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12207Paper APRIA 2007 - Madalasa V _ R Vaidyanathan

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					Home Ownership and Reverse Mortgage
As Components of Portfolio Choice in India




             Madalasa Venkataraman
           Fellow Program in Management
            Indian Institute of Management
            Bilekahalli, Bannerghatta Road,
               Bangalore, India 560076
              Venkatm03@iimb.ernet.in




                 R. Vaidyanathan
            Professor of Finance & Control
            Indian Institute of Management
            Indian Institute of Management
            Bilekahalli, Bannerghatta Road,
               Bangalore, India 560076
                 vaidya@iimb.ernet.in
                   91-80-26993086
Introduction


Rational forward-looking agents ought to smooth consumption over the life cycle and
exhaust the asset stock accumulated during the working career in retirement. In the life
cycle-permanent income framework, total lifetime resources available to the economic
agent are an important determinant of consumption. Lifetime resources are comprised
both of the stream of labor income expected to receive over the remainder of the working
life and of assets owned by the consumer.
The portfolio of individuals about to retire consists of stocks, bonds, other financial and
real assets, such as housing. While stocks and bonds and financial assets are more or less
liquid and can be traded in the market, housing does not have a well-developed liquid
market, and housing incorporates both consumption and investment motives.
Real estate assets differ substantially from financial assets due to their dual role. Housing
wealth is demanded both as an investment good and as a durable consumption good.
While risk averseness in an individual might preclude him from holding financial assets,
the individual will still have to consume housing services (as in taking a house on rent)
and one way to hedge against inflation in rental values is to purchase the house itself.
Therefore, housing also has another role to play as a hedge against fluctuations in rental
value.
The lifecycle hypothesis of saving suggests that asset accumulation happens during the
early stages when there is an income inflow, and during the later years the elderly ‗live
off‘ the accumulated assets. However, housing, by its very nature, is inseparable into the
investment and the consumption components. The elderly would like to decumulate the
investment component, yet retain the consumption component of housing by living in the
same house. Given the virtually illiquid nature of housing and the high volatility of real
estate prices, the value of the housing investment is not guaranteed of a certain rate of
return. One such instrument that allows the elderly citizens to tap into housing equity
without selling the house and moving out is the reverse mortgage.
A reverse mortgage is primarily an instrument that allows the elderly homeowners
borrow against the accumulated home-equity without moving out of or being forced to



                                             2
sell the home asset. Traditionally, reverse mortgages were designed as a product for
elderly homeowners, who are ‗house-rich‘ but ‗cash-poor‘, whereby they could liquidate
a portion or the whole of their home equity over a period of time to create a regular
stream of income.
Reverse mortgages are in existence in countries such as the US, Canada and Australia.
With the strained solvency of the state sponsored system, the onus in these countries has
shifted to private savings for retirement. Shifting the onus to private savings needs some
structural changes in the financial sector. Households will need to insure against
uncertainty related to real estate prices, rates of appreciation of property, interest rate
movements, inflation, and adjust for increasing longevity. While increasing the level of
precautionary savings can do this, this tends to reduce the level of disposable income
available for consumption, and therefore reduces net welfare. Shifting the risk to the
insurance agents, who are better equipped to handle this risk, is a more efficient solution.
A pareto optimal solution is for the individual to hold a part of his wealth as annuities to
aid in asset decumulation. (Yaari, 1965).
This paper attempts to investigate the effect of introducing a reverse mortgage into a
lifetime asset and liability scenario, measuring the impact of the reverse mortgage
product in terms of the net welfare increase for the individual. The introduction of a
product like the reverse annuity mortgage helps in smoothing consumption over the
lifetime of the individual by allowing her to tap into long term assets and by helping to
separate the consumption and the investment components of housing.
The rest of the paper is organised as follows: Section II gives a brief overview of
literature, section III discusses the Model development, Section IV defines the data
collection and Section V gives an overview of the results. This is followed by the
limitations and conclusions of the current study.




