Factor That Influence Housing Price in Malaysia

FACTORS THAT INFLUENCE HOUSING PRICE IN MALAYSIA

MOHD SHAKIB BIN ISHAK 2012619382

BACHELOR OF BUSINESS ADMINISTRATION WITH HONOURS (FINANCE) FACULTY OF BUSINESS MANAGEMENT UNIVERSITY TECHNOLOGY MARA SHAH ALAM

JANUARY 2017

FACTORS THAT INFLUENCE HOUSING PRICE IN MALAYSIA

MOHD SHAKIB BIN ISHAK 2012619382

Submitted in Partial Fulfilment of the Requirement for the Bachelor in Office Systems Management with Honours

FACULTY OF BUSINESS MANAGEMENT UNIVERSITY TECHNOLOGY MARA SHAH ALAM

JANUARY 2017

i

DECLARATION OF ORIGINAL WORK

BACHELOR OF BUSINESS ADMINISTRATION WITH HONOURS (FINANCE) FACULTY OF BUSINESS MANAGEMENT UNIVERSITY TECHNOLOGY MARA “DECLARATION OF ORIGINAL WORK” I,

, (I/C Number :

)

Hereby, declare that: 

This work has not previously been accepted in substance for any degree, locally or overseas, and is not being concurrently submitted for this degree or any other degrees.



This project paper is the result of my independent work and investigation work, except where otherwise stated.



All verbatim extracts has been distinguished by quotation marks and sources of my information have been specifically acknowledged.

Signature : _____________________________

Date :

January 2017

ii

LETTER OF SUBMISSION

Mohd Shakib Bin Ishak 2012619382 Bachelor of Bachelor Of Business Administration With Honours Faculty of Business Management

2017

Puan Fatimah binti Setapa Head of Program (BM222) Faculty of Business Management University Technology Mara, Shah Alam

TITLE: SUBMISSION OF FINAL PROJECT PAPER JANUARY 2017 I am submitting herewith 1 hard bound report and 1 CD of my thesis entitle “FACTORS THAT INFLUENCE HOUSING PRICE IN MALAYSIA” for your kind perusal and consideration. Thank You. Your faithfully,

__________________________ MOHD SHAKIB BIN ISHAK 2012619382

iii

ACKNOWLEDGEMENTS

I would like to take this opportunity to express my sincere gratitude to Puan Fatimah binti Setapa for his expert guidance, give me valuable suggestions and encouragement at every stage during the completion of this research. It was pleasant and inspiring experience for me to work under her guidance. A special note of admiration and gratitude to my families and friends, without their moral support, it would have been impossible for me to go through this piece of work.

Mohd Shakib bin Ishak January, 2017 Faculty of Business Management University Technology MARA Malaysia

iv

TABLE OF CONTENT Page TITLE

i

DECLARATION OF ORIGINAL WORK

ii

LETTER OF SUBMISSION

iii

ACKNOWLEDGEMENTS

iv

TABLE OF CONTENT

v

LIST OF FIGURE

xi

LIST OF TABLES

xii

LIST OF ABBREVIATIONS

xiii

ABSTRACT

xiv

CHAPTER 1 - INTRODUCTION

1

1.1

Introduction

1

1.2

Background of The Study

2

1.3

Problem Statement

3

1.4

Research Objective

5

1.5

Research Questions

5

1.6

Significance of Study

6

1.7

Scope and Limitation of Study

7

1.8

Definition of Terms

9

1.8.1

Housing Price

9

1.8.2

Interest Rate

9

v

TABLE OF CONTENT Page 1.8.3

Inflation Rate

9

1.8.4

Gross Domestic Product

9

1.8.5

Population (Malaysia)

10

1.8.6

Stock Price

10

CHAPTER 2 - LITERATURE REVIEW AND RESEARCH FRAMEWORK

11

2.1

Introduction

11

2.2

The relationship of the interest rate with the housing price

11

2.3

The relationship of the inflation rate with the housing price

13

2.4

The relationship of the gross domestic product with the housing price

15

2.5

The relationship of the population with the housing price

16

2.6

The relationship of the stock price with the housing price

17

2.7

The Conceptual Framework

18

CHAPTER 3 - RESEARCH METHODOLOGY

19

3.1

Introduction

19

3.2

Data Collection

19

3.3

Variables

19

3.3.1

Dependent Variable

19

3.3.2

Independent Variables

20

3.4

Population and Sample

20

3.5

Study Design

20

vi

TABLE OF CONTENT Page 3.6

Unit of analysis

21

3.7

Time Horizon

21

3.8

Data Analysis and Treatment

21

3.8.1

Linear Multiple Regression Model

21

3.8.2

Economic a Priori Sign

23

3.8.3

Test of Coefficient

24

3.8.3.1

Determination of Coefficient (R2)

24

3.8.3.2

Correlation Coefficient (Simple Linear Correlation)

24

3.8.4

Test of Significant - Probability (P-valued method)

25

3.8.5

Test for Multicollinearity

25

3.8.5.1

Variance Inflation Factor

25

3.8.5.2

Durbin Watson

26

CHAPTER 4 - ANALYSIS AND FINDINGS

27

4.1

Introduction

27

4.2

Graph

27

4.2.1

Malaysia Housing Price Index – Dependent Variable

27

4.2.2

Interest Rate – Independent Variable 1

28

4.2.3

Inflation Rate – Independent Variable 2

28

4.2.4

Gross Domestic Product – Independent Variable 3

29

4.2.5

Population – Independent Variable 4

30

4.2.6

Stock Price – Independent Variable 5

30

vii

TABLE OF CONTENT Page 4.3


31

4.3.1


31

4.3.1.1 The Equation

32

4.3.2


33

4.3.3

Test of Coefficient

33

4.3.3.1

Determination of Coefficient (R2)

33

4.3.3.2

Correlation Coefficient (Simple Linear Correlation)

34

4.3.4


34

4.3.7


35

4.3.7.1

Variance Inflation Factor

35

4.3.7.2

Durbin Watson

35

CHAPTER 5 - CONCLUSION

37

5.1

Introduction

37

5.2

Conclusion

37

5.3

Recommendation

38

REFERENCE

39

Journals

39

Websites

42

viii

TABLE OF CONTENT Page APPENDICES APPENDIX 1

APPENDIX 2

APPENDIX 3

43 Final Data had been choose for analyse by annually basis with 3 Independent variable

43

APPENDIX 1.1 : Historical data

43

APPENDIX 1.2 : Model of Multiple Linear Regression

45

APPENDIX 1.3 : Model of Correlation

45

APPENDIX 1.4 : Model of Variance Inflation Factors

45

APPENDIX 1.5 : Scatter Plots Relationship MHPI with INT

46

APPENDIX 1.6 : Scatter Plots Relationship MHPI with INF

46

APPENDIX 1.7 : Scatter Plots Relationship MHPI with KLCI

47

Data Analysis using 5 independent variable by Annually Basis

48


48


48


48

Data Analysis using 4 independent variable by Annually Basis

49


49


49


49

ix

TABLE OF CONTENT Page APPENDIX 4

APPENDIX 5

APPENDIX 5

Data Analysis using 4 independent variable by Semi-Annually Basis

50


50


50


50

Data Analysis using 4 independent variable by Quarterly Basis

51


51


51


51

Turn It In – Final Result

52

x

LIST OF FIGURE

Page Graph 1

Malaysia Housing Price

27

Graph 2

Interest Rate

28

Graph 3

Inflation Rate

28

Graph 4


29

Graph 5

Population

30

Graph 6

Stock Price

30

Graph 7

Durbin Watson

35

xi

LIST OF TABLES

Page Table 1

Economic a Priori Sign Before Result

23

Table 2

Range Correlation Coefficient (Simple Linear Correlation)

