a65 the determinants of house prices from …

A65

THE DETERMINANTS OF HOUSE PRICES FROM

MACROECONOMICS PERSPECTIVE IN MALAYSIA

BY

KHOO MIAW RU

LEE YIN ZI

LOH LE HAN

TAN JIA MEI

WONG PUI KHUAN

A research project submitted in partial fulfillment of the

requirement for the degree of

BACHELOR OF BUSINESS ADMINISTRATION

(HONS) BANKING AND FINANCE

UNIVERSITI TUNKU ABDUL RAHMAN

FACULTY OF BUSINESS AND FINANCE

DEPARTMENT OF FINANCE

AUGUST 2017

The Determinants of House Prices from Macroeconomics Perspective in Malaysia

Undergraduate Research Project ii Faculty of Business and Finance

Copyright @ 2017

ALL RIGHTS RESERVED. No part of this paper may be reproduced, stored in a

retrieval system, or transmitted in any form or by any means, graphic, electronic,

mechanical, photocopying, recording, scanning, or otherwise, without the prior consent

of the authors.


Undergraduate Research Project iii Faculty of Business and Finance

DECLARATION

We hereby declare that:

(1) This undergraduate research project is the end result of our own work and that

due acknowledgement has been given in the references to ALL sources of

information be they printed, electronic, or personal.

(2) No portion of this research project has been submitted in support of any

application for any other degree or qualification of this or any other university,

or other institutes of learning.

(3) Equal contribution has been made by each group member in completing the

research project.

(4) The word count of this research report is 24288 words.

Name of Student: Student ID: Signature:

1. Khoo Miaw Ru 14ABB07429 __________________

2. Lee Yin Zi 14ABB00983 __________________

3. Loh Le Han 14ABB06938 __________________

4. Tan Jia Mei 14ABB07495 __________________

5. Wong Pui Khuan 14ABB06982 __________________

Date: 23 AUGUST 2017


Undergraduate Research Project iv Faculty of Business and Finance

ACKNOWLEDGEMENT

We are indebted to many people for their support and contributions towards the

successful and timely completion of this research project.

Our first and deep gratitude goes to our supervisor, Dr. Yii Kwang Jing for his

professional support, guidance, encouragement and commitment through the entire

project. His wise counsel, patience and useful suggestions made it possible for us to

complete the research project. We also like to thank to our second examiner, Ms. Liew

Feng Mei who give some advice and useful suggestion so that we can enhance our

research project. With these relevant suggestions, we able to amend the mistakes that

had made and improve our research project.

Besides, we would like to thank project coordinator, Ms. Nurfadhilah Binti Abu Hasan

for coordinating everything that related to the research project and inform us about any

latest information. Next, our heartfelt appreciation goes to our parents whose

encouragement and moral support left us stronger every day in the entire duration of

our studies.

Lastly, we would like to thank to Universiti Tunku Abdul Rahman for providing us

complete and sufficient facilities such as Bloomberg and Science Direct. This enables

us to get the data and information that is important for the research easily. The

cooperation between groupmates also allow us to complete the project in time. They

also give their best effort and valuable time to finish it.


Undergraduate Research Project v Faculty of Business and Finance

DEDICATION

We would like to dedicate this thesis work to our most beloved supervisor, family,

friends and group members. We appreciate the helpful assistance given by our

knowledgeable supervisor, Dr. Yii Kwang Jing as he provides us a lot of useful

guidance and advices. He also spent his precious time to guide us throughout the whole

research project. Meanwhile, this research project is dedicated to our families and

friends for their encouragement and unconditional support when we conducting

research project. Lastly, all the members who have played their roles with the full

cooperation and contribution from each of the members should not be forgotten during

the process of completing the research project. In brief, this research project was

impossible to complete without their sincerity and unlimited support.


Undergraduate Research Project vi Faculty of Business and Finance

TABLE OF CONTENTS

Page

Copyright Page ………………………………………………………………...ii

Declaration …………………………………………………………………….iii

Acknowledgement ……………………………………………………………..iv

Dedication ……………………………………………………………………...v

Table of Contents ……………………………………………….………..........vi

List of Tables …………………………………………………………………..xi

List of Figures …………………………………………………………………xii

List of Abbreviations ………………………………………………………….xiii

List of Appendices ………………………………………………………........xv

Preface …………………………………………………………………………xvi

Abstract ……………………………….……………………………………….xvii

CHAPTER 1 RESEARCH OVERVIEW …………………..…………………..1

1.0 Introduction ……………………….……………………………...…1

1.1 Research Background ……………………………………….……....2

1.1.1 Property Market in Malaysia………………………………4

1.1.2 Macroeconomics Factor Affect House Price Index …...….6

1.1.2.1 Gross Domestic Product in Malaysia …………...6

1.1.2.2 Inflation Rate in Malaysia ………………….…...8

1.1.2.3 Exchange Rate in Malaysia …………………….11

1.1.2.4 Unemployment Rate in Malaysia ………………12


Undergraduate Research Project vii Faculty of Business and Finance

1.2 Problem Statement …………………………..…………….….……15

1.3 Research Questions …………………….….……………………….18

1.3.1 Main Research Question ………………………………....18

1.3.2 Specific Research Questions ……………………………..18

1.4 Research Objectives ………………………………………………..18

1.4.1 General Objective ……………………………….……......18

1.4.2 Specific Objectives …………………………………….....18

1.5 Hypotheses of Study …………………………………….……….....19

1.5.1 Gross Domestic Product …………………………...……..19

1.5.2 Inflation Rate ……………………………………………..20

1.5.3 Exchange Rate ……………………………………………21

1.5.4 Unemployment Rate ……………………………………...21

1.6 Significance of the Study …………………………………………...22

1.7 Chapter Layout …………………………………………………..…24

1.8 Conclusion ……………………………………………………….....25

CHAPTER 2 LITERATURE REVIEW ……………….………………………26

2.0 Introduction …………………………………………………….…..26

2.1 Review of Literature ………………………………………..……...26

2.1.1 House Price Index …………………………………….….26

2.1.2 Gross Domestic Product and House Price Index ………...28

2.1.3 Inflation Rate and House Price Index …………………....30

2.1.4 Exchange Rate and House Price Index …………….…….32

2.1.5 Unemployment Rate and House Price Index ………….…34


Undergraduate Research Project viii Faculty of Business and Finance

2.2 Review of Relevant Theoretical Models …………………………….37

2.2.1 Demand and Supply Theory ………………………………37

2.2.2 Purchasing Power Parity …………………………………..38

2.2.3 Portfolio Balance Theory ………………………………….38

2.3 Proposed Theoretical/ Conceptual Framework ………………….…..40

2.4 Conclusion …………………………………………………….……..41

CHAPTER 3 METHODOLOGY …………………………………………….….42

3.0 Introduction ………………………………………………..…………42

3.1 Proposed Empirical Model …………………………………………...43

3.2 Source of Data ……………………………………………………..…43

3.3 Variables Description ….………………………………...………...…45

3.3.1 House Price Index ………………………………………….45

3.3.2 Gross Domestic Product …………………………………....45

3.3.3 Inflation Rate …………………………………………….....46

3.3.4 Exchange Rate ………………………………………..…….47

3.3.5 Unemployment Rate ……………………………………..…47

3.4 Empirical Testing Procedure ………………………………...…….…48

3.4.1 Unit Root Test ……………………………………………...48

3.4.1.1 Augmented Dickey-Fuller Test …………………..50

3.4.1.2 Phillips-Perron Test ……………………………....51

3.4.2 Auto Regressive Distributed Lag …………………………..52

3.4.3 Non-Auto Regressive Distributed Lag ……………………..54

3.4.4 Granger Causality Test ……………………………………..55


Undergraduate Research Project ix Faculty of Business and Finance

3.5 Diagnostic Checking ……………………………………………….57

3.5.1 Multicollinearity ………………………………………….57

3.5.2 Heteroscedasticity ………………………………….……..57

3.5.3 Autocorrelation …………………………………………...58

3.5.4 Model Specification ………………………………………59

3.5.5 Normality Test ………………………………………….…60

3.5.6 CUSUM and CUSUMSQ Test ………………………...….60

3.6 Conclusion …………………………………………………………...61

CHAPTER 4 DATA ANALYSIS ………………………………………..……...62

4.0 Introduction ………………………………………………………….62

4.1 Descriptive Statistics …………………………………………….…..62

4.2 Unit Root Test ……………………………………………………….64

4.3 Auto Regressive Distributed Lag ……………………………………66

4.3.1 Diagnostic Checking of the model …………….…….……66

4.3.1.1 Multicollinearity ……….…………….……….…66

4.3.1.1.1 High pair-wise correlation among

independent variables …………………68

4.3.1.1.2 Variance Inflation Factor ……………...69

4.3.1.2 Normality Test …………………………………..70

4.3.1.3 Autocorrelation …………………………….…....71

4.3.1.4 Heteroscedasticity ……………………………….72

4.3.1.5 Model Specification …………….……………….73

4.3.1.6 CUSUM Test and CUSUM Square Test ………..74


Undergraduate Research Project x Faculty of Business and Finance

4.4 Non-Auto Regressive Distributed Lag ………………………….…74

4.4.1 Diagnostic Checking of the model …………….…….…..77

4.4.1.1 Normality Test ………………………………....78

4.4.1.2 Autocorrelation ………………………………...79

4.4.1.3 Heteroscedasticity ……………………………..79

4.4.1.4 Model Specification …………..……………….80

4.4.1.5 CUSUM Test and CUSUM Square Test ………82

4.5 Granger Causality Test ………………………….…………………82

4.6 Discussions of Major Findings …………………………………….85

4.6.1 Gross Domestic Product …………………………………85

4.6.2 Inflation Rate …………………………………………….87

4.6.3 Exchange Rate ………………………………………...…89

4.6.4 Unemployment Rate ……………………………………..91

4.7 Conclusion …………………………………………………………92

CHAPTER 5: Conclusion ………………………………….…………………..94

5.0 Introduction ……………………………………………………..….94

5.1 Summary ………………………….………………………………..94

5.2 Policy Implication ……………………………………………….....97

5.3 Limitations of Study ………………………..……………………...99

5.4 Recommendations of Future Research ………………….……...…100

5.5 Conclusion ……………………………………….…………….….101

References ………………………………..……………….…………….….…102

Appendices …………………………………………………………………....113


Undergraduate Research Project xi Faculty of Business and Finance

LIST OF TABLES

Pages

Table 3.1: Data Measurement ……………………………………......................44

Table 4.1: Descriptive Statistic …………………………..…………..................63

Table 4.2: Unit Root Test …………………………….………………………...64

Table 4.3: ARDL Bound Test ………………………………….……………....66

Table 4.4: Long Run Coefficients Result ……………………………….……...67

Table 4.5: Correlation Analysis ………………………………..……………....69

Table 4.6: Variation Inflation Factor …………………..………………………70

Table 4.7: Result of Variation Inflation Factor …..………………….………....70

Table 4.8: Test Statistics of Breusch-Godfrey Serial Correlation LM Test ...…72

Table 4.9: Test Statistics of ARCH Test …...………………………….…….…72

Table 4.10: Test Statistics of Ramsey Reset Test …………………….…..…....73

Table 4.11: NARDL Bound Test ………………………………………….…...75

Table 4.12: NARDL Estimation Results ……………………………………....76

Table 4.13: Test Statistic of Wald Test ……………………………..…………77

Table 4.14: Test Statistics of Breusch-Godfrey Serial Correlation LM Test ….79

Table 4.15: Test Statistics of ARCH Test …...………………………………...80

Table 4.16: Test Statistics of Ramsey Reset Test ………...…………….……...81

Table 4.17: Granger Causality Results based on VECM ………………………83


Undergraduate Research Project xii Faculty of Business and Finance

LIST OF FIGURES

Pages

Figure 1.1: Global Real House Price Index ……………..…………….……………3

Figure 1.2: Malaysia House Price Index and House Price 1 Year Percentage

Change ……………………………………………………………..…...5

Figure 1.3: GDP in Malaysia from year 1994 to year 2015 …………………….….7

Figure 1.4: Inflation rate in Malaysia from year 1994 to year 2015 …………….…9

Figure 1.5: Malaysia Exchange Rate (Ringgit to US Dollar) from year 1999 to

year 2016 ………………………………………………………………11

Figure 1.6: Unemployment Rate in Malaysia from year 1994 to year 2015 ………13

Figure 2.1: Determinants of house prices from macroeconomic perspective

in Malaysia ………………………………………………………….….40

Figure 4.1: Jarque-Bera Normality Test ………………………………………...….71

Figure 4.2: CUSUM Test ……………………………………………………….….74

Figure 4.3: CUSUM Square Test ………………………………………………..…74

Figure 4.4: Jarque-Bera Normality Test …………………………………………...78

Figure 4.5: CUSUM Test ……………………………………………………….….82

Figure 4.6: CUSUM Square Test ……………………………………………..……82


Undergraduate Research Project xiii Faculty of Business and Finance

LIST OF ABBREVIATIONS

ADF Augmented Dickey Fuller

AIC Akaike Information Criterion

ARCH Autoregressive Conditional Heteroscedasticity

ARDL Auto Regressive Distributed Lag

BNM Bank Negara Malaysia

BVAR Bayesian Vector Auto-regression

CPI Consumer Price Index

CUSUM Cumulative Sum

CUSUMSQ Cumulative Sum of Square

ECM Error Correction Model

EXG Exchange Rate

GDP Gross Domestic Product

HPI House Price Index

IMF International Monetary Fund

JB Jarque-Bera

JPPH Valuation and Property Services Department

NAPIC National Property Information Centre

NARDL Non-Auto Regression Distributed Lag

NEER Nominal Effective Exchange Rate

OLS Ordinary Least Square


Undergraduate Research Project xiv Faculty of Business and Finance

PP Phillips-Perron Test

PPP Purchasing Power Parity

RESET Ramsey’s Regression Specification Error Test

REER Real Effective Exchange Rate

RMB Ren Min Bi

SARS Severe Acute Respiratory Syndrome

SBC Schwarz Bayesian Criterion

UK United Kingdom

UNEMPT Unemployment Rate

USD United States Dollar

UTAR Universiti Tunku Abdul Rahman

Q1,Q2,Q3,Q4 Quarter 1,2,3,4

VECM Vector Error Correction Model

VIF Variance Inflation Factor


Undergraduate Research Project xv Faculty of Business and Finance

LIST OF APPENDICES

Page

Appendix 4.1: Descriptive Statistics …………………………………………...….113

Appendix 4.2: Augmented Dickey-Fuller unit root tests results

(without trend, level) ……………………………………………....113

Appendix 4.3: Augmented Dickey-Fuller unit root tests results

(with trend, level) ……………………………………………….....116

Appendix 4.4: Phillips Perron unit root tests results

(without trend, level) ……………………………………………....120


(with trend, level) ……………………………………………….....124

Appendix 4.6: Augmented Dickey Fuller unit root tests results

(without trend, First Difference) …………………………………..128

Appendix 4.7: Augmented Dickey Fuller unit root tests results

(with trend, First Difference) ……………………...……………....131


(without trend, First Difference) …………………………………..135


(with trend, First Difference) ……………………………………...139

Appendix 4.10: Autoregressive Distributor Lag Model (ARDL) ………………....143

Appendix 4.11: Diagnostic Checking for ARDL Test ………………………….…144

Appendix 4.12: Multicollinearity …………………...……………………………..147

Appendix 4.13: Non-Auto Regressive Distributed Lag (NARDL) ……...…….…..149

Appendix 4.14: Diagnostic Checking for NARDL Test ……………………….….151

Appendix 4.15: Vector Error Correction Estimates …………………………..…...154

Appendix 4.16: Granger Causality Tests ………………………………………......155


Undergraduate Research Project xvi Faculty of Business and Finance

PREFACE

Housing price has always been the main concerning issue for households to make a

decision for owning a house. Nevertheless, we observed that Malaysian house prices

keep on increase in recent years. The consequences are the citizens in Malaysia are

having difficulty in purchasing their dream houses. Thus, the rising price of houses in

Malaysia has raised the attention from investors, home buyers, economist, policy

makers and other relevant parties.

In addition, the household spending and borrowing behaviours would affected by the

changes of house prices in a country. The household behaviours can be link to the Law

of Supply and Demand. When the unemployment rate is low, it indicates that the

income of household is high. When the income level is high, household will demand

more on housing and this action will lead to increase in GDP in the long run. Next, the

relationship between inflation rate and exchange rate will affect the housing prices as

well. When inflation rate of goods and services in a country increase, it will cause the

depreciation of currency. The decrease in value of currency will influence the demand

on housing, however, foreign investors will demand on the local property. Hence, the

price of residential property is continues to increase due to their intervention in the

property market.

This purpose of this study is to determine the relationship between the house price

index in Malaysia with the macroeconomic determinants such as gross domestic

product (GDP), inflation (CPI), exchange rate (EXG) and unemployment rate

(UNEMPT). This study will be able to provide empirical results for readers such as

policy makers, investors, economists, homebuyers on the consequences and

relationship of these four independent variables on Malaysian house prices and

determine whether those variables are the essential determinants.


Undergraduate Research Project xvii Faculty of Business and Finance

ABSTRACT

Nowadays, housing price in Malaysia has continuously growing compare to previous

years. It had caused plenty of problems for countries especially in economic

development. The increasing in house prices has brings a lot of negative effect to the

households. Hence, this study examines the macroeconomic determinants with

residential housing price in Malaysia from period year 2001 first quarter until year 2015

fourth quarter, which consist of quarterly data of 60 observations. Thus, this study

would like to investigate the significant relationship among the housing price and

macroeconomics variables that affect the housing prices. Hence, we employed Unit

Root Test, Autoregressive Distributor Lag Model (ARDL), Non-Autoregressive

Distributor Lag Model (NARDL), and Granger Causality Tests, to investigate the long-

run and short-run relationship between gross domestic product (GDP), inflation rate

(CPI), exchange rate (EXG) and unemployment rate (UNEMPT) with housing price in

Malaysia.

The macroeconomics variables chosen for the study are gross domestic product (GDP),

inflation rate (CPI), exchange rate (EXG) and unemployment rate (UNEMPT) in

Malaysia. This study concludes that the inflation rate (CPI), exchange rate (EXG) and

unemployment rate (UMEMPT) are significant toward the Malaysian residential

housing price, however gross domestic product (GDP) is not significant toward the

residential housing price of Malaysia. Besides, inflation rate (CPI) and unemployment

rate (UNEMPT) showed positive relationships with the house price index, whereas

exchange rate (EXG) showed a negative relationship with the house price index. The

result that obtained enables benefits to various parties such as investors, housing

developers, speculators, home buyers, government and policy makers. Among these

four variables, the results concluded that inflation rate have the highest impact in

determining on Malaysian housing price.


Undergraduate Research Project Page 1 of 155 Faculty of Business and Finance

CHAPTER 1: RESEARCH OVERVIEW

1.0 Introduction

This study aims to examine the determinants of house prices from macroeconomic

perspective in Malaysia from year 2001 quarter one until 2015 quarter four. There are

a total of four macroeconomic elements which include Gross Domestic Product (GDP),

inflation rate, exchange rate and unemployment rate were employed in this study

together with house price index (HPI) in Malaysia. The reason we choose the

determinants of housing price from macroeconomics perspectives in Malaysia as our

topic is that we noticed the Malaysian house prices keep on rising in recent year. The

consequences are the citizens in Malaysia are having difficulty in purchasing their

dream houses in present time. Thus, through this study, we could find out which

determinants would affect the house prices the most.

Firstly, background of the study consists of general concept, real estate market and each

macroeconomic factors that affect house price index in Malaysia will be further

discussed. Next, this chapter will continue with problem statement which provides

readers an in-depth understanding of this study followed by the research questions

which consist of both main research question and specific research questions. Besides,

this chapter also listed out the general objective and specific objectives regarding to

this study. Hypotheses and significance of study are discussed as well as chapter layout

will be outlined accordingly. In the last part of this chapter, a short conclusion will be

reviewed.



1.1 Research Background

House is important for every human being and everyone is pursuing a key goal of life

is to own a house. Housing is a basic need for every human being because these assets

could function as a shelter for individuals to stay and give protection to them. Hence,

the main concerning issue for households to make decision for owning a house is the

changes in house prices (Liew & Haron, 2013). For instance, the increase in house

prices caused young generation with low and medium income level unable to own a

desirable house. Furthermore, the household spending and borrowing behaviours

would affected by the changes of house prices in a country. This will influence the

household’s perceived lifetime wealth. Le (2015) added that the fluctuation in house

prices might affect changes in household wealth significantly as households is the

largest investment group for housing in most countries. Besides providing protection

and shelter, house also act as a long term investment vehicle. When the public

infrastructures like highway, public transport is upgraded, it will bring convenience to

those households who live near with the advanced public infrastructure. Therefore,

demand for houses increase and it will lead to house prices rise. Besides that, when the

government continued to invest in infrastructure projects, it could stimulate the

economic growth in the country. Furthermore, due to the increase in market demand,

landlord or broker will bid up the prices along with the movement of housing price.



Figure 1.1 Global Real House Price Index

Source: International Monetary Fund

The global real house price index from year 2001 quarter one until year 2016 quarter

three is calculated by International Monetary Fund (IMF) and compile in graph as

above. In year 2008 first quarter, the global real house price index has experienced a

stable growth and reached a peak of 159.31. However, the global house price index

decreases dramatically by around 14 which was year 2008 first quarter (159.31) until

year 2009 second quarter (145.32). This was due to the United Stated subprime

mortgage crisis and global financial crisis in year 2008. After that, the global house

price index starts to fluctuate in between 140 and 155 after year 2009 onwards. It clearly

reveals that house prices from all over the world have been start to recovery and going

up steadily. In short, the global financial crisis happened in year 2008 had caused

collapse in financial markets and subsequently lead to the global economic recession

and housing bubble. Hence, fluctuations in housing price have the potential to affect

the regional economic activity (Lean & Smyth, 2013).

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1.1.1 Property Market in Malaysia

In 2016, the property price index in Malaysia has been ranked as the 24th highest

among 32 Asia countries (Numbeo, 2016). Price of the “average house” for all

houses in Malaysia recorded as RM 339,552 in 2016 third quarter which rise

gradually since the first quarter of 2000 with the amount of RM 135,293 based

on the information from Valuation and Property Services Department (JPPH).

Moreover, the rapid economic development in Malaysia has leads to increase

in demand of residential housing located in urban areas (Ong, 2013). It has

resulted the residential property market price has go through severe expansion

over the past ten years. Thus, a significantly rising in house prices will grab the

attention from individual, institution and government successfully.

However, when there is low borrowing cost and excessive bank-lending that

leads to investment and speculation, the house bubble in Malaysia will formed.

Housing price will rise until they attain unsustainable levels relative to national

incomes and other economic elements. Hence, while the non-performing loans

cases start to appear, banks will faced capital shortages. Economy and housing

price will be influenced when banks start to cut back credit (Hussain, Rahman,

Husain, Lyndon & Ibrahin, 2012).

