The impact of real estate on the mixed-asset portfolio in periods of financial stress

The case for holding real estate in the mixed-asset portfolio is typically made on its stabilising effect as a result of its diversification benefits. However, portfolio diversification often fails when it is most needed, i.e. during periods of financial stress. In these periods, the variability of returns for most asset classes increases thus reducing the stabilising effect of a diversified portfolio. This paper applies the approach of Chow et al (1999) to the US domestic mixed-asset portfolio to establish whether real estate, represented by REITs, is especially useful in times of financial stress. To this end monthly returns data on five assets classes: large cap stocks, small cap stocks, long dated government bonds, cash (T-Bills) and real estate (REITs) are evaluated over the period January 1972 to December 2001. The results indicate that the inclusion of REITs in the mixed-asset portfolio can lead to increases or decreases in returns depending on the asset class replaced and whether the period is one of calm or stress. However, the inclusion of REITs invariably leads to reductions in portfolio risk that are greater than any loss in return, especially in periods of financial stress. In other words, REITs acts as a stabilising force on the mixed-asset portfolio when it is most needed, i.e. in periods of financial stress.

The return due to diversification of real estate to the US mixed-asset portfolio

Booth and Fama (1992) observe that the compound return and so the terminal wealth of a portfolio is greater than the weighted average of the compound returns of the individual investments, a difference referred to as the return due to diversification (RDD). Thus assets that offer high RDD should be particularly attractive investments. This paper test the proposition that US direct real estate is such an asset class using annual data over the period 1951-2001. The results show that adding real estate to an existing mixed-asset portfolio increases the compound return and so the terminal wealth of the fund. However, the results are dependent on the percentage allocation to real estate and the asset class replaced.

When does direct real estate improve portfolio performance?

For over twenty years researchers have been recommending that investors diversify their portfolios by adding direct real estate. Based on the tenets of modern portfolio theory (MPT) investors are told that the primary reason they should include direct real estate is that they will enjoy decreased volatility (risk) through increased diversification. However, the MPT methodology hides where this reduction in risk originates. To over come this deficiency we use a four-quadrant approach to break down the co-movement between direct real estate and equities and bonds into negative and positive periods. Then using data for the last 25-years we show that for about 70% of the time a holding in direct real estate would have hurt portfolio returns, i.e. when the other assets showed positive performance. In other words, for only about 30% of the time would a holding in direct real estate lead to improvements in portfolio returns. However, this increase in performance occurs when the alternative asset showed negative returns. In addition, adding direct real estate always leads to reductions in portfolio risk, especially on the downside. In other words, although adding direct real estate helps the investor to avoid large losses it also reduces the potential for large gains. Thus, if the goal of the investor is offsetting losses, then the results show that direct real estate would have been of some benefit. So in answer to the question when does direct real estate improve portfolio performance the answer is on the downside, i.e. when it is most needed.

Different risk measures: different portfolio compositions?

Traditionally, the measure of risk used in portfolio optimisation models is the variance. However, alternative measures of risk have many theoretical and practical advantages and it is peculiar therefore that they are not used more frequently. This may be because of the difficulty in deciding which measure of risk is best and any attempt to compare different risk measures may be a futile exercise until a common risk measure can be identified. To overcome this, another approach is considered, comparing the portfolio holdings produced by different risk measures, rather than the risk return trade-off. In this way we can see whether the risk measures used produce asset allocations that are essentially the same or very different. The results indicate that the portfolio compositions produced by different risk measures vary quite markedly from measure to measure. These findings have a practical consequence for the investor or fund manager because they suggest that the choice of model depends very much on the individual’s attitude to risk rather than any theoretical and/or practical advantages of one model over another.

The case for REITs in the mixed-asset portfolio in the short and long run

The poor performance of the Stock Market in the US up to the middle of 2003 has meant that REITs are increasingly been seen as an attractive addition to the mixed-asset portfolio. However, there is little evidence to indicate the consistency of the role REITs should play a role in the mixed-asset portfolio over different investment horizons. The results highlight that REITs do play a significant role over both different time horizons and holding periods. The findings show that REITs attractiveness as a diversification asset increase as the holding period increases. In addition, their diversification qualities span the entire efficient frontier, providing return enhancement properties at the lower end, switching to risk reduction qualities at the top end of the frontier.

Real estate in the mixed-asset portfolio: the question of consistency

The recent poor performance of the equity market in the UK has meant that real estate is increasingly been seen as an attractive addition to the mixed-asset portfolio. However, determining whether the good return enjoyed by real estate is a temporary or long-term phenomenon is a question that remains largely unanswered. In other words, there is little or no evidence to indicate whether real estate should play a consistent role in the mixed-asset portfolio over short- and long-term investment horizons. Consistency in this context refers to the ability of an asset to maintain a positive allocation in an efficient portfolio over different holding periods. Such consistency is a desirable trait for any investment, but takes on particular significance when real estate is considered, as the asset class is generally perceived to be a long-term investment due to illiquidity. From an institutional investor’s perspective, it is therefore crucial to determine whether real estate can be reasonably expected to maintain a consistent allocation in the mixed-asset portfolio in both the short and long run and at what percentage. To address the question of consistency the allocation of real estate in the mixed-asset portfolio was calculated over different holding periods varying from 5- to 25-years.

Selecting from amongst non–nested conditional variance models: information criteria and portfolio determination

We consider the finite sample properties of model selection by information criteria in conditionally heteroscedastic models. Recent theoretical results show that certain popular criteria are consistent in that they will select the true model asymptotically with probability 1. To examine the empirical relevance of this property, Monte Carlo simulations are conducted for a set of non–nested data generating processes (DGPs) with the set of candidate models consisting of all types of model used as DGPs. In addition, not only is the best model considered but also those with similar values of the information criterion, called close competitors, thus forming a portfolio of eligible models. To supplement the simulations, the criteria are applied to a set of economic and financial series. In the simulations, the criteria are largely ineffective at identifying the correct model, either as best or a close competitor, the parsimonious GARCH(1, 1) model being preferred for most DGPs. In contrast, asymmetric models are generally selected to represent actual data. This leads to the conjecture that the properties of parameterizations of processes commonly used to model heteroscedastic data are more similar than may be imagined and that more attention needs to be paid to the behaviour of the standardized disturbances of such models, both in simulation exercises and in empirical modelling.