24

Table 3

Range Test for Multicollinearity – Variance Inflation Factor

25

Table 4

Multiple Linear Regressions Analysis

31

Table 5

Economic a Priori Sign Result

33

Table 6

Correlation Coefficient

34

Table 7

Variance Inflation Factors

35

xii

LIST OF ABBREVIATIONS

MHPI

Malaysia Housing Price Index

INT

Interest Rate

INF

Inflation Rate

GDP


POP

Population

KLCI

Kuala Lumpur Composite Index

CPI

Consumer Price Index

BLR

Base lending Rate

PR1MA

Projek Perumahan Rakyat 1Malaysia

PPA1M

Perumahan Penjawat Awam 1Malaysia

KRI

Khazanah Research Institute

xiii

ABSTRACT

The purpose of this study is to provide an analysis on the “FACTORS THAT INFLUENCE HOUSING PRICE IN MALAYSIA”. The researcher had revised the independent variable and had chosen interest rate (BLR), inflation rate (CPI), and stock price (KLCI) as an independent variables and dependent variable is Malaysia housing price index (MHPI). The researcher has achieved the main objective from the research of Factors That Influence Housing Price in Malaysia. The objective is to identify whether interest rate, inflation rate, and stock price are significant or not significant in affecting Malaysia housing price index. Another objective is to determine the relationship and to identify the most dominant that has significant between independent variables and Malaysia housing price index as a dependent variable. Using Multiple Linear Regression Model, the result show the inflation rate is 1 of 3 independent variables have significant positive relationship with Malaysia housing price index. In other hand, interest rate and stock price as independent variables has not significant positive relationship with Malaysia housing price index. However, inflation rate is the most dominant that has significant between independent variables.

xiv

CHAPTER 1

INTRODUCTION

1.1

Introduction

Chapter one will discuss regarding the background of study, problem statement, research question, research objective, significant of study and summary regarding Factors That Influence Housing Price in Malaysia.

In background of study, Malaysia Housing Price index is dependent variable and the independent variables are the interest rate, inflation rate, gross domestic product, population and stock price. In this chapter also discuss about the problem that relate in problem statement.

1.2

Background of The Study

Refer to Maslow's Hierarchy of Needs, the basic physiological are needs human being is a shelter or home. The house is not just a place for shelter but it is also a place to live.

In Malaysia, the Government has played an important role in implementing policies and programs that people can get their own house. Furthermore to the essential component in the economy, it is also a basic requirement for all Malaysians who have been recognized by the Government. In the two parties play an important role in the construction of housing is the public sector and the private sector that will

1

develop low-cost, medium cost and high cost. Although, by Othman on year 1999, through the policies implemented by the Government on aspects of housing production in the private sector focus mainly housing development schemes.

Government had launched the first project of housing production. On the topic in The Star on 15th October 2011, My First Home Scheme had launched by Government in their Budget 2011 with intent to help young people whose earning RM3,000 and below for their first home. In the scheme, its allows young people to get 100% approval of the financial institutions and enabling them to own their first home without having to pay 10% of down payment. Projek Perumahan Rakyat 1Malaysia (PR1MA) was the second project had launched by the Government. The program is on purpose to encourage home ownership especially among the middle earners income by providing affordable residential properties in major cities across the country.

Early 2013, other project was launched by Government. It is focus to allow the lower and middle earners income to buy a house especially in the cities. The project named Perumahan Penjawat Awam 1Malaysia (PPA1M). PPA1M was a project of Government that affordable housing scheme for help Government staff to own a house (Sinar Harian on 18 April 2013).

Thru New Straits times on 6th April 2016, a special product that was introduced by the Government to help especially for the middle-income is Skim Pembiayaan Deposit Rumah Pertama (MyDeposit). Government had allocate RM200 million as a contribution to the deposit for the purchase of a first home by one household. MyDeposit Scheme was launched by the Government through the Ministry of Housing and Local Government in objectives of the National Housing Policy to increase the capacity and accessibility, for those to own households. Now a

2

days, housing price are increase, therefore Government's initiatives to introduce the scheme and so that Malaysia people can buy a house. MyDeposit Scheme was officially opened for application on 6th April 2016.

The Valuation and Property Services Department of the Ministry of Finance is compile Malaysian House Price Index (MHPI) for semi-annually. The index is only for house property and they are distinguished by their index of terraced houses, highrise buildings, detached and semi-detached.

There are many factors that influence housing prices. The overall factors have a strong relationship with dependent variable. In this topic will be focus on four factors or variables to determine the relationship with housing prices. Some variables have a positive or negative relationship but some have both negative and positive.

1.3

Problem Statement

House is a requirement for people to stay and live. With comfortable and complete house is one of dreams. Developer are offer various types of house for the example,

low-cost

houses,

terraced

houses,

bungalows,

apartments

and

condominiums.

However, in Berita Harian on 12th September 2016 had mentioned that there are several factors is cause to have a house. The several factors that cause the problem such as increase in house prices between 5 to 15 percent a year, developer are depending on the location, type of home and the current economic situation. The common factor are factor of manipulating the increase in supply and demand, price increases, improvement of infrastructure facilities are in perfect developed, repair and

3

renovation of residential as well as increased cost of the construction and management of the house.

Thru Utusan Malaysia on 1st September 2015, a research from Khazanah Research Institute (KRI) regarding price of house was too expensive are not reasonable with meaning that, by international standards the house price was 4.4 times higher than the average salary a year. On the others side, factor that cause to the house price increase in the market are, growth of population. More population increase, will rise the demand for own their own house.

Nowadays, because of the increasing of house price, the opportunity for worker those are new or fresh graduate to get their own house are very difficult. Normally, income that their earned a month is enough to cover the cost of living and others loan such as study loan. Based on Sinar Harian on 8th April 2016, most of them are facing financial limit for who have a family to own their own house, due to rising of house price and cost of living.

4

1.4

Research Objective

(i)

To identify whether interest rate, inflation rate, gross domestic product,

population and stock price are significant or not significant in affecting housing price;

(ii)

To determine the relationship between the interest rate, inflation rate, gross

domestic product, population and stock price with housing price ; and

(iii)

To identify the most dominant variable that has significant influence towards

the housing price.

1.5

Research Questions

(i)

Which of the factors are significant or not significant in affecting housing

price? ;

(ii)

What is the relationship between interest rate, inflation rate, gross domestic

product, population and stock price with housing price? ; and

(iii)

Which is variable has the most significant influence towards the affecting

housing price?

5

1.6

Significance of Study

This analysis is to identify the relationship between the interest rate, inflation rate, gross domestic product, population and stock price with housing price in Malaysia which is, based on the relationship between the dependent variable and independent variables. The analysis mentioned was a tasks and modules required for the program BM222 Bachelor of Business Administration with Honours (Finance), Faculty of Business Management, University Technology Mara Shah Alam.

Therefore, from this analysis for the learning process to guide researcher to identify the problem based on the analysis and able to understand how the information obtained. Mostly, other researcher use DataStream that provided by the UITM to obtain accurate data and appropriate according to the study.

By refer to the relevant journal for reference and tutored by lecture it was once of the practices to preparing a thesis and researcher also used E-View system applicable to research data as a benchmark result.

To the end, based on this study researcher hope this study can assist as a reference for other researcher, those who is under the program of Bachelor of Business Administration with Honours (Finance) at University Technology Mara Shah Alam.

6

1.7

Scope and Limitation of Study

This study focuses on the relationship between housing prices with interest rate, inflation rate, gross domestic product, population and stock price whether the relationship between them is positive or negative. This study uses data collected from DataStream. This study was prepared by chapter as follows. Chapter 1 introduces the study used as the focus and problem. Chapter 2 is a literature review. It is associated relationship between housing prices with interest rate, inflation rate, gross domestic product, population and stock price. Followed by chapter 3, the methodology of the model used in this study. Chapter 4 discusses the results obtained and lastly is chapter 5, the conclusions and recommendations of the study.

This subject focuses on the relationship between housing prices with interest rate, inflation rate, gross domestic product, population and stock price whether the relationship between them is positive or negative and it use data collected from DataStream. Therefore, it was prepared by chapter as follows; Chapter 1 introduces the study used as the focus and problem. Chapter 2; is a literature review. It is associated relationship between housing prices with interest rate, inflation rate, gross domestic product, population and stock price. Followed by chapter 3, the methodology of the model used in this study. Chapter 4; discusses the results obtained and lastly is chapter 5; the conclusions and recommendations from the results.

7

The scopes of this study are as follows:

(i)

The study will use secondary data from Malaysia Housing Price Index, Interest Rate (BLR), Inflation Rate (CPI), Gross Domestic Product (GDP), Population and Stock Price (KLCI) from 1980 till 2015 with 36 observation;

(ii)

Data are analysed by using the multiple regression analysis method performed by computer software called The Econometric Views (E-View9).

Below are several limitation occurred in this study;

(i)

Time constraint will be one of the limitations due to commitment and nature of the work of the researcher, it was unable to spend more time to reviewed on more related literature and have limited precious time for personal consultation with the advisor.