Furthermore, there are also different sub-sectors on real estate, including

residential, commercial, development land, agricultural and industrial in

Malaysia. In year 2015, the total volume and value of property transaction by

sub-sector on the real property of Malaysia is 362,105 and RM 149,897.95

million. Residential is the largest sub sectors with the value of RM 73,469.89

million among these sub-sectors in year 2015. The second is the commercial

followed by development land, agricultural and industrial (National Property

Information Centre (NAPIC), 2015). According to the statistic done by NAPIC,

residential remains the largest sub sectors over the past ten years. Thus, the

residential can be considered as the major player of real property markets.



Moreover, refer to the graph below, it has clearly showed the housing price as

proved by the Malaysian house price index had experienced a strong increase

in price appreciation by index of 144.5 which is from index of 100.6 in year

2001 first quarter to 245.1 in year 2016 third quarter. Furthermore, the

Malaysian house price index had growing steadily and reached doubles from

year 2001 first quarter to 2016 third quarter (Valuation and Property Services

Department, 2016). The change in house price index has showed positive sign

which indicates that the housing price were keep growing.

Figure 1.2 Malaysia House Price Index and House Price 1 Year Percentage Change

Source: Valuation and Property Services Department

Based on the data showed in the graph above, HPI in Malaysia shows an

increment trend from year 2001 to 2015. From the year 2001 to 2009, the

growth of Malaysia house price is growing up steadily while for the year 2010

to 2015 the Malaysia house price index had increase rapidly. In another words,

Malaysia house price index growth rate was fluctuating but relatively steady

with around an average 3.28 percent of growth rate per annual from year 2001

to 2008. However, Global Financial Crisis in year 2008 has affected Malaysia’s

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GDP dropped from 3.32 percent (2008) to -2.52 percent (2009). Due to the

economic crisis in 2008, many firms started to cut down expenses such as

reduce the labour working hours. This had indirectly affected the growth rate

for housing market has dropped sharply within three quarters which is from

year 2008 third quarter (5.0 percent) to 2009 first quarter (0.7 percent). After

that, the housing price market is started to increase. The growth rate was

significantly increase by around 4.1 percent from year 2009 third quarter (1.5

percent) to 2009 fourth quarter (5.6 percent). Furthermore, from the period of

year 2010 first quarter to 2012 first quarter the house prices had experienced a

strong average increase of 8.83 percent annually. This is because Malaysia was

recovering from the Global Financial Crisis during that period of time. During

the period of 2001 first quarter to 2015 fourth quarter, the growth rate of house

price index with 12.2 percent had recorded the highest and hit the peak at year

2012 fourth quarter and 2013 third quarter. After the growth rate hit the peak,

it started to fall till 2016 third quarter. In brief, since the house price index in

Malaysia has a rising trend from year 2001 until 2016, thus it raises the

importance for this study to identify and analyse the significant determinants of

house price in Malaysia.

1.1.2 Macroeconomics Factor Affect House Price Index

1.1.2.1 Gross domestic product (GDP) in Malaysia

GDP usually is the best way to measure a country’s economy. In general,

the GDP of a country consists of personal consumption, government

expenditure, business investment, and net exports. GDP of a nation

holding imports constant will be directly affected when exports has been

increased. GDP is a leading indicator that reflect a country’s

performance in the long run. Therefore, all parties such as economist,



policy maker, businessman, speculators, even consumers would always

look out on the GDP in their country. If there is a fluctuation on GDP in

their country, it may result in financially loss or profited. Thus, the

growth in GDP will bring an impact towards investment, personal

finance, and job growth. In short, the Central Bank will use the change

in growth rate to determine whether implement contractionary monetary

policy to prevent inflation or expansionary monetary policy to avoid

recession.

Figure 1.3 GDP in Malaysia from year 1994 to year 2015

Source: World Bank

Based on the data showed in the graph above, it was clearly showed that

there is a fluctuation in some certain periods. The GDP growth had drop

significantly in year 1998 which is -7.35 percent due to Asian Financial

Crisis. This had caused the recession in economy and lead to higher

unemployment as well as increase in inflation rate. Other than that,

many companies had experienced difficulties during these periods.

Moreover, in line with the strong recovery, the economy began to

growth again in 1999 and it had risen up to 8.86 percent in 2000. GDP

growth was fluctuating but relatively stable with the averaged 5.95

percent from year 2002 to 2008. However, the growth fell sharply to -

2.52 percent in 2009 was due to the falls in global demand and world

-10

-8

-6

-4

-2

0

2

4

6

8

10

12

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P (

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Gross Domestic Product in Malaysia (1994-2015)



economic downturn. In 2010, GDP growth had recovered strongly and

bounced back to 6.98%. This is due to expansionary monetary policy

has been used as a tool to cushion the financial system against share

market collapse and capital outflows (Athukorala, 2010). Furthermore,

the GDP growth continue keep maintain within 4 percent to 6 percent

from year 2011 to 2015. In addition, the Malaysian Institute of

Economic Research (Mier) has forecast for Malaysia GDP growth of

4.5 percent in 2017 against an estimated growth of 4.2 percent projected

for 2016. National demand is expected to be the driver of GDP growth

in 2017 (“Mier forecast”, 2017).

In addition, GDP can be contributed by housing through consumption

spending on housing services and housing investment. Thus, GDP is

associate with housing in order to explain their effect towards the

economic growth. Growth in GDP could indicate that the economy

condition of the country is healthy. Furthermore, Ong and Chang (2013)

discussed and concluded that GDP has significant relationship towards

house price index. Meanwhile, there are others researchers found that

the significant relationship exist between GDP and house prices.

Besides that, the rising in construction cost and economic activity would

influence more on the house prices for lower income countries compare

to higher income countries (Chien, Lee & Cai, 2014). Lastly, it has led

to the policy of house prices growth can be considered as economic

growth for developing countries. In brief, GDP is an important variable

that must be included as one of the housing price determinants.

1.1.2.2 Inflation rate in Malaysia

Inflation defined as the rise in the price degree in an economy. When a

country experiences inflation, the prices of goods or services are



increasing and causing less export to other country as foreign buyers

would decrease their demand on the goods that are costly to them. Hence,

the depreciation of currency will lower down the purchasing power of

domestic consumer as now one unit of currency only allowed to

purchase small quantity of products as compared to previous time.

Higher inflation rate will have a lot of negative impacts to the citizens

and to the country itself such as investment lost, economic recession,

obstruct economic growth and the most important is the value of ringgit

depreciate.

Figure 1.4 Inflation rate in Malaysia from year 1994 to year 2015

Source: World Data Atlas

The graph above showed that inflation rate in Malaysia is fluctuation

from time to time. The inflation rate in 1998 and 2008 is high as

compared to other years. The inflation rate in 1998 is 5.3 percent due to

global financial crisis which can means the financial assets that hold by

public is worth less than their previous expectation. When there is a

global financial crisis, the currency over the world will be affected and

could cause inflation happen as the raw material over the world will

0

1

2

3

4

5

6

Infl

ati

on

Ra

te (

An

nu

al

%)

Year

Inflation Rate in Malaysia (1994 -2015)



become expensive. The rate in 2008 is 5.4 percent which caused by the

rising in global commodity prices such as cost of oil, food, construction

materials and petrol. In contrast, the inflation rate in 2009 is the lowest

as compared to other years due to global oil prices. The drop in global

oil prices has reduced the transportation from mowing the products

among markets regardless what countries produce the finished goods.

However, The Statistics Portal (2017) has predicted the inflation rate in

Malaysia may keep on increase and reach 2.7 percent in year 2017 as

compared with year 2015 which is only 2.1 percent and a rate of 3

percent is estimated for the following years. The data that predicted by

Malaysia’s government enables citizens or foreign investors to predict

the changes of inflation rate in the future.

Based on International Construction Cost Survey 2016, which held by

Turner and Townsend, the government plans for building the high-speed

rail that can link from Malaysia to Singapore had to be hold on because

inflation had increase the price of construction material and petrol. The

increase in inflation is considered to influence the housing price with

respect to the increasing in the general price levels of goods and services

(Gaspareniene, Remeikiene & Skuka, 2016). When people expect that

the housing price will continue to increase, they will demand more from

now. However, when the demand of housing is more than supply, the

price of housing will be increase sharply as compared to previous years.

Although the price of housing continue to upswing, but the increasing

in the cost of living and economic uncertainties had led to decreasing in

the number of property transaction (“Malaysia real estate market”,

2017). Inflation rate will cause a lot of goods increase in price, including

the construction cost, price of raw material for building, maintenance

fee and other costs. Hence, the effect of inflation will cause housing



price in the country continue to increase although the purchasing power

had decreased.

1.1.2.3 Exchange Rate in Malaysia

Exchange rate is defined as exchanging one currency with another

foreign currency from finance perspectives. The movement on the

exchange rate denominated in a currency will determine the inflow and

outflow of funds into a country.

Figure 1.5: Malaysia Exchange Rate (Ringgit to US Dollar) from year 1999 to 2016

Source: World Bank

Before 1999, Ringgit Malaysia keeps depreciating against US dollar.

Therefore, in September 1998, Malaysia government has imposed

“drastic measures” to fix the value of the Ringgit at RM3.80 to the USD

and capital controls on international trade toward the company in

Malaysia. The action taken by the Central Bank of Malaysia to control

on the monetary policy from continuously depreciating in the value of

currency. In the year of 2005 to 2008, the graph shows that there is an

2

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3.5

4

4.5

5

Exch

an

ge

Ra

te (

RM

to

US

Do

lla

r)

Year

Exchange rate in Malaysia (1999 to 2016)



appreciation of currency of Ringgit Malaysia and slightly depreciate in

2009. In year 2009 to 2011, its shows an appreciating of Ringgit

Malaysia to a peak at RM3.06 per dollar in year 2011.

After 2011, the currency of Malaysia keep depreciates until today and it

shows a serious depreciates from year 2014 to 2016. It is consider as

high pressure to most of the households, businessman and government

today. For example, they tend to spend more and save less, prices of

goods keep on increasing, and suffering in their normal living cost.

According to Stephy (2016), the economy in Malaysia is slowing as the

depreciation in ringgit Malaysia lead to the cost of living increases when

the housing prices keep increasing.

Nowadays, there was few breaking news that reported about the weak

currency in Malaysia. Due to the weak currency in home country, it has

increased the purchasing power of foreigners to invest in our home

country. The increase of purchasing power has increase the demand of

house and consequently pushed up housing price by the foreign buyers.

Glindro, Subhanij, Szeto and Zhu (2011) stated that when there is a

depreciation of currency in home country, foreigners are able to buy our

product at a cheaper price. It will directly affect the housing price in

Asia countries whereby most of the foreigners demand the house for

investment purpose. Foreign investors who have the affordability to

purchase houses are due to their strong currency.

1.1.2.4 Unemployment Rate in Malaysia

Unemployment rate means that where someone is not able to get a job

but would actively seek for work. Unemployment rate calculate by the

total number of unemployed workers divided by total number of labour



force. The rate of unemployment is economic indicators to determine

the performance of the economy. The nation economic growth might

become slower with higher unemployment rate due to the decrease in

consumer spending. When unemployment is high, the spending powers

of consumer is reduce and more likely to save their money. Hence, less

spending causes to a weak economic expansion. It will lead to lower

income per person and production of the country. Other economy

factors might significantly affected such as the stress and health

problems arises, potential homelessness, increase in social problems and

the low quality of living standard level.

Figure 1.6 Unemployment rate in Malaysia from year 1994 to year 2015

Source: Department of Statistics Malaysia.

From the graph, it shows that unemployment rate in Malaysia was

fluctuating for some certain period. The unemployment rate was

fluctuating between 3.80 percent and 3.0 percent during the year 1994

to 2000. This trend was happened due to the financial crisis that started

in middle year of 1997. It has affected the Malaysian real GDP growth

to become slower. Hence, a weak economy expansion causes the

1.5

2

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3.5

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Un

emp

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men

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Unemployment rate in Malaysia (1994-2015)



unemployment rate was increasing during that period of time.

Unemployment rate was increase steadily at approximate 0.10 percent

from year 2001 to 2004. Because of the 911 Incident (US World Trade

Centre was attacked by terrorists), the economy of US has experienced

slump and indirectly affect the global economy. Thus, the global

economy will be sluggish as US has decrease their import activity. It

then continued to fluctuate between 3.50 percent and 3.20 percent

during the year 2005 to 2015. Severe Acute Respiratory Syndrome

(SARS) and Iraq War occur was hit the economy of Malaysia because

our country has to conduct a large amount of inter-regional trade in the

worldwide. It can lead to the world economy to become downturn. After

this, Malaysia has the ability to survive during the sub-prime crisis.

Therefore, policy maker has implemented fiscal policy and monetary

policy to stimulate the economy of Malaysia. Thus, the unemployment

rate remained moderate at approximate 3.0 percent at the end of the

recent year (UKESSAYS, 2015).

According to Reed and Ume (2016), higher housing prices adversely

affect employees income in the labour market if the unemployment rate

is higher it can eventually affect the ability the buyer to purchase a home.

On the other words, turnover in the labour market also affect the demand

and supply of housing. If the workers success to find a job, after earning

labour market income, they can begin for searching a new house to

purchase in order to enjoy the benefits of home ownership. It can cause

the consumer have more confidence to put more money enter into real

property market for investment purpose or to enjoy the benefits of home

ownership for their living purpose. Therefore, prices and tightness of

the housing market may be affected by the labour market conditions

through labour force participation and wages.



1.2 Problem Statement

Housing plays a very important role in our life. Housing is a basic need for the citizens

for living as well as treats it as a profitable investment regardless local citizens or

foreign investors. However, the affordability of buyers to own a house is closely linked

with house prices (Osmadi, Kamal, Hassan & Fattah, 2015). Nevertheless, it cannot be

deny that the housing price today in Malaysia is much higher compare to the price in

the last few decades. For instance, developers mentioned that in Klang Valley or others

major urban areas to purchase a house with the price of RM 250 000 would not be a

viable price tag. This is due to the continuous increasing of many aspects in the building

cost especially the result of escalating land prices (Anwar, 2016). Hence, rising of the

housing price and unaffordability in owning a house has become the main issue recently.

From the whole population in Malaysia, only 72.5% of Malaysian citizens have their

own houses (Ismail, Jalil & Muzafar, 2015). From the figure that we obtained, we know

that 27.5% of the population do not owning a house regardless of their income level or

their age. This issue had reflect the performance of the economy in Malaysia whether

it is good or bad in recent era. According to Goh (2015), Malaysia young generation

with low or medium income group which occupy about 50 percent of the whole

population are also not able to purchase a dream house in present time. The percentage

revealed that most of the young generation or fresh graduates are not able to own a

house as the increase in housing price is greater than the increase in income level.

Besides, the percentage of property investment hold by foreign investors is 0.3 percent

in 2016 (Chow, 2017). The lower price of property in Malaysia had attracted the foreign

investors to own the property as the value of ringgit had drop in recent years.

Several consequences or problem may arise if we overlook the impact of rising in

housing prices on macroeconomics variables. Increasing in housing prices can be

recognized as a serious issue and brings negative consequences like stress of financial

insolvency and slowdown the economic growth. Although the rising in housing price

can increase the wealth of landlord, but it will affect the affordability of individuals



who want to purchase houses. According to Rahman (2008), first home buyers will

suffer in paying higher cost when purchase a house. They need to reserve a lot of money

to pay higher down payment and monthly mortgage payments. This is due to the

housing can be considered as the largest expenses in the budgets of most households

and individuals. The high proportions revealed that small changes in housing price will

have large impacts on the households who want to purchase a house.

In fact, most of the buyers are fear that they are incapable to meet the price increase in

housing (Ong, 2013). Lack of sufficient and reasonably priced of housing as one of the

serious issue facing in the country has identified by the governments and policy

analysts. It has causes the households with medium income level also facing the

difficulties to purchase a house due to the house and land prices are increase rapidly.

Despite the high price of housing in Malaysia, some developers had faced negative

reaction from the citizens as they added the prices of petroleum products and natural

gas on the housing. In other words, citizens need to bear the high construction cost

because of the high price of fuel. According to Al-Aees (2017), the increase in fuel

price will cause the construction cost increase as well, which is about 15%. Hence, the

growth of inflation will cause the housing price increase.

Besides that, it is impossible to expect the developer to build the house which is cheaper

with good quality as they do not know about the exchange rate of Malaysia in future

whether it will appreciate or depreciate. For instance, when the depreciation of

domestic currency happened, it will attract foreign investors to look into the properties

in Malaysia. Based on the news that reported in Daily Express (2014), the decreasing

in the value of ringgit allow Singaporeans and Bruneians to buy property in Malaysia

with lower price as compared to Malaysians. Consequently, local citizens will reduce

their demand on buying houses when the foreign investors pushed up the housing price

in domestic country with a weak currency. However, most of the peoples believe that

the continuous pricing of properties in Malaysia is due to real estate developers which

are the main constraint of the housing challenge. This means that developers might sell

it at higher cost for the purpose of obtaining more revenue without considering the



difficulties faced by the low-income level’s citizens. According to Zainal, Teng, and

Mohamed (2016), developers will passed all the costs and taxes to the end housing

buyers instead of bear it by themselves. In another words, when the real estate

enterprises obtain great amount of investment, the number of developments project will

increase and lead to the rising in supply of property. Thus, in this case, it will drive the

domestic investment when the currency appreciation (Liu & Hu, 2012).

In brief, the residential house price performance is important to a country. However,

there are not a good news for households especially for fresh graduates or households

with low-medium income level when the house prices is keep increasing. This had

weaken their purchasing power and unable to afford in purchasing a house whereas

there are also some other researchers with the opinion that increasing in house prices

is good for economic since the households spending will contribute to national gross

domestic product. Hence, by emphasizing how vital and necessary housing is,

government has to implement some suitable policies and programs to ensure citizens

in the country have a chance to obtain a place to stay.

Generally, the continuous bloom of house prices in Malaysia has raises the attention

for this study to analyse and identify the significant determinants of the house price.

Thus, this study aims to obtain a better understanding and an in-depth analysis on the

relationship of macroeconomic factors with HPI in Malaysia. This has supported by the

past researchers (Liu, Miao & Zha, 2016; Mahalik & Mallick, 2011; Ong, 2013).



1.3 Research Questions

1.3.1 Main Research Question

What are the determinants of house prices from macroeconomic perspective in

Malaysia?

1.3.2 Specific Research Questions

i. Does any significant relationship exist between GDP and HPI in

Malaysia?

ii. Does any significant relationship exist between inflation rate and HPI

in Malaysia?

iii. Does any significant relationship exist between exchange rate and HPI

in Malaysia?

iv. Does any significant relationship exist between unemployment rate and

HPI in Malaysia?

1.4 Research Objectives

1.4.1 General Objective

The purpose of this study is to examine the determinants of house prices from

macroeconomic perspective in Malaysia. This study is to determine the



association between the macroeconomics factors namely GDP, inflation rate,

exchange rate, unemployment rate with the house prices in Malaysia.

1.4.2 Specific Objectives

i. To examine the significant interconnection between GDP and HPI in

Malaysia.

ii. To examine the significant interconnection between inflation rate and

HPI in Malaysia.

iii. To examine the significant interconnection between exchange rate and

HPI in Malaysia.

iv. To examine the significant interconnection between unemployment rate

and HPI in Malaysia.

1.5 Hypotheses of Study

Based on this study, there are four hypotheses had been conducted to identify the

relationship between the macroeconomic factors and the housing price in Malaysia.

1.5.1 Gross Domestic Product (GDP)

H0: There is no important connection between GDP and HPI in Malaysia.

H1: There is an important connection between GDP and HPI in Malaysia.

Ong (2013) and Guo and Wu (2013) have find that there is positive and

significant correlated between GDP and housing price. GDP has a linkage with

the macroeconomics activities in the housing market. When the country has



greater GDP as well as economy development performing well, it will lead to

increase in housing prices. In another word, when the housing demand rises but

with the shortage of housing supply, this will also cause the housing price

increase. Consequently, GDP will affect the housing price indirectly through

various factors and aspects. Hence, GDP is a significant factor in affecting

housing prices.

1.5.2 Inflation Rate

H0: There is no important connection between inflation rate and HPI in

Malaysia.

H1: There is an important connection between inflation rate and HPI in

Malaysia.

The research conducted by Ong (2013) mentioned that a long-lasting effect is

exist between inflation rate and HPI in Malaysia. In other words, inflation rate

is positively correlated with housing price. He also found that the cost to build

a house will increase as inflation rate increase. This statement is supported by

Shaari, Mahmood, Affandi and Baharuddin (2016) as construction costs will

rise during inflation, therefore, the housing price will increase significantly.

This is due to some seller may take this “opportunity” to increase the price of

goods or services in order to earn more profit. Thus, the rising in inflation will

lead to the increase in house prices.



1.5.3 Exchange Rate

H0: There is no important connection between exchange rate and HPI in

Malaysia.

H1: There is an important connection between exchange rate and HPI in

Malaysia.

According to Abelson, Joyeux, Milunovich and Chung (2005), the relationship

between exchange rate and house prices is negative. It will increase the

attractiveness of housing assets when the exchange rate is lower for foreigners.

Another research that done in India by Mahalik and Mallick (2011) which stated

that the real effective exchange rate will bring negative influence on housing

prices. The appreciation of domestic currency discourage foreign investors

from investing in property project. This can explained that the home currency

is overvalued against the foreign currency, which subsequently leads to a

reduction of purchasing power in the foreign country.

1.5.4 Unemployment Rate

H0: There is no important connection between unemployment rate and HPI in

Malaysia.

H1: There is an important connection between unemployment rate and HPI in

Malaysia.

House price are extremely correlated to the home buyer or investor who want

to buy or invest in the housing sector. When the housing boom, the more

construction workers will be hire for building the house. This indicates that

fluctuations in employment have large effect on the unemployment rate.

Panagiotidis and Printzis (2015) found that decrease in employment growth rate



on the labour market leads to an unemployment rise and decline to the housing

price. When the unemployment rate is rising, the households will faced

difficulties in purchasing a new house because of the decrease in household

income and thus decrease the house prices (Aspden, 2012). Therefore, there is

an opposite relationship between house price and unemployment rate.

1.6 Significance of the study

Nowadays, housing price in Malaysia has continuously growing compare to previous

years. It had caused plenty of problems for countries especially in economic

development. The growing price of houses in Malaysia has raised the attention from

investors, home buyers, economist, and policy makers. The increasing in house prices

has brings a lot of negative effect to the households with low and medium income level.

It has leads to them choosing to rent a house instead of enjoying the benefits of home

ownership. Based on the study prepared by Ong (2013) on the macroeconomic

determinants of Malaysian house prices has showed the importance of macroeconomics

affect the housing price and called for future studies in this area. Hence, this study will

reveal the relationship of macroeconomic factor which are GDP, inflation rate,

exchange rate and unemployment rate with respect to Malaysia’s housing price level.

This allow the home buyers or investors to gain a basic knowledge about which

determinants would affect the house prices the most.

It is a vital step for government and policy makers to determine which macroeconomic

factors that will affect housing price in Malaysia the most before making any decisions.

