Multiple Linear Regression Model for Project’s Risk Profile and DSCR

Several studies have been conducted in the field of project risk management to understand the bankability criteria for PPP projects. However, the relationship between risk factors and financial variables requires increasing analysis for project finance projects such as Public-Private Partnerships (PPPs). Debt providers may benefit by understanding how project risk impacts debt financing ratios. This study focuses on identifying the main factors that may influence the target Debt Service Coverage Ratio (DSCR). To achieve bankability, PPPs must demonstrate that the minimum DSCR is greater than or equal to the target DSCR pre-established. In this study, potential factors that may affect the DSCR are analyzed, namely: demand revenues, debt/equity ratio, repayment period, concession period, CAPEX, interest rate, rate of return, public subsidies, and construction period. This study aims to develop a linear model and an analysis of variance to understand how risk profile variables affect the DSCR established by debt providers in PPPs. The research is based on a dataset of six hospital PPP projects in PPP healthcare facilities. The regression model presented brings to the fore the correlation between the internal rate of return and the target DSCR. The Repayment period also demonstrated correlation but with lower significance. The reliability of the model is strongly dependent on the data taken into account for some Italian PPP projects. The analysis shows that it is potentially acceptable for debt providers to reduce the target DSCR based on a high IRR.