(ii)

The data is limited to only a few users. Especially in the using of database Thomson Reuters Eikon.

(iii)

During the research process, lack of experience is one of the factor that prevent researcher to produce a good research study.

8

1.8

Definition of Terms

1.8.1

Housing Price

Data released by the National Property Information Centre Malaysia. It consists of 4 types of terrace houses, high rise, detach and semi detach. The overall data collected and consolidated and categorized as Malaysia Housing Price Index (MHPI). MHPI is benchmark for housing price.

1.8.2

Interest Rate

Base Lending Rate is a minimum interest rate calculated by financial institutions based on a certain formula. This formula takes into account the institutions cost of funds and other administrative costs. Base Lending Rate is benchmark for interest rate.

1.8.3

Inflation Rate

Measures change in the price level of consumer goods and services purchased by household. Consumer price index is benchmark for inflation rate.

1.8.4


Gross Domestic Product is the market value (money-value) of all final goods and services produced in a geographical region, usually a country. GDP is benchmark for gross domestic product.

9

1.8.5

Population (Malaysia)

The population estimates based on the 2000 Population and Housing Census data which had earlier been revised for under-enumeration based on underenumeration rates from the Census Coverage Evaluation Survey. This population estimates are subsequently projected to current years. These projections are derived based on assumptions of components of fertility, mortality and migration.

1.8.6

Stock Price

In Malaysia, selling and buying stock is done by Bursa Malaysia and Kuala Lumpur Composite Index (KLCI) currently known as the FTSE Bursa Malaysia KLCI is use as indicator of how these performances stock. Stock prices can be affected by a number of things including volatility in the market, current economic conditions, and popularity of the company. Kuala Lumpur Composite Index is benchmark for stock price.

10

CHAPTER 2

LITERATURE REVIEW AND RESEARCH FRAMEWORK

2.1

Introduction

The literature review basic to any research organization. Information research that have been used in this study. This chapter will discuss the empirical literature in housing prices and independent variables interest rate, inflation rate, gross domestic product, population and stock price. In particular, will be discussing on previous studies that have been carried out by others about the variables that affect the housing price.

2.2

The relationship of the interest rate with the housing price

During the research from year 2001 to 2010 by monthly basis in Malaysia, there is not significant positive relationship between the interest rate and the housing price according to Ong (2013). Buyer or speculators also had been inform that do not care of the interest rate charged by the bank, demand or supply that is not balance. Because of that, the investor are confident and optimistic about the housing market. Therefore, it mentioned that the speculator in perspective, they might not hold the houses for the long term and in the short term the houses will be sell. The impact is the gain are more than the cost. Currently, developers try to design and develop a number of houses based on demand and buyer is willing to pay more to obtain their desired type of house. So the price of the houses will increase because of the fact.

11

The interest rate on housing price in Lagos, Nigeria during the research by Suvita and Olanipekun (2015), for the year of 2000 to 2014 by annually basis, there is a significant correlation regarding the housing price and the interest rate. Though, 17% of interest rate is still high and not many people can afford to take a loan because of the percentage and it is shows that, this correlation is strong negative relationship.

There is differential price expectation perceived by housing consumers in differing economic situations. On year 1997, the empirical result suggest that the interest rate effect on housing prices in Hong Kong is contras significantly positive and negative in the deflationary by Tak Yun (2003). Research from year 1981 to 2001 by quarterly basis in Hong Kong, found evidence found that the higher correlation displayed between housing prices, nominal interest rates and in year 1998 to 2001 it is relative to those in year 1981 to 1997.

Research by Y.Zheng (2014), in China for the year December 2012 to June 2015 thru monthly basis, stated that the high interest rate is not necessarily indicate low housing price, although it does calm down the housing price to some extent and more specific in the correlation is strong for the long term but not significant in the short-term. In specific, interest rate adjustment is lagging and inflation rate has been high end the years and caused the real interest rates to decrease.

In the other view, the positive sign on interest rate could be that high house prices are usually a sign of high activity in the economy and interest rate is increased to cool down the economy, which could result in high house prices at a high interest rate and vice versa by Jonas (2007), by annually basis in Sweden and Australia from year 1980 to 2006.

12

By annually basis from Alabama, USA in year 1988 to 2007, the interest rate coefficient is negative and the significant is 1 percent. Towards that, when loan interest rate are increases by 1 percent, causally the demand for new single family houses decreases by 0.1 percent, research by John (2009).

2.3

The relationship of the inflation rate with the housing price

The relationship between housing prices and inflation rate is significantly positive. The impact of the CPI on housing prices is greater than that of housing prices on the CPI, which indicates that housing purchase has been used as effective hedge for inflation rate. However, by Wu & Tidwell (2015), have make a research from year 1996 to 2010 by using annually basis in China mention that the inflation rate in order to curb housing prices have to control.

Besides that, refer to the Mahdi & Masood (2011), investigates the relationship between housing price and inflation rate from empirical perspective. There is a positive and significant relationship between inflation housing prices. Research from year 1989 to 2007 by using quarterly basis in Iran.

In Kenya, by using quarterly basis data from year 2004 to 2014, shows that the negative relationship occur between the house price and inflation rate. According to Kibunyi (2015), inflation rate affects the demand side more than the supply side as the purchasing power of consumers is eroded. The strength of this relationship is observed to be weak and therefore not significant. Furthermore, by the result of the regression test which show a negative coefficient which is not significant given the pvalue of 13.3%. Inflation rate correlation negatively with house price at -0.0706 which

13

means as inflation rate increases, the purchasing power of the buyers is eroded, the disposable incomes therefore decrease and this in turn slows demand for new housing. This is consistent with regression results which give a coefficient of -0.5915. As the law of supply and demand takes effect, this is bound to slow down prices unless there is a house price bubble. This relationship is however quite weak and is not significant at p-value 0.6610.

There is strong long term relationship between the inflation rate and the housing price during the research period from 2000 to 2010 by quarterly basis in Malaysia according to the Pillaiyan (2015). Same goes to Datta & Mukhopadhyay, (2011), relationship between Inflation and Economic Growth in Malaysia - An Econometric Review market. . Inflation rate is a constant increase in price indices on account which also increase cost of living in the country. During inflation rate, general public feel relatively poor because the increase in price of goods and services will reduce the purchasing power. In the short run inflation rate plays the vital role for affecting economic growth negatively on the other hand in the long run economic growth leads to change positive in inflation rate by data taken from annually basis in Malaysia for period 1971 to 2007.

However, refer to Ong (2013), by monthly basis in Malaysia during the period of 2001 till 2010. Inflation rate is not a significant determinant of housing price and conclude the other variables such as capital income and employment have a significant with housing price.

14

2.4

The relationship of the gross domestic product with the housing price

The relationship of the gross domestic product has a positive connection with housing price. While the level of gross domestic product arises, it will manipulate the housing price according to Taltavull (2003), by using annually basis in Spain from year 1989 to 1999.

View from Ong (2013), the gross domestic product is found to be significant positive correlation with the housing price. By taken data from year 2001 to 2010 using monthly basis in Malaysia, conclude that increase in the gross domestic product because of the increase in personal consumption.

The economic growth fluctuations play a major role in determining the changes in house prices and the relationship is positive. No long run relationship exists

among

the

housing

price

and

gross

domestic

product,

by

Batayneh & Al-Malki (2015), during research period of 1985 to 2012 with using annually basis data in Saudi Arabia.

Data taken from Kenya, using quarterly basis from year 2004 to 2014 by Kinbunyi (2015), the strong positive correlation was observed between house prices and gross domestic product. Its means that, the house prices move in the same direction as the economic variables. In other words, as the economic variables increase, the house prices also increase. However, the long run relationships were observed between house prices and gross domestic product which lead to the exclusion of a house bubble.

Otherwise by monthly basis data from year 1996 to 2004 in Ukraine, results of the empirical estimation confirm that macro factors indeed influence real estate

15

market. Increase in gross domestic product level appear to have positive effect on real estate prices. It was also concluded that real estate market respond to changes in gross domestic product that is to economic progress with the lapse of time by Mavrodiy (2015).

Refer case of The Economic Factors Affecting Residential Property Price in Penang Island, the gross domestic product show positive correlation with housing price and strong correlation towards housing price by Zandi (2015), data taken from year 2007 to 2014 using annually basis.

