The Real Estate Investment Trust Finance Essay

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The origins of the real estate investment trust or REIT (pronounced reet") date back to the 1880s. At that time, investors could avoid double taxation because trusts were not taxed at the corporate level if income was distributed to beneficiaries. This tax advantage, however, was reversed in the 1930s, and all passive investments were taxed first at the corporate level and later taxed as a part of individual incomes. Unlike stock and bond investment companies, REIT were unable to secure legislation to overturn the 1930 decision until 30 years later. Following WWII, the demand for real estate funds skyrocketed and President Eisenhower signed the 1960 real estate investment trust tax provision which reestablished the special tax considerations qualifying REIT as pass through entities (thus eliminating the double taxation). This law has remained relatively intact with minor improvements since its inception. REIT investment increased throughout the 1980s with the elimination of certain real estate tax shelters. Investments in real estate provided investors with income and appreciation. The Tax Reform Act of 1986 allowed REIT to manage their properties directly, and in 1993 REIT investment barriers to pension funds were eliminated. This trend of reforms continued to increase the interest in and value of REIT investment. Singapore REIT commonly referred to as S-REIT. There are currently 20 REIT listed on the SGX, starting with CapitaMall Trust in July 2002. They represent a range of property sectors including retail, office, industrial, hospitality and residential. S-REIT hold a variety of properties in countries including Japan, China, Indonesia and Hong Kong, in addition to local properties. S-REIT are regulated as Collective Investment Schemes under the Monetary Authority of Singapore's Code on Collective Investment Schemes, or alternatively as Business Trusts. S-REIT benefit from tax advantaged status.

BACKGROUND OF STUDY

Real estate is the world's biggest business accounting for 15 percent of global gross domestic product (GDP) with assets of US$50 trillion compared with US$30 trillion in equities (Bloomberg, 2004). Moreover more than 50 percent of the world's total assets are invested in direct real estate and securitized real estate investment vehicles such as real estate investment trusts (REIT) or real estate stocks. In the US, REIT was created by Congress in 1960 to allow small investors to have indirect access to large-scale income producing real estate investment. In Singapore, REIT offers an indirect vehicle for developers with large portfolio of low-yield income producing commercial properties to divest the asset holdings and unlock book values of the assets. This is an "exit strategy" for some developers to liquidate their equity stake in real estate projects, such as the divestment of Suntec retail mall and office towers via Suntec REIT that ended the joint ownership relationship of the 11 Hong Kong tycoons that initiated the project in 1988. The road to a buzzing REIT market today in Singapore was not smooth-sailing. The idea was mooted back in 1986, when Singapore property market was in doldrums following its first post-independent economic recession in 1985. The idea did not take off, but subsequently interest in REIT was re-ignited in late 1990s when the Monetary Authority of Singapore (MAS) revealed the guidelines for Property Funds in May 1999. The first attempt to launch a REIT was made by CapitaLand with its SingMall Property Trust (SPT) in November 2001, but the listing was aborted due to under-subscription of the new issues. Market timing and lack of tax transparency were cited by analysts as the main causes for the lukewarm response to the maiden S-REIT at the time. (Tien Foo, 2005) In July 2002, SPT was restructured with the same portfolio of three retail properties (Junction 8, Tampines Mall, Funan IT Mall) and re-launched under the new name "CapitaMall Trust" (CMT). The CMT IPOs were priced at $0.96, which was estimated at 2% discount to NAV compared to the earlier attempt that priced the IPO at 2.7% premium. The re-launch of CMT was a success with 5-time oversubscription, and it has been an important cornerstone in the REIT chapter in Singapore. Ascendas- REIT (A-REIT) was subsequently listed on the Singapore Exchange in November 2002, and it IPO was also over-subscribed by 5 times. As of July 2005, there are six REIT listed and traded on Singapore Exchange, and their details at IPO are summarized in Figure 1.

