Chapter Three: Literature Review

In todays economic environment, organizations normally need to optimize all the resources and assets at their disposal, to manage them efficiently in order to perform better. (Varcoe, 2001, p.117). This statement exactly interpret the real estate management and especially for the S-REITs. In this dissertation, the authors research comprise of the property of return related to investor behaviour, different sectors activity and major economic factors. I will present my literature review in this chapter in terms of the academic research of my field.

3.1Financial TheoriesIn well-known finance literature, academics combine return and risk as two main interests. There are only two theories that provide a rigorous foundation for computing the trade-off between risk and return: the Capital Asset Pricing Model (CAPM) and the Asset Pricing Theory (APT).according to Burmeister, Roll and Ross (2003, p.2), Both the single factor model (CAPM) and multifactor model (APT) assist to investors to make financial decisions when investors evaluate the risk-return performance in terms of the systematic and unsystematic risks. A number of journals and articlesscrutinised the pricing of real estates in the risk-return performance and the macro-economic context relating to the literature on real estate return.

Lizieri et al. (2003) examine the REITs underlying return-generating factors applying a principle components analysis approach. Ling and Naranjo (1997) focus their analysis on the economic risk factors and commercial real estate returns utilizing a multifactor asset pricing model according to the CAPM model and the APT model. Additionally, Chaudhry et al. (2004) started with the CAPM theory and decomposing the CAPM theory into systematic and unsystematic risk to investigate the idiosyncratic risk of REITs. Liow et al. (2006) analyse the influence and relations between some major macroeconomic factors and the expected risk premia on property stocks by three step methods: principal component analysis, GARCH and GMM. Kim et al. (2007) study the REITs dynamics between microeconomics variable and financial markets. However, on the contrary, in order to determine the explanatory power on movements in real estate return, they prefer to use the vector auto regression (VAR) model. In this dissertation, the author presents an overview of the REITs literature indicating that the disputing about risk-return assessment method are normally mixed and is not close as the results.

3.1.1Modern Portfolio Theory and Single Factor Model

The basic Markowitz portfolio theory derives the expected return rate of return for a portfolio of assets and measure of expected risk, which is the standard deviation of the expected rate of return. Markowitz showed that the expected rate of return of a portfolio is the weighted average of the expected return for the individual return investment in the portfolio. The standard deviation of portfolio is a function not only of the standard deviations for the individual investment but also of the covariance between the rates of return for all the pair of assets in the portfolio.The Modern Portfolio Theory (MPT) was developed by Harry Markowitz. He assumed that most investors want to be cautious when investing and that they want to take the smallest possible risk in order to obtain the highest possible return, optimizing return to the risk ratio. MPT states that it is not enough just to look at the expected risk and return of one particular stock. By investing in more than one stock, an investor can obtain the benefits of diversification, a reduction in the volatility of the whole portfolio (Markowitz,1959).

The CAPM is built on a set of assumptions: Individual investors Investors evaluate portfolios by the mean and variance of returns over a one period horizon Preferences satisfy non-satiation Investors are risk averse

Trading conditions Assets are infinitely divisible Borrowing and lending can be undertaken at the risk-free rate of return There are no taxes or transaction costs The risk free rate is the same for all Information flows perfectly The set of investors All investors have the same horizon Investors have identical expectationsThe CAPM model assumes that risk is a function of only one factor, which is the relationship between a securitys return and the market return. This relationship is defined by the securities beta. It also assumes investors fully diversified therefore only systematic needs considering. The CAPM model also refer to the efficient market hypothesis that assumes the investors are rationally and act in a predictable way. The CAPM argues that these assumptions imply that the tangency portfolio will be a value-weighted mix of all the assets in the world. The proof is actually an equilibrium argument. It begins with the assertion that all risky assets in the world may be regarded as slices of a global wealth portfolio.The major factor that allowed portfolio theory to develop into capital market theory is the concept of a risk-free asset. Following the development of the Markowitz portfolio mode. Several authors considered the implications of assuming the existence of a risk-free asset, that is, an asset with zero variance. As we will show, such an asset would have zero correlation with all other risky assets and would provide the risk-free rate of return (RFR). It would lie on the vertical axis of a portfolio graph. (TEXT BOOK, P232). The direct implications are:i. All investors face the same efficient set of portfoliosii. All investors choose a location on the efficient frontieriii. The location depends on the degree of risk aversioniv. The chosen portfolio mixes the risk-free assets and portfolio M of risky assetsThis assumption of a risk-free asset allows us to derive a generalized theory of capital asset pricing under conditions of uncertainty from the Markowitz portfolio theory. This achievement is generally attributed by William Sharpe (1964), but Linter (1965) and Mossin (1966) derived similar theories independently. Consequently, we see references to the Sharpe-Lintner-Mossin (SLM) capital asset pricing model.The CAPM model also introduced two fundamental concepts that are the Capital Market Line (CML) and Security Market Line (SML). The Capital Market Line indicates that all optimal investment portfolios should be split between a percentage investment in the risk-free asset and percentage investment in market portfolio M, this line defined by every combination of the risk-free asset and the market portfolio, presenting the superior return you earn for taking each extra risk. (http://www.nasdaq.com/investing/glossary/c/capital-market-line). An investor is only willing to accept higher risk if the return rises proportionally. The optimal portfolio for an investor is the point where the new CML in tangent to the old efficient frontier when only risky securities were graphed. (http://www.researchgate.net/publication/264547651_Capital_market_line_based_on_efficient_frontier_of_portfolio_with_borrowing_and_lending_rate). SML is a linear (straight) line showing the relationship between systematic risk and expected rates of return for individual assets (securities). According to the capital asset pricing model the return above the risk-free rate of return or a risky asset is equal to the risk premium for the market portfolio multiplied by the beta coefficient. (http://www.lse.co.uk/financeglossary.asp?searchTerm=&iArticleID=969&definition=security_market_line). The CML only deals with composition of optimal investment portfolios. But Security Market Line (SML) says that the expected return of any stock or portfolio is related to three factor. i. The risk-free rate in the market rfii. The stocks market risk is measured by beta (), iii. The expected return of the market rMFormula 1: CAPM Model

Where,E(rit) = Expected return of security i at time trft = Risk-free rate of return at time tit = Beta of security at time trmt = Return of the market at time t[rmt - rft] = Market risk premiumThe Formula 1 above is the CAPM model.

