Let us now look into challenges and opportunities associated with estimating the variables in equation (12.1) above, as well as ways and means for improving upon this algorithm. A good proxy for risk-free rate of interest, F, is a long-term bond by the US, UK, or other Western European government. More difficult is to evaluate in advance the house’s risk premium, because it depends on:
● Technical factors, like steady maintenance, and
● The public’s propensity to buy newhouses, a trend evident in the United States
● A possible deterioration in the appeal of the neighbourhood, and similar variables.
By contrast, it is not difficult to have a fairly good estimate on V, based on macro-economics (more on this later) as well as the house’s location and its appeal to future home owners for primary residence or as a second house.
Notice, however, that estimates of ∆Vinclude uncertainties due to social factors and government policies. Also, most evidently, interest rate and taxation.
Moreover, in its present form equation (12.1) by no means covers all important fac- tors affecting house prices. It can be made more sophisticated by including into it:
● Transaction costs associated to real estate (as distinct from taxation)
● Liquidity affecting the market at large, and the housing sector, in particular
● Borrowing criteria and facilities available for mortgage lending
● Changes in land prices which have an evident effect on the price of houses
● Changes in construction costs, and7
● The population’s propensity to change houses every X years, X being a one-digit or low two-digit number.
All of these factors help to explain either lasting differences or spikes between house prices and rents. For instance, the French socialist government’s imple- mentation of the 35 hour week increased construction costs by 20% in the year it became effective. In terms of rotation in ownership, on the French Riviera, British and American owners tend to change their secondary residence every 5 or 6 years, while Greeks and Russians keep their real estate out of the market for 20 or 30 years.
Moreover, as it now stands, equation (12.1) presupposes that homeowners can quickly change rents to accommodate an increase in house prices. Generally
speaking, this is not a valid hypothesis because national regulations existing in some jurisdictions prevent them from doing so.
Neither is it possible to exclude a priori that there is, or will be, a misalignment in house prices. When such a misalignment exists, this is often due to the fact that one or more of the hypotheses entering into equation (12.1) does not hold when tested in the market. Or, alternatively, conditions have changed and one or more of these hypotheses is no longer valid.
In spite of these shortcomings, the factors entering in equation (12.1), and even better an improved version of it incorporating the foregoing references to other variables affecting the P/R ratio, could serve as a predictor. It is appropriate to notice that, so to speak, the accuracy of a model is elastic. As an example, fac- tors other than government regulations to be taken into account, in the relation between house prices and rentals, are:
● Household disposable income, and
● Equity market bulls or bears (see also the discussion in section 6).
To substantiate this statement, Figure 12.3 and Figure 12.4 provide statistics from Japan. The pattern in Figure 12.3 presents the ratio of house prices to rents.
That in Figure 12.4 maps the ratio of house prices to household disposable income. Both of them convey a significant message:
● The more a model reflects what is happening in real life
● The more complex and less controllable this artifact becomes.
So much for the asset pricing approach. For its part, the structural economic modelfor house price valuation can be seen both as a self-standing simulator and as a worthwhile complement to the asset pricing model (which, in my judgment, is the better alternative).
Basically, the structural model involves estimation of supply and demand for the housing market, as well as certain empirical factors. Take the French commercial real estate market as an example. Among professionals, the value of commercial real estate, particularly in an area for shops and retail outlets, is generally com- puted in either of two ways:
● Economic performance measured in revenue and profits
● Established evaluation tariffs, if any, and their variables.
In the case of economic performance, the investor searches for accounting ele- ments and statistics that allow him or her to form an opinion about the real
estate’s likely value, subject to certain ranges. For instance, a restaurant is gen- erally sold at a price between 50 and 200% of its annual turnover; while a mar- keting outlet for foodstuff masters a much lower ratio – between 10 and 25% of annual turnover.
The downside of this approach is that tariffs permitting a more accurate evalua- tion are not always available. Their development requires a diagnostic study which considers location – as well as commercial activities taking place at that location – plus a number of technical, financial and juridical elements associated to the commercial real estate and its use.
