Modeling risks in real estate development projects: a case for Egypt
Abstract
Risk analysis is a vital step in the succession of construction projects. However, no adequate researches have been conducted to assess, and quantify risk events in real estate projects in developing countries, and particularly in Egypt.This research recommends Fuzzy Quantitative Risk Assessment Model to quantify risk factors participated in real estate development projects. Model is composed of two components: 1) Fuzzy Fault Tree (FT) that determines root causes of each risk, probability of its occurrence, and probability of mitigation strategies failure; and 2) Fuzzy Event Tree (ET) that calculates crisp value of Expected Monetary Value (EMV) of allowance of mitigation of the identified risks. Causes of risk are determined through literature review and interviews with experts in field. Risk probability occurrence is determined using five linguistic terms, defined either triangular or trapezoidal membership functions which are developed using modified horizontal approach and an interpolation technique. Two-step Delphi technique is used to achieve consensus on the root causes and logical representation of the Fault Tree. Fuzzy importance analysis is performed to rank different root causes for identified risks according to their criticality to probability of occurrence. A Case Study is presented to evaluate results obtained from model, in terms of Expected Monetary Value (EMV), and fuzzy probability of failure for each risk participated in case study.
Keyword : risk management, fuzzy sets, fault tree, event tree, real estate development projects, mitigation strategy, Egypt
How to Cite
Marzouk, M., & Aboushady, A. (2018). Modeling risks in real estate development projects: a case for Egypt. International Journal of Strategic Property Management, 22(6), 447-456. https://doi.org/10.3846/ijspm.2018.6270
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abdelgawad, M. (2011). Hybrid decision support system for risk criticality assessment and risk analysis (Thesis dissertation). Faculty of Graduate Studies and Research, Department of Civil and Environmental Engineering, University of Alberta, Edmonton, Alberta, Canada.
Abdelgawad, M., & Fayek, A. R. (2011). A comprehensive hybrid framework for risk analysis in the construction industry using combined failure mode and effect analysis, fault trees, event trees, and fuzzy logic. Journal of Construction Engineering and Management, 135(5), 642-651. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000471
Abdelhai, M. I. (1993). A new methodology based on fuzzy set theory and fault tree analysis for failure diagnosis in nuclearpower plants (Thesis dissertation). Faculty of Graduate Studies and Research, University of Tennessee, USA.
Aboushady, A. (2012). A framework for risk assessment in Egyptian real estate projects using fuzzy approach (M.Sc. Thesis). Cairo University, Cairo, Egypt.
Al-Bahar, J. F., & Crandall, K. C. (1990). Systematic risk management approach for construction projects. Journal of Construction Engineering and Management, 116(3), 533-546. https://doi.org/10.1061/(ASCE)0733-9364(1990)116:3(533)
Alexandria Bank Economic Research. (2012). Egypt’s real estate industry. Working Papers. Retrieved from http://alexbank.smetoolkit.org/egypt/en/file/promotion/ 2552/en/EgyptsRealEstateIndustry2.pdf
Al-Sobiei, O. S., Arditi, D., & Polat, G. (2005). Predicting the risk of contractor default in Saudi Arabia utilizing artificial neural network (ANN) and genetic algorithm (GA) techniques. Construction Management and Economics, 23(4), 423-430. https://doi.org/10.1080/01446190500041578
Amadeo, K. (2012). How does real estate affect the U.S. economy? US Economy. Retrieved from http://useconomy.about.com/od/grossdomesticproduct/f/ Real_estate_faq.htm
Angelini, E., Giacomo, T., & Roli, A. (2008). A neural network approach for credit risk evaluation. Journal of Quarterly Review of Economics and Finance, 48(4), 733-755. https://doi.org/10.1016/j.qref.2007.04.001
Ayyub, B. M. (2003). Risk analysis in engineering and economics (pp. 33-113). Chapter 2. New York: Chapman & Hall/CRC. https://doi.org/10.1201/9780203497692
Chan, A., Chan, D., & Yeung, J. (2009). Overview of the application of fuzzy techniques in construction management research. Journal of Construction Engineering and Management, 135(11), 1241-1252. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000099
Elbarkouky, M., & Fayek, A. R. (2011). Fuzzy similarity consensus model for early alignment of construction project teams on the extent of their roles and responsibilities. Journal of Construction Engineering and Management, 137(6), 432-441. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000310
Foong, O. M., & Nordin, M. I. (2010). Decision support system for alarm rationalization using risk assessment matrix. International Journal of Computer Applications, 4(9), 8-13. https://doi.org/10.5120/857-1174
Hauptmanns, U. (1988). Fault tree analysis for process plants. In A. Kandel & E. Avni (Eds.), Engineering risk and hazard assessment, Vol. I. Boca Raton: CRC Press.
