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[THE IMPACT OF ECONOMIC INDICATORS TOWARDS GDP] MARCH11

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CHAPTER 1: INTRODUCTION

1.1 CHAPTER DESCRIPTION

This chapter consists of an introduction and background for Study the impact of

economic indicators including housing price index towards gross domestic

product. This chapter also includes the problem statement, objectives of the study,

scope and hypotheses of the study. Besides that, it also briefly discusses the

significance of the study, limitations and definition of terms during the process to

completing this study.

1.2 BACKGROUND TO THE STUDY

1.2.1 OVERVIEW OF GDP

What will come across to our mind when we talk about Gross Domestic Product

(GDP) ? Basically, a good way to judge how well someone is doing economically is

to look at his or her income (N.Gregory Mankiw, C.Ron). It is just the same if we

want to judge or estimate how well a country is doing economically, which is take a

look on the total income that everyone in the economy is earning (N.Gregory

Mankiw, C.Ron).The main idea, GDP is our measure of the economys that total

income, often called national income (N.Gregory Mankiw, C.Ron). Other than

that, GDP also measures total expenditure on the goods and services produced in

the economy and the value of the economys output (production) of goods and

services. Thus, GDP is also referred to as output (N.Gregory Mankiw, C.Ron).

The measure of standard of living is GDP per capita (production measure) or GNP

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per capita (income measure) (P.Michaela, 2010). The formula as below derived by

Michaela Podmanicka, 2010 :

GNP = GDP + net factor income from abroad

GDP = GDP x EMPLOYMENT

There are two ways to increase GDP per capita :

Firstly, by raising labor productivity, e.g. by increasing physical and human capital

per employed (P.Michaela, 2010). Secondly, by a larger proportion of the population

in employment (P.Michaela, 2010). Refer to Michaela Podmanicka, 2011, the

evidence on convergence in per capita income across the Swedish Counties is

consistent with predictions of textbook model :

Low per capita income little capital (physical + human) per worker

Low wages, high rate of return to capital

Large investments in capital

High capital per worker

High production per worker

High income per capita

Low wages out-migration

High capital per worker

High production per worker

For this study the researcher used the GDP per capita as the proxy for GDP because its

value indicates in bigger value and easy to be measured.

POPULATION POPULATIONEMPLOYMENT

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1.3 PROBLEM STATEMENT

The uniqueness of the GDP per capita characteristic really encourage myself to

explore more about it. There may be many indicators that can give the impact or

influence the appreciation or depreciation of the GDP per capita in any country.

The key economic indicators that can influence the GDP per capita are

population, base lending rate, unemployment rate, and also housing price index.

The reason why the researcher choose the housing price index as the

independent variables for this study rather than others key economic indicators

because I really want to know does housing price index will affect the nations

GDP per capita and does it has the relationship with GDP per capita. Thus, the

researcher undergo this study to know the accuracy level of those key economic

indicators including housing price index can give an impact towards nations

GDP per capita. The focus of the study is to examine whether the population

(POP), base lending rate (BLR), unemployment rate (UE), and housing price

index (HPI) can affect the nations GDP per capita. The economists also agree

that those economic indicators really can help in bearing and bulling of nations

GDP per capita.

1.4 OBJECTIVES OF THE STUDY

The general purpose of this study is to examine whether the population (POP),

base lending rate (BLR), unemployment rate (UE) and housing price index (HPI)

can affect the GDP per capita (GDPPC). In defining the limitations of this study,

the researcher identified study areas to be addressed. A careful review of those

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questions areas led to the development of the following specific research

objectives:

To determine whether there are significant relationship between population

and gross domestic product.

To examine the significant relationship between base lending rate and gross

domestic product.

To study the unemployment rate affect the gross domestic product.

To inspect the effect of housing price index towards gross domestic product.

1.5 SCOPE OF THE STUDY

This study is conducted to focus on the key economic indicators including housing

price index that influenced the gross domestic product. The scope of the study

involves the Population (POP), Base Lending Rate(BLR), Unemployment rate

(UE), and Housing Price Index (HPI) which will help investors to measure the

nations gross domestic product per capita.

1.6 THEORETICAL FRAMEWORK

In the study, the theoretical framework is needed in order to know the relationship

from one variable to the other variables. A variable is anything that can take on

differing or varying value. In the theoretical framework there are two variables are

used to identify for each other which is:

Dependent variable (criterion variable) - is the primary interest to the researcher.

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Independent variable (predictor variable) is one that influence the dependent

variable in either a positive or negative way.

The schematic diagram for the theoretical is as follow:

Figure 1

INDEPENDENT VARIABLES DEPENDENT VARIABLE

1.7 RESEARCH QUESTIONS AND HYPOTHESIS OF THE STUDY

1.7.1 Research Questions

1.7.1.1 Does there are significant relationship between population and gross

domestic product?

1.7.1.2 What is the significant relationship between base lending rate and gross

domestic product?

1.7.1.3 How does unemployment rate affect the gross domestic product?

1.7.1.4 What is the effect of housing price index and gross domestic product?

Population

Base Lending Rate

Unemployment rate

Housing Price Index

Gross Domestic product

per capita

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1.7.2 HYPOTHESIS OF THE STUDY

Hypothesis can be defined as a logically conjectured relationship between two or

more variables expressed in the form of a testable statement. By testing the

hypotheses and conforming the conjectured relationship, it is expected that

solutions can be found to correct the problem encountered.

There are two differential hypotheses; the first one is null hypothesis (Ho) which

is the proposition that the states a definitive, exact relationship between two

variables. In general the null statement is expressed as no (significant)

relationship between two variables or no (significant) difference between two

groups. The second type is the alternate hypothesis (Ha), which is the opposite

of the null that is expressing a relationship between two variables or indicating

differences between groups.

In this study, five hypotheses have built that are related with the objective of the

study:

Hypothesis 1

Ho: There is no relationship between population and gross domestic product.

Ha: There is a significant relationship between population and gross domestic

product.

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Hypothesis 2

Ho: There is no relationship between base lending rate and gross domestic

product.

Ha: There is a significant relationship between base lending rate and gross

domestic product.

Hypothesis 3

Ho: There is no relationship between unemployment rate and gross domestic

product.

Ha: There is a significant relationship between unemployment rate and gross

domestic product.

Hypothesis 4

Ho: There is no relationship between housing price index and gross domestic

product.

Ha: There is a significant relationship between housing price index and gross

domestic product.

1.8 SIGNIFICANT OF THE STUDY

1.8.1 To the Researcher

It is the best way for the researcher to apply all those theories that have been

learned in class. As a Finance student, this study will be positively beneficial

for them to apply all those theories that have been learned in class. Through

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this study, they might gain knowledge and share his ideas and findings

regarding to the area of study.

1.8.2 To the Investor

It might be the research that can be as the references by the investor. In

order to do the investment, the investors really need to analyze the economic

indicators to make sure they do the right investment decisions. With the

knowledge of GDP per capita, the investors can predict and estimate which

investment alternatives that will give high profit with the low risk.

1.9 LIMITATIONS OF THE STUDY & DEFINITION OF TERMS

In completing this research, the researcher has to face a few limitations that could

affect the result of the study. However these limitations differ from one researcher

to another researcher where it depends on the subject matter of the study itself.

Several limitations have been identified while completing this research such as:

1.9.1.1 Time constraint

It is difficult to complete the research within a specified time frame. The

studies conducted are accomplished within a short period. In this short

time period, it will not be able to obtain more information for completing

the research. Time period also becomes a constraint for the researcher to

get more accurate and reliable data. At a same time, the researcher also

needs to concentrate on the practical training during the working hours.

So that also restrains the researchers effort in completing this project

paper.

