Investment in Listed Property Trusts (LPTs) - Case Study Example

Investment in Listed Property Trusts (LPTs) CHAPTER 1 1.1 Introduction 1) identify the primary contribution of your dissertation (eg is it an exploratory study of the relationship between firm specific characteristics and downside risk?) 2) re-write your abstract to state clearly what it is that your dissertation does, and what are the main findings. 3) re-write your introduction to make it clear what is the question that you are asking/addressing in your dissertation, why is it important, what is the context of the question (in terms of both business understanding and academic understanding), what are the contirbution s that you make to the literature, and how do you go about doing that 1.1.1 The Key Issue Investment in Listed Property Trusts (LPTs) or Real Estate Investment Trusts (REITs) has often considered risk as an important factor, which has been a traditional way of thinking for a considerably long time. Numerous studies on risk have also demonstrated that REIT’s financial condition and management structure have serious implications for its risk. Most of these studies have utilised the cross-sectional difference of REIT financial conditions and REIT management structure in estimating the relationship between these variables and risk. A REIT’s risk is measured by the beta coefficient in the Capital Asset Pricing Model (CAPM) in which it is measured in a variance framework1. However, it has been observed that the appropriateness of using CAPM and in particular the use of beta as a measure risk has been debated in recent years. On the other hand, some studies propose downside systematic risk (downside beta) is comparatively better than systematic risk for measuring market-related risk for an asset in line with the conjectural superiorities of downside risk. One important reason that makes the use of downside risk over the traditional beta is that the downside risk does not require an assumption about the distribution of assets returns; instead, it is more consistent with investor’s expected utility function and combines the information presented by variance and skewness engrossed in one measure. However, the contradiction between beta and downside beta raises several important questions. Does the financial condition of a REIT or LPT have implication on risk? Is the management structure in some way responsible for risk? If yes, then how to measure these risks and what would be the most suitable element to capture the variables? What is the exact relationship between firm specific characteristics and downside risk? This study tries to find answers to some of these questions and explores the relationship between firm specific characteristics and downside risk for Australian LPTs. 1.1.2 Why is it important? Illiquidity, high transaction costs and intensive management involvement are some drawbacks of direct real estate investments. On the other hand, indirect real estate investment through securitization has been considered by institutional investors in Australia and the United States (U.S.) as a favourable alternative to solve the liquidity problem in the real estate markets. Real estate securities have attracted a growing share of institutional funds in these markets. These securitized forms of real estate also offer individual investors a chance to invest in primary real estate without having to buy the physical real estate, which is a great advantage to the investors. Investors are required to have only minimal involvement in the day-to-day management of properties. They could also achieve internal and external diversification through holding the securitized real estate assets. This study is important since by analysing downside risk to real estate, the investors can make use of protection which provides a cushion should the underlying asset suffer a loss. 1.1.3 Academic and Business Context Downside risk was first introduced by Roy (1952) primarily based on the safety first rule. It appears to be a secure risk measure compared to variance due to several reasons such as downside risk does not require an assumption about the return distribution of an asset. It is more consistent with the investor’s expected utility function and the combining information provided by variance and skewness into one measure (Nawrocki, 1999, Estrada, 2002). Hogan and Warren (1974) and Bawa and Linderberg (1977) demonstrated that downside risk (lower partial moment) can be generalised into CAPM and they developed a Mean-lower Partial Moment Capital Asset Pricing Model (MLPM-CAPM). The results from Price et al. (1982) and Nantell et al. (1982) depicted that if the return distributions are not normally distributed, downside systematic risk is different from traditional systematic risk, which is a reasonable conclusion. Over the years, a primary concern remains is to examine why the downside systematic risk is more appealing than systematic risk and distinguishable from traditional systematic risk. There is a link between systematic risk and financial determinants and management structures as the investors dislike downside volatility and are concerned about downside risk, which is the likelihood of returns falling below a target return rate. Downside risk being robust and sensible risk measure, both practitioners and academicians have suggested it to be used in portfolio analysis. Studies have also depicted that systematic risk differs in downside risk framework than systematic risk in mean variance framework if the distribution of returns are in lognormal form. This is also the reason why downside risk replaced variance as the risk of underperformance of the benchmark is the real risk for fund manager rather than the volatility of the returns. The size of the portfolio does put an impact on the portfolio’s downside risk. Semivariance is used as the risk measure in the downside risk measure