime Science, 10(1), 47–60.
Dandan, T. H., Sweis, G., Sukkari, L. S., & Sweis, R. J. (2020). Factors affecting the accuracy of cost estimate during various design stages. Journal of Engineering Design and Technology, 18(4), 787-819.
De Paoli, S. (2024). Performing an inductive thematic analysis of semi-structured interviews with a large language model: An exploration and provocation on the limits of the approach. Social Science Computer Review, 42(4), 997-1019.
Do, S. T., Nguyen, V. T., Tran, C. N. N., & Aung, Z. M. (2023). Identifying and evaluating the key claim causes leading to construction delays. International Journal of Construction Management, 23(12), 1999-2011.
Easwaramoorthy, M., & Zarinpoush, F. (2006). Interviewing for research. Imagine Canada, 425(1), 1-2.
Glette-Iversen, I., Flage, R., & Aven, T. (2023). Extending and improving current frameworks for risk management and decision-making: A new approach for incorporating dynamic aspects of risk and uncertainty. Safety Science, 168, 106317.
Gómez-Cabrera, A., Gutierrez-Bucheli, L., & Muñoz, S. (2024). Causes of time and cost overruns in construction projects: a scoping review. International Journal of Construction Management, 24(10), 1107–1125.
Goyal, P. K., & Sharma, S. (2022). Evaluating cost contingency for construction projects: A fuzzy risk analysis approach. Lecture Notes in Networks and Systems, 356, 607-622.
Guillén-Mena, V., Quesada-Molina, F., Astudillo-Cordero, S., Lema, M., & Ortiz-Fernández, J. (2023). Lessons learned from a study based on the AHP method for the assessment of sustainability in neighborhoods. MethodsX, 11, 102440.
Haggarty, L. (1996). What is content analysis? Medical Teacher, 18(2), 99–101.
Krechowicz, M., & Piotrowski, J. Z. (2021). Comprehensive risk management in passive buildings projects. Energies, 14(20), 6830.
Lee, K., Hasanzadeh, S., & Esmaeili, B. (2022). Assessing hazard anticipation in dynamic construction environments using multimodal 360-degree panorama videos. Journal of Management in Engineering, 38(5), 04022039.
Li, B., Zhang, L., & Yuan, J. (2024). A simple Monte Carlo simulation method for geotechnical reliability-based design. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 18(3), 558-569.
Mahamid, I. (2015). Factors affecting cost estimate accuracy: Evidence from Palestinian construction projects. International Journal of Management Science and Engineering Management, 10(2), 117–125.
Mahamid, I. (2024a). Effect of conflicts on the contracting business failure in the construction industry. Journal of King Saud University-Engineering Sciences, 36(7), 498-503.
Mahamid, I. (2024b). Risk management in construction projects in palestine: Contractors’ perspective. Revista Ingenieria de Construccion, 39(2), 140–150.
Mahmoud, S. A., Ibrahim, E., Thakre, B., Teddy, J. G., Raheja, P., Ganesan, S., & Zaher, W. A. (2021). Evaluation of pooling of samples for testing SARS-CoV-2 for mass screening of COVID-19. BMC Infectious Diseases, 21(1), 360.
Marks, T. (2020). Quantitative risk management. In the Practitioner Handbook of Project Controls, pp. 223–230. Routledge.
Millet, M. S. (2009). Sources: Encyclopedia of survey research methods. Reference & User Services Quarterly, 49(2), 196-197.
Mosly, I. (2025). Multivariate analysis of factors influencing construction costs in Saudi Arabia. Buildings, 15(5), 687.
Palestinian Central Bureau of Statistics. (2022). Labour force survey. Extracted from http://www.pcbs.gov.ps
Pichery, C. (2014). Sensitivity analysis. In Encyclopedia of Toxicology (pp. 236–237). Elsevier.
Pineda, M., & Stamatakis, M. (2022). Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status, and challenges. The Journal of Chemical Physics, 156, 120902,
Popovic, Z. N., & Knezevic, S. D. (2022). Dynamic reconfiguration of distribution networks considering hosting capacity: A risk-based approach. IEEE Transactions on Power Systems: A Publication of ‘the Power Engineering Society, 1–10.
Rasshyvalov, D., Portnov, Y., Sigaieva, T., Alboshchii, O., & Rozumnyi, O. (2024). Navigating geopolitical risks: Implications for global supply chain management. Multidisciplinary Reviews, 7, 23-38.
Riveros, C., Ruiz, A. L., Mesa, H. A., & Guevara, J. A. (2022). Critical factors influencing early contract termination in public design–build projects in developing and emerging economies. Buildings, 12(5), 614.
Saaty, T. L., & Kearns, K. P. (1985). The analytic hierarchy process. In Analytical Planning (pp. 19–62). Elsevier.
Salciccioli, J. D., Crutain, Y., Komorowski, M., & Marshall, D. C. (2016). Sensitivity analysis and model validation. In Secondary Analysis of Electronic Health Records (pp. 263–271). Springer International Publishing.
Seddeeq, A. Bin, Assaf, S., Abdallah, A., & Hassanain, M. A. (2019). Time and cost overrun in the Saudi Arabian oil and gas construction industry. Buildings, 9(2) 41.
Shaheen, R., Ağa, M., Rjoub, H., & Abualrub, A. (2020). Investigation of the pillars of sustainability risk management as an extension of enterprise risk management on palestinian insurance firms’ profitability. Sustainability, 12(11), 4709.
Suarez-Burgoa, L. O. (2023). The saltbox-roof probability distribution. http://arxiv.org/abs/2305.03108
Ullah, S., Mufti, N. A., Qaiser Saleem, M., Hussain, A., Lodhi, R. N., & Asad, R. (2021). Identification of factors affecting risk appetite of organizations in selection of mega construction projects. Buildings, 12(1), 2.
Weyant, E. (2022). Research design: Qualitative, quantitative, and mixed methods approaches. Journal of Electronic Resources in Medical Libraries, 19(1-2), 54–55.
Wiegand, H. (1968). Kish, L. Survey sampling. John Wiley & sons, inc.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