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1.9.1.2 Cost constraint

This study was the liability of the researcher. Since the research was not

sponsored, the researcher had to bear all the cost. A lot of money had to

be spent in gathering data and information. As a student, the researcher

has a financial constraint to carrying out the project paper. Limited budget

and energy prevented the researcher to get further information.

1.9.1.3 Computer expertise

This research uses lots of data for analyzing. All data will be collected and

analyzed. The researcher needs to know how to use the SPSS program

to analyze data. So, researcher needs to be familiar with SPSS function

and program. But, most students are not experts in using this program. In

other words, it gave a lot of problems to the researcher in the process of

interpreting data precisely. Therefore, the researcher must spend more

time to understand and learn this program.

1.9.1.4 Lack of experience and information.

As this the first time for the researcher to do this research, therefore it

may quite difficult to collect data and manage time. Besides, the

researcher has limited knowledge about general information about the

GDP per capita in Malaysia. The researcher also faces with the challenge

to find the related journal to be as the guide and references.

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1.9.2 Definition of Terms

GDP per capita

An approximation of the value of goods produced per person in the country,

equal to countrys GDP divided by the total number of people in the country.

Population

A group of individuals or items which share one or more characteristics from

the data can be gathered and analyzed.

Base Lending Rate

Base Lending Rate (BLR) is a base interest rate calculated by financial

institutions according to a formula which takes into account the institutions

cost of funds and other administrative costs. Table below is the latest BLR

published by commercial banks in Malaysia.

Unemployment rate

Percentage of employable peoples that actively seeking work, out of the total

number of employable peoples.

Housing Price Index

The housing price index is measures the price of residential housing.

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CHAPTER 2: LITERATURE REVIEW

2.1 Gross Domestic Product (GDPPC)

The gross domestic product is the godfather of the indicator world ( B.Ryan, 2010).

As an aggregate measure of total economic production for a country, GDP

represents the market value of all goods and services produced by the economy

during the period measured, including personal consumption, government

purchases, private inventories, paid-in construction costs and the foreign trade

balance (exports are added, import are subtracted) ( B.Ryan, 2010). Presented only

quarterly, GDP is most often presented on a annualized percent basis ( B.Ryan,

2010). Most of the individual data sets will also be given in real terms, meaning that

the data is adjusted for price changes, and is therefore net of inflation ( B.Ryan,

2010). The GDP is an extremely comprehensive and detailed report. Real GDP is

the one indicator that says the most about health of the economy and the advance

release will almost always move markets ( B.Ryan, 2010). It is by far the most

followed, discussed and digested indicator out there- useful for economists, analysts,

investors and policy makers ( B.Ryan, 2010). The general consensus is that 2.5 % -

3.5 % per year growth in real GDP is the range of best overall benefit ; enough to

provide for corporate profit and jobs growth yet moderate enough to not incite undue

inflationary concerns ( B.Ryan, 2010). If the economy is just coming out of

recession, it is OK for the GDP figure to jump into the 6-8% range briefly, but

investors will look for the long term rate to stay near the 3% level ( B.Ryan, 2010).

The general definition of an economic recession is two consecutive quarters of

negative GDP growth ( B.Ryan, 2010).

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The strengths of GDP are it is considered the broadest indicator of economic output

and growth. Other than that, the real GDP takes inflation into account, allowing for

comparisons

against other historical time periods ( Bureau of Economic Anaysis, BEA). On the

other hand, the GDP also have its own disadvantages or weaknesses which are the

data is not very timely it is only released quarterly and the revisions can change

historical figures measurably ( the difference between 3 % and 3.5 % GDP growth is

a big one in terms of monetary policy) ( B.Ryan, 2010).

The real GDP per capita of an economy is often used as an indicator of the average

standard of living of individuals in that country, and economic growth is therefore

often seen as indicating an increase in the average standard of living (Trading

Economics, 2010). However, there are some problems in using growth in GDP per

capita to measure general well being. GDP per capita does not provide any

information relevant to the distribution of income in a country (Trading Economics,

2010). GDP per capita does not take into account negative externalities from

pollution consequent to economic growth (Trading Economics, 2010). Thus, the

amount of growth may be overstated once we take pollution into account (Trading

Economics, 2010). GDP per capita does not take into account positive externalities

that may result from services such as education and health (Trading Economics,

2010). GDP per capita excludes the value of all the activities that take place outside

of the market place ( such as cost-free leisure activities like hiking) (Trading

Economics, 2010). The economists are well aware of these deficiencies in GDP,

thus, it should always be viewed merely as an indicator and not absolute scale

(Trading Economics, 2010). The economists have developed mathematical tools

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to measure inequality, such as the Gini Coefficient (Trading Economics, 2010). The

flaws of GDP may be important when studying public policy, however, for the

purpose of economic growth in the long run it tends to be very good indicator

(Trading Economics, 2010). There is no other indicator in economics which is as

universal or as widely accepted as the GDP (Trading Economics, 2010). Economic

growth is exponential, where the exponent is determined by the PPP annual GDP

growth rate (Trading Economics, 2010). Thus, the differences in the annual growth

from country A to country B will multiply up over the years (Trading Economics,

2010).

Figure 2 : Chart of Malaysia GDP per capita (Purchasing Power Parity-PPP)

start year 1998-2008

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2.2 Population (POP)

Real GDP growth rate in developed countries is found to be a sum of two terms (

K.O. Ivan,2006). The first term is the reciprocal value of the duration of the period of

mean income growth with work experience, Tcr ( K.O. Ivan,2006). The current value

of Tcr in the USA is 40 years ( K.O. Ivan,2006). The second term is inherently

related to population and defined by the relative change in the number of people with

a specific age ( 9 years in the USA) ( K.O. Ivan,2006). The Tcr grows as the square

root of real GDP per capita. Hence, evolution of real GDP is defined by only one

parameter-the number of people of the specific age ( K.O. Ivan,2006). Predictions

for the USA, the UK, and France are presented and discussed ( K.O. Ivan,2006).A

similar relationship is derived for real GDP per capita ( K.O. Ivan,2006). Annual

increment of GDP per capita is also a combination of economic trend term and the

same specific age population term ( K.O. Ivan,2006). The economic trend term

during last 55 years is equal to $400 (2002 US dollars) by the attained level of real

GDP per capita ( K.O. Ivan,2006). Thus,the economic term has an asymptotic value

of zero ( K.O. Ivan,2006). Inversion of the measured GDP values is used to recover

the corresponding change of the specific age population between 1955 and 2003 (

K.O. Ivan,2006). The population recovery method based on GDP potentially is of a

higher accuracy than routine censuses ( K.O. Ivan,2006).

Per capita GDP growth rate in the USA was used by Kitov (2005a) as an external

parameter in prediction of the observed evolution of the personal income distribution

(PID), its components and derivatives ( K.O. Ivan,2006). The PID has been

expressed as a simple and predetermined function of GDP per capita and the age

structure of the working age population in the USA ( K.O. Ivan,2006). The current

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study interprets this relationship in the reverse direction ( K.O. Ivan,2006). The

observed PID is considered as a result of each and every individual effort to earn

(equivalent-to produce goods and services) money in the economically structured

society as exists in the USA ( K.O. Ivan,2006). Thus, the individual money

production (earning) aggregated over the US working age population is the inherent

driving force of the observed economic development ( K.O. Ivan,2006). The working

age means the age eligible to receive income, i.e. 15 years of age and above ( K.O.

Ivan,2006). This effectively includes all retired people ( K.O. Ivan,2006). Per capita

GDP growth rate is uniquely determined by the current distribution of personal

income which, in turn, depends on population age distribution ( K.O. Ivan,2006).As

shown by Kitov (2005a), the mean personal income distribution is only governed by

two values- at the starting point of the distribution and the Tcr- the value of work

experience characterized by the highest mean income ( K.O. Ivan,2006).