1.2 PROBLEM STATEMENT

In effect, due to the fact that the inputted parameters are unstable, the estimated optimal allocations can differ markedly between periods. This is made even more acute as optimiser typically produce portfolios with extreme holdings in a limited number of assets with some assets taking zero weights while others have very large allocations. Black and Litterman (1992) refer to these as corner solutions. Although the resulting portfolios are optimal in the statistical sense, the results would be unacceptable to any prudent portfolio manager (Jorion, 1985). Such corner solutions portfolios quickly become sub-optimal with changes in the means over time, leading to a further reduction in ex ante performance. In addition such extreme portfolio allocations assets seem to be against the spirit of diversification, Michaud (1989). One way to control for such extreme holdings are to place constraints (upper and lower bounds) on the amount any one asset, or group of assets, can have in the optimum portfolio. Indeed papers such as Frost and Savarino (1988) and Chopra (1993) suggest portfolio optimisations, which are subject to such constraints, have better ex ante performance compared with unconstrained solutions. However, any constraints are likely to be arbitrary, leading to the results being hard to generalise. For example, one possible constrained portfolio is the equally weighted naive portfolio. Morrell (1993), however, argues that that it is generally not possible for property funds achieve equal-weighting in a portfolio and at the same time be represented in key market segments. In addition fund managers typically desire to maintain weights similar to a benchmark portfolio. Also at a practical level due to the indivisibility of property and the marked differences in lot size between say the office and industrial sectors an equal-weighted portfolio strategy would be impossible to implement. Thus an equal-weighted portfolio is therefore probably not a realistic, or even a desirable goal of fund managers. In addition such an approach fails to tackle the fundamental reason for the major shifts in portfolio allocations over time, the instability in the sample means. In contrast the estimation error in variances and covariances is not as much of concern since these parameters are relatively stable over time and therefore are more precisely estimated. Thus the use of a portfolio selection procedure based on historical parameters that ignores the estimation risk due to the uncertain in mean returns is likely to produce sub-optimal results in subsequent periods. Indeed previous work on the application of MPT to the real estate portfolio shows this to be the case.

1.3 RESEARCH QUESTIONS

1.3.1 Are Economic effect really gives impacts to REIT? 1.3.2 Which Economic effect gives most changes to REIT indices?

1.4 OBJECTIVES OF STUDY

Based on the research question, thus, two research objectives were formulated. 1.4.1 To find out whether the macroeconomic variables have impact on REIT performance. 1.4.2 To determine the macroeconomics factor that influenced most on REIT performance.

1.5 SIGNIFICANT OF THE STUDY

The main purpose of carrying out this research is to determine the REIT performance and analyze it throughout the macroeconomics variables and how REIT react on it. The group of people who gain the benefit from this study: 1.5.1 Researcher - The study gives benefits to college's people and can be used in their study and references. 1.5.2 Company - The research may give important information to the company and corporation who are related in REIT and sectors related to REIT activity for them make decision. 1.5.3 Investor - Gives overview and guidelines before they can make decision on investment.

1.6 SCOPE OF STUDY

The scope of this study would cover S-REIT index performance within ten years period. The macroeconomics variables that used in this study will be GDP, industrial production output, inflation, money supply, and exchange rate.

1.7 LIMITATION OF STUDY

REIT performances are sometimes outperform the stock market. Due to the characteristics of real estate that have high in value, it will give the confident to the investors and increase the demand towards REIT. This kind of demands is tremendous but sometimes it will not following the macroeconomic variables trend. For example, for production variable, the production may not match the demand on REIT and it will gives unusual result in regression. What are the limitations of your study? Here, it seems that you are talking about an issue/problem statement.