Javid, M., & Seneviratne, P. N. (2000). Investment risk analysis in airport parking facility development. Journal of Construction Engineering and Management, 126(4), 298-305. https://doi.org/10.1061/(ASCE)0733-9364(2000)126:4(298)
Kangari, R., & Riggs, L. S. (1989). Construction risk assessment by linguistics. Transactions on Engineering Management IEEE, 36(2), 126-131. https://doi.org/10.1109/17.18829
KarimiAzari, A., Mousavi, N., Mousavi, S. F., & Hosseini, S. B. (2011). Risk assessment model selection in construction industry. Expert Systems with Applications, 38(8), 9105-9111. https://doi.org/10.1016/j.eswa.2010.12.110
Khan, F., & Abbasi, S. A. (1999). PROFAT: a user-friendly system for probabilistic fault tree analysis. Process Safety Progress, 18(1), 42-49. https://doi.org/10.1002/prs.680180109
Levner, E., Ganoulis, J., Linlov, I., & Benayahu, Y. (2007). Multi objective risk/cost analysis of artificial marine systems using decision trees and fuzzy expert estimations. In I. Linkov, G. A. Kiker, & R. J. Wenning (Eds.), Environmental security in harbors and coastal areas (pp. 161-174). NATO Security through Science Series (Series C: Environmental Security). Dordrecht: Springer. https://doi.org/10.1007/978-1-4020-5802-8_12
Maria-Sanchez, P. (2005). Neural-risk assessment system for construction projects. Construction Research Congress, ASCE, San Diego, California, US.
Markowski, A. S., & Mannan, M. S. (2008). Fuzzy risk matrix. Journal of Hazardous Materials, 159(1), 152-157. https://doi.org/10.1016/j.jhazmat.2008.03.055
Markowski, A. S., Mannan, M. S., & Bigoszewska, A. (2009). Logic for process safety analysis. Journal of Loss Prevention in the Process Industries, 22(6), 695-702. https://doi.org/10.1016/j.jlp.2008.11.011
Marsh, K., & Fayek, A. R. (2010). Fuzzy expert system to assist surety underwriters in evaluating construction contractors for bonding. Journal of Construction Engineering and Management, 136(11), 1219-1235. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000224
NASA. (2002). Fault tree handbook with aerospace applications. NASA Office of Safety and Mission Assurance, NASA Headquarters, Washington, USA.
PMI. (2008). A guide to the project management body of knowledge (PMBOK) (4th ed.). The Project Management Institute (PMI), Pennsylvania, USA.
Robert, S. C. (2004, 29-31 March). 3D model for qualitative risk assessment. In SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production. Calgary, Alberta, Canada.
Sadeghi, N., Fayek, A. R., & Pedrycz, W. (2010). Fuzzy Monte Carlo simulation and risk assessment in construction. Computer-Aided Civil and Infrastructure Engineering, 25(4), 238-252. https://doi.org/10.1111/j.1467-8667.2009.00632.x
Shaheen, A. A., Fayek, A. R., & AbouRizk, S. M. (2007). Fuzzy numbers in cost range estimating. Journal of Construction Engineering and Management, 133(4), 325-334. https://doi.org/10.1061/(ASCE)0733-9364(2007)133:4(325)
Song, H., Zhang, H., & Chan, C. (2009). Fuzzy fault tree analysis based on T–S model with application to INS/GPS navigation system. Journal of Soft Computing, 13(1), 31-40. https://doi.org/10.1007/s00500-008-0290-3
Tyagi, S. K., Pandey, D., & Kumar, V. (2011). Fuzzy fault tree analysis for fault diagnosis of cannula fault in power transformer. Journal of Applied Mathematics, 2(11), 1346-1355. https://doi.org/10.4236/am.2011.211188
Verma, A. K., Srividya, A., & Gaonkar, R. S. P. (2007). Fuzzy-reliability engineering: concepts and applications (pp. 88-127). Chapter 4. New Delhi: Narosa Publishing House.
