Demographic Profile as a Determinant of Default Risk

8/6/2019 Demographic Profile as a Determinant of Default Risk

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International Research Journal of Finance and EconomicsISSN 1450-2887 Issue 58 (2010) EuroJournals Publishing, Inc. 2010http://www.eurojournals.com/finance.htm

Demographic Profile As a Determinant of Default Risk

in Housing Loan Borrowers Applicable to Indian Condition

Mritunjay Kumar

Doctoral Student, XLRI, Jamshedpur, India, Resident: Nageshwar colony, Boring Road, Patna

Tel: +91-9431017287, 09234877342E-mail: [email protected]

Abstract

One of the major objectives of this paper is to identify the demographicdeterminants of the Housing loan Default risk in the Indian condition (with a case study of

one of the urban centre, city like Patna). I) The need for testing some of the hypothesisarises as the demographic profile of the country is different in many ways with rest of thedeveloped mortgage market. II) The other objective is to identify the type of households inthe population more prone to default on payments. III) How demographic and situationalfactors (such as employment status, family type, income level, locations) coupled withbehavioral aspect affect default risk.

The proposed study employs collection of relevant secondary data about housingloan account in Default from the select Bank branches / processing centres of the Bankwithin Patna. Secondly primary data has been collected from the relevant borrowers bycontacting them, administering them a set of questionnaire to check on the efficacy of thevariables and to register the dynamic changes within the variable.

Logistic regression method has been used to predict the probability of housing loandefault, based on financial and non financial variable. The findings are that thoughdemographic factors determine the default risk and model built on it is more reliableindicator than not using the model. However, there is no significant difference inrepayment habits across age groups.

Keywords: Default Risk, Mortgage Finance, Housing Finance, Housing Loan,Demographic profile.


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International Research Journal of Finance and Economics - Issue 58 (2010) 151

IntroductionThe Housing Finance is the major driver of Retail growth for Banks & Financial institutions in India.The Housing Finance constitutes roughly 47% of total outstanding retail loans and 12 % of Total Loans& advances for all scheduled commercial banks as on March 2007, as evident from Table 1.

Item Outstanding as on

March 2006

(10 millions)

% of Total Retail

Loan

Outstanding as on

March 2007

(10 millions)

% of Total Retail

Loan

Housing Loans 1,79,060 47,66% 2,24,481 46,01%Consumer Durables 4,469 7,296Credit CardReceivable

12,434 18,317

Auto Loans 61,369 82,562Other Personal Loans 1,18,351 1,55,204Total Retail Loans 3,75,683 4,87,860

Total Loans &

Advances

14,73,723 4,87,860

Source: RBI Report on trend and progress of banking in India, 2006-07.

The mortgage loan is the most commonly used terminology in USA and many of the developed

and developing countries. In India, the synonyms for Mortgage loan are Housing Loan.The relative size of the domestic mortgage market in India is small as compared to USA &

some of the European & Asian countries in terms of the proportion of their respective GDP Size. TheMortgage market size in India at Rs.2.25 trillion is still just 5% of the GDP size compared to 70% inUSA, 80% in UK, 94% in Denmark, 52% in Germany, 50% in Hong kong, 46% in Canada, 36% inSingapore, 26% in Malaysia, 16% in Thailand, 14% in South Korea, 11% in China. While some of themarket is showing maturity in terms of growth, India has a different story. The CAGR (compoundedannual growth rate) in terms of Housing loan disbursement by the banks for the period 2000-01 to2007-08 has been around 30 percent. Further, the growth in Housing loans by some of the leadingMortgage lender & key player in the market has been targeted at 33%. According to Crisil Research, itis estimated to grow at 12 percent over next five years. The present trend of growth combined with

existing house shortage of around 24.71 million as on 2007(11th Five year plan working group reporton urban housing) makes it an attractive potential market. As the number of players joins in to quicklybuild the volume, there is somewhere concern that rapid expansion of credit will increase thepossibility of relaxation of income criteria for consideration of loan and financial institutions will dilutethe lending standards to accommodate those whose income stream is not guaranteed or secure.

This concern is based on the trend observed in USA and other developed market. 100 percentlending became much common in USA. This easing of lending norms is one of the major driver ofobserved increase in early payment defaults in the USA ( Kiff and Mills 2007: Gerardi, Lehnert,Sherlund and Willen, 2008) has shown that lending standards declined more in areas that experiencedlarger credit booms and house price increases.

There seems to be a great variation in lending standards (as data suggests) across major players

in India. The major player can be categorized into four types, namely SBI & Associate Banks,Nationalized Banks, Foreign Banks and other scheduled commercial banks (OSCBs) which alsoinclude Private Banks like HDFC, ICICI, Axis Bank etc. (Figures as on 31

stMarch2007)

Location Type SBI& Associate

Rs.mn/borrower

Nationalized

Rs.mn/borrower

Foreign Banks

Rs.mn/borrower

OSCB

Rs.mn/borrower

Rural 0.26 0.24 2.71 0.57Semi urban 0.22 0.25 --- 0.38Urban 0.32 0.33 1.40 0.70Metropolitan 0.54 0.66 1.49 0.94

Source: derived on the basis of Basic Statistical Returns Figure, RBI.


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152 International Research Journal of Finance and Economics - Issue 58 (2010)

The average ticket size variation between SBI & Associates: Nationalized Banks: ForeignBanks: OSCBs (Pvt Banks) in urban area are in the ratio of 1:1:4:2.

Variation in standards across the industry imposes systemic risk which can be a potential threatto the sector.

While there is perceived opportunity by the financial Institutions (FIs) to profitably deploy itsfund into housing sector, which is likely to give an attractive returns for the medium term of 5 years tolong terms of 20 years, there is also enhanced risk of default, leading to substantial locking of funds.

The Bank has to choose the optimal tradeoff between profitability and risk.Default risk is the uncertainty surrounding a borrowers ability to service its debts and

obligation. The Bank, particularly the Public sector bank cannot discriminate between variousborrowers in lending money, as Housing sector upto Rs.30 lacs also comes under priority sectorlending. The bank at best can make probabilistic assessments of the likelihood of default. The bank atany point of time holds millions of loan accounts under various categories. Like other rare events withhigh costs, default risk can be effectively managed in a portfolio. (Source: Managing Bank risk Anintroduction to Broad base credit engineering by Morton Glantz, Academic press).

The portfolio approach requires to measure the default correlations. Correlations measure thedegree to which the default risk of the various borrowers and counterparties in the portfolio are related.The probability that a borrower will default (Default probability) and the extent of the loss incurred in

the event that the borrower defaults (loss given default) together constitutes to arrive at the total likelydefault to help bank make provision against Bad Debts.

The Banks frequently use broad indicator approach that makes use of a number of variables tohelp build a credit scoring Model. The variables used in the model are sound in estimating theprobability of default of an applicant and secondly their explanatory power is used in analyzing theloan application. The variables are broadly divided into four main categories, namely: DemographicIndicators, Financial Indicators, Employment Indicators and Behavioral Indicators.

