The empirical research on housing market in India is scarce due to the paucity of information. The monograph on “A Study of Residential Housing Demand in India” is the outcome of a Study conducted by the National Institute of Bank Management (NIBM) for National Housing Bank (NHB) and partially addresses advice of Reserve Bank of India to NHB on studying the housing and real estate sector. This study provides exhaustive empirical research and detailed analysis (both micro and macro level) of current status and future growth potential of housing industry in India, its back-ward and forward linkages, financing structure and nature of underlying risk in the housing market in India

Baid, Shilpa

Bandyopadhyay, Arindam

Basu, Sanjay

Kuvalekar, S V

Saha, Asish

Munich RePEc Personal Archive

MPRAMunich Personal RePEc ArchiveA Study of Residential Housing Demandin IndiaArindam Bandyopadhyay and S V Kuvalekar and SanjayBasu and Shilpa Baid and Asish SahaNational Institute of Bank Management (NIBM), India, NationalHousing Bank (NHB), India1. April 2008Online at http://mpra.ub.uni-muenchen.de/9339/MPRA Paper No. 9339, posted 29. June 2008 02:55 UTC 1   2   ------------------------------------------------------------------------------------------------------ Author’s Addresses:  Dr. Arindam Bandyopadhyay is Assistant Professor (Finance), NIBM. He can be contacted at: Email: arindam@nibmindia.org. Dr. S. V. Kuvalekar is Associate Professor (Finance), NIBM Prof. Sanjay Basu is Assistant Professor (Finance), NIBM Dr. Shilpa Baid is Assistant Professor (Finance), NIBM Dr. Asish Saha is Professor and Director, NIBM  Acknowledgement:   We, the Study Team, thank National Housing Bank for funding this project. We would like to acknowledge the help and cooperation of Mr S Sridhar, CMD, NHB and Mr V K Badami, DGM, NHB. We also thank participating HFCs and Banks in sharing the crucial information. Finally, we would like to acknowledge the Publications Department (especially John T D) and secretarial assistance by Rosamma Peter. We look forward to continue the academic exchange with NHB in future.   3 Abstract  The empirical research on housing market in India is scarce due to the paucity of information. The monograph on “A Study of Residential Housing Demand in India” is the outcome of a Study conducted by the National Institute of Bank Management (NIBM) for National Housing Bank (NHB) and partially addresses advice of Reserve Bank of India to NHB on studying the housing and real estate sector. This study provides exhaustive empirical research and detailed analysis (both micro and macro level) of current status and future growth potential of housing industry in India, its back-ward and forward linkages, financing structure and nature of underlying risk   Broadly, the study analyzes the following key issues:  a) Nature of the residential housing demand at present ownership vs. rental (individual and corporate), gender-wise, age-wise, education-wise, profession-wise, income class-wise break-up (i.e. based on the purchasing power), etc. and estimation of current demand for housing b) Correlation of projected housing demand with other economic factors such as induced demand for cement, steel, (backward linkage), employment benefits, etc. so that the multiplier effect of housing can be estimated at the national level. Other relevant correlations such as with other assets say car, two-wheeler, TV, (for low income group) may also be considered. c) Factors reckoned at time of purchase of houses and nature of their financing. d) Based on published data on demographics, household expenditure, etc., projection of housing demand for the next 5 years using econometric models. e) Broad profile and trends in the projected demand for housing using time series forecasting techniques. f) Identification of emerging trends in housing and housing finance. g) The study also includes correlating the existing profile of housing loan borrowers of select banks and HFCs to understand the correlation between borrower characteristics and loan parameters such as asset quality, delinquencies, period of loans, collateral values etc. We also examine the link between loan delinquency and value of collateral to further pinpoint the importance of valuation in the housing sector. h) Study specifically looks at the issues regarding collateral of the housing loan portfolio including their value, based on data collected from few banks and HFC i) Suggestions for the Development of Housing Loan Mortgage market    Key Words: Housing Demand Estimation, Micro & Macro Analysis, Default Risk, Financial Institutions   4 Key Findings Macro Findings • Net 68 million Indians (assuming average size of households is 3) will require independent housing and thereby