An analysis of housing credit program for urban hoausehold case study in HCMC Housing development bank(HDBANK)

=======oOo======= VIETNAM- NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS AN ANALYSIS OF HOUSING CREDIT PROGRAM FOR · URBAN HOUSEHOLD CASE STUDY IN HCMC HOUSING DEVELOPMENT BAN

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VIETNAM- NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS

AN ANALYSIS OF HOUSING CREDIT PROGRAM FOR ·

URBAN HOUSEHOLD CASE STUDY IN HCMC HOUSING DEVELOPMENT

BANK (HDBANK)

A THESIS PRESENTED BY DOHONGNGOC

BQ GIAO D~C E>AO Tl;\0 TRUONG Dl;\1 H9C KINH TE TP.HCM

1

IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE

DEGREE OF MASTER OF ARTS IN DEVELOPMENT ECONOMICS

SUPERVISOR

DR TRAN TIEN KHAI

Ho Chi Minh City, February 2009

All my classmates, especiaily my group have encouragement, cooperation and help during my stUdying and doing the thesis

All ofmy family members have help and encouragement for me to try my best to finish this research

ABSTRACT

VietNam is going on the way of modernization, industrialization and especially of globalization with the purpose of economic development and enhancing the life standard

of the resident This implies· to· increase further with a rise in the urbanization levels and

in the population along with increasing the demand for housing in urban of VietNam To solve the d~mand of urban household, the government has many policies to support the resident such as housing finance system from bank or other financial institution, housing program for low inconie : This study only concentrates to analysis housing credit program from bank for urban household on two major aspects The first is the determinants of probability to get a housing loan And the second is the determinants of housing loan amount SO the household should improve their character and capacity to be able to borrow housing loan from bank Conversely, the bank also should have-suitable credit policy and condition for the customer to speed up effective and sustainable credit development

TABLES OF CONTENTS

C ertl 1cat1on ···~··· .·fi 1

Acknowledgement : ii

Abstract iii

Contents :··· iv

List of tables and figures viii

··Abbreviation ix

CHAPTER I·: INTRODUCTION • · ~ !

1.1 Prob-em statement ~··· I 1.2 Objectives ofihe stlldy ~ l 1.2.1 General objective ~ 2

1.2.2 Specific objective 2

· 1.3 Research questions ~·~···•···2

·1.4 SuJi:J.macy on rese8.rch· methQdology- and data • 2

1.5 ·The organization of the thCsis ···~···3

CHAPTER II: LITERATURE REVIEW 4

2 1 TheOry ba~kground 4

2.1.1 Major concepts 4

Household Credit Borrowing Household credit market 2.1.2 Banking theory · 4

Credit management Credit analysis and loan decision Issues in credit market 2.2' Experiences of housing development in some Asian countries •• • •• ; 7

2.2.1 In Singapore ; , 8

2.2.2 In China , 9

2.2.3 In Thailand 10

2.2.4 In Korea 11

2.3 Theoretical models and empirical studies for house demand 12

2.3.1 General observations 12

2.3.2 Determinants ofhousingloan.- 13

2.3.3.Determinants of loan amount 15

2.3.4 Theoretical models 16

The basic models 16

:The extended models ; 16

2.3 .5 Empirical models applied in previous studies 17

CHAPTER III: RESEARCH METHODOLOGV 20

· 3·.1 AnalyticiJJ· framework ···~···•···20

3.1.1 Hypotheses , 20

3.1.2Detenninants ofthe probability 20

3, 1.3 Determinants of loan amount • 21

3.1·.4 Specific empirical models • 22

Model1 _ 22

Description of the model1 22

Definition and explanation of variables of the model 1 22

Expected signs of the variables' coefficients 23

Model2 24

Description of the model 2 , • ; 24

·Definition and explanation of variables of the model2 24

Expected signs of the variables' coefficients 25

3.2 Data source· aild sa~pling 26

3.2.1 Data source 26

Where is data source? : 26

Wh • d ? 26 at-Is Its va I Ity _

When are they collected? 26

Population : : ; 26

3.2.2 Sampling • 27

.sample size 27

Sampling method 27

3.3 Analysi-s methods ···~···29

3.3 !Statistical tests for descriptive analysis 29

3.3 2 Correlation analysis 29

3.3.3 Statistical tests for validity of specific models 30

3.4 Analysis· too1 _ ~ 30

CHAPTER IV: ~SU~TS AND DISCUSSION , 31

4.1 Situation ofhousingcreditprogram for urban household in Ho Chi Minh City 31

4.1.1 Housing demand of urban household : 31

4.1.2 National strategy on housing up to the year 2010 31

4.1.3 The overview of urban finaricial system, borrowing by urban households and .housing finance project 32

Financial system and source of credit to urban households in VietNam 32

Overview ofborrowing by urban households 32 ·

4.1.4 HCMC Housing development program 35

4.2 IiCMC Housing D-evelopment Bank ···~···-···35

·4~2.l.Overview ofHDBank 35

4.2.2 Housing credit program ofHDBank 38

4.3 HCMC ·aousing D.eveloprri.ent Bank ···~···40

.4J~1Analysis ofborrower' characteristics 40

4.3.2 Correlation.analysis 44

4.3.3 Statistical tests for validity of specific models 47

4.3 4 Description collecting data and choosing suitable method 48

·4.4 Results ·of ~odels ~esting and explanation 49

4.4.1 ·:Empirical result- Model I ; 49

4.4.1.1 Enter method ; 49

4.4.1.2 Backward LR method : ; ;· 50

4.4.1.3 Analysis the factor affect to the probability to get housing loan for

household : 50

4.4~2 Empiricalresult- Mod.el2 : 54

4.4.2.1 Enter method 54

4.4.2.2 Backward LR method 54

4.4.2.3 Analysis the factor affectto the probability to get housing loan for household , 54

CHAPTER V: CONCLUSION AND IMPLICATION 58

5.1 Conclusion on the applied methodology and limitation • • •.••.•.• • •• 58

5.2Conclusi0n On the studie.d results 58

5.3 Po.licy impli~ation in macro levCI • 59

5.4 Policy implication for HDBank as well as housing credit program • • •• •• 59 References

Annex

LIST OF TABLES

Table 1.1: Compare mean of variable

Table 1.2: Correlation Matrix

Table L3: Durbin:_ Watson in model!