Demographic Indicators Age, Sex, Marital status, Number of dependents, addressFinancial Indicators Total assets, Gross Income, Net Income, Monthly cost of householdEmployment Indicators Type of employment- Public sector / Pvt sector, Government / Non

Government, length of employment, Number of employment over last 5yearsBehavioral Indicators Saving / Current account, Average balance in the account, Loan

Outstanding, Status of the previous loan- Standard or NPA, Any previousdefault, Collateral Guarantee, Number of payments per year

The demographic variables establish the identity of the borrower for the purpose of the loan andlooks at legal aspects. These variables do not have the highest importance but they capture variousregional, gender and other relevant differences. For example, it is often found that old man is less riskythan young men. In general, the risk of default decreases with age. Home owners also represent a lessrisky category due to a house as collateral.

The Financial indicators are used to determine the quantum of loan. The bank considers 40 to60 times of the Net monthly Income (NMI) or 75% to 85% of the value of the property, whichever islower.

The employment indicator is used for fixing repayment period and for the purpose ofdocumentations.

While financial indicators initially sets the limit of advance for each borrower, at later stage,more than financial, it is non -financial parameters that affects financial aspect and matters in thedefault risk. For example, credit scoring model do not consider education as a variable. Empiricalevidence suggests that a factor such as age, education, income and marital status affects an individualsrisk tolerance. The risk loving nature of some of the individuals may jeopardize the repaymentschedule towards housing loan instilments.


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One of the major objectives of this paper is to identify the demographic determinants of theHousing loan Default risk in the Indian condition (with a case study of one of the urban centre, city likePatna), since the demographic profile of the country is different in many ways with rest of thedeveloped mortgage market, where some of the hypothesis has been tested. ii) The other objective is toidentify the type of households in the population more prone to default on payments. iii) Howdemographic and situational factors (such as employment status, family type, income level, locations)coupled with behavioral aspect affect default risk. The essence of the study is that none of the broad

indicators categorized into four category work in silos, rather they interact in a dynamic way and therelative importance increases or decreases over the life time of the mortgage loan.

The rest of the paper is divided into Literature review. The third section will focus on Housingmarket condition and the demographic profile of the borrower. The fourth section is concerned withResearch questions and Research design which deals with data, construction of variables anddescriptive statistics. The fifth section will present the empirical findings, and result of hypothesistesting, followed by conclusion in sixth section.

Literature ReviewThe most commonly mentioned causes of default in the literature are: Easing of lending norms, lending

standards, negative equity, borrowers personal characteristics, Loan to value ratio (LTV), Purpose ofpurchase, income of the borrower, credit score, contemporary economic condition, payment to incomeratio, loan tenure, location, unemployment, marital status, credit history, age, trigger events (likedivorce, loss of a job, accident or sudden death), default cost, default as a rational decision, decision torelocate, wealth maximization etc.

There are two competing theories on default of Mortgage loans: Equity maximization modeland Ability-to-pay model. The equity theory of default suggest that borrower base their defaultdecisions on a rational comparison of the financial costs and benefits in continuing (or discontinuing)the periodic payments on the mortgage loans. The borrower will choose to default if the financialbenefits are less than financial cost. The borrower thus engages in optimizing behavior. The modelhowever does not include other cost like Psychic costs involved like argument with the financial

institutions on a regular basis, etc. The ability to pay theory suggests that the borrower will not like todefault as long as their income flow is sufficient to meet the periodic payment without placing anyundue burden on the household. The term household is used as because the house property is one of themajor assets of the household and even if the mortgage property is in name of the borrower, it involvesmany times collective decision of the family members, though the degree of the decision making varyamong family members. The equity theory implies that probability of default is positively related tothree things: the market value of the mortgage property, the outstanding loan and Loan to value (LTV)ratio. Jerry and David (1980) while testing the two hypotheses finds that the equity theory of defaultsdominates the ability-to-pay hypothesis.

The Disaggregate data allows us to study the determinants of mortgage arrears at the householdlevel, allowing us to capture the idiosyncratic factors like income shocks, relationship breakdown.

Coles (1992) found that unemployment and relationship breakdowns could each explain around 25%of arrears, and those in arrears were typically self-employed, mostly working in an industry withexposure to the construction industry, or working in sales-oriented businesses.

Burrows(1997) used a logistic regression to model the likelihood of households being in arrearsof three months or more. The results suggested that households were more likely to be in arrears if theyhad a 100% mortgage, were employed part-time or unemployed or unable to work, worked in theprivate sector (relative to the public sector). Bheim and Taylor found that age was important: oldheads were less likely to experience housing finance problems. Households with higher income and atleast two members earning in a households were also less likely to face housing finance problems. Insome findings it is found that the proportion of households reporting repayment problems is higher if


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the head of the household is female. The house has problem if the head of the household is currentlyunemployed, the household faces problem if the partner is disabled or unemployed.

Orla and Tudela(2005) found that persistence in mortgage payment problems was greateramong households in which the head was 35 years old or over than it was among households headedby younger individuals. That is, younger households are more capable of getting out of problems thanthose aged 35 or over.

Lending standards declined more in areas that experienced large credit booms and house price

increases,(Ellis, 2008). Loans within the same geographical area and property type tend to exhibitcorrelation in default incidence (Yildiary Yildirium, May2007). Lawrence and Arshadi(1995) used thelogit model using a series of borrower and bank variable to analyze the management of problem loansand to determine the resolution choice.

Logit model was also used by Campbell and Dietrich (1983) to show that the age of themortgage, the LTV ratio, interest rates and unemployment rates significantly explain mortgageprepayment, delinquencies and defaults.

Logit, probit and Discriminant analysis are the most commonly used research methodology toexamine factors for different level of default risk. Jackson and kaserman (1980) used multivariateregression and probit analysis to measure default risk. Charitou, Neophytou and Charalanbous (2004)found that the logit method is superior to other methods in predicting defaults. The various methods are

often comparable in results due to the fact that there exist some mathematical relationship betweenvarious models being used. However, the popularity of logit method is mainly due to the fact that noassumptions are imposed on variables, with the exception of missing values and multi collinearityamong variables. Contrary to this, non-parametric methods can deal with missing values andmulticollinearity (or correlations) among variables, but often are computationally demanding.

The housing loan borrower exhibit four types of payment behavior: become delinquent (delaypayment), default thereby inviting foreclosure, prepay through refinance or resale, pay the regularinstallments. The delinquency or the default are somewhat related and varies in degree, it points toproblem in borrower and lender relationship. Gardner and Mills (1989), recognize that delinquentborrowers do not necessarily end up in default, employ a logit regression model to estimate theprobability of default for currently delinquent loans. They recognize that the Bankers use this method

to identify the severity of the problem loan to enable them to formulate an appropriate strategy to dealwith such delinquencies. The Indian banks identify the delinquencies which are in the early stage assoft NPAs. NPA are the abbreviation for non performing assets.

