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Cost of capital parameters for Sydney Desalination Plant

10 August 2011

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Cost of capital parameters for Sydney Desalination Plant (10 August 2011)

Contents

1. INTRODUCTION ........................................................................................................ 1

2. BETA ESTIMATES OF LISTED WATER UTILITIES ................................................ 4

2.1 Introduction ........................................................................................................ 4 2.2 Methodology ...................................................................................................... 6

2.2.1 Regression equations .......................................................................................... 6 2.2.2 Bias correction ..................................................................................................... 7 2.2.3 Leverage ............................................................................................................. 8

2.3 Data ................................................................................................................. 10 2.4 Results ............................................................................................................. 11

2.4.1 Mean estimates of individual firms ..................................................................... 11 2.4.2 Beta estimate from an index of sample firms ..................................................... 13 2.4.3 Stability analysis ................................................................................................ 15 2.4.4 Results summary ............................................................................................... 18

2.5 Conclusion with respect to OLS beta estimates ............................................... 19 2.5.1 Bias versus no bias correction ........................................................................... 19 2.5.2 Inclusion versus exclusion of outliers based upon stability analysis ................... 19 2.5.3 Consideration of asymmetric exposure to market conditions ............................. 20 2.5.4 Conclusion ......................................................................................................... 21

3. INTERNAL CONSISTENCY OF COST OF CAPITAL PARAMETER ESTIMATES 22

3.1 Introduction ...................................................................................................... 22 3.2 No imputation ................................................................................................... 22 3.3 Imputation ........................................................................................................ 23 3.4 Capital structure choice ................................................................................... 25 3.5 Conclusion with respect to internal consistency ............................................... 27

4. RISKS ASSOCIATED WITH THE SYDNEY DESALINATION PLANT ................... 28

4.1 Introduction ...................................................................................................... 28 4.2 Conceptual issues ............................................................................................ 28

4.2.1 Impact on beta estimates ................................................................................... 28 4.2.2 Impact on leverage ............................................................................................ 30

4.3 Financial analysis ............................................................................................. 30 4.3.1 Financial analysis assuming initial leverage equal to the WACC assumption..... 30 4.3.2 An alternative scenario – 60% leverage and an A– credit rating ........................ 34 4.3.3 Impact on SDP’s financial position ..................................................................... 36

5. CONCLUSION ......................................................................................................... 38

6. REFERENCES ......................................................................................................... 40


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1. Introduction SFG Consulting has been engaged to provide advice to the Independent Pricing and Regulatory Tribunal (“IPART” or “the Tribunal”) on estimates of equity beta and leverage for the Sydney Desalination Plant, for the purposes of setting the regulated rate of return. In this report we present three pieces of analysis which form the basis of our recommendations.1 First, we present estimates of equity beta for listed water utilities using ordinary least squares (“OLS”) regression, along with estimates of leverage from market capitalisation and book value of debt. Our listed firm analysis includes analysis of the differing systematic risk exposure of comparable firms to months in which market returns are above- or below the risk-free rate of interest. It also accounts for the impact of outliers, identified as the observations which have the largest absolute impact on beta estimates. This analysis does not account for issues specific to the Sydney Desalination Plant, internal consistency of cost of capital parameter estimates, or the reliability of using OLS regression estimates of beta for estimating regulated rates of return. If the OLS estimates are considered in isolation, without consideration of these factors, the estimated equity beta would be 0.65. Second, we discuss the general issue of internal consistency amongst parameters which underpin the regulated rate of return. The basic premise association with this discussion is that the return required by equity holders must be at least equal to the return required by debt holders in the same firm. In other words, except in unusual circumstances associated with distressed firms, an investor purchasing debt will pay at least the same price as that investor would be prepared to pay for equity in the same firm. One alternative view of this premise, with which we disagree, is that there should be internal consistency from the perspective of an Australian resident investor. According to this view the minimum internally-consistent equity beta would be 0.52. Our preferred view is that there should be internal consistency with respect to resident and non-resident investors, because observed prices of debt and equity are determined by the trading of these investors. According to this view, the equity returns from dividends and capital gains (but not the returns associated with imputation credits) should be at least equal to the cost of debt capital. According to this view the minimum internally-consistent equity beta would be 0.74. An additional issue of internal consistency is with respect to assumptions relating to the joint estimation of leverage/debt premium, the estimated value of imputation credits (gamma) and the market risk premium. This discussion implies that at relatively high debt premiums, high values for gamma and/or low values for the market risk premium are inconsistent with capital structure theory. If IPART maintains its assumed values for gamma of 0.4 and market risk premium of 6.0% then it would not make sense for a firm to finance with debt at high premiums – the firm’s WACC would be higher for the geared firm than the ungeared firm. We present alternative assumptions which resolve this inconsistency. Third, we discuss risks specific to the Sydney Desalination Plant (SDP). In its submission, SDP identified a number of risks which were used in support of an equity beta estimate of 0.9. The rationale for this argument was that there would be no allowance in cash flow estimates to compensate SDP for bearing these risks, leaving compensation for risk exposure to be accounted for in the regulated rate of return. The principal involved here is that regulated charges should be set such that expected cash flows are sufficient for capital providers to earn their cost of capital. The term expected cash flows has its

1 Note that our analysis relies upon other cost of capital assumptions to be determined by IPART. For the purposes of this report we have assumed a risk-free rate of 4.80%, a market risk premium of 6.00%, a debt premium of 3.10%, a corporate tax rate of 30%, an imputation credit (gamma) value of 0.40 and a debt beta of 0.20. The debt beta is used in discussion of asset betas and does not form part of the regulated rate of return.


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statistical meaning, namely the probability-weighted average of potential cash flows. So if a risk exposure has a higher chance of causing cash flows to fall below, rather than above expectations, then regulated charges should reflect these asymmetric consequences of risk. However, if in fact there are asymmetric consequences of risks, the appropriate treatment of these risks is to set a series of charges which reflect these risks, and not to have them reflected in the regulated rate of return. In addition, none of the risks identified could be considered to be systematic in nature, in that they would be more likely to eventuate, or have greater consequences, during economic downturns. There may be other unidentified cash flow risks which have a systematic component. Even if SDP’s volumes were to fluctuate with economic conditions, the proposed pricing structure in which variable charges are set to expected variable costs removes this volume-related risk. This does not mean that equity investors in SDP will have zero systematic risk exposure. Regardless of the cash flow risks associated with economic conditions, asset values fluctuate with changes in risk premiums. Investors in assets with relatively low systematic risk of cash flows still have systematic risk exposure associated with changes in risk premiums. SDP investors are not immune to this risk. To place these risks in perspective we analysed the sensitivity of SDP’s earnings and valuation to changes in fixed costs, variable costs and discount rates. This sensitivity analysis incorporates an assumption that at the end of the five-year regulatory period the value of the assets equals the regulated asset base plus working capital. For a 10% change in fixed costs there is an approximate 0.5% change in asset value and an approximate 3.0% change in average earnings before interest and tax (“EBIT”) measured in 2010 – 11 dollars. For a 10% change in variable costs there is an approximate 0.8% change in asset value and an approximate 4.5% change in EBIT. For a 1% change in the discount rate there is an approximate 4.1% change in asset value but no change in EBIT. At this stage the sensitivity of value and earnings to variation in costs has not impacted on our estimates of beta. Variation in costs from expectations due to company-specific events should be accounted for in setting regulated charges such that expected cash flows are sufficient for investors to earn the cost of capital. Variations in costs associated the economic conditions would contribute positively to the beta estimate if revenue also fluctuated in the same direction with economic conditions. However, there is no evidence before us which suggests that revenues will be associated with economic events. Sensitivity of value to variation in discount rates supports the premise that the equity beta estimate has a minimum value to be internally consistent with estimates for the required returns to debt holders and the market risk premium. It is not used in support of a separate computation of beta. In reaching a conclusion on equity beta we consider the limited ability of OLS beta estimates to predict realised returns. If OLS beta estimates, when incorporated into the CAPM, provide reliable estimates of equity holders’ required returns then on average we should observe an association between expected returns and realised returns. We have previously analysed this issue and question the precision which can be attached to these estimates (Gray, Hall, Klease and McCrystal, 2009). The current analysis incorporates improvements to the OLS beta estimates – accounting for asymmetric exposure to market returns and the elimination of outliers – but the returns forecasting ability of these improvements has not yet been analysed. Hence, we would not place full weight on OLS estimates. Finally, the beta estimate should be considered in light of IPART’s most recent determinations with respect to water utilities, namely the reviews of bulk water pricing for State Water in 2010 and metropolitan pricing for Sydney Water in 2008. In both instances IPART determined than an equity beta of 0.9 was appropriate. It also provided an indication of the imprecision of this estimate by reporting a range of 0.8 – 1.0. There is reason to believe that SDP is exposed to lower systematic risk than the other water businesses regulated by IPART, given the nature of its contracts and the pricing proposal by SDP. SDP’s cash flows are expected to be insensitive to volume, given that the variable


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charge has been set equal to expected variable costs. SDP’s cash flows are also expected to be insensitive to shut downs, given that the proposal that the fixed charge differ depending upon whether the plant supplies water or is shut down. Given these considerations an equity beta estimate of 0.80 is appropriate if IPART were to agree with our view of internal consistency in WACC parameters, and 0.70 otherwise. These estimates lie marginally above the estimate of 0.65 based purely upon the returns analysis of listed firms and below the 0.90 estimate used by IPART in its bulk and metropolitan water pricing decisions. The figure of 0.80 lies above the minimum beta estimate of 0.74 which is consistent with our view of internal consistency, and the figure of 0.70 lies above the minimum beta estimate of 0.52 which is consistent with the alternative view of internal consistency. With respect to the issue of appropriate leverage, the project-specific characteristics of SDP suggest that it could sustain leverage of 70% and still maintain a BBB credit rating, compared to the typical assumption in regulatory determinations of 60% leverage. At 60% leverage in the base case SDP has debt service coverage ratios which marginally exceed those required for a typical water utility to sustain an investment-grade credit rating from S&P. At 70% leverage these coverage ratios fall to the point where a typical water utility would be on the margin of sustaining a BBB rating. But SDP can be considered to have relatively lower business risk than the typical water utility, which would facilitate lower coverage ratios. Ultimately default risks are determined by the variation in operating costs. While we do not have specific estimates of this potential variation for SDP, if the standard deviation of operating costs was within an approximate range of 6 – 19% then the risk of default would approximate that of BBB rated debt. On average, 2.0% of S&P BBB rated debt incurs at least one incident of default over a five year period. We would not recommend leverage above 70% because this assumption already exceeds the maximum average leverage of the 16 listed firms in our comparable firm set. The mean leverage observed for listed water utilities in our sample was 43% and 15 out of 16 firms analysed had average historical leverage within a range of 32 to 65%, with just two firms having average leverage which exceeded 60%. So while it is reasonable to conclude that SDP has lower business risk than the typical water utility, we would recommend against adopting a leverage assumption substantially above the high leverage firms we have observed. An alternative to the 70% leverage/BBB debt assumption is to hold leverage at 60% but adopt a higher credit rating due to SDP’s relatively lower risk. In this event we would recommend an A– credit rating rather than an A credit rating on the basis that there are, in fact, relatively few credit ratings assigned by S&P at levels of A and above. At 1 January 2009 only 12% of non-financial corporations were assigned ratings of A or above, while 19% were assigned ratings of A– or above. The median credit rating assigned by S&P is BB+. Given the imprecision with which credit ratings are assigned, the lower A– rating appears appropriate to distinguish SDP from a regulated water utility with the same 60% gearing assumption. In conclusion, our recommended equity beta assumption is 0.8 and our recommended leverage assumption is 70%, conditional upon assuming a BBB credit rating. If IPART were to adopt a view of internal consistency of WACC parameter estimates which differs from ours, a beta assumption of 0.7 would be appropriate. Finally, an alternative to the 70% leverage/BBB rating assumption is a 60% leverage/A– rating assumption.


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2. Beta estimates of listed water utilities 2.1 Introduction Systematic risk, also termed market or economic risk, represents risks associated with overall economic conditions, as opposed to risks associated with company-specific events. In the regulatory setting it is used as an input into the required return to equity holders, estimated via the Capital Asset Pricing Model (“CAPM”) in which equity holders are only compensated for their systematic risk exposure, given their ability to mitigate against company-specific risks through diversification. The specific parameter estimate which forms part of the CAPM estimate of the required return to equity holders is the equity beta. A common technique for estimating systematic risk is to perform an OLS regression of excess stock returns against excess market returns for a set of listed companies considered to have the same systematic risk exposure of the firm of interest.2 We have examined the returns of 16 United States- and United Kingdom-listed water utilities. We adopt two approaches to analysis of this set of comparable firms. The first approach treats the equity beta estimate for each firm as an independent estimate of the equity beta, from which we derive mean estimates and confidence intervals based upon the variation in the equity beta estimates across the sample. In the second approach we form an index comprising a portfolio of firms using whichever firms are listed during each returns month. Both approaches are designed to mitigate against the estimation error inherent in the systematic risk estimate for each individual firm. Estimation error results from the use of realised returns for measurement of systematic risk exposure, rather than expected returns, which are more difficult to observe. Individual stock returns are volatile and driven by company-specific events, so there is considerable imprecision in the estimated systematic risk exposure of individual firms. While portfolio analysis mitigates against this imprecision to some degree we still observe wide confidence intervals amongst beta estimates derived from analysis of historical returns. In other work (Gray, Hall, Klease and McCrystal, 2009) we document the limited reliance which can be placed upon equity beta estimates derived from historical returns and do not repeat that discussion in this paper. We also adjust the beta estimates of each firm and the indices to account for differences between observed leverage of listed firms and the assumed leverage in regulated rates of return and a bias in OLS beta estimates. If actual leverage differs from leverage inherent in the regulated rate of return, and if we assume that listed entities have the same operational risk exposure of the regulated entities (that is, the same risk exposure in the absence of financial risk) then we need to re-compute the beta estimate derived from listed entities to account for this systematic risk difference. The bias in OLS beta estimates results from an increased probability of sampling error the further an OLS beta estimate lies from a prior estimate of one. This bias has been documented in the finance literature for a substantial period of time (Vasicek, 1973) and data providers routinely make corrections to account for this bias (Bloomberg, Datastream). However, a bias correction of this type has not been previously accepted by Australian regulators. Importantly, we also demonstrate the asymmetric risk exposure inherent in the returns of the comparable listed firms. In this context, asymmetric risk represents the relatively greater association between market returns and stock returns during low market returns periods, relative to periods of high market returns. In other words, when the market falls the stocks exhibit considerably higher market risk than during rising markets. Under the CAPM, returns are expected to exhibit a constant exposure to market returns, regardless of the direction of market movements. However, when we examine actual 2 The term “excess returns” refers to returns above the risk-free rate of interest.