                                             3
Literature Survey
The idea of smoothing consumption over the lifetime has had more than its share of
research, both theoretical and empirical. There is however, no single accurate
explanation, although the life cycle models of Modigliani and Brumberg (1954), Ando
and Modigliani (1963) form the basis, with or without a bequest motive. Phelps (1962),
Samuelson (1969), Merton (1969), Hakansson (1970), have also put forward their
theories of portfolio decision making in an infinite horizon with certainty. On the
contrary, Yaari (1965), Levhari and Mirman (1977), and others studied the impact of
uncertain lifetime on planned consumption with actuarially fair insurance.
After imposing the simplifying assumption that interest rate is zero, Modigliani and
Brumberg (1980) arrived at the individual consumption function. Because the retiree
finances his consumption by running down the assets accumulated over the working life,
wealth holdings follow a humpshaped age profile with a peak at the end of the earning
span as stressed in Modigliani (1986).
Deaton (1992) asserts that the introduction of a positive interest rate does not
significantly change the main features of the life cycle model. In the beginning of the life
cycle consumption path shifts downwards, whereas at the end it shifts upwards. Income
path is more realistically described by a hump-shaped age profile. During the early years
of working career income is typically low, but increases along with ageing. Eventually
earnings decline at the end of the working career. According to Deaton (1992), in this
case consumption smoothing implies that a young worker might in fact want to borrow
rather than to save during the early years of his earning span.
Zeldes (1989b) proved that once the benchmark life cycle model is substituted by a
model with random labor income and constant relative risk aversion utility function
(isoelastic utility function) the low dissaving of the elderly accords with rational
optimizing behaviour. In a model like this, the agent is allowed to insure oneself against
future contingencies, such as health shocks, with saving motivated by precaution. With
respect to the behaviour of an economic agent, the implications of the CRRA utility are in
general more plausible than those of quadratic utility.




                                             4
Among the proposed causes for the slow dissaving of the elderly is uncertainty about the
date of death. Hurd (1989, 1990) has shown using a life cycle model with uncertain
lifetime that slow decumulation of wealth is associated with a rather smooth consumption
path. According to his interpretation, modest current consumption and postponed
decumulation are a means for a highly risk averse consumer to hedge against the
possibility of having insufficient resources if he should live unexpectedly long.
Davies (1981), on the other hand, claims that uncertainty over the length of life depresses
consumption by a fraction that increases with age. The reduction in consumption then
leads to lack of dissaving in old age.
Kotlikoff and Spivak (1981), Kotlikoff et al. (1987) and Abel (1985) also find that with
longevity being certain, or in an actuarially fair market, people tend to consume much
more in their retirement phase, following the consumption smoothing stipulated by
finance theory, and it is the risk aversion of individuals and uncertainty of their life-span
that causes them to leave unintended bequests.
The presence of housing in the portfolio, however, skews the patterns of investment and
consumption predicted by the above models. The investment in housing is much larger
than should be the weightage given to a single asset, given that housing is a fairly risky
proposition. Economic and finance theory looks at housing as a deviation from the
standard models of asset allocation. For most homeowners, the house is the single largest
asset in their portfolio. Housing differs from stocks and bonds and other investment
assets because a house combines both investment and consumption. The household's
ownership of residential real estate determines the level of its consumption of housing
services, and the level of real estate ownership that is optimal as consumption good is
different from the level of real estate from an investment viewpoint.
With developed rental markets, a household can separate the consumption and the
investment aspects. However, rental housing is by no means a perfect substitute for
owner-occupied housing, because of various agency and transaction costs over the
lifetime of home ownership. Housing can thus be reduced to a make-or-buy decision.
Buying a house insulates the consumption of housing services from variation in the rental
price of housing, but housing is an illiquid asset and subject to liquidity and interest rate
risks. Homeowners‘ wealth is exposed to house price risk whereas renters‘ wealth is not.