1.8 DEFINITION OF TERMS

1.8.1 REIT/S-REIT Real Estate Investment Trust/Singapore REIT 1.8.2 S-REIT index FTSE ST Real Estate Investment Trust Index 1.8.3 GDP Gross Domestic Product 1.8.4 FX Foreign Exchange 1.8.5 M2 Money supply 1.8.6 GNP Gross National Product

1.9 SUMMARY

Issuing of REIT is some kind of investment trust that have significant the assets hold are real estate compared to shares. Macroeconomic variables seem effect the REIT performance as well as the stock market, unit trusts, bonds, and other instruments. This study will examine the effects of microeconomic on REIT performance. Few variables are taken to test whether there are significant correlation between the dependent and independent variables. The dependent are S-REIT index and the independent variables are macroeconomic variables. For final report, there's no by chapter summary. Just concluded them and put it under the last topic - 'conclusion'.

CHAPTER 2

LITERATURE REVIEW

INTRODUCTION

S-REIT yields have normalized back to a market cap weighted 7.4% FY10 DPU or 480bps over the 10-year bond yield, in tune with long term historical levels. Hence, the S-REIT sector is expected to trade in line with the broader market in 2010 with a projected total return of 15.6%. Balance sheets are now healthier with gearing at 31% and refinancing risks have abated with only 16% of the S-REIT indebtedness due for review next year. At this point, with little prospect of significant asset reflation, we believe that S-REIT are unlikely to favour gearing up much from here. Hence, any capital management exercises to fund new purchases are likely to be smaller and opportunistic in nature. Investors are likely to refocus back on earnings growth potential, driven by both organic and acquisition means as a catalyst for stock performance.

PREVIOUS STUDY

Islam (2003) conducted the studies in examining the short run dynamic adjustment and long term equilibrium relationships between the Kuala Lumpur Composite Index and the selected macroeconomic variables (interest rates, exchange rates, inflation rates and industrial productivity). His found that the existence of the short runs (dynamic) and the stable long run (equilibrium) relationships between stock returns of Kuala Lumpur stock market and that selected macroeconomic variables by using the monthly data from January 1990 to June 2002. Maysami, Lee and Mohamad (2004) also investigated the relationship between macroeconomic variables (interest rates, industrial production, price levels, exchange rates and money supply) and Singapore stock market index (STI) as well as with various Singapore Exchange Sector Indices (finance index, property index and hotel index). His concludes that the Singapore's stock market and the property index form cointegrating relationship with changes in the short term and long term of the selected macroeconomic variables. Maghyereh (2002) investigated the long-run relationship between the Jordanian stock prices and selected macroeconomic variables, again by using Johansen's (1988) cointegration analysis and monthly time series data for the period from January 1987 to December 2000. The study showed that macroeconomic variables were reflected in stock prices in the Jordanian capital market. Reily and Brown (2000), however, complicated the matter a bit by stating that cash flows from stocks can change along with interest rates and it is not certain whether this change in cash flows will augment or offset the change in interest rates. Ibrahim (1999) also investigated the dynamic interactions between the KLSE Composite Index, and seven macroeconomic variables (industrial production index, money supply M1 and M2, consumer price index, foreign reserves, credit aggregates and exchange rate). Observing that macroeconomic variables led the Malaysian stock indices, he concluded that Malaysian stock market was informationally inefficient. Omran (2003) focused on examining the impact of real interest rates as a key factor in the performance of the Egyptian stock market, both in terms of market activity and liquidity. The cointegration analysis through error correction mechanisms (ECM) indicated significant long-run and short-run relationships between the variables, implying that real interest rates had an impact upon stock market performance. Chong and Goh's (2003) results were similar: they showed that stock prices, economic activities, real interest rates and real money balances in Malaysia were linked in the long run both in the pre- and post capital control sub periods. Maysami and Koh (2000) examined such relationships in Singapore. They found that inflation, money supply growth, changes in short- and long-term interest rate and variations in exchange rate formed a cointegrating relation with changes in Singapore's stock market levels. Islam and Watanapalachaikul (2003) showed a strong, significant long-run relationship between stock prices and macroeconomic factors (interest rate, bonds price, foreign exchange rate, price earning ratio, market capitalization, and consumer price index) during 1992-2001 in Thailand. Vuyyuri (2005) investigated the cointegrating relationship and the causality between the financial and the real sectors of the Indian economy using monthly observations from 1992 through December 2002. The financial variables used were interest rates, inflation rate, exchange rate, stock return, and real sector was proxied by industrial productivity. Maghyereh (2002) investigated the long-run relationship between the Jordanian stock prices and selected macroeconomic variables, again by using Johansen's (1988) cointegration analysis and monthly time series data for the period from January 1987 to December 2000. The study showed that macroeconomic variables were reflected in stock prices in the Jordanian capital market. Gunasekarage, Pisedtasalasai and Power (2004) examined the influence of macroeconomic variables on stock market equity values in Sri Lanka, using the Colombo All Share price index to represent the stock market and (1) the money supply, (2) the treasury bill rate (as a measure of interest rates), (3) the consumer price index (as a measure of inflation), and (4) the exchange rate as macroeconomic variables. Mansor and Wan Sulaiman (2001) analyzed the dynamic interactions among three macroeconomic variables (real output, price level and money supply), exchange rates and equity prices in Malaysia. Their findings show that the Malaysian stock prices response more by changes in domestic factors mostly on money supply by exerts a positive effect on stock prices in the short run and negatively associated in the long run. Check your line spacing.