Formula 2: Single-index model

Where,Rit = Expected return of security i at time t = Risk-free rate of return at time timt = Market Beta of security i at time tRmt = Return of the market at time teit = Non-systematic risk or idiosyncratic error term of security i at time t

Bordie et al. (2008) explain that the CAPM is a model about expected returns, whereas I practice all anyone can observe directly are ex post or realised return (Bodie et al., 2008, p.308). The author use the CAPM model from a single factor model point of view because the purpose of this research is not to examine the expected returns but the influences of factors on Singapore Real Estate Investment Fund Trust returns. The Formula 2 above presents the index model which can be interpreted as a regression equation through which estimates of the alpha and beta can be obtained by Ordinary Least Squares (OLS). OLS is a statistical technique which attempts to find the function which most closely approximates the data (a best fit). In general terms, it is a method to fitting a model to the observed beta. This model is specified by an equation with free parameters. In technical terms, the Least Squares method is used to fit a straight line through a set of data0points, so that the sum of the squared vertical distances (called residuals) from the actual data-points is minimised. (http://www.strath.ac.uk/aer/materials/4dataanalysisineducationalresearch/unit4/ordinaryleastsquaresmethod/)

Formula 3: Calculation of the Beta () with CAPM model

Where,imt = Market Beta of security iRi = Expected return of security iRm = Return of the market

Beta can be viewed as a standardized measure of systematic risk because it relates this covariance to the variance of the market portfolio. (text book p240). Beta measures the sensitivity of the stocks return to the markets return. If a stock has a high beta, then when the market goes up, the stock goes up even more (and vice versa). The price movements of a low beta stock are less sensitive to variations in the market. As convention, beta on the market is one and stocks are thought of as being more or less risky than the market, according to whether their beta is larger or smaller than one (Elton et al., 2007, p137). Therefore, the beta fluctuates negatively or positively, a beta coefficient of 1 presents that the stock has the same risk as the overall market, and will not earn more extra return than market. A coefficient below 1 suggests the risk and return of the stock will be less than the average (where the average means the overall market). On the other hand, the coefficient higher than 1 suggests the risk of the stock will be more risky and profitable than the overall market risks and return. (http://accountingexplained.com/misc/corporate-finance/beta-coefficient)Throughout our presentation of the CAPM, we noted that the market portfolio included all the risky assets in the economy. Further, in equilibrium, the various assets would be included in the portfolio in proportion to their market value. Therefore, this market portfolio should contain not only U.S. stocks and bonds but also real estate, options, art, stamps, coins, foreign stocks and so on, with weights equal to their relative market value. (text book, p257)Although this concept of a market portfolio of all risky asset is reasonable in theory, its difficult to implement when testing or using CAPM. Most studies have been limited to using a stock or bond series alone due to it is difficult to derive the monthly financial data in a timely fashion for numerous other assets. Most academicians recognize this potential problem but assume that the deficiency is not serious. Several articles by Roll (1977a, 1978, 1980, 1981), however, concluded that, on the contrary, the use of these indexes as a proxy for the market portfolio had very serious implications for tests of the models and especially for using the model when evaluating portfolio performance. Roll referred to this problem as a benchmark error because the practice is to compare the performance of a portfolio manager to the return of an unmanaged portfolio of equal risk that is, the market portfolio adjusted for risk would be the benchmark. Rolls point it that, if the benchmark is mistakenly specified, you cannot measure the performance of a portfolio manager properly. (test book, p257)The CAPM has been one of the most useful and most frequently used financial economic theories ever developed. However, many empirical studies cited also point out some of the deficiencies in the model as an explanation of the link between risk and return. For example, assuming the sample periods are long enough and the trading volume is adequate, tests of the CAPM presented that the beta coefficients for portfolio generally were stable while the beta coefficient for individual securities were not stable. Another challenge to the CAPM was that it is possible to use knowledge of certain firm or security characteristics to develop profitable trading strategies, even after adjusting for investment risk as measured by beta. Banz (1981) showed that portfolio of stocks with low market capitalizations (i.e., small stocks) outperformed large stock portfolios on a risk-adjusted basis, and Basu (1977), who documented that stocks with low price-earnings (P-E) ratios similarly outperformed high P-E stocks. Fama and French (1992) demonstrates that value (i.e., those with high book value-to-market price ratios) tend to produce larger risk-adjusted returns than growth stocks (i.e., those with low book-to-market ratios).(text book p270)

3.1.2Modern Portfolio Theory and Multifactor ModelIn the early 1970s, the academic community searched for an alternative asset pricing theory to the CAPM that was reasonably intuitive, required only limited assumptions, and allowed for multiple dimensions of investment risk.The result was the arbitrage pricing theory (APT), which was developed by Ross (1976, 1977) in the mid -1970s. Unlike the CAPM, it does not depend critically on the notion of an underlying market, which Roll (1977) critique of the CAPM. In Formula 2 single-factor model introduced a manner of breaking up the market or systematic risk due to macroeconomics factors, against the firm-specific risk of idiosyncratic effects (Chaudhry et al., 2004). The single-factor generates the multifactor by the integration of several sources of systematic risk. This model divides the risks into systematic and unsystematic risk where systematic risk is non-diversifiable and unsystematic risk is diversifiable, in the same way as the single model does (Burmeister et al., 2003, p.2). Groeneworld and Fraser (1997) empirically examined the CAPM and APT models on the Australian market and as result indicated APT model outperforms CAPM as written in Burmeister et al. (2003, p. 16) the multifactor model has far greater explanatory power than the CAPM.

Chen, Roll and Ross (1986) was the first study to select macroeconomic variables to estimate U.S. stock returns and apply the APT models. They employed seven macroeconomic variables, namely: term structure, industrial production, risk premium, inflation, market return, consumption and oil prices in the period of Jan 1953-Nov 1984. During the tested period in their research, they found a positive relationship between the macroeconomic variables and the expected stock returns. They note that industrial production, changes in risk premium, twists in the yield curve, measure of unanticipated inflation of changes in expected inflation during periods when these variables are highly volatile, are significant explaining expected returns. Consumption, oil prices and market index are not priced by the financial market has been discovered. They conclude asset prices react sensitively to economic news, especially to unanticipated news.

