An analysis of housing credit program for urban hoausehold case study in HCMC housing development bank(HDBANK) (2)

Problemstatement .I 1.2Objectivesofthestudy

Generalobjective

Analysiso f housingc r e d i t programf o r urbanh o u s e h o l d s b y a n a l y s i s the probabilityofcreditforhouseholdandthedeterminantsaffecttoloanamount.

Specificobjective

- Theresearchanalyzestherelationshipbetweenurbanhouseholdsandcreditmarkett o f i n d o u t relation o f characteristics a n d e n d o w m e n t o f urbanhousehol dsandcharacteristicsofloantochancetoborrow,loanamount.

-Inaddition,housing financeprojectdostrengthenandcapacitybuilding to developVietNam'shousingfinancesystem.

Researchq u e s t i o n

Summaryonresearchmethodology a n d data

Themethodisused,includingstatisticalanddescriptiveanalysis,reviewofhistoricaltrends,andcomparativ emethods.Apartform,quantitativemethodisextensivelyused.

Households are a significant focus in social science, with definitions varying among economists, feminists, and anthropologists Ringen (1991) characterizes a household as a group of at least two individuals who pool their incomes and utilize them collectively In this context, income is not strictly divided among members; instead, cooperation in income usage allows households to maximize their financial resources more effectively than if each member operated independently Despite this understanding, there is ongoing debate regarding the boundaries of the household concept Economists, in particular, regard the household as a crucial analytical unit, developing theories based on household behavior modeling Their economic models typically explore two main themes: one that examines consumption and production separately or together in an integrated framework, and another that focuses on market conditions and the interplay between households and markets for land, labor, and credit.

Creditisthetradeofmoney,goodsorservicesatthepresenttimeforapaymentinthefuture.Creditcanb eprovidedinmanydifferentformsandunderawidevarietyofarrangements( K i n n o n Sc o t t , 2000)

Borrowingisonesideofcredit.Ingeneral,borrowingisdefinedasthattobetransferredpropertyrightonagivenob ject(e.g.sumofmoney)inexchangeforimplementingobligationofaclaimonspecifiedobject(e.g.a certainsumofmoney)atspecifiedpointoft i m e i n t h e f u t u r e I n e x t e n s i o n o f t h i s d e f i n i t i o n , b o r r o w i n g b y h o u s e h o l d s , particularlyurbanhouseholdisdefinedasactivitie sofhouseholdstoobtainexternalresourcestosupportotherhouseholdactivitieswithobligationtorepa yinfuture.Inotherway,borrowingismadebyhouseholdtofinancehousehold'sbudgetdeficit.

Housingcreditmarketthepresenceofheterogeneityamongdifferenthouseholdinducestosituation wherebyhouseholdsfallintobudgetdeficitwhilesomeothersareinbudgetsurplus.Therefore,th edeficithouseholdsdesiretoborrowinordertocovertheirdeficitwhilesurplushouseholdsa l s o desiretolendoutfortheiri n t e r e s t Consequently, householdcreditmarketise stablishedtofacilitateborrowingandlendingofhouseholds.Creditmarketfunctionistotransferpurchasing powerfromsurplushouseholdstodeficitonesbyissuingandacquiringofmoney-denominateddebt.

Increditscoringmethod,thecreditworthinesscanbemeasuredbythevaluesbasingonthecharacteristi cs.However,therearesomecorefactorsthatareusuallyusedincreditmanagement(Kapoor,Dlabay,Hughes,20 01).Theyare:

Credita n a l y s i s i s a p r o c e s s o f c o l l e c t i n g i n f o r m a t i o n , a n a l y z i n g i n f o r m a t i o n w i t hsciencesmethodfortheaimtounderstandclearaboutclientsandtheirbusin essprojects,servingprocessofshort- termloandecision.Creditanalysisisanimportantstepwiththepurposeusingtomakeloandecision:a cceptorrejectissuingcredit.Inordertomakecreditdecisionbanksmustdothreestepsfollowing:

- Collectingsu ffi cie nt &accurateinformation.

Bycreditanalysis,bankscanreplacetheirexperiencesabouthouseholdandtheirprojectappliedforloan withscientificallyargumentandevidencesrelyoninformationanddataprocessing.Therefore,credit analysishelpbanksavoidingtwotypesofmistake:(1)givecreditforbadclient,(2)andrejectingthegoodone.

Analysiscontents:analysisshouldbetargetedevaluationofcapacityrepayingloanthatmean,iteva luatewhethercustomercanrepayseedmoneyandinterestornot.Toappreciaterepa ymentcapacityofcompany,wehavetodeterminedfactorseffectingtocustomer’srepaying- debtcapacity.Inotherhand,creditanalysiscontentsshouldbeconcentratedintoanalyzefactorse ffectingtoabilitytomeetobligation.Basically,abilityofrepayingloanisinfluencedby:

Chung(2000).Creditrationingisaconditionofloanmarketinwhichthelendersupplyoffundislesst hanborrowerdemandatthequotedcontractterm.Inotherwords,itmeansthatthereisexcessdemand. Increditmarket,creditrationingappearstobeaninefficientsituationofcreditmarket,wherei nterestratedoesnotworkwelltobalancesupplyanddemandsides.

Therearetwomajorwaystoexplorecausesofcreditrationing.Firstly,traditionalviewsconsider creditrationingresultedfromgovernmentinterventionsoncreditmarketbyimposinginte restrateceilingonlendinginstitutions.Interestrateisexogenouslyheldundermarketclearin glevel.Withinterestratekeepartificiallylow,somepotentialborrowerswhowant toborrowarerationed.S e c o n d l y , afterthedemise ofthetraditionaltheories,anewapproach tocreditrationingwasdeveloped.T h e n e w a p p r o a c h a r g u e d t h a t p e r m a n e n t creditr a t i o n i n g i s consideredasanequilibriumphenomenonratherthanatemporaryphenomenon.Mod erntheoriesidentifyproblemsofmoralhazardandadverseselectionincreditmarketas a sourceof creditrationingwheninformationis distributedasymmetiicallya m o n g marketparticipants.

Fragmentationofcreditmarketstronglyaffectstheborrowingbehaviorsofurbanhousehold s.Fragmentationofcreditmarket,firstly,isthoughtasaconsequenceoftherepressi veg o v e r n m e n t p o l i c y C e i l i n g r a te ondepositan d loanrateimposedb ycentralbankinducestoseverecreditrationinginformalfinancialsector.Resultingfro minter- linkagebetweenformalandinformalfinancialmarketstheunsatisfieddeman dofhouseholdsforformalloanflowsintoinformalfinancialsectorandputdemandforinfor malloanstoriseup.Secondly, it isviewedthatfragmentation ofhouseholdcreditmarketiscausedbystructuralandinstitutionalf e a t u r e s ofcreditm arketi n developingcountries I n addition, fragmentationofcreditmarketmay alsoresultfromweaknessintheinfrastructure

6 thatsupportsthefinancialsystem.Thirdly,anotherperspectiveonfragmentationofcreditmarket arguesthatformalandinformalfinancial sectorsareparalleldevelopedbecausetheyservedifferentsegmentsofcreditmarket.

In conclusion, under perfect credit market conditions, borrowing and saving enable urban households to maximize their utility over time The demand for loans is influenced by household resources, incomes, and activities, which are referred to as "demand-side effects." Conversely, credit market conditions and the relationship between urban households and financial intermediaries impact their borrowing capabilities Inefficiencies in the credit market can prevent potential borrowers from accessing credit, while the severity of credit rationing explains the constraints faced by urban households Additionally, the fragmentation of the credit market accounts for varying borrowing behaviors among these households Ultimately, borrowing by urban households is significantly influenced by both demand-side and supply-side factors.

Asia is experiencing rapid urbanization, with a significant portion of its population living in poverty Approximately 25% of urban residents in Asia are below the poverty line, with countries like India and China housing about one-third of the region's urban population in challenging conditions For instance, in Mumbai, around 50% of its 12 million residents live in slums or on the streets This contributes to the global homeless population, which exceeds 100 million To address the basic needs of its urban population, Asia will require an investment of approximately $280 billion annually over the next 30 years.

Thelackofhousingaccessisoneofthemostseriousandwidespreadconsequencesandcausesofpo vertyinAsiancities.Theimprovementsinhousingthatareimportanttoimprovingthequ alityoflifeamongthepooroftendoesnotreceivetheattentiontheydeservefrompolicymakers(Da niere,1 9 9 6 )

T o makeanyappreciableimprovement,substantialgovernmentspendingisneeded,bot hinthephysicalexpansionofthecity’sinfrastructurea ndimplementationo f povertya lle viationprograms.Buttressedbytheheritageofliteraturethatarguestheimportanceofa ffordableandimprovedhousingin urbanpovertyreduction(see,forexample,Mitlin,2001),theimmediateresearchissueis howpoorfamiliescanaccessurbansheltermoreaffordably.