Kau and keenan(1998) treats default as a rational decision and in his research paper providesthe entire distribution of defaults severity. The distributions of severity are both disperse and skewed.The severity distribution shifts more than in proportion to the rise in the loan to value (LTV) ratio.Further, he has demonstrated that severity of default rise as we increase the LTV ratio.

According to empirical model, negative mortgage value motivates financial defaults. Themortgage value is equity (the amount paid by the borrower from his savings to the developer apartfrom Bank loan and some more investment in the house for furniture & fittings, registration cost etc.),house value less mortgage balance, and the value of prepayment and default options imbedded in

mortgage contract.The default imposes personal costs on borrowers that include limits on occupational and credit

opportunities, social stigma and damage to reputation (Kau, keenan and Kim, 1993; and Vandell andThibodeau,1985). If these costs exceed the absolute value of negative equity, the borrower will notdefault.

Dennis and Cross (The delinquency of subprime mortgage) using nested and multinomial logit,finds that credit scores and loan characteristic play an important role in loan defaults and thatdelinquency and defaults are sensitive to current economic conditions and housing markets. In many acases, the trigger events are potential cause of loan defaults and payments. Typical trigger eventsinclude losing a job, a severe illness, or the breakup of the household.


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The locations matter in many a case like frequent cyclone devastations in some places,proximity to seismic zone, where earthquakes are severe, etc. Brent Ambrose and Sanders (2001) findsthat the properties located in the west (USA) are more likely to prepay relative to other regions.Further, the mortgage on southern are less likely to go to full term relative to those in the west. They donot find any statistical relationship between LTV and prepayment or default. He finds that mortgagewith higher LTV at origination are more likely to go to the full term. Von Furstenberg has documenteda consistent nonlinear relationship between default incidence and the age of the mortgage.

Some studies suggest that income reductions or interest rate increase should be more importantthan equity levels in influencing the decision to delay payments.

The loan-to-value (LTV) ratio has been found as a key variable in explaining the probability ofMortgage loan default in several studies by Vandell(1978), Ingram and Frazier (1982), Campbell andDietrich(1983), Vandell and Thibodeau(1985), Mills and Lubuele(1994), Deng et al. (1995).

Another important variable in explaining the probability of default is debt-to-equity ratio (page,1964). Stansell and Millar (1976), Vandell (1978), Ingram and Frazier (1982), have found thatpayment-to-income ratio is positively correlated with the probability of default (i.e, higher the paymentto income ratio, greater is the default risk). Some Banks consider repayment (equated monthlyinstallments to Net Monthly income as 40 % to 55%). Banks also permitting higher LTV ask foradditional collaterals or guarantee. William, Baranek and Kenkel (1974) have found that borrowers

having initial payment to income ratio exceeding 30% have higher default rate.Simulation analysis has been used by Vandell and Thibodeau (1985) to demonstrate several

non-equity factors overshadowing the equity effect on default which helps to explain why somehouseholds with zero or negative equity may not default, while others with positive equity may.Clauretie ( 1987) has also argued that other non-equity factors like sources of income, property valueand borrower characteristics clearly play a larger role in affecting default levels.

Lee (2002) has identified the purpose of purchasing real estate property is one of the keydeterminants of default risk. Therefore, when the market price of collateral falls sharply or economicperformance becomes much worse, the property frequently will be abandoned by the owners therebylimiting their loss.

Follian, Huang, and Ondrich (1999) include in their model tenure, location, demographic and

economic variables as covariates to explain default.Studies of von Furstenberg and Green (1974), Avery et al (2004) have assessed local situational

factors as factors of default risk. They find that inclusion of situational factors (like unemploymentstatus, marital status, credit history etc.) and other borrower specific characterrs (joint account or singleaccount, age, location of the borrower etc.) improves the performance of the scoring models.Riddiough (1991) has found various trigger events, such as divorce, loss of a job, and accidents orsudden death has influence on default behavior. Eichholtz ( 1995)has observed the relationshipbetween regional economic stability and mortgage default risk in Netherlands.

Housing Market Condition & Demographic profile of IndiaThere are more than 5 million housing loan outstanding as on 31 st March 2007, by all Schedulecommercial Banks in India. The total aggregates for all scheduled commercial banks are at Rs.2.28trillion. All schedule commercial Banks (ASCBs) constitute roughly 65% of the market share. Rest35% is shared by Housing Finance companies (HFCs). The Total housing loan outstanding wouldexceed Rs.3.5 trillion. There are 219 million households in India. The total number of housing loanoutstanding comprises of 2.3 % of total number of households. There is urban housing shortage of24.71 million as per 11th five year plan (2007-12) report on urban housing. On average, roughly, thereis need for 5 million houses per year. The present rate of disbursement by ASCBs of approx 0.5 millionper year, will hardly be able to fill this gap. There is serious resource constrain on the supplier side,like Housing Developers, Housing Boards, Private builders in form of Man, material and Financeconstrain. The house due to its price is hardly affordable by many of the households. The low cost


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house is the only solution. This would also ensure that the financial institutions, even if they are askedto lend to people under financial inclusion, will able to contain their loss given default (LGD). ForPSBs, lending to housing sector is not only mere a commercial decision, but also carries obligation tofulfill social commitment. The Banks has to necessarily lend 40% of their total advances towardspriority sector. The lending to housing sector up to Rs.30 lakhs comes under priority sector.

The Bank raises deposits for short term which range from 91 days to maximum period of 10years. The Housing loans are generally long term. It ranges from 5 years (Minimum) to 52 years

(Difference of maximum permissible 70 years eligibility age 18 years). This requires matching ofasset and liabilities, maturity wise and Interest rate wise, on ongoing basis and constant tracking ofloan account. According to Basel Norms and Income recognition norms, the Bank has to classify itsloan account into standard, sub-standard, Doubtful and Loss Assets. The bank has to keep provisionsfor all its assets depending on type of classification, varying between 0.40% on Standard assets to100% on loss assets.

In case of residential mortgage loans, when such loans are past due for more than 90 days theywill be risk weighted at 100%, net of specific provisions. Thus the moment such loans becomedelinquent for more than 90 days, the bank has to carry higher risk weight from 20% in case ofstandard account to 100% in case on NPA. This seriously limits the lending ability of the banks.

There are three kinds of players in the Housing Finance: Financial institutions, Scheduled

commercial banks, and other Institutions. Presently there are 88 SCBs including 28 PSBs & 18 PvtBanks; and 29 HFCs. Towards the end of the 1990S, against backdrop of lower interest rates, industrialslowdown and financial deregulation, commercial Banks shifted their focus from the wholesalesegment to retail portfolios. Housing Finance traditionally has been characterized by lownonperforming assets (NPAs). However, the asset prices over the last few years rise has beendisproportionate to the income level of a first time house loan Borrower. Past trend has shown Houseloan disbursement of CAGR OF 31 percent during 2000-01 to 2007-08. The disbursement made in last3 years constitutes 68 percent of the outstanding portfolio. Presently the SCBs among themselvesconstitute roughly two-third of the total outstanding Housing loans.