Read more: http://www.ukessays.com/dissertation/literature-review/literature-review-of-arbitrage-pricing-theory.php#ixzz3iu2skI91

The relationships between the Singapore stock index and chosen macroeconomic variables over a seven-year period from 1988 to 1995 were experimented by Maysami and Koh (2000). It resulted in existence of a positive relationship between stock returns and changes in money supply but negative relationships between stock returns with changes in price levels, short- and long-term interest rates and exchange rates.

Read more: http://www.ukessays.com/dissertation/literature-review/literature-review-of-arbitrage-pricing-theory.php#ixzz3itsOz81ATo examine the interdependence between stock markets and fundamental macroeconomic factors in the five South East Asian countries (Indonesia, Malaysia, Philippines, Singapore, and Thailand) was the main purpose of Wongbangpo and Sharma (2002). Monthly data from 1985 to 1996 is used in this study to represent GNP, the consumer price index, the money supply, the interest rate, and the exchange rate for the five countries. Their results showed that high inflation in Indonesia and Philippines influences the long-run negative relation between stock prices and the money supply, as the money growth in Malaysia, Singapore, and Thailand induces the positive effect for their stock markets. The exchange rate variable is positively related to stock prices in Indonesia, Malaysia, and Philippines, yet negatively related in Singapore and Thailand.

Read more: http://www.ukessays.com/dissertation/literature-review/literature-review-of-arbitrage-pricing-theory.php#ixzz3iu2l8HSiIn contrast to the CAPM model, the APT advocates that the risks not only from the suggested market-systematic risk of the CAPM but from many other systematic risks. APT asserts that an assets expected return depends on a linear combination of a set of factors whose identify must be determined empirically. Examples of such factors might include such macro-economic variables as real economic growth, exchange rate, inflation, interest rates, employment level etc, or such financial variable as dividend yield, capital structure etc. According to Roll and Ross (1980) the few conditions in the use of APT such as random asset return follows a multivariate normal distribution and investors behave rationally in the market (Roll and Ross, 1980, p. 1074-1075). As written by Bodie: The price of this generality is that APT does not guarantee this relationship for all securities at all times (Bordie et al., 2008,p.350). Arbitrage Pricing Theory has three major assumption: Capital market are perfectly competitive Investors always prefer more wealth to less wealth with certainty The stochastic process generating asset returns can be expressed as a linear function of a set of K risk factors (or indexes).The operational form of the APT can be expressed in Formula 4 as follows; Formula 4: Multifactor APT model

Ri = Return on asset i, for i = 1 nij =Sensitivity parameters of asset i to risk factor j, for i = 1 k and for j = 0 nFj = j-th risk factor for j = 1 nei = Non-systematic risk or idiosyncratic error term of asset i

The advantage of this approach is that the investor knows precisely how many and what things need to be estimated to fit the regression equation. However, the major disadvantage of a multifactor model is that it is developed with little theoretical guidance as to the true nature of the risk-return relationship. (test book p280)

Although the APT is considered newer than the CAPM, it has undergone numerous empirical studies. Roll and Ross produced one of the first large-scale empirical tests of the APT. Cho, Elton and Gruber (1984) tested the APT by examining the number of factors in the return-generating process that were priced. Because APT model contends that more factors affect stock returns than are implied by the CAPM, they examined different sets of data. Dhrymes, Friend, and Gultekin (1984) re-examined the methodology used in prior studies and contended that these techniques have several major limitations. Roll and Ross (1984) acknowledged that the number of risk factors differ with 30 stocks versus 240 but contended that the important consideration is whether the resulting estimates are consistent because it is not feasible to consider all of the stocks together. Dhrymes, Friend and Guitekin (1985) repeated the prior tests for larger groups of securites. They found that the unique or total standard deviation for a period was as good at predicting subsequent returns as the factor loadings. These results are not favourable to the empirical relevance of APT because the model depends on group size and the number of observations. Finally Cornnor and Korajczyk (1993) argued that most tests for the number of priced risk factors are valid only for strict factor models in which diversifiable returns are uncorrelated across the set of stocks in the sample. Reinganum (1981) addressed the APTs ability to account for the differences in average returns between small firms and large firms. The small-firm portfolio experienced a positive and statistically significant average excess return, whereas the large-firm portfolio had a statistically significant negative average excess return. The mean difference in excess returns between the small and large firms was about 25 percent a year. Also, the mean excess returns of smallest through largest portfolios were perfectly inversely ordered with firm size. Given the so-called January effect, where returns in January are significantly larger than in any other month. Gultekin and Gultekin (1987) tested the ability of the APT model to adjust for this anomaly. The APT model was estimated separately for each month, and risk premia were always significant in January but rarely priced in other months. It was concluded that the APT model can explain the risk-return relation only in January. Burmeister and McElroy (1988) estimated a linear factor model (LFM), the APT, and a CAPM. They found a significant January effect that was not captured by any of the models. They rejected the CAPM in favour of the APT. Kramer (1994) shows that an empirical form of the APT accounts for the January seasonal effect in average stock returns while the CAPM cannot. (Test book P276-278)

3.2Economics Theories

In respect to volatility, both capital market and real estate market approach are seems to be in the contradiction while both of them lead to a perfect equilibrium (Brown and Matysiak, 2000). According to Marty (2008), the long-term strategy are more secured investment which can avoided, however, the short-term strategy is dependent from the daily variation of market and cannot be avoided. Singapore REITs, underlying the properties on the stock-exchange market, is supposed as long-term oriented strategy of the investors. Investors have to take the economic conjunction along the period into consideration when they concern about the performance of these investments. The author believe that non-anticipated event and fundamental economic aggregates affect asset prices at different levels from financial theories perspective. (Roll and Ross, 1980; Chen et al., 1986; Burmeister et al., 2003; Lizieri et al. 2007). In this section, I will review the specific risks factors of holding a real estate assets portfolio and the main factors that influence the performance of Singapore real estate investment trust.