They need to handle it carefully and implement a suitable policy that will benefit to

investors, home buyers or the whole nation. Thus, government need to adopt valid

approaches and make decision effectively and efficiently to assure that citizens have

an opportunity to obtain a place to stay in the future. However, decreasing in house

price has brings disadvantage to the investors because it will reduce in their wealth and

affect the house values, while it is favourable to the home buyers as they can purchase



a house with lower price in the future. In brief, the decision to invest or purchase a

house is a major decision because it involved a high cost and potential of a substantial

loss.

Besides that, a large number of breaking news that reported a weak domestic currency

is a good case in point. This is due to the overseas investors can affordable the sales

price with their strong currency. Hence, when there is more attraction of the foreign

buyers look into the real estate in Malaysia, it can boost up our economy. The

depreciation of domestic currency will increase housing demand from overseas

investors to scoop up low priced asset and outbidding than domestic buyers.

Consequently, domestic country with a weak currency has pushed up housing price by

the foreign buyers. It has leads to the local citizens will less demand on purchasing

house. As a result of, when the Bank Negara Malaysia (BNM) announced the reduction

of Overnight Policy Rate from 3.25% to 3%, there are more people willing to borrow

money to purchase a new house with the lower interest rates as it will lead to lower

interest payment in the future. It also can enhance the domestic currency to be improved

against numerous major currencies in the world (Chandrasekaran, 2016). In short, it is

important to include the exchange rate as an independent variable to determine whether

it will affect the housing price in Malaysia.

Furthermore, we able to provide an empirical result on the consequences of these four

variables on Malaysian house prices and determine whether those variables are the

essential determinants by using Auto Regressive Distributed Lag (ARDL) and Non-

Auto Regressive Distributed Lag (NARDL) in this study. So far for our knowledge,

there were very less researches using NARDL in their study to test on housing market.

According to Yeap and Lean (2017), there are several advantages on NARDL. First,

NARDL approach allows us to examine the asymmetry and nonlinear relationship for

both short run and long run at the same time. Second, the variables do not require to

have the same order of integration in NARDL. Regardless of the variables are

stationary, long-term relationship between the variables can be forecasted. Third, the

asymmetric adjustment patterns of the disequilibrium can still be observed through the



dynamic multipliers although the NARDL does not directly model asymmetric error

correction. At the same time, it will provide the overall view of the asymmetry

relationship between the macroeconomic variable and house prices.

In a nutshell, this study provides indicators to policy makers, government, future

economist researchers and others relevant parties towards the housing prices in

Malaysia. The useful suggestions allow government to enhance the growth of property

market for the benefits of citizens. For instance, this study could act as a reference to

help home buyers in purchasing their house at the right timing from macroeconomic

perspective. Furthermore, with the further understanding in this study, investors can

have a well investment planning and make an effective investment decision to reduce

the exposure risk. Lastly, it could be a guidance and reference for future researchers.

Towards to the end, this study will explore the most significant relationship between

these macroeconomic factors that affected the rise in Malaysia housing price.

1.7 Chapter Layout

Chapter 1 explains the research background of the linkage of residential housing prices

in Malaysia with macroeconomic factors. Next, this chapter also discussed the problem

statement, research objectives, research questions, hypotheses and significance of the

study. Lastly, a list of chapter layout and the briefly conclusion have been included in

this chapter.

Chapter 2 presents the literature review for each variables. The review of literature

included the findings and results are conducted by the past researches on their studies

and the relevant theoretical models in this chapter. In addition, the proposed theoretical

or conceptual frameworks of residential house prices are displayed in this chapter.



Chapter 3 will display the methodology used in this study. The proposed empirical

model of this study, the sources of data, variables description and method used to

analyse the data that will be discussed in this chapter.

Chapter 4 presents the data analysis with empirical results, diagnostic checking and

interpretation which involve the methodology stated in chapter 3. Lastly, it also

summarize and discussion on the major findings from chapter 4.

Chapter 5 is the conclusion chapter. Hence, this chapter has includes the summary of

chapter 1 to chapter 4, policy implication, identify the limitations and provide

recommendations for future study.

1.8 Conclusion

The Malaysia house prices are in continuous increment trend. Hence, the most widely

discussed issue in recent years is regarding the variables that lead to the continuous

increasing of house prices in Malaysia. This study are mainly describes on the housing

market in Malaysia with the macroeconomic variables. The rationale of this study is to

investigate the determinants of house prices from macroeconomic perspectives in

Malaysia, which are GDP, inflation rate, exchange rate, and unemployment rate for

better understanding purpose. In brief, in the end of the studies, it will be able to

determine which macroeconomic factors are affect the housing price the most and also

a guideline to home buyers or investors as it provides the knowledge of the relationship

between housing prices and macroeconomic factors. It is suitable for home buyers or

investors to have a review when planning to purchase a house or make decisions on

housing investment.



CHAPTER 2: LITERATURE REVIEW

2.0 Introduction

The earlier chapter has outlined our research background for each variables, problem

statements, general and specific objectives, and significance of study about the housing

price in Malaysia. Before proceeding to the empirical analysis, we had study the

previous researchers’ studies that are related to the determinants of housing price in

different countries. The researchers had used different variables in their studies, which

including microeconomic and macroeconomic factors.

In this chapter, we will examine the relationship between the independent variables

(GDP, inflation rate, exchange rate and unemployment rate) and dependent variable

which are house price index in Malaysia. In addition, we also will discuss the theories

that used by the previous researchers when conducting their research.

2.1 Review of the Literature

2.1.1 House Price Index

According to the Valuation and Property Service Departments (JPPH), HPI has

been on an increase between 1999 and 2014. Kaur (2017) stated that although

the economic slowdown and the ringgit depreciation, but the house prices will

not decrease for the next year. About 38 percent of consumers are expect to buy

a new house while 15 percent were to focus attention on the secondary market

such as an investment based on the PropertyGuru’s Consumer Sentiment

Survey. A potential house owner will look for a good transportation



infrastructure or other urban locations which is near to their workplace. Based

on the survey, the result shows that high property prices consist of 59 percent

concern by the potential house owners, followed by timing consist of 44 percent

and inability to pay in advance for the initial installment which consists of 39

percent. Furthermore, although there are a large discount offered by developers

due to market downturn but almost two-thirds of consumers are dissatisfy with

the house prices based on the PropertyGuru’s Affordability Sentiment Index. It

indicates that a low score which is 37 percent. This happened because of the

people unaffordability to deal with the rising living costs, income insufficient

and a big burden in qualifying for home loans. Therefore, it would be a

challenge for the generation Y demographic bracket who wants to own a home

ownership.

In addition, there was a great number of past researches that reviewed about the

determinants of the house price index in the particular country. Two Stage

Vector Error Correction Model (VECM) are used to investigate the relationship

between house prices and the other macroeconomic factors. The results shows

that GDP and inflation are positively related to the house prices, while

unemployment and exchange rate are an inverse relationship with the house

prices (Fereidouni & Bazrafshan, 2012). When the GDP rises, it indicates the

economic is growing well in the country at the same time tend to promote the

overall demand prices to comprise with the rise of house prices. Moreover,

when the general prices level of products and services increases, it indicates

that the rise of inflation causes the house prices tend to rises as well. On the

other hand, when the real estate activities decrease, the employment rate also

will decrease. It could lead to the unemployment rate increase. If the

unemployment rate increase by 1 unit, house prices will decrease by 1 percent

as well (Panagiotidis & Printzis, 2015). When the expected RMB became more

appreciation, it tends to promote the rise of real estate prices. Hence, it could

attract the investor to speculate capital in domestic housing prices which

accelerated the momentum of rising house prices as well (Liu & Zhang, 2013).



2.1.2 GDP and House Price Index

GDP is a tool used to measure a country economic performance. All the relevant

parties such as government, policy makers, home buyers are concerning on the

GDP in their country. It may lead to profited or financially loss when there is

any changes in the GDP. Meanwhile, housing price also determined by the GDP.

Most of the studies showed positive relationship between housing price and

GDP. As a result, housing and income are strongly connected. When the

country economy performing well and keep on growing, greater GDP will lead

to higher economic growth as well as increase in national income level. When

people have capacity to purchase their desired house, it will lead to rising in

housing demand. Thus, this has leads to increase in housing prices. Housing

demand among citizen is contribute to the increase in house prices directly.

Based on the study conducted by Liew and Haron (2013), there are 44 percent

of respondent opinion agreed that the high gross domestic product will lead to

the house prices increase. Furthermore, the housing developer has develops

more houses in the country in order to get profit earnings from the situation

where the expanding economy has improve the living standard as well as

increase in income level. However, when there is a recession happens in the

country, the income falls, unemployment rate will increase and leads to the

demand of houses drop. This situation will affect and imposes supplier to reduce

the house prices for the purpose of selling out the houses.

Overall, GDP plays an important role in contributing to housing prices. Ong

(2013) found that there is significantly positive relationship between GDP and

housing prices in Malaysia. Furthermore, housing investment is one of the

components in GDP. Therefore, when there is an increase in investment activity,

it will lead to growth in GDP. Studies from Guo and Wu (2013), Xu and Tang

(2014), Fereidouni and Bazrafshan (2012), Ong and Chang (2013) also

supported that GDP has a positive relationship towards the housing price in

others country. In other words, when a country economic growth is increased,



the house prices will increase because of the high demand from people who

have extra money to spend. Since the people have excess cash, they will invest

in the real estate to earn a higher return (Shaari, Mahmood, Affandi &

Baharuddin, 2016). However, according to Pillaiyan (2015), there could be a

real danger that the house prices are in bubble as the GDP is not classified as

an indicator towards house prices in the long run. Additionally, Feng, Lu, Hu

and Liu (2010) stated there is a stable equilibrium relationship between housing

price and GDP in the long run. The elasticity of GDP is more than one which

indicates the GDP has a major impact on the house prices. Thus, GDP increases

will result in the housing price grows largely. Besides that, Valadez (2010) also

found that there is a strong relationship exists between house price index and

real gross domestic product. However, there will be a challenge to study the

scientific causal effect because it is hard to manage control group in this stage.

It might be indirect or overlap on the causes of underlying relationship in this

way. Moreover, we cannot always take as granted that the relationship is linear,

it might be not linear over the time.

Moreover, the study of housing price in Iran using the new time series method

with quarterly data from 1990:1 to 2008:3 known as Toda-Yamamoto method

showed there is a significant multidirectional connection between housing price

and the macroeconomic variables. There is bidirectional causality between

GDP and house prices when tested using the Granger Causality method

(Meidani, Zabihi & Ashena, 2011). Based on the study conducted by Hii,

AbdLatif and Nasir (1999), a fluctuation movement in GDP has a significance

relationship related to the number of long houses, semi-detached and terraced

constructed in Sarawak. However, detached has no lead or lag relationship with

GDP which means the buyer purchase decision is not influenced by the GDP.

All the houses show positive co-movement with the GDP except detached.

However, GDP does not seem to be influential in the overall of housing prices.

For instance, Chui and Chau (2005) stated that there is a lack relationship



between the real estate investment and GDP because of the significant variation

in project's duration in Hong Kong. In short, it does not mean that change in

real estate demand has no effect on economic performance. Nevertheless, the

change in real estate demand is reflect more precisely and rapidly in real estate

prices as the property market in Hong Kong is efficient. From the result, the

real estate prices especially residential and office prices were found a strong

leading relationship towards GDP growth. Furthermore, Cohen and

Karpaviciute (2017) supported that the GDP are causal determinants of housing

prices in Lithuania. Hence, people who have ability to purchase a house with

their own funds are important as GDP will influence on the income. Besides

that, Li and Chiang (2012) have stated that the GDP does not granger caused

house prices, indicating personal gain does not catch up with GDP in China. On

the other hand, it has indicates that the appreciation in housing price does not

result in immediate capital gain. This is also shows that improvement purposes

or self-ownership have exceeds the speculative purpose in buying houses.

2.1.3 Inflation Rate and House Price Index

In theoretical view, housing price is positively correlated to inflation rate. The

higher the inflation rate, the higher the housing price in the economy. Inflation

rate is one of the factors of house prices volatility as it can push up the prices

of housing in the long run (Hossain & Latif, 2009). Inflation may affect the

housing price in Malaysia as it will affect people’s expenditure for consumption

and thus influence their demands for housing. This means that when the prices

of goods and services in the country continue to increase dramatically, people

will decrease their demand for the housing although it is an important and basic

asset to an individuals. According to Apergis (2003), increasing in inflation will

lead to an increase in housing price and thus reduce citizen’s incentive to invest

in property or real estate market. During inflation, most of the manufacturer or

seller will take advantage to increase their price of goods or services in order to



gain more profit. For example, there may have an increase in the price of raw

material for building a house which will cause the housing price increase

significantly (Ong, 2013). Piazzesi and Schneider (2009) also support the

positive relationship between the two variables as they found that Great

Inflation can led to a shifting of portfolio by making housing more profitable

than equity although both might increase in price. People will increase their

saving and reduce their expenditure for consumption for the purpose of

investing in house. They expect the value of property will increase sharply after

the change in inflation, and the value of stock or bond may continue to drop or

only increase in a small amount. Therefore, the investors are more confidence

and willing to invest in housing compared to equity, and caused the price of

housing increase when demand is more. In addition, the studies that conducted

by Abelson, Joyeux, Milunovich and Chung (2005) and Lee (2009) also found

a positive relationship between inflation rate and housing prices.

Although some of the researchers had found positive relationship between

housing price and inflation rate, but there are also negative relationship between

these two variables. Guo, Wang and Ma (2015) argued that increase in inflation

can stimulate the rising of housing price in the short run, but the rise of housing

prices can curb inflation in the long run. In other word, the effect of inflation

towards housing price is less than that of housing prices on inflation, which

means that housing price can hedge inflation rate in a certain period of time

(Kuang & Liu, 2015). When the prices of housing rise until a certain level, the

investors will shift their capital to other hedging products, thus this method can

curb the rise of housing prices in future. This can be explained when some of

the property buyers or foreign investors are not affordable to invest in housing

as they found that more money are needed to buy or invest on that housing.

Now, the demand towards housing is less than the supply and able to stop the

increase in housing price. Thus, the rise of housing price in long term will curb

inflation from happening in that country.



Next argument is insignificant relationship between inflation and housing price.

The increase in housing price may not affected by inflation rate, but depends on

other factors such as population and household income. For instance, developer

who wish to earn more profit or the differences in the price of lands in big cities

will show the housing price is much higher as compared to the property in rural

area. According to Zandi, Supramaniam, Aslam and Lai (2015), the correlation

between inflation rate and housing price is not statistically significant. This

indicates that housing prices is keep on increase when compared to previous

time as the economy growth in a country is good. When the economy growth is

good, it will attract more foreign investors to expand their business or become

resident in that country. Therefore, more housing is needed to fulfill the basic

needs of the people as housing can protect them from any disasters. Tan (2011)

also supported the insignificant relationship in his studies because the inflation

rate that had used is only for durable goods. Building such as houses or factories

are not considered as durable goods and it is calculate separately from other

durable goods such as food, clothing and other items as they are from different

categories. Cohen and Karpaviciute (2017) also had concluded insignificant

relationship between inflation rate and housing price in their studies. They

found that inflation rate is not causal determinants of the HPI in Lithuania. The

prices of goods and services may be change from year to year which is depends

on the economic condition of a country. Therefore, the result show insignificant

relationship between the two variables.

2.1.4 Exchange Rate and House Price Index

The depreciation in home currency makes the domestic residents to have less

purchasing power in demanding the houses because of the economic downturn

in Malaysia. In foreigners view, the house in Malaysia is cheaper for them

because of foreign currency is appreciating against Ringgit Malaysia.

Foreigner’s demand the houses in Malaysia will increase and caused the



housing price in Malaysia to increase. Therefore, an adverse correlation

relationship is expected between exchange rate and housing prices.

Some of the researchers examined that the exchange rate is negatively relation

with the housing price. According to Mahalik and Mallick (2011) and Abelson

et al., (2005), the appreciation of home currency discourage the foreign

investors to invest in the property market because of the overvalued against

their currency. Due to the lower purchasing power and decrease in foreign

investors’ demand towards housing, it has resulted an inverse relationship

between exchange rate and housing price. Apart from this, Abelson et al., (2005)

explained the two variables by using error correction model in Australia with

long-run relationship while Mahalik and Mallick (2011) explained short-run

causal relationship by using the co-integration test and the vector error-

correction model (VECM).

However, some of the researchers found that exchange rate and the house prices

has positive relationship. A research carried out in China by Liu and Zhang

(2013), stated that real estate will have a favor return of foreign investment

capital for both consumer and investment when there is an appreciation of

currency. Zhang, Hua and Zhao (2012) have an empirical studies in China by

using non-linear modeling approach and view from economic theory

respectively. The research shows that when there is an expectation of an

increase in RMB, most investors will pay attention on the real estate market by

transferring a lots of money into China for investment purpose. It has significant

positive impact and exist more hot money in economy and leads to house

bubbles in China (Tian & Gallagher, 2015). Besides, a rising in exchange rate

lead to increase in housing demand, which will cause the housing price increase

due to imbalance between supply and demand (Meidani et al., 2011).

In addition, Jiang (2012) and Zhang and Wu (2006) mentioned that when there

is anticipation of the appreciation of currency in China, housing prices will rise.



The performance of housing prices increasing the attractiveness of foreign

capital inflows, will further enhance the inflow of foreign capital, which in turn

will increase the upward pressure on currency. A study in Shanghai by Huang,

Wu and Du (2008) stated that exchange rate growth had a positive effect

towards the rise in housing price by using time series analysis.

Moreover, Glindro et al., (2011) stated that positive relationships between

exchange rate and house prices can be found in Asia countries that pay attention

on foreign direct investment. When the real effective exchange rate is high, will

cause a lots of non-residents demand for property investment and lead to an

increases in housing price in Asia-Pacific economies. According to Ohno and

Shimizu (2015), Asian central banks that have own exchange rate regime will

accumulate their foreign reserves by employing foreign exchange intervention.

If government intervention sterilization imperfectly, foreign exchange

intervention may brought in additional liquidity into local market and leads to

an increase of loan in bank and further pushing on housing price. Restraining

the fluctuation of exchange rate has leads the housing price to rise.

2.1.5 Unemployment Rate and House Price Index

In theoretical view, Aspden (2012) and Abelson et al., (2005) stated that

housing price is negatively related to unemployment rate. When a country

experiences economic difficulties such as unemployment, then it will impact

that the price of the property value to be going up. Housing market act as an

equilibrium price based on the ‘supply and demand’ approach in the long run.

When the economic is in the great recession, the house prices decline due to the

credit crunch and lead to the unemployment rate become higher. The researcher

stated that poorer region have a greater impact compare than a wealthy region.

If the poorer region is unemployed, they are unable to pay their monthly

installment of the house and the chance of being repossessed is high. This will



resulted supply is more than demand of housing in these region. Hence, they

will increase the demand for renting than become the homeowner. The increase

in housing supply will caused the house prices decrease because of the higher

unemployment rate. It shows a more direct relationship between house price

and unemployment rate.

Besides, Liu et al., (2016) found that there are an opposite movement direction

between house prices and unemployment rate. House price, unemployment rate,

job vacancies, investment, total hours and consumption has performed a large

co-movement due to the negative shock of housing demand. By using a

Bayesian Vector Auto-regressions (BVARs) model, the results indicate that a

negative shock of the housing price give rise to the unemployment rate. The

house price decline sharply due to the negative shock in the economy. In

addition, Geerolf and Grjebine (2014) also concluded that there is an inverse

relationship between house price and unemployment rate. The study is

conducted by using a data from 34 countries over the last 40 years to significant

impact of house price and unemployment rate. When the level of consumption

expenditure towards a product is reducing, a corporation may layoff some

employees to reduce the operation costs. This will lead to increase in

unemployment rate and thus the decline in housing price as the demand has

drop.

Furthermore, Gan and Zhang (2013) found that unemployment rate and housing

price are negatively correlated. From the demand side, if the unemployment is

high restricting a household from entering the property market, it serves as a

financial constraint because of them cannot get a mortgage. Therefore, it could

reduce the number of buyer. From the supply side, an increase in the

unemployment rate, it makes homeowners less willing to move or change a

new house because of the increased job insecurity by the people is higher. They

are worried about the uncertainty of higher chances of being unemployed for

the next time. Hence, it also could reduce the number of sellers. Thus, the housing



market becomes thinner with fewer buyers and sellers, which leads to poorer

matching quality on average. As a result, the housing price and sales volume

would drop sharply. Hence, increase in the unemployment rate lowers the sales

price, reduces the transaction volume, and increases the time-to-sale in the

housing market.

Based on the review on the studies of previous researcher, most of the result

had found that there is an inverse relationship between the two variables.

However, some researchers declared that positive relationship is exist between

house prices and unemployment rate. According to Xu and Tang (2014), the

relationship between unemployment rate and housing price is positive by using

cointegration vector. However, the researchers stated that the result still logical

in explaining the UK property market because UK housing price and

unemployment rate do not have evident interrelated. Apart from this, property

sector usually hire labour from foreign country due to the cost of hiring them is

much cheaper compared to local labour. Most of the local labour is unwilling

or not interested to engage in property industries due to the riskiness of the job.

In addition, Karamelikli (2016) stated that there are a positive relationship

between house prices and unemployment rate. An increased unemployment

rate could cause a reduction in number of potential customers and reduces the

demand for real estate. It also could consider a signal effect capable of showing

the economic situation for the nearest future. Hence, these signal effect could

affect the prediction about the future housing prices. Thus, the housing sector

has become a more secure investment instrument relative economic fluctuation,

but this has the effect that any harmful economic activity can cause an increase

in expected housing prices. The direction taken by financial assets such as real

estate will be shifts to riskless sectors if unemployment rate increases. As a

result, the housing sector will be invested more by the investors when an

economic downturn is expected in the near future. The housing sector is as act

crucial sector among those of minimum attendant risk. However, a decline in



unemployment does not have a significant effect on investors’ decisions. Any

increase in unemployment could be regarded as an alarm bell ringing in respect

of the economic future on the housing sector.

2.2 Review of Relevant Theoretical Models

2.2.1 Demand and Supply Theory

Demand and supply theory is used to explain the interconnection between the

demand and supply of goods or services. Demand defined as the quantity of

goods or services wished by the buyers, while supply represent the quantity that

can offered by the sellers. In economic theory, unemployment occurs when

someone is not able to get a job but would actively seek for work (Mouhammed,

2010). Unemployment rate is measured by the total number of unemployed

divided by the number of people in labor force. When the unemployment rate

in a country is lower, it will affect the property prices. Based on the theory,

when more people is being unemployed, the demand on housing will decrease

and will lead to a fall in housing price. People are not afford to buy house as

their household income had dropped and the cost of buying a house is high as

compared to other stuff. As housing price is directly correlated to income, only

when peoples have stable and fixed income are affordable to own a house

(Aspden, 2012). This will constrain the citizens to rent a house instead of buying

it, as their lower income are not entitle them to own a house. Therefore, when

unemployment rate increase, demand on housing will decrease and lead to

lower housing price. This show negative relationship between housing price

and unemployment.