Tahie 1.4: Durbin- Watson in model 2

Table 1.5: Regression result of model 1 from step 1 to step 6 by back ward method Table 1.6: Regression result ofmodel2 froni stepl to step 6 by back ward method

LIST OF FIGURES

Figure 2.1: Structure ofprobability to get housing loan

Figure 2.2: Structure of samples possibility to get a housing loan

Figure 2.3: Growth oftotal assets and chartered capital in HDBank

Figure 2.4: Growth ofloan outstanding debts in HDBank

Figure 2.5: Outstanding ofhousing credit program and total loan outstanding in HDBank

Figure 2.6: Regression standardized residual

LIST OF ANNEXS:

Annex 1: T-Test result

Annex 2: The result of model 1- Enter method

Annex3: The resultofmodell- Backward method

Annex 4:Theresult ofniodel2- Enter method

Annex 5: The result ofmodel2- Backward method

·ABBREVIATION

HDBank: Housing Development Bank ADB: Asian Development Bank

SBV: State Bank of VietNam

SOB: State Owned Bank

· DLH: Department of Land and House

CHAPTER 1: INTRODUCTION

1.1 Problem statement

The world is flat on all fields by globalization, information technology development especially in the economic field Viet Nam also follows this tendency by joining econoinic organizations in area as well as in the world such as AFTA, WTO

Economic development and reforms in the sector has brought increased urbanization Urban population is expected to increase further with a rise in the urbanization levels and in the population In a research of JBIC (1999) stated that VietNam has been experiencing rapid urbanization owing to economic development after the Doi Moi policy, and urban population is predicted to increase to 46 million in 2025 from 15 millionin 1995,

· Urban development and housing sector, especially in Hanoi and HCMC, is urgently challenged by swelling migration from rural· areas, expanding informal settlement, emerging relocation needs by inner-city redevelopment, accelerating reform of SOEs that provided their employees with houses This implies demand for housing would increase at a faster pace in urban VietNam in the short term to medium _term

To solve urban development and housing problem for the resident, Viet Nam is learning valuable lessons from other country's different models of housing development Vietnamese authorities clearly organized and exposed a particular plan such as urban development policy, real estate institution, housing development and housing finance With· housing finance, the authority specifies two aspects are municipal housing fund and housing finance facility In municipal housing fund, respective "Housing fundl' was establish in the early 1990's in HCMC and in 1998 in Hanoi; These funds mobilized capitals primarily from the sales revenue of the former state-owned houses, and are used for housing finance Another facility named HIFU was set up in HCMC in 1997 HIFU mobilizes capitals from bank loans, equity and trust fund entrusted by the People's Committee, and it invests in urban development including housing projects of the public developers Besides that, housing loan activities are chiefly supplied by state-owned commercial bank, private joint-stock bank, municipal fund

This study just concentrates to analyze housing finance especially housing credit program for household In real, a large of the urban population lacks access to affordable finance for housing construction or improvement, so they have to resort to informal sources such as moneylenders, friends, relatives and credit associations So investigating the determinants of access to credit and the determinants of loan amount

help household and· bank have change to quickly find the common result It means that the household satisfy bank's·loan condition to get the housing loan and the bank has housing creditto the customer safely and effectively

Another further issue concerns this study is the housing finance project will set up a facility to provide mortgages and loans for urban low-income and poor people to improve their property or build or buy new homes It will also promote institutional strengthening and capacity building to develop VietNam's housing finance system

1.2 Research objectives

1.2.1 General objective

Analysis of housing credit program for urban households by analysis the probability of credit for household and the deteirninants affeCt to loaiiamomil 1.2.2 Specific objective

, The demand of house and housing system in urban VietNam

- The demand of housing loan of urban household

- The_ ~eterminants of housing credit program for urban household including· probability of credit and loan value

- The research analyzes the relationship between urban households and credit market to find -out relation of characteristics and endowment of urban households and characteristics of loan to chance to borrow, loan amount

"' Enrich our knowledge on credit market and borrowing by household in Viet Nam, particularly in housing credit program

- The effective of the housing finance project to the low-income and poor

·· people artd contribute the economic growth in VietNam

- In addition, housing finance project do strengthen and capacity building to develop VietNam's housing finance system

_ How is the housing demand of urban household?

What are the determinants of housing credit program for urban household?

How does the credit especially housing credit program contribute the economic development and economic growth?

1.4 Summary on research methodology and data

The method is used, including statistical and descriptive analysis, review of historical

·trends, and· comparative methods Apart form, quantitative method is extensively used;

Data is used in the analysis from the core banking of HCMC Housing Development

Banlc

1.5 Strcuture of the thesis

The thesis consists of five chapters, following chapter 1, the rest of the structure as follow:

Chapter 2: Literature review

Chapter 3: Research.methodology

Chapter 4: Result arid discussion

Chapter 5: ·Conclusion and Implication

CHAPTER II: LITERATURE REVIEW

2.1 Theory background

· 2.1.1 Major concepts

Household is area of concern in social science Concept of household is defined and discussed by economists, feminists and anthropologists Ringen (1991) defines of household that as is a group of at least two people pools their incomes and uses income collectively In house holds, income is not split up.between household members for each

to use his or her · share, at least not all of it In large measure, household members cooperate on the use of income and thereby get more out of their income than they could

if each had been on his or her own However, there is much controversy over the boundary of this definition Generally, economists view household as an essential unit of analysis and develop theory based on modeling behavior of household There are two themes of economi~ model of household behavior The first treats consumption and produce separately or simultaneously in an integrated model The second emphasizes on market condition, and relationship between household and market for land, labor, and credit

Credit is the trade of money, goods or services at the present time for a payment in the future Credit can be provided in many different forms and under a wide variety of arrangements (Kinnon Scott, 2000)

Borrowing is one side of credit In general, borrowing is defined as that to be transferred property right on a given object (e.g sum of money) in exchange for implementing obligation of a claim on specified object (e.g a certain sum of money) at specified point

of time in the future In extension of this definition, borrowing by households, particularly Urban household is defined as activities of households to obtain external resources to support other household activities with obligation to repay in future In other way, borrowing is made by household to finance household's budget deficit

Housing credit market the presence of heterogeneity among different household induces

to situation whereby households fall into budget deficit while some others are in budget

surplus Therefore, the deficit households desire to borrow in order to cover their deficit while surplus households also desire to lend out for their interest Consequently, household credit market is established to facilitate borrowing and lending of households Credit market function is to transfer purchasing power from surplus households to deficit