they will add to the housing demand of the nation due to age-demographic effect. This may be one rational estimate of housing demand in Indian by 2015 due to age-demographic effect. In case of urban population, the additional need for housing would be 31 million (1/3 of 94 million). • We have also done projections of realized demand for housing through credit supply channel till 2012. Following this method, additional demand for housing is forecasted by us for 2012-13 is around 6.79 million (which is closure to figure projected by 11th Planning Commission. We have also created three scenarios depending upon various ‘loan growth’ rates (2.35%, 250% and 3.30%) to project housing demand for 2012-13 viz. 5.97, 6.35 and 7.62 million respectively. • We studied possible Linkage between Housing Demand and Other Sectors like Steel and Cement Production The coefficients of the estimated equations depict that a 10% increase in realized demand for house (proxied by number of loans disbursed by HFCs and Banks) results in 4.59% increase in steel production and 4.67% increase in cement production.  • We also studied possible Linkage between Household Leverage, Housing Demand and GDP Growth. We have found linkage between Housing sector GDP and household sector leverage and PSBs housing loan disbursement. We also found evidence of linkage between house hold leverage and GDP.  • House price index (computed by us) moves in with the economic cycle and it becomes more evident when the growth profile is matched with the expansion in bank credit growth to housing (especially during 2004 onwards when banks stepped up the lending to housing segment).  Micro Findings In our micro-study of the determinants of housing Demand, we profiled the borrowers in terms of Location, Gender, Age, Income, House Size etc based on large sample data obtained from HFCs and Banks. Some Key findings are listed below:  • A typical borrower would most likely to be a male in the age group of 40 to 50 having an average monthly income of Rs.10000 who prefers to buy a house of the size of 100 square meters. • Though most of the borrowers are in the age group of 40-50, a significant 25% are below 35 years of age. It is also found that there is a falling trend in average age profile of the housing demand. A statistical un-paired t-test confirms that the year wise fall in average age is significant at 1% or better level except between year 2006 and 2007. • It is found that income and price elasticity of demand is less than unity. An increase in house price by 10%, ceteris paribus, results in a 4.6% decrease in housing demand as affordability comes down. With a10% increase in the monthly  5 income of the borrower leads to increase in housing demand area by 5.96% (or approximately 6%). • The demand for house-size is found to be inversely related with the age of the borrowers. The number of dependence, which capture the financial liability of the borrower, is found to have negatively significant implying thereby more the number of dependents in a family reduces the affordability and hence the size of the house. • Urban people have greater demand for bigger house in comparison to suburban counterparts. The demand for house in terms of size in the rural area is lesser than the people live in suburban area; access to housing loan, capacity/affordability might be reasons driving the same.   Risk Analysis in Housing We studied various factors that cause housing loan default. These factors were collected from the HFC’s loan history files. The borrower specific parameters normally used by the HFCs while granting loan and pricing of such loans include age, income, occupation, service & length of service, number of dependents, collateral information, guarantor etc. apart from property specific information. In a set of regression exercises, we have found the following interesting observations: o Bigger the size of house (in square meter), lower is the risk of default. o Higher the monthly income, lower the chance of default because of higher ability to pay. o Greater the value of asset (ln_asset_val), lesser the risk of default because of greater affordability due to wealth effect.   o Security value (original book value of property) as proportion to the original loan amount factor (secval_loanamt or we call it as LTV) is an important and highly significant determinant of risk of default in housing loan. Higher the security margin available to the bank (real margin in this case), lower is the chance of default in home loan. o With a 10% decrease in LTV, the odds of default increases by 2.173%.  