2.5

The relationship of the population with the housing price

Population is significantly correlated with the increase in house price. While the population in Malaysia increase, the demand for housing lead to rising the housing price. The factor cause the house price increase is, more demand than supply for housing, it will affect the price of housing and people are willing to spend more money to buy a house when there are fewer house on the market data taken from year 2000 to 2012 using quarterly basis by Ong (2013).

The Factors of Demand for Single Family Housing, throughout the research period data from 1988 till 2007 by quarterly basis in Alabama, USA. The coefficient for population was positive and significant at 1 percent level. The study found that higher population growth tends to increase demand for new single family houses by John (2009).

More increase of population are appear to have a positive effect on real estate prices by Mavrodiy (2005), in Ukraine and also mention, the assumptions that

16

economic development and increase in income level as well as rise of population induces demand and lead to increase in price level through data from year 1996 to 2004 by monthly basis.

In China, the significance of the coefficient is lower than in the static regression analysis as only the population coefficient are significant in the long-run equilibrium results and the sign of the long-run relationship between the population and the house price index is negative by Stohldreier (2012), during the period of 1998 till 2011 using annually basis.

2.6

The relationship of the stock price with the housing price

Regarding the House Prices and the Collapse of the Stock Market in Mainland China by Huang (2009), the Empirical Study on House Price Index, during the period of 2006 till 2008 by using monthly basis data, there is a strong positive correlation between the stock markets and housing price. Otherwise, it can be drawn and possible that property price will slip as a result of the stock market turmoil. However, the property market will not collapse. Furthermore, during year 1995 to 2006 by using quarterly basis in Thailand, the results from the analysis also have a positive relationship with housing prices and stock price thru Ibrahim (2009).

The analysis from year 2003 to 2012 in China by data from quarterly basis, for a long term, stock prices have a negative effect on house prices, indicating that the substitution effect dominates the relationship between stock and house prices. However, in the short term, the positive effects of stock prices on house prices are statistically significant according to Yuan (2014).

17

Furthermore, the stock prices influence a major role in determining the changes in house prices and the relationship between the house prices and stock prices is negative thru Batayneh & Al-Malki (2015), data from year 1985 to 2012 by annually basis in Saudi Arabia.

The Change of Relationship between Real Estate and Stock Markets in China by Cheng (2015), during analysis from year 2003 to 2013 using annually basis, state that, the increase of Shanghai composite index has a negative influence on housing price index will reminds investors and Chinese government of the stock market’s and real estate market’s price rational return after financial crisis.

2.7

The Conceptual Framework

Independent Variable

Dependent Variable

Interest Rate - BLR (INT)

Inflation Rate - CPI (INT)

Gross Domestic Product (GDP)

Housing Price (MHPI)

Population (POP)

Stock Price (KLCI)

18

CHAPTER 3

RESEARCH METHODOLOGY

3.1

Introduction

This study will focus on the impact of interest rate (BLR), inflation rate (CPI), gross domestic product (GDP), population (Malaysia) and stock price (KLCI) as an independent variables towards the housing prices index (MHPI) in Malaysia as a dependent variable.

3.2

Data Collection

All the data of housing price index, interest rate (CPI), inflation rate (BLR), gross domestic product (GDP), population (Malaysia) and stock price (KLCI) in Malaysia are collected from the DataStream. The latest data and previous data can get from this e-source.

3.3

Variables

3.3.1

Dependent Variable

Dependent variable must support with the independent variables to use in analyse the factors that affect the housing prices index in Malaysia. This variable will

19

be the one who affected by the independent variables that had been choose. The dependent variable in this study is Malaysia housing price index (MHPI).

3.3.2


This study will using interest rate (BLR), inflation rate (CPI), gross domestic product (GDP), population (Malaysia) and stock price (KLCI) as an independent variables. These independent variables will help to collect the information effect of Malaysia housing price index (MHPI) as a dependent variable.

3.4

Population and Sample

Population of this study is in Malaysia. The sample that being used in this study

are

Malaysia

housing

price

index

(MHPI),

interest

rate

(BLR),

Inflation rate (CPI), Gross Domestic Product (GDP), population (Malaysia) and stock price (KLCI). The sample period is annually basis from 1980 until 2015. All the variables data is obtained by DataStream.

3.5

Study Design

The purpose of this study is to investigate which of the using interest rate (BLR), inflation rate (CPI), gross domestic product (GDP), population (Malaysia) and stock price (KLCI) is a factors influence the housing prices in Malaysia.

20

This study involved the correlation to determine of selected independent variables on Malaysia housing price index either existing any significant between their means of each economic variable.

3.6

Unit of analysis

For the unit analysis, these studies use multiple regressions. These studies are analysing the Malaysia housing price index (MHPI) as dependent variable. Interest

rate

(BLR),

inflation

rate

(CPI),

gross

domestic

product

(GDP),

population (Malaysia) and stock price (KLCI) as independent variables.

3.7

Time Horizon

The collection of data Malaysia housing price index (MHPI) as dependent variable. Interest rate (BLR), inflation rate (CPI), gross domestic product (GDP), population (Malaysia) and stock price (KLCI) is obtained from the time series of year 1980 until 2015 annually.

3.8


3.8.1


Regression analysis is a statistical process for estimating the relationships among variables. In this analysis many techniques for modelling and analysing several variables, that focus is on the relationship between a dependent variable and

21

one or more independent variables. In linear multiple regressions model there is more than one independent variables. The true model contains the random error term and the true coefficients for the input variables.

Regression Model: Yi

= β0 + β1 X1 + β2 X2 + .......... + βk Xk + ei

Where; Dependent Variable

= Yi

Estimated Coefficient

=β


=X

Error Term

= ei

The regression analysis in this study is used to examine the relationship between the housing price and explanatory variables such as interest rate, inflation rate, gross domestic product, population and stock price.

Regression Model: MHPI = β0 + β1 INT + β2 INF + + β3 GDP + β4 POP + β5 KLCI +ei Where; MHPI = Malaysia Housing Price Index INT

= Interest Rate (BLR)

INF

= Inflation Rate (CPI)

GDP = Gross Domestic Product POP = Population KLCI = Stock Price

22

3.8.2


This shows whether independent variables in the equation are comparable with the postulations of economic theory; that is, if the sign and size of the parameters of economic relationships follow with the expectation of the economic theory.

DV

IV

Relationship

Result

MHPI

INT

Positive / Negative

?

MHPI

INF

Positive

?

MHPI

GDP

Positive

?

MHPI

POP

Positive

?

MHPI

KLCI

Negative

?

Table 1 : Economic a Priori Sign Before Result

Any parameter estimates with a positive sign indicates that the independent variables indirect or positive relationship with the dependent variable. This means that if that particular independent variables increase, the dependent variable will increase too. Thus, they move in the same direction. However, a negative sign implies an inverse or negative relationship meaning that if the independent variables increases, the dependent variable will decrease, and vice versa. Thus, they move in opposite directions.

23

3.8.3

Test of Coefficient

3.8.3.1 Determination of Coefficient (R2)

Coefficient of determination (R2), is a summary measure that how well the regression line fits the data. This R2 lies between 0 and 1, the closer it is to 1, the better is the fit. If the = 0, this means none of the changes in the dependent variable can be explained by the changes in the independent variables and vice versa.

3.8.3.2 Correlation Coefficient (Simple Linear Correlation) Correlation Coefficient indicates the direction, strength, and significance of the relationship among all the independent variables.

Range

Status of correlation

r=1

Perfect positive linear correlation

0.75 < r < 1

Strong positive linear correlation

0.26 < r < 0.74

Moderate positive linear correlation

0.1 < r < 0.25

Weak positive linear correlation

r=0

No linear correlation

-0.1 < r < -0.25

Weak negative linear correlation

-0.26 < r < -0.74

Moderate negative linear correlation

-0.75 < r < -1

Strong negative linear correlation

r = -1

Perfect negative linear correlation

Table 2 : Range Correlation Coefficient (Simple Linear Correlation)

24

3.8.4


Probability is used to find the degree of freedom of each variable. The study will reject null hypothesis when the probability is = 0.0000 If probability sign at 5% or 0.05 significant level, this shows at least one of the independent variables is important in explaining the dependent variable.