and downside risk is generalised into CAPM model, which becomes D-CAPM Var(Ri) = βi2Var(Rm) + Var(εi ) Where Var(Ri) is the variance of the asset i, Var(εi ) is volatility measure for unsystematic risk.2 Then this equation mentioned above is amended in which total risk is substituted by total downside risk, beta changed into downside beta and variance into semivariance and unsystematic risk is changed into unsystematic downside risk. This relationship is shown below: SVrf (Ri) = βi D2 SVrf (Rm) + SVrf (εi ) Where SVrf (Ri) = Total Downside Risk (Semivariance) SVrf (Rm) = Semivariance of market portfolio SVrf (εi ) = Unsystematic downside risk Introduction of downside risk to real estate in late 1990s by Sivitanides (1998) and Sing and Ong (2000) in their studies revealed that downside risk produces divergence portfolio allocations in comparison with the mean-variance portfolio. This was also confirmed by the findings from Byrne and Lee (2004) for U.K. real estate portfolios, and Peng (2005) for Australian real estate portfolio allocations. While Cheng (2001) argued that, bootstrapped simulated downside risk model provided a more realistic allocation for real estate. At times any two real estate investment trust (REIT) markets, regardless of geography, not only share a similar institutional structure, but also they are likely to be closely driven by common economic factors. Therefore, this study gains insight into the expected REIT market performance in by examining the post return parameters of the Australian market in detail. Downside Risk can be applied to Real Estate as an asset class as according to early research on real estate returns concluded, tentatively that real estate both earned substantial risk-adjusted excess returns and served as a good hedge against inflation2. When analysing the inherent risk and level of variances related with specific real estate investment valuation, appraisal smoothing has been an important factor. According to Patel and Olsen (1984) in their study involving a small sample size of U.S REITS and concluded that short-term financial leverage, business risk and advisor fee of a LPT are directly linked to its systematic risk. Later studies have also depicted that business risk is negatively related to the REIT systematic risk in all the models, where as no similar significant results were found for marketability and agency variables. More recently, Dickes and Delcoure (2004) founded that there is an inverse relationship between REIT systematic risk and short term and variable rate financing where as a positive and significant relationship was found between long-term debt and REIT systematic risk. It was also depicted that there is a positive relationship between total financing leverage and systematic risk and the asset variables (proportion of REIT in equity property) is insignificant in explaining beta. Apart from the above-mentioned variables, Conover et al. (1998) also mentioned the impact of role of size in systematic risk that risk and return differences between small and large foreign real estate were statistically significant. In Australia, it is shown by Tan (2004) that large size LPTs have a higher beta and in the later period from 1993-1997 it is presented that there is a low negative correlation between size and systematic risk. More recent studies have found that inverse relationship between beta and size is attributed to the economies of scale in REITS. Market driven factors such as economies of scale and management structure of LPTs do have an impact on systematic risk as the externally managed REITS have higher systematic risk than self-managed REITS. The effect of management structure is such that internally managed structures outperform externally managed LPTs, as there is an increase in systematic risk for internal management structure. IPD index has been showing capital growth of between 0.27% and 0.46% in June 2007 and then UK REIT market had moved to large discounts to NAV reaching almost 20% in September 2007. In 2008, the market was extensively damaged by realization of global capital shortage, which also led to collapse of big companies. This capital yield was even deteriorating and in December 2008, there was a largest fall in capital values in the entire history of IPD index. This shows that investment in property should be re-examined and the industry will resume growth and will achieve recognition through derivatives in the future. The decision on whether investors should invest in real estate should be is based on understanding of the current market conditions and thus, it will need to be made with utmost care keeping downside risk in consideration. Therefore, it is important to analyse downside risk to real estate so the investors can make use of protection which provides a cushion should the underlying asset suffer a loss. Most investors have learnt by now to employ some form of downside protection to hedge their portfolio positions. The main challenge investors’ face is how much downside protection should be in place given the risk and the potential return. The type and amount of down side protection depends on the investor’s perspective of the market, the sector, and the stock. A continuous monitoring of the trends assists to develop this acumen by any potential investor. According to the Capital Asset Pricing Model (CAPM), a stock’s expected excess return is proportional to its market beta, which is constant across periods of high and low market returns. What we learn from Bawa and Lindenberg (1977) as per their suggestion, a natural extension of the CAPM that takes into account the asymmetric treatment of risk is to specify asymmetric downside and upside betas collectively. We compute downside (upside) betas over periods when the excess market return is below (above) its mean. We indicate that stocks with high downside betas have, in usual circumstances, high unconditional average returns. We also find that stocks with high co-variation conditional on upside movements of the market tend to trade at a discount; however, the premium for downside risk dominates in the cross-section of stock returns. 