TheSingaporepublichousingdevelopmenthasattractedkeenresearchinterest(see,forexample,Won gandYeh,1985;Yuenetal,1999)butfewhaveclarifiedthepublichousing-urbanpoverty nexus.Thisprovidesthestartingpointforthepresent analysisoftheperformanceo f housingdevelopment.

According to Global Urban Development Magazine, significant economic development in Singapore has been crucial for housing improvements The government took deliberate actions to diversify the economy and create employment opportunities, resulting in a remarkable average GDP growth of 8.6% per year from 1965 to 1999 This economic expansion boosted nominal household income, increasing real per capita GDP from S$4,000 in 1965 to S$32,000 in 1999, while maintaining low inflation rates of around 2-3% annually Additionally, average monthly household income rose by 6.7% per year from 1988 to 1998, contributing to a surge in asset ownership Consequently, homeownership of public flats grew significantly, rising from 26% in 1970 to 92% of the housing stock by 1999.

Singapore's approach to public housing financing highlights its significant role in economic progress and job creation Established in 1960, the Housing Development Board (HDB) has successfully constructed over 850,000 housing units, alongside commercial and industrial premises, schools, community facilities, parks, and markets This extensive development not only enhances living conditions for the underprivileged but also generates numerous construction jobs, demonstrating a strong multiplier effect on the economy Scholars like Sandilands (1992) have noted that the construction sector outpaces overall GDP growth, emphasizing its critical role in economic development Additionally, the public sector housing construction is recognized for its pump-priming effect on the economy (Krause et al., 1987).

Aswithmanyothercities,Singapore’squesttoprovidei t s poorresidentswith go odlivingenvironmentcisnotnew.Adequateshelterwiththepromiseofadecentlifeofdignity,goo dhealth,safety,happinessandhopeisonethemethathasbeenrepeatedinternationallyandenshrine dinsuccessiveUnitedNationsdeclarations(see,forexample,

UNCHS,1 9 9 8 ; 1 9 9 9 ; WorldB a n k , 1 9 9 3 ) TheS i n g a p o r e d ev e l o p me n t e x p e r i e n c e , however,showsthatpublichousing(evenhigh- rise)forthelowerincomefamiliesneednotdegenerateintopolarizedandmarginalenvironments.Non etheless,thereemergenceofthehomelessunderscorestheurgency forfurtherresearch.Inparticular,thetrendtowardstallerhousingpresentschallengesTh epoorofSingaporedonothavethealternativetooptoutofthishousing.Inthisregard,wearereminde dofMitlin’s(2001,p5l2)exhortationtounderstandandfollowtherealitiesofthepoorinthecontinui ngefforttocreateaffordablehousingthatseektoaddresstheirdiverseneeds.

Insummary,Singapore’ssystemofhousingdevelopmentwithasingleempoweredauthori tyresponsibleforhousingdeliverymaynotbethemodelforallcountries,buteffectivepragmaticmana gementprinciples(suchasinclusivehousingandwideninghomeownershipopportunityforlower- income families,directedassistanceforlow- incomerenterhouseholdsa n d continualre vie w ofhousingaccess) apply inmo stcontexts.Thereisagrowingliteraturethatemphasizesacomprehensiveapproachtohousing.Sim ilartosituationofChinaandThailandorotherAsiancountries,VietNamcangetthemostsuitablelessonsforhousing marketandpolicyinthewayofeconomicdevelopment,

China’shousingmarketandpolicycontextarerelativelyunique,differingfromthoseoftheuropean dtheUS,includingthepost-

’shomeownership- orientedhousingpolices.Inthissectionandthroughoutthepaperthediscussionfocusesonurbanhousing policyonlybe.causeofthedifferentrulesandinstitutionalarrangementgoverningho usinginruralandurbanareas.

Aseconomic‘reformsdeepenedinthe1990sChina’spolicymakerssoughttoprivatizemuch ofthepublicly-ownedhousingstockthathadbeenpreviouslyrentedfromthestateorstate- ownedenterprises(SOEs).Indoingsothegovernmentwasmotivatedbyanumberoffactors ,foremostofwhichwasthefactthatmaintenancecostlevelsranwellabovethenominalrentspaidbytenants.Zhang( 2003)citesfiguresindicatingthatasof1991rentongovernment-ownedhousingaveraged0.13Y/ m2oflivingspace(enterprise- ownedhousingwasevencheaper)whileupkeepexpensesaveraged2.31 reformsdesigned t o encouraget h e development o f housingm a r k e t s L i u , Par k,a n dZheng’s(2002)analysisoftherelationshipbetweenhousinginvestmentandecono micgrowthfoundsignificantpositiverelationshipsoverboththeshortandlongruns:anotherimportant policymotivationforspurringdevelopmentofhousingmarkets.

TheHousingProvidentFund(HPF)wasdesignedin1992tohelpweanemployeesfromworkplaceh ousingprovision.Assuch,itwaspairedwithreformofthesalarysystem.Insteadofprovidinghousingdirec tlyandpayingemployeesacorrespondinglylowersalary,theprogram’sgoalwastoenlistpublicsectorempl oyeesinthedevelopmentofthecommercialhousingmarketbyraisingtheirincomes butsiphoningtheincreaseintosavingsaccountsdedicatedtohousing,whilereducingtheirin- kindhousingbenefit,therebyencouragingthemtofindhousinginthemarketplace(Wang2001 ).Thereformwaspartofamoregeneralefforttohaveindividualsandmarketsreplacegovernmentandwor kunitsastheentitiesresponsibleforhousing finance(Lee2000).Becauseemployerparticipationisnotmandatoryintheprivatesector,theprimaryH PFbeneficiariesaregovernment,party,SOE,andotherpublicsectorworkers,althoughsomeprivatefirmsandf oreignjointventuresalsomatchemployeecontributions.

InarecentoverviewofChina’shousingpolicy,Sun(2004)criticizesthetargetingoftheHPFsystem.Th isisreinforcedb y thefactthatevenmostworkingl o we r - i n c o me householdsarenotinthekind ofofficial,full- time,andtypicallypublicsectorpositionslikelytocarryanHPFbenefit.

Theotherprincipalh o m e o w n e r s h i p - o r i e n t e d p u b l i c policyi s thedevelopment o f‘affordablehousing’(J/ng/ iShiyongFangor‘economicandcomfortablehousing’).Thepolicyisdesignedforlower-middle- andmiddle-incomeurbanresidentsandinvolvesgovernmentsubsidiesandprofitcapsfordevelopers.

In May 2005, key representatives from Thailand, including Vice Minister of Finance Veerachai Veerametheekul and other notable leaders from the Government Housing Bank and National Housing Authority, participated in the international conference titled “More Than Shelter: Housing as an Instrument of Economic and Social Development,” organized by Harvard University’s Joint Center for Housing Studies in Bellagio, Italy The event also featured important leaders from the USA, Mexico, South Africa, and Kenya Following the conference, delegates from the five participating countries endorsed the “Bellagio Housing Declaration,” emphasizing the critical role of housing in fostering economic and social development.

Declaration”affirmingthatsound,sanitaryandaffordablehousingforeveryoneiscentraltothewell- beingofnations.Italsoreaffirmedthathousingismorethanshelter;itisapowerfulenginethatcreateso pportunityandeconomicgrowth.Severalotherprincipleswerealsopromulgated:

(b)Housinga s a n e n g i n e o f s o c i a l a n d e c o n o m i c d e v e l o p m e n t : H o u s i n g b r i n g ssignificantbenefitsintermsofemploymentcreation,domesticcapitalmobilizationand socialwellbeinginthefaceofthemajorchallengesposedbypopulationgrowthandurbanization. (SeeseminardetailsandBellagioHousingDeclarationinGHBNewsletter,41stedition,2005)Theconclus ionsfrom thismostimportantconferenceindicatedthatmanycountriesrecognizedthathousingismorethanshelt er.Itisoneoflife’sessentialsandaprocessorengineofeconomicandsocialdevelopment.TheGovernmentshould giveahigh-priorityforestablishingalong-termhousingpolicyandstrategy.

In1962,KoreaNationHousingCorporation(KNHC)wasestablishedwiththemissionofimprovingthepublic livingstandardsandwelfarethroughhousingconstructionandurbanredevelopment.

KNHCi s a 1 0 0 percents t a t e - o w n e d c o m p a n y D u e t o i t s s t r o n g l i n k s w i t h t h egovernment,ithasbeenprovid eddirectfundingassistancebythegovernmentintheformofequitycontributionsandloansfromtheNationalHousin gFund.