In credit scoring method, the creditworthiness can be measured by the values basing on the characteristics However, there are some core factors that are usually used in credit management (Kapoor, Dlabay, Hughes, 2001) They are:

Characte~: the borrower's attitude toward his or her credit obligations

Capacity: the borrower's financial ability to meet credit obligations

Capital: the borrower's assets or net wealth

Collateral: a valuable asset that is pledged to ensure loan payments

Conditions: the general economic conditions that can affect a borrower's ability to repay a loan · ·

Access to credit in this research can be understood as two components:

The probability to get credit from bank for household

In case of the household can get the loan, the size of the loan is also one compop.enf in access to credit

Credit analysis and Loan decision

Purpose of credit analysis:

Credit analysis is a process of collecting information, analyzing information with sciences method for the aim to understand clear about clients and their business projects, serving process of short-term loan decision Credit analysis is an important step with the purpose using to make loan decision: accept or reject issuing credit In order to make credit decision banks must do three steps following:

Collecting sufficient & accurate information

Analyzing and processing data collected

Predicting ability of repaying seed and interest of household

By credit analysis, banks can replace their experiences about household and their project applied for loan with scientifically argument and evidences rely on information and· data processing Therefore,· credit analysis help banks avoiding two types of mistake: (1) give credi(for bad client, (2) and rejecting the good one

Information using for credit analysis

Collecting good information is initial necessary when carry out credit analysis;

· quaiification of data analyzed have significant effected to result, by this, it is effected to loan decision, the quality of data is noticed with three attributions: (1) sufficiency, (2) timely, (3) accurately

Analysis contents: analysis should be targeted evaluation of capacity repaying loan that mean, it evaluate whether customer can repay seed money and interest or not To

·appreciate repayment capacity of company, we have to determined factors effecting to customer's repaying-debt capacity In other hand, credit analysis contents should be concentrated into analyze factors effecting to ability to meet obligation Basically, ability

of repayingloan is influenced by:

Finance situation

Practicability and effectiveness of projects

Customer's attitude to refund debt

Issues in credit market

- Chung (2000) Credit rationing is a condition of loan market in which the lender supply of fund is less than borrower demand at the quoted contract term In other words, it means that there is excess demand In credit market, credit rationing appears to be an inefficient situation of credit market, where interest rate does not work well to balance supply and demand sides

There are two major ways to explore causes of credit rationing Firstly, traditional view:s consider credit rationing resulted from government interventions on credit market by imposing interest rate ceiling on lending institutions Interest rate is exogenously held under market clearing leveL With interest rate keep artificially low, some poteritialborrowers who want to borrow are rationed Secondly, after the demise of the traditional theories, a new approach to credit rationing was developed The new approach argued that permanent credit rationing is considered as an equilibrium phenomenon rather than a temporary phenomenon Modem theories identify problems of moral hazard and adverse selection in credit market as a source of credit rationing when information is distributed asynimetrically among market participants

Fragmentation of credit market strongly affects the borrowing· behaviors of urban households Fragmentation of credit market, firstly, is thought as a consequence

of the repressive government policy Ceiling rate on deposit and loan rate imposed by central bank induces to severe credit rationing in formal financial sector Resulting from inter-linkage between formal and informal financial markets the.unsatisfied demand of households for formal loan flows into informal financial sector and put demand for informal loans to rise up Secondly, it is viewed that fragmentation of household credit market is caused by structural and institutional features of credit market in developing countries In addition, fragmentation of credit market may also result from weakness in the infrastructure

that supports the financial system Thirdly, another perspective on fragmentation

of credit market argues that formal and informal financial sectors are parallel developed because they serve different segments of credit market

In conclusion, under perfect credit market, borrowing, along with saving, allows urban households to maximize their utility overtime Resources or endowments, incomes, and activities of households determine the demand for loan The effects

of those factors on the demand of urban households for credit are called "demand side effectsi' On the other hand, credit market conditions and relationship between urban households and financial intermediaries affect borrowing by urban households If credit market is inefficient; some of potential household borrowers fail to access to credit market Therefore, severity of credit rationing is an explanation for credit constraint of urban households; and high fragmentation of credit market is an explanation for various borrowing behaviors of urban households Borrowing by urban households is substantially determined by

condition of credit market so called "supply side effects" Thus, borrowing by urban households is jointly affected by both demand and supply side factors 2.2 Experiences of housing development in some Asian countries:

As with Latin America and Africa, the number of urban residents is fast expanding in Asia Asia is also home to the largest concentration of poor people in the world (Chapman et al, 1999; Montgomery et al, 2001) About a quarter of the total urban population in Asia is living below the poverty line although the proportion may be higher

in some countries India and China· each holds about a third of the region's urban population with many living in relative poverty (Jacquemin, 1999) Of the 12 million

· T~e lack of housing access is one of the most serious and widespread consequences and causes of poverty ili Asian cities The· improvements in housing that are important to improving the quality of life ·among the poor often does not receive the attention they

· deserve from policy makers (Daniere, 1996) To make any appreciable improvement, substantial government spending is needed, both in the physical expansion of the city's infrastructure and implementation of poverty alleviation programs Buttressed by the heritage of literature that argues the importance of affordable and improved housing in

· urban poverty reduction (see, for example, Mitlin, 2001), the immediate research issue is how poor families can access urban shelter more affordably · Experience for VietNam from other countries in Asian as follow:

2.2.1 In Singapore

The Singapore public housing development has attracted keen research interest (see, for example, Wong and Yeh, 1985; Yuen et al, 1999) but few have clarified the public housing-urban poverty nexus This provides the starting point for the present analysis of the performance of housing development

· Follow by Global urban development magazine in Singapore (GUDS, 11/2007), in a fundamental perspective, without parallel economic development, the housing improvements would not have advanced so dramatically Deliberate action was taken to diversify the economy and provide employment in Singapore With economic growth, the nominal household income had increased Real GDP had grown at an average of 8.6% per year over the 30-year period from 1965 to 1999 This had fuelled growth in real per capita GDP from S$4000 in 1965 to S$32,000 in 1999 (while inflation remained low, around 2 to 3% per year) At the household level, the average monthly household income increased As Ng and Yap (2001} illustrated, from 1988 to 1998, average monthly household income had increased by 6 7% per year, leading to higher asset ownership The proportion of homeownership public flats expanded from 26 per cent in 1970 to 92 per cent ofthe housirig stock by 1999

Although the financing of public housing draws from the general background of the country's economic progress,· Singapore's experience also demonstrates the employment-generation potential of this sector By 2000, the HDB (Housing Development Board of

