what have we learned from asset sales?

22

© 1999, Elsevier Science Inc., 1040-6190/99/$–see front matter PII S1040-6190(99)00075-5

The Electricity Journal

What Have We Learned from Asset Sales?

The unfortunate answer, so far, is not much. Regression analyses of 33 non-nuclear utility sales provided very little guidance for making a sound estimate for units outside the sample. With sales of generating units occurring at a rapid pace, however, prospects may improve in the future.

Jonathan Falk

n conjunction with restructuring plans, many utilities have

agreed to auction off generating units. One of the salient advan-tages of these auctions is a precise estimate of stranded cost. In other jurisdictions, stranded costs are being determined administra-tively. Intervenors in these admin-istrative cases have argued that the evidence from the asset sales can be used as a check, or even a sub-stitute, for the administrative pro-cess. Economists tend to have some sympathy with this view. It is a principle of economics that, all other things being equal, market data is more reliable than data which does not come from mar-kets. Markets function as price rev-elation mechanisms.

For those of us who would like to replace (or even supplement) administrative determinations of value with observed market prices, unfortunately, those “other things” are rarely equal. Suppose I selected 30 houses which had recently sold throughout the United States and attempted to use the data on those houses to estimate the value of houses on some particular block not in the sample. Several prob-lems instantly present themselves. First, the houses on which I have market data may be very different from one another, making it diffi-cult to assess what we mean by value in the sample. Second, the idiosyncrasies of the particular block may make extending what inferences we can draw from the

Jonathan Falk

is Vice President atNational Economic Research

Associates, New York. In NERA’senergy practice, he has worked on a

variety of issues involving themodeling of investment and industry

structure, and he is the currentdeveloper of the NERA Electric MarketModel, which estimates market clearingprices in previously regulated markets.Mr. Falk also has studied market power

questions in emerging electricitymarkets, has estimated the social

benefits of real-time pricing options forelectricity, and has created a number of

models to value flexibility inutility planning. He has also done

work in such areas astelecommunications, environmental

economics, and labor economics.Mr. Falk received his B.A., M.A.,

and Ph.D. degrees in economics from

Yale University.

I

October 1999


23

market sample even more tenuous. In this instance, it may well be true that there is nothing helpful in the market data.

I have created a database of 33 sales of generating assets. I have used the characteristics of those sales to estimate the value of gen-erating assets. My conclusion so far is negative: the sales observed to date have varied so widely in characteristics and price that observed sales data cannot be use-fully employed to forecast with any reliability the price at which some other asset is likely to sell in a subsequent auction. This does not mean that the auction

method

is in any way inferior to an adminis-trative method for determining stranded costs. It simply means that there are at present no reliable inferences which can be drawn from this process to inform the administrative process. While this situation might change as more and more assets are auctioned, there are reasons to think that this may not be the case.

here is no conceptual difference between an auction method

and an appraisal method. Indeed, the auction method is nothing more than the appraisal method carried out by a different group of people. The advantage of the auc-tion method is that a buyer is “putting his money where his mouth is.” As economists, we gen-erally regard the revealed prefer-ence of markets to be superior to a potentially manipulable estimate carried out by one side or another. But to draw reliable inferences, it is vital to understand the underlying components of value.

The forward-looking value of an asset is the present discounted value of the net cash flows of that asset from the current time until the end of time. This is the only sensible value to place on an asset, which cannot be valued other than by valuing its expected cash flows. To know the cash flows which an asset can be expected to generate,

someone

must estimate:

•

Prices paid for the output of

and maintenance expenditures, excise taxes; and

•

A discount rate to move the estimates of cash flows over time into a net present value.

The “advantage” of observed sales prices is that the hard work of estimating these quantities has been done by someone else. Thus, if I want to know the current value of Microsoft, I don’t have to create an elaborate cash flow analysis: I can simply look at the price of recent trades in Microsoft stock. I can utilize these estimates because the stock market is, for the most part, efficient; by which I mean only that if the current price does not reflect the future cash flows, then one could make money by buying (if undervalued) or selling (if overvalued) the stock and lock-ing in a sure profit.