KNHC'sorganizationconsistsofMainOfficewith20Divisionsunder4HeadDivisionsand1 Resea rchI n s t i t u t e , 2RegionalH e a d D i v i s i o n s i n SeoulandGyeonggi,and 1 0Branc hOfficesinothermajorcities.

At o t a l o f 3 , 0 7 6 e m p l o y e e s i s w o r k i n g a t K N H C , i n c l u d i n g 7 e x e c u t i v e s , 8 4 4 administrativeofficials(27%ằ),1,721engineeringofficials (56%) and504researchersand clerks( 1 7% ằ)

Asoftheendof2001,assetsamountto14,374billionwon,liabilitiesto9,301billionwon,capitalto5,07 3billionwon.Itssalesareupto3,274billionwon.

However,theliabilities,theaccruedinterestofwhichshouldbepaidbyKNHC,areonly2,254billionwo noftotal9,301billionwon.

Tenantsand.housingbuyerswillpaytheinterestexpensesoftherest(7,047billionwon) housingunitfornationalhousingstabilizationuntilnow,butwillbegintolaystrong emphasisontheimprovementofqualitytocomplywiththechangeoflifefromnow.

KNHCwillb ereborna s afuture- orientedc o r p o r a t i o n w i t h higheradditivevalueto realizeurbandevelopmentandhousingwelfare.

Moody'ss a i d s u c h a trackr e c o r d o f g o v e r n m e n t a s s i s t a n c e a n d t h e possib il ityt h a tgovernmentwillcontinuetoprovidefinancial help‘ensuresKNHC'sfinancialviabilityandoperationalsoundness.(websiteofGOLIATH— Businessknowledgeondemand).

Anyfuturechangesinratingwouldoccurifthecountry'ssovereignratingischanged,itsaid.Downwar dpressuremay occurifthegovernment'sfiscalconditionweakens,orifthecorporationisprivatized.

RivalratingsagencyStandard& Poors(S&P)alsogavethecorporationa foreign- currencycreditratingof"Aminus"andadomestic- currencyratingof"A."S&P'soutlookforthecorporation'screditratingis"stable."

Thecreditratingreflectsthecorporation'sfirmstatusintheSouthKoreanhousingmarketanditshousingprojectsfo rthecountry'slow-incomepeople.

Housing loans for households are accessible in various countries, including Finland, the UK, Japan, Spain, Portugal, Italy, France, Norway, Sweden, the US, Australia, Germany, Belgium, Thailand, and the Philippines There is a notable concern regarding household indebtedness, yet simulation-based analyses on this topic are scarce, with descriptive analyses being more prevalent Theoretical frameworks, such as the Permanent Income Hypothesis introduced by Milton Friedman in 1957, suggest that individuals base their consumption on perceived 'normal' income, despite short-term income fluctuations, which can lead to borrowing Similarly, the Lifecycle Hypothesis highlights the influence of wealth changes on consumption but does not address household indebtedness issues directly This discussion centers on the determinants of housing loans and the loan amounts influenced by household indebtedness.

Housing demand is influenced by households' choices regarding neighborhood selection, whether to own or rent, and the amount of housing services required Research conducted by Carol Rapaport on Tampa, Florida households illustrates this concept through a model that considers various factors, including the price of housing services, property tax rates, income, household characteristics, and local public goods These elements interact to shape the housing decisions made by different households across communities.

Householdincomeandthepriceofthecompositecommodityareexogenous.Thepriceofhousin greflectsthesupplypriceofhousingandanycapitalizationofthecommunitymixofpublicservicesandtaxlevels.P isconstantacrossallinanygivenj,butdiffersacrossHjjurisdictionsandacrosstenurestatus.

ClusandRalph(2007)alsohadresearchofhousingdemand Beyondmoney, monetarypolicyandfinancialdevelopmentshousingmarketsarecertainlyalsoinfluencedbyotherfact ors,suchastaxes,demographicsandotherdevelopmentsdeterminingthedemandforhousing.Thisrese archalsostatedtherelationsbetweenhousingloanandhousingdemand.MarcoSalvi(2 007)alsopresentedthemodelforthedemandofhousingintheGreaterZuricharea.Besidesthat,therearejustfe wempiricalofhousingdemandsuchasBajariandKahn(2002);Ferreira(2004);Bayer,McMillan,R uben(2002).Allthesepapersexposeanalysisconcernedtohousingdemand.Theseliter aturesofhousingdemandprovethathousingdemandisalwaysburningissueinmanycountries intheworld.

Thereseemtobehardlyanytheoriesondeterminantsofhousingloanforhousehold.Instead,therearen umerousempiricalanalysesonhouseholds’debtservicingdifficulties.

Maya n d T u d e l a ( 2 0 0 5 ) u s e d Britishd a t a t o s t u d y w h i c h factorsd e t e r m i n e d thelikelihoodofh a v i n g d e b t s e r v i c i n g d i f f i c u l t i e s U n e m p l o y m e n t , highl e v e l s o f indebtednessandahighproportionofnon-collateral debtincreasedthelikelihoodofproblems.Difficultiesseemedtobeofpermanentnature;pastprob lemswereagoodpredictoroffutureproblems.Therewasalmostnoevidenceofhousingwealthpreventing difficulties.Thismightbedue serviceloans.Hence,theavailabilityofcollateralisoflimiteduseinpreventingreported difficulties,eventhoughcollateralcertainlyreducesbanks’creditrisks.

MayandTudelaintroduceanewconcept,namely‘debt-at- risk’,anindicatorofcreditrisk.Itissimplythesumofhouseholdlevelhousingdebtmultipliedbytheho useholdlevellikelihoodoffinancialdistress.Ifitispossibletoidentifythedeterminantsofthelikelihoodofdistr ess,andifmicroleveldataareavailable,itispossibletocalculatetheaggregateamountof‘debtatrisk’andhowitwou ldreacttochangesintheparameters,suchasincome,age,housevalue,housesize,etc.Thesectionsafterofthispap erapplythisconcept.

LiuandLee(1997)usedv ar i o u s methodstostudythedeterminantso f housinglo andefaultsinTaiwan.Severalfactors,includingtheloan-to- valueratioandthelevelofeducation,s e e m e d t o c o n t r i b u t e t o t h e o c c u r r e n c e o f p r o b l e m s P r o b l e m s w e r e particularlyc o m m o n p l a c e a m o n g theyoungest.

CairnsandPryce(2005)foundwithBritishdatathathouseholddebtservicingabilitywas anincreasingfunctioninthelevelofeducation,ageandmarriedstatus.Familieswithmany childrenweremorelikelytohaveproblems.Geographicfactorsseemedtoplaysomerole.Parti ally,Bowie-

Anincreaseinthenumberofchildreninahouseholdisassociatedwithhigheroutgoingsandlessavailable financestorepaydebt.Itisworthexploringtheirexperienceofmaintainingpaymen ts.However,withineachyearcomparisonbetweenhouseholdswithdifferentnumbersofchildrencon firmsthat,inmostcases,asthenumberofchildrenincreasestherateofrepaymentdifficultyandarrearsincre ases.

Onewouldexpectthathouseholdincomewould haveastrongrelationshipwiththeprobabilityofhouseholdex p e ri e nc in g mortga ge ar re ar s

Ingeneral,onewouldexpectthatasanindividualgetsolder,theirstatus,incomeandfinancialstabilitywi llalsoincrease.

SimilartothecaseinHDBank,theexpectedresult likesresultofBowie- CairnsandPryce.TheresultofregressionofthisresearchwillbeshowndetailsinchapterIV.

Research indicates that several key variables significantly influence the likelihood of obtaining a housing loan, including income, age, sex, house value, house size, principal, residential status, occupation, interest, marital status, and credit type In the context of Vietnam, the author focuses on specific variables such as sex, age, education, income, size, house value, maturity, collateral, and loan amount to assess the probability of securing a housing loan Vietnamese banks typically offer housing credit with standard conditions related to principal, income, interest, and maturity However, given the pressures of economic development and integration into the global economy, commercial banks in Vietnam should implement more flexible and diversified housing credit programs to cater to various needs This research aims to build a model that identifies significant variables affecting the probability of obtaining a housing loan, ultimately enabling commercial banks to develop more effective housing credit programs tailored to borrower characteristics.

Del-RioandYoung(2005a)concludedthatnon-collateral loansinBritainweretypicallytakenbypeoplewhowereintheirtwenties,hadnochildren, wererelativelywelleducated,wereemployedandhadoptimisticexpectations.

Brown,Garino,TaylorandPrice(2003)found thatoptimismseemedtostrengthenthedemandfornon- collateralloans,eventhoughafteracertainlevelthedegreeofoptimismhadnoimpact.Factorssuchasspous eincomeandsavingsseemedtohavenoimpact.