2.2.2 Purchasing Power Parity (PPP)

Besides, Purchasing Power Parity (PPP) theory is known by an economist,

Gustar Cassel in 1918. This theory defines about the macroeconomics role of

exchange rate. Different country may have different currency and purchasing

power. PPP theory has an assumption of ‘Law of One Price’ which described

that buyers are able to purchase the same product with same quantity in different

country with same amount of money. This theory explains that exchange rate

at the equilibrium within the both countries will have the same purchasing

powers either local citizens or foreigners (Rudiger & Paul, 1976). It also

determines on the levels of relative prices. If the price levels of goods or

services in a country decrease, the currency will be appreciated against other

currencies as the demand is decreasing. In addition, this theory states that the

exchange rate depreciation which will caused a country export to be increase,

and also making the import price relatively high. Continuously, the domestic

price of goods and services would be affected, the high prices of commodities

in economy reducing purchasing power of the people in a country (Ozor & Eze,

2016). According to Mahalik and Mallick (2011), the country which have a

higher currency value will has a higher purchasing power indeed have a strong

impact to relative prices, and vice versa. A depreciation of currency will raised

the import prices relative to export prices, and thus increase the country's

competitiveness on goods and services. A greater purchasing power with higher

exchange rate currency attracting foreigners to purchase house in Malaysia

(Meidani et al., 2011).

2.2.3 Portfolio Balance Theory

Lastly, Portfolio balance theory was created by Mckinnon and Oates in 1966.

Portfolio balance theory has assumed that most of the people has allocated their

capitals funds, income or wealth to three forms of asset. It consists of domestic



bonds, foreign bonds and monetary base. Exchange rate in the balance of

holdings all these assets in a preferable proportion. An increase in capitals

means the wealth of residence has increased and leads to rise in monetary base,

purchasing and hold government of domestic and foreign bonds because of the

surplus in current account. Increasing in wealth of residents will increase the

demand of foreign goods and assets has resulted capital outflow and lead to

decrease of exchange rate (Ozor & Eze, 2016). This theory reviews the terms

of sterilization which means that the government will trade government bond

as the same amount as the exchange currency in order maintain the currency at

the same level as the foreign currency. According to Ohno and Shimizu (2015),

if government intervention sterilization imperfectly, foreign exchange

intervention may brought in additional liquidity into local market and leads to

an increase of loan in bank and further pushing on housing prices.



2.3 Proposed Theoretical/ Conceptual Framework

Figure 2.1 Determinants of house prices from macroeconomic perspective in Malaysia

Adopted from: Ong, T. S. (2013). Factors affecting the price of housing in Malaysia.

Journal of Emerging Issues in Economics, Finance and Banking, 1(5).

Abelson, P., Joyeux, R., Milunovich, G., & Chung, D. (2005). House prices in Australia:

1970 to 2003 facts and explanations. Economic Record, 81(1), 96-103.

Dependent Variable

Gross Domestic

Product (GDP)

Independent Variables

House

Price Index

Inflation Rate

Exchange rate

Unemployment

rate



Figure 2.1 states that the four independent variables would have impacts on the

dependent variable. The macroeconomic factors included GDP, CPI, exchange rate,

unemployment rate which will affect the movement of the housing price in Malaysia.

2.4 Conclusion

In brief, the relationship between house price index and macroeconomic variables has

been explained based on literature from previous researchers. However, it is noted that

the researchers were obtained different result for the relationship between the housing

price and macroeconomic variables. The reason behind inconsistency result may due

to the researchers conducted their studies in different country and thus, the data and

policies are different. So far, housing price in Malaysia is almost reach a peak, yet the

real variables that affect the housing price are still a question to citizens. Therefore, we

would like to examine the significant relationship for house price index and the

independent variables to get the accurate result as compared to the previous finding by

other researchers.

Throughout the discussion above, those findings have declared that there are correlated

between the GDP, inflation rate, exchange rate, and unemployment rate and the house

price index. This chapter also reviewed the theoretical framework between housing

price index and its determinants. For the next chapter, this study will discuss the

methodology and technique used for the estimation of the relationship of HPI and other

macroeconomic variables for the study in Malaysia.



CHAPTER 3: METHODOLOGY

3.0 Introduction

This study examined the determinants of house prices from macroeconomic

perspective in Malaysia. It is significant to have a well-designed research methodology

in order to determine how accurate of the results of a research method are. The

methodology and tests used in this study for the purpose to meet with the objectives in

the previous chapter. More specifically, empirical model, source of data, the proxy and

unit measurement for each variable, research methodology will be proposed in this

chapter.

Initially, this study was to identify the relationship between house prices in Malaysia

with four independent variables which include GDP, inflation rate, exchange rate,

unemployment rate. This study will apply time series econometric models and data uses

in this study is quarterly data from year 2001 first quarter to year 2015 fourth quarter,

a total number of 60 observations. In addition, Eviews software is used to analyse the

results output.

In short, section 3.1 will discuss the proposed empirical model of this study. Section

3.2 is the source of data in this study and section 3.3 is the dependent variable and

independent variables description. Furthermore, the ideas, theories and functions of

each methodology will be discussed in the section 3.4 and 3.5. The last section is the

conclusion of this chapter.



3.1 Proposed Empirical Model

The main purpose in this study is to examine the association between macroeconomics

factors namely GDP, inflation rate (CPI), exchange rate (LNEXG) and unemployment

rate (UNEMPT) towards house price index in Malaysia. The empirical model of this

research can be specified as below:

𝑙𝑛HPIt = β0 + β1GDPt + β2CPIt + β3𝑙𝑛EXGt + β4UNEMPTt + μt

Where:

𝑙𝑛HPIt = House Price Index (index, 2000=100)

GDPt = Real Gross Domestic Product in Malaysia (Percentage)

CPIt = Consumer Price Index (Percentage)

𝑙𝑛EXGt = Real Effective Exchange Rate Index in Malaysia (index, 2000=100)

UNEMPTt = Unemployment rate (Percentage)

μt = Error term

ln representing the natural logarithm form, μt represents uncorrelated white-noise error

terms. β0 is the intercept of the regression model and β (1,2,3,4) represent the slope of

coefficient.

3.2 Source of Data

The aim of this study is to examine identify the determinants of house prices from

macroeconomic perspective in Malaysia. All of the pertinent information and research

data have been gathered to investigate the relationship between the macroeconomics

variables with relation to the house price index in Malaysia. In addition, all of the

research data were collected and used in this study is mainly focused on secondary data.

This study uses secondary data as it is more accurate and useful as well as it is more

cost-efficient and time saving compared to primary data.



This research uses the quarterly time series data from year 2001 first quarter to year

2015 fourth quarter, consequently a total number of 60 observations. All of the

independent variables data are retrieved from Data Stream, which is available in the

library of University Tunku Abdul Rahman (UTAR) while the dependent variable,

residential housing price which is measured by house price index (HPI) obtained from

National Property Information Centre (NAPIC).

HPI is used as the proxy of residential house price in Malaysia. Besides that, other time

series data used in this research include GDP, CPI (proxy for inflation), real effective

exchange rate index (proxy for exchange rate) and unemployment rate which is the

most relevant variables that will influence the movements of house prices in Malaysia

and the residential housing market. The details of all dependent variable and

independent variables data are summarized as shown in Table 3.1 below.

Table 3.1 Data Measurement

Variable Proxy Unit

Measurement

Source

Residential Housing

Price

LNHPI Index (2000=100) National Property

Information Centre

(NAPIC)

Gross Domestic

Product

GDP Percentage International Monetary

Fund (IMF)

Inflation Rate CPI Percentage Department of Statistics

Malaysia

Exchange Rate LNEXG Index (2010=100) Bloomberg

(Bank for International

Settlements)

Unemployment Rate UNEMPT Percentage International Monetary

Fund (IMF)

Source: Developed for the research



3.3 Variables Description

3.3.1 House Price Index (HPI)

According to Maslow’s Hierarchy of Needs, housing is one of the human basic

physiological need (Kaur, 2013). Hence, house prices is the main concern by

the citizens in a country whether they afford to own a house or not. Besides, it

also shows the whole macroeconomic condition in that country. With respect

to study the determinants of house prices, HPI is used as a proxy to measure the

price of housing. The main objective of the usage of HPI is to identify the

changes of housing price over time (Tan, 2011). Recently, the demand from

local citizens and foreign investors towards Malaysia’s housing is increasing

over the years. Therefore, when demand is more than supply in the property

market, the housing price is predicted to increase due to unequal between

demand and supply. In short, many researchers had done by capturing the

relationship between the macroeconomic variables and the housing price for the

purpose of study how the macroeconomic factors affect the housing price and

take effective ways to solve the problem. In this study, GDP and inflation rate

are assumed to have positive relationship with HPI while exchange rate and

unemployment rate have adverse relationship against HPI.


GDP was described as the overall market prices for all finished goods and

services produced in a country in a specific year (Ong, 2013). In addition, real

GDP is the most frequently measured as an indicator of a country’s economic

activity. Real GDP can provide a more accurate figure of economic growth and

account for the changes in price level compare to nominal GDP. Thus, real GDP



is considered as the best method to measure an economy of a country due to the

economic performance of a country would affect the movements of housing

price and the residential housing market. Besides that, the people will become

wealthier when there is an increase in GDP which indicates that the economic

is growing. Furthermore, it will lead to the political of a country become more

stable with the healthier economy. In brief, real GDP is included as one of the

important variables in this study. For instance, according to Le (2015), he

claimed that rapid economic development has resulted a high demand for

housing in Malaysia. Hence, house prices have appreciated swiftly throughout

the country.

3.3.3 Inflation Rate (CPI)

In general, inflation rate is measured by using CPI (Consumer Price Index). CPI

is used to calculate price movement of a basket of finished goods and services

that are vary over time in index form. The change of percentage in index reflects

changes in the amount of inflation rate of a country over that specific period.

CPI is the best indicator to examine the effect of inflation rate on purchasing

power of a currency. As we know, an increase in CPI will affect consumer’s

purchasing power, dropping in the value of the currency will cause CPI to be

sky rocketing in the future.

CPI also help to provide a good image about the ‘temperature’ of the economy

as policy makers need to understand the economy condition whether it is

overheated, under perform or just at the ideal level. There are millions of goods

and services provided in the market, and it is impossible to include all of them

in the calculation. Therefore, Malaysia’s government has been categories them

‘into a basket’ that will represent the majority of the products and services that

household consumed in a year. The computation of CPI can be based on the

formula for multiple items. The following formula is:



Consumer Price Index (CPI) = ∑ 𝐶𝑃𝐼 × 𝑊𝑒𝑖𝑔ℎ𝑡𝑛𝑖=0

3.3.4 Exchange Rate

According to Mahalik and Mallick (2011), without the existence of inflation,

nominal exchange rate will be used to determine the HPI. Therefore, in this

study, real exchange rate is used as inflation is included as one of the variable

in the model. According to Klau and Fung (2006), real exchange rate can be

performs better than nominal exchange rate in the sense of inflation included.

Real effective exchange rate index included in the study as exchange rate is

directly link and remark the consequences of consumer’s purchasing power. In

order to get the high quality data on price and cost indicators is required for

calculating the real effective exchange rate (REER). REER is conducted as an

index of the nominal effective exchange rate (NEER) adjusted against the price

level corresponding to the relative consumer prices or costs in a countries. The

formula of real exchange rate index provided below:

𝑅𝐸𝐸𝑅𝑡 = 𝑁𝐸𝐸𝑅𝑡 ∑ (𝑤𝑖𝑃𝑖𝑡

𝑃𝑡)

𝑖=1

𝑁𝐸𝐸𝑅𝑡

Where:

𝑃𝑖𝑡 – Base price index in the period t for each partner country i

𝑃𝑡 – Index of domestic prices compared with the base period

3.3.5 Unemployment Rate

The unemployment rate is one of the important indicators to determine the state

of a country’s labour market. Unemployment rate measures the percentage of

employable people who are over the age of 16 in a country’s workforce and

someone that is not able to get a job but would actively seek for work.



Besides, this study used unemployment as an independent variable and the unit

used is in percentage. High unemployment could lead to the workers have lower

disposable income, reducing their expenditures on goods and services which

reduces the demand for productive inputs. It might reflect an economy to grow

slowly and the whole country loses due to the purchasing power of these

workers is loss and decline the productivity of the goods and services. Therefore,

unemployment is a key indicator to determine housing price level. The

following formula is the calculation for unemployment rate:

Unemployment Rate =𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑈𝑛𝑒𝑚𝑝𝑙𝑜𝑦𝑒𝑑

𝑇𝑜𝑡𝑎𝑙 𝐿𝑎𝑏𝑜𝑟 𝐹𝑜𝑟𝑐𝑒

According to Liu et al., (2016) and Geerolf and Grjebine (2014) stated that

unemployment rate is negative and have significant influence toward residential

housing prices. They came out the same results and claimed that there are an

opposite movement direction between house price and unemployment rate.

Hence, the expected sign in this study would be negatively and significant

relationship toward housing price.

3.4 Empirical Testing Procedure

3.4.1 Unit Root Test

Throughout this study, unit root test is conducted to test whether the series in

the group (or it is first or second difference) are stationarity, for the purpose to

prevent obtaining any spurious and invalid results. In other word, R² value and

t-statistics cannot validly undertake hypothesis tests as they do not fulfill the

assumptions. A time series has stationarity if a change in time doesn’t cause

any change in the shape of the distribution.



At level, 𝑦𝑡 = |∅|𝑦𝑡−1+ 𝑢𝑡

At 1st difference, let ϕ =1 and subtract 𝑦𝑡−1from both left hand side and right

hand side in the equation,

𝑦𝑡− 𝑦𝑡−1= 𝑦𝑡−1− 𝑦𝑡−1+ 𝑒𝑡

Δ𝑦𝑡 = 𝑒𝑡

Due to 𝑒𝑡 is a white noise error term, hence Δ𝑦𝑡 is a stationary series. After

differencing 𝑦𝑡 can obtain stationarity.

Hypothesis statement:

H0: There is a unit root test (Non-stationary).

H1: There is no unit root test (Stationary).

Decision rule: Reject null hypothesis if p-value is less than the significance

level. Otherwise, do not reject null hypothesis.

Unit root test is employed to examine whether there are stationary or non-

stationary trend of time series data for all variables. It is necessary to check the

order of each variables integration at level and first difference. Gujarati and

Porter (2009) mentioned that stationary trend will show constant mean,

variance, and covariance of series across different periods. On the contrary,

non-stationary trend will show different or non-constant mean, variance and

covariance across different periods. If the variables in the regression model are

non-stationary, it will lead to inaccurate normal assumptions of the analysis and

the results will show biased and invalid problem. Therefore, most researchers

will conduct unit root test to determine whether a time series is stationary or

non-stationary before proceed to the next step (Hill, Griffiths & Lim, 2007).

Additionally, Asteriou and Hall (2007) stated that most of the macroeconomic

variables are non-stationary and seemed to be varied over time. Therefore, unit



root test must be carried out and make sure the model is stationary to prevent

such econometric problems and invalid results in the future. Both Augmented

Dickey-Fuller (ADF) and Phillips-Perron (PP) tests under the category of unit

root test will be run to determine whether there is stationary or non-stationary

in this study.

3.4.1.1 Augmented Dickey-Fuller (ADF) Test

Based on statistics and econometrics, ADF test is a test for a unit root with

larger and more complicated set of time series data. ADF is the most popular

test to conduct for stationarity, however, it is suffering from some shortcomings.

It does not correct for heteroscedasticity (Mahalik & Mallick, 2011). Three

probable modus of ADF:

Δy𝑡 = 𝛾𝑦𝑡−1 + ∑ 𝛽𝑖𝑝𝑖=1 Δ𝑦𝑡−𝑖+ 𝑢𝑡

Δy𝑡 = 𝛼0 + 𝛾𝑦𝑡−1 + ∑ 𝛽𝑖𝑝𝑖=1 Δ𝑦𝑡−𝑖+ 𝑢𝑡

Δy𝑡 = 𝛼0 + 𝛾𝑦𝑡−1 + 𝛼2𝑡 + ∑ 𝛽𝑖𝑝𝑖=1 Δ𝑦𝑡−𝑖+ 𝑢𝑡

The tests above will show valid result only if 𝑢𝑡 is a white noise error term.

Hypothesis Statement:

H0: βt has unit root (Non-stationary).

H1: βt has no unit root (Stationary).

Decision rule: Reject null hypothesis if absolute t-statistics is higher than

absolute critical value. Otherwise, do not reject null hypothesis.

This test was further improved by Dickey and Fuller (1979) which is conducted

by ‘augmenting’ the preceding three equation by adding lagged of dependent



variable （∆Yt）to remove autocorrelation effect and the optimal lag length of

test is based on the minimum information criterion.

3.4.1.2 Phillips-Perron (PP) Test

Phillips and Perron have developed a more comprehensive theory of unit root

non-stationarity. PP test is a modification of the ADF test, and it incorporates

an automatic correction to the ADF technique to allow for auto-correlated

residuals. The tests are usually provides the same conclusions as the ADF test,

but the calculation of the test statistics is more complex as compared to ADF.

Besides, it can strengthen the evidence of stationarity of the series in this study.

Test regression for PP as below,

Δ𝑦𝑡−1 = 𝛼0 +𝛾𝑦𝑡−1 + 𝑢𝑡


H0: βt has unit root (Non-stationary).

H1: βt has no unit root (Stationary).

Decision rule: Reject null hypothesis if absolute t-statistics is higher than

absolute critical value. Otherwise, do not reject null hypothesis.

PP test is a non-parametric test for unit root in time series data, but it is almost

similar to the ADF test. However, PP test does not take into account of lagged

difference terms as ADF, but it makes a correction to the t-statistics of the

coefficient to control serial correlation. The PP statistics are modifications of

the ADF's t-statistics that take into account the less restrictive nature of error

process, as well as investigate any serial correlation and heteroscedasticity error

(Gujarati & Porter, 2009). The PP is performed with the inclusion of a constant,

a constant and linear trend, or neither in the test regression (Asteriou & Hall,



2007). Besides, this study will follow the most researchers that tend to choose

(Newey-West automatic) using Bartlett kernel in PP test.

3.4.2 Auto Regressive Distributed Lag (ARDL)

ARDL test is employed to examine the cointegration for the whole model

between the house price index with Malaysia’s macroeconomic variables.

ARDL framework developed by Pesaran and others allow for the detection of

asymmetric effect in the long-run relationship among variables. It can be

applied regardless of whether the underlying regressors are purely I (0), purely

I (1) or combination of the both. However, the ARDL cointegration technique

is not valid in the presence of I (2). The ARDL bounds testing approach is more

suitable and provides better results underlying variables in a small sample size

(Fatukasi, Olorunleke, Olajide & Alimi, 2015). The application of ARDL

approach can help in identifying the cointegration vectors and will give realistic

and efficient estimates. By choosing the appropriate lag length for the ARDL

model, the orders of the lags are selected using a lag selection criterion such as

the Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC)

to eliminate of autocorrelation, multicollinearity and heteroscedasticity occur

in the residuals. Hence, the model of AIC and SBC to be choosing should be in

the smallest value or small standard errors and high R2 perform relatively better

in order to avoid spurious regression.

There are many advantages of using ARDL approach. The problems of

endogenous regressor become less because it is free of residual serial

correlation by including a sufficient number of lags of dependent and

independent variables. Furthermore, the ARDL procedure can differentiate

between dependent and independent variables in a single long-run relationship.

It assumes that only a single reduced form equation relationship exists between

measured variable and controlled variables. Thus, when there are multiple



cointegrating vectors occur, it can identify the cointegrating vectors more

effective and efficient. In addition, ARDL converge short-run adjustments with

long-run equilibrium in the Error Correction Model (ECM) through a simple

linear transformation so that to maintain a long-run information without losing

it. It needs to take a sufficient number of lags in the copartner of ECM model

in order to obtain the data generating process in general to specific model

framework (Nkore & Uko, 2016).

Here, the unrestricted error correction model associated with the ARDL to test

for cointegration is interpreted as follow:

∆𝐻𝑃𝐼𝑡 = 𝑎0 + 𝛼𝐻𝑃𝐼𝐻𝑃𝐼𝑡−1 + 𝛼𝐺𝐷𝑃𝐺𝐷𝑃𝑡−1 + 𝛼𝐶𝑃𝐼𝐶𝑃𝐼𝑡−1 + 𝛼𝐸𝑋𝐺𝐸𝑋𝐺𝑡−1

+ 𝛼𝑈𝑁𝐸𝑀𝑃𝑇𝑈𝑁𝐸𝑀𝑃𝑇𝑡−1

+ ∑ 𝛼𝑝𝐻𝑃𝐼𝑡−1 +

𝑔

𝑝=0

∑ 𝛼𝑞𝐺𝐷𝑃𝑡 +

ℎ

𝑞=0

∑ 𝛼𝑟𝐶𝑃𝐼𝑡 +

𝑖

𝑟=0

∑ 𝛼𝑠𝐸𝑋𝐺𝑡

𝑗

𝑠=0

+ ∑ 𝛼𝑢𝑈𝑁𝐸𝑀𝑃𝑇𝑡 + 𝑢𝑡

𝑙

𝑢=0


H0: 𝑎𝐻𝑃𝐼 + 𝑎𝐺𝐷𝑃 + 𝑎𝐶𝑃𝐼 + 𝑎𝐸𝑋𝐺 + 𝑎𝑈𝑁𝐸𝑀𝑃𝑇= 0 (Long-run relationship is not

exist).

H1: At least one 𝑎𝑡 ≠0, where t = HPI, GDP, CPI, EXG, UNEMPT (Long-run

relationship is exist).

Decision rule: Reject null hypothesis if the p-value is less than the significance

level. Otherwise, do not reject null hypothesis.



3.4.3 Non-Auto Regressive Distributed Lag (NARDL)

An asymmetric ARDL cointegration methodology is used to detect the

asymmetric effects among economic variables in the long run and short run by

using positive and negative partial sum decomposition. It allows the joint

analysis of the issues of non-stationarity and nonlinearity in the context of an

unrestricted error correction model (Katrakilidis & Trachanas, 2012).

There are many advantages by using of asymmetric ARDL. According to Yeap

and Lean (2017), asymmetric ARDL approach allows us to simultaneously

examine the asymmetry and nonlinear relationship between dependent and

independent variables for both long run and short run. In addition, asymmetric

ARDL does not require having the same order of integration among the

variables. It also can be estimated regardless of whether the variables are

stationary in the long-run relationship between the variables. Furthermore, the

asymmetric adjustment patterns of the disequilibrium can be observed through

the dynamic multipliers although the asymmetric ARDL does not directly

model asymmetric error correction. The adjustment patterns can be obtained

from initial equilibrium to the new equilibrium through the dynamic multipliers

following an economic change. The dynamic multipliers allow us to further

investigate the asymmetry adjustment patterns between dependent and

independent variables.

