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(T-test) has been used to compares the actual difference between two means in relation to the variation in the data. Correlation analysis is concern with the study of the relation between two variables.

3.8 Hypothesis Statement

In order to fulfill the answer of the research objective, hypothesis were conducted as below:

Hypothesis 1

H0: There is no significant effect between GDP growth and property stock price.

H1: There is significant effect between GDP growth and property stock price.

Hypothesis 2

H0: There is no effect relationship between interest rate and property stock price.

H1: There is significant effect between interest rate and property stock price.

Hypothesis 3

H0: There is no significant effect between money supply (M3) and property stock price.

H1: There is significant effect between money supply (M3) and property stock price.

Hypothesis 4

H0: There is no significant effect between inflation (CPI) and property stock price.

H1: There is significant effect between inflation (CPI) and property stock price.

3.9 Summary

This research will be complete according to the objective where to know whether there is any significant relationship between the property stock market performance and macroeconomic variables. The multiple linear regression method was used to examine the relationship between the property market price and relevant macroeconomic variables. The result can indicate the movement and the volatility of the price. This information will perhaps can be used by the investors especially small investors, government, economist or other financial institutions for them to make understand more on Malaysia property stock market and economic. Since study focuses on the data from 2000 until 2010, if would give reliable result on this study.

CHAPTER 4

FINDING

4.0 Introductions

This chapter will discuss the finding of relationship between the macroeconomic variable and Malaysia property stock market performance. Its explains the data analysis, using data from year 2000-2010 with multiple regression as a tool of analysis between dependent and independent variables.

4.1 Multiple Linear Regressions

4.1.1 Descriptive Statistic

Table 1: Descriptive Statistic

Descriptive analysis fall into two categories: measures of central tendency (mean) or measures of dispersion (standard deviation). From the table above N represent as a sample in this research while mean is the arithmetic average across the distribution of the data set. The measures of standard deviation are used to determine the consistency of a variable.

The larger the standard deviation, the more spread out the observations are, which explains how widely the values in a data set are spread around the mean. If the variance or standard deviation are large, the data are more dispersed.

4.1.2 Correlation

Table 2: Correlation

This research used to explain more on the overall correlation of the relationship among the variables. It is used to a measure the strength and direction of the relationship between two variables. From the table 2 its shows the relationship among other