2003.Householddebthadbeenonincrease,especiallyamongtheyoungesthouseholdsan dthosewithlowincome.Thegrowthoffinancialwealthhad takenplaceaboveall inhouseholdswithnodebt.

Magri(2002)studiedtheoccurrenceofdebtamongItalianhouseholds.Higherincomestre ngthenboththe‘demandforloansandtheavailabilityofcredit,whereasbeinganentrepreneur strengthenedthedemandforcreditbutmadeitmoredifficulttoobtainloans.

BrownandTaylor(2005)studiedthedeterminationofhouseholdfinancialwealthanddebtintheUK,German yandtheUS.Therewasaclearcorrelationbetweenfinancialwealthanddebt.Ifhouseholdswithnodebtwer eexcluded,thecorrelationvanished. overthelifecycle.Themodeloftheseauthorsmayalsobeusedtoassesstheimpactofchangesintheincom eprocess.Thefirstsimulationtheyconsiderisanunanticipateddeclineinthenon- propertyincomeofpensioners.Thiscouldbethoughtofasarisingfromacutinthestatepensionoraworse ninginpayoutsfromcompanypensions.Itismodeledas20%pointdeclineintheagepremiumin oldagethatisassumedtobepermanentandoccursunexpectedlyinthe2026—

30period(itreducesthenon-propertyincomeof thoseover60by20%).Whentheshockoccurs,allcohortsreducetheirconsumptionof goodsandhousing,butthosewhowillhavejust retiredreducetheirconsumptionthemostastheyexperiencethelargestproportionatede clineintheirlifetimeincome.Thedeclineofaround12%(forthoseaged61

65)intheirimmediatespendingissmallerthanthedeclineintheirnon- propertyincomebecausetheirspendingisalsodependentonwealthwhichisnotaffectedbythechangeininco me.ThisresultisthesimilartothisresearchandinVietNam.Whentheoldofhouseholderincreasesorthehou seholderisonretire,theprobabilityofhousingcreditdecreases.

Crook(2001)analyzedthe1995USSurveyofConsumerFinancesdata;thedemandforhousehold loansseemedtobeanincreasingfunctioninincomeandfamilysize.

DavydoffandNaacke(2005)presentedapurelydescriptivereportonthedistributionofhousinga ndconsumer loansinFrance,Britain,GermanyandItaly.HousingloanswereparticularlycommonplaceintheU KbutremarkablyexceptionalinItaly,eventhoughowner- occupiedhousingisparticularlycommonplaceinItaly.Inallthecountriesthedeterminantsofhousingloa namountwererathersimilar.

Lendingornotandhowmuchforlendingareoneofthemostimportantaspectsofboththelenderandtheborro wer.Therearealsoresearchesconcernedloanamount.Similartothemodelofhousingloan,themodelofh ousingloanamountisbuiltwithvariablesassex,age,education,income,size,housevalue,maturity.Inrea l,thismodelhasdetailanalysisofhousingcreditforthehousehold.

ThebasicmodelofChung(2000)basedonassumptionsthatthereisahouseholdlivesintwoperiodswithin aperfectmarket.Thehouseholdhasainter-temporalutilityfunctionandbehavesrationally.

Generally,thebasicmodelindicatesthefactorsaffectinghouseholddemandforloanarecurrentconsu mptionlevel,incomeandasset.Itimpliesthatthedemandofhouseholdfor

16 loanisfunctionofhouseholdasset,incomeandconsumption.Theirrelationshipsare showedinthefollowingequation:A=A;+QY;+rY

WhereAs isinitialamountofassets;YiIsthecurrentincome;Yisfutureincome;andrismarketrateofinte rest.Theequationexplicitlyindicatesthatthedemandofhouseholdsforloan risesifpreferenceforcurrentconsumptionbeinghighorexpectedincomeincreasingo r a mountofinitialassetsandcurrentincomeaswellasinterestratesdropping.

Thus,thebasicmodelshowsthatborrowing,alongwithsaving,allowsthehouseholdto smoothitsconsumptionpathandtomaximizeitsutilityovertime.

Inextensionofthebasicmodel,suchfactorsashouseholdresources,activitiesandflowsofresourcesareincl udedtoidentifydeterminantsofthedemandofhouseholdsforloan;andsomeassumptions a r e changed T h e models h o w s thatt h e de ma nd o f urbanhouseholdsforloanincrea seswithitsliquidityrequirementsforactivities.Chung(2000)writethatlevelofhouseholdbudgetdeficitdeter minesthelevelofborrowing:B=dR- ;wwhheerreeBBisthelevelofborrowing;dRisthe changeinassets;Yj isincomejthofthehousehold;andFgistheoutflowofexpendituretofinanceactivitygg.

Increasesinsuchhousehold‘activitiesasconsumption,productionandinvestmentcreateincreasingo u t f l o w s o f household r e s o u r c e s I f i n f l o w o f resources f r o m i n c o m e generatinga ctivitiesandassetswouldnotmeetoutflows,demandforloanmayrise.Theaboveequationshowsthat thedemandofthehouseholdforcreditappearswhenflowsofincomeandchangesinhousehold'sr esourcesdonotmeetdemandforfundingthehouseholdactivities.

Hence,thedemandforloanappearsand riseswhenhouseholddeficitgap appears andbroadens.Thedemandofthehouseholdforloanisdeterminedbyhouseholdresourcesandactivities. 2.3.5Empiricalmodelsappliedinpreviousstudies

Egerta n d Mih al jek ( 2 0 0 7 ) h av e b o t h s h o w n thath o u s e p r i c e d y n a m i c s a r e u suallymodeledintermsofchangesinhousingdemandandsupply(seeexHMTreasury,2003).Onthedem andside,keyfactorsaretypicallytakentobeexpectedchangeinhouseprices(PH),householdincome(Y),there alrateonhousingloans(r),financialwealth(WE),demographicandlabormarketfactors(D),theexpectedra teofreturnonhousing(e)andavectorofotherdemandshifters(X).Thelattermayinclude3proxiesforthelocation,a geandstateofhousing,orinstitutionalfactorsthatfacilitateorhinderhouseholds’ accesstothehousingmarket,suchasfinancial innovationonthemortgageandhousing loanmarkets:

Sexton(1977)suggestedsomecharacteristicsoftheborroweraffectedtothelendersuchas:goodcreditrecor d,married,single,divorcedorseparated,numberofdependents,age,primarymonthlyincome,presenceo fextraincome,homeowner,homerenter,hometelephone,creditedi n v e s t i g a t i o n made

OzlemOzdemir(2004)constructeda conceptualmodeltoexplaintherelationshipbetweenconsumercreditclients’paymentperfo rmanceandcreditcategory,interestrate,sex,age,maritalstatus,income,loansize,maturity,residentialstatu sandoccupation.Theequationofthemodelisasfollows:

Paymentperformance— § 0 +§1CreditType+§2Interest+§3Sex+§4Age+§5MaritalStatus+§6Income+§7Principal+ §8NumberofPayments(1)+§9ResidentialStatus+§10Occupation+E2

Descriptiveandregressionanalysisareusedtoanalyzethedeterminantsofintensityoflendingi nThaibanksb e t w e e n 1 9 9 2 and1 9 9 6 Sincetheird e p e n de n t variable is co ntinuoustheauthorusestheOLSmethodtoestimatethefollowingequation.

Creditriskorborrowercharacteristicsvariablesincludeavectorofjvariablesthatshowthedirectandindirectcreditofth eborrowersuchasassetsize,age,liabilities-to-assetsratio,currentratio,interestcoverageratio,andthelike,

Relationshipfactorsrepresentavectoroftvariablesthatshowtherelationshipbetweentheborrower andfirmsuchashousebankstatus,relationduration,andnumber ofbanksthattheborrowerhasrelationshipswith.

Otherrepresentsavectorofrvariablesthatcapturestheyearsoflending,banktypeofthelendingbanks,sector classification,etc. crepresentstheerrortermwithalltheclassicalassumptions,andn,§,y,‹pand$areconstantstobeestim ated.

ModelappliedbyL.Ellis,J.LawsonandL.Roberts-Thomson( 2 0 0 3 )

Fromt h e r esul t o f ane c o n o m e t r i c m o d e l , t h r e e k e y v a r i a b l e s e f f e c t t o t h eh o us in gleverageforhouseholdasageofthenhouseholdhead,householdincomeandhouseholdhou singwealth.

Ml, ,M4=dummyvariablesindicatingmaritalstatus,andL, ,L7=dum myvariablesindicatinglocation.

Insummary,therearemanystudiestoanalyzehousingcreditforhousehold.Therearealsomanycharacters effecttotheprobabilityofgettingaloanandloanamount.Inlimitedcondition,theauthorjus tconstructsthemodels withvariableswhichcanbegottenthedataandinformation.Thedetailofthemodelforthisresearchwill beshowninthenextchapterIII.