Wu, J., Yan, S., & Xie, L. (2011). Reliability analysis method of a solar array by using fault tree analysis and fuzzy reasoning Petri net. ActaAstronautica, 69(11-12), 960-968. https://doi.org/10.1016/j.actaastro.2011.07.012
Yiu, T. W., Cheung, S. O., & Lok, C. L. (2015). A fuzzy fault tree framework of construction dispute negotiation failure. IEEE Transactions on Engineering Management, 62(2), 171-183. https://doi.org/10.1109/TEM.2015.2407369
Yuhua, D., & Datao, Y. (2005). Estimation of failure probability of oil and gas transmission pipelines by fuzzy fault tree analysis. Journal of Loss Prevention in the Process Industries, 18(2), 83-88. https://doi.org/10.1016/j.jlp.2004.12.003
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
Abdelgawad, M., & Fayek, A. R. (2011). A comprehensive hybrid framework for risk analysis in the construction industry using combined failure mode and effect analysis, fault trees, event trees, and fuzzy logic. Journal of Construction Engineering and Management, 135(5), 642-651. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000471
Abdelhai, M. I. (1993). A new methodology based on fuzzy set theory and fault tree analysis for failure diagnosis in nuclearpower plants (Thesis dissertation). Faculty of Graduate Studies and Research, University of Tennessee, USA.
Aboushady, A. (2012). A framework for risk assessment in Egyptian real estate projects using fuzzy approach (M.Sc. Thesis). Cairo University, Cairo, Egypt.
Al-Bahar, J. F., & Crandall, K. C. (1990). Systematic risk management approach for construction projects. Journal of Construction Engineering and Management, 116(3), 533-546. https://doi.org/10.1061/(ASCE)0733-9364(1990)116:3(533)
Alexandria Bank Economic Research. (2012). Egypt’s real estate industry. Working Papers. Retrieved from http://alexbank.smetoolkit.org/egypt/en/file/promotion/ 2552/en/EgyptsRealEstateIndustry2.pdf
Al-Sobiei, O. S., Arditi, D., & Polat, G. (2005). Predicting the risk of contractor default in Saudi Arabia utilizing artificial neural network (ANN) and genetic algorithm (GA) techniques. Construction Management and Economics, 23(4), 423-430. https://doi.org/10.1080/01446190500041578
Amadeo, K. (2012). How does real estate affect the U.S. economy? US Economy. Retrieved from http://useconomy.about.com/od/grossdomesticproduct/f/ Real_estate_faq.htm
Angelini, E., Giacomo, T., & Roli, A. (2008). A neural network approach for credit risk evaluation. Journal of Quarterly Review of Economics and Finance, 48(4), 733-755. https://doi.org/10.1016/j.qref.2007.04.001
Ayyub, B. M. (2003). Risk analysis in engineering and economics (pp. 33-113). Chapter 2. New York: Chapman & Hall/CRC. https://doi.org/10.1201/9780203497692
Chan, A., Chan, D., & Yeung, J. (2009). Overview of the application of fuzzy techniques in construction management research. Journal of Construction Engineering and Management, 135(11), 1241-1252. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000099
Elbarkouky, M., & Fayek, A. R. (2011). Fuzzy similarity consensus model for early alignment of construction project teams on the extent of their roles and responsibilities. Journal of Construction Engineering and Management, 137(6), 432-441. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000310
Foong, O. M., & Nordin, M. I. (2010). Decision support system for alarm rationalization using risk assessment matrix. International Journal of Computer Applications, 4(9), 8-13. https://doi.org/10.5120/857-1174
Hauptmanns, U. (1988). Fault tree analysis for process plants. In A. Kandel & E. Avni (Eds.), Engineering risk and hazard assessment, Vol. I. Boca Raton: CRC Press.
