International real estate investment risk analysis.

The late 1980s and early 1990s marked a burgeoning interest in international estate investment among United States institutions. Many investors believed that investment in international real estate could enhance overall performance by increasing returns and reducing portfolio volatility. During the late 1990s, the impetus for investing in international real estate came from the poor performance of American real estate during the 1987 to 1992 period. Investors were concerned about the difficulty of selling under-performing real estate assets during a period of significant over-production and weak demand.

By 2000, the Euro was beginning to have a remarkably beneficial effect on Europe. It integrated the 11 countries' financial systems, decreasing the cost of capital by creating a deeper, more liquid market. Many European Union (EU) countries support EU enlargement to include the current ten accession (2004) candidates-Malta, Hungary, Poland, Cyprus, the Czech Republic, Slovakia, Estonia, Latvia, Lithuania, and Slovenia. Other countries being considered for membership later on in the decade include Switzerland, Norway, Iceland, Bulgaria, Romania, and Turkey.

Current EU countries would prefer that new members be wealthier nations, with Switzerland and Norway as the most popular candidates. This preference is linked to the common perception that admitting poorer countries to the EU could unleash substantial labor migration flows. The precise scale of migration flows from the accession candidates is difficult to predict. Estimates based on the post-war German experience suggest that about 3.5% of the population of the 10 new members (1% of the current EU population) will seek jobs in Western Europe.

Recently, the amount and cost of capital have respectively increased and declined. On average, European companies pay more than half a percentage point less for their capital that if the Euro did not exist. More than $600 billion were raised last year. This means that more Euro-denominated bonds were issued in 2001 than dollar-denominated bonds. A return to growing rents and overall economic health is likely to occur by 2005 in Paris, Milan, and Brussels. Markets like London, Frankfurt, Stockholm, and Madrid will continue to shift sideways during the next three years.

IMPLICATIONS OF THE NEW GLOBAL ECONOMY

The post-industrial age began soon after World War II in the United States and arrived in Western Europe and Japan in the 1960s. Its distinguishing characteristic is a declining emphasis on material goods and a growing interest in quality of life. Quaternary activities steadily expand, resulting in an elaborate division of labor and supplying a whole new set of societal needs. Service activities including banking, retail, telecommunications services, and public sector activities, such as education and medical care, have grown in importance. However, this does not mean that manufacturing is on the way out.

The key characteristic of the New Economy is increasing global interdependence. Rising trade fosters a cycle leading to greater competition and efficiency, more rapid diffusion of innovations, and greater productivity gains. However, the growing dependence of services on a sophisticated infrastructure of hardware and software has increased their cyclical vulnerability.

The New Economy reflects a willingness to undertake massive risky investment in innovative information technology. This, when combined with a decade of improvements in the U.S. financial markets, down-sizing of the Federal government, and efforts by corporations to cut costs and increase efficiency, has had a profound impact on the competitiveness of the American economy.

The Institute for International Economics, a Washington think-tank, expects that between 30% and 40% of global financial assets will end up denominated in Euros (with between 40% and 50% in dollars, and the rest in yen and a few other currencies). This would imply a shift of between $500 billion and $1 trillion into Euros, primarily out of dollars, as investors and central banks reshuffle their portfolios.

CHALLENGE OF INTERNATIONAL REAL ESTATE INVESTMENT

International real estate investment represents considerable decision-making, organization and managerial challenges above and beyond the problems of achieving the desired cash flows at the building level. These challenges are accentuated by the time-distance gap from the United States and the different socio-economic and cultural structures associated with individual national markets.

International real estate investment requires a concentrated scrutiny of the problems and opportunities linked to such decisions. A number of macro issues must be examined to reduce systematic risks for portfolio allocation across particular nations. By extension, international diversification assists, but does not remove, systematic risks.

One of the most popular means of international real estate investment is a country fund. Using country funds, investors can speculate in a single foreign market with minimal costs, construct their own personal international country portfolios using country funds as building blocks, arid diversify into emerging markets that are otherwise inaccessible. Some of the most common variables in the initial winnowing process used to determine the economic desirability of a nation for real estate investment are gross domestic product (GDP), per capita income, and the percentage of GDP devoted to service industries.

CALCULATING AND ANALYZING VALUE AT RISK

Value at Risk (VAR) is the amount of money an institution could make or lose from changes in the price of the underlying assets. VAR reduces a firm's total market risk to a single number. In other words, VAR is a statistical estimate based on historical data. Most firms are more worried about what they can lose if the markets move against them. Consequently VAR has become a measure of potential losses rather than a measure of potential gains. The VAR concept incorporates two central elements of risk: (1) the sensitivity of a portfolio to changes in underlying prices and (2) the volatility of the underlying prices.

The former reflects how well the portfolio is hedged (the better hedged it is, the less sensitive to price changes) and the latter, the likelihood of large price fluctuations. The size of the price change is inextricably linked with the holding period. The longer the holding period, the greater the possibility that price changes will lead to a higher potential loss.

There are three main approaches to calculating value at risk:

1. The correlation method, a deterministic approach also known as the variance/covariance matrix method. According to the correlation method, the change in the value of the position is calculated by combining the sensitivity of each component to price changes in the underling asset, with a variance/covariance matrix of the various components' volatilities and correlation.

2. Historical simulation. This approach calculates the change in the value of a position using the actual historical movement of the underlying asset, but starts from the current value of the asset. It does not require a variance/covariance matrix.

3. The length of the chosen historical period has an impact on the results. If the period is too short, it may not capture the full variety of events and relationships between the various assets and within each asset class. If it is too long, it may be too stale to predict the future. The advantage of this method is that it does not require the user to make any explicit assumptions about the correlation and dynamics of the risk factors, since the simulation follows every historical move.

4. Monte Carlo simulation, a technique for dealing with complex resource allocation problems that cannot be solved by mathematical analysis. This technique involves creating a typical life history of a system that represents the actual problem and its rules of operation. Repeated runs of the simulation, slightly altering the operating rules each time, enable experimentation aimed at discovering methods of improving the performance of the system.

The Monte Carlo simulation method calculates the change in the value of a portfolio using a sample of randomly-generated price scenarios. In this approach, the user makes certain assumptions about market structures, the correlation between risk factors and the volatility of these factors. Unlike the historical simulation method, a Monte Carlo simulation requires the user to rely on his views and experience in evaluating risk.

All three methods include three basic parameters: a holding period, a confidence interval and a historical time horizon, over which the asset prices are observed. One way of evaluating the accuracy of a firm's VAR methodology is to compare the estimated (ex-ante) VAR number produced by its internal model with its actual (ex-post) profit and losses. A VAR number can be calculated for individual positions and for whole portfolios. If a firm has only one position, the VAR number represents the potential loss of that instrument, for a specified time horizon and confidence interval. Once it has two instruments, it will have two VAR numbers. To arrive at one number for both positions, it is necessary to evaluate whether and to what extent the positions offset or reinforce each other when the market moves.