Factor That Influence Housing Price in Malaysia
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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
Data Analysis and Treatment
31
4.3.1
Linear Multiple Regression Model
31
4.3.1.1 The Equation
32
4.3.2
Economic a Priori Sign
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
Test of Significant - Probability (P-valued method)
34
4.3.7
Test for Multicollinearity
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
APPENDIX 2.1 : Model of Multiple Linear Regression
48
APPENDIX 2.2 : Model of Correlation
48
APPENDIX 2.3 : Model of Variance Inflation Factors
48
Data Analysis using 4 independent variable by Annually Basis
49
APPENDIX 3.1 : Model of Multiple Linear Regression
49
APPENDIX 3.2 : Model of Correlation
49
APPENDIX 3.3 : Model of Variance Inflation Factors
49
ix
TABLE OF CONTENT Page APPENDIX 4
APPENDIX 5
APPENDIX 5
Data Analysis using 4 independent variable by Semi-Annually Basis
50
APPENDIX 4.1 : Model of Multiple Linear Regression
50
APPENDIX 4.2 : Model of Correlation
50
APPENDIX 4.3 : Model of Variance Inflation Factors
50
Data Analysis using 4 independent variable by Quarterly Basis
51
APPENDIX 5.1 : Model of Multiple Linear Regression
51
APPENDIX 5.2 : Model of Correlation
51
APPENDIX 5.3 : Model of Variance Inflation Factors
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
Gross Domestic Product
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
Gross Domestic Product
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
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
Independent Variables
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
Data Analysis and Treatment
3.8.1
Linear Multiple Regression Model
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
Independent Variables
=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
Economic a Priori Sign
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
Test of Significant - Probability (P-valued method)
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
Test for Multicollinearity
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
Data Analysis and Treatment
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
Economic a Priori Sign
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
Test for Multicollinearity
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
C LOG(INT) LOG(INF) LOG(KLCI)
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
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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).
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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.
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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).
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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.
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
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
APPENDIX 2.1 : Model of Multiple Linear Regression (Least Squares) Dependent Variable: LOG(MHPI) Method: Least Squares Date: 12/27/16 Time: 22:11 Sample: 1980 2015 Included observations: 36 Variable
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
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.957089 0.949937 0.090397 0.245150 38.72750 133.8248 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
4.538716 0.404016 -1.818195 -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
Coefficient Variance
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
APPENDIX 3.1 : Model of Multiple Linear Regression (Least Squares) Dependent Variable: LOG(MHPI) Method: Least Squares Date: 12/17/16 Time: 09:12 Sample: 1980 2015 Included observations: 36 Variable
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
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.954407 0.948524 0.091665 0.260475 37.63600 162.2306 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
4.538716 0.404016 -1.813111 -1.593178 -1.736348 0.155496
APPENDIX 3.2 : Model of Correlation (Simple Linear Correlation)
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
Coefficient Variance
Uncentered VIF
Centered VIF
C LOG(INT) LOG(INF) LOG(GDP) LOG(POP)
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.
C LOG(INT) LOG(INF) LOG(GDP) LOG(POP)
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
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.950430 0.947379 0.089919 0.525552 71.88735 311.5685 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
5.250011 0.391989 -1.911067 -1.750460 -1.847272 0.086545
APPENDIX 4.2 : Model of Correlation (Simple Linear Correlation)
LOG(INT) LOG(INF) LOG(GDP) LOG(POP)
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
Coefficient Variance
Uncentered VIF
Centered VIF
C LOG(INT) LOG(INF) LOG(GDP) LOG(POP)
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.
C LOG(INT) LOG(INF) LOG(GDP) LOG(POP)
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
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.950026 0.948546 0.088616 1.060137 143.1757 641.6066 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
4.556826 0.390664 -1.973939 -1.868880 -1.931246 0.045749
APPENDIX 5.2 : Model of Correlation (Simple Linear Correlation)
LOG(INT) LOG(INF) LOG(GDP) LOG(POP)
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
Coefficient Variance
Uncentered VIF
Centered VIF
C LOG(INT) LOG(INF) LOG(GDP) LOG(POP)
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)
Gross Domestic Product
(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
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