3.2.1Differentiation between Macro and Microeconomics

A wide variety of empirical factor specifications have been employed in practice. A hallmark of each alternative model that has been developed is that it attempts to identify a set of economic influences that is simultaneously broad enough to capture the major nuances of investment risk but small enough to provide a workable solution to the analyst or investor. (test book p280)

Two general approaches have been employed in this factor identification process. First, risk factors can be macroeconomic in nature; that is, they can attempt to capture variations in the underlying reasons an assets cash flows and investment returns might change over time (e.g., changes in inflation or real GDP growth). Macroeconomics measures the natures and actions of the economy as a whole by businesses or government usually. On the other hand, risk factors can also be identified at a microeconomic level by focusing on relevant characteristics of the securities themselves, such as the size of the firm in question or some of its financial ratios. Microeconomics measure how individuals or institutions make their financial decisions. (test book p280). In order to assist investors, firms, financial institutions to make financial decisions, both macroeconomics and microeconomics aim to forecast aggregation.This table describes the difference between the two economic fields: real estate market and the sources of risks.The specific risk of the overall market is macro variable, while all risks specific to assets are micro variable. Heidra and Van Der Ploeg (2002) entails many concepts relating to the demand for money and aggregate labour market and on the opposite behaviour of individual.

Source: Ducoulombier (2007,p.41)However, the differentiation of is not so easy in terms of the case of Singapore REITs. As Brueggman Fisher (2008, p. 625) state, most of the equity REITs specialise by geographic location, property type, and sometime by both of them. As such, the risk of geography and sector is not clear and can be considered as both micro and macroeconomic factors.Initially, economists divided the economic environment into two distinct academics fields: macro and microeconomics. But nowadays these two fields influence each other and the distinction between macro and micro variable cant be established isolated because they influenced each other as the table 2 above. The asset risks and the overall market risks can interpret the returns of Singapore REITs in respect to the geographic risks and the sector. Singapore REITs returns are expected to change trends as the changing of the economic and business conditions. (Ducoulombier, 2007)The purpose of review macro and microeconomic factors separately is to answer the second hypothesis whether the performance of Singapore REITs are influenced by the economic factors. The author will use the most common economic factors related to real estate market used in the academic research.

3.2.2Macroeconomic Factors and Real Estate MarketBall, Lizieri and MacGregor (2001), among others, found that economic activity is a major driver of demand for real estate. In my study, controls for macroeconomic conditions include GDP growth, inflation and interest rate. Generally, when the Singapore doing well, GDP will grow, and investors will have more confidence to invest in Singapore. Many investors in Asia are keen to invest in real estate, and would like to buy properties in a country where the economy is growing, as this would ensure a healthy stream of demand for the real estate in the country to boost the investment yield. As they purchase more real estate assets, this would push up property prices and consequently result in rising rentals, a key ingredient of net property income. Furthermore, as the economy flourish, more companies would invest in the country, pushing up demand for commercial real estate space, leading to higher rentals as well. The reason for choosing unemployment is closely related, as higher unemployment reflects a bad state of economy, which would like mean lower business confidence and correspondingly lower demand for commercial real estate. Chen, Roll and Ross (1986) test the influence of a set of economic state variables on the US stock market returns by appying the Fama-MacBeth technique, assuming that prices of assets respond sensitively when the economic news are released. They use many factors to perform their analysis, for example, short-term treasury-bill, long-term government bonds, inflation, value weighted equities, equally weighted equities, industrial production, low-grade bonds, consumption and oil prices, and so on. They found that the expected stock return can be explained by most of these variables. Based on the research result from Deutsche Bank Research (2008, p. 23), Gross Domestic Product (GDP) growth trend is the major indicators for the real estate market as well as GPD per capita, population, median age, population growth , legal system, financial market development and average inflation. Furthermore, Ducoulombier (2007) discovered Employment, unexpected inflation and interest rates are the other sources of systematic risk. Generally speaking, almost all of them agree on the use or on a variant of GDP, interest rate, real wage, rate of employment and tax rates when macroeconomists try to study what variables influence the macroeconomics. In addition, Liow et al. (2006, p. 301) gave a series of analysis on the influence of macroeconomics, he found that the most relevant indicators are: inflation, GDP and interest rate. In this dissertation, the author will choose GDP, CPI inflaction, short term trade bill interest rate and long term government bond interest rate as macro-factors to illustrate. GDP is the most important macroeconomics measure which measures the total value of economic activity within a nation. To be more specific, GDP is the sum of the market values or prices, of all finical goods and service produced in an economy during a period of time. (http://www.sparknotes.com/economics/macro/measuring1/section1.html). GDP is the reflection of the growth of the economy, a high GDP indicates that the economic condition is healthy cause to drive the Singapore REITs positively. Government spend money in infrastructure, Investors and institutions in new construction while individuals in owning and renting houses. The difference between Real GDP and nominal GDP is in terms of the inflation whether been factors in. Generally, Real GDP measure the value of the goods and services produced expressed in the prices of some base year which nominal GDP measures the value of the goods and services produced expressed in the current prices. For example, Real GDP take five years timeline into consideration. (http://economics.about.com/cs/macrohelp/a/nominal_vs_real.htm)As one of the main macroeconomic factor, inflation is commonly accepted by academics. Inflation is defined as the rate at which price rise for goods and services. However, when economic calculate the risk relating to the inflation, they preferred to use Consumer Price Index (CPI) as a proxy (Chen et al., 1986; Ling and Naranjo, 1997). The CPI allows appreciating the movements in prices of products on a constant basis as the official instrument for measuring the inflation. Brueggma and Fisher (2008) compared the CPI and the performance of real estate and found that real estate exceed the growth rate of inflation from each category. In addition, from their research, they found that the values of inflation and real estate return are the opposite resulting in the irrelevant correlation. Nevertheless, the highlight that a positive correlation with inflation is desirable because it indicates that the asset is an inflation hedge (2008, p.666).

Interest rate is another important factor of macroeconomics accepting by academics. Investors and financial institutions notably through the interest rate and use the relatively long period to finance to the cost of purchase the real estate in order to make the financial investment decision. The interest rate is the relevant factor in the real estate market. Like the real GDP, the real GDP has a better prospective of the real cost of fund for the borrower because it removes the effects of inflation which is preferred by the investors and financial institutions. The real interest rate when a borrower pays a lender, the percentage which increasing in purchasing power. In reality, Researchers and Academics usually prefer to divide the interest rate factor into short-term and long-term rate when they study the influence of multiple factors on stocks. Three-months treasury bills and ten-years government bonds are commonly use in this purpose (Bodie et al., 2008; Chen et al., 1986).