Thisc h a p t e r w i l l p r e s e n t stepb y s t e p t h e w a y ofi m p l e m e n t a t i o n ana lyzinga n dprocessing thed a t a c o l l e c t e d T h e s t u d y w i l l e m p l o y d e s c r i p t i v e s t a t i s t i c s a n d econometricmodeltoexaminetherelationshipbetweenthedependentvariableandi ndependentvariables.Theanalysisaimsatfindingoutthedeterminantsofprobabilitytogetahousingloanan ddeterminantsofloanamount.

The aboveanalyticalgroundproposesthatcharacteristics,endowmentsofhouseholdsandloa ncharacteristicswoulddeterminetheprobabilityofgettingahousingloanofaborrower.Therefore,thefollowin ghypothesesareraised.

Hypothesis2 : forhousingborrowers,l o a n amountorlevelofborrowing,LAwould dependoncharacteristicsandendowments‘ofhousehold,andcharacteristicsofloan.

LA -f(vectorofhouseholdendowments,vectorofhouseholdcharacteristics,vectorof loancharacteristics) SubjecttoB=l

Thismodelshowsthedeterminantsoftheprobabilitytogethousingloan.Thetesteddet erminantsoftheprobabilitytogetahousingloanaresex,age,educationlevel,incomeofborrowerandHo wdothevariablesaffecttotheprobabilitytoborrowofurbanhouseholdsandwhichvaria ble isstronglyaffectstheprobabilitytoborrowofurbanhouseholds.Thevariabledefinitionofthismodelasf ollow:

Theprobabilitytoget‘ahousingloanforhousehold:thisvariableismeasuredbasingoncriterionso f creditanalysissuchas credit policy,charactersoftheborrower,paymentplanning

Sex:isoneofthehousehold’character.Thisdemographicvariableisalsoinputthemodeltoc hecktheinfluenceofborrowergendertoprobabilitytogetahousingloanandloanamount.

Ageofhouseholder:thisfactorisoneofthekeyfactorsforanalysisofhousingcreditfromba nk.Ageshowsthatthehouseholderisonworkinglaborageornot.Inaddition,inworkinglaborage,agealsosh owshouseholder’sjobexperienceandjobposition.Thisaffectsstronglytheirincome.

Incomeo f household:t h e l e v e l a n d the s t a b i l i t y of incomea r e important f a c t o r s i n evaluatingthecapacityofrepaymentfromhousehold.

Sizeofhousehold:thisfactoraffectsstronglysavingsandexpendituresofhousehold.Bec auseanincreaseinthenumberofchildreninahouseholdisassociatedwithhigheroutgoingsandlessavailablef inancestorepaydebt,itisworthexploringtheirexperienceofmaintainingpayments.

Numberofincomeearnersinhousehold:thisfactorvigorouslyimpacttosavingsand incomeofhousehold.Themoreincomeearners,themoresavingsare.

Valuesofhouse:housevalueisvaluated asacollateralsecurityincasewithoutcollateralforhousingSloan.Besideshousevalueshowthesavingsofh ouseholdatthetimewherethehouseholdgettheloanandtheloanamountwhichthehouseholdwanttoget.

Maturity:intuitively,probabilityofcreditforhousingincreaseswhenmaturityincreasesbecause thelonger term, themoreprobabilityofperiodicrepaymentfromhousehold.Itmeansthatthelongerterm,thesmallerp eriodicprincipalandinterestofhousehold.

Collateral:Assetpledgeincaseofdefault.Ifloanwasguaranteedbymortgageorpledgeasset,itwillceme ntresponsibilityandobligationofborrower.In caseofinsolvent,collateralturntobesecondreceivableofbanks,however,mortgageorsecurityas sethasaccommodatetorealisticc o n d i t i o n s

Loanamountorloansize:effecttoprobabilityofcreditdelinquencyincreases whentheloansizeincreases.Sothisfactorwilldefineapositiverelationshipbetweenloansizeandhousehol d’paybackperformance,assumingthereisnoinflation.Thelendersgenerallyprefergiving thiskindofcreditforlowerloansizeinordertodecreasetheirrisk.

Thesecondmodelattemptstoprovethat characteristicsandendowmentsofhousehold,andloancharacteristicsdetermineloanamou ntsborrowed.Thetesteddeterminantsofloana m o u n t a r e a g e o f households, h o u s e h o l d size,house o f value,householdexpenditure,occupationsofhouseholdmembersand numbersofdependants Which factoraresignificantdeterminantsofloanamount.Howdothesefactorseffecttoloan amount.Thevariabledefinitionofthismodelasfollow:

Theloanamountofhousingloan forhousehold:thisvariableismeasuredb a s i n g on privatecharacteristicsofborrowersandstatusofborrowers’household.

Firstly,twodependentvariables(Bvariable-possibilitytogetahousingloaninmodell andLAvariable-loanamountinmodel2)arechosentoanswerthestudyquestions.

Secondly,accordingtotheabovestudiesinLiteratureReviewchapter,itcanbeseenthatmanyfactorsareablet oinfluencetheprobabilitytogetahousingloananddeterminantofloanamount.Thisresearchjustconcentrates someimportantfactorseffectstronglytoresultofthemodel1&2asfollows:

B=f(SEX,AGE,EDU,INCOME,SIZE,HHNO,HOUSEVAL,MATUR,COL,LA).

LA-f(SEX,AGE,EDU,INCOME,SIZE,HHNO,HOUSEVAL,MATUR).

B- + a ; S E X + a AGE+ a EDU+ I NCOME +a5SIZE+HHNO+ a7HOUSEVAL+ agMATUR+a9COLL+aieLA

Bistheprobabilitytogetahousingloan.It’sabinaryvariablehavingtwovalues(0;1).Value[TJ meansacasethatborrowergotahousingloanwhilevalue[0]indicatesacasethatborrowerwasref used.

3 EDU:Adummyvariablethatequalsto1ifeducationlevelofhouseholdhead ishigherthancollegelevel;and0ifnot.

9 COLL:Collateralisdummyvariable.Itequals1ifborrowerofferscollateraland0ifnot.

LA:Loanamountisthecurrencyvalueofthehousingloanthattheborrowers askedthebank.It’smeasuredinmillionVND.

+ Ifthehouseholdheadisaman,theprobabilitytogetahousingloan wouldbehigherthanthecasethatthehouseholdheadisawom an.Inthiscase,theexpectedsignis(+);incontrar y,theexpectedsignis(-).

2 AGE - More agei s l e s s p r o b ab i l i t y to getloan, especiallythepersonisatretiredage.Almostof thisgrouppeoplearenotabletogetloan.

3 EDU + Ifthehouseholdheadhashighereducationlevel,theprobab ilitytogethousingloanwouldbehigher.Inthis case,theexpectedsignis(+);incontrary,theexpectedsi gnis(-).

7 HOUSEVAL + Household hashousevalueasmuchaspossiblebecauseh o u s e v a l u e i s t h e c o l l a t e r a l o f t h e housingl oa n andhousevalueis highwhichmeansthatloa n amountfrombank islowandriskfromthehouseholdalsoislow.

8 MATUR + Longtermofloanmaturityissynonymouswithincrea singpaymentformhousehold.Thelongermaturityof housingloan,thesmallertheperiodicprincip lewhichtheymusttopay.Itmeans thattheirincomeensurerepaymentfromhousehold.

9 COL + Ift h e h o u s e h o l d headhascollateral,t h epr obabilitytogethousing loanwould behigher.Inthiscase,theexpectedsignis(+);incontrary,theex pectedsignis(-).

10 LA - Thelessrequired loana m o u n t , theh i g h e r probabilitytogetthehousingloanis.

LA+biSEX+ bAGE+baEDU+ b‹INCOME+ bSIZE+b6HHNO+ b7HOUSEVAL+b8MATUR

LA:Loanamount(millionVND).LAinthesecondmodelisdifferentfromtheLAinthefi rstmodel.Here,itistherealloanamountthattheborrowergotfromthebank.

8 MATUR:Loanmaturity.It’smeasureinmonth. ằ Expectedsignsofthevariables’coefficients:

SEX + Ift h e h o u s e h o l d h e a d i s a m a n , t h e l o a namountwouldbehigherthanthecasethattheh ouseholdheadisawoman.Inthiscase,theexpected signis(+);incontrary,theexpectedsignis(-).

3 EDU + Ift h e h o u s e h o l d h e a d h a s h i g h e r e d u c a t i o n higher.Inthiscase,theexpectedsignis(+);in contrary,theexpectedsignis(-).

5 SIZE - Bigs i z e o f h o u s e h o l d ist o i n c r e a s e expe ndituretogetherwithd e c r e a s i n g incomea swellasdecreasingpayment.Itmeansthatbiggersi zeislessloanamount.