2.2 MICROECONOMICS INDICATORS AND REIT

While much works have been done on stock markets and USA REIT, this research provides an alternative perspective on the dynamic relationship between listed real estate market and the macroeconomic. Specifically, we examine whether the expected risk premium on property stocks of Singapore could be linked to the conditional volatilities of a set of principal components derived from six chosen observable macroeconomic variables. Another contribution of the paper stems from the use of a three-step methodological framework in addressing the objectives of the paper. Since property stock combines the investment characteristics of direct real estate and general stock, property stock market return and volatility profiles are likely to be different from those of stock markets (especially) in the long term. Moreover, property stocks are also different from REIT in their organization form, tax status, institutional framework and risk-return performance. Thus, we propose to extend the inquiries of this study to cover major property stock markets and examine the potential impact that macroeconomic risk may have on property stock excess returns. In a preview of our results, we are able to find that the expected risk premium on property stocks of the four markets are time-varying and dynamically linked to the conditional volatilities of the macroeconomic factors. Significant results are obtained from the macroeconomic volatilities as useful predictors for the expected risk premium and their conditional variances. But these significant results depend upon the individual markets involved.

THEORETICAL FRAMEWORK

Six economic factors are selected to test the relationships of these factors and S-REIT performance. Growth rate in GDP Growth rate in Industrial production output Unexpected inflation Interest rate Growth in money supply Changes in exchange rate I suggest you should exclude this part (2.3) since you will illustrate the framework in Chapter 3. It's redundant.

SUMMARY

Collectively, the evidence from this study indicates that for all the four markets studied, the six macroeconomic variables, GDP growth, INDP growth, unexpected inflation, money supply, interest rate and exchange rate can be represented by three principal components which are time-varying. In addition, between five and six macroeconomic risk factors are highly correlated with the retained principal components. Furthermore, GARCH and GMM evidence suggests that the expected risk premium and conditional volatilities of the risk premium for the four markets are time-varying and dynamically linked to the conditional volatilities of the three retained principal components (and hence the macroeconomic factors). However the impact of the macroeconomic risk factors on the expected risk premium in terms of direction and significance do vary across the four markets studied. Hence there are opportunities for risk diversification in international property stock markets

CHAPTER 3

METHODOLOGY AND DATA

3.0 INTRODUCTION

The purpose of this paper is to provide an analysis of the relationship between property stocks and some major macroeconomic risk factors as reflected in the general business and financial conditions. Ten years time period will be used to find the correlation and regression between microeconomic variables and S-REIT performance.