Table 2: Regional and bank-group wise classification of outstanding housing loans of scheduled commercial

banks as on March 31(Rs million) 2004 2005 2006 2007

No. of

A/csAmount No. of A/cs Amount

No. of

A/csAmount

No. of

A/csAmount

SBI & associates

Rural 123,658 27,686.5 175,387 47,512.9 222,966 73,465.7 236,890 62,657.8Semi-urban 302,897 57,168.6 386,043 84,081.1 415,998 105,051.3 522,079 118,972.3Urban 322,175 74,915.3 387,035 111,779.3 440,510 140,803.6 516,735 169,698.4Metropolitan 174,714 52,715.5 207,529 98,817.6 240,954 113,834.2 299,469 160,338.9All-India 923,444 212,485.9 1,155,994 342,190.8 1,320,428 433,154.8 1,575,173 511,667.3Nationalised banksRural 216,223 33,547.3 252,404 46,817.5 298,794 63,602.9 331,523 80,731.7Semi-urban 319,244 55,480.9 385,624 79,242.7 428,255 100,070.0 456,034 116,261.8Urban 466,630 110,405.0 555,711 151,584.2 671,526 198,636.1 663,954 221,843.2

Metropolitan 386,122 142,128.1 416,374 196,986.9 628,504 360,900.8 699,786 465,496.3All-India 1,388,219 341,561.4 1,610,113 474,811.3 2,027,079 815,272.3 2,151,297 884,332.9Foreign banks

Rural 60 38.2 2,074 1,052.4 1,305 1,162.4 171 465.8Semi-urban 0 0.0 0 0.0 0 0.0 0 0.0Urban 1,511 1,666.8 2,617 2,388.2 1,704 2,534.3 2,105 2,952.6Metropolitan 61,643 55,352.6 95,852 84,167.2 121,438 159,787.8 73,553 110,132.2All-India 63,214 57,057.6 100,543 87,607.8 124,447 163,484.5 75,829 113,550.7RRBsRural 100,012 8,494.8 125,175 11,080.2 134,222 12,450.5 145,922 14,512.3Semi-urban 51,561 5,865.1 61,628 7,600.9 67,018 7,986.5 70,755 9,237.1Urban 30,980 4,459.9 33,087 4,915.5 40,431 5,739.4 45,696 6,993.3Metropolitan 2,185 315.3 2,310 355.1 5,237 792.0 5,642 927.5All-India 184,738 19,135.0 222,200 23,951.8 246,908 26,968.3 268,015 31,670.3


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Other SCBs

Rural 27,090 7,354.1 55,786 23,779.0 68,835 31,452.1 72,331 41,864.6Semi-urban 75,564 18,595.5 83,446 21,744.9 89,888 26,177.8 102,772 39,172.2Urban 99,705 39,283.5 152,293 74,400.3 189,045 110,582.3 224,507 157,099.6Metropolitan 273,052 157,991.4 286,075 219,484.5 454,901 306,642.2 539,989 509,875.9All-India 475,411 223,224.5 577,600 339,408.6 802,669 474,854.4 939,599 748,012.3

Source: Basic Statistical Returns, RBI

The SBI& Associates account for 1.57 mn number of loan accounts, almost 31.44% of totalnumber of loan accounts. However, in value terms, it accounts for only 22.35% of the market share.There seems to be disproportionate holdings of loan portfolio among different banks. The Foreignbanks and other scheduled commercial Banks (other SCB) holding high value advance. It could be dueto liberal finance or excessive funding approach.

The Foreign Banks has larger presence in Metropolitan cities. Other SCB dominates in theMetropolitan centre where they have large exposure compared to Nationalized Banks or SBI &Associates. It seems the Private Banks have followed aggressive lending policy in Metropolitan center,where they clearly left behind the Nationalized Banks in terms of value of loan disbursement, despitethe fact that Nationalized Banks hold much larger number of loan accounts vis a vis Private Banks /other SCB. The SBI & Associates hold only 12.86% market share in Metropolitan centers. TheNationalized banks are leader in Rural Market, followed by SBI & Associates. The SBI & Associatesare leader in Semi urban centre, followed by Nationalized Banks. There seems to be greatercompetition among all Banks in urban centre.

98.25% of loan size is covered under range of Rs.25, 000/- to Rs.10 million, that is $ 560 to $ 2,22,220. There seems to be a great variation in asset price from Rural to urban. Within each centre,Rural, Semi urban, Urban, and Metro there seems to a wide variation in type & quality of the House.The Wide variation in asset price could be explained in terms of location, type of House, Size of theHouse, Purpose, Income level etc.

Table 5: Size of credit limit-wise classification of outstanding credit of scheduled commercial banksaccording to occupation March 2007

Loan slab 2004 2005 2006 2007

No. of A/cs Amount% in Value

TermsNo. of A/cs Amount No. of A/cs Amount No. of A/cs Amount

% inValue

Terms

Rs 25,000 andbelow

240,759 3,358 0.3934554 205,307 2,921 220,483 3,096 311,209 4116 0.179

Above Rs25,000 and uptoRs 2 lakh

1,532,541 160,415 18.795755 1,643,585 171,110 1,886,439 192,950 1,859,875 183510 8.01

Above Rs 2lakh and uptoRs 5 lakh

847,832 259,311 30.383355 1,174,191 360,992 1,441,806 440,201 1,537,198 469227 20.49


292,757 189,022 22.147624 447,699 290,869 635,462 413,452 796,254 500975 21.88

Above Rs 10

lakh and uptoRs 25 lakh 104,558 139,028 16.289849 164,352 219,582 277,189 372,150 369,131 475558 20.77


13,783 41,211 4.8286747 23,740 68,835 47,111 134,721 66,161 185571 8.1

Above Rs 50lakh and uptoRs 1crore

2,142 12,368 1.4491531 5,475 32,029 9,547 56,516 67,801 435084 19.00

Above Rs 1crore and uptoRs 4 crore

462 6,866 0.8044862 1,624 25,030 2,829 43,516 2,117 24872 1.00


52 2,111 0.2473449 147 5,578 199 8,298 102 4547 0.2


40 2,733 0.3202244 128 8,332 159 10,175 60 3648 0.16


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44 6,443 0.7549235 114 15,881 160 22,007 1 234 0.01

Above Rs 25crore

56 30,598 3.5851541 88 66,812 147 124,589 4 1893 0.08

Total 3,035,026 853,464 3,666,450 1,267,970 4,521,531 1,821,671 5,009,913 2289234


The loan ticket size (Rs.25, 000/- to Rs.2, 00,000/-) constitute the greatest number, 50.49% of

the total number of housing loan accounts in 2004. Within three years, from 2004 to 2007, there hasbeen gradual shift, as percentage has fallen from 50.49% to 37.12% in this category, and shift istowards Rs.2, 00,000 to Rs.5, 00,000. The median value in value terms in 2004 has been Rs.5, 00,000to Rs.10, 00,000 (Rs.0.5 million to Rs.1 million). The shift in 2007 is clearly towards Rs. 1 million toRs. 2.5 million, which points towards rapid rising asset price and emergence of the new income class,liberal finance available from the lending institutions.