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returns to measure this risk, we observe an asymmetric risk exposure. Asymmetric exposure to market risks is an explanation for the relative performance of value versus growth stocks (Zhang, 2005) and the substantial returns to purchasing takeover targets, referred to as merger arbitrage (Mitchell and Pulvino, 2001). On average, listed water utilities have relatively greater exposure to market movements during periods of poor market returns, which is a pattern of returns opposite to what would be desirable to investors. Our beta estimates are summarised as follows and are drawn from a sample and estimation technique which we consider most relevant for analysis. We present complete results tables for each sample and estimation technique considered and summarise the mean and 90% confidence intervals for these estimates in Table 10. In Section 2.5 we outline the rationale which underpins our preferred estimates. The results presented below are drawn from a sample which excludes 20 observations (0.4% of the sample) on the basis of stability analysis used to identify outliers, although we also present results for the full sample of firms. Where we refer to an index, it is an equal-weighted index comprised of five to 16 stocks. The OLS beta estimates have been corrected for the bias referred to above and adjusted to an estimate consistent with 60% leverage, often referred to as “re-geared” beta estimates.3 Results are also presented without the bias correction and re-gearing. On average the mean OLS beta estimate from 16 listed firms is 0.55, within a 90% confidence

interval of 0.40 to 0.70. From this point onwards we simply present the 90% confidence interval in brackets next to the point estimate.

During periods when market returns were below the risk-free rate, the mean OLS beta estimate is 0.69 (0.42 to 0.97). During periods when market returns were above the risk-free rate, the mean OLS beta estimate is 0.38 (0.27 to 0.50).

The OLS beta estimate from an equal-weighted index of 16 listed firms is 0.52 (0.45 to 0.58).

During periods when market returns were below the risk-free rate, the OLS estimate is 0.61 (0.50 to 0.72). During periods when market returns were above the risk-free rate, the OLS estimate is 0.43 (0.32 to 0.55).

The issue is what use we can make of the OLS estimates for determining regulated rates of return, in particular the different beta estimates associated with rising and falling markets. There are two alternative scenarios to consider.

First, there is the possibility that the sample of historical returns does provides a reasonable basis for estimating the systematic risk exposure of an Australian water utility. That sample returns series is characterised by asymmetric risk exposure. Under this assumption, equity investors’ required returns will at least incorporate systematic risk exposure inherent in the period of poor market performance. In other words, if the historical returns provide useful information to measure systematic risk exposure, the beta estimate incorporated into the CAPM should have a lower bound determined with respect to the down market beta estimates and associated confidence intervals. This represents a lower bound because, under this scenario, returns exhibit relatively low association with market movements when the market is rising, the very time when investors would prefer returns which are positively associated with market movements.

3 In our subsequent analysis relating to leverage we conclude that an assumption of 70% leverage for SDP would still be consistent with a BBB credit rating. That conclusion does not affect the re-geared equity beta estimates presented here. In this section we consider the risk to equity holders in a typical water utility re-geared to 60% leverage.


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Second, there is the alternative possibility that the sample of historical returns does not provide a reasonable basis for estimating the systematic risk exposure of a listed water utility. We have previously documented that there is little ability of beta estimates derived from historical returns to predict subsequently-realised returns when incorporated into the CAPM (Gray et al., 2009). In the absence of reliable information from historical returns about the systematic risk exposure of equity holders, an equity beta estimate of one is appropriate, given that we do not have information which allows us to determine whether the risk exposure is above or below that of the typical firm in the market. While the systematic risk exposure of operational earnings is likely to be below average, an assumed capital structure of 60% debt finance has accounted for this relatively low risk exposure of operational earnings.

In arriving at an appropriate equity beta estimate for determining the regulated rate of return, we would consider whether the outcome is economically-reasonable in the context of other parameter estimates. We would also consider whether OLS beta estimates are sufficiently stable to use at all in estimating the required return to equity holders, given their limited association with subsequently realised returns. Those issues do not form part of the analysis presented here. However, if we were to use this returns information in isolation, without consideration of these factors, an appropriate beta estimate would be 0.65. This estimate is the average of the down market beta estimates of 0.69 and 0.61 from the individual firm means and the equal-weighted index. It lies within the 90% confidence interval for individual firm means when market risk is assumed to be constant (0.40 to 0.70). It lies above the confidence interval for the equal-weighted index when market risk is assumed to be constant (0.45 to 0.58). But if a lower figure than 0.65 were adopted, then the asymmetry present in the beta estimates would in effect be given almost zero consideration. It is entirely plausible that the constant OLS beta estimate from the sample is driven downwards by the relatively low returns during rising markets. If this was in fact the case, regulated rates of return would be set below the true cost of capital to equity holders because of relative underperformance of listed water utilities during periods of strong market performance. 2.2 Methodology 2.2.1 Regression equations We compiled OLS beta estimates for individual firms and an equal-weighted index according to two regression equations on monthly excess returns, where excess returns refers to stock or market returns minus the risk-free rate. The first equation assumes a constant relationship between excess stock and market returns:

𝑟𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛼 + 𝛽�𝑟𝑚,𝑡 − 𝑟𝑓,𝑡� + 𝜀𝑖,𝑡 where: ri,t, rm,t and rf,t = return on stock i, the return on the equity market and the risk-free rate, respectively in month t; and εi,t = an error term for stock i during month t. The second equation allows the association between excess stock and market returns to vary according to whether excess market returns are positive or negative:

𝑟𝑖,𝑡 − 𝑟𝑓,𝑡 = 𝛼 + 𝛽𝑢𝑝�𝑟𝑚,𝑡 − 𝑟𝑓,𝑡 − 𝑡ℎ�𝐼 + 𝛽𝑑𝑜𝑤𝑛�𝑟𝑚,𝑡 − 𝑟𝑓,𝑡 − 𝑡ℎ�(1 − 𝐼) + 𝜀𝑖,𝑡 where:


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I = an indicator variable which takes on a value of 1 when excess market returns exceed a threshold of th (set equal to zero in this case) and 0 otherwise. The differential exposure to systematic risk is illustrated in Figure 1 which is extracted from Mitchell and Pulvino (2001, p.2143). They investigated whether there was differential exposure to market risk from buying stocks for which takeover offers had been announced, a trading strategy known as merger arbitrage. The same regression equation is used to examine the risk of this trading strategy in the Australian market (Maheswaran and Yeoh, 2005). The figure is presented to aid understanding of the beta estimates which follow. Whether or not asymmetric risk is present in merger arbitrage is independent of whether asymmetric risk is present in our sample of returns on water utilities. The presentation of the equations in the papers listed above is different to the presentation of our equation, but the actual computations are the same. In those papers the authors estimate two different slope coefficients (that is, beta estimates) and two different intercept terms, but restrict the equation so that there is continuity at the point labelled “Threshold” in the figure. In our case we have a constant value (α) at this point and incorporate the threshold for excess market returns into the independent variables. The result is the same – at the threshold for excess market returns is an estimated value for excess stock returns, which then increases or decreases depending upon the up and down market beta estimates. Figure 1. Asymmetric relationship between stock returns and market returns

2.2.2 Bias correction The beta estimates computed from the equations specified above are then adjusted to account for bias in OLS beta estimates. In statistical terms, a coefficient derived from sample data is biased if it does not represent the expected value in the population. This bias was previously documented by Vasicek (1973)


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and verified in recent empirical data (Gray et al., 2009). The bias occurs because the further a computed beta estimate lies from one, the higher the probability that we have observed the extreme beta estimate by random chance and the greater the probability that the true, unobservable beta of the stock lies closer to one. Hence, the bias correction places some weight on the OLS beta estimate and some weight on a prior estimate of one, with the weights computed according to the precision of those two values. The weight on the OLS beta estimate will increase as the standard error of the estimate decreases. Our bias correction equation is that presented by Vasicek, given below, and we assume a prior beta estimate of one and a standard error of the prior estimate equal to 0.5 (implying that approximately 95% of true betas will lie between zero and two if betas are normally distributed): 𝛽𝑏𝑖𝑎𝑠−𝑐𝑜𝑟𝑟𝑒𝑐𝑡𝑒𝑑 =𝛽𝑂𝐿𝑆 × �1 − 𝑆𝑡𝑑 𝑒𝑟𝑟𝑜𝑟 𝑜𝑓 𝛽𝑂𝐿𝑆

2

𝑆𝑡𝑑 𝑒𝑟𝑟𝑜𝑟 𝑜𝑓 𝛽𝑂𝐿𝑆2+𝑆𝑡𝑑 𝑒𝑟𝑟𝑜𝑟 𝑜𝑓 𝛽𝑃𝑟𝑖𝑜𝑟

2�+ 𝛽𝑃𝑟𝑖𝑜𝑟 × � 𝑆𝑡𝑑 𝑒𝑟𝑟𝑜𝑟 𝑜𝑓 𝛽𝑂𝐿𝑆2

𝑆𝑡𝑑 𝑒𝑟𝑟𝑜𝑟 𝑜𝑓 𝛽𝑂𝐿𝑆2+𝑆𝑡𝑑 𝑒𝑟𝑟𝑜𝑟 𝑜𝑓 𝛽𝑃𝑟𝑖𝑜𝑟

2� In commentary on this bias adjustment, Davis (2011) states that “the rationale for such an adjustment, particularly using a long data sample of specifically chosen water utility stocks is not strong (p.2).” He refers to Vasicek (1973) himself who provided the example that “if a utility stock is included, and it is known from previous measurements that betas of utilities are centered around 0.8 with a dispersion of 0.3, the estimated [β] is adjusted toward 0.8…with [βPrior] = 0.8 and [Std error of βPrior] = 0.3.”4 The issue is that, prior to conducting the regression analysis, we do not have information which suggests that our prior estimate should be different from the average stock in the market. To adopt a prior expectation different from one, we would need evidence aside from the returns information, which supports this prior expectation. It is possible to argue that the beta estimate for an ungeared utility will be lower than the beta estimate for an ungeared stock in another industry, on the basis of the stability of the revenue stream and if there is quantitative information to support this view. But this does not tell us whether the relatively high leverage of the utility will result in an equity beta estimate which differs from one. 2.2.3 Leverage Sample firms do not necessarily have the same leverage as the 60% figure typically assumed in setting regulated rates of return. But the standard technique is to assume that the listed comparable firms have the same underlying risk at the operational level as the entity which is being regulated. This is generally referred to as saying the listed comparable firms are assumed to have the same asset beta. The term asset beta refers to the equity beta the firm would have if it was financed entirely by equity. This assumption raises the question as to why, if 60% leverage is considered to be the value-maximising level, why we often see listed firms adopt leverage well below this level. In our sample the average leverage across firms, weighting each firm equally, is 43%. The time-series average of the equal-weighted index has leverage of 45%, as firms with longer returns history in the sample have higher leverage. The assumption of 60% gearing in setting regulated rates of return relies upon the assumption that sample firms were marginally under-geared during the sample period. The alternative assumption is that a lower level of gearing is the value-maximising capital structure. However, discussion of this issue is beyond the scope of the current paper. In computing our re-geared beta estimates we estimate asset betas for each individual firm, or a single asset beta in the case of an index, by stripping out the effects of the mean monthly leverage for the firm, or the mean time-series leverage for the index. Leverage is computed as book value of debt ÷

4 We altered the symbols quoted in Vasicek (1973) to correspond to the symbols used in the equation presented above.


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(market capitalisation + book value of debt). The equation used in this computation accounts for debt bearing a small amount of systematic risk, as given by a debt beta of 0.2. It represents the levering equation which we consider to be most appropriate and is derived simply by applying the CAPM to expected returns to debt, levered equity and unlevered equity in a one-period model. The Tribunal uses an alternative equation. However, provided the same equation is used for computing asset betas and then estimating re-levered equity betas, the choice of equation typically makes little difference to the result, especially when observed leverage is close to assumed leverage, as it is in this case. Our equation is as follows and incorporates a corporate tax rate of 30%:

𝛽𝑒𝑞𝑢𝑖𝑡𝑦 = 𝛽𝑎𝑠𝑠𝑒𝑡 × �1 +𝐷𝑒𝑏𝑡𝐸𝑞𝑢𝑖𝑡𝑦

× (1 − 𝑐𝑜𝑟𝑝 𝑡𝑎𝑥 𝑟𝑎𝑡𝑒)� − 𝛽𝑑𝑒𝑏𝑡 ×𝐷𝑒𝑏𝑡𝐸𝑞𝑢𝑖𝑡𝑦

× (1 − 𝑐𝑜𝑟𝑝 𝑡𝑎𝑥 𝑟𝑎𝑡𝑒)