6 HHNO Morehouseholdnumbercanearnmoreincomealongwith increasingpaymentcapacity.Somorehouseh oldnumbercanearnismoreloanamount.

+ Householdha s housevalueasmuchaspossi blebecausehousevalueisthecollateralofthehousin gl o a n and h o u s e v a l u e i s highwhichmeans thatloanamount frombankislowandriskfromthehouseholdalsoislo

8 MATUR + Longt e r m o f l o a n m a t u r i t y i s s y n o n yw. m o u swithincreasingpaymentformhousehold. Thelongermaturityofhousing loan,thesmallertheperiodicprinciplewhichtheymustto pay.Itmeansthattheirincomeensurerepaymentfro mhousehold.S o morematurityismoreloa namount.

2isthesimilartothemodel1.Butthesignofagevariableisdifferentfromthemodel2.Theagevariableint hemodel2willbepositivesign.Inlaborage,themoreageisthehigherworkingpositionandincome.

Dataincludes bothindividualinformationandhouseholdinformation oftheborrower.Theborrowersarethepersonswhocangethousingloan(inmodel1&2)andcannotgethousingl oan(inmodel1).

The authorwanttoapplytheresultofthisresearchtorealityofVietnameseeconomyespecially HDBankcase.NowVietNamhasmanydifficultiestofacethehighinflation,economic crisisallindustries,tighteningmoneypolicyofSBV Oncemoreimportantthingistheviolentco mpetitioninbankingfieldatthetimeofundertakingtoparticipateinW T O T h i s r e s e a r c h w i l l h a v e m a n y m e a n i n g f o r ban kt o f i n d o u t t h e s t a b l e developingstrategyparticularinc ustomerlendingbusiness.The author hasafortunatechangetojoinmodemprojectofbankinallmodule(kernel,generalledger,customerlen ding,branchteller ).That’scorebankingproject.Almostofdataforthisthesiscanbeextractedfromthecorebanki ngsystem.AnotherfortuneissupportandpermissionofHDBankgeneraldirectortoallowandtosignad ecisionfortheauthortogetthedata.

- Loaninformationandcustomerinformationfromheadofficeandbrancheso fHDBankinHCMC.Becauseofgeographicconditions,it’sdifficulttogetdataofsomevaria blessuchassizeofhousehold,thenumberofhouseholdcan earnincome fromarchivesofbranchesareoutHCMC.

- Loaninformationandcustomerinformationmusthasenoughforallvariablesofthesissucha s:sex,age,education,incomeofhousehold,sizeofhousehold,housevalue,maturityloan,collateral,loa namount.

DataisusedintheanalysisfromthecorebankingofHCMCHousingDevelopmentBank.Thea nalyzeddataareoftheyear2005,2006and2007.Thereasonwhychoosetheseyearare:

Thesamplesizeconsistsof306observedcases.Ofwhich,thereare198casesgettingahousingloanfromtheHD Bankand108casesrefusedtogettheloan.ComparedtothetotalborrowingcasesoftheHCMCbranchoftheH DBank,thesampleoccupies21.7%.

Themethodisusedwhichcalled“proportionatestratifiedsampling”(eachstratumisproperlyreprese ntedsothatthesamplesizedrawnfromthestratumisproportionatetothestratum’sshareofthetotalpopulati on).

Bythepossibilitytogetaloan:thepopulationisdividedintotwostratums:thecasest hatw e r e a c c e p t e d a n d r e f u s e d t o getthehousing l o a n ( 6 5 % a n d 3 5 % ofc asesaskingloanfromtheHDBank—figure2.1).

2007.Ineachyear,t h e sa mp le s aredrawnfollowingthepercentageo f eachstr atum ( a c c e p t e d andrefusedtogetahousingloan).

Alldataforhousingl o a n waspickedu p fromcorebankings y s t e m Afterthat,da ta will begroupbyyearbybranchandselectedbycommonconditionsasfollows:

- Observationn u m b e r o f e v e r y y e a r w i l l b e e x t r a c t e d b y b e l o w g r o w t h r a t e o f housingloan amount.S o withthecustomerw h o c a n gethousingl o a n , thereare24observationsinyear2005,48inyear2006and126in year2007.Similartothecustomerwhocannotgethousingloan,thereare12observationsiny ear2005,24inyear2006,72inyear2007correspondingtothegrowthratesof43%and2 83%incomparisontothepreviousyears.

Figure2.1sho ws thest ru ct ur e ofthepossibilityt o gethousingloanornot.Totally, thesamplesizeconsistsof306cases,ofwhich,1 9 8 casesareoftheoneswhocouldgeth ousingl o a n du r i n g theperiod2 0 0 5 -

Figure2 2 s h o w s thestructureofs a m p l e s in everyyearbyprobabilityandimprobability.Theobservationwillbepickedfollowbygro wthratepercentageoftotalhousingloanamount.Totalhousingloanamountinyear2005,2006, 2007are413,591,2268billionVNDwithinturnpercentageare43%

(year2007comparewith2006).Sowiththecustomercan‘gethousing loan,thereare24observationsinyear2005,48inyear2006and126iny e a r 2 0 0 7 Similart o t h e c u s t o m e r c a n n o t g e t h o u s i n g loan,thereare12observationsinyear2005,24inyear2006,72inyear2007. Figure2.2:Structureofsamplespossibilitytogetahousingloan.

Descriptiveanalysiswillbeappliedinthestudytounderstandthecentraltendency,thedispersionandthedistri butionofthesamples.DetailofstatisticaltestfordescriptiveanalysiswillbepresentedinchapterIV(item4 3).

- Multico-linearitytest:Multico-linearitytestslinearrelationshipsbetweentwoor moreexplanatoryvariables.

- Autocorrelationtest:Durbin-Watson(DW)testwillbeusedforautocorrelationof variables.

AlldataofthisthesiswillberunbySPSSsoftwareincludesanalysessuchas:statistical testsfordescriptiveanalysis,correlationanalysis,testsforvalidityofspecificmodels.

4.1 Situationofhousingcreditprogramf o r urbanho us eho l d i n HeChiMinhC i t y 4.1.1Housingdemandsofurbanhouseholdandsupplyofhousing

3m'/person.InHCMC,thereare150,000temporarydwellings,including43,000dilapidated housesand24,000slumhousesalongthecanalsandsmallrivers.Ofthetotal54.4millionin'ofurbanho using,32millionin'requiresimmediaterepairandmaintenance,and1.7millionin'needstoberem ovedfornewhousingconstruction.

SinceDoiMoi,theprivatesectorhasproducedabout70%ofthenewhousinginHanoiandabout60%i nHCMC(DLHofHanoi,1995).Therearedifferencesintheconceptofthe"privatesector"ofurbanhousi ngproductionbetweenVietNamandotherlowincomecountries.InVietNamtheprivatesecto rcovershousingproductionbyhouseholdusingtheirownfundstoproduce housingfortheirpersonal use.Becauseit’sdifficultforhouseholdtogetahousingloan,housingfinanceandtheboomofhousingconstr uctionincivilsectorafterDoiMoiaswellasthederegulationinhousingsector.Theauthoritieshavethehousing loanprogramasfollows:

TheNationalStrategyonHousinguptotheYear2010isstillunder studybytheMOC.Althoughthedraftstrategyatthismomentjusts h o w s somegeneral strategyhaspresentedtheprimaryconceptsonhousingdevelopmentwhichisconcerned withfinancingfromthecommercialbanksandfinancialorganizations.

Vietnam's financial system, despite significant reforms since 1988, remains underdeveloped and heavily dominated by state-owned banks Non-bank financial institutions and the securities market are relatively small, highlighting the system's weaknesses The transition from a planned to a market economy has led to institutional weaknesses that affect both the overall economy and the financial market Additionally, Vietnam's financial system shares characteristics with those of other developing countries, such as a monopolistic credit market dominated by state-owned banks, an incomplete credit market, and the absence of certain markets like credit insurance These challenges are exacerbated by repressive financial policies.

,Governmenthasbeentaking strictly controlover interestratesandoperationsofbankingsector.Therefore,interestrateshavebeenkeptunderthe marketclearingrates;andextensionofcreditisbiasedtowardsprioritysectors.

Theurbancreditmarketishighlyfragmented.Theformalandinformalsectorsco- exist.Theformalfinancialsect or includesgovernmentbanks,privatebanks,a ndcreditcooperativesandcreditschemes.Amongtheformalfinancialinstitutions,alargenumberoffo rmalloansconcentrateonruralhouseholdsratherthanurbanhouseholds.Onthehand, theinformalfinancialsectorincludesprivatemoneylenders,relativesandfriendsandotherin dividuals.Theyhavebeenactiveinthecreditmarket.