1

y the same token, however, it would be foolish to value

Microsoft stock simply by looking at the price paid for, say, Intel. While the cash flows which one generates has a similar underlying component with the other (the “microcomputer market”), there are too many differences between them for one to reliably be used to gauge the value of the other. Indeed, there are enough differ-ences such that it is generally unre-liable even to use

changes

in Intel stock prices to predict

changes

in Microsoft prices.

The same is true in electricity. While the generating assets of New England Electric System and the assets of Southern California Edison are similar in many ways, they are different in at least as many ways. Prices are different

The auction method is nothing more than the appraisal method carried out by a different group

of people.

the unit, which will be some com-bination of market prices which prevail in the future and con-tracted prices for the output of the unit. In addition, estimate of these prices will depend on assumptions about future market rules and reg-ulatory regimes;

•

Output of the unit, which will depend on the expected lifetime of the unit and reliability of the unit and/or its fuel supply;

•

Cost of the unit’s output, which will depend on (among other things) fuel prices, the heat rate of the unit, the specific run-ning pattern of the unit, future capital expenditures, operation

B

T

24



between NEPOOL and Southern California. The operating charac-teristics of the two portfolios are different. The fuel mix and fuel availabilities are different. If SCE units and NEES units sold for the same price per kilowatt, it would be by accident.

ven for a fixed set of genera-tors, values can vary widely

over time as we gradually learn more about the future. Look at the stock prices of a set of companies (AES, CalEnergy, Calpine, Power-Gen, and National Power) that derive almost all of their profits from electric generation. The prices of their stocks do not move together, and all of them have exhibited sub-stantial fluctuations over the last two-and-a-half-years. AES sold for $55 per share in April 1998, and for $28 per share in August 1998. National Power was valued at $37 per share in October 1997, and $43

per share in January 1998. Calpine shares, worth only $19 in Novem-ber 1997, are worth over $80 today. The value of a portfolio of generation assets can easily change by a factor of two over the span of several months, and by a factor of four over the span of several years.

Figure 1

displays the market price of these companies monthly from September 1996 to January 1999. Although all are in the same business, it is quite clear that the fortunes of any particular generation portfolio vary widely over time and not in any consis-tent fashion. Not only is the electric generation business quite volatile overall, but the values of specific portfolios are impossible to judge in isolation. Specific factors relating to geog-raphy, regulatory changes, and other idiosyncratic features

can dominate the valuation of generation assets.

All is not necessarily hopeless, however. It may be possible to get reliable estimates of plants which have not been auctioned off by comparing them to plants which have already been auctioned off, if we can express the value of an auctioned unit as a function of the relevant characteristics which affect value. While it is impossible to get all such measures, control-ling for the most important factors may get an “acceptable” measure of value.

2

The standard method for making inferences outside of a sample for results observed inside a sample is, of course, linear regression. Given enough observations and accurate quantitative measures of the underlying characteristics, we can recover an approximation to the valuation function implicitly used by the winning bidders in bidding for generation assets. Once we have such a function, we can take the characteristics of the portfolio of units whose value we are attempting to assess, substi-tute them into the function, and get an estimate of how this port-folio would be valued if it were auctioned.

In practice, however, we are severely limited by two facts. First, we have quite crude data with which to estimate the characteris-tics within the sample. A great deal of relevant data about units is not publicly available. These data were made available to bidders during the due diligence phase of bidding, but as analysts sitting outside the process, we cannot recover it. The

Figure 1: Market Prices of Five Companies That Derive Their Profits From Electric Generation, September 1996 to January 1999.

E

October 1999


25

deals themselves contain a num-ber of side payments (e.g., contin-uation of the current labor force for some number of years or below-market buyback provi-sions) which are either unre-ported or reported in terms so sketchy that quantitative assess-ment is impossible.

Second, we have fewer than 40 sales from which to estimate the valuation function. This sharply limits our flexibility to figure out what the bidders thought even if we had perfect data. Even theoreti-cally, we could not include more than 40 factors to account for 41 observations. In practical terms, we are probably limited to no more than five. Even for these, the spe-cific functional form must remain

terra incognita

, subject to no more than an educated guess.