Chapter Four: MethodologyMy literature review and research methodologyare from secondary sources, the text books, study notes, statistical databases and scientific journal s are the main study source. The methodology chapter outlines the business research strategy, data selection, specifications on the regression model and lastly the dataset.

4.1 Scientific Point of DepartureMany alternatives and orientations can be selected when choose a specific business strategy for dissertation. The reader are informed these assumptions and viewpoints by these specifications that the authors have taken.4.1.1 Business Research StrategyThe business research strategy entails all the methodological choices done by the author. In this section, the blueprint aims at presenting the three different steps followed in order to fulfil our business research problem: the major influential factors on S-REITs performance.

In reference to traditional business research methods, two general methods of reasoning exist and are known as inductive and deductive approaches. The first one starts from specific observations to broader generalizations and theories, whereas the second one starts from hypotheses and theories to achieve the research purpose (Bell and Bryman, 2003, p. 9). Hence, I utilize a deductive approach which requires, as premise, to state the hypotheses related to our research problem. As a reminder from part one (Section 1.2.3.), the two hypotheses that need to be scrutinized are respectively linked to financial, economic and theories and they are synopsized below: Hypothesis 1: Some categories of S-REITs generate superior performance than others. Hypothesis 2: Some economic factors affect the S-REITs performance.

4.1.2Research DesignThe research design specifies the process that will be followed in the data collection.The case study approach and comparative design were the two most appropriate for our paper as they fulfil our objectives. As indicated by its name, the case study is an intensive analysis of a single variable whereas the comparative one comprehends at least two different cases with distinctive sets of observations and are compared. In reference to earlier sections I intend to conduct an intensive case-study analysis by examining one specific country, Singapore, in the real estate market and for one specific class of assets within seven years. I narrowed down my field of research questions to closely determine the circumstances in which our two hypotheses will and will not be validated (Bell and Bryman, 2003, p. 55). This choice of focus is firstly on the major influences that I defined as a classification of real estate, Singapore listed companies, then on economic factors and financial behaviour.

4.1.3Choices of the SourcesOur main sources of information are based on secondary sources. After a comparison on main specialized websites in finance such as Bloomberg, Morningstar, Reuters, Yahoo Finance, Straits Time, Singapore News Paper, SGX.com, DBS Vickers.

4.1.4DataTo foster the quality of our research, the literature review and theoretical framework have been updated in a continuous flow depending on the empirical data and literature that I get. Furthermore, as the structure of S-REITs is new in Singapore (seven years), the research available on the property stock market and S-REITs may not be peer-reviewed or relevant enough. Thus, I prefer reliable sources and decided to mostly use the scientific articles for our literature review. This practical consideration has been initiated as I am fully aware of limitations due to the amount of data available, and also by using a case analysis method combined with a sampling process which delimited our field of research even more.

The choice of the model and the explanatory factors may be seen as restricted. The author is aware of biases that may occur when it comes to interpreting the collected data, notably the performance, due to the short time-period and small sample of 14 S-REITs chosen due to the limited available data. I believe that additional factors should be examined on a wider scale. Furthermore the choice of the multifactor model can be criticized as it is used predominately in academic research rather than in practice. While it is acknowledged that other alternatives exist I feel more familiar and confident with the multifactor model derived from the APT and I employed it by preference.The sources of information were difficult to obtain and required costs that I could not afford. In order to overcome this problem, I used, Datastream database and national statistics as quasi-unique resources in the extractions of the stock prices, classification, index and market trend indicators.

The overall credibility of the paper dealing with the reliability, replication and validity (Bell and Bryman 2003, p. 33) will be developed in our last part.

4.2Data Selection ProcessAs a result of the literature research, the data has been carefully selected. In this section, I explain all the considerations and decisions that have been operated.

The 14 S-REITs were selected in Datastream in accordance with our time frame and geographical considerations. In order to get the most complete and representative set of observations, a weekly period of seven years is examined from 2008 - 2014. The S-REITs market is only scrutinized in Singapore currency. Based on the Datastream classification, these 14 firms are divided into five categories. The S-REITs dataset is composed of 5 areas: Office, Retail, Industrial, Hospitality and Healthcare.

4.3.Specifications of the Regression ModelWhen researchers deal with financial time series, statistics usually appear with their models to help them on financial issues, for example assessing and predicting the performance of assets or portfolios. However before using statistics which are trying to match performance results to the real world researchers have to be aware of the properties of the models and their assumptions. To determine the stakes of S-REITs and to emphasize our practical considerations, I analyse their performance through their respective return and risk. Then, the data used is the adjusted price and the price index. These extracted results are integrated in an Excel spread sheet and Eview software to perform most of the computations and analysis.

4.3.1Determination of Beta from the CAPM Model

The first step in the calculation of the beta consists of computing the returns of S-REITs. The log-return presents better statistical properties than the simple return. For instance, Chen et al. (1986), Campbell and Shiller (1988) actually use the log-return in their research. I computed the log-return by employing the logarithm function on the adjusted prices due to the continuous compounding effect. Formula 5 shows how each log-return is calculated.

Formula 5: Calculation of the Log-Return

Where,Log-return t = Logarithmic return of the asset at time tPt = Price of the asset at time tEven if the net return is commonly utilised in finance, researchers prefer the log-return, additive in time, due to its closer link to the reality and as it measures the continuous compound return. However this distinction is not so important as long as the returns are low (Ruppert, 2006, p. 76). As a second step in the performance evaluation of our S-REITs, I calculate precicley the risk sensitivity with the market through Formula 3 of the betas calculation derived from CAPM model.

Formula 6: Calculation of the Beta () adapted to our case analysis

The risk of each stock and index is calculated through Formula 7 with the variance (VAR) and standard deviation (SD) computed by Excel function; respectively VARA () and STDEVA (). I use the traditional formula named COV () and VARA () functions due to the similar results I get and to the convenience of Excel. The calculation of the beta is reiterated for each S-REITs and is presented on an annual basis in order to catch the variation of sensitivity between the firm and the market each year instead of having it for a seven year period. In addition, the use of the CAPM model implies the acceptation of the related assumptions (Section 2.1.1.).

4.3.2. The Multifactor Model

The single factor model CAPM needs to be extended (Section 2.2.1.). Indeed, I replace the latter by a more comprehensive factor model by applying a multifactor model such as APT, based on the fact that economy-wide factors affect the return of S-REITs. I decompose the analysis in two models to capture better the sensitivity of all factors. The model one integrates our main macro- and micro-factors, and is presented in the Formula 7.