Ramanathan(2005)statethatADBwillhelpabout137,000lowincomeurbanresidentsinVietNa mtobuyorimprovetheirhomesthroughanapprovedloanforUS$30millionequivalentthatwillsupportth eestablishmentofanaffordablehousingfinancesystem.

AccordingtoADBsource(2003),ADBProjectEconomistAlfredoPerdiguerostatedthat"About65%oftheurban populationlacksaccesstoaffordablefinance forhousingconstructiono r improvement,s o theyhavetoresortt o informal s o u r c e s su ch asmoneylenders,friends,relativesandcreditassociations"and“Theprojectwillim prove

Vietnam's urban population has been growing at an annual rate of 3.8%, reaching 19 million people, which is nearly a quarter of the total population as of 2000 Despite government efforts to improve housing for low-income residents, urban housing conditions have worsened, with only 25% of urban households living in permanent structures and nearly 40% occupying areas smaller than 36 square meters Projections indicate that urbanization could increase to 46 million people, or 45% of the population, by 2020 The government has implemented various policies and strategies for housing development, including establishing a housing finance facility within the State Bank of Vietnam This facility will enable participating financial institutions to originate and refinance loans, eventually evolving into an independent mortgage refinance agency to attract additional funding through bonds or securities The total cost of the housing project is estimated at $51.8 million, with 58% funded by the Asian Development Bank (ADB), which provides loans with a 32-year term and a grace period of eight years at a low interest rate The State Bank of Vietnam serves as the executing agency for this project, set for completion by the end of 2007.

GovernmentandADBhaveparticularplanningforhousingloanandhousingdevelopment.Fo llowingtheinclusioninthe2002countryprogramoftechnicalassistancetopreparetheHou singFinanceProject,anADBFact-FindingMissionvisitedVietNamd u r i n g 4-

8March2002.TheMissionhelddiscussionswithofficials fromtheSBV,theMinistr yo f Construction,t h eMinistryo f Planning a n d I n v e s t m e n t , a n d theMinistryofFi nance.F o l l o w i n g d i s c u s s i o n s w it h allstakeholders, th e issuesreacheda nunders tandingwiththeGovernmentincludedas:

(1)FollowingarequestfromtheGovernmentforsupporttothehousingfinancesectorinVietNam,ADB approvedthepreparationofastudytoassessthehousingfinancesectoratthenationallevel,includingthephysical,in stitutional,andfinancialaspects.

(2)ThehousingfinancesectorisrelativelyundevelopedinVietNam.Thisreflectstheunderdevelopednature ofthewiderbankingandcapitalmarketsectorsthat arealsoinneedofsignificantreformand restructuring. recognizinglegalownershiprightsthroughtheissuanceofbuildingownershipandland usecertificates.

(4)Althoughthesereformshavehadasignificantimpactonthehousingsector,manyconstraintsstillr emain.Theseincludelackof(i)coordinationbetweenthegovernmentagenciesresponsible forthehousingsectorandthefinancialsector;

(iii)aninstitutionalandregulatoryframeworkforthehousingfinancesector; (iv)savingsschemeslinkedtohousingimprovementsanddevelopment;(v)long- termcapitalfinancingforhousing;and(vi)asystemoftransparent,well-justified,andtargetedsubsidies.

(5)Althoughnofinancialinstitutionsfocusexclusivelyonhousingfinance,manyhavesho wninterest inthe sectorandhaveinvestedpartoftheirportfoliosinthehousingsector.Thestructureofthehousingfinan cemarketinVietNamhasfourtiers.AtthetopisSBV,responsibleforsettingthe policyandregulatoryenvironmentforfinancialinstitutionsandmonitoringtheirprudenti alandoperationalperformance.Atthesecondlevel,therearestatebankswhoarerarelyengagedinprov idinghousingloanstohomeownersalthoughtheymayfinanceloanstohousingdevelopme ntandconstructioncompanies.ThisgroupincludestheBankforInvestmentandDevelopmentof VietNam,IndustrialandCommercialBankofVietNam,andBankforForeignTradeofVietNam.Asu bcategoryofthesecondtieristheprovincialhousingdevelopmentfunds,whichprovideconstruc tionanddevelopmentfinanceloanstohousingd e v e l o p me n t andconstructionc ompaniesaswellasfacilitatehousingloanfinancetohomepurchasersassociatedwiththefinancedhousi ngdevelopmentprojects.ThemostactiveinstitutioninthissubcategoryistheHoChiMinhCityInvestmentFu ndforUrbanDevelopment.

Athirdlevelcomprisestheretailhousingbankswhicharecurrentlyengagedinprovidinghousingloans.Th eyincludeAsiaCommercialBan k, HaNoiHousingBank,HoChiMinhCityHousi ngB a n k, MekongDelta HousingBank,andVietNamBankforAgricultureandRu ralDevelopment.Finally,thereareotherfinancialinstitutionsandconsumercreditorganizationsthatprovides mallloanstohomeowners.

(6)SocialsurveyspreparedundertheADB- fundedstudyandothersurveyspreparedbyotherorganizationsinHoChiMinhCity(HCM C)revealedthatmostlow- incomefamilieswouldpayaroundD50millionfornewhousing,whilesomecouldpayuptoD70mi llion.Theresultsoftheaffordabilityanalysisillustrateclearlythatmostlow- incomehouseholdscanaffordloansatmarketinterestratesifmedium-andlong- termfinancingwouldbeavailable.

The HCMC People's Committee is set to submit the official housing development program to the Government following the establishment of the National Strategy on Housing Although the program's details are not finalized, the Department of Planning and Investment (DPI) aims to increase housing availability from the current 5.8 square meters per person to 7.0 square meters per person To achieve this target, the city requires the development of 48 million square meters of housing HCMC plans to contribute 12 million square meters through expansion and an additional 36 million square meters through new developments, necessitating 5,400 hectares of land The city intends to secure 900 hectares for high-rise buildings and 4,500 hectares for residential areas in the suburbs The housing program consists of five key components, as stated by the Department of House-Land.

In 2007, Vietnam experienced significant economic growth after joining the WTO, achieving a GDP increase of 8.5%, the highest in a decade Recognized by global economic experts as the safest country for investment in Asia and second worldwide, Vietnam faced challenges such as unprecedented oil and gold prices, high inflation, and a struggling stock market Despite these obstacles, Vietnam capitalized on environmental changes that positively impacted its socio-economic development With a young, educated, and motivated workforce, HDBank, supported by its Executive Board and Management, has successfully set and pursued ambitious targets.

In2 0 0 7 , t h e c h a r t e r e d c a p i t a l w a s V N D 5 0 0 b i l l i o n ; t o t a l a s s e t s o f H D B a n k a p p r o x reachedVND14,000billion,equivalentto244%rise;themobilizedcapitalgainedVND12 ,456billion,withariseof284%;thetotalliabilitiesreachedVND8,912billionofthe233%oincrease.Wit hthegoodcontrol,thebaddebtwasunder0.3%oftotalliabilities.TheprofitbeforetaxwasVND168 billionequivalentto78%rise.Thedividendratein2006was1 6 % T h e HDBank’networkwas continuallyopenedmore20businesslocationsnationwideint o t a l l y 40branches.The staffintotalisupto1 0 0 0 T h e technologym o d e r n i z a t i o n p r o j e c t —

C o r e bankingw a s g o i n g the“ G o Live” l a u n c h issuccessful-atthisrighttime.

In 2008, the finance and monetary market faced significant uncertainty, marked by intense competition among banks, a shortage of skilled management, and limited government policies regarding securities trade, which raised potential risks in the real estate market To address these challenges, HDBank implemented a strategy to become a leading, diverse, and operational bank, focusing on enhancing its financial capacity by increasing its chartered capital to VND 2,000 billion, strengthening its brand, and diversifying its service offerings based on core banking principles The bank also prioritized improving management practices to align with international standards, boosting asset value and share liquidity Additionally, HDBank sought strategic partnerships to enhance competitive capacity and expand business performance sustainably while developing human resources through motivational policies In 2008, the bank committed to ensuring shareholder interests with a minimum dividend rate of 12% per year.

In2007,withyoung,higheducatedandenthusiasticstaffaswellasExecutiveBoard’sfullsupportsandonconsider ableattentionofBoardofManagement,HDBank’operationgainedgreatresultsbyitsnewmanagementstruct ure,orientationandchangesshowedasfollow:

TheprofitbeforetaxreachedVND168billion,equivalentto178%risecomparedtothat oftheyear2006.

Totalincome:777billion,suchasLoaninterest433billionfor55.8%oftotalincome,Non-

TotalExpenses:609billion,suchasOperationexpense490billionfor80.8%oftotalexpe nses,Managementexpense81billion(13.4%),theprovisionofcreditlosses27billion(4.4%),other expenses10billion(1.7%).