Following is the nonlinear asymmetric cointegrating regression:

𝐻𝑃𝐼𝑡 = 𝐵+𝐸𝑋𝐺𝑡++𝐵−𝐸𝑋𝐺𝑡

− + 𝑢𝑡

Where β+ and β− are the associated long-run parameters and 𝑥𝑡 (which is the

exchange rate in this study) is a k×1 vector of regressors decomposed as:

𝑥𝑡 = 𝑥0 + 𝑥𝑡+ + 𝑥𝑡

−



Where, xt+ and xt− are partial sum processes of positive and negative changes

in xt:

𝑥𝑡+ = ∑ ∆𝑥𝑗

+

𝑡

𝑗=1

= ∑ max (∆𝑥𝑗

𝑡

𝑗=1

, 0) 𝑎𝑛𝑑 𝑥𝑡− = ∑ ∆𝑥𝑗

−

𝑡

𝑗=1

= ∑ min(∆𝑥𝑗

𝑡

𝑗=1

, 0)

The asymmetric responses of the dependent variable to positive and negative

variations of the independent variable are captured by the positive and negative

dynamic multipliers associated with unit changes in x+ and x− as follows:

𝑚ℎ+ = ∑

𝜕𝑦𝑡+𝑗

𝜕𝑥𝑡+

ℎ

𝑗=1

, 𝑚ℎ− = ∑

𝜕𝑦𝑡+𝑗

𝜕𝑥𝑡−

ℎ

𝑗=1

, ℎ = 0,1,2 … ….

Note that as h→∞, then mh+→β+ and mh−→β−, where β+ and β− are the

asymmetric long-run coefficients calculated as β+=−θ+/ρ and β−=−θ−/ρ

respectively.

3.4.4 Granger Causality Test

Granger-Causality test had proposed by Granger (1969) is one of the common

way that used by past researchers to examine the relationship between two

variables. The test can only built on the smooth variable or cointegrated non-

stationary variables (Liu & Zhang, 2013).

Below is the estimation of the following VAR model,

𝑦𝑡 = 𝑎1 + ∑ 𝛽1

𝑛

𝑖=1

𝑋𝑡−1 + ∑ 𝛾𝑗𝑌𝑡−𝑗

𝑚

𝑗=1

+ 𝑒1𝑡

𝑥𝑡 = 𝑎2 + ∑ 𝜃1

𝑛

𝑖=1

𝑋𝑡−1 + ∑ 𝛿𝑗𝑌𝑡−𝑗

𝑚

𝑗=1

+ 𝑒2𝑡




H0: X does not Granger cause on Y.

H1: X does Granger cause on Y.

And

H0: Y does not Granger cause on X.

H1: Y does Granger cause on X.

Decision rule: Reject null hypothesis if Chi-square test is greater than critical

value at 1 percent, 5 percent or 10 percent level of significance.

Granger Causality Test used to test on the causality relationship between two

variables. According to Abu-Libdeh and Harasheh (2011), there is a short

explanation for Granger Causality test which conducted to examine the

causality relationship between two variables and whether one variable is having

advantageous to be applied in predicting another variable. Nevertheless, we

cannot estimate what is the effect and impact on the dependent variable in short

run, we are only able get to know the direction of causality between variables.

In brief, VEC Granger Causality / Block Exogeneity Wald Tests will be carried

out in this study and apply the pair wise Granger Causality test to check for the

causality. This test is used to analyze the causality between two variables,

whether bidirectional, unidirectional or no relationship in short run. In addition,

Granger Causality Test is one of the general test will applied by past researchers

to determine the causality relationship between house price and its determinants

(Chui & Chau, 2005; Li & Chiang, 2012; Liu & Zhang, 2013; Meidani et al.,

2011).



3.5 Diagnostic Checking

3.5.1 Multicollinearity

Researchers would develop relevant independent variables and used it to clarify

and forecast the value of dependent variable. With the presence of

multicollinearity, part or all independent variables are highly correlated with

each other. Therefore, researchers will run some test whereby relating to

covariance analysis to check whether there is any multicollinearity problem

exists. The problem of multicollinearity will cause the problem to the regression

model, such as the different result between actual and expected sign in the

regression coefficient. Hence, researchers cannot get the accurate result if it

shows that there was high degree of multicollinearity in the model. The result

will be spurious because the important variables used would become

insignificant and huge standard errors in the model (Larget, 2007). To identify

the presence of multicollinearity, high pair wise correlation and Variance

Inflation Factor (VIF) approach will be used. When the value of R-squared and

VIF is high, means that there is a multicollinearity problem in the model

(Motulsky, 2002).

3.5.2 Heteroscedasticity

According to Zhu, Chen, Guo & Zhu (2016), heteroscedasticity testing is

importance in regression analysis. The variance of the error term in not constant

when the heteroscedasticity happened. Furthermore, heteroscedasticity may be

due to the omission of some important variables from the model, distribution

of one or more regressors is uneven, incorrect data transformation and incorrect

functional form. Moreover, there were many ways such as graphical method,



Park test, Glesjer test, Breusch-Pagon-Godfrey test, White’s test and

Autoregressive Conditional Heteroscedasticity (ARCH) test are used to detect

the heteroscedasticity problem occur. If there is heteroscedasticity problem, re-

estimate the model should be done. When time series data is adopted in the

study, ARCH test is used to identify heteroscedasticity problem.


H0: There is no heteroscedasticity problem.

H1: There is heteroscedasticity problem.

Decision Rule: Reject null hypothesis if p-value is less than significance level

at 1 percent, 5 percent and 10 percent. Otherwise, do not reject null hypothesis.

3.5.3 Autocorrelation

It is quite common for researchers to test for autocorrelation when they deal

with time series data in their studies. Correlation between series of observations

ordered in time could be defined as autocorrelation terms (Gujarati & Porter,

2009), whereas serial correlation is the lag correlation between the two different

series. Serial correlation is occurs when the error terms are correlated with each

other in the model. Another possible reason that autocorrelation will present is

because of omitting relevant variables, including unnecessary and irrelevant

independent variable or using incorrect functional form in the model. If the

model has autocorrelation problem, the result will be bias. Therefore, Breusch-

Godfrey Serial Correlation LM Test is manipulated to identify whether there is

an autocorrelation problem in the model. Higher orders of autocorrelation will

be detected after used this test.




H0: There is no autocorrelation problem

H1: There is autocorrelation problem



3.5.4 Model Specification

Model specification is a process of converting a theory into a regression model.

A specification error results when any one of these choices is made incorrectly.

Specification error can occur in many ways such as omission of relevant

explanatory variable, include irrelevant explanatory variable and adopt the

wrong functional form (Gujarati & Porter, 2009). A serious model specification

can causes to multicollinearity, heteroscedasticity, autocorrelation, biased and

inconsistent of the estimators. If found that there is a specification error occur

in the regression model, we can detect by Ramsey’s Regression Specification

Error Test (RESET). This test was developed by Ramsey (1969). But this test

only can detect the adopting of the wrong functional model.


H0: Model specification is correct.

H1: Model specification is incorrect.


at 1 percent, 5 percent and 10 percent. Otherwise, do not reject null

hypothesis.



3.5.5 Normality Test

Jarque-Bera test is used to identify the normality of the error term. Jarque-Bera

test can test whether the skewness and kurtosis match with the skewness and

kurtosis of a normal distribution. If the error term is normality and able to fulfill

the nine assumptions of OLS, the OLS estimator can easily to expound (Gujarati

& Porter, 2009).


H0: The error term is normally distributed.

H1: The error term is not normally distributed.



The Jarque-Bera test statistic in manual is defined as:

Where N represents sample size, S represent sample skewness, and K represent

sample Kurtosis.

3.5.6 CUSUM and CUSUMSQ Test

In general, the constancy of coefficients in a model is tested by the CUSUM

and CUSUMSQ test. The tests is used on the first observations and plotted

against breaking point. It can be used without prior knowledge about the date

of structural breaks. The coefficients are stable when the plot is falls within the

range of 5 percent significant level.

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3.6 Conclusion

In a nutshell, this chapter had discussed about the proposed empirical model, sources

of data, variables description and methodologies that we will be carried out for testing

and analyzing in this study. This chapter has clearly defined and elaborated the ideas

for each of the methodology. In addition, the data analysis and findings of each

methodology will be discussed and explained in details in the next chapter with the

result of E-views software. Consequently, diagnostic checking is conducted in order to

ensure there is no econometric problems in the model.



CHAPTER 4: DATA ANALYSIS

4.0 Introduction

The goal in this chapter is to examine the interaction between housing price and

macroeconomic perspectives in Malaysia. Thus, this chapter will be focus on analyzing,

explaining and reporting the empirical result by using the methodology that stated in

the previous chapter. Descriptive statistics for all variables, and the Unit Root Test by

using Augmented Dickey Fuller (ADF) test and Philips Perron (PP) test will be

discussed in detail. Next, it is follow by the empirical results based on Granger

Causality test, Auto Regressive Distributed Lag (ARDL) and Non-Auto Regressive

Distributed Lag (NARDL). In addition, a series of diagnostic checking such as Jarque-

Bera normality test, Breusch-Godfrey Serial Correlation LM Test, Autoregressive

Conditional Heteroscedasticity (ARCH) Test, and Ramsey RESET test will be

conducted in order to make sure all the variables in ARDL and NARDL model are

valid and accurate. Each of the empirical test’s result will be discussed with a full

detailed of interpretation. Lastly, discussions of major findings of the test results will

be concluded in this chapter as well as a short conclusion will be outlined briefly.

4.1 Descriptive Statistics

Descriptive statistics are method use to describe the basic features, patterns, and trends

of the data sets in the study. They provide summarize of the data sets of all variables

which include mean, median, maximum, minimum, standard deviation in the Table 4.1.

Table 4.1 shows the descriptive statistics of house price index (LNHPI), inflation rate

(CPI), GDP, exchange rate (LNEXG), and unemployment rate (UNEMPT) in Malaysia

over the period of year 2001 quarter one until year 2015 quarter four. The data had been

transformed and expressed into natural logarithm term except CPI, GDP and UNEMPT.



Table 4.1: Descriptive statistic

LNHPI CPI GDP LNEXG UNEMPT

Mean 4.933563 2.311667 4.870333 4.580857 3.338333

Median 4.859425 2.100000 5.375000 4.582413 3.200000

Maximum 5.440251 8.200000 10.18000 4.672174 4.000000

Minimum 4.611152 -2.000000 -5.760000 4.434738 2.700000

Std. Dev. 0.251923 1.586072 2.753230 0.042153 0.318413


According to the Table 4.1, the mean for the LNHPI, CPI, GDP, LNEXG, and

UNEMPT are amounted to 4.933563, 2.311667, 4.870333, 4.580587 and 3.338333

respectively. This shows that the average change in LNHPI is higher than the averages

rates for CPI, GDP, LNEXG and UNEMPT. Furthermore, CPI and GDP are found to

have the largest difference between the maximum value and minimum value. The

highest CPI was 8.2% while the lowest was -2% whereas the highest GDP was 10.18%,

but the lowest was -5.76% over the period of year 2001 quarter one to year 2015 quarter

four. Besides that, standard deviation for GDP is the highest, and following by CPI,

UNEMPT and LNHPI with the values of 2.753230, 1.586072, 0.318413 and 0.251923

respectively. It might cause the volatility of housing price when the standard deviation

indicates the high variation in GDP and CPI. At the same time, the lowest variation

with standard deviation is LNEXG with the values of 0.042153.



4.2 Unit Root Test

Table 4.2 Unit Root Test

Unit Root Tests

Augmented Dickey Fuller (ADF) Phillips Perron (PP)

Level

Variable Constant

Without Trend

Constant

With Trend

Constant

Without Trend

Constant

With Trend

LNHPI 3.697587

(1.0000)

-0.505315

(0.9806)

3.552810

( 1.0000)

-0.530380

(0.9793)

CPI -4.305318***

(0.0011)

-4.302400***

(0.0061)

-3.551394***

(0.0099)

-3.549088**

(0.0433)

GDP -5.195505***

(0.0001)

-5.126500***

( 0.0005)

-3.069865**

(0.0344)

-3.010484

(0.1382)

LNEXG -1.471726

(0.5409)

-1.617795

(0.7740)

-1.603875

(0.4743)

-1.844688

(0.6702)

UNEMPT -4.459124***

(0.0006)

-5.728674***

(0.0001)

-4.538934***

(0.0005)

-5.583115***

(0.0001)

First Difference

LNHPI -3.606572***

(0.0086)

-7.514309***

(0.0000)

-6.334654***

(0.0000)

-7.541763***

(0.0000)



CPI -6.689566***

(0.0000)

-6.615670***

(0.0000)

-6.447256***

(0.0000)

-6.388426***

(0.0000)

GDP -6.731443***

(0.0000)

-6.703706***

(0.0000)

-4.474285***

(0.0006)

-4.439151***

(0.0041)

LNEXG -7.817996***

(0.0000)

-7.772591***

(0.0000)

-7.816453***

(0.0000)

-7.772832***

(0.0000)

UNEMPT -8.352933***

(0.0000)

-8.303736***

(0.0000)

-15.03188***

(0.0000)

-14.77329***

(0.0000)

Note: ***, ** and * denotes significant at 1%, 5% and 10% significance level,

respectively. The figure in parenthesis (…) represents optimum lag length selected

based on Akaike Info Critirion. The figure in bracket […] represents the Bandwidth

used in the KPSS test selected based on Newey-West Bandwidth critirion.


Hypothesis:

H0: There is a unit root (Non-stationary).

H1: There is no unit root (Stationary).

Decision rule: Reject null hypothesis if P-value is less than the significance level.

Otherwise, do not reject null hypothesis.

ADF and PP unit root test are conducted to determine the stationary level among the

variables. Referring to the table 4.2 above, at level form, house price index (LNHPI)

and exchange rate (LNEXG) are failed to reject the null hypothesis from the ADF and

PP results. As a result, p-value of these two variables is more than 1 percent, 5 percent

or 10 percent significance levels, which explains that these two variables are not

stationary and contain of unit root in the level form. On the other hand, both results



from ADF test and PP test for inflation rate (CPI), GDP and unemployment rate

(UNEMPT) are able to reject the null hypothesis since the P-value of CPI, GDP and

UNEMPT are less than all significance level. Hence, GDP, CPI and UNEMPT are

stationary and do not contain of unit root at level form except for GDP in the PP test

constant with trend is found to be non-stationary in level.

Furthermore, when both ADF and PP unit root test proceed to the first difference, all

the variables are able to reject the null hypothesis as their P-value is less than

significance levels which is 1 percent, 5 percent or 10 percent. In brief, ADF and PP

tests resulted that all the variables are stationary and do not contain of unit root in first

differences.

In short, all the variables must ensure to reject the null hypothesis at the level form.

However, there are some variables in the results presented are only able to be rejected

after the first difference. Hence, ARDL bound test is suitable to be carried out to

determine the long-run cointegration between housing price index and its

macroeconomic factors because all the variables are stationary in level and first

difference without integrated of order two.

4.3 Auto-Regressive Distributed Lag (ARDL)

Next, we proceeded to ARDL model to examine the long-run relationship between the

house price index (LNHPI) and its determinants such as inflation rate (CPI), gross

domestic product (GDP), exchange rate (LNEXG) and unemployment rate (UNEMPT).

Table 4.3 ARDL Bound Test

Equation F-statistic Conclusion

Fhpi(lnhpi|cpi, gdp, lnexg, unempt) 10.09653*** Cointegrated



Optimal lag [2,0,4,0,0]

Critical value I(0) I(1)

1% Significance level 4.176 5.676



***, ** and * denote significant level at 1%, 5% and 10% respectively.

Unrestricted intercept and no trend (k=4, T=60).


According to the Table 4.3, the F-statistics of 10.09653 in ARDL bound test was greater

than the upper critical value of 5.676 at 1 percent significant level. Hence, the null

hypothesis of no cointegration is being rejected. The empirical findings lead to

conclusion that there is a long-run relationship between the LNHPI with CPI, GDP,

LNEXG and UNEMPT exists.

Table 4.4 Long Run Coefficients Result

Variables Coefficients Standard Errors Probability

C 18.14473 0.171536 0.0912*

CPI 0.219434 0.001592 0.0295**

GDP 0.020334 0.000779 0.6723

LNEXG -3.956611 0.033336 0.0591*

UNEMP 0.974701 0.005530 0.0061***

***, ** and * denote significant level at 1%, 5% and 10% respectively.




Furthermore, table 4.4 showed the long-run coefficients of result between the

relationship of HPI and its determinants. The null hypothesis was set to reject when the

p-value is lower than 10 percent significance level. There is significant relationship

between CPI, LNEXG and UNEMPT with house prices index in Malaysia at 10 percent

significant level in the long run. Thus, we concluded that all the variables have co-

integrated with house prices index except GDP. Besides, based on the result, CPI and

UNEMPT showed positive relationship with LNHPI which can be seen from the

positive coefficient 0.219434 and 0.974701 respectively in the regression. Nevertheless,

LNEXG is negative related to the house prices index due to the negative coefficient in

the regression, which is -3.956611. It indicates that when there is one percent increase

in exchange rate, house prices is decreased by 3.956611 percent.

4.3.1 Diagnostic Checking of the model

Diagnostic checking such as Jarque-Bera normality test, Breusch-Godfrey rial

Correlation LM test, ARCH test, Ramsey RESET test, CUSUM and CUSUM

square test were applied in the model and conducted in order to ensure that there

is no econometric problems and all the variables in ARDL model are valid and

accurate.

4.3.1.1 Multicollinearity

4.3.1.1.1 High pair-wise correlation among independent variables

Correlation matrix represents the relationship among the independent variables.

The correlation coefficient (r) ranges between -1 and +1. Higher correlation

between two independent variables, which is more than 0.8 means that they are

strongly correlated. Moreover, it may consists of high possibility of serious



multicollinearity problem that will lead to the result become inefficient. As

shown in the Table 4.5, there is no any serious multicollinearity problem

happened between each of the independent variables since the highest value is

0.360709.

* r > 0, implying that the two variables have a positive correlation.

* r < 0, implying that the two variables have a negative correlation.

* r = 0, implying that the two variables have no correlation.

Table 4.5 Correlation Analysis

CPI GDP LNEXG UNEMPT

CPI 0.000267 -2.41E-05 7.03E-05 0.000166

GDP -2.41E-05 9.10E-05 0.000318 0.000153

LNEXG 7.03E-05 0.000318 0.360709 0.000394

UNEMPT 0.000166 0.000153 0.000394 0.006720


4.3.1.1.2 Variance Inflation Factor (VIF)

Variance Inflation Factor (VIF) test is used to determine whether there is the

presence of serious multicollinearity problem in the model for each of the

variables. As a result, based on the table 4.7 that calculated, all the VIF are

between 1 and 10. Hence, it can be concluded that this model is no serious

multicollinearity problem among the variables.



Table 4.6 Variance Inflation Factor (VIF)

VIF = 1 No Multicollinearity

1 < VIF <10 No serious Multicollinearity

VIF Serious Multicollinearity

Table 4.7 Result of Variance Inflation Factor (VIF)

Variables VIF = 1

1−𝑅2

Consumer Price Index (CPI) 1

1−0.048780 = 1.0513

Gross Domestic Product (GDP) 1

1−0.073537 = 1.0794

Exchange Rate (LNEXG) 1

1−0.003359 = 1.0034

Unemployment Rate (UNEMPT) 1

1−0.062410 = 1.0666


4.3.1.2 Normality test

Hypothesis:

H0: The Error term is normally distributed.

H1: The Error term is not normally distributed.

Decision rule: Reject H0, if the p-value of Jarque-Bera (JB) statistic < α (0.05).

Otherwise, do not reject H0.



Figure 4.1 Jarque-Bera Normality Test

Adopted from: E-views

Conclusion:

Do not reject H0 since the p-value for the JB statistic is 0.835259 which is

greater than α=0.05. Therefore, the error term is normally distributed in this

model.

4.3.1.3 Autocorrelation

Hypothesis:

H0: There is no autocorrelation problem.

H1: There is autocorrelation problem.

Decision rule: Reject H0, if the p-value of Breusch-Godfrey Serial Correlation

LM test < α (0.05). Otherwise, do not reject H0.

0

2

4

6

8

10

-0.02 -0.01 0.00 0.01 0.02

Series: Residuals

Sample 2002Q1 2015Q4

Observations 56

Mean 1.23e-15

Median 0.000326

Maximum 0.019096

Minimum -0.019505

Std. Dev. 0.008213

Skewness -0.065467

Kurtosis 2.629657

Jarque-Bera 0.360028

Probability 0.835259



Table 4.8: Test Statistics of Breusch-Godfrey Serial Correlation LM Test


Conclusion:

Do not reject H0 since the p-value for the Breusch-Godfrey Serial Correlation

LM test is 0.8841 which is greater than α=0.05. Therefore, there is no

autocorrelation problem.

4.3.1.4 Heteroscedasticity

Hypothesis:



Decision rule: Reject H0, if the p-value of ARCH test < α (0.05). Otherwise, do

not reject H0.

Table 4.9: Test Statistic of ARCH Test




Conclusion:

Do not reject H0 since the p-value for the ARCH test is 0.5634 which is greater

than α=0.05. Therefore, there is no heteroscedasticity problem.

4.3.1.5 Model Specification

Hypothesis:

H0: There is no error in the model.

H1: There is model specification error in the model.

Decision rule: Reject H0, if the p-value of Ramsey Reset Test < α (0.05).


Table 4.10: Test Statistic of Ramsey Reset Test




Conclusion:

Do not reject H0 since the p-value for the Ramsey Reset test is 0.0750 which is

greater than α=0.05. Therefore, there is no model specification error in the

model.

4.3.1.6 CUSUM Test and CUSUM Square Test

CUSUM Test CUSUM Square Test

Figure 4.2 Figure 4.3


Cumulative sum (CUSUM) and CUSUM of squares (CUSUMSQ) tests are

used for investigating the structure stability in the model and demonstrated in

Figure 4.2 and 4.3. It has showed the coefficients are stable, as the plot of

CUSUM and CUSUMSQ falls within the range of 5% significant level.

4.4 Non-Auto Regressive Distributed Lag (NARDL)

In light of this, this study adopts the NARDL approach, which is an asymmetric

extension to the standard ARDL model. The NARDL model is designed to capture the

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

04 05 06 07 08 09 10 11 12 13 14 15

CUSUM of Squares 5% Significance

-20

-10

0

10

20

04 05 06 07 08 09 10 11 12 13 14 15

CUSUM 5% Significance



asymmetric relation between house price index and its macroeconomic variables in

Malaysia.

Table 4.11: NARDL Bound Test

Equation F-statistic Conclusion

Fhpi(lnhpi|cpi, gdp, lnexg, unempt) 6.602057*** Cointegrated

Critical value I(0) I(1)




***, ** and * denote significance level at 1%, 5% and 10% respectively.

Unrestricted intercept and no trend (k=4, T=60).


From the Table 4.11, the Wald F statistics values of 6.602057 in NARDL Bound test

was greater than the upper critical value of 5.676 at 1 percent significance level. The

result supports that there is a strong evidence of cointegrating relationship between

house price index and its macroeconomic determinants in Malaysia. The results also

provide the reason for estimating the long-run elasticity of each variable on changes of

house price index.