Formula 7: Model 1 derived from APT

Where,Rit = Expected return of the S-REITs i at time t = Interceptbk = Sensitivity variable between our S-REITs return and the factor k = Non-systematic risk or idiosyncratic error termFormula 8: Model 2 derived from APT

Where,Rit = Expected return of the S-REITs i at time t = Interceptbk = Sensitivity variable between our S-REITs return and the factor k = Non-systematic risk or idiosyncratic error termRit represents our dependent variable which is the actual S-REITs returns. The classification granted by Datastream enables us to follow the performance of S-REITs according to its activity in the real estate market. Besides to increase the significance of our results I generated the model 1 and 2 with different variables such as GDP (instead of Real GDP) or interest rates. Nevertheless the significance obtained was lower, thus I have chosen to scrutinize the influences of the variables. Additionally, to capture a maximum of information about the factors that can influence the S-REITs returns I use both systematic and unsystematic explanatory variables. All of these factors are used as possible explanatory factors. Some dummy variables are used in developing the regression model as they are not readily measurable with quantitative values. (Keating and Wilson, 1986, p. 150-151).

4.3.3. Presentation of the Dataset in Descriptive StatisticsThis last section presents the dataset used after our linear regression model one and two derived from APT that have been applied by the author.

Chapter V Empirical FindingsThis chapter presents an overview of FTSE ST Real Estate Investment Trusts Index performance through a benchmark as well as including the outcome of the multifactor model one and two. The descriptive statistical results are generated from Eview software and Excel.5.1 Overview of the performance of FTSE ST Real Estate Investment Trusts Index

FTSE ST REITsSTISSEDow Jones

Mean718.77455263136.2327112546.11882714286.40187

Standard Error2.0408200335.59194573117.5477641369.33248141

Median716.013163.4099122345.113593.37

Mode646.043124.3798832655.6611478.13

Standard Deviation69.23765822189.7145368595.33230552352.200867

Sample Variance4793.85331635991.60548354420.55395532848.918

Kurtosis-0.641801584-0.1272043664.524506563-1.327571124

Skewness-0.007128069-0.5227825012.0326499740.147693922

Range321.05925.53216.348326.58

Minimum569.112614.4499511950.019985.81

Maximum890.163539.9499515166.3518312.39

Sum827309.513609803.8512930582.7716443648.55

Count1151115111511151

The three benchmarks STI (Straits Time Index), SSE compositeindex (Shanghai Securities Composite Index) and Dow Jones Index are shown the brief statistics given by the table above. Total No. of 1151 counts are used. The mean of FTSE ST REITs performance of 718.7745526 is smaller than STI (3136.232711), SSE(2546.118827) and Dow Jones (14286.40187). In the meantime, the standard deviation of the FTSE ST Real Estate Investment Trusts Index is lower than STI, SSE, Dow Jones. In terms of the risk and return, FTSE ST Real Estate Investment Trusts Index doesnt show a better performance than STI, SSE and Dow Jones as shown in table above. The Figure below presents the three benchmarks in comparison with my sample. Figure: Comparison of STI, SSE, Dow Jones. Sample (as of 22 July, 2015)

The performance of FTSE ST REITs remains stable over the past 5 years. The overall trend is positively correlated to the Straits Time Index (STI),and SSE. As we can see the from the gaph, Dow Jones is leading the overall trend and present the best performance over the other stock index in other countries.

Table xx: Correlation matrix of FTSE benchmarkFTSESTISSEDow Jones

FTSE10.8547940.1519940.699655844

STI0.85479410.3811030.711915673

SSE0.1519940.38110310.221535089

Dow Jones0.6996560.7119160.2215351

The correlation matrix table above indicates that FTSE returns are positively correlation to STI (0.854794) and SSE (0.151994) and Down Jones (0.699656). As we can see from the table above, the correlation between FTSE and STI have the higher degree of correlation, this is mainly due to they are in the Singapore stock exchange market, and they varies according to the national economic and investment environment, so the correlation is very strong. Additionally, I also observed that FTSE has a quite stronger correlation with Dow Jones Index (0.699656). This is because the overall stock exchange market follows the trend of the USA markets. HealthcareIndustrialOffice RetailHospitalityFTSE

Beta10.5493678123.5565252422.1921962259.1868390163.13298144-17.85754567

Table 11. Beta per SIIC sector

HealthcareIndustrialOffice RetailHospitalitySTI

Beta1.195504021-11.4341955917.9129496726.34411038.65889857643.06921819

As we know, beta is to measure the stocks risk relating to the overall market. If beta is 1, meaning the level of risk is the same as the overall market. In a bullish market, the stocks price increases. Versa Vice. If beta is greater than 1, this stock has more risk and more volatile than the market. It will move the same trend as the market but will move to greater rate. In a bullish market, the stocks price will go up at a faster speed than the market. If beta is zero, meaning to say that the stock has no relationship with the market at all. If beta is negative, means the moving director of beta will be the opposite to the stock market. (http://efinancialresourcecenter.com/stocks-negative-beta/)Applying the Formula 7 from the first model introduced in this dissertation, the table 11 presents the betas value and each category of Singapore REITs. We noticed that industrial class has the only negative beta (-11.43419559), which means industrial category moves the opposite direction of the overall stock market in Singapore. While retail has the highest beta (26.3441103) flowed by the beta of office (26.3441103), as the overall market go up, more investors have the more power to purchase the industrial and office property which return is more than the industrial property. The beta of healthcare presents the smallest value of beta which is the similar to the overall market risk.

5.2 Overview of the HypothesesI will illustrate the empirical findings in this section in order to solve my two hypotheses.

5.2.1 Hypothesis 1: Some categories of FTSE ST REITs Generate Superior performance than others.In order to analyse further in terms of the performance of FTSE ST REITs, the following graphic illustrates the price evolution of fourteen property stocks over the past six year. To better understand the overview of Singapore real estate market, I have chosen fourteen Singapore real estate stocks and divided them into 5 areas.