3.8.5


3.8.5.1 Variance Inflation Factor

Variance inflation factors (VIF) use to measure how much the variance of the estimated regression coefficients are inflated as compared to when the predictor variables are not linearly related. Also to describe how much multicollinearity (correlation between predictors) exists in a regression analysis. The problem of multicollinearity can increase the variance of the regression coefficients, making them unstable and difficult to interpret.

Range

Status of predictors

VIF = 1

Not Correlated

1 < VIF < 5

Moderately Correlated

VIF > 5 to 10

Highly Correlated

Table 3 : Range Test for Multicollinearity – Variance Inflation Factor

25

3.8.5.2 Durbin Watson

The Durbin Watson test is a simple numerical method for checking serial dependence. The model is assumed to be free from the autocorrelation if the Durbin

Watson

value

is

around

2.

It

can

easily

demonstrated

as

1.5 < Durbin Watson Value > 2.5.

26

CHAPTER 4

ANALYSIS AND FINDINGS

4.1

Introduction

This study will focus on the results of relationship between the dependent variable which is Malaysia housing price and the independent variables which are interest rate, inflation rate, gross domestic product, population and stock price. From the results, this study can get the answer whether the dependent variable have strong or weak relationship with the dependent variable.

4.2

Graph

4.2.1

Malaysia Housing Price Index – Dependent Variable MHPI 240

200

160

120

80

40 1980

1985

1990

1995

2000

2005

2010

2015

Graph 1 : Malaysia Housing Price Index

27

Graph 1 show the movement Malaysia housing price index from 1980 till 2015 by annually basis. The minimum index is 49.32 for the year in 1980 and the maximum index is 224.7 for the year in 2015.

4.2.2

Interest Rate (BLR) – Independent Variable 1 INT 12 11 10 9 8 7 6 5 1980

1985

1990

1995

2000

2005

2010

2015

Graph 2 : Interest Rate (BLR)

Graph 2 show the movement interest rate from 1980 till 2015 by annually basis. The minimum percentage is 5.51% for the year in 2009 and the maximum percentage is 11.25% for the year in 1984.

4.2.3

Inflation Rate (CPI) – Independent Variable 2 INF 120 110 100 90 80 70 60 50 40 1980

1985

1990

1995

2000

2005

2010

2015

Graph 3 : Inflation Rate (CPI)

28

Graph 3 show the movement inflation rate from 1980 till 2015 by annually basis. The minimum index is 41.4 for the year in 1980 and the maximum index is 112.8 for the year in 2015.

4.2.4

Gross Domestic Product – Independent Variable 3 GDP 1,200

1,000

800

600

400

200

0 1980

1985

1990

1995

2000

2005

2010

2015

Graph 4 : Gross domestic Product

Graph 4 show the movement gross domestic product from 1980 till 2015 by annually basis. The minimum amount is RM57.61 million for the year in 1980 and the maximum amount is RM1,157.14 million for the year in 2015.

29

4.2.5

Population – Independent Variable 4 POP 32

28

24

20

16

12 1980

1985

1990

1995

2000

2005

2010

2015

Graph 5 : Population

Graph 5 show the movement population in Malaysia from 1980 till 2015 by annually basis. The minimum population is 13.83 million for the year in 1980 and the maximum population is 30.33 million for the year in 2015.

4.2.6

Stock Price – Independent Variable 5 KLCI 2,000

1,600

1,200

800

400

0 1980

1985

1990

1995

2000

2005

2010

2015

Graph 6 : Stock Price

Graph 6 show the movement stock price from 1980 till 2015 by annually basis. The minimum index is 233.46 for the year in 1985 and the maximum index is 1866.96 for the year in 2013.

30

4.3


The researcher had used Malaysia Housing Price as a dependent variable and interest rate, inflation rate, gross domestic product, population and stock price as a dependents variable by using data from year 1980 to 2015 included 36 observation. By using system E-View, the data are not fulfil the analysis. Therefore, researcher had convert the data by using semi-annually (S1 1980 to S2 2015) and quarterly (Q1 1981 to Q4 2015). However, the result are same as annually analysis had mentioned above.

By using the annually data (year 1980 to 2015) that including 36 observation, researcher had revised the independent variable and had choosen interest rate (BLR), inflation rate (CPI), and stock price (KLCI) as an independent variables.

4.3.1

Multiple Linear Regression Model

e-View command : LS LOG(MHPI) C LOG(INT) LOG(INF) LOG(KLCI) Dependent Variable: LOG(MHPI) Method: Least Squares Date: 12/27/16 Time: 22:18 Sample: 1980 2015 Included observations: 36 Variable

Coefficient

Std. Error

t-Statistic

Prob.

C LOG(INT) LOG(INF) LOG(KLCI)

-1.565996 0.085542 1.315105 0.046392

0.515713 0.120896 0.123385 0.053241

-3.036565 0.707571 10.65855 0.871355

0.0047 0.4843 0.0000 0.3901

R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)

0.949878 0.945179 0.094596 0.286349 35.93132 202.1456 0.000000

Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat

4.538716 0.404016 -1.773962 -1.598016 -1.712552 0.118924

Table 4 : Multiple Linear Regressions Analysis

31

4.3.1.1 The Equation

Regression Model MHPI = β0 + β1 INT + β2 INF + + β3 KLCI + ei MHPI = -1.565996 + 0.085542 INT + 1.315105 INF + 0.046392 KLCI

The correlation coefficient explained that all independents variable which is interest rate (BLR), inflation rate (CPI) and stock price (KLCI) have a positive relationship with the Malaysia housing price index (MHPI).

It explains for every 1% increase in interest rate, the Malaysia housing price index will increase by 0.09%. The interest rate variable has the correct priori sign, it show positive relationship between variables and Malaysia housing price index. For inflation rate, every 1% increase in inflation rate, the Malaysia housing price index will increase by 1.32%. Inflation rate also shows the correct priori sign positive the relationship between Malaysia housing price index.

Lastly, the researcher had the negative relationship stock market and Malaysia housing price in economic priori sign. However the result shows positive relationship with stock price and Malaysia housing price index. It mean, every 1% increase in stock price, the Malaysia housing price index will increase by 0.05%.

32

4.3.2


DV

IV

Relationship

Result

Correlation

MHPI

INT

Positive / Negative

Positive

Conform

MHPI

INF

Positive

Positive

Conform

MHPI

KLCI

Negative

Positive

Does Not Conform

Table 5 : Economic a Priori Sign

Table 5 show, all the results independent variables which is interest rate, inflation rate and stock price have a positive relationship with Malaysia housing price index as a dependent variable. It explain when increase in independent variables, it will increase in Malaysia housing price index.

4.3.3

Test of Coefficient

4.3.3.1 Determination of Coefficient (R2)

Show the valued R2 is equal 0.949878 or 94.99% of the variation in dependent variable is explained by the independent variables which are interest rate, inflation rate and stock price. Only 5.01% of the dependent variable cannot be explained by this not included independent variables and it may be explained by other factor.

33

4.3.2.2 Correlation Coefficient (Simple Linear Correlation)

The correlation coefficient is the degree of linear association amongst the two variables. The coefficient of correlation undertakes the degree of ranking between +1 and -1.

INT

INF

INT

1.000000

INF

-0.663668

1.000000

KLCI

-0.586448

0.873003

KLCI

1.000000

Table 6 : Correlation Coefficient

From table 6 above, there have 3 relationship with the independent variables. There are moderate negative linear relationship between interest rate and inflation rate, also interest rate and stock price. The lastly, there is strong positive linear correlation relationship between inflation rate and stock price.

4.3.4

Test of Significant - Probability (P-valued method) By test of significant result, it shows 1 of 3 independent variables are

significant with dependent variable. The test of significant at 5% significance level. Refer to the research objective, inflation rate is significant positive and dominant variable with Malaysia housing price index at 0.0000 or 0.00%. The interest rate and stock price the result are not significant positive with Malaysia housing price index at 0.4843 or 48.43% and 0.3901 or 39.01%.

34

4.3.7


4.3.7.1 Variance Inflation Factor Variance Inflation Factors Date: 12/27/16 Time: 22:20 Sample: 1980 2015 Included observations: 36

Variable

Coefficient Variance

Uncentered VIF

Centered VIF


0.265960 0.014616 0.015224 0.002835

1069.971 236.5679 1126.717 498.7502

NA 1.787838 4.931198 4.205631

Table 7: Variance Inflation Factors

According to the regression results for all variables Variance Inflation Factors is range in 1 to 5. It means this model does not have multicollinearity.