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2.3 Data We analysed monthly returns on 16 United States- and United Kingdom-listed water utilities from January 1973 to June 2011, a period of 462 months. The analysis begins in January 1973 because this is the first month in which data is available for analysis. The minimum number of observations is provided by American Water Works with 38 months and the maximum number of observations is provided by American States Water, Aqua America, Middlesex Water and SJW with 462 months. On average each firm contributes 294 months of returns to the sample. Our initial sample comprised 32 securities which were classified as ICB Subsector Water Utilities and were listed on the New York, NASDAQ, Toronto, London and Australian exchanges. We eliminated 16 securities which were American Depository Receipts, comprised water technology companies rather than water utilities, for which returns data was unavailable or which were thinly traded.5 The number of returns months and the start and end dates for each firm are summarised in Table 1. This table also presents the average leverage ratio and market capitalisation for each firm over the sample period. Debt figures were obtained on an annual basis from Datastream. On average, observations have market capitalisation of US$675 million and leverage of 43%, which is below the leverage ratio of 60% typically observed in regulatory determinations and that submitted by SDP. Table 1. Sample of listed water utilities Firm Country Mths (N) Mths (%) First month Last Month Lev (%) MC (US$m) American States Water US 462 9.8 Jan-1973 Jun-2011 46 206 American Water Works US 38 0.8 May-2008 Jun-2011 61 3730 Aqua America US 462 9.8 Jan-1973 Jun-2011 44 797 Artesian Res.'A' US 158 3.4 May-1998 Jun-2011 54 82 Cadiz US 313 6.6 Jun-1985 Jun-2011 38 128 Cal.Water Ser. US 462 9.8 Jan-1973 Jun-2011 37 272 Connecticut Water US 432 9.2 Jul-1975 Jun-2011 45 93 Consolidated Wt. US 193 4.1 Jun-1995 Jun-2011 9 125 Middlesex Water US 462 9.8 Jan-1973 Jun-2011 47 89 Northumbrian Water Gp. UK 97 2.1 Jun-2003 Jun-2011 65 739 Pennichuck US 214 4.5 Sep-1993 Jun-2011 44 57 Pennon Group UK 258 5.5 Jan-1990 Jun-2011 48 669 Severn Trent UK 258 5.5 Jan-1990 Jun-2011 41 1523 SJW US 462 9.8 Jan-1973 Jun-2011 36 174 United Utilities Group UK 258 5.5 Jan-1990 Jun-2011 44 2042 York Water UK 178 3.8 Sep-1996 Jun-2011 32 71 Mean 294 100.0 Jan-1987 Jun-2011 43 675 The indices we use are the Datastream Total Market Indices for the United States and the United Kingdom, which are large stock market capitalisation-weighted indices with long time series. When we compile an equal-weighted index of water utilities we convert United Kingdom stock returns and the United Kingdom Index returns to United States dollar returns. Our water utilities index and the market index are then an equal-weighted index of stock returns and a weighted index of market returns, in which the weights placed on United Kingdom and United States markets are the number of stocks comprising the sample at that point in time.

5 There was no specific criteria used to determine whether a stock was thinly traded or not. Given the small sample size it was clear from inspection of daily trading volumes the stocks which were thinly traded, as there were several instances in which no shares were traded for days at a time. In a larger sample study it is appropriate to apply an objective criteria to all stocks to determine which stocks should be excluded on the basis of thin trading.


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2.4 Results 2.4.1 Mean estimates of individual firms In Table 2 we present beta estimates for individual firms derived from an OLS regression of excess stock returns against excess market returns. We also present the mean estimate and a confidence interval for this mean estimate, under the assumption that each firm represents an independent estimate of the equity beta. The table shows a mean re-geared and bias-corrected beta estimate of 0.57 (0.39 to 0.75). In the absence of a bias correction the mean re-geared beta estimate would be 0.55 (0.36 to 0.74).6 On average excess market returns were able to explain 7.5% of the variation in excess stock returns, which leaves 92.5% of variation in excess stock returns attributable to factors other than overall market movements. This occurs both because company- and industry-specific events are the primary determinants of stock returns on an individual firm basis, and because market returns are not necessarily a perfect proxy for systematic risk exposure. Table 2. Beta estimates for individual firms Returns-based estimates Re-geared to 60% Firm Adj R-sq (%) OLS Bias-corrected OLS Bias-corrected American States Water 4.4 0.28 0.29 0.30 0.32 American Water Works 14.7 0.41 0.46 0.40 0.45 Aqua America 5.4 0.36 0.37 0.41 0.43 Artesian Res.'A' 3.2 0.22 0.24 0.22 0.25 Cadiz 5.1 1.14 1.11 1.55 1.51 Cal.Water Ser. 5.5 0.29 0.29 0.32 0.34 Connecticut Water 7.0 0.33 0.34 0.37 0.38 Consolidated Wt. 13.5 0.95 0.96 1.65 1.66 Middlesex Water 9.4 0.37 0.37 0.41 0.42 Northumbrian Water Gp. 4.4 0.34 0.39 0.32 0.37 Pennichuck 0.6 0.17 0.21 0.16 0.21 Pennon Group 6.8 0.42 0.44 0.47 0.50 Severn Trent 7.5 0.39 0.41 0.47 0.49 SJW 14.5 0.56 0.57 0.73 0.74 United Utilities Group 14.6 0.51 0.52 0.61 0.62 York Water 4.0 0.33 0.36 0.40 0.45 Mean 7.5 0.44 0.46 0.55 0.57 StdDev 4.5 0.26 0.24 0.43 0.42 StdErr 0.06 0.06 0.11 0.10 Low 0.33 0.35 0.36 0.39 High 0.55 0.57 0.74 0.75 We repeated this analysis after accounting for potential asymmetric exposure to market risk. This allows us to compute beta estimates during periods of high and low market performance. All else being equal, investors will prefer stocks whose returns exhibit high covariance with market returns during periods of high market returns and stocks whose returns exhibit low covariance with market returns during periods of low market returns. Results are presented in Table 3. We refer to months when excess 6 In commentary on our OLS beta estimates, Davis (2011) presents OLS beta estimates for the 16 comparable firms from Thomson-Reuters using 60 months of data ending on 7 August 2011. The mean estimate using this recent sample period is 0.49 (10 per cent higher than the mean estimate of 0.44 presented above) and the standard deviation is 32 per cent (26 per cent higher than the standard deviation of 26 per cent presented above). Once outliers are excluded the standard deviation falls further to 22 per cent, while the mean is almost unchanged at 0.43. These differences in the mean and standard deviations are not statistically significant. However, the higher standard deviation associated with the most recent five years means that there is an increased chance that the estimates using a short sample period will be materially affected by random chance.


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market returns are negative as the “down market” and months when excess market returns are positive as the “up market.” Table 3. Beta estimates for individual firms according to market conditions Panel A: Beta estimates during months when excess market returns are positive Returns-based estimates Re-geared to 60% Firm Adj R-sq (%) OLS Bias-corrected OLS Bias-corrected American States Water 4.2 0.28 0.29 0.30 0.31 American Water Works 13.9 0.18 0.25 0.18 0.25 Aqua America 5.5 0.50 0.51 0.59 0.60 Artesian Res.'A' 3.8 0.01 0.04 -0.01 0.02 Cadiz 6.4 0.13 0.32 0.09 0.38 Cal.Water Ser. 6.3 0.47 0.48 0.60 0.61 Connecticut Water 6.9 0.38 0.39 0.43 0.44 Consolidated Wt. 13.0 0.99 0.99 1.72 1.72 Middlesex Water 9.2 0.36 0.37 0.41 0.42 Northumbrian Water Gp. 3.7 0.19 0.25 0.19 0.25 Pennichuck 0.4 0.32 0.35 0.36 0.40 Pennon Group 8.0 0.10 0.13 0.08 0.11 Severn Trent 8.1 0.17 0.19 0.16 0.19 SJW 14.5 0.46 0.47 0.58 0.59 United Utilities Group 14.4 0.44 0.45 0.52 0.53 York Water 3.5 0.40 0.43 0.51 0.55 Mean 7.6 0.34 0.37 0.42 0.46 StdDev 4.3 0.23 0.21 0.40 0.38 StdErr 0.06 0.05 0.10 0.10 Low 0.24 0.28 0.24 0.29 High 0.44 0.46 0.59 0.63 Panel B: Beta estimates during months when excess returns are negative Returns-based estimates Re-geared to 60% Firm Adj R-sq (%) OLS Bias-corrected OLS Bias-corrected American StatesWater 4.2 0.28 0.29 0.30 0.32 American Water Works 13.9 0.57 0.61 0.56 0.60 Aqua America 5.5 0.23 0.25 0.24 0.26 Artesian Res.'A' 3.8 0.39 0.41 0.41 0.43 Cadiz 6.4 1.98 1.76 2.75 2.44 Cal.Water Ser. 6.3 0.11 0.12 0.07 0.09 Connecticut Water 6.9 0.29 0.30 0.32 0.33 Consolidated Wt. 13.0 0.92 0.93 1.59 1.60 Middlesex Water 9.2 0.37 0.37 0.41 0.42 Northumbrian Water Gp. 3.7 0.47 0.51 0.44 0.48 Pennichuck 0.4 0.04 0.09 -0.01 0.05 Pennon Group 8.0 0.70 0.71 0.83 0.84 Severn Trent 8.1 0.59 0.60 0.74 0.76 SJW 14.5 0.66 0.66 0.87 0.88 United Utilities Group 14.4 0.57 0.58 0.69 0.70 York Water 3.5 0.27 0.31 0.31 0.37 Mean 7.6 0.53 0.53 0.66 0.66 StdDev 4.3 0.45 0.40 0.68 0.60 StdErr 0.11 0.10 0.17 0.15 Low 0.33 0.36 0.36 0.40 High 0.73 0.71 0.95 0.92


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On average the re-geared and bias-corrected up market beta estimate is 0.46 (0.29 to 0.63) and the mean down market beta estimate is 0.66 (0.40 to 0.92). In the absence of a bias correction the mean beta estimates and confidence intervals would be 0.42 (0.24 to 0.59) during up markets and 0.66 (0.36 to 0.92) during down markets. Ten of the 16 sample firms have beta estimates which exhibit this asymmetric exposure to market movements. It is important to note that asymmetric risk exposure has not been imposed on the empirical relationship. If there was indeed a constant relationship between excess stock returns and excess market returns in the sample data, the line of best fit would be identical to that which assumes constant risk exposure. The only issue is how the empirical relationship present in the sample data should be used to form parameter estimates in setting the regulated rate of return. The CAPM is not a model which accounts for asymmetric risk exposure, implying that expected returns on each asset will reflect the asset’s linear (that is, constant) exposure of asset returns to market returns. But in the historical time series we do not have a sample of expected returns on assets, nor do we have a series of expected returns on the market portfolio. We have observations of actual returns on assets which are used as a proxy for the relationship between expected stock and market returns. So it is not necessarily the case that imposing a linear relationship between actual stock returns and market returns provides the most reliable estimate of a linear relationship between expected stock and market returns. At this stage of the report we merely present the results of the analysis and reconcile the alternative beta estimates in our concluding section. 2.4.2 Beta estimate from an index of sample firms In the results presented in Section 4.1 estimation error is mitigated by analysing a sample of 16 firms, under the assumption the company-specific events which negatively impact upon the OLS beta estimate for one firm will be offset by company-specific events which positively impact upon the OLS beta estimate for another firm. However, a limitation of this approach is that the 16 beta estimates within the sample are not necessarily independent estimates, and independence is an assumption which underpins the confidence intervals presented above. Specifically, if there is an event which affects industry returns in a given month, and which is unrelated to market returns, that event will influence the beta estimate for all firms in the same direction. The noise created by this event will not be entirely reflected in the confidence interval because it could reduce the standard error of the estimate, rather than increase it. In simple terms, the more material is the violation of the independence assumption, the more risk that a confidence interval is too narrow relative to the true amount of imprecision in the beta estimate. An alternative to analysis of individual firms is to form an index returns series and estimate the systematic risk of this index. It remains the case that the industry-specific event described above will impact upon the beta estimate for the index, but there is less likely to be violation of the independence assumption and therefore less chance that the width of the confidence interval will be understated. The limitation of the index analysis is that there are a number of months in which the index is comprised of a small number of firms, in some cases just five firms. For this reason our preference is to use an equal- rather than a value-weighted index, because there is a risk that the returns series for a firm with relatively larger market capitalisation carries undue weight in the analysis. On average, larger firms are likely to be more heavily traded than small firms, so they are more likely to be efficiently priced. That is, the observed price for a large market capitalisation firm is relatively more likely to reflect publicly available information, with lower risk of price distortions due to a small number of trades. However, this does not eliminate the noise in beta estimates associated with company-specific events which affect returns and we have no reason to believe that the beta estimates of large market


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capitalisation firms will be relatively less influenced by these company-specific events than small market capitalisation firms. The re-geared and bias-corrected beta estimate from the equal-weighted index is 0.54 (0.47 to 0.60). In the absence of a bias correction the beta estimate would be 0.53 (0.47 – 0.60). The bias correction makes relatively less difference in this instance, compared to individual firm estimates, because of the lower standard error inherent in the index estimates (0.04) compared to the standard errors of individual firm estimates (which are unreported and ranged from 0.05 to 0.27). Table 4. Beta estimates for indices of listed water utilities Returns-based estimates Re-geared to 60% OLS Bias-corrected OLS Bias-corrected Estimate 0.45 0.46 0.53 0.54 Lower 0.39 0.40 0.47 0.47 Upper 0.51 0.52 0.60 0.60 Standard error 0.04 0.04 0.04 0.04 Mean leverage (%) 45 Number of observations 462 Adjusted R-squared (%) 240.9 As with the individual firm estimates, we observe an asymmetric exposure of index returns to market returns. This asymmetric exposure is illustrated in Figure 2 which plots the OLS beta estimates assuming constant risk exposure and asymmetric risk exposure. The estimates presented in the figure have not been re-geared or bias-corrected. In the sample, there is a divergence in the association between stock returns and market returns during different market conditions, with the beta estimate rising to 0.50 during down markets and falling to 0.40 during up markets. The estimate drawn from an assumption of constant risk exposure is 0.45, presented in the upper-left hand corner of Table 4. Figure 2. Excess stock returns and market returns for an equal-weighted water utility index

-22%

-20%

-18%

-16%

-14%

-12%

-10%

-8%

-6%

-4%

-2%

0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

22%

-22% -20% -18% -16% -14% -12% -10% -8% -6% -4% -2% 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 20% 22%

Equa

l-wei

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of st

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Market return minus risk-free rate