I have implemented the graph above to demonstrate the price evolution of FTSE ST REITs stock over the past six-years to encourage the thoughts of FTSE ST REITs properties. I divided sixteen Singapore property shares into 5 classifications. From the graph above, we can see some stocks are perform much better than other classification stock. Investors can forecast a stocks performance in the future according to the current performance of the stock in the market. It is important for an investor to choose which category of the stock to invest. From the graph above, we can see that the healthcare category performed better than other category shares on the equity market from the financial data from 2008 to 2015. While office and industrial category has been seen a steady growth over the period of 2008 2015. Hospitality and retails performed better than office and industrial categories from 2008 to 2013, but since Feb 2014, these four categories presented the similar performance from Feb 2014 onwards. Overall, healthcare category dominated have the highest price on the equity market. A quick benchmark from mid of July 2009 to mid of July 2015 is provided by FTSE ST REITs to get the general trend of Singapore REITs using the daily financial data in Singapore. Table 12: Descriptive statistics for FTSE indexHealthcareIndustrialOfficeRetailHospitalityFTSE ST REITs

Mean2.1030415671.248557781.3237589131.381116441.501840144239.6765761

Standard Error0.0089929970.0063668450.0057785790.0039173140.0032158310.659889053

Median2.211.337511.36251.3751.4975240.0333333

Mode2.351.426251.526251.27251.4825259.8333333

Standard Deviation0.3180776030.2251919950.2043853210.138553360.11374225723.33993159

Sample Variance0.1011733610.0507114350.0417733590.0191970340.012937301544.7524067

Kurtosis-1.274570644-1.123334908-1.14902605-1.047995738-0.224877707-0.658525471

Skewness-0.167499541-0.414875933-0.2647224110.169123526-0.303829101-0.027128876

Range1.371.2869950.78250.580.641085107.0166667

Minimum1.420.3982350.9151.10251.127189.7033333

Maximum2.791.685231.69751.68251.768085296.72

Sum2630.9051561.9457831656.02241727.7766671878.80202299835.3967

Count125112511251125112511251

The average return for all the five categories are positive. The highest mean return is healthcare (2.103041567) followed by hospitality industry category (1.501840144), the average return of Healthcare is much higher than the other 4 categories. However, none of the classes return exceeds the return of FTSE ST REITs (239.6765761). The coefficient (beta) is to evaluate the performance of the Singapore REITs each sector and assist us to answer the first hypothesis. According to the table 13 below, I will illustrate the coefficient of 14 Singapore REITs based on each classification.

Table 17: Coefficient, classification and FTSE ST Reits (monthly data)Coefficient Standard Errort Sig

Intercept-807.258210.3437-3.83780.000287

Healthcare459.893955.17455.174968.3351938.28E-12

Hospitality-457.103135.9031-3.363450.001304

Industrial48.83247111.67590.437270.663388

Office204.3861193.95021.0538070.295934

Retail814.8362209.28143.8934940.000238

According the table above, at the level of 5%, hospitality and retail classification are statistically signification which present 0.001304 and 0.00238 respectively. In other words, hospitality and retail classifications have real influence on the dependent variable as the coefficient are inferior to 5%. The other three classifications are less than 5% which means they dont have real influence on the dependent variables. Therefore, investing in the retail area will cause to a rise by 814.8362 units which is the most return of FTSE ST Reits. On the other hand, the performance of the industrial area shows the slowest compare to the other categories, with only an increase of 48.83247. Retail independent variable contribute the most in the explanation of FTSE ST Reits return. Hospitality contribute the opposite to the return of FTSE ST Reits. Table 18: Coefficient, Retail and ST and LT interest rate Standard Error t Sig

Intercept1.1059170.05670719.50232.5E-29

ST Interest Rate0.1812850.0385034.7083941.3E-05

LT Interest Rate0.0766740.0273692.8014880.006643

As we can see from the above table, long term interest rate presents the Beta of 0.006643 in absolute value which is less than 5% level, therefore, its the statistically significant variable. Meaning to say that long term interest rate has the influence on the dependent variable. Table 19: Coefficient, Healthcare and ST and LT interest rateStandard Error t Sig

Intercept2.59490.19843113.077074.11E-20

ST Interest Rate-0.252790.134729-1.87630.064971

LT Interest Rate-0.421750.095771-4.403783.92E-05

From the table above, the Beta for both ST and LT interest rate show more than 5%, indicating that none of them have a real influence on the dependent variable which is 0.064971 and 3.92 respectively. Table 20: Coefficient, Hospitality and ST and LT interest rateCoefficientsStandard Errort StatP-value

Intercept1.5427180.05540127.846511.49E-38

ST Interest Rate0.0174510.0376160.4639290.644202

LT Interest Rate-0.02690.026739-1.005870.318097

Table 20 shows that interest rate has no influence on the return of hospitality classification Reits return. Table 21: Coefficient, Industrial and ST and LT interest rate

CoefficientsStandard Errort StatP-value

Intercept0.5593960.0859936.5051231.15E-08

ST Interest Rate0.109290.0583871.8718280.065596

LT Interest Rate0.2693230.0415046.4891481.23E-08

Table 20 shows that interest rate has no influence on the return of industrial classification Reits return.

Table 22: Coefficient, Office and ST and LT interest rate

CoefficientsStandard Errort StatP-value

Intercept0.7592390.0931418.1515211.28E-11

ST Interest Rate0.168110.063242.6582820.009813

LT Interest Rate0.1969680.0449534.3816154.25E-05

Table 20 shows that interest rate has no influence on the return of office classification Reits return.

5.2.2 Hypothesis 2: Some Economic Factors Affect the FTSE ST REITs Performance.

CoefficientsStandard Errort StatP-value

Intercept-1801.152146.554-0.839090.416584

REALGDP0.037730.0238841.5797220.138185

CPI1.24217512.873320.0964920.924601

ST233.5148239.47960.9750920.347311

LT7.54124944.753690.1685060.868779

According to the table of coefficient table, none of the factors have a real influence on the dependent variable because all of them are more than 5% significant level. The parameter under coefficient presents different value in terms of unstandardized coefficients which means the contribution on the FTSE ST REITs return of different independent variable are varied. The statistically significant variable is ST interest rate which presents 233.5148 while REALGDP (0.03773) is the lowest Beta. CPI and LT interest rate present 1.242175 and 7.541249 respectively. I found the ST and LT interest have more relationship with the return of FTSE ST REITs, in the following sections, I will focus on analyse the coefficient with ST and LT interest with FTSE ST REITs.Table 16: Coefficients, interest rates and FTST ST REITs (Daily data)CoefficientsStandard Errort StatP-value

Intercept858.125912915.5371125355.230720092.5504E-273

LT interest rate-5.0909717515.857424999-0.8691484320.385030261

ST interest rate-556.037173157.21576022-9.7182519463.65986E-21

Table 20 shows that interest rate has no influence on the return of office classification Reits return.