o This collateral margin is also a significant determinant of rate of recovery of defaulted loans. A 10% increase in the ratio (secval_loanamt) lead to 0.38% increase in the recovery rate (rr). Further, due to the presence of guarantee, bank’s likelihood of recovery is more. o EMI to Income ratio is positively associated with the estimated likelihood of default. A 10% increase in EMI to Income ratio is estimated to increase the likelihood of default by 4.52%! o The likelihood of default in housing loan decreases significantly with the presence of additional collateral. The presence of more number of co-borrowers reduces the risk of default. Interestingly, higher the co-borrower’s monthly income, lesser is the chance of default because of availability of second line of source of income. o As no. of dependents increases, probability of default also significantly rises because of higher financial burden.   6 o Rural and semi urban people are riskier than the urban borrowers. However, we have found that borrowers located in big cities are riskier than medium and smaller cities. This may be because with the activation of personal loan segment by commercial banks and easy access to such loans especially in the metro area prompted the existing home loan borrowers to overstretch their financial commitments with a consequential deleterious effect on home loan default rate in this segment.   o An increase in GDP growth rate reduces the likelihood of default.  Target Audience: Who will benefit from this report?   This report would be of immense relevance to Government, RBI, NHB, banks and HFCs as well as academia. It can also act as guidance to local authorities and sub-regions on understanding their local housing market. The study would provide guidance on housing needs assessments and can be used as a manual for undertaking housing market assessments and facilitate future projections and valuation. Finally, we believe that such report will benefit the industry in facilitating relevant financing, banking, and monetary policy formulation and provide them a better risk perspective to meet the growing financial demands of the nation in coming future.                              7        8                9   Bibliography  Ambrose, B. and C. Capone, 2000. The Hazard Rates of First and Second Defaults. Journal of Real Estate Finance and Economics 20, 275-293. Bajari, Patrick and Matthew E Kahn (2003), "Estimating Housing Demand with an Application to Explaining Racial Segregation in Cities", NBER Working Paper No. 9891. Baku, E. and M. Smith, 1998. Loan Delinquency in the Community Lending Organizations: Case Studies of NeighborWorks Organizations. Housing Policy Debate 9, 151-175. Barot, Bharat and Zan Yang (2002), "House Prices and Housing Investment in Sweden and the United Kingdom : Econometric Analysis for the Period 1970-1998", Review of Urban & Regional Development Studies, Vol. 14, No. 2. Calem, P. and S. Wachter, 1999. Community Reinvestment and Credit Risk: Evidence from Affordable-Home- Loan Program. Real Estate Economics 27, 105-134. Chinloy, P. 1995. Privatized Default Risk and Real Estate Recessions: The U.K. Mortgage Market. Real Estate Economics 23(4): 410-420. Cowan, A., and Charles Cowan, 2004. Default Correlation: An Empirical Investigation of a Subprime Lender. Journal of Banking and Finance 28, 753-771. Dholakia, B. and R. H. Dholakia (2000), “Impact of Investment in Housing Sector on GDP and Employment in Indian Economy”, IIMA, Working Paper, July. Égert, Balázs and Dubravko Mihaljek (2007), "Determinants of House Prices in Central and Eastern Europe", BIS Working Paper No. 236, September. Furstenberg, G. Von, and R. Green, 1974. Estimation of Delinquency Risk for Home Mortgage Portfolios. American Real Estate and Urban Economics Association 2, 5-19.  10 Getter, D. 2003, Contributing to the Delinquency of Borrowers. The Journal of Consumer Affairs 37, 86-100. Hanushek, Eric A and John M Quigley (1979), "The Dynamics of the Housing Market : A Stock Adjustment Model of Housing Consumption", Journal of Urban Economics, Vol. 6, pp. 90-111. Hanushek, Eric A and John M Quigley (1980), "What is the Price Elasticity of Housing Demand?" Review of Economics and Statistics, Vol. 62, pp. 449-454. Mankiw, N Gregory and David N Weil (1989), "The Baby Boom, the Baby Bust and the Housing Market", Regional Science and Urban Economics, Volume 19, pp. 235-258. Sirmans, G. Stacy and David A Macpherson (1999), "Forecasting the Demand for Housing in South Carolina", Center for Applied Real Estate Education and Research, University of South Carolina. Tiwari, Piyush and Jyoti Parikh (1998), "Affordability, Housing Demand and Housing Policy in Urban India", Urban Studies, Vol. 35, No. 11, pp. 2111-2129. Tiwari, Piyush; Kirit Parikh and Jyoti Parikh (1999), "Effective Housing Demand in Mumbai (Bombay) Metropolitan Region", Urban Studies, Vol. 36, No. 10, pp. 1783-1809.  