In my experience, those who attempt to use the auction data to derive values for units which have not been auctioned try to glide over these issues through the use of “average” values. Some of them take more “sophisticated” averages than others, but all of them ignore a central lesson of statistics and econometrics: any inference about an average which ignores the uncertainty with which that result is measured is meaningless.

With statistical analysis we can solve two problems at once. First, we can include all sales on which we have reliable data and adjust for the characteristics which we know to be relevant in determin-ing value, such as the operating costs of the units. Given the lim-ited number of sales, there will be

a limited number of variables we can control, but at least we can approach the question of causa-tion in a systematic fashion. Sec-ond, when we are done, we will have a good sense of the consis-tency with which the auction data speak to us. If the data are consis-tent, we can be reasonably confi-dent that the values produced in previous auctions will carry over

capacity factor of the portfolio, the region in which the plants are located, and the total size of the bundle sold.

Comparing this list with the list of value components listed above, I believe that we have captured a major part (though by no means all) of the components of value. Some of these components are sim-ply unavailable (e.g., estimates of future capital expenditures). Others are proxied for in a very simple way (e.g., North American Electric Reliability Council region dum-mies to account for regional price differences). Variation over time, which my study of the stock price of generation firms demonstrates to be an important factor, is simply ignored, since there is no obvious way to measure its effects.

Various functional forms were tried with similar results. I do not claim that the functional forms I have employed are optimal, but I do not think there are any other models which explain the data so much better as to make the specific functional forms I have used impossible to credit.

These statistical models aggre-gate the information about these sales in the most efficient manner. Thus, rather than simply take an average after ignoring some set of units, this method optimally weights all relevant factors to the extent that they can be controlled for and creates the weights which best reflect the data. More impor-tant, however, these models gener-ate a confidence interval around the predictions which might be made for other units.

Table 1

gives the parameters of

Some ignore the central lesson that any inference about an average which ignores the uncertainty with which that result is measured

is meaningless.

to the valuation of other units. If the data are highly inconsistent, we can draw the conclusion that no reasonable inferences can be made from the data, no matter what they say on average.

Embarking on this approach, I first created a database of 33 non-nuclear utility sales for which I could get data, with one excep-tion.

3

Second, I created a series of statistical models which related the price paid per kilowatt to various factors which must influence the price paid. These driving factors include the fuel cost of the units, the technological composition of the portfolio sold, the historic

26



the estimated models. All six models generate plausible and consistent effects for the factors examined. For example, each addi-tional dollar per megawatt-hour in observed fuel cost yields a decrease in value of around $3.50 per kilowatt. A one percentage point increase in observed capacity factor increases the value of a unit by about $5.50 per kilowatt. The purchase of a minority share of a unit is less valuable than purchase of a controlling interest. The West-ern Systems Coordinating Council and Northeast Power Coordinat-ing Council units have sold, on average, for somewhat lower prices than other units, most of which are in the Pennsylvania-New Jersey-Maryland Intercon-nection. None of the results are particularly surprising. Indeed, had any of these coefficients been

around $500 per kW for some unit and we required our estimate to be right 95 percent of the time (assuming that our model is cor-rect), the most we could say is that the value of the unit lies somewhere between $120 per kW and $780 per kW. I daresay that any knowledgeable observer could have gotten that degree of precision without any study at all.

This standard error of prediction rises when we consider units which are further and further from the mean set of characteristics because any extrapolation has more error at the extremes than around the mean. It is, I think, fair to say that these data are currently useless for estimating the likely sale value of a unit inan auction.

Second, we can look across the

dramatically different, we would have suspected a flaw in the regression procedure, not in the underlying components of value.

Despite the qualitative fit between these models and our expectations, however, it is clear that the data on these sales give very little guidance to making a sound estimate for units outside the sample. First, we can look at the root mean squared error (RMSE), a direct estimate of the precision of the model. For a unit which had values at the mean of the observed sample, the linear form suggests a standard error of around $140 per kW—meaning that an estimated value for some unit outside the sample will have a 95 percent confidence interval which is $550 per kW wide. Thus, if the model predicted a value of

Table 1:

Auction Value Regressions

Regression

1 2 3 4 5 6

Factors controlled for

Size of portfolio purchased 0.013 0.006 0.149

Fuel cost

2

3.341

2

2.869

2

3.909

2

3.904

2

0.553

2

0.423

Fuel variance -0.265

Capacity factor 5.472 5.680 5.402 5.447 0.373 0.474

Capacity factor variance 2.722

WSCC

2

86.979

2

89.871

2

69.267

2

66.518

2

0.433

2

0.410

NPCC

2

48.481

2

54.962

2

49.103

2

50.985 0.052 0.002

Minority stake

2

101.299

2

118.791

2

90.173

2

93.679

2

0.355

2

0.805

Hydro share 418.676 432.307 411.457 413.496 1.811 2.082

Constant 241.113 244.815 249.472 253.543 5.123 5.434

Log/linear Linear Linear Linear Linear Log Log

Adjusted R

2

0.618 0.629 0.590 0.606 0.645 0.632

Root mean squared error

136.420

134.590

141.430

138.580

0.526

0.536

Dependent variable is $/kW or ln($/kW), depending on specification.

October 1999


27

specifications. While they all give broadly similar results, it is easy to construct examples (particu-larly away from the mean charac-teristics of the sample) in which a given unit will have 25 percent differences in predicted price, depending on the model employed. Since there is no way to determine whether any of these models is in fact more accurate than any other, the choice of model in the assessment pro-cess will tend inexorably to be a political choice rather than a scientific one.

Third, ignoring changes over time undoubtedly skews the esti-mates, though it is difficult to say in what direction. The problem is that the values of units do not fol-low any simple trends, and are likely to rise and fall rapidly based on effects which we simply cannot measure.

All of this is not to say that the explanatory variables employed have no power; these regressions explain between 59 percent and 65 percent of the variance in values per kilowatt. The problem is that the variability in the results is so high that explaining 60 percent of it is just not enough to give any confidence in extrapolation.

While there is no method of making reliable extrapolations today, the future situation is not quite so bleak. Sales of generating units are occurring at a fairly rapid pace. As we gain more data, three things will happen:

1. We will be able to more pre-cisely choose between various functional forms.

2. We will be able to include more factors.

3. The more units which are sold, the more likely it is that we can find a subset of units which have already sold whose charac-teristics closely resemble the units we wish to value. At some point, we can get closer to the real estate agent’s method of comparables. Using the example cited above, we would be more confident about the value of a house when compared

value which we would have expected: the relative bids for units reflect fuel costs, capacity factors, and regional differences. But what we

should

have learned is that the specific values obtained are not yet generalizable. To change our meta-phor, we have sold a bunch of used cars of different makes, models, vintages, and maintenance his-tory. We do not yet have enough sales to create a Blue Book for elec-tric generating facilities which data, rather than guesswork, can support.

j

Endnotes:

1.

This “simple” notion of efficiency has many tricky requirements to make it even approximately accurate. For example, if everyone can discover the value of the stock by simply observing the market price, why would anyone bother to invest time and effort in estimating the value? While there are good (if not perfect) answers to this question, they lie well beyond the scope of this paper.

2.

What “acceptable” means in this con-text depends on the use to which the value will be put, and the gains and losses which follow from an “accept-able” estimate which nonetheless turns out to be wrong.

3.

I exclude the Florida Power and Light purchase from Central Maine Power on the grounds that the FPL decision to sue to have the sale overturned undermines the notion that the purchase price repre-sented an informed arm’s-length trans-action. The fact that the suit failed is immaterial to this conclusion. Nuclear sales were excluded because such unavailable items as future capital and maintenance costs are clearly so critical to any sensible estimate of value that adding those sales to the database would simply add noise: variations in their value are much less likely to reflect differences in measured characteristics than in unmeasured ones.

with 30 similar houses sold in the same neighborhood. We could use the value of those houses as an appropriate starting point, and make qualitative adjustments for particularities of the unit in ques-tion. While these qualitative adjustments would still create uncertainty, we might be able to get the major part of value through assessment of comparable sales and avoid aggregate statistical techniques like regression altogether.

So, what have the asset sales taught us? On a qualitative level, we have learned that the sales reflect exactly the components of

While there is no method of making reliable extrapolations today, the future situation is not quite

so bleak.

what have we learned from asset sales?

Documents