· Singapore was established in the beginning of year 1960) in providing a total housing environment forallwho lack has initiated the construction of more than 850,000 dwelling units, 19,500 commercial premises, 12,800 industrial premises, more than 1460 schools and community facilities, 45 parks, 17;347 markets/hawker centers, and numerous car parks The construction of these facilities while providing improved housing and better quality of life for the poor has created construction jobs and has· a high multiplier effect Reflecting on the economic impact, some housing scholars such as Sandilands (1992) have described the construction sector as a leading sector since its growth rates are above the rate of growth of overall GDP Others have written about the pump priming effect of public sector housing construction (see Krause et al, 1987)

As with many other cities, Singapore's quest to provide its poor residents with good living environment is not new Adequate shelter with the promise of a decent life of dignity, good health, safety, happiness and hope is one theme that has been repeated internationally and enshrined in successive United Nations declarations (see, for example,

UNCHS, 1998; 1999; World Bank, 1993) The Singapore development expenence, however; shows thatpublic housing (even: high-rise) for the lower income families need not degenerate irito polarized and marginal environments Nonetheless, the reemergence

of the homeless underscores the urgency for further research In particular, the trend towards taller housing presents challenges The poor of Singapore do not have the alternative to opt out of this housing In this regard, we are reminded of Mitlin's (2001, p512) exhortation to understand and follow the realities of the poor in the continuing effort to create affordable housing that seek to address their diverse needs

In sumrilary, Singapore's system of housing development with a single empowered authority responsible for housing delivery may not be the model for all countries, but effective pragmatic management principles (such as inclusive housing and widening homeownership opportunity for lower-income families, directed assistance for low-income· renter households and continual review of housing access) apply in most contexts There is a growing literature that emphasizes a comprehensive approach to housing Similar to situation of China and Thailand or other Asian countries, VietNam can get the most suitable lessons for housing market and policy in the way of economic development

·these conditions housing costs accounted for only 1 percent of the average worker's earnings

Inl994 the Housing Reform Steering Group of the State Council unveiled several

reforms designed to encourage the development of housing markets Liu, Park, and Zheng's (2002) analysis of the relationship between housing investment and economic growth found significant positive relationships over both the short and long runs: another important policy motivation for spurring development of housing markets

Housing Provident Fund

The Housing Provident Fund (HPF) was designed in 1992 to help wean employees from workplace housing provision As such, it was paired with reform of the salary system Instead of providing housing directly and paying employees a correspondingly lower salary, the progranfs goal was to enlist public sector employees in the development of the coriunercialliousing market by raising their incomes but siphoning the increase into savings accounts dedicated to housing~ while reducing their in-kind housing benefit, thereby encouraging them to find housing in the marketplace (Wang 2001) The reform was part of a more general effort to have individuals and markets replace government and work units as the entities responsible for housing finance (Lee 2000) Because employer participation is not mandatory in the private sector, the primary HPF beneficiaries are government, party, SOE, and other public sector workers, although some private firms and foreign joint ventures also match employee contributions

In a recent overview of China's housing policy, Sun (2004) criticizes the targeting of the HPF system This is reinforced by the fact that even most working lower-income households are not in the kind of official, full-time, and typically public sector positions likely to carry an HPF benefit ·

Affordable Housing

The other principal homeownership-oriented public policy is the development of 'affordable housing' (Jingji Shiyong Fang or 'economic and comfortable housing') The policy is designed for lower-middle- and middle-income urban residents and involves

· government subsidies and profit caps for developers

2.2.3 In Thailand

In May 2005, Veerachai Veerametheekul, Vice Minister of Finance, Khan Prachuabmob, President of· Government Housing Bank, Pomsak Boonyodom, National Housing Authority Governor; Kitti Pattanapongpiboon, President of Housing Loan Association and Ballobh K.rittayanawat of the Government Housing Bank, represented Thailand at the

"More Than Shelter: Housing as an Instrument of Economic and Social Development",

an international conference organized by Harvard University's Joint Center for Housing Studies (JCHS) in Bellagio, Italy Important leaders from four other countries, including

· the USA, Mexico, South Africa and :kenya also attended the conference After the conference, the participants from five countries endorsed the "Bellagio Housing

Declaration" affirming that sound, sanitary and affordable housing for everyone is central

to the well-being of nations It also reaffirmed that housing is more than shelter; it is a powerful engine that creates opportunity and economic growth Several other principles were also promulgated:

(a) Housing as a sustained national priority: Housing is a long.; term process that requires

a stable policy framework and national priority attention

(b) Housing as an engine of social and economic development: Housing · brings significant benefits in terms of employment creation, domestic capital mobilization and social wellbeing in the face of the major challenges posed by.population growth and urbanization (See seminar details and Bellagio Housing Declaration in GHB Newsletter, 41st edition, 2005) The conclusions from this most important conference indicated that many countries recognized that housing is more than shelter It is one of life's essentials and a process or engine of economic and social development The Government should give a high :priorityfor establishing a long-term housing policy and strategy

2.2.4 In Korea

In 1962, Korea Nation Housing Corporation (KNHC) was established with the mission of improving the public living standards and welfare through · housing construction and urban redevelopment

KNHC is a 100 percent state-owned· company Due to its strong links with the government, it has been provided direct funding assistance by the government in the form of equity contributions and loans from the National Housing Fund

· KNHC's organization consists of Main Office with 20 Divisions under 4 Head Divisions and 1 Research Institute, 2 Regional Head Divisions in Seoul and Gyeonggi,and 10 Branch Offices in other major cities

A total of 3,076 ·employees is working at KNHC, including 7 executives, 844 administrative officials (27%), 1,721 engineering officials (56%) and 504 researchers and clerks (17%)

.As of the end of 2001, assets amol:mt to 14,374 billion won, liabilities to 9,301 billion won, capital to 5,073 billion won Its sales are up to 3,274 billion won

However, the liabilities, the accrued interest of which should be paid by KNHC, are only 2,254 billion won oftotal9,301 billion won

Temmts andhousing buyers will pay the interest expenses of the rest (7,047 billion won) which chiefly comes.from National Housing Funds

The National Corporation Realizing Urban Development and Housing Welfare Korea National Housing Corporation (KNHC) has focused on the massive construction of

housing unit for national housing stabilization uritil now, but will begin to lay strong emphasis on the improvement of quality to comply with the change of life from now KNHC will be reborn as a future-oriented corporation with higher additive value to realize urban development and housing welfare