Table 4.12: NARDL Estimation Results


The NARDL test also designed to capture the asymmetric responses of the house price

index to positive and negative variations of the exchange rate in the long-run coefficient.

To calculate the long run coefficient, it has to divide for each LNEXG_P and

LNEXG_N by the coefficient of LNHPI(-1). The calculation as below:

Long-run coefficient of LNEXG_P is -0.199406 / -0.030697 = 6.495944

Long-run coefficient of LNEXG_N is -0.076598 / -0.030697 = 2.495293

In short, both long-run coefficients are positive. Hence, the long run equation is:

Long run equation: LNHPI = 6.495944 LNEXG_P + 2.495293 LNEXG_N + 𝜇

It indicates that 1 percent point increase in exchange rate leads to 6.4959 percent point

increase in house price index and 1 percent point decrease in exchange rate leads to

2.4953 percent point decrease in house price index, which means both are in positive



relationship. However, the house price index response more towards exchange rate

positive change compare to negative change because the coefficient is larger.

Table 4.13: Test Statistic of Wald Test


Both of the positive change and the negative change have the long-run positive effect

on house price index. Thus, it has showed the null hypothesis of equality is rejected as

p-value is less than 5 percent significance level. Wald test indicates that there is

asymmetry in the long run impact of exchange rate on house price index in Malaysia.

4.4.1 Diagnostic Checking of the model

Jarque-Bera normality test, Breusch-Godfrey Serial Correlation LM test,

ARCH test, Ramsey RESET test, CUSUM and CUSUM square test has being

applied in the model and used in order to identify whether the model is consists

of economic problems. Moreover, it also used to evaluate all the variables in

NARDL model whether those variables are valid and accurate.



4.4.1.1 Normality Test

Hypothesis:

H0: The Error term is normally distributed.

H1: The Error term is not normally distributed.

Decision rule: Reject H0, if the p-value of Jarque-Bera (JB) statistic < α (0.05).


Figure 4.4 Jarque-Bera Normality Test


Conclusion:

Do not reject H0 since the p-value for the JB statistic is 0.747582 which is

greater than α=0.05. Therefore, the error term is normally distributed in this

model.



4.4.1.2 Autocorrelation

Hypothesis:

H0: There is no autocorrelation problem.

H1: There is autocorrelation problem.

Decision rule: Reject H0, if the p-value of Breusch-Godfrey Serial Correlation

LM test < α (0.05). Otherwise, do not reject H0.

Table 4.14: Test Statistics of Breusch-Godfrey Serial Correlation LM Test


Conclusion:

Do not reject H0 since the p-value for the Breusch-Godfrey Serial Correlation

LM test is 0.2233 which is greater than α=0.05. Therefore, there is no

autocorrelation problem.

4.4.1.3 Heteroscedasticity

Hypothesis:



Decision rule: Reject H0, if the p-value of ARCH test < α (0.05). Otherwise, do

not reject H0.



Table 4.15: Test Statistic of ARCH Test


Conclusion:

Do not reject H0 since the p-value for the ARCH test is 0.3816 which is greater

than α=0.05. Therefore, there is no heteroscedasticity problem.

4.4.1.4 Model Specification

Hypothesis:

H0: There is no model specification error in the model.

H1: There is model specification error in the model.

Decision rule: Reject H0, if the p-value of Ramsey Reset Test < α (0.05).




Table 4.16: Test Statistic of Ramsey Reset Test


Conclusion:

Do not reject H0 since the p-value for the Ramsey Reset test is 0.2130 which is

greater than α=0.05. Therefore, there is no model specification error in the

model.



4.4.1.5 CUSUM Test and CUSUM Square Test

CUSUM Test CUSUM Square Test

Figure 4.5 Figure 4.6


Cumulative sum (CUSUM) and CUSUM of squares (CUSUMSQ) tests are used for

checking the structure stability in the model and illustrated in Figure 4.5 and 4.6. It has

showed the coefficients are stable, as the plot of CUSUM and CUSUMSQ falls within

the range of 5% significant level.

4.5 Granger Causality Test

Granger causality test is carried out in this study to examine the direction of causality

and the lead lag relationships between the house price index in Malaysia and selected

macroeconomics variables. The results are stated in the table and summarized in the

figure below.

Short-term granger causality test results:

Hypothesis:

-30

-20

-10

0

10

20

30

03 04 05 06 07 08 09 10 11 12 13 14 15

CUSUM 5% Significance

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

03 04 05 06 07 08 09 10 11 12 13 14 15

CUSUM of Squares 5% Significance



H0: There is no granger cause relationship between dependent variable and independent

variable in short run.

H1: There is a granger cause relationship between dependent variable and independent

variable in short run.

Table 4.17: Granger Causality Results based on VECM

Independent Variables

Dependent

variable

2 -statistics of lagged 1st differenced term

[p-value]

ECTt-1

coefficient

LNHPI CPI GDP LNEXG UNEMPT (t-ratio)

LNHPI

--

11.38072***

[0.0034]

0.820506

[0.6635]

3.051661

[0.2174]

1.524152

[0.4667]

0.020689**

(2.60469)

CPI

2.410408

[0.2996]

--

6.593250**

[0.0370]

0.684018

[0.7103]

3.223263

[0.1996]

0.066896

(0.07147)

GDP 2.842092

[0.2415]

23.57201***

[0.0000]

--

1.825253

[0.4015]

4.679058*

[0.0964]

-3.302958**

(-3.42252)

LNEXG

0.392502

[0.8218]

4.391175

[0.1113]

9.812108***

[0.0074]

--

5.851769*

[0.0536]

-0.031571

(-1.82763)

UNEMPT 3.498057

[0.1739]

5.788369*

[0.0553]

4.129843

[0.1268]

0.090744

[0.9556]

--

-0.705193**

(-3.82538)

Note: ***, ** and * denotes significant at 1%, 5% and 10% significance level,

respectively. The figure in the parenthesis (…) denote as t-statistic and the figure in the

squared brackets […] represent as p-value.




Referring to the table 4.17 above illustrate the results on Granger Causality test based

on VECM model. The findings reveal that the null hypothesis of inflation rate (CPI)

does not granger cause on house price index (LNHPI) in Malaysia is rejected. It is

because the p-value of CPI is 0.0034 which are less than 1 percent significance level.

Hence, there is enough evidence to conclude that unidirectional granger causality is

happening from CPI to LNHPI in the short run at 1 percent significant level. Besides,

gross domestic product (GDP), exchange rate (LNEXG) and unemployment rate

(UNEMPT) are not granger cause LNHPI at any level of significant.

Meanwhile, there is a bidirectional causality between CPI and GDP since the p-value

is less than 5 percent significance level. Other than that, there is unidirectional granger

causality from UNEMPT to GDP and following by UNEMPT to LNEXG at 10 percent

significance level respectively. In addition, there is a unidirectional granger causality

is from GDP to LNEXG at 1 percent significance level. Lastly, CPI has short run

dynamic granger cause on UNEMPT at 10 percent significant level.

In a nutshell, all the dynamic causal interactions among the variables are clarify and

stated out. Apart from that, others variables do not have any causality relationship

among the variables due to the null hypothesis cannot be rejected, as well as its p-value

are all less than the significance level.

The causal channels can be summarized as below:


CPI UNEMPT

LNHPI GDP

LNEXG



On the other hand, based on the results of t-test of the error correction term in the right

hand side, the results has showed that there are two variables have significant negative

coefficients of the error correction term due to its t-statistic for GDP is (-3.42252) and

UNEMPT is (-3.82538), which are greater than the upper critical value at 5 percent

significant level. In another words, it indicate that GDP need take at least three quarter

to adjust their equilibrium from short run to long run equilibrium while UNEMPT

would need to take approximately one quarter. In addition, it demonstrated these two

variables are able to diverge from equilibrium and adjust to correct for any deviations

from the long-run relationship. Furthermore, the remaining variables which are CPI

and LNEXG have insignificant coefficients of the error correction term due to its t-

statistics are fallen in the rejection area.

4.6 Discussions of Major Findings


This is quite surprisingly in explaining GDP is insignificant with the house

prices at 5 percent significance level. It indicates that house price will not

influenced by the GDP in Malaysia. The result is inconsistent with most of the

studies of previous researchers such as Ong (2013), Guo and Wu (2013), Xu

and Tang (2014), Fereidouni and Bazrafshan (2012), Ong and Chang (2013).

The property prices increase rapidly was worrying as it was not backed by a

rapid economy growth. GDP has found to be insignificant with housing price

in Malaysia due to several causes. Malaysia’s economy has experienced an

average growth of 4.75 percent, which is far apart from the growth of 8% to 9%

in the early 1990s (Azhar, 2016). The reason behind the weaker in national

economy might be due to the falling of crude oil price and fluctuation of

currency rate. For instance, today Malaysia’s ringgit has slid past 4.29 against



US Dollar and the continuous ringgit slide is alarmingly disquieting. The global

financial crisis that peaked in 2008 has affect differently in various countries.

For instance, stock price in Malaysia has decrease sharply by 20% between year

2007 to 2009 (Athukorala, 2010). When stock price decrease, investors might

pull out their funds and invest in other countries which provide low risk and

higher return. Apart from this, a drop in crude oil price can ruin the global

investment especially the oil and energy sectors. However, falling of crude oil

price may present another window of opportunity to the country such as reduce

oil prices can increase purchasing power, reduce industries’ production cost and

lower inflation. Nevertheless, as reported by Kana (2017), inflation is expects

to increase notably till 3.8% due to the high transportation cost and depreciation

of the Ringgit. Since Malaysia is a net importer of petroleum products, it has

resulted we will be paying more due to weak Ringgit when the crude oil price

has decrease. In short, the causes that mentioned above have influence the GDP,

but do not affect the housing price in Malaysia.

Based on the Granger Causality test result, there was no causality running from

GDP to house prices in Malaysia. This statement is supported by Li and Chiang

(2012) that the GDP does not granger cause on house prices. However, Cohen

and Karpaviciute (2017) is rejected the result since their finding is show that

GDP will cause housing price whereas for Meidani et al., (2011) supported

there is bidirectional causality between GDP and house prices. Nevertheless,

GDP will indirectly affect the housing price through inflation rate based on the

Granger Causality test result. Assumed that inflation rate continue to increase

over the time, it will lead to decline in consumption spending. Hence, it will

indirectly affect the housing price when the GDP is decrease. The changes in

consumption will affect the housing price through the wealth effect. As a result,

households will choose to save their money instead of spending when the

overall price of goods and services increase. This will lead to decrease in GDP

and thus housing price will drop in the long run.



4.6.2 Inflation rate

According to the Autoregressive Distributed Lag model (ARDL) test result,

indicated that inflation rate is having significantly positive long-run relationship

with house price index at 5 percent significance level in Malaysia. The

estimated coefficient of inflation rate verifies that house price will be increased

by 0.2194 as inflation rate increase by 1 percent. This is consistent with the

research done by Hossain and Latif (2009), Apergis (2003), Ong (2013),

Piazzesi and Schneider (2009), Abelson et al., (2005) and Lee (2009). The

increase in inflation rate could be due to various reasons such as excess money

supply, fiscal deficit or fluctuation in the price of diesel fuel (Cheng & Tan,

2002). The result is compatible with research concluded by Ong (2013)

showing that increasing of money supply and cost of material to build a house

will lead to increasing in housing price. Nowadays, global oil prices have

become major factor to the higher inflation in Malaysia. The price of diesel fuel

is fluctuated over time and cause the price of goods and services in the market

increase dramatically. According to Neely (2015), changes in oil prices tend to

increase the inflation rate as most of the industries will consume oil, especially

for purpose of transportation. Since diesel fuel is the main transportation fuel,

the transportation costs will increase and lead to increase in the cost of moving

a raw material from one location to another location. The finding in the study

is consistent with the expected sign as discussed in previous chapter, which is

positive relationship between inflation rate and house price index. As refer to

previous research that conducted by Piazzesi and Schneider (2012), Great

Inflation will led to the shifting of portfolio and encourage peoples invest in

residential property as housing become more attractive than equity. In order

word, inflation will impact organization profits through higher input costs and

thus, affect the stock return to be paid. Besides, the shift in portfolio would

occur because high inflation promote tax effect that favor property investment.

This can be explained as the returns on housing are tax exempted and the

mortgage interest rate payment is tax deduction.



However, some studies that conducted by previous researchers generally find

that the interconnection between inflation rate and house price index is

insignificant and negative. These different results may due to the countries

involved, the period tested or the use of methodology in their research. For

instance, the research that carried out by Mariadas, Selvanathan and Tan (2016)

founds that inflation rate will not influence house price. Negative relationship

between inflation rate and house price index can be explained by Kuang and

Liu (2015) which house price can restraint inflation rate from increasing and

inflation would not stimulate the rise of housing price. This result against the

explanation above as house price will curb inflation in the long run. When the

housing price continue to increase until a certain level, investors or citizens will

shift their investment to lower investment security. This is due to the higher

price of housing cause the investors unable or unwilling to invest in higher cost

investment which could bring some risks to them in the future.

Next, the causality tests confirmed that inflation ganger cause house price in

unidirectional way at 1 percent significance level. This is similar with the result

of ARDL tests which agree the significance effect of inflation rate on housing

prices in Malaysia. In the research that carried out by Guo et al., (2015),

inflation is the granger causality of house price fluctuation, but changes in

housing prices have a weak effect on inflation rate. This indicate that expansion

in inflation rate will prompt the rising of house price and the house price might

not be the dominant matter to determine the inflation rate in Malaysia. This is

consistent with the research conducted by Meidani et al., (2011) as construction

cost will increase as well. Based on the International Construction Cost Survey

2017, which was held by Turner and Townsend, the construction cost inflation

in 2016 is 3.7 percent. Construction costs not only include the cost of material

to build a house, but it also include labour cost, cost of transportation and

equipment in developing of house and the list goes on. Therefore, when the

inflation rate continue to increase, the overall price of goods and services in the

economy will increase at the same time. In the end, developers would not bear



the costs and will transfer them to the buyers in property market. Hence, during

the high-level inflation rate in a country, the housing price will increase as well.

4.6.3 Exchange rate

The results of Autoregressive Distributed lag (ARDL) shows that there is a

significantly negative connection between exchange rate and housing price

index at 5 percent significant level. The housing price will increased when the

exchange rate depreciates. This implies that exchange rate has a significant

impact on the housing price in Malaysia. The results convince both the expected

sign in this and past studies by the previous researchers. According to Mahalik

and Mallick (2011) and Abelson et al., (2005), when there is an appreciation of

exchange rate in local country will discourage foreign investors to invest in

local investment. In other words, an undervalued of domestic currency against

other country currency, foreigners will choose to invest in Malaysia. With a

weak currency in domestic country, housing price will be pushing up by the

foreign investors. From the foreign investors’ perspective, foreign currency is

more valuable and increases their purchasing power to demand the houses in

Malaysia. The higher the housing demand will increase the value of the asset.

Hence, it leads to an increase in house price. This concludes that an inverse

relationship is existed between exchange rate and housing price.

Besides, most of the previous researchers examine techniques of time series

data by using the cointegration, error-correction modelling and granger

approach. This study adopts nonlinear ARDL cointegration approach (NARDL)

as an asymmetric extension which derive from the well-known of the ARDL

model. The NARDL results prove that there is asymmetry in the long-run effect

of exchange rate on housing price in Malaysia. Both long-run coefficient are

positive which is positive 6.495944 and 2.495293 therefore the long run

equation or the cointegrating equation is:



Long run equation: LNHPI = 6.495944 LNEXG_P + 2.495293 LNEXG_N + 𝜇

Therefore, the house price index is shows to react more towards the positive

change because the coefficient of LNEXG_P is larger. In order words, there

will be a larger impact on positive changes than negative changes in the long

run. The positive impact will be rely on 1 percent point increase in exchange

rate leads to 6.4959 percent point increase in house price index. According to

Liu and Zhang (2013), Meidani et al., (2011), Zhang, Hua and Zhao (2012), an

appreciation of currency will increase the housing prices because the real estate

will have a favor return due to economy well-being. When the economy is

continue to flourish, most of the people might get extra salaries and bonus.

Hence, appreciation of exchange rate increase the housing demand to increase

the value of the assets, which in turn housing price will increase.

NARDL test is more dominated towards the positive impact than the negative

impact while ARDL test shows negative relationship between the exchange rate

and housing price. The findings in NARDL test resulted on positive impact

might cause the speculative activities by those speculators to earn profit from

the expectation of appreciation of currency. When the speculators expect that

there will be an appreciation of currency in future, it indicates that the currency

is undervalued currently. At this moment, the foreign investors will start to

demand the houses in Malaysia before the exchange rate appreciate. This will

attracting international speculative to highly demand on the houses in Malaysia

and cause the housing price in Malaysia to grow. The high demand cause the

Malaysia currency to appreciate. As a result, when there is an appreciation of

Malaysia currency, the foreigners who own a house in Malaysia would able to

sell it at a higher price as compare to the purchase price. Hence, the arbitrage

opportunity has inspired foreign investment speculating in Malaysia real estate

market and leads to housing bubble in Malaysia (Zhang and Wu, 2006).



4.6.4 Unemployment rate

The result showed that unemployment rate is statistically and positively related

with housing price at 1 percent significance level. The result is different from

the expected sign that mentioned in the previous chapter. Based on Department

of Statistics Malaysia, Labor Force Survey Report 2016 has mentioned the

highest percentage for unemployment is fell in the age group of 20 to 24, which

is 37.7 percent over total unemployed. According to Mwalili (2014), mostly

unemployment happen among the teenagers whom do not participate actively

in purchasing residential house. In addition, unemployed persons usually will

choose to rent a house instead of purchasing a house which is costly to them.

This situation has explains why unemployment rate do not affect the price of

housing in Malaysia.

Furthermore, when the unemployment rate is lower, the public is more

willingness to move but there is not only depend on the employment status, but

also affect by other variety factors such as the housing prices, rental prices,

market value of the house and potential offered wage. Hence, the city areas

generate more growth in housing market however many relations notes that

most of the areas suffer from housing problems so that this will decrease the

demand of houses in the market. As a result, the house prices decrease.

Therefore, this study draws a conclusion by indicating that unemployment rate

has a positive association with Malaysian housing price (Forslund & Sandra,

n.d.).

Moreover, Karamelikli (2016) stated that an increased unemployment rate

could cause a reduction in number of potential customers and reduces the

demand for real estate. It also could consider a signal effect capable of showing

the economic situation for the nearest future. Hence, these signal effect could

affect the prediction about the future housing prices. Therefore, the direction

taken by financial assets such as real estate will be shifts to riskless sectors if



unemployment rate increases. As a result, the housing sector will be invested

more by the investors when an economic downturn is expected in the near future.

Any increase in unemployment could be regarded as an alarm bell ringing in

respect of the economic future on the housing sector.

In theory, this study expected unemployment rate are negatively related with

housing price index. When the unemployment rate is low, it indicates the

employment is growth in that particular country, it has reveals the households

feel wealthier spurring consumer confidence in spending, it raises consumer

purchasing power and increasing the demand of housing, which causes housing

prices to rise (Gaspareniene, Remeikiene & Skuka, 2017). In contrast, when the

unemployment rate is high, it indicates the employment is slowdown in that

particular country and the household feel poorer lead to pessimism or lack of

consumer confidence in spending. It causes household saving their money

rather than spend. Hence, it reduces the consumer purchasing power and

decreasing the demand of housing, which causes housing prices to fall

(Unconventional Economist, 2011). Therefore, this study draws a conclusion

by indicating that unemployment rate has a positive association with Malaysian

housing price.

4.7 Conclusion

In a nutshell, this study has analysed the dynamics data with a series of time series

econometrics test. In the beginning, the descriptive statistics of each variable is being

reviewed. Besides that, Unit Root Test which consists of ADF and PP test, ARDL test,

NARDL test, diagnostic checking and Granger Causality Test have been conducted in

this chapter. Overall, all the empirical results from the methodologies used in this study

have been interpreted and showed in figure, diagram and table form. The clear and

precise interpretation of the results have been showed on the below of each of the test

in this chapter. Before the end of this chapter, there will be a discussion for major

https://www.macrobusiness.com.au/author/leith/



finding to justify on each independent variables. Lastly, the summary for whole study

will be discussed in the next chapter.



CHAPTER 5: CONCLUSION

5.0 Introduction

In this chapter, an overall outline from Chapter 1 to Chapter 4 will be provided. The

result that obtained from Chapter 4 allow us to propose the policy implication for each

variables in this study. Moreover, limitations that arise in the study will be thoroughly

discussed. In addition, recommendation for future studies will be also provided too.

5.1 Summary

The housing prices indicate the economic performance in Malaysia. When the house

prices in Malaysia keep increases, it has leads to many people especially fresh graduate

household and household with low-medium income level are worried and unable afford

to purchase a house. This issue has raised Malaysian citizen attention and driven us to

discovery the main variables that affect the increasing in housing prices by using the

time series data from year 2001 quarter one to year 2015 quarter four in this study.

In brief, chapter 1 explains that the housing price today in Malaysia is much higher

compared to the price in the last few decades. It has causes the household with low and

medium income level facing the difficulties to purchase a house due to the house and

land prices are increase rapidly. Besides that, the depreciation of domestic currency

will increase housing demand from overseas investors to scoop up low priced asset and

outbidding than domestic buyers. Thus, this issue unconsciously raised Malaysian

citizen attention and driven us to discover which macroeconomic factors that affect the

increase in house prices.



Furthermore, chapter 2 explains the relationship between HPI and macroeconomic

variables has been explained based on literature from previous researchers. From the

review, it might be getting a different result from the past researchers on their studies

between the housing price and macroeconomic variables. The reason behind

inconsistency result may due to the researchers conducted their studies in different

country and thus, the data and policies are different. Thus, it is important to determine

the interrelation between macroeconomic variables and housing price in Malaysia in

order to get the precise result as compared to the previous finding by other researchers.

In addition, theoretical models are developed which are supply and demand theory,

portfolio balance theory and purchasing power parity theory between housing price

index and its determinants in Malaysia.

Next, this chapter 3 discusses all the methodologies and statistical test that will be

implementing in this study. It has clearly defined and elaborated the ideas for each of

the methodology. Firstly, Unit Root Test that consists of ADF and PP tests is carried

out to test whether there are stationary or non-stationary trend of time series data for

all variables. It is necessary to check the order of integration of the level variables for

an appropriate econometrics method in order to avoid obtaining any spurious and

invalid results. Next, ARDL test is employed to examine the co-integration for the

whole model between the measured variable and controlled variables in the long-run

relationship. It can help in identifying the co-integration vectors and will give realistic

and efficient estimates. The next method that we employed in this study is NARDL test

is allow to detect the asymmetric effects among economic variables in the long run and

short run by using positive and negative partial sum decomposition. Moreover, Granger

Causality Test has been run to analyse the causality between two variables, whether

bidirectional, unidirectional or no relationship in short run. Lastly, diagnostic checking

has been conducted in order to ensure no econometric problems in the model.