English

A Study of Residential Housing Demand in India

Munich Personal RePEc Archive

MPRA
Munich Personal RePEc Archive
A Study of Residential Housing Demand
in India
Arindam Bandyopadhyay and S V Kuvalekar and Sanjay
Basu and Shilpa Baid and Asish Saha
National Institute of Bank Management (NIBM), India, National
Housing Bank (NHB), India
1. April 2008
Online at http://mpra.ub.uni-muenchen.de/9339/
MPRA Paper No. 9339, posted 29. June 2008 02:55 UTC
 1 
 
 2 
 
 
------------------------------------------------------------------------------------------------------ 
Author’s Addresses: 
 
Dr. Arindam Bandyopadhyay is Assistant Professor (Finance), NIBM. He can be contacted at: Email: 
arindam@nibmindia.org. 
Dr. S. V. Kuvalekar is Associate Professor (Finance), NIBM 
Prof. Sanjay Basu is Assistant Professor (Finance), NIBM 
Dr. Shilpa Baid is Assistant Professor (Finance), NIBM 
Dr. Asish Saha is Professor and Director, NIBM 
 
Acknowledgement:  
 
We, the Study Team, thank National Housing Bank for funding this project. We would like to acknowledge 
the help and cooperation of Mr S Sridhar, CMD, NHB and Mr V K Badami, DGM, NHB. We also thank 
participating HFCs and Banks in sharing the crucial information. Finally, we would like to acknowledge the 
Publications Department (especially John T D) and secretarial assistance by Rosamma Peter. We look 
forward to continue the academic exchange with NHB in future. 
 
 3 
Abstract 
 
The empirical research on housing market in India is scarce due to the paucity of 
information. The monograph on “A Study of Residential Housing Demand in India” is 
the outcome of a Study conducted by the National Institute of Bank Management 
(NIBM) for National Housing Bank (NHB) and partially addresses advice of Reserve 
Bank of India to NHB on studying the housing and real estate sector. This study provides 
exhaustive empirical research and detailed analysis (both micro and macro level) of 
current status and future growth potential of housing industry in India, its back-ward and 
forward linkages, financing structure and nature of underlying risk  
 
Broadly, the study analyzes the following key issues: 
 
a) Nature of the residential housing demand at present ownership vs. rental 
(individual and corporate), gender-wise, age-wise, education-wise, profession-
wise, income class-wise break-up (i.e. based on the purchasing power), etc. and 
estimation of current demand for housing 
b) Correlation of projected housing demand with other economic factors such as 
induced demand for cement, steel, (backward linkage), employment benefits, etc. 
so that the multiplier effect of housing can be estimated at the national level. 
Other relevant correlations such as with other assets say car, two-wheeler, TV, 
(for low income group) may also be considered. 
c) Factors reckoned at time of purchase of houses and nature of their financing. 
d) Based on published data on demographics, household expenditure, etc., projection 
of housing demand for the next 5 years using econometric models. 
e) Broad profile and trends in the projected demand for housing using time series 
forecasting techniques. 
f) Identification of emerging trends in housing and housing finance. 
g) The study also includes correlating the existing profile of housing loan borrowers 
of select banks and HFCs to understand the correlation between borrower 
characteristics and loan parameters such as asset quality, delinquencies, period of 
loans, collateral values etc. We also examine the link between loan delinquency 
and value of collateral to further pinpoint the importance of valuation in the 
housing sector. 
h) Study specifically looks at the issues regarding collateral of the housing loan 
portfolio including their value, based on data collected from few banks and HFC 
i) Suggestions for the Development of Housing Loan Mortgage market  
 
 
Key Words: Housing Demand Estimation, Micro & Macro Analysis, Default Risk, 
Financial Institutions 
 