1.1.4 Likely Contribution to Literature The study is likely to make significant contribution to the existing literatures as it evaluates the risk- and return-characteristics of sector-specific REITs and diversified REITs. The downside risk measures are used to determine the impact of holding these REIT assets on the optimal asset allocation. It would highlight their diversification benefits for the institutional investor. In addition, its attempts to compare diversification gains between specialized trusts and diversified trusts in institutional investors’ portfolios on a risk-adjusted basis would be highly useful for efficient frontiers for a mixed asset portfolio comprising stocks, bonds, sector specific and diversified REITs in a downside-risk asset allocation framework. Further, this study shows how the different firm specific variables are related to downside risk and shows that there is negative relation between size and unsystematic risk, therefore, investors can benefit by diversifying their unsystematic risk through size but would not gain any reduction in systematic downside via this approach. The investors can gain full diversification benefit in unsystematic downside risk by investing in Large Market LPTs. In addition, managerial surveys suggests that downside concepts of risk in terms of failure to perform at a target level are more appropriate to practicing managers than performance variability including the upside and downside outcomes. There is alos the role of organisational slack in determining the risk-return relations in downside risk framework. The more interesting issue is whether downside risk has an impact on improvement in a firm’s own performance. By controlling the prior performance, it is shown that downside risk has a positive relation with financial performance as the downside risk puts manager’s attention on performance-enhancing organisational strategies to avoid downside risk. Firm’s performance and downside risk have a negative relation due to autocorrelation in firm’s returns data and this is the reason why firm with higher average performance experience a greater decline in downside risk than others are. At the same time, financial performance has a negative relation with downside risk as organisational slack provides a cushion and motivation to seek out revenue enhancing changes and strategies to performance shortfalls. Therefore, slack satisfy the positive effect of downside risk on financial performance. Agency theory also presents another domain on organisational downside risk, which explains that shareholders are more interested in downside stock returns variability of a diversified portfolio. Agency problems such as changes in organisational structure, compensation – management systems, monitoring, leverage etc results in manager’s attention to maximising the returns for shareholders rather than reducing downside risk. Employees’ stock ownership concerns managers being exposed to both the upside and downside-stock price movements that could be solved through stock options that provide the prospective to unlimited gains but limiting downside risk. 1.1.5 Modus Operandi The study employs positivist philosophy in approaching the area of investigation. This philosophy takes a closer look at in the dissertation’s methodology. The study is approached with a deductive method (Saunders et al, 2007) that suggests that all theoretical models should be practically tested in order to reach conclusion. The present research aims to analyse the understanding of the economic concept of downside risk. In delivering this effectively, this dissertation seeks to extend the paper written by Andrew Ang, Joseph Chen and Yuhang Xing entitled 'Downside Risk'3. It applies the understanding of downside risk and downside beta in relation to both real estate and equities through consideration of mainly the Australian Real Estate Investment Trusts (REITs) by way of a comparative along with equity market methodology.4 The data for the study were collected from authentic financial statistical databases. To apply downside risk modelling to equity and real estate markets, major stock price indexes was used along with major real estate indexes such as S&P REIT index and Dow Jones REIT Composite index. The data comprise all LPTs listed on the Australian Stock Exchange (ASX) from 1993 to 2005. For reliably assessing a LPT’s economic and financial situation, 3-year intervals are employed in which it is argued that annual accounting information cannot show the real situation of a company (Alexander et al., 2003). Accordingly, the study was sub-divided into six different periods in line with the arguments of time-variation in systematic risk. The strategy of the research was to conduct regression analysis in order to measure the level of downside risk in the equity and real estate market of Australia. Regression analysis will employ linear models such as CAPM to measure securities’ beta and thus assess their downside risk. The ultimate objective is to study the relationship between firm specific characteristics and downside risk with focus. The study is organised in the following sequence. The next chapter delineates the theoretical underpinning of downside risks and risk measuring. It also delves to explore the theoretical advances and current research underway and critically reviews the empirical findings and conclusions made by researchers concerning the models that are most appropriate for estimating downside risk. Subsequently, the research method and the selection of the operational model is discussed, which is followed by the summation of the results, its analysis followed by a discussion. Read More