In the chapter 4, a series of test have been conducted and the results we obtain are

clearly explained. Initially, this study has overviews the descriptive statistics of all the

measured variable and controlled variables. The results from both ADF and PP tests



reveal that house price index and exchange rate are non-stationary at level. After the

first difference of both ADF test and PP test, all the variables which consist of house

price index (LNHPI), GDP, inflation rate (CPI), exchange rate (LNEXG) and

unemployment rate (UNEMPT) are stationary at the first difference. Then, we proceed

to ARDL test to examine the long run co-integration between the house price index and

its determinants. Since the value of F-statistics is greater than the upper critical value,

thus, it can conclude that housing prices is co-integrated with CPI, GDP, LNEXG and

UNEMPT in the long run. However, the empirical results for long-run coefficients of

ARDL test showed that CPI and UNEMPT are having significant positive relationship

towards LNHPI while LNEXG have a significant negative relationship with HPI in the

long run. In addition, NARDL test were used to capture the asymmetric relation

between HPI and its macroeconomic variables in Malaysia. There is a strong evidence

of co-integrating relationship between HPI and its macroeconomic variables in

Malaysia due to value of F-statistics is greater than the upper critical value. Besides

that, the NARDL model results has shown that LNHPI response more towards to

LNEXG positive change compare to negative change because the coefficient is larger.

Consequently, Wald test indicates that there is asymmetry in the long-run impact of

LNEXG on HPI in Malaysia at 5 percent significance level.

Next, the short-run relationship and the causality direction of the model will be

examined by using Granger Causality test. Based on Granger Causality test results, CPI

has a unidirectional Granger Causality LNHPI in the short run, but GDP, LNEXG and

UNEMPT are not granger cause LNHPI at any level of significance. Furthermore, there

is bidirectional causality between CPI and GDP in the short run, whereby the

movement of UNEMPT tends to influence the movement of GDP in the short run.

Moreover, there is a unidirectional granger causality is occurring from GDP to LNEXG

and UNEMPT to LNEXG. Lastly, CPI also has short-run dynamic granger cause on

UNEMPT. Therefore, the empirical result showed there is only CPI have granger

causality and short-run relationship to the HPI.



5.2 Policy Implication

As with most economies, the important fundamental of the economy is property market

which have become an attractive form of investment for local homebuyers or foreign

investors. It is vital for investors to understand which macroeconomic variables are

bringing the utmost effect to house price in Malaysia.

As explained in the discussion, there are negatively correlated between exchange rate

and housing prices in Malaysia. In another words, it indicates that appreciation in the

currency value will led to decrease in house prices. Although investors cannot control

the exchange rate whether experienced appreciate or depreciate in the value of currency,

however, they could have a better knowledge and overview for the fluctuation of the

exchange rate in Malaysia economy. Thus, they can try performing hedge on the

currency when investing in residential properties in Malaysia with the adequate

information. Hedging can help those investors to eliminate the risk and limit the loss

resulting from the transactions in foreign currencies. Hence, when the investors are

making their investment decisions on the Malaysia residential properties, they can

avoid facing undesirable exchange rate fluctuations.

Besides, government should concern when the foreigners who take advantage when

there is depreciation on currency in Malaysia. Government should maintain a rational

price fluctuation and promote a healthily developing market by establish more strict

rules and regulations to control capital from foreign country and speculations by

utilizing taxation. Besides, government should also crack down on the speculations and

to make the yield to the certain level that speculators do not aim for speculation and

gain speculative profits on property market in Malaysia.

In addition, it is concluded that there is positive relationship and significantly related

between house prices in Malaysia and inflation rate. Consequently, it is important for

the government and policy makers to know that inflation rate may be one of the



important factors that will bring an effect toward increase in housing price. Thus,

government should concern more on the price stability policy. It indicates that the

overall price level in an economy does not change much over the period of time. As a

result, government should enhance more on promotion of stable prices to avoid severe

inflation. This is due to inflation has recognizes as a dangerous impact which can

reduce the economic growth. Hence, price stability helps to maintain financial and

economic activity at satisfied level. In short, price stability policy is a useful tool to

lower down and stable inflation.

Besides that, the government should develops plan to encourage more foreign direct

investment into our country to create more job opportunity. Those citizens can easily

obtain a job when the job opportunity is increase. Consequently, unemployment rate

will decline gradually and it will lead to the income increase. When the income is

increase, the affordability to own a house will be increase. Apart from this, developers

should cooperate with authorities in order to control the housing price and take any

corrective action to prevent occurrence of unfair price in the market. The developers

should set housing price based on buyers from different group of income level.

Moreover, government can offer subsidies for the developers to construct low-cost

housing projects or offer housing allowance to benefit the lower income group of

buyers.

As one of the developing country, the demand for housing in Malaysia is increase

especially in urban areas due to rapid economic development in recent years. The

Malaysian government understand that housing is a necessities for every citizen (Ong,

2013). Therefore, government works hard to implement variety of policies to achieve

the target. Moreover, the fresh graduate households and households with the lower and

middle income level may not afford to own a house due to the housing price rise swiftly.

There are a lot of citizens are in doubt such the high annual grow in house prices is

inconsistent with annual income, as the increase in house price shift quickly than the

increase in household income. In fact, most of the citizens are fear to deal with the high

priced of residential properties (Ong & Chang, 2013). Therefore, the government



should more concern on the citizen who owns a first home ownership by giving them

a subsidy. According to the 10th Malaysia Plan from 2011-2015, the government

launched several housing programmes and financing schemes such as Perumahan

Rakyat 1Malaysia (PR1MA), Skim Perumahan Mampu Milik Swasta (MyHome),

Youth Housing Scheme in order to assist those citizens who does not have the ability

to afford the high priced of residential properties in Malaysia (“Providing Adequate

and Quality Affordable House”, 2015).

5.3 Limitation of Study

Throughout this study, there are few limitation found in this paper. Indeed, it is difficult

to get perfect research without any limitation during the process of study as well. First

and foremost, the housing prices not only can be affected by macroeconomics variables,

it also can affect by microeconomics variables such as education level and personal

income. However, in this study, we only adopt from macroeconomics variables which

may not provide the whole picture that causes the housing price to increase.

In addition, the data we used in this study was based on the whole Malaysia housing

market. It might be resulted in inaccuracy if the researches would like to examine the

case study from a particular geographical region in Malaysia. For instance, with the

same independent variables used, the overall housing price might give the result of

rising but the housing price in some states such as Perak and Perlis might have a drop

in housing price. Thus, the result in this study might be sufficient if studied for the

whole Malaysia housing market but not specific enough to studied in one particular

geographical area.

Lastly, in this study, we only adopt those secondary data without concerning on primary

data which result from the survey in Malaysia. Primary data collection might provide

us more information from a large number of respondents as larger sample size provide

us more accurate result.



5.4 Recommendations for Future Research

Research recommendations deliver suggestions about what can be improved in this

study and what could future studies practice in order to produce a better result. The

main objective for recommendation is to discuss the gaps and uncertainties throughout

the whole study and develop guidelines for improving further research (Brown, 2006).

It is an important part of a project as it can avoid similar mistakes to be repeated and

provide more accurate results for future research.

Firstly, researchers are highly recommended to conduct further study by using

microeconomic variables such as personal income and education level. For example,

individuals’ personal income can influence their home-ownership. Individuals with

higher income level are more highly to own a house compared to individual with lower

income level. According to Tsai and Peng (2011), personal income and housing price

is closely connected because demand for housing depends on personal income. Seen

housing price not only affected by macroeconomic factors, thus we can include more

microeconomic variables to enhance the result. When a similar study is conducted,

researchers may include more variables that haven’t test before to enhance the model.

However, the variables that decided to test must be relevant and its characteristics must

be significant to the study.

Next, residential properties are the only type of property that we examine in our study.

Future researchers can include industrial or commercial property in their studies to

identify the effect caused by macroeconomic variables. Industrial property is used for

industrial purpose while commercial property is used solely for business purpose.

Hence, future researchers are encouraged to investigate the relationship and effect of

macroeconomic variables with other types of real estate. The result that obtained enable

government and policy makers to take any action immediately to protect the economy

condition and citizens.



Besides, future researchers can carry out research on housing price based on various

geographical areas. They can narrow down the scope of study in either a rural or urban

areas to find out the differences in setting of housing price. For example, the housing

price in Perak is much cheaper than housing in Penang. The housing price in Penang

may increase from year to year, while the housing price in Kampar will increase slowly.

Difference in the performance of housing price in different location will show different

housing price change in Malaysia. The result of the study provided will be more correct

to make specific estimation of the performance of property market in the particular

areas.

Lastly, future researchers may advise to use other types of research method for data

collection such as primary data method. Primary research involves collecting data

directly from the residents in Malaysia. It encompasses interviews, surveys,

observations and questionnaire. With the using of two types of method, we can obtain

a better result in the study. Besides, we can get some suggestions or opinions of

residents toward our study or about the housing price in Malaysia. Primary data

collection also enable us to get more information from a large number of respondent as

larger sample size provide us desired result.

5.5 Conclusion

This chapter will provide an overall outline from chapter 1 to chapter 4. Housing price

today in Malaysia is much higher compare to the price in the last few decades. Based

on the empirical results and discussion, this result found that unemployment rate,

exchange rate, and inflation rate are significant determinants of Malaysia’s house price

index, while GDP is insignificant. Moreover, the model of this study is free from

econometric problem by using diagnostic checking. Therefore, the result of this study

is reliable and can be trusted. In addition, several policies and implication are provided

is this study. Furthermore, limitation and recommendation are discussed for researcher

who will be doing the relevant topic in the future.



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APPENDICES

Appendix 4.1: Descriptive Statistics

Table 4.1: Descriptive Statistic

Appendix 4.2: Augmented Dickey-Fuller unit root tests results (without trend,

level)

1. House Price Index

Null Hypothesis: LNHPI has a unit root

Exogenous: Constant

Lag Length: 0 (Automatic based on SIC, MAXLAG=10) t-Statistic Prob.* Augmented Dickey-Fuller test statistic 3.697587 1.0000

Test critical values: 1% level -3.546099

5% level -2.911730

10% level -2.593551 *MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(LNHPI)

Method: Least Squares

Date: 06/14/17 Time: 22:13

Sample (adjusted): 2001Q2 2015Q4

Included observations: 59 after adjustments Variable Coefficient Std. Error t-Statistic Prob. LNHPI(-1) 0.021147 0.005719 3.697587 0.0005

C -0.090094 0.028200 -3.194782 0.0023 R-squared 0.193459 Mean dependent var 0.014053

Adjusted R-squared 0.179309 S.D. dependent var 0.011782

S.E. of regression 0.010674 Akaike info criterion -6.208731

Sum squared resid 0.006494 Schwarz criterion -6.138306

LNHPI CPI GDP LNEXG UNEMP

Mean 4.933563 2.311667 4.870333 4.580857 3.338333

Median 4.859425 2.100000 5.375000 4.582413 3.200000

Maximum 5.440251 8.200000 10.18000 4.672174 4.000000

Minimum 4.611152 -2.000000 -5.760000 4.434738 2.700000

Std. Dev. 0.251923 1.586072 2.753230 0.042153 0.318413

Skewness 0.638816 1.042411 -1.741247 -0.859121 0.424464

Kurtosis 2.122071 7.337769 6.938252 4.996767 2.259834

Jarque-Bera 6.007763 57.90682 69.09399 17.34860 3.171315

Probability 0.049594 0.000000 0.000000 0.000171 0.204813

Sum 296.0138 138.7000 292.2200 274.8514 200.3000

Sum Sq. Dev. 3.744461 148.4218 447.2364 0.104838 5.981833

Observations 60 60 60 60 60



Log likelihood 185.1576 Hannan-Quinn criter. -6.181240

F-statistic 13.67215 Durbin-Watson stat 1.993219

Prob(F-statistic) 0.000491

2. Inflation Rate

Null Hypothesis: CPI has a unit root

Exogenous: Constant

Lag Length: 1 (Automatic based on SIC, MAXLAG=10) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -4.305318 0.0011


5% level -2.912631



Dependent Variable: D(CPI)


Date: 06/14/17 Time: 22:37


Included observations: 58 after adjustments Variable Coefficient Std. Error t-Statistic Prob. CPI(-1) -0.429439 0.099746 -4.305318 0.0001

D(CPI(-1)) 0.357828 0.125591 2.849161 0.0062

C 1.009758 0.272857 3.700687 0.0005 R-squared 0.267664 Mean dependent var 0.020690


S.E. of regression 1.113484 Akaike info criterion 3.103203

Sum squared resid 68.19156 Schwarz criterion 3.209778

Log likelihood -86.99289 Hannan-Quinn criter. 3.144716



3. Gross Domestic Product (GDP)

Null Hypothesis: GDP has a unit root

Exogenous: Constant



5% level -2.912631

10% level -2.594027



*MacKinnon (1996) one-sided p-values.


Dependent Variable: D(GDP)


Date: 06/14/17 Time: 22:50


Included observations: 58 after adjustments Variable Coefficient Std. Error t-Statistic Prob. GDP(-1) -0.380760 0.073286 -5.195505 0.0000

D(GDP(-1)) 0.576172 0.106989 5.385360 0.0000








4. Exchange Rate

Null Hypothesis: LNEXG has a unit root

Exogenous: Constant



5% level -2.911730



Dependent Variable: D(LNEXG)


Date: 06/14/17 Time: 23:03


Included observations: 59 after adjustments Variable Coefficient Std. Error t-Statistic Prob. LNEXG(-1) -0.115263 0.078319 -1.471726 0.1466

C 0.525317 0.358939 1.463529 0.1488 R-squared 0.036609 Mean dependent var -0.002923









5. Unemployment Rate

Null Hypothesis: UNEMPT has a unit root

Exogenous: Constant



5% level -2.911730



Dependent Variable: D(UNEMPT)


Date: 06/14/17 Time: 23:11


Included observations: 59 after adjustments Variable Coefficient Std. Error t-Statistic Prob. UNEMPT(-1) -0.477015 0.106975 -4.459124 0.0000








Appendix 4.3: Augmented Dickey-Fuller unit root tests results (with trend, level)



Exogenous: Constant, Linear Trend





5% level -3.487845





Date: 06/14/17 Time: 22:14


Included observations: 59 after adjustments Variable Coefficient Std. Error t-Statistic Prob. LNHPI(-1) -0.011101 0.021969 -0.505315 0.6153

C 0.054441 0.099150 0.549083 0.5851

@TREND(2001Q1) 0.000476 0.000313 1.519046 0.1344 R-squared 0.225377 Mean dependent var 0.014053







2. Inflation Rate





5% level -3.489228





Date: 06/14/17 Time: 22:38





D(CPI(-1)) 0.361750 0.126725 2.854599 0.0061

C 0.892763 0.364766 2.447493 0.0177








3. Gross Domestic Product





5% level -3.489228





Date: 06/14/17 Time: 22:52



D(GDP(-1)) 0.577046 0.108409 5.322854 0.0000

C 1.882288 0.515613 3.650582 0.0006








4. Exchange Rate







5% level -3.487845





Date: 06/14/17 Time: 23:04



C 0.593316 0.366391 1.619351 0.1110

@TREND(2001Q1) -0.000173 0.000183 -0.945944 0.3482 R-squared 0.051760 Mean dependent var -0.002923












5% level -3.487845





Date: 06/14/17 Time: 23:11


Included observations: 59 after adjustments



Variable Coefficient Std. Error t-Statistic Prob. UNEMPT(-1) -0.736089 0.128492 -5.728674 0.0000

C 2.675006 0.479048 5.584005 0.0000





Log likelihood 1.307832 Hannan-Quinn criter. 0.098598



Appendix 4.4: Phillips Perron unit root tests results (without trend, level)



Exogenous: Constant

Bandwidth: 3 (Newey-West using Bartlett kernel) Adj. t-Stat Prob.* Phillips-Perron test statistic 3.552810 1.0000


5% level -2.911730


Residual variance (no correction) 0.000110

HAC corrected variance (Bartlett kernel) 0.000118

Phillips-Perron Test Equation



Date: 06/14/17 Time: 22:21


Included observations: 59 after adjustments Variable Coefficient Std. Error t-Statistic Prob. LNHPI(-1) 0.021147 0.005719 3.697587 0.0005










2. Inflation Rate


Exogenous: Constant

Bandwidth: 1 (Newey-West using Bartlett kernel) Adj. t-Stat Prob.* Phillips-Perron test statistic -3.551394 0.0099


5% level -2.911730







Date: 06/14/17 Time: 22:42












Exogenous: Constant

Bandwidth: 4 (Newey-West using Bartlett kernel)



Adj. t-Stat Prob.* Phillips-Perron test statistic -3.069865 0.0344


5% level -2.911730







Date: 06/14/17 Time: 22:58










4. Exchange Rate


Exogenous: Constant



5% level -2.911730









Date: 06/14/17 Time: 23:07












Exogenous: Constant



5% level -2.911730







Date: 06/14/17 Time: 23:15












Appendix 4.5: Phillips Perron unit root tests results (with trend, level)






5% level -3.487845







Date: 06/14/17 Time: 22:22


Included observations: 59 after adjustments Variable Coefficient Std. Error t-Statistic Prob. LNHPI(-1) -0.011101 0.021969 -0.505315 0.6153

C 0.054441 0.099150 0.549083 0.5851










2. Inflation Rate





5% level -3.487845







Date: 06/14/17 Time: 22:43



C 0.666158 0.365305 1.823568 0.0736













5% level -3.487845









Date: 06/14/17 Time: 22:59



C 1.278084 0.595616 2.145818 0.0362

@TREND(2001Q1) -0.001859 0.013738 -0.135302 0.8929 R-squared 0.127801 Mean dependent var 0.051864







4. Exchange Rate





5% level -3.487845







Date: 06/14/17 Time: 23:08





Variable Coefficient Std. Error t-Statistic Prob. LNEXG(-1) -0.128968 0.079718 -1.617795 0.1113

C 0.593316 0.366391 1.619351 0.1110













5% level -3.487845







Date: 06/14/17 Time: 23:15



C 2.675006 0.479048 5.584005 0.0000










Appendix 4.6: Augmented Dickey Fuller unit root tests results (without trend,

First Difference)


Null Hypothesis: D(LNHPI) has a unit root

Exogenous: Constant



5% level -2.913549



Dependent Variable: D(LNHPI,2)


Date: 06/14/17 Time: 22:15


Included observations: 57 after adjustments Variable Coefficient Std. Error t-Statistic Prob. D(LNHPI(-1)) -0.599131 0.166122 -3.606572 0.0007

D(LNHPI(-1),2) -0.250926 0.133704 -1.876731 0.0660

C 0.008574 0.002805 3.056455 0.0035 R-squared 0.432221 Mean dependent var -8.06E-05







2. Inflation Rate

Null Hypothesis: D(CPI) has a unit root

Exogenous: Constant



5% level -2.915522





Dependent Variable: D(CPI,2)


Date: 06/14/17 Time: 22:39


Included observations: 55 after adjustments Variable Coefficient Std. Error t-Statistic Prob. D(CPI(-1)) -1.535680 0.229563 -6.689566 0.0000

D(CPI(-1),2) 0.659360 0.194579 3.388648 0.0014

D(CPI(-2),2) 0.501679 0.158419 3.166796 0.0026

D(CPI(-3),2) 0.538008 0.125064 4.301871 0.0001









Null Hypothesis: D(GDP) has a unit root

Exogenous: Constant



5% level -2.915522



Dependent Variable: D(GDP,2)


Date: 06/14/17 Time: 22:56


Included observations: 55 after adjustments Variable Coefficient Std. Error t-Statistic Prob. D(GDP(-1)) -1.341284 0.199256 -6.731443 0.0000

D(GDP(-1),2) 0.664192 0.159496 4.164308 0.0001



D(GDP(-2),2) 0.482702 0.140812 3.427986 0.0012

D(GDP(-3),2) 0.419969 0.125620 3.343170 0.0016








4. Exchange Rate

Null Hypothesis: D(LNEXG) has a unit root

Exogenous: Constant



5% level -2.912631



Dependent Variable: D(LNEXG,2)


Date: 06/14/17 Time: 23:05


Included observations: 58 after adjustments Variable Coefficient Std. Error t-Statistic Prob. D(LNEXG(-1)) -1.051122 0.134449 -7.817996 0.0000









Null Hypothesis: D(UNEMPT) has a unit root

Exogenous: Constant

Lag Length: 2 (Automatic based on SIC, MAXLAG=10) t-Statistic Prob.*



Augmented Dickey-Fuller test statistic -8.352933 0.0000


5% level -2.914517



Dependent Variable: D(UNEMPT,2)


Date: 06/14/17 Time: 23:13


Included observations: 56 after adjustments Variable Coefficient Std. Error t-Statistic Prob. D(UNEMPT(-1)) -2.267612 0.271475 -8.352933 0.0000

D(UNEMPT(-1),2) 0.884823 0.188681 4.689507 0.0000

D(UNEMPT(-2),2) 0.374996 0.124330 3.016121 0.0040

C -0.019316 0.033655 -0.573920 0.5685 R-squared 0.708139 Mean dependent var -0.005357







Appendix 4.7: Augmented Dickey Fuller unit root tests results (with trend, First

Difference)






5% level -3.489228







Date: 06/14/17 Time: 22:16



C 0.004263 0.002944 1.447738 0.1534

@TREND(2001Q1) 0.000344 9.71E-05 3.541435 0.0008 R-squared 0.507038 Mean dependent var -4.66E-05







2. Inflation Rate





5% level -3.493692





Date: 06/14/17 Time: 22:40



D(CPI(-1),2) 0.659562 0.196723 3.352750 0.0015

D(CPI(-2),2) 0.501860 0.160193 3.132845 0.0029

D(CPI(-3),2) 0.538021 0.126334 4.258726 0.0001

C 0.030786 0.349554 0.088073 0.9302















5% level -3.493692





Date: 06/14/17 Time: 22:57



D(GDP(-1),2) 0.672679 0.161298 4.170407 0.0001

D(GDP(-2),2) 0.487814 0.142071 3.433585 0.0012

D(GDP(-3),2) 0.424377 0.126726 3.348763 0.0016

C 0.302697 0.475095 0.637130 0.5270








4. Exchange Rate







5% level -3.489228





Date: 06/14/17 Time: 23:06



C -0.000354 0.006598 -0.053721 0.9574













5% level -3.492149





Date: 06/14/17 Time: 23:13



D(UNEMPT(-1),2) 0.888793 0.190241 4.671936 0.0000

D(UNEMPT(-2),2) 0.377690 0.125365 3.012727 0.0040



C -0.051868 0.074245 -0.698611 0.4880

@TREND(2001Q1) 0.001032 0.002093 0.492835 0.6242 R-squared 0.709522 Mean dependent var -0.005357







Appendix 4.8: Phillips Perron (PP) unit root tests results (without trend, First

Difference)



Exogenous: Constant



5% level -2.912631







Date: 06/14/17 Time: 22:23



C 0.011201 0.002414 4.640461 0.0000 R-squared 0.394627 Mean dependent var -4.66E-05









2. Inflation Rate


Exogenous: Constant



5% level -2.912631







Date: 06/14/17 Time: 22:45












Exogenous: Constant

Bandwidth: 6 (Newey-West using Bartlett kernel) Adj. t-Stat Prob.*



Phillips-Perron test statistic -4.474285 0.0006


5% level -2.912631







Date: 06/14/17 Time: 23:00










4. Exchange Rate


Exogenous: Constant



5% level -2.912631









Date: 06/14/17 Time: 23:08












Exogenous: Constant



5% level -2.912631







Date: 06/14/17 Time: 23:16












Appendix 4.9: Phillips Perron (PP) unit root tests results (with trend, First

Difference)






5% level -3.489228







Date: 06/14/17 Time: 22:25



C 0.004263 0.002944 1.447738 0.1534

@TREND(2001Q1) 0.000344 9.71E-05 3.541435 0.0008 R-squared 0.507038 Mean dependent var -4.66E-05







2. Inflation Rate







5% level -3.489228







Date: 06/14/17 Time: 22:46



C 0.038833 0.351398 0.110511 0.9124













5% level -3.489228









Date: 06/14/17 Time: 23:01



C 0.210700 0.482564 0.436626 0.6641








4. Exchange Rate





5% level -3.489228







Date: 06/14/17 Time: 23:09


Included observations: 58 after adjustments Variable Coefficient Std. Error t-Statistic Prob.