4.3.7.2 Autocorrelation Test – Durbin Watson

Durbin-Watson stat

0.118924

N / Observation

36

K / Total Independent Variables

3

Graph 7 : Durbin Watson

35

Since Durbin-Watson = 0.118924, stay in the positive auto correlation region. There is auto correlation problem in the estimated model.

36

CHAPTER 5

CONCLUSION

5.1

Introduction

By using the annually data (year 1980 to 2015) that including 36 observation, the result from the chapter 4 has been discuss in this chapter. Overall after the researcher get the data and revised to get the result from the system E-Eview, this is the conclusion to summarize in this research and recommendation.

5.2

Conclusion

The researcher had revised the independent variable and had chosen interest rate (BLR), inflation rate (CPI), and stock price (KLCI) as an independent variables and dependent variable is Malaysia housing price index (MHPI). The researcher has achieved the main objective from the research of Factors That Influence Housing Price in Malaysia. The objective is to identify whether interest rate, inflation rate, and stock price are significant or not significant in affecting Malaysia housing price index. Another objective is to determine the relationship and to identify the most dominant that has significant between independent variables and Malaysia housing price index as a dependent variable. Using Multiple Linear Regression Model, the result show the inflation rate is 1 of 3 independent variables have significant positive relationship with Malaysia housing price index. In other hand, interest rate and stock price as independent variables has not significant positive relationship with Malaysia housing price index. However, inflation rate is the most dominant that has significant between

37

independent variables. This is confirming by another result from this research in chapter 4.

5.3

Recommendation

For future researcher, there are advices to do more analysis for the Factors That Influence Housing Price in Malaysia. Increase the observation is one of to get the better result. The analysis can make by annually, semi-annually and quarterly basis by the time series analysis. In addition, selection of the appropriate independent variables studying in journal, not only in Malaysia but the other country. It can make a different result from analysis with difference independent variables. For example to choose independent variables that can be made in parallel with this research study are as unemployment, tax, money supply, monetary policy, income and capital gains.

Also, the future researcher can use E-Views (Econometric Views) software for their analysis. From the econometric method, analysis can be use more deeply to get more result. Example for another analysis is "Breusch-Godfrey (BG) test: A General test of autocorrelation", "Newey-West standard error of procedure", "White test (White's General Heteroscedasticity test)".

38

REFERENCES

Journals

Batayneh, K. I., & Al-Malki, A. M. (2015). The Relationship between House Prices and Stock Prices in Saudi Arabia: An Empirical Analysis. International Journal of Economics and Finance, 7(2), 156.

Cheng, Z., & Zheng, S. (2015). The Change of Relationship between Real Estate and Stock Markets in China.

Datta, K., & Mukhopadhyay, C. K. (2011). Relationship between inflation and economic growth in Malaysia-an econometric review. Int. Ferenc. Econ. Finance. Re., IPEDR, IACSIT Press, Singapore, 4.

Huang, Y., & Ge, X. J. (2009). House prices and the collapse of the stock market in mainland China: An empirical study on house price index. Lantian College, Zhejiang University, Manuscript.

Ibrahim, M. H., Padli, J., & Baharom, A. H. (2009). Long-run relationships and dynamic interactions between housing and stock prices in Thailand. Asian Academy of Management Journal of Accounting and Finance, 5(1), 93-105.

John M. Kagochi Lesley M. Mace, (2009),"The determinants of demand for single family housing in Alabama urbanized areas", International Journal of Housing Markets and Analysis, Vol. 2 Iss 2 pp. 132 – 144

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Jonas Berglund, (2007), Determinants and Forecasting of House Prices. Department Of Economics, Uppsala University, Sweden

Kibunyi, D. (2015). Real estate prices in Kenya: is there a bubble? (Doctoral dissertation, Strathmore University).

Mahdi, S., & Masood, S. (2011). The long run relationship between interest rates and inflation in Iran: Revisiting Fisher's hypothesis. Journal of Economics and International Finance, 3(14), 705.

Mavrodiy, A. (2005). Factor Analysis of Real Estate Prices (Doctoral dissertation, Economics Education and Research Consortium).

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American

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of

Business,

Economics

and

Management. Vol. 3, No. 6, 2015, pp. 384-394.

Ong, T. S., & Chang, Y. S. (2013). Macroeconomic determinants of Malaysian housing market. Journal of Human and Social Science Research, 1(2), 119127.

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Pillaiyan, S. (2015). Macroeconomic drivers of house prices in Malaysia. Canadian Social Science, 11(9), 119-130.

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Stohldreier, M. T. (2012). The Determinants of House Prices in Chinese Cities. Master Thesis of University of Zurich.

Tak Yun Joe Wong Chi Man Eddie Hui William Seabrooke, (2003),"The impact of interest rates upon housing prices: an empirical study of Hong Kong’s market", Property Management, Vol. 21 Iss 2 pp. 153 – 170

Taltavull de La Paz, P. (2003). Determinants of housing prices in Spanish cities. Journal of Property Investment & Finance, 21(2), 109-135.

Wu, Y., & Tidwell, A. (2015). Inflation-hedging properties of regional Chinese real estate market: evidence from 35 cities in China. Applied Economics, 47(60), 6580-6598.

Y. Zheng, (2014), Effectiveness of monetary policies in affecting Chinese housing prices. Faculty of Economics and Business, University of Amsterdam

Yuan, N., Hamori, S., & Chen, W. (2014). House Prices and Stock Prices: Evidence from a Dynamic Heterogeneous Panel in China (No. 1428).

Zandi, G., Mahadevan, A., Supramaniam, L., Aslam, A., & Theng, L. K. (2015). The Economic Factors Affecting Residential Property Price: The Case of Penang Island. International Journal of Economics and Finance, 7(12), 200

41

Websites

http://www.bharian.com.my/node/192014

http://www.nst.com.my/news/2016/04/137711/mydeposit-scheme-launched-assistmiddle-income-first-time-house-buyers

http://www.simplypsychology.org/maslow.html

http://www.sinarharian.com.my/nasional/pm-lancar-projek-rumah-penjawat-awam-diputrajaya-1.151428

http://www.sinarharian.com.my/nasional/sebahagian-masalah-beli-rumah-selesai1.507519

http://www.thestar.com.my/story/?file=/2011/10/15/business/3304372&sec=business

http://www.utusan.com.my/rencana/harga-rumah-bertindaklah-segera-1.130406

42

APPENDICES APPENDIX 1 : Final Data had been choose for analyse by annually basis with 3 Independent variable

APPENDIX 1.1 : Historical data of Malaysia Housing Price, Interest Rate, Inflation Rate, Gross Domestic Product, Population and Stock Price from 1980 until 2015 (36 observation).