3.1 DATA COLLECTION

Data concerning on the macroeconomic variables and the S-REIT performance index were gathered to see whether there are any correlation between them as being the main objective of this study. In completing this study, the researcher will use secondary data. The data are collected from DataStream, related journals, news articles, and views from analysts, comments from professionals, books references, magazines, news paper, investors' reports and updates of financial industry reviews. 'Data' means your raw data (the one with figures) and it usually collected by students from the datastream. Those that you get from journals, articles etc. are NOT data. They are 'information'. So, rearrange your sentence to separate between sources of data and information.

3.2 SAMPLING FRAME

To secure an acceptable result, this study decided to use S-REIT index within 1999 to 2010. How about your IV?

3.3 SOURCES OF DATA

The data on S-REIT performance index will be collected from the Data Stream provided by University of Technology Mara and also related website. The data from all Singapore companies REIT listed within 2000 to 2009 will be used to identify the movements of S-REIT performance index.

3.4 VARIABLES AND MEASUREMENT

The variables used in this study can be categorized into two main types which are; the dependent and the independent variables. 3.4.1 Dependent Variables The dependent variable for this study is the Singapore REIT index. 3.4.2 Independent Variables The independent in this study is macroeconomic variables. The macroeconomic variables used in this study are GDP, Money Supply, Inflation, Industrial Production and Exchange Rates and Unexpected inflation.

RESEARCH DESIGN

This research is designed to explore the relationship between dependent and independent variables. In this study, it engages in hypotheses testing that were explaining the certain significant correlations between economic variables and the decision of going public. 3.5.1 Purpose of the Study The purpose of this study is to determine the relationship between the index of REIT performance and the macroeconomic variables. 3.5.2 Types of Investigation This study involved the correlation types of investigation where the researcher intends to investigate whether the dependent and independent variables have positive or negative relationship. 3.5.3 Unit of Analysis In this study, all the data regarding the REIT performance index and economic variables of Malaysia (Consumer Price Index, Exchange Rates, Money Supply and Industrial Production Index) as a whole were taken to test the relationship between them. 3.5.4 Time Horizon Time horizon of this study is cross-sectional studies, which the data are gathered just once over a period. This study was used yearly basis data from 2001 until 2009 as a time frame for this study.

THEORETICAL FRAMEWORK

There is a classical theory that explained the high correlation between the macroeconomic variables and the stock market index. This study tries to look at the possibility of the relationship between the dependent and the independent variables in Malaysia market. Dependent variable: S-REIT Performance Index Independent variables: Gross Domestic Product, Inflation, Exchange Rates (US$/RM), Money Supply (M2) and Industrial Production Index, Interest rate. Below is the schematic diagram to show the relationship between the relationship between the dependent and independent variables: Figure 2: Schematic Diagram (Relationship Diagram) GDP Inflation Exchange Rates Money Supply REIT Performance Index Industrial Production Index Interest rate Independent Dependent According to the schematic diagram above, it can be elaborated that the performance of S-REIT index are determine by the GDP, inflation, exchange rates, money supply, iInterest rate and industrial production index. The dependent variables will reflect in any changes in independent variables. Recheck your spelling and grammar. Make sure they are past tense since you've completed your study.

3.7 DATA ANALYSIS AND TREATMENT

The statistical tools use in the study is Multiple Linear Regression Model. This model was examined the real-time effects of several independent variables towards dependent variable that is interval scaled. Multiple linear regression analysis aids in understanding how much of the variance in the dependent variable is explained by a set of predictors. This type of analysis is also done to trace the sequential previous that cause the dependent variable through path analysis. This model is more appropriate to be used since it can explain the correlation between the dependent and independent variables much better. SPSS Software was used to generate the result from the data that have been collected. Multiple Linear Regression Model: (Equation 1) Where; Y = Dependent variable which represent S-REIT index = The constant number of equation = Coefficient Beta value = Growth rate in GDP = Growth rate in Industrial production output = Inflation = Interest rate = Growth in money supply = Changes in exchange rate = Error