D(LNEXG(-1)) -1.060350 0.136422 -7.772591 0.0000

C -0.000354 0.006598 -0.053721 0.9574













5% level -3.489228







Date: 06/14/17 Time: 23:19



C -0.032032 0.083029 -0.385792 0.7011










Appendix 4.10: Autoregressive Distributor Lag Model (ARDL)

ARDL Long Run Form and Bounds Test


Selected Model: ARDL(2, 0, 4, 0, 0)

Case 2: Restricted Constant and No Trend

Date: 06/19/17 Time: 23:24

Sample: 2001Q1 2015Q4

Included observations: 56

Conditional Error Correction Regression

Variable Coefficient Std. Error t-Statistic Prob.

C -0.296059 0.171536 -1.725932 0.0912

LNHPI(-1)* 0.016317 0.007067 2.308702 0.0256

GDP** -0.000332 0.000779 -0.425830 0.6723

CPI(-1) -0.003580 0.001592 -2.248330 0.0295

LNEXG** 0.064558 0.033336 1.936596 0.0591

UNEMPT** -0.015904 0.005530 -2.875649 0.0061

D(LNHPI(-1)) -0.179756 0.137104 -1.311085 0.1965

D(CPI) -0.000969 0.001196 -0.810116 0.4221

D(CPI(-1)) 0.002987 0.001606 1.859860 0.0694

D(CPI(-2)) -0.001517 0.001303 -1.164146 0.2505

D(CPI(-3)) 0.002304 0.001244 1.851764 0.0706

* p-value incompatible with t-Bounds distribution.

** Variable interpreted as Z = Z(-1) + D(Z).

Levels Equation

Case 2: Restricted Constant and No Trend


GDP 0.020334 0.049585 0.410091 0.6837

CPI 0.219434 0.134668 1.629442 0.1102

LNEXG -3.956611 2.280607 -1.734894 0.0896

UNEMPT 0.974701 0.651429 1.496252 0.1416

C 18.14473 9.526683 1.904622 0.0632

EC = LNHPI - (0.0203*GDP + 0.2194*CPI -3.9566*LNEXG + 0.9747*UNEMPT

+ 18.1447 )

F-Bounds Test Null Hypothesis: No levels relationship

Test Statistic Value Signif. I(0) I(1)

F-statistic 10.09653 10% 2.2 3.09

k 4 5% 2.56 3.49

2.5% 2.88 3.87

1% 3.29 4.37



Appendix 4.11: Diagnostic Checking for ARDL Test

1. Breush-Godfrey Serial Correlation LM Test

Breusch-Godfrey Serial Correlation LM Test:

F-statistic 0.095013 Prob. F(2,43) 0.9096

Obs*R-squared 0.246388 Prob. Chi-Square(2) 0.8841

Test Equation:

Dependent Variable: RESID

Method: ARDL

Date: 06/19/17 Time: 23:28

Sample: 2002Q1 2015Q4


Presample missing value lagged residuals set to zero.


LNHPI(-1) -0.011884 0.253671 -0.046848 0.9629

LNHPI(-2) 0.012233 0.259194 0.047198 0.9626

GDP 1.99E-05 0.000801 0.024892 0.9803

CPI 1.17E-08 0.001270 9.19E-06 1.0000

CPI(-1) 3.38E-05 0.001533 0.022036 0.9825

CPI(-2) -1.79E-05 0.001601 -0.011156 0.9912

CPI(-3) -2.67E-05 0.001812 -0.014744 0.9883

CPI(-4) 5.42E-05 0.001316 0.041201 0.9673

LNEXG 0.002582 0.034809 0.074188 0.9412

UNEMPT -2.74E-05 0.005672 -0.004832 0.9962

C -0.013488 0.182041 -0.074094 0.9413

RESID(-1) 0.010757 0.288808 0.037245 0.9705

RESID(-2) -0.068082 0.156185 -0.435903 0.6651

R-squared 0.004400 Mean dependent var 1.23E-15

Adjusted R-squared -0.273442 S.D. dependent var 0.008213








2. ARCH test

3. Normality Test (Jarque-Bera)

Heteroskedasticity Test: ARCH



Test Equation:

Dependent Variable: RESID^2


Date: 06/19/17 Time: 23:28




C 7.02E-05 1.48E-05 4.734919 0.0000

RESID^2(-1) -0.077858 0.136840 -0.568968 0.5718

R-squared 0.006071 Mean dependent var 6.50E-05

Adjusted R-squared -0.012682 S.D. dependent var 8.56E-05

S.E. of regression 8.61E-05 Akaike info criterion -15.84568

Sum squared resid 3.93E-07 Schwarz criterion -15.77269




0

2

4

6

8

10

-0.02 -0.01 0.00 0.01 0.02

Series: Residuals

Sample 2002Q1 2015Q4

Observations 56

Mean 1.23e-15

Median 0.000326

Maximum 0.019096

Minimum -0.019505

Std. Dev. 0.008213

Skewness -0.065467

Kurtosis 2.629657

Jarque-Bera 0.360028

Probability 0.835259



4. Ramsey RESET Test

Ramsey RESET Test

Equation: UNTITLED

Specification: LNHPI LNHPI(-1) LNHPI(-2) GDP CPI CPI(-1) CPI(-2) CPI(

-3) CPI(-4) LNEXG UNEMPT C

Omitted Variables: Squares of fitted values

Value df Probability

t-statistic 1.823772 44 0.0750

F-statistic 3.326143 (1, 44) 0.0750

F-test summary:

Sum of Sq. df Mean Squares

Test SSR 0.000261 1 0.000261

Restricted SSR 0.003710 45 8.24E-05

Unrestricted SSR 0.003449 44 7.84E-05

Unrestricted Test Equation:

Dependent Variable: LNHPI

Method: ARDL

Date: 06/19/17 Time: 23:27

Sample: 2002Q1 2015Q4


Maximum dependent lags: 4 (Automatic selection)

Model selection method: Akaike info criterion (AIC)

Dynamic regressors (4 lags, automatic):

Fixed regressors: C

Variable Coefficient Std. Error t-Statistic Prob.*

LNHPI(-1) 1.200421 0.238897 5.024846 0.0000

LNHPI(-2) 0.320218 0.154290 2.075430 0.0438

GDP -0.000292 0.000760 -0.384691 0.7023

CPI -0.001761 0.001244 -1.415324 0.1640

CPI(-1) 0.000565 0.001452 0.389425 0.6988

CPI(-2) -0.006578 0.001904 -3.455600 0.0012

CPI(-3) 0.005656 0.001910 2.961231 0.0049

CPI(-4) -0.003676 0.001428 -2.574996 0.0135

LNEXG 0.081856 0.033862 2.417355 0.0198

UNEMPT -0.022754 0.006572 -3.462262 0.0012

C -1.628790 0.749654 -2.172722 0.0352

FITTED^2 -0.049095 0.026919 -1.823772 0.0750

R-squared 0.998963 Mean dependent var 4.955904







*Note: p-values and any subsequent tests do not account for model

selection.



Appendix 4.12 Multicollinearity

Dependent Variable: LNHPI


Date: 06/14/17 Time: 21:47

Sample: 2001Q1 2015Q4



CPI -0.000671 0.016339 -0.041062 0.9674

GDP -0.002234 0.009537 -0.234261 0.8157

LNEXG -1.399612 0.600591 -2.330392 0.0235

UNEMPT -0.498856 0.081975 -6.085441 0.0000

C 13.02276 2.771822 4.698268 0.0000








1. Correlation Analysis

2. Regression Analysis

A. Inflation Rate

Dependent Variable: CPI


Date: 06/14/17 Time: 21:48

Sample: 2001Q1 2015Q4



GDP 0.090183 0.077067 1.170198 0.2469

LNEXG -0.263416 4.911976 -0.053627 0.9574

UNEMPT -0.621260 0.665298 -0.933807 0.3544

C 5.153088 22.65967 0.227412 0.8209


C CPI GDP LNEXG UNEMPT

C 7.682997 -0.001376 -0.002352 -1.655381 -0.025363

CPI -0.001376 0.000267 -2.41E-05 7.03E-05 0.000166

GDP -0.002352 -2.41E-05 9.10E-05 0.000318 0.000153

LNEXG -1.655381 7.03E-05 0.000318 0.360709 0.000394

UNEMPT -0.025363 0.000166 0.000153 0.000394 0.006720









B. Gross Domestic Product

Dependent Variable: GDP


Date: 06/14/17 Time: 21:49

Sample: 2001Q1 2015Q4



CPI 0.264675 0.226179 1.170198 0.2469

LNEXG -3.492277 8.402175 -0.415640 0.6793

UNEMPT -1.677554 1.126501 -1.489172 0.1421

C 25.85635 38.68314 0.668414 0.5066








C. Exchange Rate

Dependent Variable: LNEXG


Date: 06/14/17 Time: 21:50

Sample: 2001Q1 2015Q4



CPI -0.000195 0.003635 -0.053627 0.9574

GDP -0.000881 0.002119 -0.415640 0.6793

UNEMPT -0.001091 0.018239 -0.059820 0.9525

C 4.589239 0.065271 70.31067 0.0000










D. Unemployment Rate

Dependent Variable: UNEMPT


Date: 06/14/17 Time: 21:52

Sample: 2001Q1 2015Q4



CPI -0.024680 0.026429 -0.933807 0.3544

GDP -0.022707 0.015248 -1.489172 0.1421

LNEXG -0.058564 0.979012 -0.059820 0.9525

C 3.774250 4.490202 0.840552 0.4042








Appendix 4.13 Non-Auto Regressive Distributed Lag (NARDL)


Method: Stepwise Regression

Date: 06/19/17 Time: 21:40



Number of always included regressors: 7

Number of search regressors: 8

Selection method: Uni-directional

Stopping criterion: p-value = 0.1 Variable Coefficient Std. Error t-Statistic Prob.* C 0.115527 0.101435 1.138931 0.2603

LNHPI(-1) -0.030697 0.021291 -1.441808 0.1557

LNEXG_P(-1) 0.199406 0.046093 4.326214 0.0001

LNEXG_N(-1) 0.076598 0.038336 1.998065 0.0513

GDP(-1) 0.000731 0.000480 1.524008 0.1339

CPI(-1) -0.001615 0.000809 -1.995626 0.0515

UNEMP(-1) 0.009110 0.005411 1.683577 0.0986

DLNEXG_P(-2) -0.340120 0.137064 -2.481470 0.0166 R-squared 0.454919 Mean dependent var 0.014234








Prob(F-statistic) 0.000058 Selection Summary Removed DLNEXG_N

Removed DLNEXG_P

Removed DLNEXG_N(-1)

Removed DLNHPI(-2)

Removed DLNEXG_P(-1)

Removed DLNHPI(-1)

Removed DLNEXG_N(-2) *Note: p-values and subsequent tests do not account for stepwise

selection.

1. NARDL Bound test Wald Test:

Equation: Untitled Test Statistic Value df Probability F-statistic 6.602057 (6, 49) 0.0000

Chi-square 39.61234 6 0.0000

Null Hypothesis Summary: Normalized Restriction (= 0) Value Std. Err. C(2) -0.030697 0.021291

C(3) 0.199406 0.046093

C(4) 0.076598 0.038336

C(5) 0.000731 0.000480

C(6) -0.001615 0.000809

C(7) 0.009110 0.005411

Restrictions are linear in coefficients.



2. Test statistics for Wald test

Appendix 4.14: Diagnostic Checking for NARDL Test

1. Breush-Godfrey Serial Correlation LM Test

Breusch-Godfrey Serial Correlation LM Test: F-statistic 1.304827 Prob. F(2,47) 0.2809


Test Equation:

Dependent Variable: RESID


Date: 06/19/17 Time: 22:24

Sample: 2001Q4 2015Q4


Presample missing value lagged residuals set to zero. Variable Coefficient Std. Error t-Statistic Prob. C 0.005139 0.100860 0.050949 0.9596

LNHPI(-1) 0.001951 0.021195 0.092037 0.9271

LNEXG_P(-1) -0.013416 0.046556 -0.288174 0.7745

LNEXG_N(-1) -0.004394 0.038212 -0.115002 0.9089

GDP(-1) 3.34E-05 0.000480 0.069569 0.9448

CPI(-1) -0.000155 0.000814 -0.190482 0.8498

UNEMP(-1) -0.003870 0.005895 -0.656444 0.5147

DLNEXG_P(-2) 0.002407 0.136471 0.017635 0.9860

RESID(-1) -0.245375 0.158640 -1.546740 0.1286

RESID(-2) -0.132483 0.153744 -0.861710 0.3932 R-squared 0.052604 Mean dependent var -7.14E-18






Wald Test:

Equation: Untitled

Test Statistic Value df Probability

t-statistic 2.562644 49 0.0135

F-statistic 6.567147 (1, 49) 0.0135

Chi-square 6.567147 1 0.0104

Null Hypothesis: -C(3)/C(2)=-C(4)/C(2)

Null Hypothesis Summary:

Normalized Restriction (= 0) Value Std. Err.

-C(3)/C(2) + C(4)/C(2) 4.000603 1.561123

Delta method computed using analytic derivatives.




2. ARCH Test Heteroskedasticity Test: ARCH



Test Equation:

Dependent Variable: RESID^2


Date: 06/19/17 Time: 22:24


Included observations: 56 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C 8.68E-05 1.65E-05 5.265958 0.0000

RESID^2(-1) -0.116523 0.134684 -0.865159 0.3908 R-squared 0.013672 Mean dependent var 7.78E-05

Adjusted R-squared -0.004594 S.D. dependent var 9.54E-05

S.E. of regression 9.56E-05 Akaike info criterion -15.63763

Sum squared resid 4.94E-07 Schwarz criterion -15.56530




3. Normality Test (Jarque-Bera)



4. Ramsey RESET Test

Ramsey RESET Test

Equation: UNTITLED

Specification: D(LNHPI) C LNHPI(-1) LNEXG_P(-1) LNEXG_N(-1) GDP(-1)

CPI(-1) UNEMPT(-1) DLNEXG_P(-2)

Omitted Variables: Squares of fitted values

Value df Probability

t-statistic 1.262181 48 0.2130

F-statistic 1.593101 (1, 48) 0.2130

Likelihood ratio 1.861091 1 0.1725

F-test summary:

Sum of Sq. df Mean Squares

Test SSR 0.000140 1 0.000140

Restricted SSR 0.004358 49 8.89E-05

Unrestricted SSR 0.004218 48 8.79E-05

LR test summary:

Value df

Restricted LogL 189.2692 49

Unrestricted LogL 190.1997 48

Unrestricted Test Equation:



Date: 06/20/17 Time: 23:43

Sample: 2001Q4 2015Q4



C 0.042964 0.116065 0.370170 0.7129

LNHPI(-1) -0.011666 0.025985 -0.448928 0.6555

LNEXG_P(-1) 0.077871 0.106634 0.730263 0.4688

LNEXG_N(-1) 0.031279 0.052357 0.597412 0.5530

GDP(-1) 0.000278 0.000596 0.466828 0.6427

CPI(-1) -0.000614 0.001130 -0.543626 0.5892

UNEMPT(-1) 0.004545 0.006482 0.701274 0.4865

DLNEXG_P(-2) -0.151656 0.202131 -0.750287 0.4567

FITTED^2 24.20968 19.18083 1.262181 0.2130










Appendix 4.15: Vector Error Correction Estimates

Vector Error Correction Estimates

Date: 06/19/17 Time: 23:31



Standard errors in ( ) & t-statistics in [ ]

Cointegrating Eq: CointEq1

LNHPI(-1) 1.000000

CPI(-1) 0.051945

(0.02610)

[ 1.99055]

GDP(-1) 0.098937

(0.01468)

[ 6.73906]

LNEXG(-1) 2.879863

(0.78437)

[ 3.67158]

UNEMPT(-1) 1.019159

(0.15402)

[ 6.61720]

C -22.12920

Error Correction: D(LNHPI) D(CPI) D(GDP) D(LNEXG) D(UNEMPT)

CointEq1 0.020689 0.066896 -3.302958 -0.031571 -0.705193

(0.00794) (0.93600) (0.96507) (0.01727) (0.18435)

[ 2.60469] [ 0.07147] [-3.42252] [-1.82763] [-3.82538]

D(LNHPI(-1)) 0.155942 -10.82592 26.69337 0.115763 -0.391865

(0.13058) (15.3871) (15.8650) (0.28398) (3.03050)

[ 1.19424] [-0.70357] [ 1.68254] [ 0.40765] [-0.12931]

D(LNHPI(-2)) 0.224810 24.25486 -4.620160 -0.165035 5.851729

(0.13654) (16.0901) (16.5898) (0.29695) (3.16897)

[ 1.64643] [ 1.50744] [-0.27849] [-0.55576] [ 1.84657]

D(CPI(-1)) -0.001170 0.077857 0.527978 -0.000324 0.016742

(0.00137) (0.16184) (0.16687) (0.00299) (0.03188)

[-0.85169] [ 0.48106] [ 3.16402] [-0.10833] [ 0.52524]

D(CPI(-2)) -0.004724 -0.195082 -0.492604 0.006147 0.078234

(0.00140) (0.16510) (0.17023) (0.00305) (0.03252)

[-3.37172] [-1.18157] [-2.89371] [ 2.01724] [ 2.40590]

D(GDP(-1)) -1.56E-05 0.171219 0.516182 0.002720 -0.016379

(0.00082) (0.09627) (0.09926) (0.00178) (0.01896)

[-0.01907] [ 1.77851] [ 5.20025] [ 1.53116] [-0.86386]

D(GDP(-2)) -0.000857 0.136519 -0.031077 0.004529 0.047534

(0.00101) (0.11930) (0.12301) (0.00220) (0.02350)

[-0.84620] [ 1.14429] [-0.25264] [ 2.05677] [ 2.02298]

D(LNEXG(-1)) 0.034144 -6.006350 2.233357 -0.018882 0.400872

(0.06392) (7.53247) (7.76643) (0.13902) (1.48353)

[ 0.53415] [-0.79739] [ 0.28757] [-0.13583] [ 0.27022]

D(LNEXG(-2)) -0.134631 2.431498 12.64153 0.126035 -0.270950

(0.07978) (9.40072) (9.69271) (0.17350) (1.85149)

[-1.68760] [ 0.25865] [ 1.30423] [ 0.72644] [-0.14634]

D(UNEMPT(-1)) -0.008760 -0.274069 1.866105 0.032272 0.195239

(0.00710) (0.83723) (0.86323) (0.01545) (0.16489)

[-1.23290] [-0.32735] [ 2.16177] [ 2.08860] [ 1.18403]

D(UNEMPT(-2)) -0.003563 1.029747 0.886057 -0.000268 -0.067354

(0.00639) (0.75259) (0.77596) (0.01389) (0.14822)

[-0.55786] [ 1.36827] [ 1.14188] [-0.01930] [-0.45441]

C 0.008807 -0.185006 -0.208540 -0.002381 -0.078057

(0.00269) (0.31652) (0.32635) (0.00584) (0.06234)

[ 3.27878] [-0.58451] [-0.63902] [-0.40763] [-1.25215]

R-squared 0.378142 0.258535 0.637174 0.262010 0.466595

Adj. R-squared 0.226132 0.077288 0.548483 0.081613 0.336207

Sum sq. resids 0.004971 69.03068 73.38544 0.023513 2.677693

S.E. equation 0.010511 1.238554 1.277023 0.022858 0.243935

F-statistic 2.487616 1.426421 7.184222 1.452405 3.578515

Log likelihood 185.5135 -86.33724 -88.08071 141.2287 6.276228

Akaike AIC -6.088193 3.450429 3.511604 -4.534341 0.200834

Schwarz SC -5.658077 3.880546 3.941720 -4.104225 0.630950

Mean dependent 0.014234 0.022807 0.085965 -0.002870 0.000000

S.D. dependent 0.011948 1.289382 1.900473 0.023852 0.299404

Determinant resid covariance (dof adj.) 6.45E-09

Determinant resid covariance 1.98E-09

Log likelihood 166.7961

Akaike information criterion -3.571793

Schwarz criterion -1.241997



Appendix 4.16: Granger Causality Tests

VEC Granger Causality/Block Exogeneity Wald Tests

Date: 06/19/17 Time: 23:31

Sample: 2001Q1 2015Q4


Dependent variable: D(LNHPI)

Excluded Chi-sq df Prob.

D(CPI) 11.38072 2 0.0034

D(GDP) 0.820506 2 0.6635

D(LNEXG) 3.051661 2 0.2174

D(UNEMPT) 1.524152 2 0.4667

All 17.36690 8 0.0265

Dependent variable: D(CPI)


D(LNHPI) 2.410408 2 0.2996

D(GDP) 6.593250 2 0.0370

D(LNEXG) 0.684018 2 0.7103

D(UNEMPT) 3.223263 2 0.1996

All 11.87534 8 0.1569

Dependent variable: D(GDP)


D(LNHPI) 2.842092 2 0.2415

D(CPI) 23.57201 2 0.0000

D(LNEXG) 1.825253 2 0.4015

D(UNEMPT) 4.679058 2 0.0964

All 35.99294 8 0.0000

Dependent variable: D(LNEXG)


D(LNHPI) 0.392502 2 0.8218

D(CPI) 4.391175 2 0.1113

D(GDP) 9.812108 2 0.0074

D(UNEMPT) 5.851769 2 0.0536

All 15.68287 8 0.0472

Dependent variable: D(UNEMPT)


D(LNHPI) 3.498057 2 0.1739

D(CPI) 5.788369 2 0.0553

D(GDP) 4.129843 2 0.1268

D(LNEXG) 0.090744 2 0.9556

All 10.52364 8 0.2302

a65 the determinants of house prices from …

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