Clearly, one fifth of the total outstanding loans are catered to the rich class, where House pricebecomes affordable in the range of Rs. 5 million to Rs.10 million. Such numbers are in the range of70,000 people and probably distributed in Metropolitan centers. This is to be examined in case ofPatna, one of the urban centers.

Disbursements in the housing finance industry are affected by factors such as the Averageticket size (ATS) value and the number of housing loans disbursed. Average ticket size (ATS) is in turnaffected by factors like average area of the house, average loan-to-value (LTV) ratio and the price persquare feet of the house.

Demographic Pattern in India

The Greatest advantage of India is that it has the largest working young population. While some of thedeveloped countries like Japan, Europe are facing aging population, it is not so, in case of India.Population and Age wise Analysis of selected countries

Age Group India China USA Japan0-14 32 % 23% 21% 14%15-29 28% 24% 21% 19%30-44 20% 25% 22% 20%45-64 15% 20% 24% 28%

Above 65 5 % 8% 12% 19%Population (million) 1066 1291 290 127

Source: Mckinsey Report 2005


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There are 484 million workforces in the country. Out of which 273 millions are engaged inAgriculture, 61million are in Manufacturing sector and 150 million in Service sector. The educationlevel among the workforce is that 400 millions are under matriculate, 48 millions are intermediate, andanother 48 million are diploma holders or graduate.

The 784 million population lives in Rural area, 224 millions in 2nd

Tier Cities and 112 millionsin top 20 cities. The household is divided into three categories: Lower Income household with incomeless than Rs.71000/- per annum, Middle Income household with income range of Rs. 71000 to Rs.

2,85,000/- per annum, and upper income household with income more than Rs. 2,85,000/- per annum.There are 46 million lower income household, 136 million middle income household and 37

million upper income household.There urban population in India is 336 million (30%). There are 607 cities with a population

size of more than 50,000. Out of 607 cities, 35 cities are with a million plus population. The NHB hascreated a housing index of 15 cities that keeps track of Housing prices. These cities are : Delhi,Bangalore, Mumbai, Kolkatta, Bhopal, Hyderabad, Faridabad, Patna, Ahmedabad, Chennai, Jaipur,Lucknow, Pune, Surat and Kochi.

Presently the Rural & semi urban centre commands 21% of the outstanding housing loanswhere as Urban and Metro centre commands 79% of the outstanding loans in terms of value. The sevenMetros alone command around 55% of the Housing market. The urban centre among themselves

command 24% of the housing market. The southern States clearly dominates in Housing constructionand Housing Loans. The 4 southern state corner roughly 1.9 million housing loans out of country totalof 5 million, which constitutes 38% of the total housing loans. This may be attributed to 3 Metropolitancentres in the southern region, namely, Hyderabad, Chennai, and Banglore. Each of the 4 Southernstate covers nearly 0.5 million housing loan.

Table 3: State and population group-wise classification of outstanding credit of scheduled commercial banksaccording to occupation

(Rs million) 2004 2005 2006 2007

Region /

State/UT

No. of

A/c s

Amount

outstanding

No. of

A/c s

Amount

outstanding

No. of

A/c s

Amount

outstanding

No. of

A/c s

Amount

outstanding

Northern region ( Haryana, Himachal Pradesh, J&K, Punjab, Rajasthan, Delhi, Chandigarh)

Region total 460,908 162,237 578,866 239,044 775,513 379,389 756,008 425,512

North-eastern region (Arunachal Pradesh, Assam, Manipur, Meghalay, Mizoram, Nagaland,Tripura)

Region total 51,658 9,485 77,530 17,749 95,274 25,747 100,287 28,044

Eastern

region

Bihar 61,811 11,624 68,866 15,591 90,299 22,600 120,880 38,258Jharkhand 30,545 5,314 198,469 32,532 31,258 8,586 36,655 10,627Orissa 163,344 23,176 28,250 6,288 215,324 38,191 206,611 41,081West Bengal 170,195 37,431 205,642 55,235 246,467 80,432 260,640 108,521

Region total 429,764 78,503 507,712 111,625 590,943 152,560 634,481 202,567

Central region (Chattisgarh, Madhya Pradesh, Uttar pradesh, Uttrakhand)

Region total 365,121 87,024 468,970 131,320 545,308 174,243 575,113 215,658

Western region( Goa, Gujrat, Maharashtra, Dadra & Nagar Haveli, Daman & Diu)

Gujarat 139,250 30,266 162,078 42,821 213,172 65,572 256,292 100,664

Maharashtra 435,090 166,117 491,433 247,762 688,221 369,949 753,423 487,506Region total 586,151 199,444 667,583 294,887 918,327 441,322 1,029,772 596,097

Southern region (Andhra Pradesh, Karnataka, Kerela, Tamil Nadu, Lakshadweep, Puducherry)

AndhraPradesh

279,997 71,186 350,494 111,835 377,219 138,971 442,999 191,860

Karnataka 279,465 93,695 332,708 142,940 416,788 221,233 515,603 283,142Kerala 270,818 55,591 1,035,382 88,015 392,327 111,110 517,005 137,983Tamil Nadu 305,928 95,075 335,958 128,621 401,937 174,321 429,828 215,560

Region Total 1,141,424 316,772 2,061,047 473,344 1,596,166 648,412 1,914,252 832,178

Total all-India 3,035,026 853,465 4,361,708 1,267,970 4,521,531 1,821,672 5,009,913 2,300,056

(figures in parenthesis indicate percentage growth in amount outstanding)



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12/22


The National Housing Bank for the purpose of Housing Index called NHB residex hasdivided Patna among 5 zone.

Zone 2 Nehru Nagar (Punaichak), Shiv puri, Jagnarayan path, Patel Nagar, Anandpuri, Indra Nagar, Dujra,Chakaram, Uttari Mandir, Baldev Bhawan Road

Zone 3 R-Block, Mountasari Lane, Gandhi Nagar, Basant Vihar colony, Patliputra Colony, Rajapur, Kidwaipuri, SriKrishna Nagar, Buddha Colony, Sri Krishna Nagar Park, Mandiri Kathpul, Shanti vihar

Zone 4 Akashwadi

Zone 5 Paschim Boring Road, Purvi Boring Canal Road, R K Bhattacharya RoadOther Zone Kankarbagh, Phoolwari, Bailey Road (New), Gola Road

The current market prices for residential purpose in Patna outskirts & within the heart of thecity range from Rs.2500/- sq.feet to Rs.4500/- sq.feet. In 2000, the price for the same was in the rangeof Rs.350/- to Rs.700/- per sq. feet. The rise in prices has made the existing house of the borrowersmore valuable to them and would thus prevent them from default in the Housing loans. Further, therehas been rise in income of the salaried employees, as well as business man. The average rises inincome level of the employees as a case shows increase in gross income of 29 times over a period of 23years. This is more likely to make it easier for the borrower to repay debts if he has been meeting theinterest payment on regular basis. For Example, a loan of Rs. 1 lac availed in 1984 would seem smaller

amount by the year 1994.