3.8 HYPOTHESIS STATEMENT

Hypothesis 1 H0: Growth rate in GDP has no relationship with S-REIT movements H1: Growth rate in GDP has a relationship with S-REIT movements Hypothesis 2 H0: Growth in money supply has no relationship with S-REIT movements H1: Growth in money supply has a relationship with S-REIT movements Hypothesis 3 H0: Inflation has no relationship with S-REIT movements. H1: Inflation has a relationship with S-REIT movements Hypothesis 4 H0: Changes in Exchange Rate has no relationship with S-REIT movements H1: Changes in Exchange Rate has a relationship with S-REIT movements Hypothesis 5 H0: Growth rate in Industrial production output has no relationship with S-REIT movements H1: Growth rate in Industrial production output has a relationship with S-REIT movements Hypothesis 6 H0: Interest rate has no relationship with S-REIT movements H1: Interest rate has a relationship with S-REIT movements

SUMMARY

In the nutshell, this chapter will provide the research design that will be used in this study. The study aims to determine the relationship between economic factors with the index movements of S-REIT. This research will be done in accordance to the objective where to know whether there is any significant relationship between the economic factors with the timing and the decision of the investors who willing to get into S-REIT. Since study focuses on the data from 1999 until 2009, if would give a better picture on the decision and timing of buying S-REIT and using this information for gaining future return as well.

CHAPTER 4

FINDING AND ANALYSIS

INTRODUCTION

As mentioned in the previous chapter, the objective of this study is to determine the significance correlation between the macroeconomics variables and the S-REIT performance. In addition, the study also tends to identify which macroeconomic factors that have a relationship with the index. Multiple linear regression is one of the useful method to examine the relationship between independent and dependent variables. In testing the hypotheses, the tests have been run for both dependent variables (S-REIT index) and independent variables GDP, Inflation Rate, Exchange Rate, Money Supply, Industrial Production Index and Interest Rate.

4.1 MULTIPLE LINEAR REGRESSION ANALYSIS OF S-REIT AND MACROECONOMIC VARIABLES (January 2000 - June 2010)

The data have been converted into rate [X= (P1 - P0) / P0] and be regressed in order to get the solid model to explained the conclusion derive from the analysis. Table 4.1: Multiple Linear Regression of S-REIT index (2000-2010)

Coefficientsa

Model Unstandardized Coefficients Standardized Coefficients T Sig. B Std. Error Beta 1 (Constant) 2.959 2.419 1.223 .229 GDP -4.480E-5 .000 -1.457 -2.569 .015 MONEYSUPPLY 2.130E-6 .000 .937 2.277 .029 INFLATIONRATE -.095 .032 -.304 -2.978 .005 FOREX .898 .512 .341 1.755 .088 IND.PROD .011 .013 .263 .898 .375 INT.RATE .799 .139 .767 5.743 .000 a. Dependent Variable: SREITINDEX Table 4.1 shows the model summary of S-REIT index and the selected macroeconomic variables. Before analyzing all the data above, residual analysis on that model has been tested to ensure that the model will meet all the assumptions in multiple regression method.

4.2 FINAL FINDING OF MULTIPLE LINEAR REGRESSION OF S-REIT INDEX

Table 4.2: Final Model Summary of S-REIT index (2000-2010)