· Moody's said ·such a track record of government assistance and the possibility that government will continue to provide financial help ensures KNHC's financial viability and operational soundness (website of GOLIATH -Business knowledge on demand) Any future changes in rating would occur if the country's sovereign rating is changed, it said.· Downward pressure tnay occur if the government's fiscal condition weakens, or if the corporation is privatized

Rival ratings agency· Standard & Poors (S&P) also gave the corporation a currency credit rating of "A minus" and a domestic-currency rating of "A." S&P's outlook for the corporation's credit rating is "stable."

foreign-The credit rating reflects the corporation's firm status in the South Korean housing market and its housing projects for the country's low-income people

· 2.3 Theoretical models and empirical literature for housing demand

2.3.1 General observations

Housing loan for household· seem to be available in many countries; including at least Finland, UK, Japan, Spain, Portugal, Italy, France, Norway, Sweden, US, Australia, Germany, Belgium, thailand, Philippines; This applies especially well to contributions that consider indebtedness as a problem It seems to be particularly difficult to find simulation based analyses on household debt Instead, descriptive analyses seem to be more commonplace There are a few theoretical contributions on household debt The permanent income hypothesis, presented by Milton Friedman in 1957, states that people base consumption on what they consider their 'normal' income even though their incomes may vary considerably in the short term Thi~ may create occasional needs to incur loan The life cycle approach is somewhat similar to the permanent income hypothesis, but it emphasizes the impact ofchanges in wealth on consumption Neither of

· these approaches focuses on problems related to indebtedness of household This part focuses on determinant of housing loan and loan amount caused by household indebtedness.·

Before going to the literature of housing loan and loan amount, reviewing some literature

of housing demand and housing credit in some countries help know deeply below literatures

Firstly, there are some theories mentioned the housing demand of household Housing demand reflects the household's simultaneous choice of neighborhood, whether to own or rent the dwelling, and the quantity of housing services demanded Existing literature with using a sample ofTampa, Florida household was showed by Carol Rapaport as follows:

where P is the price of a unit of housing services in community/tenure choice j, t is the

· statutory Hj j property tax rate, P i~ the price of the composite good that is the numeric, Y

is income, X is a vector of household characteristics that are independent of the choice of ccirn.inunity/tenure, Z is a j vector of local public goods and other community

·characteristics, and contains unobservable ij characteristics that vary across households and communities

Household income and the price of the composite commodity are exogenous The price

of housing reflects the supply price of housing and any capitalization of the community mix of public services and tax levels Pis constant across all in any given j, but differs across Hj jurisdictionsand across tenure status

Clus and Ralph (2007) also had research of housing demand Beyond money, monetary policy and financial developments housing markets are certainly also influenced by other factors, such as taxes, demographics and other developments determining the demand for

· housing This research also stated the relations between housing loan and housing demand Marco Salvi (2007) also presented the model for the demand of housing in the Greater Zurich area Besides that, there are just few empirical of housing demand such as Bajari and Kahn (2002); Ferreira (2004); Bayer, Me Millan, Ruben (2002) All these papers expose analysis concerned to housing demand These literatures of housing demand prove that housing demand is always burning issue in many countries in the world

2.32 Determinants of housing loan

There seem to be· hardly ·any theories on determinants of housing loan for household Instead, there are numerous empirical analyses on households' debt servicing difficulties May and· Tudela (2005) used British data to study which factors determined the likelihood of having debt servicing difficulties · Unemployment, high levels of indebtedness and a high proportion of non-collateral debt increased the likelihood of problems Difficulties seemed to be of permanent nature; paSt problems were a good predictor offuture problems There was almost no evidence of housing wealth preventing difficulties This might be due to the possibility that many interviewees considered themselves to be in difficulties if the time schedule of repayments had been renegotiated with the bank A typical household is certainly unwilling to sell the dwelling in order to

· service loans Hence, the availability of collateral is of limited use in preventing reported difficulties, even though collateral certainly reduces banks' credit risks

May and Tudela introduce a new conCeP,t, namely 'debt-at-risk', an indicator of credit risk It is simply the sum of household level housing debt multiplied by the household · level likelihood of financial distress If it is possible to identify the determinants of the likelihood of distress, and if micro level data are available, it is possible to calculate the

· aggregate amoimt of 'debt at risk' and how it would react to changes in the parameters, such· as income, age, house value,· house size, etc The sections after of this paper apply this concept

Lh.t and Lee (1997) used various methods to study the determinants of housing loan defaults in Taiwan Several factors, including the loan-to-value ratio and the level of education, seemed to contribute to the occurrence of problems Problems were

· ·· particularly commonplace among the youngest

.Bowie-Cairns and Pryce (2005) found with British data that household debt servicing ability was an increasing function in the level of education, age and married status Families with many children were more likely to have problems Geographic factors seem~d to play some role Partially, Bowie-Cairns and Pryce mention some major characters effect to the loan payment as follow:

Number of children in a household and payment difficulties

An increase in the number of children in a household is associated with higher outgoings and less available finances to repay debt It is worth exploring their experience of maintaining payments However, within each year comparison between households with different numbers of children confirms that, in most cases, as the number of children increases the rate of repayment difficulty and arrears increases

Annual household income and mortgage payment difficulties

One would expect that household income would have a strong relationship with the probability of household experiencing mortgage arrears

Age and mortgage repayment difficulties

In general, one would expect that as an individual gets older, their status, income and financial stability will also increase

In these researches, major variables strongly effect to the probability of housing loan which are income, age, sex, house value, house size, principal, residential status, occupation, interest, marital status, credit type, etc In real condition of Viet Nam and ability to get the data, the author will choose some variables in the model as sex, age, education, income, size, house value, maturity, collaterals and loan amount for the probability to get a housing loan Banks in Viet Nam usually have housing credit with common conditions as principal, income, interest, maturity But in situation of economic developing and pressure of joining the world economic, commercial bank in Viet Nam should has more credit program especially housing credit as mentioning with flexible and diversified condition for many objectives This research builds a model with many variables to get meaning variables and main variables have deeply effect to the probability to get a housing loan From the result of this research, commercial bank will has more housing credit program based on characteristic of the b()rrower_in order to undertake effectively and successfully

2.3 3 Determinants of housing loan amount

There seem to be some empirical regularity that has been corroborated by observations ina:de in many different countries