INDEX 2000 = 100

BLR INT %

INDEX 2010 = 100

MYR MILLION

PERSON MILLION

INDEX

YEAR

MHPI

INT

INF

GDP

POP

KLCI

1980

49.3220

8.5000

41.4000

57.6130

13.8300

366.7000

1981

53.9835

8.5000

45.5000

62.5790

14.1800

380.8100

1982

57.0399

8.5000

48.1000

69.9410

14.5400

291.4400

1983

59.1221

10.0000

49.9000

79.5500

14.9300

401.5900

1984

61.4090

11.2500

51.9000

77.5470

15.3300

303.5600

1985

61.6740

9.7500

52.0000

71.5940

15.7600

233.4600

1986

62.0240

9.2500

52.4000

79.6250

16.2200

252.4300

1987

62.5496

7.0000

52.9000

90.8610

16.7000

261.1800

1988

64.1304

6.7500

53.9000

102.5870

17.2000

357.3800

1989

65.9215

6.7500

55.4000

119.0810

17.7100

562.2800

1990

67.9440

7.2500

56.9000

135.1240

18.2100

500.8900

1991

70.8863

8.2500

59.3000

150.6820

18.7100

556.2200

1992

74.2752

9.0000

62.2000

172.1940

19.2000

643.9600

1993

76.9073

7.8000

64.4000

195.4610

19.7000

1275.3200

1994

79.7432

6.5500

66.7000

222.4730

20.2100

971.2100

1995

82.5084

7.7000

69.0000

253.7320

20.7300

995.1700

1996

85.3786

9.1800

71.4000

281.7950

21.2600

1237.9600

1997

87.6448

10.3300

73.4000

283.2430

21.8100

594.4400

1998

92.2855

8.0400

77.2000

300.7640

22.3600

586.1300

43

INDEX 2000 = 100

BLR INT %

INDEX 2010 = 100

MYR MILLION

PERSON MILLION

INDEX

YEAR

MHPI

INT

INF

GDP

POP

KLCI

1999

94.8000

6.7900

79.3000

356.4010

22.9000

812.3300

2000

100.4000

6.7900

80.5000

352.5790

23.4200

679.6400

2001

101.5750

6.3900

81.7000

383.2130

23.9200

696.0900

2002

104.7750

6.3900

83.2000

418.7690

24.4000

646.3200

2003

109.0250

6.0000

84.1000

474.0480

24.8700

793.9400

2004

113.3250

5.9800

85.3000

543.5780

25.3300

907.4300

2005

116.3000

6.2000

87.8000

596.7840

25.8000

899.7900

2006

119.3500

6.7200

91.0000

665.3400

26.2600

1096.2400

2007

124.5500

6.7200

92.8000

769.9490

26.7300

1445.0300

2008

129.5000

6.4800

97.8000

712.8570

27.2000

876.7500

2009

132.8000

5.5100

98.4000

821.4340

27.6600

1272.7800

2010

142.0250

6.2700

100.0000

821.4340

28.1200

1518.9100

2011

155.9750

6.5300

103.2000

911.7330

28.5700

1530.7300

2012

174.4000

6.5300

104.9000

971.2520

29.0200

1688.9500

2013

193.4500

6.5333

107.1000

1018.6140

29.4700

1866.9600

2014

209.8250

6.7865

110.5000

1106.4660

29.9000

1761.2500

2015

224.7000

6.7865

112.8000

1157.1390

30.3300

1692.5100

44

APPENDIX 1.2 : Model of Multiple Linear Regression (Least Squares) Dependent Variable: LOG(MHPI) Method: Least Squares Date: 12/27/16 Time: 22:18 Sample: 1980 2015 Included observations: 36 Variable

Coefficient

Std. Error

t-Statistic

Prob.


-1.565996 0.085542 1.315105 0.046392

0.515713 0.120896 0.123385 0.053241

-3.036565 0.707571 10.65855 0.871355

0.0047 0.4843 0.0000 0.3901


0.949878 0.945179 0.094596 0.286349 35.93132 202.1456 0.000000


4.538716 0.404016 -1.773962 -1.598016 -1.712552 0.118924

APPENDIX 1.3 : Model of Correlation (Simple Linear Correlation) LOG(INT)

LOG(INF) LOG(KLCI)

LOG(INT) 1.000000 -0.663668 -0.586448 LOG(INF) -0.663668 1.000000 0.873003 LOG(KLCI) -0.586448 0.873003 1.000000

APPENDIX 1.4 : Model of Variance Inflation Factors Variance Inflation Factors Date: 12/27/16 Time: 22:20 Sample: 1980 2015 Included observations: 36 Variable C LOG(INT) LOG(INF) LOG(KLCI)

Coefficient Uncentered Variance VIF 0.265960 0.014616 0.015224 0.002835

1069.971 236.5679 1126.717 498.7502

Centered VIF NA 1.787838 4.931198 4.205631

45

APPENDIX 1.5 : Scatter Plots Relationship MHPI with INT 240

200

160 MHPI INT

120

80

40

0 40

80

120

160

200

240

200

240

MHPI

APPENDIX 1.6 : Scatter Plots Relationship MHPI with INF 240

200

160 MHPI INF

120

80

40

0 40

80

120

160 MHPI

46

APPENDIX 1.7 : Scatter Plots Relationship MHPI with KLCI 2,000

1,600

1,200 MHPI KLCI 800

400

0 40

80

120

160

200

240

MHPI

47

APPENDIX 2 : Data Analysis using 5 independent variable by Annually Basis


Coefficient

Std. Error

t-Statistic

Prob.

C LOG(INT) LOG(INF) LOG(GDP) LOG(POP) LOG(KLCI)

-1.058780 -0.074815 2.499252 0.105619 -1.927349 0.054967

1.776106 0.142901 0.739781 0.238329 0.881648 0.069914

-0.596124 -0.523543 3.378368 0.443163 -2.186076 0.786203

0.5556 0.6044 0.0020 0.6608 0.0367 0.4379


0.957089 0.949937 0.090397 0.245150 38.72750 133.8248 0.000000


4.538716 0.404016 -1.818195 -1.554275 -1.726080 0.256697

APPENDIX 2.2 : Model of Correlation (Simple Linear Correlation)

LOG(INT) LOG(INF) LOG(GDP) LOG(POP) LOG(KLCI)

LOG(INT)

LOG(INF)

LOG(GDP)

LOG(POP)

LOG(KLCI)

1.000000 -0.663668 -0.688931 -0.700842 -0.586448

-0.663668 1.000000 0.994278 0.995250 0.873003

-0.688931 0.994278 1.000000 0.995405 0.901454

-0.700842 0.995250 0.995405 1.000000 0.879276

-0.586448 0.873003 0.901454 0.879276 1.000000

APPENDIX 2.3 : Model of Variance Inflation Factors (VIF) Variance Inflation Factors Date: 12/27/16 Time: 22:15 Sample: 1980 2015 Included observations: 36

Variable


Uncentered VIF

Centered VIF

C LOG(INT) LOG(INF) LOG(GDP) LOG(POP) LOG(KLCI)

3.154553 0.020421 0.547276 0.056801 0.777303 0.004888

13897.29 361.9441 44354.03 8138.867 32339.62 941.7976

NA 2.735355 194.1201 228.1630 196.4011 7.941556

48

APPENDIX 3 : Data Analysis using 4 independent variable by Annually Basis


Coefficient

Std. Error

t-Statistic

Prob.

C LOG(INT) LOG(INF) LOG(GDP) LOG(POP)

5.659022 -0.025133 0.949373 0.362106 -0.967400

4.874596 0.030554 0.731478 0.189613 0.747752

1.160921 -0.822573 1.297884 1.909712 -1.293744

0.2545 0.4170 0.2039 0.0655 0.2053


0.954407 0.948524 0.091665 0.260475 37.63600 162.2306 0.000000


4.538716 0.404016 -1.813111 -1.593178 -1.736348 0.155496


LOG(INT) LOG(INF) LOG(GDP) LOG(POP)

LOG(INT)

LOG(INF)

LOG(GDP)

LOG(POP)

1.000000 -0.342653 -0.330144 -0.339337

-0.342653 1.000000 0.995960 0.996009

-0.330144 0.995960 1.000000 0.994940

-0.339337 0.996009 0.994940 1.000000

APPENDIX 3.3 : Model of Variance Inflation Factors (VIF) Variance Inflation Factors Date: 12/18/16 Time: 15:57 Sample: 1980 2015 Included observations: 36

Variable


Uncentered VIF

Centered VIF


23.76168 0.000934 0.535060 0.035953 0.559133

101806.3 10.60368 42172.97 24039.37 238740.6

NA 1.153957 184.5745 146.0948 145.3904

49

APPENDIX 4 : Data Analysis using 4 independent variable by Semi-Annually Basis

APPENDIX 4.1 : Model of Multiple Linear Regression (Least Squares) Dependent Variable: LOG(MHPI) Method: Least Squares Date: 12/17/16 Time: 16:26 Sample: 1981S1 2015S2 Included observations: 70 Variable

Coefficient

Std. Error

t-Statistic

Prob.


6.024076 -0.021292 1.888669 0.123403 -1.087003

3.714368 0.021646 0.620033 0.172604 0.519910

1.621831 -0.983661 3.046077 0.714948 -2.090752

0.1097 0.3289 0.0033 0.4772 0.0405


0.950430 0.947379 0.089919 0.525552 71.88735 311.5685 0.000000


5.250011 0.391989 -1.911067 -1.750460 -1.847272 0.086545



LOG(INT)

LOG(INF)

LOG(GDP)

LOG(POP)

1.000000 -0.358354 -0.341730 -0.346321

-0.358354 1.000000 0.997351 0.995589

-0.341730 0.997351 1.000000 0.995217

-0.346321 0.995589 0.995217 1.000000

APPENDIX 4.3 : Model of Variance Inflation Factors (VIF) Variance Inflation Factors Date: 12/17/16 Time: 16:30 Sample: 1981S1 2015S2 Included observations: 70

Variable


Uncentered VIF

Centered VIF


13.79653 0.000469 0.384441 0.029792 0.270306

119444.3 21.42374 83076.78 36505.15 267189.3

NA 1.214538 245.8879 221.6511 127.9571

50

APPENDIX 5 : Data Analysis using 4 independent variable by Quarterly Basis

APPENDIX 5.1 : Model of Multiple Linear Regression (Least Squares) Dependent Variable: LOG(MHPI) Method: Least Squares Date: 12/15/16 Time: 23:23 Sample: 1981Q1 2015Q4 Included observations: 140 Variable

Coefficient

Std. Error

t-Statistic

Prob.