Model Summaryb

Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change Sig. F Change 1 .906a .821 .791 .23921 .821 26.792 .000 Predictors: (Constant), INT.RATE, GDP, INFLATIONRATE, FOREX, IND.PROD, MONEYSUPPLY b. Dependent Variable: SREITINDEX Table 4.3 shows the model summary of S-REIT index and the selected macroeconomic variables. From the table above, it shows that adjusted R2 is 0.791 in which represents the total amount of variance in the population. The value of adjusted R2 can be interpreted that only 79.10 percent of variability of data regressed can be explained between the independent and dependent variables. In this case, it can be concluded that GDP, Inflation Rate, Exchange Rate, Money Supply, Industrial Production Index and Interest Rate were explained 79.1% of the variance in S-REIT index. It also explained by adjusted R2 that there are strong correlations between the variables since the adjusted R2 is above 50%. Then, the Analysis of Variance (ANOVA) test has been conducted again to test the null hypothesis that several groups mean are equal in the population by comparing the sample variance estimated from the group means to that estimated within the group. Below is the hypothesis of this model: H0: None of the 6 independent variables GDP, Inflation Rate, Exchange Rate, M2, Industrial Production Index and Interest Rate will significantly explain the variance in S-REIT index. (β1=β2=β3=β4 = 0) You mentioned that you have 6 IVs H1: The 6 independent variables of GDP, Inflation Rate, Exchange Rate, M2, Industrial Production Index and Interest Rate will significantly explain the variance in S-REIT index. (At least one of ¢ is not equal to zero) After the test that has been conducted, the null hypothesis (H0) will be rejected if the p-value < 0.05. The F-significant value (p-value) stands at 0.0000, which shows that the model is significant with 99 percent confidence level. The finding shows that this test will reject the null hypothesis. Means that, at least one of the ¢ is not equal to zero. Thus, following findings and analysis will interpret the result for the independent variables and test on the hypothesis. Table 4.3: Final Coefficient of Multiple Linear Regression Analysis Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 2.959 2.419 1.223 .229 GDP -4.480E-5 .000 -1.457 -2.569 .015* MONEYSUPPLY 2.130E-6 .000 .937 2.277 .029* INFLATIONRATE -.095 .032 -.304 -2.978 .005* FOREX .898 .512 .341 1.755 .088 IND.PROD .011 .013 .263 .898 .375 INT.RATE .799 .139 .767 5.743 .000* a. Dependent Variable: SREITINDEX Table 4.3 represents the result from multiple linear regression analysis of S-REIT index from year January 2000 to June 2010. It reports the coefficient values for each independent variable that has been regressed against the S-REIT index. The test has been conducted to test the H0 of the S-REIT index and the selected macroeconomic variables. The H0 will be rejected if the p-value > 0.05. Hypothesis 1 H0: Growth rate in GDP has no relationship with S-REIT movements Looking the analysis between S-REIT index and GDP (table 4.3), it is found that the t-significant value stand at 0.015, which is significant. The finding hereby rejects the null hypothesis for GDP. It also explained that there is significance correlation between GDP and S-REIT index. Hypothesis 2 H0: Growth in money supply has no relationship with S-REIT movements Based on analysis between S-REIT index and Money Supply (table 4.3), this study found that there is significant correlation between S-REIT index and Money Supply, which is the t-significant value stand at 0.029. The finding shows that this study will reject the null hypothesis for exchange rate. It explained that there is relationship between Money Supply and S-REIT index. Hypothesis 3 H0: Inflation has no relationship with S-REIT movements Based on analysis between S-REIT index and Inflation (table 4.3), this study found that there is significant correlation between S-REIT index and Inflation, which is the t-significant value stand at 0.005. The finding shows that this study will reject the null hypothesis for Inflation. It explained that there is correlation between Inflation and S-REIT index. Hypothesis 4 H0: Changes in Exchange Rate have no relationship with S-REIT movements Based on analysis between S-REIT index and exchange rate (table 4.3), this study found that there is no significant correlation between S-REIT index and Exchange Rate, which is the t-significant value stand at 0.088. The finding shows that this study failed to reject the null hypothesis for Exchange Rate. Hypothesis 5 H0: Growth rate in Industrial production output have no relationship with S-REIT movements Looking the analysis between S-REIT index and Industrial production output (table 4.3), it is found that the t-significant value stand at 0.375, which is not significant. The finding hereby failed to reject the null hypothesis for Industrial production output. It also explained that there is no significance correlation between Industrial Production Output and S-REIT index. Hypothesis 6 H0: Interest rate has no relationship with S-REIT movements Looking the analysis between S-REIT index and Interest rate (table 4.3), it is found that the t-significant value stand at 0.000, which not significant. The finding hereby rejects the null hypothesis for Industrial production output. It also explained that there is significance correlation between Industrial Interest rate and S-REIT index.