The basic formula and scenarios with respect to activity rates and growth of labour

productivity ( V.Thierry, S.C.Ronald, 2010) are :

GDP inhabitant = ( GDP / P employed ) x ( Pemployed / Pworking age ) x ( Pworking age / P total

= (labour productivity) x (employment rate) x (share of total

population at working age )

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Table 1 : The table of Malaysias Population Growth Rate in for 2005 and 2010

Average annual Growth rate (%)

2005 % 2010 % 8 MP 9MP

Total population 26.75 100.0 28.96 2.6 1.6

Citizens 24.36 65.9 26.79 100 2.0 1.9

Bumiputera 16.06 25.3 17.95 67.0 2.3 2.2

Chinese 6.15 7.5 6.52 24.3 1.3 1.2

Indian 1.83 1.3 1.97 7.4 1.6 1.4

Others 0.32 0.35 1.3 2.6 2.2

Non-citizens1 2.39 2.17 11.0 -1.8

Age Structure

0 - 14 8.72 32.6 9.18 31.7 1.7 1.0

15 - 64 16.88 63.1 18.42 63.6 3.0 1.8

65 and above 1.15 4.3 1.36 4.7 4.3 3.4

Dependency ration (%) 58.5 57.2

Median age (years) 23.3 24.2

Total fertility rate 2.76 2.48

Bumiputera 3.18 2.80

Chinese 2.19 2.04

Indian 2.34 2.15

Urban (%) 63.0 63.8

Rural (%) 37.0 36.2

Source : Department of statistic and economic planning unit.

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2.3 Base Lending Rate (BLR)

BLRis a minimum interest rate calculated by banking institutions based on a formula

which takes into account the institutions cost of funds and other administrative costs.

This is defined by central bank of the countries (Bursa Malaysia, 2011). The

Overnight Policy Rate (OPR) from Bank Negara Malaysia is reference for banks in

BLRadjustments, but there might differ from bank to others bank (Bursa Malaysia,

2011). At the global money market down turn, BLR will get lower and if the money

market on uptrend, it will correlation upward (Bursa Malaysia, 2011). It is wisely and

timely to consider take up mortgage loan and start to own your property at the lower

BLR as current (Bursa Malaysia, 2011).

Interest rate of a loan is commonly taken as an indicator of the cost of a loan, and

the borrowerss ability to afford said loan (Jeni, Estate 123). However, as pointed

out by in Money3.com .my, it is not the most accurate (Jeni, Estate 123). The

interest rate is made up of two components-the BLR and the Spread (Margin) (Jeni,

Estate 123). The formula is as following :

Interest rate = Base Lending Rate (BLR) + Spread (Margin)

While the loans spread/margin is usually fixed by the lender (e.g. the bank) at +1%

or -2%, the BLR is a changing variable, and usually overlooked or not fully

understood by most borrowers (Jeni, Estate 123). In Malaysia, the BLR takes into

account the institutions cost of funds and other administrative costs related to the

loan and is adjustable (Jeni, Estate 123). Adjustment to the BLR are made almost
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simultaneously among all banks nationwide, usually during the time in correlation to

the adjustments of the OPR, determined by Bank Negara Malaysia during Monetary

Policy Meeting (Jeni, Estate 123).

During the few current advertisements we seen, especially on housing loans from

banks, low BLR made part of the taglines on every ad (Jeni, Estate 123). This

seems to be an attractive deal, and the BLR for now is not too high, being 6.75 % at

the present (Jeni, Estate 123). However, the same cannot be said in a few years

time : whether if it will increase or decrease (though experts observation is on the

opinion that BLR will be on the rising trend starting from now and onwards) (Jeni,

Estate 123). For those cutting down on the spread, lets not forget that interest rate

is the sum of the BLR and the spread (Jeni, Estate 123). So, particularly for those

who are having a BLR-based loan, the sum that you are paying may not be the sum

that you will continue to pay for in the next few years (Jeni, Estate 123). As a

borrower, one should look at the loan being taken over a time period of 25-30 years

(the usual payment period for a home loan), instead of just a few years ahead (Jeni,

Estate 123).

A lower interest rate leads to expansion of bank credit and thus acts as a propelling

force of economic growth (South Indian Bank, 2010). In the aftermath of the global

economic recession, all economies followed a concerted policy action of monetary

easing to gear up both consumption and investment expenditure in the economy

(South Indian Bank, 2010). Thus liquidity was injected and interest rates were

reduced (South Indian Bank, 2010). There is a wide spectrum of interest rates in

the economy, representing interest rate as a reward for savings and as a cost of

credit from the banks (South Indian Bank, 2010). In a deflationary situation, lower

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interest rates, devoid of inflation, are ideal and in an inflationary condition, interest

rates should account for inflation factor also, and are thus likely to be higher (South

Indian Bank, 2010). What really matters most to the borrowers and from the

economys point of view is the real interest rates (South Indian Bank, 2010). The

nominal interest rates move with inflation (South Indian Bank, 2010). The real

interest rate can be computed as nominal interest rate minus inflation rate (South

Indian Bank, 2010).An examination of the historical trend of real interest rates and

Hong Kongs GDP and its domestic demand components, however, does not reveal

any definite pattern on how real interest rate movements would impact on the

economy in a very significant way (Economic background of Hong Kong, 2005).

This may not to be at all surprising given that the levels of indebtedness of the

household and corporate sectors are low in Hong Kong, as suggested by the

persistently high savings ratio and the strong net external financial asset position of

Hong Kong (Economic background of Hong Kong, 2005). Also, it is worth noting

that the interest rate policy of the US Federal Reserve is to forestall the inflation risk

that may arise from a buoyant US economy (Economic background of Hong

Kong, 2005).

Thus the negative impacts arising from higher interest rates have often been diluted

by other positive economic developments (Economic background of Hong Kong,

2005). Correlation analysis indicates a much higher correlation between domestic

demand and GDP, and between domestic demand and total exports of goods and

services, which are usually the main drivers of economic growth (Economic

background of Hong Kong, 2005).

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Figure 3 : Malaysia Base Lending Rate as at December 2006-December 2010

2.4 Unemployment Rate (UE)

In order to know broadly about the unemployment rate in Malaysia, we must

know about the meaning of unemployment rate first. According to

Investopedia.com the meaning is the percentage of the total labor

force that is unemployed but actively seeking employment and willing to

work.

The unemployment rate rose to 5.7%, in July, up from 5.5% in May and June

and up from a near-term low of 4.4% in March of 2007 (S.Jim, 2008). Payroll

employment declined for the seventh consecutive month in July (by 51,000

jobs) and revised data on the inflation-adjusted (real) gross domestic product

(GDP) show that real GDP declined in the 4 th quarter of 2007, raising

concerns about a recession (S.Jim, 2008). Perhaps buoyed by stimulus-

Source : Bank Negara Malaysia

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payment effects on consumer spending, GDP growth was 1.9% in the 2 nd

quarter, up from 0.9% growth in the 1st quarter(S.Jim, 2008).

Common sense dictates that some interdependence should exist between

unemployment and GDP (F.Farzad, 2003). However, if a causal relation

between these variables is to be identified, it should mostly be from

unemployment to GDP (F.Farzad, 2003). It is true that at times changes in

output cause unemployment to change in an opposite direction (F.Farzad,

2003). For example, a booming economy that is producing more output may

require more workers (less unemployment) to account for the rise in

production (F.Farzad, 2003). However, many exogenous factors can weaken

or eliminate this negative causal relationship (F.Farzad, 2003). First, as

Prachowny (1993), Blinders (1997), and Altig, Fitzgerald, and Ruperts

(1997) point out, at times, increases in output may be due to changes in

labor productivity (F.Farzad, 2003). Indeed, productivity changes may very

well disrupt the otherwise stable negative relationship between output and

unemployment (F.Farzad, 2003).A rise in labor productivity increases GDP

(F.Farzad, 2003). However, the resulting increase in output may not lead to

a decline in unemployment (F.Farzad, 2003). In fact, this has mostly been

the case in the US and other western countries in recent decades (F.Farzad,

2003). What we have been witnessing is that advances in technology have

contributed to substantial increases in productivity, which in turn has

heightened the amount of output produced in the economy (F.Farzad, 2003).

