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Amadi, C, Carrillo, P and Tuuli, M (2019) PPP projects: improvements in stakeholder management. Engineering, Construction and Architectural Management, 27(02), 544–60.

Huo, X, Yu, A T W, Zezhou, W and Jayantha, W M (2019) Site planning and design of green residential building projects: case studies in China. Engineering, Construction and Architectural Management, 27(02), 525–43.

Hyung, W, Kim, S and Jo, J (2019) Improved similarity measure in case-based reasoning: a case study of construction cost estimation. Engineering, Construction and Architectural Management, 27(02), 561–78.

Johari, S and Jha, K N (2019) Challenges of attracting construction workers to skill development and training programmes. Engineering, Construction and Architectural Management, 27(02), 321–40.

Li, X, Wang, X and Lei, L (2019) The application of an ANP-Fuzzy comprehensive evaluation model to assess lean construction management performance. Engineering, Construction and Architectural Management, 27(02), 356–84.

Mellado, F, Lou, E C and Becerra, C L C (2019) Synthesising performance in the construction industry. Engineering, Construction and Architectural Management, 27(02), 579–608.

Nwaogu, J M, Chan, A P, Hon, C K and Darko, A (2019) Review of global mental health research in the construction industry. Engineering, Construction and Architectural Management, 27(02), 385–410.

Salvado, F, Almeida, N and Vale e Azevedo, A (2019) Aligning financial and functional equivalent depreciations rates of building assets. Engineering, Construction and Architectural Management, 27(02), 441–57.

Wang, X, Shi, L, Wang, B and Kan, M (2019) A method to evaluate credit risk for banks under PPP project finance. Engineering, Construction and Architectural Management, 27(02), 483–501.

  • Type: Journal Article
  • Keywords: Optimization; Engineering; Risk management; Simulation; Estimating;
  • ISBN/ISSN: 0969-9988
  • URL: https://doi.org/10.1108/ECAM-06-2018-0247
  • Abstract:
    The purpose of this paper is to provide a method that can better evaluate the credit risk (CR) under PPP project finance. Design/methodology/approach The principle to evaluate the CR of PPP projects is to calculate three critical indicators: the default probability (DP), the recovery rate (RR) and the exposure at default (EAD). The RR is determined by qualitative analysis according to Standard & Poor’s Recovery Scale, and the EAD is estimated by NPV analysis. The estimation of the DP is the focus of CR assessment because the future cash flow is not certain, and there are no trading records and market data that can be used to evaluate the credit condition of PPP projects before financial close. The modified CreditMetrics model and Monte Carlo simulation are applied to evaluate the DP, and the application is illustrated by a PPP project finance case. Findings First, the proposed method can evaluate the influence of the project’s cash flow uncertainty on the potential loss of the bank. Second, instead of outputting a certain default loss value, the method can derive an interval of the potential loss for the bank. Third, the method can effectively analyze how different repayment schedules and risk preference of banks influence the evaluating result. Originality/value The proposed method offers an approach for the bank to value the CR under PPP project finance. The method took into consideration of the uncertainty and other characteristics of PPP project finance, adopted and improved the CreditMetrics model, and provided a possible loss range under different project cash flow volatilities through interval estimation under certain confident level. In addition, the bank’s risk preference is considered in the CR evaluating method proposed in this study where the bank’s risk preference is first investigated in the CR evaluating process of PPP project finance.

Yevu, S K and Yu, A T W (2019) The ecosystem of drivers for electronic procurement adoption for construction project procurement. Engineering, Construction and Architectural Management, 27(02), 411–40.

Zarghami, S A and Gunawan, I (2019) A domain-specific measure of centrality for water distribution networks. Engineering, Construction and Architectural Management, 27(02), 341–55.

Zhou, S, Ng, S T, Lee, S H, Xu, F J and Yang, Y (2019) A domain knowledge incorporated text mining approach for capturing user needs on BIM applications. Engineering, Construction and Architectural Management, 27(02), 458–82.

Zohrehvandi, S, Vanhoucke, M, Soltani, R and Javadi, M (2019) A reconfigurable model for implementation in the closing phase of a wind turbines project construction. Engineering, Construction and Architectural Management, 27(02), 502–24.