The alternative to these practical inputs, when they are not available, is a theoret- ical evaluation. A hypothesis underpinning the structural economic model is that supply of housing is usually determined by the profitability of housing construc- tion. In a free economy, this is a reasonable assumption. The downside is that sup- ply is relatively inert in the short term, quite often leaving demand as the main force driving house prices at a one year, or so, time horizon. Furthermore, housing supply might be relatively slow to adjust to demand, even in the longer term.
1978 1986 2002
1970 1974 1982 1990 1994 1998
AVERAGE BETWEEN 1970 AND 2002 HIGH
LOW
RATIO (JUST NOTE DIFFERENCE)
Figure 12.3 Ratio of house prices to rents in Japan (Source: European Central Bank, based on its own statistics and statistics by Japan Real Estate Economic Research Institute)
As far as the demand side of the equation is concerned, as we have seen in Figure 12.4, a key factor is household disposable income. Prevailing mortgage interest rates also have a major impact on growing or dampening demand (see section 6).
Housing demand is also influenced by demographic trends.
Some experts suggest that a measure of affordabilityshould also be used, beyond the ratio of house prices to household disposable income. Affordability is a fairly complex factor taking into account: disposable income, interest rates, future prospects of employment, and some social variables. When interest-adjusted affordability declines, this might indicate a change in house price dynamics.
As the last few paragraphs have shown, a structural economic model is not sim- pler than that of asset pricing, and it also involves more tentative statements than asset pricing. The downside of using only a structural model lies in the fact that available explanatory variables are often limited, and they might not take all rele- vant information into account. Also, as with all econometric approaches, this one:
● Is based on average behaviour, and
● Averages are typically misleading in the presence of structural changes in the demand or supply of housing.
1985 1989 2001
1981 1983 1987 1991 1993 1995 1997 1999 2003 AVERAGE
BETWEEN 1970 AND 2003 HIGH
LOW
RATIO (JUST NOTE DIFFERENCE)
Figure 12.4 Ratio of house prices to household disposable income in Japan (Source: European Central Bank, based on its own statistics and statistics by Japan Real Estate Economic Research Institute and national accounts)
Precisely because housing values are affected by a plurality of factors, house prices should be assessed by different approaches; cross-checking information obtained from various models, such as asset-based and structural; looking for discrepancies between the two; then targeting improved versions whose results may converge.
Convergence might be obtained by homogenizing common factors, such as bor- rowing costs and increased or decreased fragility of borrowers – as a function of changes in interest rates, employment, and other variables. A good example is what was observed in the early 1990s during the Swedish banking crisis.
The Swedish case is particularly relevant in connection to the commercial real estate market. Though commercial real estate and private housing are often seen as two different markets, a British research finding by a major British bank sug- gests that the two correlate up to 88%. This correlation is most significant to the structural economic model. The Swedish real estate crisis has affected the banks’
expected cash flows from borrowers through:
● A deterioration of their intrinsic repayment capacity, and
● Lower values of real estate collateral, in case the debtors defaulted.
Therefore, in equation (12.1), the evolution of expected future cash flows should also reflect credit risksince it leads to lower asset values. This is another exam- ple of improvement which may help to provide greater accuracy to be obtained from a given model.
Finally, the prevailing accounting standards for financial reporting should also be taken into consideration. The combined effect of value changes would need to be fully reflected in financial statements under fair value accounting (FVA).
By contrast, under historical cost, if specific provisioning behaviour of the bank is disregarded, credit quality deterioration would have no impact until impair- ment – a fact that can mislead investors in their asset allocation decisions.
Notes
1 D.N. Chorafas, Statistical Processes and Reliability Engineering, D. Van Nostrand Co., Princeton, NJ, 1960.
2 D.N. Chorafas, Modelling the Survival of Financial and Industrial Enterprises: Advantages, Challenges, and Problems with the Internal Rating-Based (IRB) Method, Palgrave/Macmillan, London, 2002.
3 D.N. Chorafas, The Management of Equity Investments, Butterworth-Heinemann, London, 2005.
4 ECB, Financial Stability Review, June 2005.
5 T. Helbling and M. Terrones, ‘When Bubbles Burst’, World Economic Outlook,IMF, April 2003, Chapter II. Busts are defined as bottom quartile peak to trough real price decreases. The authors base their results on a sample of 14 countries (for housing prices) or 19 countries (for equity prices), between 1959 and 2002.