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Moreover, improvement in human intellectual assets of the producing sector

has enabled the labor force to produce products and services more

efficiently and faster (F.Farzad, 2003). The resulting growth in GDP,

however, has not necessarily contributed to lower unemployment rates,

since many unskilled workers have lost their jobs to new technology

(F.Farzad, 2003). The fact is that enhancement in labor productivity can be

responsible for higher structural unemployment rate (F.Farzad, 2003). In

short, changes in productivity can disrupt the negative causal relation from

output to unemployment (F.Farzad, 2003). Second situation, in which

increases in output may not cause a decline in the unemployment rate is

when workforce works longer hours and more days per week (F.Farzad,

2003). The rise in working hours, while contributing to the rise in output,

would have no impact on the conventional unemployment rate (F.Farzad,

2003). In this case, GDP will rise with no effect on the unemployment rate

(F.Farzad, 2003).

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Figure 4 : The line chart of the unemployment rate in Malaysia during

2000 until 2009

2.5 Housing Price Index (HPI)

A potential investor before making an investment decision for a particular

type of investment would like to know its past performance and related risk-

return characteristics (H.K.Ting, 2003). Similarly, an investor who has made

an investment would like to know how the investment has performed in

comparison with similar assets and with different types of investment options

is necessary (H.K.Ting, 2003). Residential property investment is a popular

form of investments among Malaysians apart from fixed deposits (FD), unit

trusts and equities (H.K.Ting, 2003). The launching of the Malaysian House

Price Index (MHPI) in February 1997, provides an opportunity to measure the

Source : http://www.indexmundi.com

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investment performance of residential properties in Malaysia (H.K.Ting,

2003). Prior to the publication of the MHPI, there is difficulty in measuring

and comparing the performance of residential properties (H.K.Ting, 2003).

The heterogeneous nature of residential properties has prevented direct

comparison of performance even with the same type of property in the same

locality (H.K.Ting, 2003). House price indices are relatively new in Malaysia

(H.K.Ting, 2003). Interests in setting-up house price indices arise after the

sharp real property asset inflation in 1995 (H.K.Ting, 2003). Factors that

have fuelled the investment and speculative demand is the availability of

easy credit, low interest rate for housing loans and the increasing wealth per

capita as evidenced by higher deposits in financial institutions, savings in

provident funds and stock market investment (H.K.Ting, 2003). Among the

effort to come out with house price indices is the Maybank-RAM Property

Index (H.K.Ting, 2003). It is an index based on the average actual

transaction values of three types of houses in housing estates in Kuala

Lumpur, Petaling Jaya, and Shah Alam ; covering single storey terrace

house, double storey terrace and semi-detached houses (H.K.Ting, 2003).

The Malaysian House price Index is a national house price index initially

prepared and published by the National Institute of Valuation (INSPEN)

(H.K.Ting, 2003). Thereafter with the establishment of the National Property

Information Centre (NAPIC) the index is now produced by NAPIC (H.K.Ting,

2003). The objective of the MHPI is to monitor the trend of house prices and

as a barometer for measuring the general performance of the residential

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property market (H.K.Ting, 2003). The MHPI has more than 60 sub-indices

apart from the national and state house price indices (H.K.Ting, 2003).

The current state of the UK housing market is state of 37% over valuation

against GDP, down from an extreme reading of more than 60%, so still far

from reaching a bottoming state associated with 0% and below (W.Nadeem,

2010). However, against this if year on year economic growth materialises as

forecast, then even if UK house prices did not change from the current levels

then UK house prices will have trended towards a state of normalization

against GDP to an estimated reading of 10% (W.Nadeem, 2010). GDP

growth trend analysis points to a continuing depression in the UK housing

market for the next 3-4 years, with the most probable outcome being for a

gradual shallow drift lower in prices over the next 1-2 years (6-12%), followed

by a further 1-2 years of base building (W.Nadeem, 2010).

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Figure 5 : The graph of quarterly housing price index start year 2001- 2010

Source :http://www.malaysiapropertyinc.com

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CHAPTER 3: RESEARCH METHODOLOGY

3.1 CHAPTER DESCRIPTION

This chapter consists of the research methodology used in order to complete the

study. This chapter will explain in detail about the sources of data and the

formula being used in order to gain the results.

3.2 DATA COLLECTION

3.2.1 Secondary Data

Secondary data is the data gathered and recorded by someone else prior to the

current needs of the researchers. Secondary data is usually a historical data in

nature which has already been assembled and do not require any access to

respondents and subjects. All the data and information is obtained from the

LIBRARY TENGKU ANIS at Kota Bharu and LIBRARY TEMERLOH,

JOURNALS, INTERNET which is BURSA MALAYSIA, ECONOMIC PLANNING

UNIT, MINISTRY OF FINANCE, VALUATION AND PROPERTY SERVICES

DEPARTMENT, DEPARMENT OF STATISTICS and also DATASTREAM as the

sources to get the data of Growth Domestic Product Per Capita (GDPPC),

Population (POP), Base Lending Rate (BLR), Unemployment Rate (UE), and

Housing Price Index (HPI).

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3.3 PROJECT TIME FRAME

ACTIVITIES JAN - FEB MAR-APR

Define the problem

Formulate the hypotheses

Collect data using proper methods of dataobtainedEvaluate the accuracy of the data obtained

Do statistical data analysis

Interpret the result

Submits the final report

3.4 DATA ANALYSIS

3.4.1 Statistical Procedure Of Social System (SPSS)

The analysis will be done to get the finding to the study after all the data is

collected. All the data is treated and interpreted by using relevant information

from analysis method. An analysis of the data can be made by using the

Statistical Procedure of Social System (SPSS), then interpret the result findings.

3.4.2 Hypothesis testing

The theory of hypothesis testing is concerned with developing rules or

procedures for deciding whether to reject or not reject the null hypothesis. There

are two mutually complementary approaches for devising such rules, namely,

confidence interval and test of significance. Both these approaches predicate that

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the variable (statistic or estimator) under consideration has some probability

distribution and that hypothesis testing involves making statements or assertions

about the value of parameter of such distribution.

3.4.3 Statistical Method Of Analysis

Regression analysis is statistical technique in observed data to relate a variable

of interest which is called the dependent or response variable, to one or more

independent or predictor variables. The objective is to build a regression model

or prediction equation that can be used to describe, predict, and control the

variables. There are two types of regression analysis namely single regression

and multiple regression analysis but only multiple regressions will be used in this

study. The multiple regression is a useful statistical technique that can be used to

predict the relationship of 2 or more variables. For this research, it will use the

multiple regression technique such below:

Y = + 1 X1 + 2 X2 + 3 X3 + 4 X4 +

Y = Value of dependent variable

(Gross Domestic Product- GDP per capita)

= Constant

1 = coefficient to be estimated




X1 = Housing Price Index (HPI)

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X2 = Base Lending Rate (BLR)

X3 = Unemployment Rate (UE)

X4 = Population (POP)

= Random Error Term

From the Multiple Regression Model, GDP will be analyzed with the independent

variables which economic indicators are including housing price index, base

lending rate, unemployment rate and population.