Income

Level

1984

(Rs.per

month)

1987

(Rs.per

month)

1992

(Rs.per

month)

1998

(Rs.per

month)

2002

(Rs.per

month)

2007

(Rs.per

month)

Bank officer(If continue inthe samegrade)

Rs1175-Rs.2675

Rs.2100-Rs.4020

Rs.4250-Rs8050

Rs.7100-Rs12540

Rs.10000-Rs18240

Rs.18500-Rs.33740

Bank Officer(NormalCase)

Rs1415 Rs3180 Rs7360 Rs14620 Rs25380 Rs44000

Gross pay per

month

Rs2250 Rs 4770 Rs11050 Rs21950 Rs 38100 Rs66000

Gross salaryper annum

Rs.27000 Rs 57240 Rs. 132600 Rs263400 Rs457200 Rs 792000

The default in the loan amount occurs generally in the first 5 to 6 years of the disbursement ofthe loan, given the fact that there is generally the moratorium period of 12 to 18 months in the case ofthe housing loans, after which the repayment starts. The rising price of the real estate on one handmakes the value of the House valuable for the borrower, on the other hand, the rise in income level andthus rise in disposable income & decreased value of the outstanding loan amounts makes it much moreattractive for the borrower to repay the loan and free the property from all encumbrance, that is to freethe house property from the equitable charge of the banks. The rate of default decreases with later halfof the loan tenure is evidenced from the data of the delinquent loans as under:

Year 2009 2008 2007 2006 2005 2004 2003

No. ofDefaultaccounts

9 17 15 19 60 65 28

Year 2002 2001 2000 1999 1998 1996 1993No. ofdefaultaccounts

30 17 3 5 3 3 2


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Resource Questiions of the StudyHow does the demographic profile of a housing Loan borrower matters in terms of default risk facedby the Banks in India?

The demographic profile contains many things like income, age, gender, marital status, Type ofprofession, location, education, number of dependents, etc.

RQ.1. Is there significant difference in repayment habits across age groups of the borrower?RQ.2. Is there significant level of difference in the repayment habits (regular repayment) among

Lower Income household, middle Income Household, and upper Income household?.RQ.3. Is there significant level of difference in the regular repayment of Installments across

male and Female Borrower?RQ.4. Is the profession a significant factor in regular repayment of Installments?RQ.5. Is the physical difference in Place of construction of the house and place of Job (place of

stay) a factor in regular repayment of the installment?RQ.6. Is education a factor in repayment habits of the installments?.Repayment habit is defined as the regular number of installment paid out of due installments

before default occurs. If the borrower has missed due installments on more than three occasions, thedefault seems to have taken place. Such frequent lapses may result into account becoming in NPA(Non-performing) category, where bank cannot book interest as their income.

Demographic Profile: Definition and HypothesisThe choice of variable used for the study is as under:

Variable Definition

Age 1:Below 30, 2: 30-35, 3: 35-40, 4:40-45, 5: 45-50, 6:50-55, 7:55-60, 8: Above 60Sex 0: female, 1:Male, 2: Male & Female Joint BorrowerNo of Income earner in the Household 0: one income, 1: double income, 2: three earner, 3:four earner, 4: more than 4 earnerMarital status 0: single, 1: MarriedProfession 0: salaried, 1: self-employed professionals, 2: Traders, 3: Agriculturist, 4: Contractual,

5:UnemployedEducation level 0: under matriculate, 1: Intermediate, 2: Bachelor degree / Diploma Holder, 3: Postgraduate,

4: DoctoralNet income of the Borrowers at time ofloan

Gross income minus expenses

Total gross income of the borrowers attime of loan

Total gross income

Present Net monthly incomePresent Gross incomeAmount of loan availedAmount of loan OutstandingNumber of co-borrowers presentCo-borrowers monthly incomeOriginal tenure of the loan 0: 5 year, 1: 5 to 10 years, 2: 11 years to 15 years, 3: 16 years to 20 years, 4: over 20 yearsNumber of members joined in job marketafter loan

0: 0, 1: 1, 2:2, 3:3, 4:4 and over

Property located in top 20 ciitiesProperty located in other citiesProperty located in Rural areasRemaining period of the loanLoan to value RatioMarket value of CollateralCurrent location of the professionIncome category of the HouseholdNo of Installment fixedNo of installment paid before default


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Research DesignChoice of Respondents

The Housing loan delinquents.

Data Collection Procedure

The proposed study employs collection of relevant secondary data about housing loan account in

Default from the select Bank branches / processing centres of the Bank within Patna. Secondly tocollect primary data from the relevant borrowers by contacting them, administering them a set ofquestionnaire to check on the efficacy of the variables and to register the dynamic changes within thevariable. The Data will relate to the age profile of the borrower, education level, Income level at thetime of loan, present income, valuation of the property at the time of the loan, Loan amount, MarginMoney with the Bank, Nature of collateral, co-borrower if any, Nature of relationship with the co-borrower, Place of stay at the time of loan, present stay, Location of the House Rural, Semi urban,Urban, Metro.

Sampling Method

The sample selected from the banks & FIs will be random sample. The sample will be classified intodelinquent and default groups, and analyzed by factors.The research methodology used will be logit, probit and discriminant analysis.The Population mean would be All India Figure and Bihar State Figure. The Sample mean

would be Figure for Patna. The number of defaulter would be arrived to project a default risk at thenational level.

The analysis of data will be carried out using Statistical package for the Social Sciences(SPSS). The Statistics tests will be t-test, chi-square test and ANOVA.

Questionnaire Description

The Questionnaire will comprise of 25-30 questions based on Age profile, Initial Mortgage loan,

Occupation, Income level, co borrower, family size, tenure of the loan, the period when first defaulttook place, education level, asset price, nature of profession, etc. would be administered to therespondents to incorporate the dynamic changes within the variables.

Product Selected

The housing loan availed across Banks in Patna and currently in delinquent stage would be gathered.

Analytical Techniques

To predict the probability of housing loan default, based on financial, non financial, situational factor,we need to use logistic regression exercise. A logistic regression has the flexibility of incorporating

both the qualitative and quantitative factors and is more efficient than the linear regression probabilitymodel.Here we choose the outcome variable, repayment habit, as the Dependent variable and the

predictor variable such as Age of the borrower, household income, gender of the borrower, profession,place of construction and place of residence as independent variable.

Anticipated FindingsOut of total number of 7353 housing loan accounts, there are 6511 male accounts holders and 825female accounts holders and remaining 17 are joint account holders. Further there are 3751 staff loanaccounts and 3602 public housing accounts. There are 6309 low value loan accounts (low value loan


15/22


accounts are referred to as the outstanding loan amount is below 30 % of the property value) of amountof loan limit below Rs.9 lacs and only 1044 loan accounts have loan exposure limit above Rs.9 lacs.