6 Merrill Lynch, Global Research Highlights, 17 June 2005.
7 For instance, the implementation of the 35 hour week by the socialist government in France, in the early years of the 21st century, increased construction costs by 20% in one year.
Part 4
Corporate Governance and the Balance Sheet
13
Balance Sheets and Income Statements as Management Tools
1. Introduction
A balance sheet (B/S) is a written representation of assetsand liabilities of an individual, a partnership, a quoted company, or other entity, such as a city. The term B/S is not crisp, its best definition being expressed as a list of balances in assets, liabilities, or net worth accounts. Notice that this definition, by the American Institute of Certified Public Accountants (AICPA), is accurate, but it is not so meaningful in management terms.
A more meaningful statement about the balance sheet is that it shows sources from which funds, presently used to operate the entity, have been obtained; for instance, owner(s) equity and other liabilities. It is also meaningful to state that the B/S documents the types of property, and property rights, on which funds are currently used. These are the assets (see section 2).
Just as important is to bring into perspective that a balance sheet may be made in an honest manner, or be subject to creative accounting by ‘cooking the books’
(see Chapter 11). When it is honestly made, and the representation of its details is correct, the balance sheet portrays the financial condition of the entity to which it belongs.
● The assets and liabilities in a balance sheet, and profit and loss (P&L) income statementshow, at year’s end, the results of the exercise.
● By contrast, as Chapter 9 explained, the budgetis a financial plan, which must be carefully established a priori, documented, and approved to become effective.
The yearly closing of the balance sheet, for financial reporting reasons, is based on accounting conventions – like those advanced by IFRS (see Part One) or US GAAP. This is a different way of saying that accounting and reporting through financial statements is, to a substantial extent, regulated by standards setters and supervised by government authorities.
Well-managed companies are driven by a strong focus on their balance sheet and P&L; also on the B/S of other companies in which they may be investing or to which they extend a line of credit. As an investment advisor pointed out in a recent discussion, ‘We never invest our client’s money in leveraged companies.
We look for strong, free cash flow, low relative debt to asset ratios, high tangible book value, and solid sales growth, among other metrics.’
Top tier companies appreciate that the balance sheet can become a great man- agement tool. Quite often, however, financial information tends to be misrepre- sented or misinterpreted, for a variety of reasons. The most frequent is that management is dishonest, and what is shown in the B/S and P&L has little to do with the facts. Enron, Global Crossing, WorldCom, Adelphia Communications, Parmalat and many other firms are testimony to this.
The profit and loss statement, too, is an important tool of management. The prob- lem is that quite often definitions are blurred, either in textbooks or in the mind of managers. Many entrepreneurs can never grasp the difference between sales and profits; when they say revenuethey mean either one, says Peter Drucker.1 Another reason for misinterpretation of financial information is the limited imagination of analysts. It is almost second nature that when we see a new phe- nomenon we try to fit it into the framework we already have. This might have been acceptable at the beginning of the Industrial Revolution, but today, it is an aberration. Until we have made enough tests and experiments, we don’t know whether there is really a difference between ‘this’ and ‘that’ figure.
Still another reason for misinterpretation is that while background and fore- ground business factors have changed, the B/S may show the same figures over and over again. Sometimes both companies and markets have more than one way of doing things, but:
● They repeat their story over time, and
● The accounting system fails to capture the ongoing change, or does so with considerable latency.
The opposite also may be true. Data reported in financial statements may look different, while it describes aspects of the same thing. There is a larger picture underneath, from which things can be broken into parts, but these parts don’t differ more than the fingers of the same hand.
These are good enough reasons to make one most careful when reading balance sheets. Expert investment advisors suggest that there are other important facets of the research process beyond analysing numbers. To get insight, equity analysts talk to the management of companies they study, and also participate in confer- ence calls.2
● Moreover, they make extensive use of macro information, and
● Examine industry background, trends and prospects.
This qualitative approach to analysis diminishes in noting the importance of having available reliable B/S and P&L. Focusing not on one year but on 10 and 20 years of balance sheet reporting allows identification of the upside and down- side of the company’s prospects. On the other hand, a factual and documented process of prognostication, or detection of events as they develop, requires qual- itative input.