Table 14 : Coefficients, L-T interest rate and FTST ST REITs (monthly data)

CoefficientsStandard Errort StatP-value

Intercept939.28702345.521401520.633965392.13E-50

LT interest rate-122.097835915.3477886-7.9554025061.57E-13

(Data from Aug 1999 to July 2015)

Table 1 : Coefficients, S-T interest rate and FTST ST REITs (monthly data)

CoefficientsStandard Errort StatP-value

Intercept521.916125.01220.866632.16E-48

ST40.1737*16.927362.3732990.018767

(Data from Aug 1999 to Aug 2013)Table 20 shows that short term interest rate (0.018767) has influence on the return of FTSE ST RETIs. At 5% level, ST interest rate is less than 5% so become significant, therefore, we should reject the hypothesis 2. FTSE ST REITsReal GDPCPIShort term interest rateLong term interest rate

FTSE ST REITs10.8536349360.82603119-0.28619579-0.310435072

Real GDP0.85363493610.960420315-0.494849766-0.300249326

CPI0.826031190.9604203151-0.522271686-0.44887364

Short term interest rate-0.28619579-0.494849766-0.52227168610.392401628

Long term interest rate-0.310435072-0.300249326-0.448873640.3924016281

Chapter VI AnalysisThis chapter targets to achieve the research purpose of the performance of FTSE ST REITs in six year time series and analyse the economic factors which influence the performance of S-REITs. In order to better understand the outcomes, 2 hypotheses are applied. 6.1 Hypothesis 1: Certain classification in FTSE ST REITs performance is superior to othersFrom the table 17 in chapter V, the FTSE ST REITs returns are influenced by the hospitality and retail classification. From Appendix 1 the correlation matrix, I observed that five different classification are daily correlated to the S-REITs returns. Therefore, the classification can be considered as a representative indicator in the choice of strategy and evaluation of S-REITs performance. All five categories are positively and significantly correlated to the S-REITs, healthcare (0.83643076), industry (0.84792949), office (0.94524881), retail (0.94503900), hospitality (0.51206844), no negative correlation is observed. Hospitality and retail classifications have the strong influence on the performance of FTSE ST REITs as the presentation of table 17 with an advantage of 0.001304 and 0.00238 respectively. As office and retail classifications have the best positive coefficient with FTSE ST REITs, an opportunistic speculator in real estate would invest in office and retail area. In addition, these two categories can be regarded as more competitive than other classes according to my samples. Natale (2000) stated that investors and institutions expect their stocks would increase depending on which subgroup is currently in favour. Applying to my dissertation, real estate investors expect the performance of REITs depending on the FTSE ST REITs classification. According to Ducoulombier (2007), every investment has its own age, structure, localisation, architecture, context, etc. which affect each asset individually. Nevertheless, due to the evaluation of the model is weak, the interpretation need to be more cautious. From financial results in the table 11, the industrial classification is the only one category to have a negative beta (-11.43419559) according to the CAPM theory. In contrast, the multifactor model observes that the performance of industrial is not the worst which is positive 48.83247.

Hypothesis 2: ST REITs Performance is affected by Certain Economic Factors Affect the FTSETake both marco- and micro factors into consideration from the economic perspective, I found the results of multifactor model are interesting. As the studying in Chapter V, the real GDP, CPI, the short-term and long-term interest rate have an influence on the sample FTSE ST REITs returns in terms of the significant level, The empirical findings as the table shown above presents a positive correlation and a significant null sensitivity between the return of FTSE ST REITs and real GDP which is 0.853634936. The beta coefficient of real GDP 0.03773 tends to express that FTSE ST REITs are correlated or extremely highly correlated to the evolution of real GDP. This fact is acceptable and reasonable as GDP plays an influencing role in the real estate market. It is the reflection of the favourable and unfavourable economic climate. Additionally, GDP represents one of the most relevant macro-economics aggregate in the sense that it depicts the level of wealth in a nation (Liow et al.,2006). Therefore, a high real GDP results in a positive reaction from the investors whereas a low real GDP leads to a negative reaction from them. This means the return of FTSE ST REITs and Real GDP move together in a positively and completely linear manner. As for the CPI inflation variable, the examine result is that CPI inflation contributes heavily to the FTSE ST REITs returns. Its correlation and beta coefficient are positively significant which is 0.82603119 and 1.242175 respectively. The values are large, the result corroborate with the hypothesis that an increase in inflation implies an increase of FTSE ST REITs return. So when the inflation rises, investors can expect a higher return form FTSE ST REITs. Concerning the interest rate: it is important to distinct between short term and long term interest rate. 3 months T-bill yield and 10 year bond yield present the biggest parameter and both are negative correlated, -0.28619579 and -0.310435072 respectively. The intrinsic nature of FTSE ST REITs is to explain the influence between the interest rate and this kind of investment vehicle. The level of significance 10 year bond yield makes the determination of its real influence quite hard. Nevertheless, statistics 3 months T-bill yield makes a high contribution on the return of FTSE ST REITs. The difference in contribution absolutely comes from the interest in FTSE ST REITs investment. As investment in real estate is considered as the prudent long-run investment in the view of the conventional sight, as a result, the long-term interest rate prevails on the short term interest rate by determining a less volatile fluctuation during transaction processes. For example, when the interest rate rises, it will impact directly real estate market due to business rate such as credit rate are mostly based on these reference rates. Therefore, an increase of these rates leads to higher interest cost resulting in the less return. Another impact is that an increase in interest rate resulting in a rise in FTST ST REITs return because it will lead to economic growth and more demand. From the correlation table, which shows that a negative correlation, ST interest rate (-0.28619579), LT interest rate (-0.310435072), from the monthly data on the coefficient table, showing that there is significant null sensitivity between the short term interest rate (0.018767) and the return of FTSE ST REITs. The beta coefficient of short term interest rate (40.1737) tends to present that the return of FTSE ST REITs are uncorrelated or very lowly correlated to the varying of short term interest rate.

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