Constant risk exposure

Asymmetric risk exposure

Beta = 0.50 (0.39 - 0.61)

Beta = 0.45 (0.39- 0.51)

Beta = 0.40 (0.29- 0.52)


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We present the beta estimates and confidence intervals for the returns indices, incorporating asymmetric risk exposure, in Table 5. The down market beta estimate for the index is 0.60 (0.49 to 0.72). In the up market the beta estimate falls to 0.48 (0.36 to 0.60). In the absence of a bias correction the down market estimate would be 0.59 (0.48 to 0.71); and the up market beta estimate would be 0.47 (0.34 to 0.59). Table 5. Beta estimates for indices of listed water utilities according to market conditions Panel A: Beta estimates during months when excess market returns are positive Returns-based estimates Re-geared to 60% OLS Bias-corrected OLS Bias-corrected Estimate 0.40 0.41 0.47 0.48 Lower 0.29 0.30 0.34 0.36 Upper 0.52 0.53 0.59 0.60 Standard error 0.07 0.07 0.07 0.07 Mean leverage (%) 45 Number of observations 462 Adjusted R-squared (%) 240.9 Panel B: Beta estimates during months when excess returns are negative Returns-based estimates Re-geared to 60% OLS Bias-corrected OLS Bias-corrected Estimate 0.50 0.51 0.59 0.60 Lower 0.39 0.40 0.48 0.49 Upper 0.61 0.62 0.71 0.72 Standard error 0.06 0.06 0.07 0.07 Mean leverage (%) 45 Number of observations 462 Adjusted R-squared (%) 240.9 2.4.3 Stability analysis In order to examine the influence of a small number of data points we performed stability analysis. Under this technique, one at a time, we systematically remove observations where the removal of that observation would either maximise or minimise the beta estimate. Specifically, we first rank the 4707 firm-month observations by the squared difference between excess stock returns and excess market returns. We consider the 200 observations with the highest squared difference for subsequent evaluation. Then, we repeat the regression analysis 200 times on 4706 observations, where a different observation is removed each time. From these 200 regression analyses we observe which observations, when removed, generate the highest and lowest mean beta estimate from the individual firm analysis (that is the mean estimate presented in the right-hand column of Table 2). These two observations are considered to be outliers because their inclusion or exclusion resulted in the greatest shift in the mean beta estimate. We then repeat this analysis another 10 times which allows us to report beta estimates and confidence intervals resulting from the removal of up to 20 outliers, representing 0.4% of observations. Mean estimates for individual firms With the removal of 20 outliers the mean beta estimate is almost unchanged at 0.55 (compared to 0.57 reported in Table 2. But width of the the confidence interval has decreased to 0.40 to 0.70, from 0.39 to 0.75. The standard error of the beta estimates across firms has fallen by 17%, from 0.104 to 0.086. This occurs because the beta estimate for Consolidated Water fell to 1.28 from 1.66, and five other firms had increases or decreases in their beta estimates within the range of 0.05 to 0.07.


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The asymmetric exposure to market conditions is more prevalent with the removal of 20 outliers. The mean down market beta estimate increases to 0.69 (from 0.66) when outliers are excluded and the mean up market beta estimate decreases to 0.38 (from 0.46). For the down market beta estimates there is a marginal 3% increase in the standard error of beta estimates across the sample 3% when outliers are removed (the standard error increases from 0.151 to 0.155). However, there is a substantial 32% reduction in the standard error associated with the up market beta estimate, which falls from 0.095 to 0.064. These results are presented in Table 6 and Table 7. What these results demonstrate is that the inclusion or exclusion of a small number of returns observations can have on the precision of beta estimates derived from analysis of historical returns. Our analysis will be more reliable if we draw conclusions from the results of 99.6% of returns months, rather than being unduly influenced by the share price movements occurring in 0.4% of returns months. Table 6. Beta estimates for individual firms excluding outliers Returns-based estimates Re-geared to 60% Firm Adj R-sq (%) OLS Bias-corrected OLS Bias-corrected American States Water 4.4 0.28 0.29 0.30 0.31 American Water Works 14.7 0.47 0.21 0.13 0.21 Aqua America 5.4 0.36 0.51 0.59 0.60 Artesian Res.'A' 3.2 0.22 0.04 -0.01 0.02 Cadiz 5.1 1.09 0.12 -0.21 0.09 Cal.Water Ser. 5.5 0.29 0.48 0.60 0.61 Connecticut Water 7.0 0.33 0.39 0.43 0.44 Consolidated Wt. 13.5 0.73 0.63 0.95 1.03 Middlesex Water 9.4 0.37 0.37 0.41 0.42 Northumbrian Water Gp. 4.4 0.41 0.22 0.15 0.21 Pennichuck 0.6 0.17 0.35 0.36 0.40 Pennon Group 6.8 0.42 0.13 0.08 0.11 Severn Trent 7.5 0.39 0.19 0.16 0.19 SJW 14.5 0.56 0.47 0.58 0.59 United Utilities Group 14.6 0.46 0.31 0.32 0.34 York Water 4.0 0.37 0.44 0.53 0.57 Mean 7.5 0.43 0.32 0.33 0.38 StdDev 4.5 0.22 0.16 0.28 0.26 StdErr 0.06 0.04 0.07 0.06 Low 0.34 0.25 0.21 0.27 High 0.53 0.39 0.46 0.50


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Table 7. Beta estimates for individual firms according to market conditions excluding outliers Panel A: Beta estimates during months when excess market returns are positive Returns-based estimates Re-geared to 60% Firm Adj R-sq (%) OLS Bias-corrected OLS Bias-corrected American States Water 4.2 0.28 0.29 0.30 0.31 American Water Works 18.4 0.13 0.21 0.13 0.21 Aqua America 5.5 0.50 0.51 0.59 0.60 Artesian Res.'A' 3.8 0.01 0.04 -0.01 0.02 Cadiz 8.3 -0.08 0.12 -0.21 0.09 Cal.Water Ser. 6.3 0.47 0.48 0.60 0.61 Connecticut Water 6.9 0.38 0.39 0.43 0.44 Consolidated Wt. 8.6 0.59 0.63 0.95 1.03 Middlesex Water 9.2 0.36 0.37 0.41 0.42 Northumbrian Water Gp. 6.4 0.15 0.22 0.15 0.21 Pennichuck 0.4 0.32 0.35 0.36 0.40 Pennon Group 8.0 0.10 0.13 0.08 0.11 Severn Trent 8.1 0.17 0.19 0.16 0.19 SJW 14.5 0.46 0.47 0.58 0.59 United Utilities Group 13.2 0.29 0.31 0.32 0.34 York Water 5.1 0.42 0.44 0.53 0.57 Mean 7.9 0.28 0.32 0.33 0.38 StdDev 4.4 0.19 0.16 0.28 0.26 StdErr 0.05 0.04 0.07 0.06 Low 0.20 0.25 0.21 0.27 High 0.37 0.39 0.46 0.50 Panel B: Beta estimates during months when excess returns are negative Returns-based estimates Re-geared to 60% Firm Adj R-sq (%) OLS Bias-corrected OLS Bias-corrected American States Water 4.2 0.28 0.29 0.30 0.32 American Water Works 18.4 0.77 0.79 0.75 0.78 Aqua America 5.5 0.23 0.25 0.24 0.26 Artesian Res.'A' 3.8 0.39 0.41 0.41 0.43 Cadiz 8.3 2.06 1.86 2.87 2.58 Cal.Water Ser. 6.3 0.11 0.12 0.07 0.09 Connecticut Water 6.9 0.29 0.30 0.32 0.33 Consolidated Wt. 8.6 0.87 0.89 1.50 1.52 Middlesex Water 9.2 0.37 0.37 0.41 0.42 Northumbrian Water Gp. 6.4 0.66 0.69 0.61 0.63 Pennichuck 0.4 0.04 0.09 -0.01 0.05 Pennon Group 8.0 0.70 0.71 0.83 0.84 Severn Trent 8.1 0.59 0.60 0.74 0.76 SJW 14.5 0.66 0.66 0.87 0.88 United Utilities Group 13.2 0.61 0.62 0.74 0.75 York Water 5.1 0.33 0.37 0.41 0.45 Mean 7.9 0.56 0.56 0.69 0.69 StdDev 4.4 0.47 0.42 0.69 0.62 StdErr 0.12 0.11 0.17 0.16 Low 0.36 0.38 0.39 0.42 High 0.77 0.75 0.99 0.97


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Beta estimates from indices of sample firms With the removal of 20 outliers the beta estimate for an equal-weighted index is almost unchanged at 0.52, compared to 0.54. However, as with the dispersion of beta estimates for individual firms there is a reduction in estimation error when a small number of observations is removed. The standard error falls by 5%, from 0.040 to 0.038 (the table reports both figures as 0.04 to two decimal places). This implies a 90% confidence interval of 0.45 to 0.58, compared to 0.47 to 0.60 for the full sample. As with the analysis of individual firm estimates, there is an increase in the estimate of asymmetric risk exposure when outliers are excluded. When 20 outliers are removed the down market beta estimate increases to 0.61 (0.50 to 0.72) and the estimated up market beta decreases to 0.43 (0.32 to 0.55). These results are presented in Table 8 and Table 9. Table 8. Beta estimates for indices of listed water utilities excluding outliers Returns-based estimates Re-geared to 60% OLS Bias-corrected OLS Bias-corrected Estimate 0.44 0.44 0.51 0.52 Lower 0.38 0.38 0.45 0.45 Upper 0.50 0.50 0.57 0.58 Standard error 0.03 0.03 0.04 0.04 Mean leverage (%) 45 Number of observations 462 Adjusted R-squared (%) 25.5 Table 9. Beta estimates for indices of listed water utilities according to market conditions excluding outliers Panel A: Beta estimates during months when excess market returns are positive Returns-based estimates Re-geared to 60% OLS Bias-corrected OLS Bias-corrected Estimate 0.37 0.38 0.42 0.43 Lower 0.26 0.27 0.31 0.32 Upper 0.48 0.49 0.53 0.55 Standard error 0.06 0.07 0.07 0.07 Mean leverage (%) 45 Number of observations 462 Adjusted R-squared (%) 25.7 Panel B: Beta estimates during months when excess returns are negative Returns-based estimates Re-geared to 60% OLS Bias-corrected OLS Bias-corrected Estimate 0.50 0.51 0.60 0.61 Lower 0.40 0.41 0.49 0.50 Upper 0.60 0.61 0.71 0.72 Standard error 0.06 0.06 0.07 0.07 Mean leverage (%) 45 Number of observations 462 Adjusted R-squared (%) 25.7

2.4.4 Results summary The analysis presented above includes eight re-geared beta estimates under four alternative estimation techniques and two samples. The eight estimation techniques are comprised of (1) two methods of mitigating against noise from firm-specific events (means of firm-specific beta estimates and an equal-weighted index); (2) two regression equations (imposing constant market risk exposure versus allowing


19

asymmetric market risk exposure); and (3) including and excluding a bias correction. The two samples either include or exclude outliers from stability analysis. In the table below we present the beta estimates and confidence intervals from these alternative sets of analysis. Presentation of the complete set of results in this manner does not necessarily mean that equal weight should be placed on each beta estimate from the table. We discuss the trade-offs between each of these sets of analysis and reach a conclusion in the following section. It also does not imply that analysis of historical returns provides reliable beta estimates for setting regulated rates of return. The estimates have wide confidence intervals and have limited ability to predict future realised returns. However, if historical returns are to be used to inform beta estimates used in setting regulated rates of return, in the table we highlight the region which we believe should be given the greatest weight in this analysis. Table 10. Results summary of re-geared beta estimates OLS Bias-corrected Est Low High Est Low High Full sample Mean of individual firm estimates: Constant estimate 0.55 0.36 0.74 0.57 0.39 0.75 Down market estimate 0.66 0.36 0.95 0.66 0.40 0.92 Up market estimate 0.42 0.24 0.59 0.46 0.29 0.63 Estimates from equal-weighted index: Constant estimate 0.53 0.47 0.60 0.54 0.47 0.60 Down market estimate 0.59 0.48 0.71 0.60 0.49 0.72 Up market estimate 0.47 0.34 0.59 0.48 0.36 0.60 Outliers removed Mean of individual firm estimates: Constant estimate 0.53 0.37 0.68 0.55 0.40 0.70 Down market estimate 0.69 0.39 0.99 0.69 0.42 0.97 Up market estimate 0.33 0.21 0.46 0.38 0.27 0.50 Estimates from equal-weighted index: Constant estimate 0.51 0.45 0.57 0.52 0.45 0.58 Down market estimate 0.60 0.49 0.71 0.61 0.50 0.72 Up market estimate 0.42 0.31 0.53 0.43 0.32 0.55 2.5 Conclusion with respect to OLS beta estimates In this section we outline our rationale for our preferred beta estimates, based upon this historical returns series, before reaching a conclusion. 2.5.1 Bias versus no bias correction We have previously outlined the need to correct OLS beta estimates for a bias inherent in the estimation process. Simply, when the observed OLS beta estimate is low there is an increased probability that we have observed that low beta estimate purely by chance, and a decreased probability that we have observed that low beta estimate because it represents the true – that is, unbiased – estimate of the systematic risk faced by equity holders. The bias correction does not account for precision of the estimate, merely whether the observed figure represents an unbiased expectation of the systematic risk we will observe in the future. 2.5.2 Inclusion versus exclusion of outliers based upon stability analysis The results summarised in Table 10 suggest that stock returns in the historical series exhibited an asymmetric association with market returns. Beta estimates are higher during months when market returns are below the risk-free rate and the magnitude of this asymmetry is affected by a relatively small


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number of observations. Once 20 outliers are excluded, representing 0.4% of the sample, the asymmetric exposure to market conditions is more pronounced. Furthermore, the standard error of beta estimates across the sample of individual firms, and the standard error of the beta estimate derived from the equal-weighted index are lower with the exclusion of these observations. Hence, our preference would be to place relatively more weight on the sample which excludes 20 outliers on the basis of stability analysis. 2.5.3 Consideration of asymmetric exposure to market conditions According to the CAPM, expected returns have a linear relationship with the market risk premium. Assuming that the CAPM is an appropriate model for estimating expected returns, the purpose of analysing historical returns is to make the most reliable estimate of that linear relationship. What is exhibited in the historical returns series is a relatively greater exposure to market returns during down markets and relatively less exposure to market returns during up markets. The issue then becomes how to incorporate this information in a beta estimate for setting regulated rates of return. A way to cut through the alternatives is to consider the following reasoning. First, suppose that the historical time series does provide a reasonable basis for estimating systematic risk to equity holders. We would recommend analysis of the historical data in its entirety and that historical data exhibits evidence of beta estimates which vary according to market conditions. If this is the case investors will require returns which are consistent with the down market beta estimate, otherwise they will exhibit below-market performance during falling markets, and will also exhibit below-market performance during rising markets because their returns will be less sensitive to market performance. Second, suppose that the true relationship between expected returns and excess market returns is indeed linear, meaning that the historical return series does not provide a reasonable basis for estimating systematic risk to equity holders. Under this scenario it is questionable whether we can place any reliance upon the OLS beta estimates. We would, in effect, be imposing a linear relationship on two variables whose relationship in the sample is known to be non-linear. Hence, if we are to place any reliance upon historical returns for estimating beta in a regulatory setting, we should consider what that returns series tells us about the risks facing investors. The data suggests an asymmetric exposure to market risk which we expect investors would account for in their required returns. To clarify why investors would account for this asymmetric exposure to market conditions, consider the following four three cases: First, suppose the risk-free rate was 5.0%, the market risk premium was 6.0% and a stock’s beta was

0.5 across up and down markets. In this instance the equity holders’ required return is 8.0%, computed as 0.050 + 0.5 × 0.06 = 8.0%.