There are regression techniques that include:

1. Test of Correlation:

(a) Coefficient of Correlation (R)

(b) Coefficient of Determination (R)

2. Test of Significant

(a) F- Statistics

(b) T- Statistics

3. Test of Multicollinearity

(a) High R2

(b) Tolerance and variance inflation factors (VIFs)

4. Test of Autocorrelation

(a) Durbin Watson

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3.5 TEST OF CORRELATION

3.5.1 Correlation Coefficient

Correlation coefficient, R,is the degree of association between independent and

dependent variable. The coefficient changes from +1 to 1. It is categorized into

one of the following category listed for linear correlation coefficient result:

r = 1 : Perfect positive linear correlation

0.75 < r r > 0.25 : Weak positive linear correlation

r = 0 : No linear correlation

-0.25 < r < 0 : Weak negative linear correlation

-1 < r < -0.75 : Strong negative linear correlation

r = -1 : Perfect negative linear correlation

3.5.2 Coefficient of Determination

It is used as a way to measure the accuracy of a regression lines

prediction. It is denoted as R-square. The R-square figure is a value which

ranges between zero and unity. Values of R square between zero and

unity indicate the relative strength of the relationship between the X

variable and the Y variable in the regression equation, The R-square

values that are close to unity indicate that the regression equation will give

relatively accurate predictions of the Y figures once the associated X value

is known and entered into the regression equation.

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3.6 TEST OF SIGNIFICANT (Hypothesis Testing)

3.6.1 F-Statistic

This study will also use the F-test in order to analyze how reliable the overall

model is. It provides an overall appraisal of the regression equation to evaluate

the significance of each individual component to the entire regression model. In

other words, it is used to test the hypothesis in which variation in dependent

variable explained a significant proportion of the variation in the independent

variable.

To compute F-value: = (k 1, n k1)

Where; n = no. of observation

k = no. of independent variables

To conduct test:

Computed F-value > Critical F-value, reject Ho

Computed F-value < Critical F-value, accept Ho

If the computed -statistic is higher than the critical value of , there is a

significant relationship between the independent variables and dependent

variable.

3.6.2 T- Statistic

T- Statistic is used to determine if there is significant relationship between the

independent variable and dependent variable. It also measures the probable

error in the predictive value. It is calculated by dividing the coefficient by the

standard error and the confidence interval used is normally 95%. It is used when

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we test one population correlation coefficient when both variables are

quantitative and when the sample is small.

Degree of Freedom (Df) = n k -1

Whereby:

k = Number of independent variable

n = number of observation

To conduct test:

If the calculated T-value is greater than the T-distribution table, the independent

variable is said to be statistically significant. If the calculated T-value is less then

T-distribution table, the independent variable is said to statistically insignificant.

3.7 TEST OF MULTICOLLINEARITY

Multicollinearity

The term multicollinearity is due to Ragnar Frisch. Originally it meant the

existence of a perfect, or exact, linear relationship among some or all

independent variables of a regression model. The several sources of

multicollinearity may be due to the following factors :

Computed T-value > Critical T-value, reject Ho

Computed T-value < Critical T- value, accept Ho

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a) The data collection method employed. For example, sampling over a limited

range of the values taken by the regressors in the population.

b) Constraints on the model or in the population being sampled.

c) Model specification. For example, adding polynomial terms to a regression

model, especially when the range of the X variable is small.

d) An over determined model. This happens when the model has more

explanatory variables than the number of observations.

Practically, the consequences of multicollinearity are wider confidence intervals,

insignificant t-ratios, a high R square but few significant t-ratios. The detection of

multicollinearity can be done by consider these rules :

a) High R2 but few significant t ratios

b) High pair-wise correlations among regressors

c) Examination of partial correlations

d) Auxiliary regressions

e) Tolerance and variance inflation factor

f) Scatterplot

After we know on how to detect the multicollinearity, now it will be the time to

know remedy it. Here there are two choices that can be taking into

considerations 1: do nothing, 2: follow some rules of thumbs. The rules of thumb

procedures that can be followed as below :

a) A prior information

b) Combining cross-sectional and time series data

c) Dropping a variable and specification bias

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d) Transformation of variables

e) Additional or new data

3.8 TEST OF AUTOCORELLATION

It can be defined as correlation between members of series of observations

ordered in time. It also can be detect by looking at the patterns of autocorrelation

and non autocorrelation. The detection of autocorrelation can be determine by

using the graphical method and runs test, durbin-watson d-test and Breusch-

Godfrrey (BG) test. For this study, I decide to do the durbin-watson d-test. Durbin

Watson Statistic- d test, is a common test for serial correlation or also known as

auto correlated. It appears when data in time series are used. The major cause

of auto correlated error term in the model is that the model is mis-specified. This

happens whenever one or more key explanatory variables have been omitted.

Durbin Watson Statistic shows that the model is good when the value of the

Durbin Watson statistic is in between 1.5 to 2.5. If the value is below 1.5, then the

model has an error and if the value is higher than 2.5, this indicates the presence

of negative autocorrelation. Negative autocorrelation is not particularly common.

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CHAPTER 4: FINDINGS AND ANALYSIS

4.1 DATA INTERPRETATION

The purpose of this study is to find out whether the dependent variable and

independent variables are related to each other or not. This analysis will prove

how relationship between the variables by using SPSS programmed test. In this

chapter, regression and correlation analysis have been done in order to test the

hypotheses. Regression analysis provides the basis for predicting the values of

one or more variables and correlation analysis. The result will enables researcher

to assess the strength of the relationship (correlation) among the variables. Once

the results have been derived from the program, the results will be interpreted.

4.2 MULTIPLE LINEAR REGRESSIONS

As a result, the multiple linear regression equation for hypothesis in this paper

could be analyzes and know how the relationship between inflation rate and

interest rate on gross national saving. Refer to table 4.1, the equation can be

derived as follow:

Y = + 1 X1 + 2 X2 + 3 X3 + 4 X4 +

GDPPC = 30, 920.081 - 1225.564 POP - 1202.843 BLR - 3251.543 UE +

55.437 HPI +

Where; GDP = Gross Domestic Product

POP = Population

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BLR = Base Lending Rate

UE = Unemployment Rate

HPI = Housing Price Index

= Random Error Term

= Constant

Table 2 : Result of coefficient value

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) 30920.081 8176.934 3.781 .0

POP -1225.564 281.757 -.567 -4.350 .0

BLR -1202.843 493.316 -.284 -2.438 .0

UE -3251.543 897.740 -.397 -3.622 .0

HPI 55.437 19.492 .399 2.844 .0

a. Dependent Variable: GDPPC

INTERPRET EACH COEFFICIENT

30920.081

The intercept is positive.

So, when POP is zero, GDP per capita is 30920.081

When BLR is zero, GDP per capita is 30920.081

When UE is zero, GDP per capita is 30920.081

When HPI is zero, GDP per capita is 30920.081

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-1225.564

Population growth rate is negative and there is negative direct relationship

between population and GDP per capita.

When population increase by 1 %, GDP per capita decrease by 1225.564

- 1202.843

Base lending rate is negative and there is negative direct relationship between

base lending rate and GDP per capita.

When base lending rate increase by 1 %, GDP per capita decrease by 1202.843

-3251.543 Unemployment rate is negative and there is negative direct relationship between

unemployment rate and GDP per capita.