The female borrower has higher percentage of default rate @ 6.65% compared to 3.83% defaultrate of the male borrower. The Asset code of Non Performing Assets(NPA) and the gender aresummarized in the table as under:

NPA accounts Total Male borrower % of Default

Male 250 6511 3.83%Female 55 827 6.65%

The loan concentration is more in some areas compared to the other parts of the city. It could besummarized in tabular form as under:

PIN CODE Number of

Housing Loan

accounts

Areas covered

800001 2428 B.C.Road, B.P.SC, Bank Road, Central Revenue Building, Chiraiyatand, Darul Mallick,Gardanibagh, Hotel Republic, Jamal Road, Kidwaipuri, LIC, Mithapur, Navshakti, NewJakkanpur, Patna Collectoriate, Patna High Court, Postal Park, Punaichak, R-Block,Rajapur Mainpura, Shri Krishna puri

800020 646 Ashok Nagar, Chitragupta Nagar, Dhelwan, K.Sector, LohiaNagar, R.M.S Colony800014 443 B.V.College, Patna Aerodrame, Sheikhpura800013 412 Patliputra Colony800015 395 Patna Sectt800023 313 Lal Bahadur Shastri Nagar800004 250 Bankipore, J.C.Road, Machuatoli, Naya Tola, PMCH, Ashok Rajpath800002 232 Anisabad, Beur, Pakri800025 202 Ashiananagar800006 150 M.Y. Sandalpur, Mahendru800007 150 Bairia, Dental College, Gulzarbagh, Mangala Devi, Pahari, Nadghat, Sonagopalpur801503 200 Danapur Bazar, Danapur Cantt

The Ashiananagar, Saguna More, Digha bye pass, Khagaul are rapidly getting concentrated

with new settlements in these areas.Out of total number of loan outstandings, around 1014 loan accounts comprising, 13.79 % of

the total loan accounts have borrower working outside Patna. The Bank official have to make at leastan annual inspection of the house in case of the regular loan accounts and more frequent visit in case ofdelinquent accounts. In such cases, where the borrowers are working outside the city where loan hasbeen granted and equitable charge has been created, the bank has to devise a method other thaninspection part to recover regular installments from these borrowers and also arrange for Inspectionand compilation of the opinion reports on these borrowers.

The Study of the delinquent accounts (NPA accounts) reveal that borrowers locally employeddefault more than the outside borrower as banks are very selective in granting loans to borrowersemployed outside the city and have better check on the credential of the borrower. The locally

employed borrowers have defaulted @ 4.40% whereas outside employed borrower have defaulted @2.56%. It could be placed in tabular form as under:

Number of loan accounts Number of NPA Accounts % of default

Borrower Locally employed 6339 279 4.40%Borrower employed outside 1014 26 2.56%

Further it is observed that the percentage default is more among the educated & professionalclass and the borrowers defaulting are employees of Labour commissioner, police, Advocate, Defencepersonnel, Businessman, Small business & retail traders, Executive engg, Judicial officers, Telecomemployees, Doctors & veterinary Doctors, Power sector employees like Electricity supply division,


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Magistrate, Minor irrigation deptt, government employees, Beautician, hotel industry, Book vendoretc.

However, the income is linked to the borrower education level and borrower skill, that is theborrower with higher education level and higher skills have generally better income than the borrowerwith lower education level and thus with lower skills. Thus it is observed that a post graduate & a PhDstart with starting salary of Rs. 3 lakhs & above, we see that the percentage default for loan limits ofRs. 3 lacs & below is 6.07% compared to borrower with loan limits of Rs. 3 lacs & above with default

percentage of 3.20%. The Loan category in the bracket of Rs.6 lacs to Rs. 15 lacs has lower percentageof default @ 2.25%. Thus borrowers with better higher education level are less likely to default thanborrowers with lesser education level.

Limit in Rs. lacs No.of Default loan

accounts

Total number of loan

accounts

% of loan default

20 lakhs & above 1 136 0.73 %15 lakhs - 20 lakhs 9 198 4.54%12 lacs - 15 lacs 6 226 2.65%9 lacs 12 lacs 10 484 2.06%6 lacs 9 lacs 29 1273 2.27%3 lacs 6 lacs 103 2618 3.93%

Below 3 lacs 147 2418 6.07%

Average ticket (loan) size is more in branch located in professional areas like in case of P.B.BDoctors colony with ticket size of Rs.10.03 lacs is ahead of Personal Banking Br.

Branch No. of

Loan

accounts

No.of loans

in last 5

years

Avg.

Ticket

Size

Branch No. of

Loan

accounts

No.of loans

in last 5

years

Avg.

Ticket

Size

Patna Sectt. 654 406 4.16 Patliputra 172 77 5.36PBB Patna 572 383 9.47 Maurya Lok Complex 168 99 6.14J.C.Road 286 191 4.34 Fraser Road 151 72 4.21AshianaNagar 272 97 4.87 Bailey Road 146 88 4.52Boring Road 272 125 6.32 P.B.B Doctors Colony 132 132 10.03

The anticipated findings are like there is likelihood of significant difference in repaymentacross age, gender, income, and education level. Also, place plays an important role, the geographicaldistribution.

Running Logistic Regression & Hypothesis TestingHypothesis 1 : H0: There is no significant difference in repayment habits across age groups of theborrower? (Simple Regression)

Regression

Variables Entered/Removed

Model Variables Entered Variables Removed Method

dimension0 1 Age at the time of Sanctiona Enter

a. All requested variables entered.b. Dependent Variable: Whether regular repayment or Default occurs


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Model Summary

Model R R Square Adjusted R

Square

Std. Error of the

Estimate

dimension0 1 .367a .135 .123 .425

a. Predictors: (Constant), Age at the time of Sanction

ANOVAb

Model Sum of Squares df Mean Square F Sig.

1 Regression 2.024 1 2.024 11.193 .001a

Residual 13.017 72 .181

Total 15.041 73

a. Predictors: (Constant), Age at the time of Sanctionb. Dependent Variable: Whether regular repayment or Default occurs

Coefficientsa

Model

Unstandardized Coefficients

Standardized

Coefficients

t Sig.B Std. Error Beta

1 (Constant) 1.969 .378 5.214 .000Age at the time of Sanction -.035 .010 -.367 -3.346 .001

a. Dependent Variable: Whether regular repayment or Default occurs

The value of R2 is. 135, which tells us that age of the borrower at the time of sanction of theloans, can account for only 13.5% of the variation in repayment habits. That means that 86.5% of thevariation in repayment habits and therefore chances of default cannot be explained by the age of theborrower. Therefore, there must be other variables that have an influence also.

The F- ratio is 11.193. The result tells us that there is less than 0.1% chance than an F-ratio thislarge would happen by chance alone.

As the probability of F is greater than the significance level , H0 is not rejected. That is, thereis no significant difference in repayment habits across age groups.