Second, suppose that an otherwise comparable stock had a beta estimate of 0.6. For this stock, the equity holders’ required return is 8.6%, computed as 0.050 + 0.6 × 0.06 = 8.6%.

Third, now suppose that the stock’s exposure to the market is 0.6 during falling markets and 0.5 during rising markets. During falling markets this stock is expected to perform worse than the first stock considered above, and during rising markets to perform equally well with stock one. It also has a an expected performance equal to the second stock during falling markets and worse than the second stock during rising markets. All else being equal, the market would incorporate a cost of capital which is at least equal to that considered for stock two.


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At a minimum, investors would price in the risk associated with down markets. They would also price in the lack of returns exposure to rising markets, the very period when exposure to market risk would be desirable. 2.5.4 Conclusion The discussion immediately above implies that beta estimates we consider the more relevant for analysis are those presented the shaded section of Table 10. Arriving at an appropriate equity beta estimate for determining the regulated rate of return, we would consider whether the outcome is economically-reasonable in the context of other parameter estimates. For example, we would consider whether the required return to equity holders is at least equal to the required return to debt-holders in the same firm. We would also consider whether OLS beta estimates are sufficiently stable to use at all in estimating the required return to equity holders, given their limited association with subsequently realised returns. Finally, this analysis does not consider factors specific to the SDP. However, if we were to use this returns information in isolation, without consideration of these factors, an appropriate beta estimate would be 0.65. This estimate is the average of the down market beta estimates of 0.69 and 0.61 from the individual firm means and the equal-weighted index, and lies within the 90% confidence interval for the individual firm beta estimates in which constant market risk exposure is assumed. The figure of 0.65 lies above the 90% confidence interval for the equal-weighted index when constant risk exposure is imposed (0.45 to 0.58). However, if a lower figure were imposed, then the asymmetry present in the beta estimates would in effect be given almost zero consideration. It is entirely plausible that the constant OLS beta estimate from the sample is driven downwards by the relatively low returns during rising markets. If this was in fact the case, regulated rates of return would be set below the true cost of capital to equity holders because of relative underperformance of listed water utilities during periods of strong market performance.


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3. Internal consistency of cost of capital parameter estimates 3.1 Introduction In this section we discuss the issue of internal consistency of parameters which underpin the regulated rate of return. Each parameter estimate should make economic sense when considered in the context of all other parameter estimates. This issue becomes particularly important in the context of the value of imputation credits, the estimated equity beta and the debt risk premium. The analysis below is presented in three separate sections, first with respect to the simplest case in which there is no dividend imputation, second with respect to a more complicated case of an imputation system in which equity prices incorporate some positive value for imputation tax credits and in a third section we incorporate discussion of consistency with optimal capital structure choice. 3.2 No imputation IPART uses the CAPM to estimate the required return to equity holders and estimates the required returns to debt holders directly with reference to the yield to maturity on traded corporate debt. The basic premise associated with this discussion is that the return required by equity holders must be at least equal to the return required by debt holders in the same firm. So in the case where there is no dividend imputation, at a minimum the required return to equity holders and the associated equity beta can be estimated as follows.

𝑀𝑖𝑛 𝑟𝑒 = 𝑟𝑓 + 𝑀𝑖𝑛 𝛽𝑒 × �𝑟𝑚 − 𝑟𝑓� = 𝑟𝑓 + 𝐷𝑒𝑏𝑡 𝑝𝑟𝑒𝑚𝑖𝑢𝑚

𝑀𝑖𝑛 𝛽𝑒 =𝐷𝑒𝑏𝑡 𝑝𝑟𝑒𝑚𝑖𝑢𝑚

𝑟𝑚 − 𝑟𝑓

So for example if the debt premium were estimated at 3.1% and the market risk premium were estimated at 6.0%, the minimum internally consistent estimate of the equity beta would be 0.52. This would be the equity beta assumption which is just sufficient for the cost of equity capital to be at least equal to the cost of debt capital. At this point we should highlight the difference between the yield to maturity on debt and the expected return on debt. The expected return on debt is the probability-weighted average of two scenarios, one in which the firm makes all the promised payments to debt holders and one in which it defaults. For example, suppose the yield to maturity on debt was 7.9%, there is a 2% probability of default, and the expected return to debt holders in the event of default is –50.0% (that is, half of the initial investment is returned and the other half is lost). In this case the expected return to debt holders is 6.742%, computed as 0.98 × 0.079 + 0.02 × –0.50 = 0.07742 – 0.0100 = 6.742%. Furthermore, if the expected return on debt can be characterised by the CAPM, the yield to maturity on debt can thus be characterised by the following equation:

𝑌𝑖𝑒𝑙𝑑 𝑡𝑜 𝑚𝑎𝑡𝑢𝑟𝑖𝑡𝑦 = 𝑟𝑓 + 𝑆𝑦𝑠𝑡𝑒𝑚𝑎𝑡𝑖𝑐 𝑟𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑚 + 𝐷𝑒𝑓𝑎𝑢𝑙𝑡 𝑟𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑚= 𝑟𝑓 + 𝛽𝑑 × �𝑟𝑚 − 𝑟𝑓� + 𝐷𝑒𝑓𝑎𝑢𝑙𝑡 𝑟𝑖𝑠𝑘 𝑝𝑟𝑒𝑚𝑖𝑢𝑚

In the example presented above, the yield to maturity of 7.900% would comprise a risk-free return of 4.800%, a systematic risk premium of 1.942% (and by implication a debt beta of 0.32 if the market risk premium is estimated at 6.000%) and a default risk premium of 1.158%. Of course, this disaggregation is just an example because for an individual bond one would need specific estimates of the probability of default and the loss given default in order to perform the disaggregation. One example where this disaggregation has been performed for a large sample of bonds


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is the paper by Elton, Gruber, Agrawal and Mann (2001) who rely upon estimates of recovery rates (one minus the proportion of the initial investment that is lost in the event of default) from Altman and Kishore (1998). The estimated recovery rate for BBB-rated debt was 49.42%. Recovery rates vary systematically with credit ratings, with an upper bound of 68.34% for AAA rated debt and a lower bound of 37.54% for B rated debt (Table 3, p.258). From 1987 to 1996 the average spread between the yield to maturity on 10-year BBB rated debt and US Treasuries was 1.337% for the financial sector and 1.180% for the industrial sector. The authors then estimate that 0.409% of this return is compensation for bearing the risk of default, which leaves 0.928% as compensation for other factors. In the Elton et al. (2001) study, part of this spread is attributed to state and local taxes and part to systematic risk. In an Australian setting, local and state taxes would not contribute to the spread. While Elton et al. attribute 0.4% from around 1.2 – 1.3% of the debt spread to default risk, there is no evidence of which allocation might be appropriate in the case where BBB rated debt spreads are substantially higher at 3.1%. In summary, there is a distinction between the yield to maturity and the expected return on debt, and disaggregating the yield into an expected return and a default premium is a substantial task. However, for some purposes it is the yield itself that is the appropriate reference point, which makes the disaggregation and the estimation of default premiums unnecessary. On such case is the estimation of WACC, in which it is standard practice to use the yield on debt rather than an estimate of the expected return. In a capital budgeting context, for example, the proponent would have to establish that the proposed new project would generate sufficient cash flows to cover all of the promised payments to debt holders, and that there would be sufficient residual cash flows to enable equity holders to earn their required return. That is, the WACC is estimated on a going concern basis as an estimate of the returns that will be required by investors in order to continue funding the firm as a going concern. A similar rationale underpins the use of the yield when estimating the regulated rate of return, rather than the expected return on debt, and why the regulated return to equity holders should be at least equal to this amount. If the regulated return to equity holders was set at less than the yield to maturity on debt, then in the most likely business case (that is, the firm continuing to operate as a solvent going concern), the return on equity would be lower than the return on debt in the same firm, despite equity holders bearing more risk. In the alternative case in which business conditions deteriorate to the point where there is a default and debt holders take control of the assets, equity holders as the residual claimant will certainly earn a return less than the debt holders (in fact, a return of minus 100% in the absence of a capital restructure). That is, the return on equity would be lower than the return in both scenarios, even though the equity holders bear more risk. The regulated rate of return is the regulator’s best estimate of the return which capital providers require to entice them to provide finance for the asset, contingent upon its risk. It would be a highly unusual corporate investment decision for an investor to accept a return on equity that is lower than that same investor would obtain on investment grade debt in the same firm in the most likely business case. 3.3 Imputation The problem becomes more complex with the introduction of imputation tax credits. According to the WACC equation used by IPART, the figure for gamma allocates the cost of equity capital into a proportion derived from dividends and capital gains, and a proportion from the assumed value for imputation credits. An individual investor doesn't actually earn the cost of equity capital – Australian resident investors earn a return above this level because they realise full value from imputation credits, while non-resident investors only earn the proportion of return from dividends and capital gains. The proportion of required returns to equity holders associated with dividends and capital gains is given by


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the following expression, which takes on a value of 0.85 under a corporate tax rate of 30% and a figure for gamma of 0.40.

𝐸𝑞𝑢𝑖𝑡𝑦 ℎ𝑜𝑙𝑑𝑒𝑟𝑠′𝑟𝑒𝑡𝑢𝑟𝑛 𝑓𝑟𝑜𝑚 𝑑𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑠 𝑎𝑛𝑑 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑔𝑎𝑖𝑛𝑠 =1 − 𝜏

1 − 𝜏 × (1 − 𝛾)

=1 − 0.30

1 − 0.30 × (1 − 0.40) =0.700.82

= 0.85

Under an imputation system, our view of internal consistency differs from an alternative view put to us by IPART. As our advice pre-dates the conclusions reached by the Tribunal, we do not have specific information about what those conclusions are and the rationale behind them. Hence, when we refer to the “IPART view” below we are simply referring to an alternative characterisation of internal consistency which pre-dates our report, which is not necessarily the conclusions to be reached by the Tribunal. For the purposes of our analysis we do not need to agree on the appropriate view of internal consistency. We simply present our analysis under both alternative views. IPART’s view is that it has adopted a version of the CAPM for which parameter inputs consider only resident investors, commonly referred to as a domestic CAPM. The risk-free rate of interest is estimated with reference to Australian government bonds, the market risk premium is estimated with respect to Australian equity market conditions and informed by historical returns on that market, and the firm’s beta estimate is meant to capture an association between the firm’s stock returns and domestic market returns. Hence, the IPART view of internal consistency on this issue would be that the cost of equity capital without the imputation adjustment mentioned above should be at least equal to the cost of debt capital. In the example, presented above the minimum internally-consistent equity beta would be 0.52. In our view, the internally-consistent set of parameters are those for which the equity holders’ required return from dividends and capital gains is at least equal to the debt holders’ required return. Our rationale is that the risk-free asset, corporate bonds and equities are priced according to the trading activity of resident and non-resident investors. While the cost of capital parameters are estimated with reference to Australian domestic securities, all the parameter estimates in the regulated rate of return reflect the trading activity of resident and non-resident investors, which is the very reason why the value of distributed imputation credits is estimated at levels significantly below one. Were the return from dividends and capital gains set at a level below the debt holders’ return, the implication would be that non-resident investors would no longer be equity holders because they could instead purchase debt at higher rates of return in the same firm. This rationale is independent of the equation used to estimate the cost of equity capital and does not depend upon whether a resident or non-resident investor is the marginal price-setting investor. It merely relies upon the observation that non-resident investors participate in both the equity and debt markets and that a set of assumptions which offers those investors lower returns on equity versus debt would be inconsistent with that observation. In the example presented above, if the minimum required return from dividends and capital gains is set to the yield to maturity on debt, then the return from dividends and capital gains is 7.90%. According to the expression above, this return is 85% of the cost of equity capital, implying a minimum cost of equity capital equal of 9.25%.7 In turn this implies a minimum equity beta of 0.74.8

7 Min re = 0.0790 ÷ 0.8537 = 0.0925. 8 Min βe = (Min re – rf) ÷ (rm – rf) = (0.0925 – 0.0480) ÷ 0.0600 = 0.0445 ÷ 0.0600 = 0.74.