5.810040 -0.020165 1.867125 0.124107 -1.063956

2.430295 0.014835 0.423825 0.117359 0.359563

2.390673 -1.359272 4.405410 1.057496 -2.959030

0.0182 0.1763 0.0000 0.2922 0.0036


0.950026 0.948546 0.088616 1.060137 143.1757 641.6066 0.000000


4.556826 0.390664 -1.973939 -1.868880 -1.931246 0.045749



LOG(INT)

LOG(INF)

LOG(GDP)

LOG(POP)

1.000000 -0.355987 -0.338942 -0.344011

-0.355987 1.000000 0.997219 0.995524

-0.338942 0.997219 1.000000 0.995091

-0.344011 0.995524 0.995091 1.000000

APPENDIX 5.3 : Model of Variance Inflation Factors (VIF) Variance Inflation Factors Date: 12/16/16 Time: 00:08 Sample: 1981Q1 2015Q4 Included observations: 140

Variable


Uncentered VIF

Centered VIF


5.906333 0.000220 0.179628 0.013773 0.129285

105297.5 10.46728 59324.90 30832.48 230131.1

NA 1.211818 236.6419 211.0913 126.0471

51

APPENDIX 5 : Turn It In – Final Result

52

FACTORS THAT INFLUENCE HOUSING PRICE IN MALAYSIA MOHD SHAKIB BIN ISHAK 2012619382

BACHELOR OF BUSINESS ADMINISTRATION WITH HONOURS (FINANCE) FACULTY OF BUSINESS MANAGEMENT UNIVERSITY TECHNOLOGY MARA SHAH ALAM

JANUARY 2017

PROBLEM STATEMENT Increase In House Prices Between 5 To 15 Percent A Year • Berita Harian on 12th September 2016 had mentioned that there are several factors is cause to have a house. • Developer are depending on the location, type of home and the current economic situation.

House Price Was 4.4 Times Higher Than The Average Salary A Year • Utusan Malaysia on 1st September 2015, a research from Khazanah Research Institute (KRI). • Factor that cause to the house price increase in the market are, growth of population.

New Workers Or Fresh Graduates To Get Their Own House Are Very Difficult • Sinar Harian on 8th April 2016, most of them are facing financial limit for who have a family to own their own house, due to rising of house price and cost of living.

RESEARCH OBJECTIVE & QUESTION To identify whether interest rate, inflation rate, gross domestic product, population and stock price are significant or not significant in affecting housing price; To determine the relationship between the interest rate, inflation rate, gross domestic product, population and stock price with housing price ; and To identify the most dominant variable that has significant influence towards the housing price.

 Which of the factors are significant or not significant in affecting housing price? ;  What is the relationship between interest rate, inflation rate, gross domestic product, population and stock price with housing price? ; and  Which is variable has the most significant influence towards the affecting housing price?

CONCEPTUAL FRAMEWORK INDEPENDENT VARIABLE

DEPENDENT VARIABLE

Interest Rate BLR (INT)



Inflation Rate CPI (INF)



Malaysia Housing Price Index (MHPI)


(GDP)

X

Population (POP)

X

Stock Price

(KLCI)



Revised the independent variable and had choosen interest rate BLR (INT), inflation rate CPI (INF), and stock price (KLCI) as an independent variables.

LITERATURE REVIEW The Relationship Of The Interest Rate With The Housing Price STATE

REFERENCE

SIGNIFICANT

RELATIONSHIP

Malaysia

Ong, T. S., & Chang, Y. S. 2013 “Macroeconomic Determinants of Malaysian Housing Market”

X

+

Lagos, Nigeria

Olanipekun T. Alaba, O. J. Adegoke, 2015 “Effects of Interest Rate on Housing Prices in Lagos Metropolis”



-

Hong Kong, China

Tak Yun Joe Wong Chi Man Eddie Hui William Seabrooke, 2003 “The impact of interest rates upon housing prices: an empirical study of Hong Kong's market”



+

LITERATURE REVIEW The Relationship Of The Inflation Rate With The Housing Price STATE

REFERENCE

SIGNIFICANT

RELATIONSHIP

China

Wu, Y., & Tidwell, A., 2015 “Inflation-hedging properties of regional Chinese real estate market: evidence from 35 cities in China”



+

Iran

Mahdi, S., & Masood, S. 2011 ” The long run relationship between interest rates and inflation in Iran: Revisiting Fisher's hypothesis”



+

Kenya

Kibunyi, D. 2015 “Real estate prices in Kenya : is there a bubble?”

X

-

LITERATURE REVIEW The Relationship Of The Inflation Rate With The Housing Price STATE

REFERENCE

SIGNIFICANT

RELATIONSHIP

Thailand

Ibrahim, M. H., Padli, J., & Baharom, A. H. 2009 “Long-Run Relationships And Dynamic Interactions Between Housing And Stock Prices In Thailand”



+

Saudi Arabia

Batayneh, K. I., & Al-Malki, A. M. 2015 “The Relationship between House Prices and Stock Prices in Saudi Arabia: An Empirical Analysis”

X

-

X

-

Cheng, Z., & Zheng, S. 2015

China

“The Change of Relationship between Real Estate and Stock Markets in China”

RESEARCH METHOD DATA COLLECTION

: DATASTREAM PROVIDE BY UITM

ANALYSIS DATA

: TIME SERIES

SELECTED SAMPLE

: ANNUALLY FROM 1980 – 2015 (36 OBSERVATION)

AND MEASUREMENT. DATA ANALYSIS

: THE ECONOMETRIC VIEWS (E-VIEW9)

ANALYSIS AND FINDINGS EVIEW COMMAND : LS LOG(MHPI) C LOG(INT) LOG(INF) LOG(KLCI)

ANALYSIS AND FINDINGS DETERMINATION OF COEFFICIENT (R2) AND F-STATISTIC

RESULTS

NOTES

R-Squared : 94.99% 5.01%

1

Prob (F-statistic) : 0.000000 < 0.05

2

ANALYSIS AND FINDINGS THE EQUATION MHPI MHPI

= β0 + β1 INT + β2 INF + + β3 KLCI + ei = -1.565996 + 0.085542 INT + 1.315105 INF + 0.046392 KLCI

RESULTS

C MHPI

3

PRIORI SIGN

-1.565996

MHPI INT 0.085542

CONFORM

MHPI INF 1.315105

CONFORM

MHPI KLCI 0.046392

DOES NOT CONFORM

ANALYSIS AND FINDINGS CORRELATION COEFFICIENT (SIMPLE LINEAR CORRELATION)

INF INT

-0.663668

Range Moderate

KLCI INT

-0.586448

Range Moderate

KLCI INF

0.873003

Range Strong

ANALYSIS AND FINDINGS TEST OF SIGNIFICANT - PROBABILITY (P-VALUED METHOD)

4

ANALYSIS AND FINDINGS

5 Since Durbin-Watson = 0.118924, stay in the positive auto correlation region. There is auto correlation problem in the estimated model.

ANALYSIS AND FINDINGS TEST FOR MULTICOLLINEARITY VARIANCE INFLATION FACTORS

IMPLICATION

Significant And Relationship

• the results from regression analysis explained that the result show the inflation rate is 1 of 3 independent variables have significant positive relationship with Malaysia housing price index. In other hand, interest rate and stock price as independent variables has not significant positive relationship with Malaysia housing price index.

• identify the most dominant variables that have influence to the Malaysia housing price index founds that inflation rate is the most dominant that has significant between independent variables. Most Dominant

FACTORS THAT INFLUENCE HOUSING PRICE IN MALAYSIA MOHD SHAKIB BIN ISHAK 2012619382

END SLIDE

THANK YOU

Factor That Influence Housing Price in Malaysia

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