When unemployment rate increase by 1 %, GDP per capita decrease by

3251.543

55.437

Housing price index is positive and there is positive direct relationship between

housing price index and GDP per capita. When housing price index increase by 1 %, GDP per capita increase by 55.437

4.3 TEST OF CORRELATION

Table 3 : Result of Model Summary b

Model Summaryb

Model R R Square Adjusted R Square

Std. Error of the

Estimate Durbin-Watson

1 .995a

.991 .984 203.903 1.713

a. Predictors: (Constant), HPI, UE, BLR, POP

b. Dependent Variable: GDPPC

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4.3.1 Coefficient of correlation (R)

The correlation coefficient (R) ranges from +1.0 to -1.0. If the value of R is +1.0,

there is a perfect positive linear relationship, if the value of R is -1.0; a perfect

negative linear relationship is indicated. No correlation is indicated if R equal to 0.

For the relationship studied between independent variables and dependent

variable, it show R valued at 0.995 which indicates a very strong positive linear

correlation.

4.3.2 Coefficient of Determination (R)

(R) = 0.991

Coefficient of determination shows the explanatory power of the equation. It is

determined how much percentage that changes in the dependent variable to the

independent variables. According to figures 4, R value resulted at 0.991 it

indicates that 99.1% of the variation in the dependent variable (GDP per capita)

is explained by the variability of the independent variables (POP, BLR, UE and

HPI) remains of 0.9% cannot be explained by these independent variable and

may be explained by other factors.

4.4 TEST OF SIGNIFICANT (HYPHOTESIS)

Table 4 : ANOVA

ANOVAb

Model Sum of Squares Df Mean Square F Sig.

1 Regression 2.272E7 4 5680519.025 136.628 .000a

Residual 207882.000 5 41576.400

Total 2.293E7 9



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4.4.1 F statistic

F-test and F-statistic is used to explain the overall models. In this study, the

result for F-statistic is as follows:

n = 10 number of observation (Year 2000-2009)

k = 4 independent variables

Significance level = 0.05

H0 = B2= B3 = B4 = 0

H1 = B2 = B3 = B4 =0

Df numerator = K - 1

= 4-1

= 3

Df denumerator = N - K

= 10 - 4

= 6

F-test = R2 / (K-1)

F-table = 4.76

Calculated F-value Critical F-value

136.628 > 4.76

The overall is significant to explain the changes in GDP per capita.

(1-R2) / (N-K)

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Based on table 4.3, F-statistics value is equal to 136.628. The value is more than

F-table or the critical value of F, which only 4.76. Therefore, the regression

equation is considered significant. Besides that, it also shows that the model

used fits fairly well at 95% confident level in explaining the relationship between

the population, base lending rate, unemployment rate, housing price index and

gross domestic product per capita. From the analysis, I as the researcher can

conclude that, the models are significant.

4.4.2 T statistic

Degree of Freedom (Df) = n k - 1

Whereby:

k = Number of independent variable

n = number of observation

Therefore, according to this study:

Df = 10 4 1

= 5

T-table = 2.015

From the above calculation, the degree of freedom at 19 at 95% confidence

interval is equal to 2.015. Therefore, the relationship and hypothesis testing

between the independent variables and dependent variable can be known as

below:

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Table 5 : A calculated t-value and value of 95% confident interval for t-test

The negative sign be ignored.

T-value (POP) = -4.350 > 2.015

It shows that t-value is greater than t-table and result we should reject H0.

When t-value is greater than t-table, there is significant relationship between

population (POP) and GDP per capita (GDPPC).

T-value (BLR) = - 2.438 > 2.015



base lending rate (BLR) and GDP per capita (GDPPC).

T-value (UE) = -3.622 > 2.015


When t-value is greater than t-table, there is significant relationship betweenunemployment rate (UE) and GDP per capita (GDPPC).

Model T Sign Table t-value Result

POP - 4.350 > 2.015 Significant

BLR - 2.438 > 2.015 Significant

UE -3.622 > 2.015 Significant

HPI 2.844 > 2.015 Significant

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T-value (HPI) = 2.844 > 2.015



housing price index (HPI) and GDP per capita (GDPPC).

4.5 TEST OF MULTICOLLINEARITY

Multicollinearity occurs because two(or more) variables are related they are

measure essentially the same thing. It is frequently both a theoretical problem

and problem with a particular sample of data.The greater the multicollinearity, the

greater the standard errors. This multicollinearity problem can be seen in our

regression equation that involve the GDP per capita as the dependant variable

while Population, BLR, Unemployment rate and Housing Price Index as the

independent variables.

Refer to G.N.Namodar and P.C.Dawn, the way to detect the multicollinearity is as

followings :

High R2 but few significant t-ratios. When the R2 > 0.8, this shows the

multicollinearity problem. From my regression result, the R2 is 0.991 @ 99.1 %.

Tolerance and variance inflation factors (VIFs)

The tolerance level is the 1 R2 value when each f the independent variables is

regressed on the other independent variables. The low tolerance level (closer to

0) means high multicollinearity while if the tolerance level is close to 1 means

have little multicollinearity.

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Based on the regression result, if we see at our R2, it is too high at 99.1 %. Therefore,

there will be the multicollinearity problem. Simultaneously, when we take a look at the

VIF table the value is greater than 5. So, there are multicollinearity problem. This is

because if VIF > 10 mean high multicollinearity problem and if VIF > 5 mean there is

possibility multicollinearity problem.

How to fix the multicollinearity problem ?

For this study, I already choose one method or way to fix the multicollinearity problem

which is one of the rule of thumb procedures :-

Drop the variable

I choose to drop the Housing Price Index from my regression because as at

Table 4.5 its VIF value is more than 10 which indicate having high

multicollinearity problem.

Coefficientsa

Model


Standardized

Coefficients

T Sig.

Collinearity Statistics

B Std. Error Beta Tolerance VIF

1 (Constant) 30920.081 8176.934 3.781 .013

POP -1225.564 281.757 -.567 -4.350 .007 .107 9.35

BLR -1202.843 493.316 -.284 -2.438 .059 .134 7.45

UE -3251.543 897.740 -.397 -3.622 .015 .151 6.61

HPI 55.437 19.492 .399 2.844 .036 .092 10.86


TABLE 6 : Table of Coefficients with Collinearity Statistics

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Here, is the result after eliminate the HPI variable.

Firstly, we take a look on its R2.

TABLE 7 : R2 after eliminate HPI

Model Summaryb


Std. Error of the


1 .988a

.976 .964 301.16321 2.420

a. Predictors: (Constant), UE, POP, BLR


We can see that the changes of R2 from 0.991 @ 99.1 % to 0.976 @ 97.6 % much better

than before, so that the multicollinearity problem in this regression already at the

minimum level.

Secondly, take a look on the changes of VIF value

TABLE 8 : Table of Coefficient with Collinearity Statistic after eliminate HPI

Coefficientsa

Model


Standardized

Coefficients

t Sig.



1 (Constant) 50211.699 6744.796 7.445 .000

POP -1981.991 137.380 -.916 -14.427 .000 .981 1.0

BLR -2142.467 541.100 -.505 -3.959 .007 .243 4.1

UE -4831.831 1041.474 -.589 -4.639 .004 .245 4.0


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The VIF value also show the multicollinearity problem at the minimum level

because for the 3 variables above give VIF value that less than 5.

4.6 TEST OF AUTO CORRELATION ( Durbin Watson)

Other than multicollinearity, our regression may also have the autocorrelation

problem. Autocorrelation problem can be define as the correlation between

members of series observations ordered in time ( as in the series data) or space .

In the classical linear regression model assumes that such autocorrelation is not

exist in the disturbance or error term (i). In order to make sure our regression has

the autocorrelation problem or not, here we go for the Durbin-Watson test.

Figure 6 : The Durbin Watson Test

Durbin-Watson test

n = 10 dL = 0.376 4-dU= 1.586k = 4 dU= 2.414 4-dL = 3.624

0 dL 4-dU 1.713 2 dU 4- dL 4d

0.376 1.586 2.414 3.624

According to the Durbin-Watson table in regression result, the value is 1.713. This value

is in the range of 1.5 until 2.5. The value in this range means we do not have to reject H0

of H*0 . So, there is no autocorrelation problem.