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3.4 Capital structure choice Adding another level of complexity to the analysis is the issue of internal consistency with respect to the firm’s decision to take on leverage. The firm will incorporate debt into its capital structure when this decision is expected to minimise its weighted average cost of capital. By taking on debt the firm receives tax benefits from the tax deductibility of interest expense and increases the proportion of funds obtained at lower rates of return. This is offset by an increase in the equity holders’ required return due to an increase in their systematic risk and the reduction in assumed tax benefits of imputation. We would not expect to see the firm take on leverage if this actually increased its WACC. In this section we demonstrate that the observed cost of debt capital places boundaries on the estimates for the value of imputation credits and the market risk premium which allow this principle to hold. If the assumed value for imputation credits is too high, or the assumed market risk premium is too low, the WACC will be higher for the levered firm compared to the all-equity financed firm. In other words if debt is trading at high yields to maturity, we would only observe high leverage if the value of imputation credits is relatively low and/or the market risk premium is also high. The analysis proceeds below. Given an estimate of the equity beta we can derive the asset beta which is consistent with this input and assumed leverage. The asset beta is an estimate of the systematic risk exposure of equity holders in an unlevered firm. The equation adopted by IPART which relates asset and equity betas is presented below.9

𝛽𝑒 = 𝛽𝑎 + (𝛽𝑎 − 𝛽𝑑) ×𝐷𝐸

× �1 −𝑟𝑑

1 + 𝑟𝑑× 𝜏 × (1 − 𝛾)�

Suppose that the equity beta were set equal to one. According to the equation presented above and adopting the additional assumption that the debt beta is equal to 0.20, the implied asset beta would be 0.52. This is an estimate of the systematic risk exposure the equity holders would be exposed to if the firm was unlevered. The required return to equity holders in the levered firm would be 10.80% and the required return to equity holders in the unlevered firm would be 7.94%.10 We can then compare WACC estimates for this firm under the assumptions of zero leverage and 60% leverage. At zero leverage, the after-tax nominal WACC would be 6.77%, computed as follows:

𝑊𝐴𝐶𝐶𝑛𝑜𝑚𝑖𝑛𝑎𝑙,𝑢𝑛𝑙𝑒𝑣𝑒𝑟𝑒𝑑 = 𝑟𝑒 × �1 − 𝜏

1 − 𝜏 × (1 − 𝛾)� ×𝐸𝑉

+ 𝑟𝑑 × (1 − 𝜏) ×𝐷𝑉

= 0.0794 × �1 − 0.30

1 − 0.30 × (1 − 0.40)� × 1.00

= 0.0794 × 0.8537 = 6.77%

At 60% leverage, the after-tax nominal WACC would be 7.01%, computed as follows:

𝑊𝐴𝐶𝐶𝑛𝑜𝑚𝑖𝑛𝑎𝑙,𝑙𝑒𝑣𝑒𝑟𝑒𝑑 = 𝑟𝑒 × �1 − 𝜏

1 − 𝜏 × (1 − 𝛾)� ×𝐸𝑉

+ 𝑟𝑑 × (1 − 𝜏) ×𝐷𝑉

9 As discussed in our OLS regression analysis, when the same equation is used to unlever and relever beta estimates, the equation used does not generally make a material difference to the outcome. However, when the asset beta is separately interpreted the equation selected can make more difference to the analysis. For this reason we adopt the IPART unlevering equation. 10 These figures are computed as 0.0480 + 1.0000 × 0.0600 = 10.80% and 0.0480 + 0.5234 × 0.0600 = 7.94%.


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= 0.1080 × �1 − 0.30

1 − 0.30 × (1 − 0.40)� × 0.40 + 0.0790 + (1 − 0.30) × 0.60

= 0.1080 × 0.8537 × 0.40 + 0.0790 × 0.7000 × 0.60 = 0.0369 + 0.0332 = 7.01%

The implication of this result is that the parameter inputs are inconsistent with the firm’s capital structure choice. If a firm were faced with an asset beta of 0.52, it could choose to take on leverage which brings tax benefits of debt finance. However, these benefits are more than offset by the increase in systematic risk to the equity holders, a relatively high debt premium and the reduction in the tax benefits of imputation. In this example, the firm would face the same WACC at zero and 60% leverage if (a) gamma were estimated at 0.21 instead of 0.40, implying nominal post-tax WACC of 7.28%; (b) the market risk premium were set at 8.20%, implying nominal post-WACC of 7.76%; (c) the debt premium were 2.55%, implying nominal post-tax WACC of 6.77%; or (d) the risk-free rate were set at 7.30%, implying post-tax nominal WACC of 8.91%. Given that the risk-free rate is the parameter estimate which can be most readily observed, especially in comparison to the market risk premium and the value of imputation credits, we are not saying that this parameter estimate should be adjusted. The resolution of this inconsistency relates to estimates for the value of imputation credits, the market risk premium and the debt premium/leverage combination. The key point is that, when the cost of debt is high, there are combinations of market risk premium and gamma estimates which are implausible. The firm will only take on debt at these high costs if the cost of equity capital is also high, or the value of imputation credits is relatively low. With respect to our report we have been asked to provide advice with respect to estimates of equity beta and leverage and were not engaged to provide advice with respect to the value of imputation credits or the market risk premium. However, the leverage assumption will directly map on to an estimate for the debt premium, which should be consistent with other parameter estimates. In isolation, we later reach a conclusion that 70% leverage is a reasonable assumption for SDP, jointly with a BBB credit rating. It is a low risk business, discussed further in Section 4, but there were only two out of 16 comparable listed firms which had higher average leverage over our sample period and the maximum average value was 65%. To adopt a figure substantially above 65% leverage would mean that we are confident that SDP can support more much debt than the most highly levered firm in our comparable firm set. The issue for consistency is that a 70% leverage assumption will flow through to a debt premium of 3.10%. This would not make economic sense in the context of a market risk premium of 6.00% and an estimated value for gamma of 0.40. So to make a recommendation on leverage must necessarily involve consideration of alternative values for imputation credits or the market risk premium. The specific implications of this analysis are: If IPART assumes an equity beta or 0.7, a market risk premium of 6% and leverage of 70%/debt

premium of 3.1%, the maximum internally-consistent value for gamma is 0.20. We note that an assumption of this level is not all that far below the recent Australian Competition Tribunal in which gamma was set equal to 0.25. In this scenario the following additional parameter estimates are also internally consistent:

o At a market risk premium estimate of 6.5%, the maximum internally-consistent value for gamma is 0.25.


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o At a market risk premium estimate of 7.0%, the maximum internally-consistent value for gamma is 0.29.

If IPART assumes an equity beta of 0.7, a market risk premium of 6% and gamma of 0.40, the maximum internally-consistent debt premium is 2.53%. In this case the leverage assumption for SDP should be set at the level which allows it to achieve a credit rating consistent with the premium of 2.53%. If a BBB rating maps on to a debt premium of 3.1%, SDP would need to achieve a higher credit rating than BBB.11

3.5 Conclusion with respect to internal consistency In the absence of dividend imputation, or if the value of imputation credits is set to zero, a minimum equity beta of 0.52 would be consistent with a debt premium of 3.10% and market risk premium of 6.00%. If imputation credits were valued at 0.40, our view is that the minimum equity beta would need to increase to 0.74 to ensure that returns from dividends and capital gains were at least equal to returns to debt holders. IPART’s view is that there only needs to be internal consistency between the cost of capital and debt holder returns and under this view the minimum equity beta estimate remains 0.52, regardless of the assumed value for imputation credits. Finally, there is a relationship between the leverage/debt premium assumption and the assumed value for imputation credits and market risk premium. At a value for gamma of 0.40 and market risk premium of 6.00%, the assumption of 70% leverage/3.1% debt premium is inconsistent with capital structure theory. The inconsistency is resolved with either a lower assumption for gamma, a higher market risk premium, or a lower leverage/debt premium assumption. There is reason to believe that the equity risk premium will move in the same direction as the debt risk premium, and a lower value for gamma would move IPART closer to the figure of 0.25 determined by the Australian Competition Tribunal with respect to electricity networks.

11 In subsequent analysis we assume a premium of 2.33% for A rated debt, based upon the differential spreads for short-term A and BBB rated debt reported by the RBA. If IPART adopts a higher credit rating assumption that BBB it will need to estimate the debt premium for this higher rating in a manner consistent with its BBB spread estimate.


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4. Risks associated with the Sydney Desalination Plant 4.1 Introduction In this section we discuss risks specific to the SDP and the implications for the equity beta estimate and leverage assumption. In conducting this analysis we first considered conceptually the risks associated with SDP with a specific focus on whether these risks could be considered systematic or non-systematic in nature. The risks identified by SDP appear to be non-systematic, as their probability of occurrence, or their consequences, appear to be independent of economic conditions. Hence, while these risks should be considered in setting regulated prices – namely in estimating leverage, providing a working capital allowance, or by ensuring that expected cash flows compensate for expected returns (in a statistical average sense) – they should not be considered in setting the estimated equity beta. We than discuss the component of systematic risk not presented by SDP, namely the systematic risk associated with changing risk premiums. As investor risk premiums fluctuate, asset prices change in the opposite direction, and SDP is not immune from this risk. Second we performed a financial analysis of SDP’s earnings, cash flow and balance sheet over the five-year regulatory period, deriving input estimates from the financial information presented in SDP’s submission. Our objectives were (a) to determine whether the financial ratios of SDP, at leverage expectations of 60% or 70%, were consistent with an investment-grade credit rating; and (b) the sensitivity of earnings and value to fluctuations in variable costs, fixed costs and discount rates. 4.2 Conceptual issues 4.2.1 Impact on beta estimates In its submission, SDP identified a number of risks which were used in support of an equity beta estimate of 0.9. Risks include changes in drinking water specifications resulting in changes to the cost of chemicals;12 changes in the quality of seawater;13 plant defects or deficiencies occurring after ten years;14 in the event that IPART does not allow a two-tiered fixed cost structure, variation in fixed costs associated with shutdown and restart;15 variation in costs associated with changes in law;16 variation in costs associated with insurance;17 loss of revenue associated with performance or non-performance of operations and maintenance services in excess of any amounts recovered from insurance;18 and loss of revenue and increased costs associated with force majeure events in excess of any amounts recovered from insurance.19 This list is not exhaustive and SDP is not necessarily exposed to the full economic impact of these events, given that substantial exposure is take on by its contractor and the insurers of SDP and its contractor. The rationale for this argument was that there would be no allowance in cash flow estimates to compensate SDP for bearing these risks, leaving compensation for risk exposure to be accounted for in the regulated rate of return. The principal involved here is that regulated charges should be set such that expected cash flows are sufficient for capital providers to earn their cost of capital. The term expected cash flows has its statistical meaning, namely the probability-weighted average of potential

12 Clause 11.20, page 23, of the Operation and maintenance contract. 13 Clause 17.3, page 46, of the Operations and maintenance contract. 14 Clause 18.5, page 49, of the Operations and maintenance contract. 15 Clause 19.35, page 55, of the Operations and maintenance contract. 16 Clause 31.6, page 74, of the Operations and maintenance contract. Cost recovery extends to amounts exceeding $100,000 in real terms in a given year. 17 Clause 33.9, page 80, of the Operations and maintenance contract. 18 Clause 35.1.2 (b), page 90, and clause 35.3, page 91, of the Operations and maintenance contract. 19 Clauses 36.8 and 36.92, page 93, of the Operations and maintenance contract.


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cash flows. So if a risk exposure has a higher chance of causing cash flows to fall below, rather than above expectations, then regulated charges should reflect these asymmetric consequences of risk. However, if in fact there are asymmetric consequences of risks, the appropriate treatment of these risks is to set a series of charges which reflect these risks, and not to have them reflected in the regulated rate of return. None of the cash flow risks identified could be considered to be systematic in nature, in that they would be more likely to eventuate, or have greater consequences, during economic downturns. Even if SDP’s volumes were to fluctuate with economic conditions, the proposed pricing structure in which variable charges are set to expected variable costs removes this volume-related risk. Hence, the discussion of risks presented by SDP has not impacted upon our estimates of equity beta. Variation in costs from expectations due to company-specific events should be accounted for in setting regulated charges such that expected cash flows are sufficient for investors to earn the cost of capital. Variations in costs associated the economic conditions would contribute positively to the beta estimate if revenue also fluctuated in the same direction with economic conditions. However, there is no evidence before us which suggests that revenues will be associated with economic events. This does not mean that equity investors in SDP will have zero systematic risk exposure. Regardless of the cash flow risks associated with economic conditions, asset values fluctuate with changes in risk premiums. Investors in assets with relatively low systematic risk of cash flows still have systematic risk exposure associated with changes in risk premiums. SDP investors are not immune to this risk. An important academic paper in this regard is the analysis by Campbell and Mei (1993) who decompose equity portfolio beta estimates into contributions from systematic risk of information about cash flows and systematic risk of information about future risk premiums. Two important conclusions are (a) that fluctuations in risk premiums make a substantial contribution to systematic risk; and (b) that there was no association between risks associated with cash flows and risks associated with changing risk premiums. The first conclusion is supported by the evidence of Vuolteenaho (2002) who estimated that approximately 50 per cent of the variance in firm-level stock returns, relative to the risk-free rate, can be attributed to news about expected returns.20 Even for the case of Australian government debt, in which for all practical purposes the promised cash flows are assumed to be certain, estimated betas are positive. Davis (2005) estimated betas of Australian government bonds using monthly returns from December 1979 to February 2004. Over the full sample period the beta estimates were 0.08 for debt with two years to maturity, 0.18 for debt with five years to maturity and 0.25 for debt with ten years to maturity.21 This provides an explanation for why beta estimates for regulated utilities are substantially positive, even when operational cash flows are relatively stable. It also underpins the need for equity beta estimates of be consistent with other parameter estimates, even when there is no obvious reason why operational cash flows will vary with economic conditions. It would be unreasonable to assume that equity holders, as the residual claimant, would require returns below those earned by debt holders in the most likely business case.

