# Infrastructure asset valuation approach

When dealing with infrastructure assets privately held in institutional portfolios, the market prices are not readily available. Therefore, the valuation of unlisted infrastructure equity investments relies on the guiding principles of the International Financial Reporting Standards (IFRS) 13 – a framework for fair value measurements (IFRS, 2023) – and of the modern asset pricing theory to value unlisted infrastructure equity investments.

One of the most commonly used methods for this purpose is the income or discounted cash flow (DCF) approach, which equates the value of an investment at any time *t* to the sum of all future cash flows (i.e., future dividends) that the investment generates from *t+1* until maturity (date when the principal amount invested is expected to be returned to the investor). To account for the fact that the value of money is different (and usually higher) today than tomorrow, the future dividends are discounted by a factor called discount rate:

where *i*, *t* and *s* are indices for assets, time (year), and scenario, respectively, and *T* is the maturity date of an asset’s investment.[1] We calculate the dividends (equity payout) as:

where the retention rate represents companies’ tendency to retain free cash (e.g., for investment opportunities, debt reduction, etc.), and *FCFE* is the free cash flow to equity. Here, *CFADS* is the cash flow available for debt service.

The discount rate is calculated as the sum of the risk-free rate and the equity risk premium:

where the risk-free rate is interpolated from the government bond yield curves provided by NGFS and Oxford Economics in each climate scenario at the country level (no index *i*).

Accordingly, we determine the infrastructure assets’ NAV in each climate scenario by i) the risk-free rate sourced at the country level from each scenario’s projections and ii) the company’s CFADS, debt service, retention rate, and risk premium. These variables are estimated by *Scientific Infra & Private Assets'* asset pricing models that use factor models and Kalman filters to identify major risk factors and estimate their coefficients (factor prices) based on observed transaction prices in the global unlisted infrastructure market. In particular, we find that revenue is a key risk factor common to CFADS, debt service and retention rate, while total assets (size) and term spread are important risk factors of equity risk premium. The specific macroeconomics of each climate scenario impact these key risk factors. More details can be found in *Scientific Infra & Private Assets' *Unlisted Infrastructure Asset-Pricing Methodology.

The calibration consists of three regressions involving GDP and inflation for total assets, revenues and OPEX. To ensure stationarity and avoid spurious correlations, we consider the growth rates of all variables rather than their raw values, except for inflation, which is already a growth rate. The variables are then log-transformed to estimate elasticities better after they are topped by 1 to limit the occurrence of negative numbers. The variables thus take the form *log(1 + variable)*. The *log(1 + x)* part is dropped in all equations for better readability.

_{[1]}_{ It needs to be noted that we use the “constant maturity” assumption to freeze the companies’ maturities and their financial status (e.g., the starting values of revenue and size) when calculating NAV at different horizons. This is a common technique used in the analysis of fixed income portfolios. Without this assumption, project companies would disappear when their maturity is reached, which could significantly alter the portfolio’s composition.}

_{IFRS (2023). IFRS 13 Fair Value Measurement. }_{International Financial Reporting Standard}_{. }_{https://www.ifrs.org/issued-standards/list-of-standards/ifrs-13-fair-value-measurement}_{ }