ThecapitalmobilizationplayedthekeyroletomeetthecapitalneedsofHDBank.In2007,thetotalca pitalmobilizationin HDBank’networkgainedVND1 2 ,4 56 bi l li on , increaseby284%compared t o 2006.Thisachievementw a s fro mtheapp licationofflexibleinterestratesinthemarket,servicequalityenhancement,networkopeni ngsnationwidewhichmade thecustomerssatisfiedandcomfortablewithHDBank’services.

As of December 31, 2007, credit operations accounted for 56% of total income, with outstanding debts reaching VND 8,912 billion, which is 71.6% of mobilized capital This marked a 95% increase over the planned target and a remarkable 233% rise compared to 2006 The bad debt ratio was low, at 0.3% for VND 27.9 billion, while the bad debt with loss possibility was VND 9.65 billion, representing 0.1% In 2007, HDBank launched marketing programs to enhance customer satisfaction by introducing new credit services, particularly in housing credit, which offered terms ranging from 10 to 15 years for households and 25 to 30 years for Phu My Hung housing purchases Additionally, credit programs were developed for low-income individuals and small to medium enterprises under ADB-sponsored initiatives.

Fé2.4GFOW thofloanoutstandingdebtsinHDBank( S o u r c e an nualreportofHDBankintheyear2 0 0 7 )

Theorybac 1Majorconcepts

Banking theory

Increditscoringmethod,thecreditworthinesscanbemeasuredbythevaluesbasingonthecharacteristi cs.However,therearesomecorefactorsthatareusuallyusedincreditmanagement(Kapoor,Dlabay,Hughes,20 01).Theyare:

Credita n a l y s i s i s a p r o c e s s o f c o l l e c t i n g i n f o r m a t i o n , a n a l y z i n g i n f o r m a t i o n w i t hsciencesmethodfortheaimtounderstandclearaboutclientsandtheirbusin essprojects,servingprocessofshort- termloandecision.Creditanalysisisanimportantstepwiththepurposeusingtomakeloandecision:a cceptorrejectissuingcredit.Inordertomakecreditdecisionbanksmustdothreestepsfollowing:

- Collectingsu ffi cie nt &accurateinformation.

Bycreditanalysis,bankscanreplacetheirexperiencesabouthouseholdandtheirprojectappliedforloan withscientificallyargumentandevidencesrelyoninformationanddataprocessing.Therefore,credit analysishelpbanksavoidingtwotypesofmistake:(1)givecreditforbadclient,(2)andrejectingthegoodone.

Analysiscontents:analysisshouldbetargetedevaluationofcapacityrepayingloanthatmean,iteva luatewhethercustomercanrepayseedmoneyandinterestornot.Toappreciaterepa ymentcapacityofcompany,wehavetodeterminedfactorseffectingtocustomer’srepaying- debtcapacity.Inotherhand,creditanalysiscontentsshouldbeconcentratedintoanalyzefactorse ffectingtoabilitytomeetobligation.Basically,abilityofrepayingloanisinfluencedby:

Chung(2000).Creditrationingisaconditionofloanmarketinwhichthelendersupplyoffundislesst hanborrowerdemandatthequotedcontractterm.Inotherwords,itmeansthatthereisexcessdemand. Increditmarket,creditrationingappearstobeaninefficientsituationofcreditmarket,wherei nterestratedoesnotworkwelltobalancesupplyanddemandsides.

Therearetwomajorwaystoexplorecausesofcreditrationing.Firstly,traditionalviewsconsider creditrationingresultedfromgovernmentinterventionsoncreditmarketbyimposinginte restrateceilingonlendinginstitutions.Interestrateisexogenouslyheldundermarketclearin glevel.Withinterestratekeepartificiallylow,somepotentialborrowerswhowant toborrowarerationed.S e c o n d l y , afterthedemise ofthetraditionaltheories,anewapproach tocreditrationingwasdeveloped.T h e n e w a p p r o a c h a r g u e d t h a t p e r m a n e n t creditr a t i o n i n g i s consideredasanequilibriumphenomenonratherthanatemporaryphenomenon.Mod erntheoriesidentifyproblemsofmoralhazardandadverseselectionincreditmarketas a sourceof creditrationingwheninformationis distributedasymmetiicallya m o n g marketparticipants.

Fragmentationofcreditmarketstronglyaffectstheborrowingbehaviorsofurbanhousehold s.Fragmentationofcreditmarket,firstly,isthoughtasaconsequenceoftherepressi veg o v e r n m e n t p o l i c y C e i l i n g r a te ondepositan d loanrateimposedb ycentralbankinducestoseverecreditrationinginformalfinancialsector.Resultingfro minter- linkagebetweenformalandinformalfinancialmarketstheunsatisfieddeman dofhouseholdsforformalloanflowsintoinformalfinancialsectorandputdemandforinfor malloanstoriseup.Secondly, it isviewedthatfragmentation ofhouseholdcreditmarketiscausedbystructuralandinstitutionalf e a t u r e s ofcreditm arketi n developingcountries I n addition, fragmentationofcreditmarketmay alsoresultfromweaknessintheinfrastructure

6 thatsupportsthefinancialsystem.Thirdly,anotherperspectiveonfragmentationofcreditmarket arguesthatformalandinformalfinancial sectorsareparalleldevelopedbecausetheyservedifferentsegmentsofcreditmarket.

In conclusion, under an ideal credit market, urban households can optimize their utility over time through both borrowing and saving The demand for loans is influenced by household resources, income, and activities, known as "demand-side effects." Conversely, credit market conditions and the relationship between urban households and financial intermediaries significantly impact borrowing behavior Inefficiencies in the credit market can prevent potential borrowers from accessing credit, while the severity of credit rationing explains the constraints faced by urban households Additionally, the fragmentation of the credit market contributes to varied borrowing behaviors among these households Ultimately, borrowing by urban households is shaped by both demand and supply-side factors.

The urban population in Asia is rapidly increasing, similar to trends in Latin America and Africa, and it is home to the largest concentration of impoverished individuals globally Approximately 25% of Asia's urban residents live below the poverty line, with higher proportions in certain countries India and China account for about one-third of the region's urban population, many of whom experience relative poverty For instance, in Mumbai, around 50% of its 12 million residents live in slums or on the streets, contributing to a global homeless population exceeding 100 million To address the basic needs of its urban population, Asia will require an estimated investment of $280 billion annually over the next 30 years in housing and related urban sectors.

Thelackofhousingaccessisoneofthemostseriousandwidespreadconsequencesandcausesofpo vertyinAsiancities.Theimprovementsinhousingthatareimportanttoimprovingthequ alityoflifeamongthepooroftendoesnotreceivetheattentiontheydeservefrompolicymakers(Da niere,1 9 9 6 )

T o makeanyappreciableimprovement,substantialgovernmentspendingisneeded,bot hinthephysicalexpansionofthecity’sinfrastructurea ndimplementationo f povertya lle viationprograms.Buttressedbytheheritageofliteraturethatarguestheimportanceofa ffordableandimprovedhousingin urbanpovertyreduction(see,forexample,Mitlin,2001),theimmediateresearchissueis howpoorfamiliescanaccessurbansheltermoreaffordably.

TheSingaporepublichousingdevelopmenthasattractedkeenresearchinterest(see,forexample,Won gandYeh,1985;Yuenetal,1999)butfewhaveclarifiedthepublichousing-urbanpoverty nexus.Thisprovidesthestartingpointforthepresent analysisoftheperformanceo f housingdevelopment.

According to Global Urban Development Magazine in Singapore (GUDS, 2007), significant economic development has been crucial for housing improvements in the country Deliberate actions were taken to diversify the economy and create employment opportunities, resulting in an average real GDP growth of 8.6% per year from 1965 to 1999 This economic growth led to a substantial increase in nominal household income, with real per capita GDP rising from S$4,000 in 1965 to S$32,000 in 1999, while inflation remained low at around 2-3% per year Additionally, from 1988 to 1998, the average monthly household income grew by 6.7% annually, contributing to a higher rate of asset ownership The proportion of homeownership in public flats surged from 26% in 1970 to 92% of the housing stock by 1999.

Singapore's public housing financing is closely tied to the nation's economic progress, showcasing its potential for job creation Established in 1960, the Housing Development Board (HDB) has constructed over 850,000 residential units, along with commercial and industrial spaces, schools, community facilities, parks, markets, and car parks by the year 2000 This extensive construction not only enhances living conditions for the underprivileged but also generates significant employment opportunities, demonstrating a high multiplier effect on the economy Scholars like Sandilands (1992) have highlighted the construction sector's role as a leading industry, with growth rates surpassing overall GDP growth, while others have noted the stimulating effect of public sector housing projects on the economy (Krause et al., 1987).