20 With respect to the second conclusion reached by Campbell and Mei (1993), Vuolteenaho (2002) finds that news about cash flows and required returns is positively correlated, and that this correlation monotonically declines as firm size increases. This means that, on average for small stocks, news suggesting an increase in cash flows will be positively correlated with news suggesting an increase in expected returns. The impact on stock prices will be in the opposite direction for these two news sources. The stock price will rise in anticipation of higher cash flows, but this rise will be offset by an increase in the expected returns used to discount those cash flows to present value. 21 This does not imply that the minimum internally consistent beta estimate should be estimated with reference to the beta estimates for government bonds. Equity holders are subject to more risk than debt holders in the same firm, so whichever risks have been incorporated into the market price of that firm’s corporate bonds (or in a regulatory setting a benchmark corporate bond estimate for that firm) are the minimum risks that the equity holder is exposed to.


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4.2.2 Impact on leverage Firms which face risks which have symmetric consequences, regardless of whether they are systematic or non-systematic in nature, will consider these risks when making their capital structure choice. Our financial analysis places these risks in the perspective of accounting ratios and sensitivity analysis. At a conceptual level, we note that SDP has submitted an assumed leverage estimate of 60% so considers its risks to be consistent with this capital structure choice and this leverage assumption is adopted by IPART in other determinations. There is reason to think that SDP’s business risk exposure is less pervasive than the risks faced by other water utilities, so at a conceptual level there is reason to think that leverage above 60% could sustain an investment-grade credit rating. In the section below we evaluate the 70% leverage case. As the financial analysis shows, at this leverage assumption financial ratios are stretched to levels which, for a typical water utility, would put an investment-grade credit rating at risk. However, accounting for the specific risk transfer implicit in SDP’s contract structure and pricing proposal, this higher gearing level is likely to be consistent with a BBB credit rating. Of course, this assumes that the structure of fixed and variable charges allowed by the regulator is consistent with SDP’s submission. At a conceptual level, it is difficult to justify evaluating leverage above 70%. Instances of gearing exceeding this level are not typically observed for investment-grade firms, regardless of perceived business risk, and the highest average leverage for listed water utilities in our comparable firm set is 65%. It is also difficult to reconcile debt premiums of 3.10%, which indicates low investor appetite for debt, with a willingness to lend at above a gearing level of 70%. 4.3 Financial analysis 4.3.1 Financial analysis assuming initial leverage equal to the WACC assumption To place these risks in perspective we analysed the sensitivity of SDP’s earnings and valuation to changes in fixed costs, variable costs and discount rates. We derived a set of input assumptions from financial information submitted by SDP, then estimated regulated charges incorporating the parameter estimates discussed in this paper, namely leverage of 60% and 70%, equity beta of 0.7 and 0.8, risk-free rate of 4.8%, debt premium of 3.1%, gamma of 0.4 and corporate tax rate of 30%.22 In performing this analysis we evaluated the financial position of SDP independent of ownership, meaning that we have assumed that SDP will be required to pay tax. The financial statements which appear in SDP’s submission are based upon the assumption that the tax which would normally be paid by a private sector company is available to reduce debt. In turn, this means that over time as borrowings rapidly decline, the financial risk position presented by those statements is reduced. If we were to replicate this assumption we would under-estimate SDP’s financial risk exposure under private ownership, and therefore over-estimate the leverage which SDP could sustain. We also performed the analysis in this section under the assumption that SDP takes on debt as a proportion of debt plus net assets equal to the leverage assumed in the WACC, that is, either 60 or 70%. According to SDP’s financial projections, debt relative to debt plus net assets at 30 June 2012 is estimated at 91%, which declines to 73% over the following five years.23 The debt estimate is $1,732.3

22 Our financial statement analysis is designed to approximate the financial statements which would result from alternative input assumptions and will not necessarily comply with accounting standards. They also assume that tax losses can be offset against taxable income from another source, rather than having to be carried forward. 23 Borrowings are estimated at $1,732.3 million at 30 June 2012 and $1,396.0 million at 30 June 2017. Net assets are estimated at $170.5 million at 30 June 2012 and $504.5 million at 30 June 2017.


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million at 30 June 2012. In a subsequent section we briefly consider the implications on SDP’s financial position. On a levels basis, we can compare the estimated financial ratios with those published by Standard & Poor’s (S&P) for alternative credit ratings. S&P publishes financial ratios which are consistent with firms assigned different credit ratings, according to an evaluation of business risk and financial risk. Those which appeared in the S&P (2008a) Corporate Ratings Criteria appear in the table below. It is not necessarily the case that a firm with these financial ratios and business risk assessment will be assigned a particular credit rating. These ratios are merely an indication of the ratios of particular firms which have been rated. Table 11. Business and financial risk profile matrix of Standard & Poor’s Financial risk Ratio Minimal Modest Intermediate Aggressive Highly leveraged FFO/Debt (%) >60 45-60 30-45 15-30 <15 Debt/EBITDA <1.4 1.4-2.0 2.0-3.0 3.0-4.5 >4.5 Debt/Capital (%) <25 25-35 35-45 45-55 >55 Business risk: Excellent AAA AA A BBB BB Strong AA A A- BBB- BB- Satisfactory A BBB+ BBB BB+ B+ Weak BBB BBB- BB+ BB- B Vulnerable BB B+ B+ B B- As defined by Standard & Poor’s: Funds from operations (FFO) = operating profits from continuing operations, after tax, plus depreciation and amortisation, plus deferred income tax, plus other major recurring noncash items. (Note, the term “operating profits…after tax” refers to net profit after tax in our context; that is, interest expense is deducted as part of the computation.) Debt = Total short- and long-term borrowings, plus adjustments made by S&P and subtracting surplus cash. (In our analysis, no cash is considered surplus and we do not need to address off-balance sheet items). EBITDA = Operating profits before interest income, interest expense, income taxes, depreciation, amortisation and asset impairment. Capital = Debt, plus nonrecurrent deferred taxes, plus equity. (In our analysis, we do not make adjustments for deferred taxes.) We also have S&P information which specifically relates to utilities generally (S&P, 2008a, Business and financial risks in the investor-owned utilities industry) and water utilities (S&P, 1999, Water and wastewater utilities, projects and concessions). In the report relating to water, S&P mentions a number of risks, some of which have been raised in the SDP submission. These risks include regulatory risks, environmental risks and volume-related risks (which in the SDP case would only be relevant if IPART did not allow the fixed cost variation proposed by SDP for periods of supply versus non-supply). In assigning actual credit ratings, S&P considers the ownership of the utility and, all else being equal, would assign a higher credit rating to a government-owned entity compared to an entity in private ownership. However, for our purposes the regulated rate of return should not reflect this implicit government guarantee. Ultimately, S&P provides some guidance as to the financial ratios required to sustain investment-grade credit ratings (our emphasis added):


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Water projects typically display fairly standard operating cost profiles and predictable revenue streams. For this reason, financial profiles tend to display high levels of leverage (debt versus equity) and low coverage ratios. Nonetheless, operating margins should be sufficient to provide a cushion against an unexpected drop in demand or hike in operating or capital costs. The sizing of the margin should be a function of the level of protection offered by the revenue and legal structures, as well as the nature of the BOT. Wastewater plants are inherently more risky, operationally, than water plants, hence, a higher margin would be required. For the better-rated projects, minimum annual debt service coverage ratio should not fall below a range of between 1.2 times (x) to 1.5x over the life of the rated debt. Some projects can withstand lower coverage ratios depending on project structure and robustness to sensitivities. Again, for investment-grade projects, the proportion of equity should generally be at a minimum 15%–25%, depending on other project features. This equity may be made up in part by deeply subordinated debt, usually provided by the sponsors and should not be amortized in substantial preference to senior rated debt.

The table below presents minimum and maximum values of base case financial ratios, under alternative assumptions for leverage and equity beta, over the five-year period ending 30 June 2017. They represent the annual outcomes across years in the base case, not the range of outcomes which could occur in cases where revenues or costs are higher or lower than projected. We also present the revenue range in comparison to the range which appears in the SDP submission, to provide context about the economic magnitude of the alternative assumptions. Note that other assumptions relating to the risk-free rate, debt margin and gamma also affect the difference between the revenue figures below and SDP’s proposed revenue stream. We also reiterate that these financial ratios are only applicable to the case where SDP is initially financed at the leverage ratios of either 60 or 70%, and not with the higher leverage which actually is currently projected. Table 12. Financial ratios under alternative leverage and equity beta assumptions Beta Lev

(%) Revenue

(real 10–11 $m) Revenue

(% of SDP)2 FFO/Debt

(%) Debt/EBITDA EBIT/Interest Free cash

flow1/Interest 0.8 60 236 – 243 90 – 92 5.7 – 6.5 6.4 – 6.9 1.4 – 1.5 1.7 – 1.9 0.7 60 231 – 238 88 – 90 5.3 – 6.1 6.7 – 7.1 1.4 – 1.4 1.6 – 1.8 0.8 70 230 – 237 87 – 90 3.7 – 4.4 7.8 – 8.4 1.2 – 1.2 1.4 – 1.6 0.7 70 226 – 233 86 – 88 3.5 – 4.1 8.0 – 8.6 1.1 – 1.2 1.3 – 1.5

1 In this table, free cash flow is the cash available after accounting for capital expenditure and changes in working capital which is available to make interest payments. It does not include tax payments. 2 The range for revenue under SDP’s proposal is from $278 million in the year to June 2013 to $303 million in the year to June 2017. In real 2010-11 terms at an inflation rate of 2.6% this is a range of $259 to 267 million. Suppose we were to consider the benchmark financial ratios presented in Table 11 (FFO/Debt, Debt/EBITDA and Debt/Capital) and assume that SDP falls within the “excellent” business risk profile of S&P. All the financial metrics computed above would be consistent with a highly leveraged profile and would be consistent with a credit rating of BB. However, the comments of S&P in relation to water utilities suggest that the typical water utility can sustain an even more aggressive financial risk position and still maintain an investment-grade credit rating. In particular, S&P notes (for the typical water utility) that the minimum debt service coverage should not fall below a range of 1.2 – 1.5 over the life of the rated debt. We can consider debt service coverage in a couple of ways. First, we could consider EBIT/Interest which provides an indication of the cash available to make repayments to debt holders under the assumptions that (a) cash equal to depreciation is required to maintain the assets; (b) there is no cash required for expansion via capital expenditure to increases in working capital; and (c) principal debt repayments are not required, or in other words the firm will be able to refinance its entire debt at


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maturity. In the case of SDP capital expenditure and increases in working capital are only around one-sixth of depreciation over the five-year period, so there is more cash available for interest payments. Hence, EBIT/Interest can be considered a lower bound measure of debt service coverage. Second, we could consider all pre-tax cash flows available to make interest payments, which accounts for capital expenditure and changes in working capital, but still relies upon the assumption that new debt finance will be readily-available at the end of the five-year period. Given that this computation provides the firm with the benefit of all available cash, but refinancing risk is ignored, free cash flow/interest can be considered an upper bound measure of debt service coverage. If the potential volatility of EBIT was the same as that of a typical water utility this base case analysis implies that leverage of 60% would be appropriate. The range of EBIT/Interest is 1.4 – 1.5, contingent upon the beta assumption, and the range of free cash flow/interest is 1.6 – 1.9. As proxies for debt service coverage, these would be consistent with investment-grade credit ratings for water utilities. At higher gearing levels, however, the coverage ratios only just approximate the range referenced by S&P, with the lower bound for EBIT/Interest falling to 1.1. For the SDP case, however, the risk transfer inherent in its contracts and the proposed relationship between charges and costs (provided this is allowed by IPART) suggests that the higher gearing level of 70% could sustain an investment-grade credit rating. In particular, S&P states that “some projects can withstand lower coverage ratios depending upon project structure and robustness to sensitivities.” We estimated the sensitivity of earnings and value to variation in fixed costs, variable costs and discount rates. This sensitivity analysis incorporates an assumption that at the end of the five-year regulatory period the value of the assets equals the regulated asset base plus working capital. In the case where we assume an equity beta of 0.8 and leverage of 70%, our sensitivity analysis suggests that: For a 10% change in fixed costs there is an approximate 0.5% change in asset value and an

approximate 3.0% change in average earnings before interest and tax (“EBIT”) measured in 2010 – 11 dollars.

For a 10% change in variable costs there is an approximate 0.8% change in asset value and an approximate 4.5% change in EBIT.

For a 1% change in the discount rate there is an approximate 4.1% change in asset value but no change in EBIT. (This valuation sensitivity is largely a function of the regulatory period. For revenue streams locked in for a longer period of time, valuation will me more sensitive to fluctuations in the discount rate.)

The risks associated with uncertainty over fixed and variable costs impact on the firm’s capital structure choice. This could be reflected either in the leverage assumption and/or working capital allowance. According to historical data from Standard & Poor’s (S&P), 1.98% of BBB rated debt experience at least one default incident over a five-year time horizon, with the default rate increasing to 4.8% over 10 years and 7.1% over 15 years. Given the potential variation in its cash flows, and if a BBB rating is the desired outcome, then SDP should take on leverage to the point where there is a 1.98% chance of default over the regulatory period. At 70% gearing and assuming an equity beta of 0.8, we estimate that a 19.3% increase in operating costs would result in EBIT falling below interest expense in at least one year, that is, the firm incurs a pre-tax loss; and a 50.1% increase in operating costs would result in free cash flows falling below interest expense in at least one year. (At the lower equity beta assumption of 0.7 the corresponding figures are


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14.8% and 44.2%. Corresponding figures continue to appear in brackets, below.) This means that an operating cost increase within the range of 19 – 50% (or 15 – 44%) would result in a default. At the lower end of this range, a default is unlikely because the firm’s cash flows exceed its EBIT so there is non profit-related cash flows available to make interest payments. But there would also likely be a default if operating costs rose by less than 50%, because the firm’s lack of profitability would make debt re-financing difficult. We do not have a reliable estimate of just how volatile SDP’s costs are likely to be. However, this risk exposure is broadly consistent with the 1.98% default rate reported by S&P. Consider the following two cases: First, consider the case where we define a default event to be the point at which EBIT is insufficient

to cover interest, which is estimated to occur if costs are 19.3% (or 14.8%) above the base case. Also suppose that variation in costs is normally distributed with a standard deviation of 7.3% (or 5.6%). This means that a cost increase of 19.3% is a 2.7 standard deviation event. Or in other words the probability of this occurring in given year is around 0.40%. This also means that the probability of there being at least one year in five in which EBIT falls below interest is 1.98%, computed as 1 – (1 – 0.40%)5 = 1.98%.