Do not reject

H0 ofH*0 or

H0 : no positive autocorrelationH*0 : no negative autocorrelation

Reject H0Evidence

ofpositive

auto-

correlatio

Zoneof

in-

Zoneof

in-

Reject H0Evidence

ofpositive

auto-

correlatio

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CHAPTER 5 : CONCLUSION AND RECOMMENDATION

5.1 CONCLUSION

The primary focus on this study is to determine the relationship between

independent variables which consist of population, base lending rate,

unemployment rate and housing price index with the dependent variable, gross

domestic product per capita in Malaysia. The multiple regression technique has

been used in order to see the relationship. From the equation, it can conclude,

most of the independent variables are negative relationship to GDP per capita.

For another findings in this study show that, there is a strong positive linear

correlation between the independent variables and dependent variable because

Coefficient of Correlation, R valued at 99.5% which a fit model. Moreover,

Coefficient of Determination, R valued at 99.1%, that only remains at 0.9%

cannot be explain by these independent variable and may be explained by other

factors. It is possible to conclude that the changes in the dependent variables are

highly explained by population, base lending rate, unemployment rate and

housing price index. Even though, other factors also consider play vital role in

determine the changing in dependent variable.

For significant test to know which hypotheses choose for every independent

variable in this study, F- Statistics and T- Statistics have been used in order to

see the relationship. F-statistics is used to explain the overall models. F- value

showed 136.628 , that is more than F-table which 4.76. Therefore, the regression

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equation is considered significant and the models also significant. So, based on

the result, it is possible to accept alternate hypothesis and reject null hypothesis.

On the other hand, for T- Statistic test population, base lending rate,

unemployment rate and housing price index have a significant relationship with

GDP per capita. So, alternate hypotheses are accepted and null hypotheses are

rejected.

So, it is important for us to determine the significant factors that have major

impact on GDP per capita. Besides these factors, there are other factors that can

be influence GDP per capita such as foreign direct investment, exports, imports,

balance of payment and money supply. All of these factors are the determinant

that influences the level of GDP per capita.

5.2 RECOMMENDATION

Based on this research, there are several recommendations that can be made.

The primary recommendations made are to the government because this study is

on GDP per capita. The recommendations made concerns mainly on giving ideas

and opinions on the ways to increase GDP per capita. This is important because,

when GDP per capita is increasing it shows that the economy is robust, industry

growth is good and people are earning more and therefore have more disposable

income to spend. Spending in turn drives the economy and the services or

manufacturing sectors. The end result is the economy booms and grows.

Countries that successfully raise their gross domestic product, or GDP per capita

often rely on a small group of experts to put a coherent set of policy reforms in

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place, according to World Bank private sector development specialists Alberto

Criscuolo and Vincent Palmade.

In order to enhance and increase the nations GDP per capita there are several

things that can help to boost up the economy. Firstly, we should allow more

countries to invest in our country. Many American companies maintain brand

presence all over the world (M.Nathaniel, 2010). But the U.S. also tops the list of

nations that others invest in, according to "The World Factbook" of the Central

Intelligence Agency (M.Nathaniel, 2010). Therefore U.S. can boost up their

economy as well as the GDP per capita efficiently and effectively (M.Nathaniel,

2010).

Other than that, the GDP per capita will be increase if everyone contributes to the

country. When the unemployment rate is reducing there will be the high

opportunity for the country to make more money. To illustrate the situation, the

researcher use this example, if a farmer owns 90 acres of land, but he can only

plant 40 acres by himself, then if he hires a helper, he should be able to plant 80

acres of land, he's just doubled the amount produced (Yahoo answer). If he

hires a 2nd hand then they should be able to plant the full 90 acres, either by

planting 40 acres, 40 acres, and 10 acres, or by each planting 30 acres (Yahoo

answer). The 2nd helper didn't increase production as much as the 1st, but there

was still a net increase (Yahoo answer). However, if the farmer hires additional

helpers, there won't be any increase, because all of his land can be planted by

only 3 people (Yahoo answer). So, the researcher can say that the more people

working there will be more money in the country flow.

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Besides that, not just increase the number of working people, we also need to

increase the productivity of the working people which are have people work more

efficiently in order to increase the GDP per capita. This is also can be illustrate by

using this example, if the farmer was able to plant 40 acres, but only had his

seed and a shovel, then you could increase his productivity by giving him a horse

and a plow (Yahoo answer). Using the horse and plow the farmer might be able

to plant all 90 acres by him (Yahoo answer). What exactly the researcher means

is, if interest rates is reducing, you can make access to money easier, making it

more likely that more farmers will end up with plows (Yahoo answer). If the

value of our currency drops relative to other currencies, it means that we

increase demand for our products, because if the farmer can only sell 40 acres

worth of crops each year, there really isn't any point to increasing his productivity

(Yahoo answer). Increasing productivity also allows us to reduce prices,

because with the horse and plow, 1 farmer can now do as much as he and 2

helpers could do before (Yahoo answer). With the money he saves not hiring

helpers he can pay for the horse and plow, save a little something for himself,

and still reduce the price of the crops he sells (Yahoo answer).

For future research, researcher also would like to suggest to other researchers

who have interest to explore and go deeper into this domain to carefully design

the questionnaire and add more questions so that it can be more reliable.

Besides, for researchers who want to continue this research or have interest to

do the same research, researcher suggests that they should use the quarterly

data rather than annually data. Besides that, it is also suggested that future

researchers take a long period of study in order to get more reliable result.

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REFERENCES

ARTICLES

SIB (2010), Base Rate in India, Students Economic Forum, South Indian Bank.

Barnes, S. et al. (2011), The GDP Impact of Reform: A simple Simulation

Framework, OECD Economics Department Working Papers, No. 834, OECD

Publishing.

K.Stephan, L.David (2007), University Of Gottingen/University of Manchester,

The Impact of population Growth on Economic Growth and Poverty Reduction in

Uganda, 5-14.

2011, Monthly Economic Bulletin, Department of Finance, 2-12.

K.O.Ivan. (2006), Society for the Study of Economic Inequality, Working Paper

Series, Russian Academy of Sciences, GDP Growth Rate and Population, (42) 6,

2-60.

V.Thierry, S.C.Ronald. (2010), Research Center of Flemish Government,

Population Ageing and Its Effect on GDP:Can increased labour productivity

and/or employment compensate for the shrinking working age population, 2-34.

G.A.Noor. Finance Department Faculty of Business Management Universiti

Kebangsaan Malaysia, Interest Rates Cycles:Impact on the Malaysian

commercial banks lending and deposit rates, 2-14.

M.Deepak (2010), Reserve Bank of India, BIS Review, Perspectives On Lending

Rates in India, 82(2), 25-33.

H.T.Tan. (2009), Sunway University College, School of Business, Base Lending

Rate and Housing Prices:Their impacts on residential housing activities in

Malaysia.

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M.Andrew, Meen, Geoffrey. (2003), House Price Appreciation, Transactions and

Structural Change in the British Housing Market:A macroeconomic perspective,

Real Estate Economics, 31(1), 99-116.

P.Tiwari. (2001), Housing and Development Objectives in India, Habitat

International, 25, 229-253.

F.Farzad. (2003), Montana State University-Billings,An Empirical Analysis of the

Relationship Between GDP and Unemployment, 19, 1-6.

T.F.Chor. (2009), Economics Programme, School of Social Sciences, Universiti

Sains Malaysia, Journal of Economics and Management, The Linkages Among

Inflation, Unemployment and Crime rates in Malaysia, 3 (1), 50-61.