Del-Rio and Young (2005a) concluded that non-collateral loans in Britain were typically taken by people who were in their twenties, had no children, were relatively well educated, were employed and had optimistic expectations

BroW11, Garino, Taylor and Price (2003) found that optimism seemed to strengthen the demand for rion-collateralloans,.even though after a certain level the degree of optimism had no impact Factors such as spouse income and savings seemed to have no impact Riiser and Vatne {2006) presented observations on Norwegian data from 1986-2003 Household debt had been on increase, especially among the youngest households artd those with low income The growth of financial wealth had taken place above all in households with no debt

·Magri (2002) studied the occurrence of debt among Italian households Higher income strengthen both the ·demand for loans and the availability of credit, whereas being an entrepreneur strengthened the demand for credit but made it more difficult to obtain loans

Brown and Taylor (2005) studied the determination of household financial wealth and debt in the UK, Germany and the US There was a clear correlation between financial wealth and debt If households with no debt were excluded, the correlation vanished Tudela and Young (2005) analyze the detetminants of household balance-sheet in the

UK, applying the hypothesis that· households optimize their income and consumption

over the life cycle The model of these authors may also be used to assess the impact of changes in the income process The first simulation they consider is an unanticipated decline in the non-property income of pensioners This could be thought of as arising from a cut in the state pension or a worsening in payouts from company pensions It is modeled as 20% point decline in the age premium in old age that is assumed to be permanent and occurs unexpectedly in the 2026-30 period (it reduces the non-property income of those over 60 by 20%) When the shock occurs, all cohorts reduce their consumption of goods and ·housing, but those who will have just retired reduce their consumption the most as they experience the largest proportionate decline in their lifetime income The decline of around 12% (for those aged 61-65) in their immediate spending is sma:ller than the decline in their non-property income because their spending

is also dependent on wealth which is not affected by the change in income This result is the similar to this research and in Viet Nam When the old of householder increases or the householder is on retire, the probability of housing credit decreases

Crook (200i) analyzed the 1995 US Survey of Consumer Finances data; the demand for household loans seemed to be an increasing function in income and family size

Davydoff and Naacke (2005) presented a purely descriptive report on the distribution of housing and consumer loans in France, Britain, Germany and Italy Housing loans were particularly commonplace in the UK but remarkably exceptional in Italy, even though owner-occupied housing is particularly commonplace in Italy In all the countries the determinants of housing loan amount were rather similar

Lending or not and how much for lending are one of the most important aspects of both the lender and the borrower There are also researches concerned loan amount Similar to the model of housing loan, the model of housing loan amount is built with variables as sex, age, education, income, size, house value, maturity In real, this model has detail analysis of housing credit for the household

Generally, the basic model indicates the factors affecting household demand for loan are current consumption level,iricome and asset It implies that the demand of household for

loan is function of household ·asset, income and consumption Their relationships are showed in the following equation: A= AI+ ao YI + rYz

Where AI is initial amount of assets; y I is the current income; y 2 is future income; and r

is market rate of interest The equation explicitly indicates that the demand of households for loan rises if preference for current consumption being high or expected income increasing or amount of initial assets and current income as well as interest rates dropping

Thus, the basic model shows that borrowing, along with saving, allows the household to smooth its consumption path and to maximize its utility overtime

The extended model

In extension of the basic model, such factors as household resources, activities and flows

of resources are included to identify determinants of the demand of households· for loan; and some assumptions are changed The model shows that the·· demand of urban households for lmin increaSes with its liquidity requirements for activities Chung (2000) write that ·level of household budget deficit determines the level of borrowing: B = dR -aYj +a Fg; where B is the level of borrowing; dR is the change in assets; Yj is income j

th ofthe household; and Fg is the outflow of expenditure to finance activity gth

Increases in such household ·activities as consumption, production and investment create increasing outflows of household resources If inflow of resources from income generating activities and assets would not meet outflows, demand for loan may rise The above equation shows that the demand of the household for credit appears when flows of income and changes in household's resources do not meet demand for funding the household activities

Hence,· the demand for loan appears and rises when household deficit gap appears and broadens The demand of the ·household for loan is determined by household resources

· andactivities

2.3.5 Empirical models applied in previous studies

Model applied by Egert and Mihaljek (2007)

Egert and Mlhaljek (2007) have both shown that house price dynamics are usually modeled in terms of changes in housing demand and supply (see ex HM Treasury, 2003)

On the demand side, key factors are typically taken to be expected change in house prices (PH), household income (Y), the real rate on housing loans (r), financial wealth (WE),

· demographic and labor market factors (D), the expected rate of return on housing (e) and

a vector of other demand shifters (X) The latter may include 3 proxies for the location, age and state of housing, or institutional factors that facilitate or hinder households'

access to the housing market, such as financial innovation on the mortgage and housing loan markets:

Model applied by Ozlem Ozdemir (2004)

Ozlem · OZdemir (2004) constructed a conceptual model to explain the relationship

· between consumer credit clients' payment performance and credit category, interest rate, sex, age, marital status, income, loan size, maturity, residential status and occupation The equation of the model is as follows:

.· Payment performance = po + P 1 Credit Type + p2 Interest + P3 Sex + P4 Age + ps Marital Status +P6 Income +P7 Principal + ps Number of Payments (1 )+ p9 Residential Status + p 1 0 Occupation + E 2

Model applied by Chodechai Suwanaporn (1996)

Descriptive and regression analysis are used to analyze the determinants of intensity of lending in Thai banks betWeen 1992 and 1996 Since their dependent variable IS

continuous the author uses the OLS method to estimate the following equation

A ;, a + P collateral + yj credit risk or borrower variables + <pt relationship factors +~r

· other+ E

Where

Collateral is the collateral· value as percentage of loan volume,

Credit risk or borrower characteristic~ variables include a vector of j variables that show

·Other represents a vector of r variables that captures the years of lending, bank type of the lending banks, sector classification, etc

E represents the· error term with all the classical assumptions, and a, ~' y, <p and ~ are constants to be estimated

Model applied by L.Ellis, !.Lawson and L.Roberts-Thomson (2003)

From the result of an econometric model, three key variables effect to the housing leverage for household as age of the household head, household income and household housing wealth

Model applied by l Balkit Fatma Hassan Ismail and S Al,.Segini (2006)

ro measure the possible relation between variables and the amount of loan or grant allocated to the UAE nationals, three regression models are used The general

specification of the models is as follows:

Ml, , M4 =dummy variables indicating marital status, and

L, , 17 = dummy variables indicating location

· In suinmary, there are many studies to analyze housing credit for household There are also many characters effect to the probability of getting a loan and loan amount In

limited condition, the author just constructs the models with variables which can be gotten the data and information The detail of the model for this research will be shown in the next chapter III

CHAPTER III: RESEARCH METHODOLOGY

This chapter will present step by step the way of implementation analyzing and processing the data collected The study will employ descriptive statistics and econometric model to examine the relationship between the dependent variable and independent variables The analysis aims at finding out the determinants of probability to get a housing loan and determinants of loan amount

3.1 Analytical framework

3 1.1 Hypotheses

The above analytical ground proposes ·that characteristics, endowments of households and loan characteristics would determine the probability of getting a housing loan of a borrower Therefore, the following hypotheses are raised

Hypothesis 1: The probability to get a housing loan, B would depend on characteristics of and endowments of the urban household

(B)=f(vector ofhousehold characteristics, vector of household endowments)

Hypothesis 2: for housing borrowers, loan amount or level of borrowing, LA would depend on characteristics and endowments of household, and characteristics of loan

LA = f (vector of household endowments, vector of household characteristics, vector of loan characteristics) Subject to B=l

3.1.2 Determinants ofthe probability to get a housing loan

This model shows the determinants of the probability to get· housing loan The tested determinants of the probability to get a housing loan are sex, age, education level, income

of borrower ·and How do the variables affect to the probability to borrow of urban households and which variable is strongly affects the probability to borrow of urban households The variable definition of this model as follow:

• Dependent variable

The probability to get· a housing loan for household: this variable is measured basing on critedons of credit analysis such as credit policy, characters of the borrower, payment planning

• Independent variable

Sex: is one of the household' character This demographic variable is also input the model to check the influence of borrower gender to probability to get a housing loan and

·roan amount

Age of householder: this factor is one of the key factors for analysis of housing credit from bank Age shows that the householder is on working labor age or not In addition, in working labor age, age also shows householder's job experience and job position This affects strongly their income

Education of household leader: level of education affects robustly their occupation and income The stabilitY of occupation and high income ensure the capacity to pay back the housing loan

Income of household: the level and the stability of income are important factors in evaluating the capacity of repayment from household

Size of household: this factor affects strongly savings and expenditures of household Because an increase in the number of children in a household is associated with higher outgoings and Jess available finances to repay debt, it is worth exploring their experience

Maturity: intuitively, probability of credit for housing increases when maturity increases because the longer term, the more probability of periodic repayment from household It means that the longer term; the smaller periodic principal and interest of household Collateral: Asset pledge in case of default If loan was guaranteed by mortgage or pledge asset, it wilL cement responsibility and obligation of borrower In case of insolvent, collateral turn to be second receivable of banks, however, mortgage or security asset has accommodate to realistic conditions

Loan amount or loan size: effect to probability of credit delinquency increases when the loan size increases So this.factorwill define a positive relationship between loan size and household' payback performance, assuming there is no inflation The lenders generally prefergiving this kind of credit for lower loan size in order to decrease their risk

3 1.3 ·Determinants of loan amount

The second model attempts to prove that characteristics and endowments of household, and loan characteristics determine loan amounts borrowed The tested determinants of loan amount are· age of households, household size, house of value, household expenditure, occupation of household members and numbers of dependants Which

factor are significant determinants of loan amount How do these factors effect to loan amount The variable definition of this model as follow:

3.1.4 Specific empirical models

Firstly, two dependent variables (B variable-possibility to get a housing loan in model 1

and LA variable.: loan amount in model 2) are chosen to answer the study questions Secondly, according to the above studies in Literature Review chapter, it can be seen that many factors are able to influence the probability to get a housing loan and determinant

· of loan amount This research just concentrates some important factors effect strongly to result ofthemodell & 2 as follows:

+ Relationship between independent and dependent variables in model 1-the probability

to get housing loan:

B ~ f(SEX;AGE, EDU, INCOME, SIZE, HHNO, HOUSEVAL, MATUR, COL, LA) + Relationship between independent and dependent variables in model 2-the determinant of loan amount:

LA= f(SEX, AGE, EDU, INCOME, SIZE, HHNO, HOUSEV AL, MATUR)

Modell

• Equation: ·

B = ao + atSEX + a2AGE + a3EDU + a.tiNCOME + asSIZE+ Cl()HHNO + a1HOUSEVAt + asMATUR+a9COLL+ atoLA

• Definition and·explanation of variables of the model I:

+Dependent variable:

B is the probability to get a housing loan It's a binary variable having two values (0;1) Value [1] means a case that borrower got a housing loan while value [0] indicat~s a case that borrower was refused

+ Independent variables:

1 SEX: A dummy variable equals 1 if gender of household head is male; and o iffemale ·

2 AGE: Age of household head It's measured in year

3 EDU: A dunimy variable that equals to 1 if education level of household head

is higher than college level; and 0 if not

4 INCOME: Total income of household It's measured in million VND

5 SIZE: Number of household members It's measured inperson

6 HHNO: Number of income earners in household It's measured in person

· 7 HOUSEVAL: Values of house It's measure in million VND

8 MATUR: Loan maturity It's measure in month

9 COI.L: Collateral is dummy variable It equals 1 if borrower offers collateral and 0 if not

10 LA: Loan amount is the currency value ofthe housing loan that the borrowers asked the bank It's measured in million VND

• Expected signs of the variables' coefficients:

Sign

I SEX + If the household head is a man, the probability to

get a housing loan would be higher than the case that the household head is a woman In this case, the expected sign is (+); Ill contrary, the expected sign is (-)

2 AGE - More age IS less probability to get loan,

More income is more ability to get housing loan because high income guarantee high payment Big size of household is to increase expenditure together with decreasing income as well as decreasing payment

More household number· can earn more income along with increasing payment capacity

Household has house value as much as possible because house value is the collateral of the housing loan and house value is high which means that loan amount from bank is low and risk from the house hold also is low

Long term of loan maturity is synonymous with increasing payment form household The longer matuiity of housing loan, the smaller the periodic principle which they must to pay It

means that their income ensure repayment from household

If the household head has collateral, the probability to get housing loan would· be higher

In this case, the expected sign is(+); in contrary, the expected sign is (-)

The less required loan amount, the higher probability to get the housing loan is

LA:.Loan amount (million VND) LA in the second model is different from the

LA in the first modeL Here, it is the real loan amount that the borrower got from the bank