 4 
Key Findings 
Macro Findings 
• Net 68 million Indians (assuming average size of households is 3) will require 
independent housing and thereby they will add to the housing demand of the 
nation due to age-demographic effect. This may be one rational estimate of 
housing demand in Indian by 2015 due to age-demographic effect. In case of 
urban population, the additional need for housing would be 31 million (1/3 of 94 
million). 
• We have also done projections of realized demand for housing through credit 
supply channel till 2012. Following this method, additional demand for 
housing is forecasted by us for 2012-13 is around 6.79 million (which is 
closure to figure projected by 11th Planning Commission. We have also created 
three scenarios depending upon various ‘loan growth’ rates (2.35%, 250% and 
3.30%) to project housing demand for 2012-13 viz. 5.97, 6.35 and 7.62 million 
respectively. 
• We studied possible Linkage between Housing Demand and Other Sectors like 
Steel and Cement Production The coefficients of the estimated equations depict 
that a 10% increase in realized demand for house (proxied by number of loans 
disbursed by HFCs and Banks) results in 4.59% increase in steel production 
and 4.67% increase in cement production.  
• We also studied possible Linkage between Household Leverage, Housing 
Demand and GDP Growth. We have found linkage between Housing sector GDP 
and household sector leverage and PSBs housing loan disbursement. We also 
found evidence of linkage between house hold leverage and GDP.  
• House price index (computed by us) moves in with the economic cycle and it 
becomes more evident when the growth profile is matched with the expansion in 
bank credit growth to housing (especially during 2004 onwards when banks 
stepped up the lending to housing segment). 
 
Micro Findings 
In our micro-study of the determinants of housing Demand, we profiled the 
borrowers in terms of Location, Gender, Age, Income, House Size etc based on large 
sample data obtained from HFCs and Banks. Some Key findings are listed below: 
 
• A typical borrower would most likely to be a male in the age group of 40 to 50 
having an average monthly income of Rs.10000 who prefers to buy a house of the 
size of 100 square meters. 
• Though most of the borrowers are in the age group of 40-50, a significant 
25% are below 35 years of age. It is also found that there is a falling trend in 
average age profile of the housing demand. A statistical un-paired t-test confirms 
that the year wise fall in average age is significant at 1% or better level 
except between year 2006 and 2007. 
• It is found that income and price elasticity of demand is less than unity. An 
increase in house price by 10%, ceteris paribus, results in a 4.6% decrease in 
housing demand as affordability comes down. With a10% increase in the monthly 
 5 
income of the borrower leads to increase in housing demand area by 5.96% (or 
approximately 6%). 
• The demand for house-size is found to be inversely related with the age of the 
borrowers. The number of dependence, which capture the financial liability of the 
borrower, is found to have negatively significant implying thereby more the 
number of dependents in a family reduces the affordability and hence the size of 
the house. 
• Urban people have greater demand for bigger house in comparison to 
suburban counterparts. The demand for house in terms of size in the rural 
area is lesser than the people live in suburban area; access to housing loan, 
capacity/affordability might be reasons driving the same.  
 
Risk Analysis in Housing 
We studied various factors that cause housing loan default. These factors were 
collected from the HFC’s loan history files. The borrower specific parameters normally 
used by the HFCs while granting loan and pricing of such loans include age, income, 
occupation, service & length of service, number of dependents, collateral information, 
guarantor etc. apart from property specific information. In a set of regression exercises, 
we have found the following interesting observations: 
o Bigger the size of house (in square meter), lower is the risk of default. 
o Higher the monthly income, lower the chance of default because of higher ability 
to pay. 
o Greater the value of asset (ln_asset_val), lesser the risk of default because of 
greater affordability due to wealth effect.   
o Security value (original book value of property) as proportion to the original 
loan amount factor (secval_loanamt or we call it as LTV) is an important and 
highly significant determinant of risk of default in housing loan. Higher the 
security margin available to the bank (real margin in this case), lower is the 
chance of default in home loan. 
o With a 10% decrease in LTV, the odds of default increases by 2.173%.  
o This collateral margin is also a significant determinant of rate of recovery of 
defaulted loans. A 10% increase in the ratio (secval_loanamt) lead to 0.38% 
increase in the recovery rate (rr). Further, due to the presence of guarantee, 
bank’s likelihood of recovery is more. 
o EMI to Income ratio is positively associated with the estimated likelihood of 
default. A 10% increase in EMI to Income ratio is estimated to increase the 
likelihood of default by 4.52%! 
o The likelihood of default in housing loan decreases significantly with the 
presence of additional collateral. The presence of more number of co-
borrowers reduces the risk of default. Interestingly, higher the co-borrower’s 
monthly income, lesser is the chance of default because of availability of second 
line of source of income. 
o As no. of dependents increases, probability of default also significantly rises 
because of higher financial burden.  
 6 
o Rural and semi urban people are riskier than the urban borrowers. However, we 
have found that borrowers located in big cities are riskier than medium and 
smaller cities. This may be because with the activation of personal loan segment 
by commercial banks and easy access to such loans especially in the metro area 
prompted the existing home loan borrowers to overstretch their financial 
commitments with a consequential deleterious effect on home loan default rate in 
this segment.   
o An increase in GDP growth rate reduces the likelihood of default. 
 