4.4 LINEAR EQUATION

Ye = 2.959 - 0.00004480(GDP)* + 0.000002130(MS)* - 0.095(INF.R)* + 0.898(FX) + 0.011(IND.P) + 0.799(INT.R)* (t-value) (-2.569) (2.277) (-2.978) (5.743) Significant at 95% confident level Y = Dependent variable which represent S-REIT index DGP = Gross Domestic Product MS = Money Supply INF.R = Inflation Rate FX = Exchange Rate IND.P = Industrial Production INT.R = Interest Rate * sig. From the equation above, it can be said that GDP has inverse relationship with S-REIT index. These results are explained that when the GDP increases, then the S-REIT index will decrease. Decrease of GDP by 1 % in show an increase on S-REIT index by 0.00004480% when holding other variables constant. But contra with Kim Hiang Liow, Muhammad Faishal Ibrahim and Qiong Huang (2005) the stock market have positive relationship with GDP. Previously in chapter 1, it was highlighted that properties have value that increasing that can over perform the stock market. It is unnecessarily that the trend of S-REIT will follow the GDP. S-REIT also contains the real property outside of Singapore such as Malaysia, Vietnam, China, Indonesia and others. It will not necessarily will relate to Singapore GDP or other variables directly. Money Supply has positive relationship with S-REIT index. From the equation, when Money Supply increases by 1%, S-REIT index will increase 0.000002130% when holding other variables constant. Inflation Rate has negative relationship with S-REIT index. From the equation, when Inflation Rate increases by 1%, S-REIT index will decrease 0.095% when holding other variables constant. Interest Rate has positive relationship with S-REIT index. From the equation, when Interest Rate increases by 1%, S-REIT index will increase 0.799% when holding other variables constant.

CHAPTER 5

CONCLUSION AND RECOMMENDATION

CONCLUSION

The paper has examined the relationship between S-REIT index with GDP, Inflation Rate, Exchange Rate, Money Supply, Industrial Production Index and Interest Rate. The study were employed the quarterly data and used multiple linear regression method to test the relationship between the dependent and independent variables. The results found that there are strong correlations between the S-REIT indexs with the selected macroeconomic factors. It means that, there are other factors that affect the performance of S-REIT rather than the six (6) selected economic factors. From this study, it can be conclude that the S-REIT performance in the strong form efficient market hypothesis seems the S-REIT performance are not fully reflected with all information from public and private sources that included the information about economic events in Singapore. Specifically, GDP, Money Supply, Inflation Rate, and Interest Rate were significantly related with S-REIT performance. It means that, any changes in GDP, Money Supply, Inflation Rate, and Interest Rate it seems to reflect the changes in S-REIT indexs. The past studied also found there are strong relationship between exchange rate and money supply towards the performance of stock market index. Maysami, Lee and Mohamad (2004) found the co-integrating relationship between exchange rates and money supply in Singapore's stock market in their past studied. While, Islam (2003) proved that there are short run and long run relationship between stock returns of Bursa Malaysia stock market with interest rates, inflation, exchange rate and industrial productivity.

5.1 RECOMMENDATION

As a recommendation, it seems there are opportunities to other students to further study on the relationship between S-REIT index and macroeconomic variables. The future researcher is suggested to measure the relationship between stock market index and the economic factors by using other method rather than use multiple linear regression method. There are also suggested by other past researchers in the similarly research that co integration analysis and Error Correction Method it seems to give the robust result and will give the deep explanation of the relationship between the stock market index and macroeconomic factors. (Islam (2003), Mansor and Hassanuddeen (2003). Other than that, it also recommended to the future researcher to add other macroeconomic variables such as Consumer Price Index (CPI) and Unemployment Index in examining the relationship of economic factors with stock market index.
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