Javid, M., & Seneviratne, P. N. (2000). Investment risk analysis in airport parking facility development. Journal of Construction Engineering and Management, 126(4), 298-305. https://doi.org/10.1061/(ASCE)0733-9364(2000)126:4(298)
Kangari, R., & Riggs, L. S. (1989). Construction risk assessment by linguistics. Transactions on Engineering Management IEEE, 36(2), 126-131. https://doi.org/10.1109/17.18829
KarimiAzari, A., Mousavi, N., Mousavi, S. F., & Hosseini, S. B. (2011). Risk assessment model selection in construction industry. Expert Systems with Applications, 38(8), 9105-9111. https://doi.org/10.1016/j.eswa.2010.12.110
Khan, F., & Abbasi, S. A. (1999). PROFAT: a user-friendly system for probabilistic fault tree analysis. Process Safety Progress, 18(1), 42-49. https://doi.org/10.1002/prs.680180109
Levner, E., Ganoulis, J., Linlov, I., & Benayahu, Y. (2007). Multi objective risk/cost analysis of artificial marine systems using decision trees and fuzzy expert estimations. In I. Linkov, G. A. Kiker, & R. J. Wenning (Eds.), Environmental security in harbors and coastal areas (pp. 161-174). NATO Security through Science Series (Series C: Environmental Security). Dordrecht: Springer. https://doi.org/10.1007/978-1-4020-5802-8_12
Maria-Sanchez, P. (2005). Neural-risk assessment system for construction projects. Construction Research Congress, ASCE, San Diego, California, US.
Markowski, A. S., & Mannan, M. S. (2008). Fuzzy risk matrix. Journal of Hazardous Materials, 159(1), 152-157. https://doi.org/10.1016/j.jhazmat.2008.03.055
Markowski, A. S., Mannan, M. S., & Bigoszewska, A. (2009). Logic for process safety analysis. Journal of Loss Prevention in the Process Industries, 22(6), 695-702. https://doi.org/10.1016/j.jlp.2008.11.011
Marsh, K., & Fayek, A. R. (2010). Fuzzy expert system to assist surety underwriters in evaluating construction contractors for bonding. Journal of Construction Engineering and Management, 136(11), 1219-1235. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000224
NASA. (2002). Fault tree handbook with aerospace applications. NASA Office of Safety and Mission Assurance, NASA Headquarters, Washington, USA.
PMI. (2008). A guide to the project management body of knowledge (PMBOK) (4th ed.). The Project Management Institute (PMI), Pennsylvania, USA.
Robert, S. C. (2004, 29-31 March). 3D model for qualitative risk assessment. In SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production. Calgary, Alberta, Canada.
Sadeghi, N., Fayek, A. R., & Pedrycz, W. (2010). Fuzzy Monte Carlo simulation and risk assessment in construction. Computer-Aided Civil and Infrastructure Engineering, 25(4), 238-252. https://doi.org/10.1111/j.1467-8667.2009.00632.x
Shaheen, A. A., Fayek, A. R., & AbouRizk, S. M. (2007). Fuzzy numbers in cost range estimating. Journal of Construction Engineering and Management, 133(4), 325-334. https://doi.org/10.1061/(ASCE)0733-9364(2007)133:4(325)
Song, H., Zhang, H., & Chan, C. (2009). Fuzzy fault tree analysis based on T–S model with application to INS/GPS navigation system. Journal of Soft Computing, 13(1), 31-40. https://doi.org/10.1007/s00500-008-0290-3
Tyagi, S. K., Pandey, D., & Kumar, V. (2011). Fuzzy fault tree analysis for fault diagnosis of cannula fault in power transformer. Journal of Applied Mathematics, 2(11), 1346-1355. https://doi.org/10.4236/am.2011.211188
Verma, A. K., Srividya, A., & Gaonkar, R. S. P. (2007). Fuzzy-reliability engineering: concepts and applications (pp. 88-127). Chapter 4. New Delhi: Narosa Publishing House.
Wu, J., Yan, S., & Xie, L. (2011). Reliability analysis method of a solar array by using fault tree analysis and fuzzy reasoning Petri net. ActaAstronautica, 69(11-12), 960-968. https://doi.org/10.1016/j.actaastro.2011.07.012
Yiu, T. W., Cheung, S. O., & Lok, C. L. (2015). A fuzzy fault tree framework of construction dispute negotiation failure. IEEE Transactions on Engineering Management, 62(2), 171-183. https://doi.org/10.1109/TEM.2015.2407369
Yuhua, D., & Datao, Y. (2005). Estimation of failure probability of oil and gas transmission pipelines by fuzzy fault tree analysis. Journal of Loss Prevention in the Process Industries, 18(2), 83-88. https://doi.org/10.1016/j.jlp.2004.12.003
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X