Second, consider the case where default does not occur until all cash flows available to make interest payments are exhausted and refinancing risk is ignored. This is the case in which costs are 50.1% (or 44.2) above expectations. In this instance, for there to be a 1.98% chance of a default event over the five-year period (the 2.7 standard deviation event) the standard deviation of cost would have to be 18.9% (16.6%).

This analysis is only an approximation. But what it implies is that, at 70% leverage, the risk of default would approximate that of BBB rated debt if the variation in costs from the base case was typically around 7 – 19% (6 – 17%). Another way to think about this risk is that if expected operating costs were $100, and there was a one-in-three chance that operating costs could increase to somewhere within the range of $107 – 119 ($106 – 117), then the 70% leverage assumption is consistent with a BBB credit rating. In aggregate it is reasonable to conclude that SDP could sustain a BBB credit rating at 70% leverage, rather than the 60% leverage assumption typically applied to regulated water utilities. However, we note that the highest average leverage estimated for listed water utilities is 65% and the mean estimate is 43%. To adopt a leverage position any higher than this would rely upon an assumption that the contractual position of SDP provides it with lower business risk than all of the listed firms examined. We cannot directly compare the business risks of each individual firm to SDP, but the range of outcomes we observe in the listed equity market should be at least somewhat informative of the sustainable gearing for SDP. 4.3.2 An alternative scenario – 60% leverage and an A– credit rating An alternative to the 70% leverage/BBB credit rating assumption is the assumption that leverage is held constant at 60% but that the firm could sustain an higher credit rating at this lower leverage. We have not been provided with an estimate of the debt premium appropriate for higher credit rating, so for the purposes of analysis we have adopted an estimate of 2.33%, which is 0.77% lower than the estimated premium of 3.10% for BBB rated debt. The figure of 0.77% is the difference in the A and BBB rated debt spreads over 1 to 5-year government debt, estimated by the Reserve Bank of Australia at the end of July 2011. This analysis should be treated with caution because, as the computed revenue stream is contingent upon the leverage and debt premium inputs, the risk analysis does not markedly change. This is an


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artefact of the setting of regulated prices. In a competitive market the revenue stream would be determined by factors entirely independent of the firm’s capital structure choice. It should also be noted that ratings which just touch investment grade are, for lack of a better term, the default option of ratings agencies. At 1 January 2009, the median corporate credit rating issued by S&P for non-financial firms was BB+. Furthermore, 25.7% of firms were rated within the range of BBB– to BBB+, 19.3% of ratings exceeded this level and 55.0% of ratings were below this range (Standard & Poor’s, 2009). The distribution of corporate credit ratings at 1 January 2009 and the historical data from inception are presented in the table below. It shows just 19.3% of firms were rated at A– or better and 11.9% of firms were rated at A or better. Table 13. S&P ratings distribution for non-financial firms

Ratings as at 1 January 2009 All initial ratings Rating N % Cumulative % N % Cumulative %

AAA 15 0.4 0.4 143 1.4 1.4 AA+ 13 0.3 0.7 68 0.7 2.1 AA 57 1.5 2.2 278 2.8 4.9 AA- 62 1.6 3.9 202 2.0 6.9 A+ 91 2.4 6.2 295 2.9 9.8 A 215 5.6 11.9 673 6.7 16.5 A- 283 7.4 19.3 432 4.3 20.8 BBB+ 301 7.9 27.2 451 4.5 25.3 BBB 370 9.7 36.9 706 7.0 32.4 BBB- 310 8.1 45.0 603 6.0 38.4 BB+ 250 6.6 51.6 387 3.9 42.2 BB 240 6.3 57.9 660 6.6 48.8 BB- 355 9.3 67.2 1195 11.9 60.7 B+ 389 10.2 77.4 1889 18.8 79.5 B 436 11.4 88.8 1253 12.5 92.0 B- 259 6.8 95.6 520 5.2 97.2 CCC/C 167 4.4 100.0 279 2.8 100.0

Investment grade 1717 45.0 45.0 3851 38.4 38.4 Speculative grade 2096 55.0 100.0 6183 61.6 100.0

All rated 3813 100.0 100.0 10034 100.0 100.0 Adopting the lower debt premium assumption of 2.33% we first reproduce the financial ratios presented in Table 12. The financial ratios are not markedly different because the lower revenue stream has been offset by lower interest costs. Also recall that we have not adopted SDP’s actual debt position in this analysis, but rather the debt position consistent with the leverage assumption in the regulated rate of return. Table 14. Financial ratios under alternative leverage and equity beta assumptions Beta Lev

(%) Revenue

(real 10–11 $m) Revenue

(% of SDP)2 FFO/Debt

(%) Debt/EBITDA EBIT/Interest Free cash

flow1/Interest 0.8 60 228 – 235 87 – 89 5.7 – 6.5 6.8 – 7.3 1.5 – 1.5 1.8 – 2.0 0.7 60 223 – 230 85 – 87 5.3 – 6.1 7.0 – 7.5 1.4 – 1.5 1.7 – 1.9 0.8 70 220 – 227 83 – 86 3.7 – 4.4 8.6 – 9.0 1.2 – 1.2 1.4 – 1.6 0.7 70 216 – 224 82 – 85 3.5 – 4.1 8.6 – 8.2 1.1 – 1.2 1.4 – 1.6

1 In this table, free cash flow is the cash available after accounting for capital expenditure and changes in working capital which is available to make interest payments. It does not include tax payments. 2 The range for revenue under SDP’s proposal is from $278 million in the year to June 2013 to $303 million in the year to June 2017. In real 2010-11 terms at an inflation rate of 2.6% this is a range of $259 to 267 million. The more relevant analysis relates to the probability of default. The historical default rate for A rated debt is considerably lower than for BBB rated debt, at just 0.68% over a five-year period, increasing to 1.98% over 10 years and 2.95% over 15 years.


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At 60% gearing, an equity beta of 0.8 and the lower debt premium of 2.33%, an estimated 42.2% increase in operating costs would result in EBIT falling below interest expense in at least one year (36.5% for beta = 0.7); and a 76.9% increase in operating costs would result in free cash flows falling below interest expense in at least one year (69.0% for beta = 0.8). As with the above discussion, this means than an operating cost increase within the range of 42 – 79% (or 37 – 69% for beta = 0.7) would result in a default. The issue then becomes, what potential variation in operating costs is consistent with the rates of default observed for A rated debt over five years? These estimates are presented below: For the case where we define a default event to be the point at which EBIT is insufficient to cover

interest, default is estimated to occur if costs are 42.2% above base case (or 36.5%). A five-year default probability of 0.68% for A rated debt is consistent with a 3.0 standard deviation event in any one year. This implies that an A credit rating could be sustained at 60% leverage if the standard deviation in costs was below 14.1% (or 12.2%).

For the case where default does not occur until all cash flows available to make interest payments are exhausted, default is estimated to occur if costs are 76.9% above base case (or 69.0%). In this case the implied standard deviation of operating costs, for a 3.0 standard deviation event would be 25.7% (or 23.0%).

As highlighted above this analysis is only an approximation. But what it implies is that, at 60% leverage, the risk of default would approximate that of A rated debt if the variation in costs from the base case was typically around 14 – 26% (12 – 23%). Ultimately, if we conclude that a BBB rating for SDP could be sustained at 70% leverage, it is logical to conclude that at lower leverage of 60% a better credit rating could be sustained. Given the relatively low percentage of corporate debt which is actually awarded ratings of A– or above, if the lower leverage/higher credit rating assumption were adopted we would recommend the use of an A– rating for setting the regulated rate of return, rather than simply adopting an A rating. Put another way, assuming an A credit rating implies that the financial risk of SDP at 60% gearing is in the lowest 12% of all rated non-financial firms, compared to the lowest 19% at an A– rating. This conclusion is subject to the caveat that internal consistency amongst parameter estimates still holds – the return available to equity holders in the form of dividends and capital gains should still be at least equal to the yield to maturity available to debt holders; and the WACC for the levered firm should be less than or equal to the cost of equity in the unlevered firm. 4.3.3 Impact on SDP’s financial position In the above analysis, we analysed the financial position of SDP under the assumption that the firm adopted leverage equal to that assumed in the regulated rate of return. At equity beta estimates of 0.7 or 0.8 we concluded that SDP could likely sustain a credit rating of BBB at 70% leverage; or an A– credit rating at 60% leverage. However, the financial statements in SDP’s submission report a debt figure at 30 June 2012 of $1,723.3 million, which is 91% of the sum of debt plus net assets, which stands at $1,893.8 million. Under the revenue stream proposed by SDP, it remains profitable over five years in its base case projections. But as the analysis presented in the previous section showed, under the alternative assumptions adopted in our analysis, the projected revenue stream is only 82 – 92% of that submitted by SDP. The lower revenue stream means that, at the high level of debt in the SDP financial statements, it would no longer remain profitable. The reduced operational earnings associated with the relatively lower regulated rate of return would be insufficient to cover interest payments on the larger borrowings. This is partly due to high depreciation charges.


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Consideration of this issue is beyond the scope of our analysis, which relates to estimation of parameters which form part of the regulated rate of return. We simply note that SDP can only earn enough profits to cover interest on the relatively larger borrowings assumed in its financial statements at the higher regulated revenue stream presented in its submission.


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5. Conclusion24 In reaching a conclusion on equity beta we consider the limited ability of OLS beta estimates to predict realised returns. If OLS beta estimates, when incorporated into the CAPM, provide reliable estimates of equity holders’ required returns then on average we should observe an association between expected returns and realised returns. We have previously analysed this issue and question the precision which can be attached to these estimates (Gray, Hall, Klease and McCrystal, 2009). The current analysis incorporates improvements to the OLS beta estimates – accounting for asymmetric exposure to market returns and the elimination of outliers – but the returns forecasting ability of these improvements has not yet been analysed. Hence, we would not place full weight on OLS estimates. The beta estimate should be considered in light of IPART’s most recent determinations with respect to water utilities, namely the reviews of bulk water pricing for State Water in 2010 and metropolitan pricing for Sydney Water in 2008. In both instances IPART determined than an equity beta of 0.9 was appropriate. It also provided an indication of the imprecision of this estimate by reporting a range of 0.8 – 1.0. There is reason to believe that SDP is exposed to lower systematic risk than the other water businesses regulated by IPART, given the nature of its contracts and the pricing proposal by SDP. SDP’s cash flows are expected to be insensitive to volume, given that the variable charge has been set equal to expected variable costs. SDP’s cash flows are also expected to be insensitive to shut downs, given that the proposal that the fixed charge differ depending upon whether the plant supplies water or is shut down. Given these considerations an equity beta estimate of 0.80 is appropriate if IPART were to agree with our view of internal consistency in WACC parameters, and 0.70 otherwise. These estimates lie marginally above the estimate of 0.65 based purely upon the returns analysis of listed firms and below the 0.90 estimate used by IPART in its bulk and metropolitan water pricing decisions. The figure of 0.80 lies above the minimum beta estimate of 0.74 which is consistent with our view of internal consistency, and the figure of 0.70 lies above the minimum beta estimate of 0.52 which is consistent with the IPART view.25 With respect to the issue of appropriate leverage, the project-specific characteristics of SDP suggest that it could sustain leverage of 70% and still maintain a BBB credit rating, compared to the typical assumption in regulatory determinations of 60% leverage. At 60% leverage in the base case SDP has debt service coverage ratios which marginally exceed those required for a typical water utility to sustain an investment-grade credit rating from S&P. At 70% leverage these coverage ratios fall to the point where a typical water utility would be on the margin of sustaining a BBB rating. But SDP can be considered to have relatively lower business risk than the typical water utility, which would facilitate lower coverage ratios. Ultimately default risks are determined by the variation in operating costs. While we do not have specific estimates of this potential variation for SDP, if the standard deviation of operating costs was within an approximate range of 6 – 19% then the risk of default would approximate that of BBB rated debt. We would not recommend leverage above 70% because this assumption already exceeds the maximum average leverage of the 16 listed firms in our comparable firm set. The mean leverage observed for listed water utilities in our sample was 43% and 15 out of 16 firms analysed had average historical leverage within a range of 32 to 65%, with just two firms having average leverage which exceeded 60%. So while it is reasonable to conclude that SDP has lower business risk than the typical water utility, we

24 The conclusions are largely a replication of specific paragraphs from the introduction. This will be re-worded following feedback from IPART. 25 As mentioned previously this refers to our understanding of the IPART view at the time of writing.


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would recommend against adopting a leverage assumption substantially above the high leverage firms we have observed. An alternative to the 70% leverage/BBB debt assumption is to hold leverage at 60% but adopt a higher credit rating due to SDP’s relatively lower risk. In this event we would recommend an A– credit rating rather than an A credit rating on the basis that there are, in fact, relatively few credit ratings assigned by S&P. At 1 January 2009 only 12% of non-financial corporations were assigned ratings of A or above by S&P, while 19% were assigned ratings of A– or above. The median credit rating assigned by S&P is BB+. Given the imprecision with which credit ratings are assigned, the lower A– rating appears appropriate to distinguish SDP from a regulated water utility with the same 60% gearing assumption. In conclusion, our recommended equity beta assumption is 0.8 and our recommended leverage assumption is 70%, conditional upon assuming a BBB credit rating. If IPART were to adopt a view of internal consistency of WACC parameter estimates which differs from ours, a beta assumption of 0.7 would be appropriate. Finally, an alternative to the 70% leverage/BBB rating assumption is a 60% leverage/A– rating assumption.


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6. References Altman, Edward I. and Vellore M. Kishore, 1998. “Defaults and returns on high yield bonds: Analysis

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