Target Audience: Who will benefit from this report?  
 
This report would be of immense relevance to Government, RBI, NHB, banks and HFCs 
as well as academia. It can also act as guidance to local authorities and sub-regions on 
understanding their local housing market. The study would provide guidance on housing 
needs assessments and can be used as a manual for undertaking housing market 
assessments and facilitate future projections and valuation. Finally, we believe that such 
report will benefit the industry in facilitating relevant financing, banking, and monetary 
policy formulation and provide them a better risk perspective to meet the growing 
financial demands of the nation in coming future.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 7 
 
 
 
 
 
 
 8 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 9 
 
 
Bibliography 
 
Ambrose, B. and C. Capone, 2000. The Hazard Rates of First and Second Defaults. 
Journal of Real Estate Finance and Economics 20, 275-293. 
Bajari, Patrick and Matthew E Kahn (2003), "Estimating Housing Demand with an 
Application to Explaining Racial Segregation in Cities", NBER Working Paper 
No. 9891. 
Baku, E. and M. Smith, 1998. Loan Delinquency in the Community Lending 
Organizations: Case Studies of NeighborWorks Organizations. Housing Policy 
Debate 9, 151-175. 
Barot, Bharat and Zan Yang (2002), "House Prices and Housing Investment in Sweden 
and the United Kingdom : Econometric Analysis for the Period 1970-1998", 
Review of Urban & Regional Development Studies, Vol. 14, No. 2. 
Calem, P. and S. Wachter, 1999. Community Reinvestment and Credit Risk: Evidence 
from Affordable-Home- Loan Program. Real Estate Economics 27, 105-134. 
Chinloy, P. 1995. Privatized Default Risk and Real Estate Recessions: The U.K. 
Mortgage Market. Real Estate Economics 23(4): 410-420. 
Cowan, A., and Charles Cowan, 2004. Default Correlation: An Empirical Investigation of 
a Subprime Lender. Journal of Banking and Finance 28, 753-771. 
Dholakia, B. and R. H. Dholakia (2000), “Impact of Investment in Housing Sector on 
GDP and Employment in Indian Economy”, IIMA, Working Paper, July. 
Égert, Balázs and Dubravko Mihaljek (2007), "Determinants of House Prices in Central 
and Eastern Europe", BIS Working Paper No. 236, September. 
Furstenberg, G. Von, and R. Green, 1974. Estimation of Delinquency Risk for Home 
Mortgage Portfolios. American Real Estate and Urban Economics Association 2, 
5-19. 
 10 
Getter, D. 2003, Contributing to the Delinquency of Borrowers. The Journal of Consumer 
Affairs 37, 86-100. 
Hanushek, Eric A and John M Quigley (1979), "The Dynamics of the Housing Market : 
A Stock Adjustment Model of Housing Consumption", Journal of Urban 
Economics, Vol. 6, pp. 90-111. 
Hanushek, Eric A and John M Quigley (1980), "What is the Price Elasticity of Housing 
Demand?" Review of Economics and Statistics, Vol. 62, pp. 449-454. 
Mankiw, N Gregory and David N Weil (1989), "The Baby Boom, the Baby Bust and the 
Housing Market", Regional Science and Urban Economics, Volume 19, pp. 235-
258. 
Sirmans, G. Stacy and David A Macpherson (1999), "Forecasting the Demand for 
Housing in South Carolina", Center for Applied Real Estate Education and 
Research, University of South Carolina. 
Tiwari, Piyush and Jyoti Parikh (1998), "Affordability, Housing Demand and Housing 
Policy in Urban India", Urban Studies, Vol. 35, No. 11, pp. 2111-2129. 
Tiwari, Piyush; Kirit Parikh and Jyoti Parikh (1999), "Effective Housing Demand in 
Mumbai (Bombay) Metropolitan Region", Urban Studies, Vol. 36, No. 10, pp. 
1783-1809. 
 


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A Study of Residential Housing Demand in India

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