H.K.Ting. (2003), Department of Estate Management, Faculty of Architecture,

Planning & Surveying, UiTM, Investment Characteristics of the Malaysian

Residential Property Sector, 2-17.

A.Hussin, H.S.Muzafar, B.Z.Ahmad. (2009), Faculty of Economics and

Management, Universiti Putra Malaysia, International Journal of Business and

Management, The Effect of Fiscal Variables on Economic Growth in Asian

Economies:A dynamic panel data analysis, 4 (1), 56-70.

H.C.Hon. (2009), Nottingham University Business School, International Research

Journal o Finance and Economics, The Impact of Property Market Developments

on the Real Economy of Malaysia, 30, 65-86.

C.Leung. (2003), Macroeconomics and housing:A review of the literature, Journal

of Asian Economics, 18, 63-75.

MFPED (2004), Population, Growth, Fertility and Poverty and The National

Economy, Discussion Paper.

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INTERNET

Aiyar, S. & (n.d.). Economic Growth and The Demographic Dividend.

Retrieved March 03, 2011, from http://www.zeta-inst.com

Malaysia GDP Growth Rate (n.d.). Retrieved March 30, 2011, from

http://www.tradingeconomics.com

Low Base Lending Rate (n.d.). Retrieved March 28, 2011, from http://www.blr.my

Experts opinion : Impact of BLR on rise (n.d.). Retrieved March 28, 2011, from

http://www.starproperty.myUK House Prices and GDP Growth Trends Analysis (n.d.) Retrieved March 28,

2011, from http://www.marketoracle.co.ukS.Jim. (n.d.) Unemployment Rate Up ; Employment Down ; GDP Growth

Accelerates. Retrieved March 28, 2011, from http://www.house.goz.jecOn Base Lending Rate and Home Loans (n.d.). Retrieved March 28, 2011, from

http:www.estate123.com

House prices and per capita GDP (n.d.). Retrieved March 28, 2011, from

http://www.financialinsights.com

GDP Growth Rate (n.d.). Retrieved March 30, 2011, from

http://www.tradingeconomics.com

Mfalvey (n.d.). Definition and Importance of GDP. Retrieved March 30, 2011,

from http://www.thestrategicmile.com Economic indicators (n.d.). Retrieved March 28, 2011, from

http://www.investopedia.com
http://www.zeta-inst.com/http://www.tradingeconomics.com/http://www.blr.my/http://www.starproperty.my/http://www.starproperty.my/http://www.marketoracle.co.uk/http://www.marketoracle.co.uk/http://www.house.goz.jec/http://www.house.goz.jec/http://www.financialinsights.com/http://www.tradingeconomics.com/http://www.thestrategicmile.com/http://www.thestrategicmile.com/http://www.investopedia.com/http://www.investopedia.com/http://www.investopedia.com/http://www.thestrategicmile.com/http://www.tradingeconomics.com/http://www.financialinsights.com/http://www.house.goz.jec/http://www.marketoracle.co.uk/http://www.starproperty.my/http://www.blr.my/http://www.tradingeconomics.com/http://www.zeta-inst.com/

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Data of Housing Price Index (n.d.). Retrieved March 27, 2011, from

http://www.jpph.gov.myData of Unemployment rate (n.d.). Retrieved March 28, 2011, from

http://www.statistics.gov.myData of Population (n.d.). Retrieved March 25, 2011, from http://www.epu.gov.myData of Base Lending Rate (n.d.). Retrieved March 25, 2011, from

http://www.malaysiapropertyinc.com

BOOKS

Rozieana. (2009). Manual Research Methodology. Malaysia: UiTM.

Sekaran, U. (2006). Research Methodology.
http://www.jpph.gov.my/http://www.jpph.gov.my/http://www.statistics.gov.my/http://www.statistics.gov.my/http://www.epu.gov.my/http://www.epu.gov.my/http://www.malaysiapropertyinc.com/http://www.malaysiapropertyinc.com/http://www.malaysiapropertyinc.com/http://www.epu.gov.my/http://www.statistics.gov.my/http://www.jpph.gov.my/

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Appendixes

Regression result

Variables Entered/Removedb

Model Variables Entered

Variables

Removed Method

1 HPI, UE, BLR,

POPa

. Enter

a. All requested variables entered.


Model Summaryb


Std. Error of the


1 .995a

.991 .984 203.90292 1.713



ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 2.272E7 4 5680519.025 136.628 .000a

Residual 207882.000 5 41576.400

Total 2.293E7 9



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Coefficientsa

Model


Standardized

Coefficients

t Sig.

Collinearity Statistic


1 (Constant) 30920.081 8176.934 3.781 .013

POP -1225.564 281.757 -.567 -4.350 .007 .107 9.3

BLR -1202.843 493.316 -.284 -2.438 .059 .134 7.4

UE -3251.543 897.740 -.397 -3.622 .015 .151 6.6

HPI 55.437 19.492 .399 2.844 .036 .092 10.8


Collinearity Diagnosticsa

Model

Dimensi

on Eigenvalue Condition Index

Variance Proportions

(Constant) POP BLR UE HPI

1 1 4.893 1.000 .00 .00 .00 .00

2 .100 6.988 .00 .08 .00 .00

3 .006 29.433 .00 .00 .04 .03

4 .001 58.344 .00 .35 .04 .07

5 4.728E-5 321.675 1.00 .57 .92 .89


Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value 1.3824E4 1.7843E4 1.5940E4 1588.92263 10

Residual -2.35785E2 1.92049E2 .00000 151.98026 10

Std. Predicted Value -1.332 1.198 .000 1.000 10

Std. Residual -1.156 .942 .000 .745 10


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Charts

Regression result after omitted HPI

Variables Entered/Removedb

Model Variables Entered

Variables

Removed Method

1 UE, POP, BLRa

. Enter

a. All requested variables entered.


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Model Summaryb


Std. Error of the


1 .988a

.976 .964 301.16321 2.420



ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 2.239E7 3 7461920.810 82.271 .000a

Residual 544195.671 6 90699.278

Total 2.293E7 9



Coefficientsa

Model


Standardized

Coefficients

t Sig.



1 (Constant) 50211.699 6744.796 7.445 .000

POP -1981.991 137.380 -.916 -14.427 .000 .981 1.02

BLR -2142.467 541.100 -.505 -3.959 .007 .243 4.11

UE -4831.831 1041.474 -.589 -4.639 .004 .245 4.07


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Collinearity Diagnosticsa

Model

Dimensi

on Eigenvalue Condition Index

Variance Proportions

(Constant) POP BLR UE

1 1 3.916 1.000 .00 .01 .00 .00

2 .078 7.085 .00 .98 .00 .00

3 .006 26.418 .00 .01 .07 .06

4 .000 171.947 1.00 .01 .93 .94


Residuals Statisticsa

Minimum Maximum Mean Std. Deviation N

Predicted Value 1.3775E4 1.7776E4 1.5940E4 1577.11982 10

Residual -4.19360E2 4.50462E2 .00000 245.89873 10

Std. Predicted Value -1.372 1.164 .000 1.000 10

Std. Residual -1.392 1.496 .000 .816 10


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Charts

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Table of data gathered (GDPPC, POP, BLR, UE, and HPI)

YEAR GDPPC POP BLR UE HPI

2000 13939 3.4 6.78 3.1 100.0

2001 13815 2.7 6.39 3.6 101.1

2002 14338 2.5 6.39 3.5 103.6

2003 15043 2.4 6.13 3.6 107.7

2004 15748 2.3 6.13 3.5 112.9

2005 16239 2.2 5.99 3.5 115.6

2006 17109 1.3 6.61 3.3 117.8

2007 17773 1.3 6.72 3.2 123.8

2008 17786 1.3 6.7 3.3 129.8

2009 17607 1.3 5.62 3.7 131.8

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