Logistic Regression

Case Processing Summary

Unweighted Casesa

N Percent

Selected Cases Included in Analysis 3 .0

Missing Cases 7380 100.0

Total 7383 100.0Unselected Cases 0 .0

Total 7383 100.0a. If weight is in effect, see classification table for the total number of cases.b. The category variable Present Occupation of the Partner is constant for the selected cases. It will be removed from the

analysis.c. The interaction EDUCATIONAL_QUALIFICATION * PRESENT_OCCUPATION *

PRESENT_OCCUPATION_PARTNER has been reduced to EDUCATIONAL_QUALIFICATION *PRESENT_OCCUPATION.

d. The variable Gender of the Self is constant for the selected cases. Since a constant term was specified, the variablewill be removed from the analysis.

e. The interaction EDUCATIONAL_QUALIFICATION * GENDER_SELF has been reduced toEDUCATIONAL_QUALIFICATION.

f. The category variable Place of purchase/ construction is constant for the selected cases. It will be removed from theanalysis.


18/22


g. The interaction CONSTRUCTIONPLACE * INSTALMENT_PAID * PRESENTLOCATION has been reduced toINSTALMENT_PAID * PRESENTLOCATION.

h. The category variable Present Location where you reside is constant for the selected cases. It will be removed from theanalysis.

i. The interaction INSTALMENT_PAID * PRESENTLOCATION has been reduced to INSTALMENT_PAID.

Dependent Variable Encoding

Original Value Internal ValueNo Default Occurs 0Default Occurs 1

Categorical Variables Codings

Frequency

Parameter

coding

(1)

Present Occupation of theself

1 1.000

NOT KNOW 2 .000

Categorical Variables Codings

Frequency

Parameter coding

(1) (2)

Household MonthlyIncome(Total Income ofall the Family Members)

2520.00 1 1.000 .000

3736.00 1 .000 1.000

8325.00 1 .000 .000Present Occupation of theself

1 1.000

NOT KNOW 2 .000

Block 0: Beginning Block

Classification Tablea,b

Observed Predicted

Whether regular repayment or

Default occurs

Percentage

CorrectNo Default

Occurs Default Occurs

Step 0 Whether regular repaymentor Default occurs

No Default Occurs 0 1 .0

Default Occurs 0 2 100.0

Overall Percentage 66.7

a. Constant is included in the model.b. The cut value is .500


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Variables in the Equation

B S.E. Wald df Sig. Exp(B)

Step 0 Constant .693 1.225 .320 1 .571 2.000

Variables not in the Equationa

Score df Sig.Step 0 Variables AGE_AT_SANCTION 3.000 1 .083

EDUCATIONAL_QUALIFICATION byHOUSEHOLD_MONTHLY_INCOME

2.962 1 .085

EDUCATIONAL_QUALIFICATION byPRESENT_OCCUPATION(1)

3.000 1 .083

EDUCATIONAL_QUALIFICATION 3.000 1 .083

INSTALMENT_PAID 3.000 1 .083

HOUSEHOLD_MONTHLY_INCOME 2.882 1 .090

PRESENT_OCCUPATION(1) 3.000 1 .083

a. Residual Chi-Squares are not computed because of redundancies.

Forward :LR method of regression method was used for analysis.

Interpretation of Results

In this example, there are 2 cases where default occurs and 1 case where default does not occur.Therefore, if SPSS predicts that every times Default occurs, 2 times out of 3, it will be correctprediction. That is, 66.7% chance of true prediction is there that default will occur for housing loanborrower over the life time of his loan period. However, if SPSS predicts that No default will occur, itwill be correct only 33.3% of the time. As such, of the two available options it is better to predict thatDefault will occur because this results in a greater number of correct predictions.

The next part of the output summarizes the model, and at this stage this entails quoting thevalue of the constant (b0), which is equal to 0.693.

The residual chi-square statistic was not computed because of redundancies.

In this example, all excluded variables have significant score statistic at p< .083 and so all thesevariables could potentially make a contribution to the model.

ImplicationsThe findings suggest that banks can incorporate weightage to these variables in calculating theprobability of default occurring and accordingly charging risk premium.

Limitations1. There are various methods available for testing. All methods cannot be tested for the best result

because of time constrain.2. The findings may be important & help in understanding but to what extent the results will be

factored by the banks, simply because of the operational convenience for the Banks. They arehappy dealing with at most with two to three variables, that is easy to understand and operate.

3. Many a times the borrower and the household in some context have been used interchangeablydepending on the contextual aspect on basis of a strong correlation which is very oftenobserved in day to day life.


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[24] Vandell, K.D; Thibodeau, T.(1985). Estimation of mortgage defaults using disaggregate loanhistory data. AREUEA Journal 15 (3),292-317.

[25] Wiliams, A.O; Beranek W; & Kenkel, J. (1974). Default risk in urban mortgages: A pittsburgprototype analysis. American Real Estate and Urban Economics Association Journal 2, 101-112.

Questionnaire1. Loan availed during the year:4. Quantum (Amount) of the Loan:5. Projected cost of the Building at the time of the purchase / construction:6. Present loan outstanding:7. Gross monthly income at the time of the Loan:8. Gross monthly income of the partner / co-borrower at the time of the loan: Rs.9. Gross monthly income during the (0-4 years) first 4 years of the loan: Rs.10.Gross monthly income during the 5th to 8th year of the loan: Rs.11.Gross monthly income during the 9th to 12th year of the loan: Rs.12.Gross monthly income during the 13th to 16th year of the loan: Rs.13.Gross monthly income during the 17th to 20th year of the loan: Rs.14.Repayment period: installment / year.15.Gross monthly income of the partner during the first 4 years of the loan: Rs.16. Gross monthly income of the partner during the 5th to 8th year of the loan: Rs.17.Gross monthly income of the partner during the 9th to 12th year of the loan: Rs.18.Gross monthly income of the partner during the 13th to 16th year of the loan: Rs.19.Gross monthly income of the partner during the 17th to 20th year of the loan: Rs.20.Present Market value of the property:21.The place of residence at time of sanction :22.Place of purchase / construction:23.The present location where you reside:24.Age at the time of Sanction ..25.Occupational status at the time of sanction, whether employed / unemployed:26.The type of occupation at the time of sanction:27.Occupational status of the partner at the time of the sanction, whether employed / unemployed:28.Occupational type of the partner:29.Present occupation of the self:30.Present occupation of the partner:31.Educational qualification at the time of loan

c) Non Matriculated) Matriculatee) Diplomaf) Intermediateg) Graduateh) Post Graduate

32.Household Monthly income (Total income of all the family members): Rs.33.If the Housing Loan is in default, year of Default:34.The total period during which the loan is in default:35.Interest Rate charged by the Bank:36.Numbers of member in the family:37.The Financing Institution


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a) SBIb) HDFCc) ICICId) Axis Banke) Standard Chartered Bankf) PNB

38.Loan other than Housing and amount outstanding:39.Gender of the self (Male / Female):40.Gender of the Partner :41.Reasons for Default of the loan:42.Number of installment already paid:43.Number of dependent member joining the job market after the loan:44.Number of dependent family members:45.Health of the borrower:46.Equated Monthly Income / Net Monthly income at the time of the Loan:47.Present Net monthly Income (after all deductions, excluding loan installment): Rs.48.Tenure of the loan:49.Head of the family:

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Demographic Profile as a Determinant of Default Risk

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