+Independent variables:

1 SEX: A dummy variable equals 1 if gender of household head is male; and o iffemale ·

2 AGE: Age ofhousehold head It's measured in year

3, EDU: A dummy variable that equals to 1 ifeduca:tion level of household head

·is higher than college level; and 0 if not

4 INCOME: Total income of household It's measured in million VND

5 SIZE: Number of household niembers It's measured in person

6 i-IHNO: ~umber of Income earners in household It's measured in person

7 HOlJSEVAL: Valuesofhouse It's measure in million VND

8 MATUR: Loan maturity It's measure in month

• Expected signs ofthevariables' coefficients:

Sign

1 SEX + If the household head is a man, the loan

amount would be higher than the case that the household head is a woman In this case, the expected sign is (+); in contrary, the expected sign is(-)

3 EDU + If the household head has higher education

level, the housing loan amount would be

as well as decreasing payment It means that bigger size is less loan amount

More household number can earn more income along with increasing payment capacity So more household number can earn is more loan amount

Household has house value as much as possible because house value is the collateral

of the housing loan and house value is high which means that loan amount from bank is low and risk from the house hold also is low Long term of loan maturity is synonymous

· with increasing payment form household The longer maturity of housing loan, the smaller the periodic principle which they must to pay

It means that their income ensure repayment from household So more maturity is more loan amount

The sign cifthe variables in the model2 is the similar to the model1 But the sign of age variable is different from the model 2 The age variable in the model 2 will be positive sign In labor age, the more age is the higher working position and income

3~2 Data source and sampling

3 2.1 Data source

Data is usedin this thesis collected from data base system ofHDBank

Data includes both individual information and household information of the borrower

· The borrowers are the persons who can get housing loan (in model 1 & 2) and can not get housing loan (in model 1 )

The author want to apply the result of this research to reality of Vietnamese economy especially HDBank case Now Viet Nam has many difficulties to face the high inflation, economic crisis all industries, tightening money policy of SBV Once more important

·thing· is the violent competition in banking field at the time of undertaking to participate

in WTO ·.This research will have many meaning for bank to find out the stable developing strategy particular in customer lending business The author has a fortunate change to join modem project of bank in all module (kernel, general ledger, customer lending, branch teller ) That's core banking project Almost of data for this thesis can

be extracted from the core banking system Another fortune is support and permission of HDBank general director to allow and to sign a decision for the author to get the data The conditions for getting the data for this thesis are:

Loan information and customer information from head office and branches of HDBankin HCMC Because of geographic conditions, it's difficult to get data of some variables such · as size of household, the number of household can earn income from archives ofbranches are out HCMC

Loan information and customer information must has enough for all variables of thesis such·as: sex, age, education, income of household, size of household, house value, maturity loan, collateral, loan amount

Data is used in the analysis from the core banking of HCMC Housing Development Bank The analyzed data are of the year 2005,2006 and 2007 The reason why choose these year are:'

The inflation rate is low so no.needto change some variable value in fixed level; Real estate market is stable and has good growth;

Regulation and policy of HDBank are consistent, stable, no significant changes compare with the year 2008

Population (total cases of borrowers and loans that you are able to access)

Total cases of borrowers can get the housing loans are 1408 cases in HCMC branches I can not define exact cases of borrowers because many cases are not kept in archives 3.2.2 Sanipling

Sample size

The sample size consists of 306 observed cases Of which, there are 198 cases getting a housing loan from the HDBank and 108 cases refused to get the loan Compared to the total borrowing cases ofthe HCMC branch ofthe HDBank, the sample occupies 21.7%

· Sampling method

The method·is used which called "proportionate stratified sampling" (each stratum is properly represented so that the sample size drawn from the stratum is proportionate

to the stratum's share of the total population)

By the possibility to get a loan: the population is divided into two strati.uns: the cases that were accepted and refused to get the housing loan (65% and 35% of cases asking loan from the HDBank- figure 2.1)

By the observed years in combination to the growth rate in business (figure 2.2): the samples are drawn by year following the percentage of each year business in comparison to the total loan amount solved during three year 2005-2007 In each year,· the samples are· drawn following the percentage of each stratum (accepted and refused to get a housing loan)

All data for housing loan was picked up from core banking system After that, data will be group by year by branch and selected by common conditions as follows:

Data ofHCMC branch and year 2005,2006,2007

The· profile of each selected case must have enough information for all variables

in year 2006,72 in year 2007 corresponding to the growth rates of 43% and 283%

in comparison to the previous years

Figure 2.1 shows the structure of the possibility to get housing loan or not Totally, the sample size consists of 306 cases, of which, 198 cases are of the ones who could get housing loan during the period 2005-2007, and 108 cases are of the ones who could not get a housing loan in the same period

Figure 2.1: Structure of probability to get housing loan

El probability to get loan

Ill Improbability to get loan

Figure 2.2 shows the structure of samples in every year by probability and improbability The observation will be picked follow by growth rate percentage of total housing loan amount Total housing loan amount in year 2005, 2006, 2007 are

413, 591, 2268 billion VND with in turn percentage are 43% (year 2006 compare with previous year) and 283% (year 2007 compare with 2006) So with the customer can gethousingloan,there are 24 observations in year 2005,48 in year 2006 and 126

in year 2007 · Similar to the customer cannot get housing loan, there are 12 observations in year 2005, 24 in year2006, 72 in year 2007

Figure 2.2: StructUre of samples possibility to get a housing loan

The cases accepted for a housing loan The case refused to get a housing loan

ll1l year 2005 22% 1111 year 2006 24%

D year2007

3.3 Analysis niethods

3.3.1 Statistical tests for descriptive analysis

Descriptive analysis will be applied in the study to understand the central tendency, the dispersion and the distribution of the samples Detail of statistical test for descriptive analysis will be presented in chapter IV(item 4.3)

· 3.3 2 Correlation analysis

Correlation analysis will be applied to explore the interrelationship among the independent variable

3.3J Statistical tests for validity of specific models

Some test will be undertaken as follow:

Norrilality test: This test is applied to check distribution of the sample

Multi co~ linearity test: Multi co-linearity tests linear relationships between two or more explanatory variables

Autocorrelation test: Durbin-Watson (DW) test will be used for autocorrelation of variables

3.4 Analytic tool

·All data of this thesis will be run by SPSS software includes analyses such as: statistical tests for descriptive analysis~ correlation analysis, tests for validity of specific models