                                             5
In terms of taxation, owner occupied housing services are not taxed and mortgages
interest payments are deductible from the income tax base. The two most important
housing tax advantages are that the service income provided by owner-occupied housing
(generally referred to as imputed rents) is untaxed and mortgage interest payments are
deductible from taxable income. (Gervais, 2002).
However, owned housing as a hedge against rent risk. Institutional and borrowing
constraints frequently prevent young households with low levels of cash in hand from
purchasing a house that matches their lifetime consumption need. Senior homeowners, in
the meantime, are often forced to hold an equity position in their houses that lasts longer
than their expected length of occupancy. This mismatch between life cycle housing
consumption need and housing investment position is worsened by the presence of lumpy
housing adjustment costs.
Jan K. Brueckner (1997) analyzes the interaction between the consumption demand and
the investment demand for housing in a mean-variance portfolio model. Brueckner
considers a general covariance matrix and mean vector of returns and a general utility
function, and derives analytical results. Flavin and Yamashita (2002) derive the portfolio
choice of level of housing stock and why this impacts the bonds to stock ratio for
households at different age and wealth levels, and solve for housing stock in a mean
variance efficiency framework, using numerical methods and simulation.

Scope of the Study
The scope of this paper is to introduce reverse mortgage into such a scenario, specific to
the Indian context. Introduction of reverse mortgage into this framework changes the
types of risks that are faced by the participants. In an instance where the home owner
does not want to shift homes for various reasons, this model will derive a solution using
simulation of house prices, appreciation, rate of increase in income, etc to identify
whether buying a house initially and then buying a reverse mortgage is a more optimal
solution than buying the house without the reverse mortgage, or not buying a house at all.
A reverse mortgage is described as an annuity payment to the homeowner for the length
of time that she remains in the house. No repayments have to be made on the loan as long
as the homeowner is alive and still living in the house affected by the reverse mortgage,



                                            6
or till the spouse of the homeowner is alive and still living in the said property. This is
better than conventional types of mortgage where the homeowner has to either move out
or sell her property, or has to undertake to make regular annuity payments on a loan.
The valuation of the reverse mortgage and the monthly or annual receipts from it depend
on the age of the homeowner, the value of the home, the amount of loans (if any) on the
property, and even the location of the property. The homeowners can choose to receive
the reverse mortgage funds as a lump sum, as monthly income (for up to life), as a line of
credit, or any combination.
This paper analyses housing as a consumption and investment decision, in a life-cycle
model without a bequest motive. In a typical portfolio of equity and bonds, housing is
introduced. Equity has a yield that mimics the stock market behaviour in India, bonds
have a deterministic yield, and housing has both consumption and investment aspects
modelled based on the imputed rent and the home appreciation rates respectively. The
owner of the house makes payments on a Fixed rate mortgage in the initial part of his life,
and towards retirement, draws down from the home equity as a reverse mortgage. The
paper uses simulation methods to solve a life-cycle model under alternative assumptions
about the proportions of equity, debt and housing in the portfolio of the individual.
The study aims to explore the potential wealth effects of Reverse Mortgage by
considering the extent to which housing equity could change the consumption of the
elderly, if housing equity were converted to a more liquid form. The mechanism for home
equity conversion that is considered is a reverse annuity mortgage. The primary goal is to
understand the potential effect on the current income of the elderly through conversion of
illiquid housing wealth into an income stream.
Taking a median income level and a housing asset accumulation and decumulation
pattern that is in keeping with the practices in India, the increase in wealth due to
availability of a reverse mortgage instrument is contrasted with the life cycle wealth in
the absence of such an instrument. In particular, the increase in current income of elderly
families if they were to obtain reverse mortgages, and thus convert housing equity into
cash that could be used for other consumption, is studied. By this, it is hoped that a strong
case can be made for the introduction of reverse mortgage products in India. Using both




                                             7
mortgage and reverse mortgage in the lifetime housing asset plan, homeowners may be
able to stabilize levels of expenditure through the lifetime.

Data
The paper uses primary data from Housing development Finance Corporation, (HDFC), a
leading provider of housing finance in India. Primary Data from HDFC covers data on
the house mortgagee profile, age, income levels, the amount of housing equity the owner
plans to invest in, the tenure of the loan, Adjustable or Fixed rate mortgage, and the
amount of down payment. The Income-house equity distribution for various states in
India is derived from the empirical distribution and values are generated from the same
for the simulation.
The yield of the equity market is calculated from the S&P CNX NIFTY, which is an
index of the National Stock Exchange, India. The returns on bonds are taken as the
returns on the Treasury Bills (180 days) published rate of return from the Reserve Bank
of India, the Central Bank in India. World Bank, UN ESCAP data are available on
population, ageing, pension coverage, etc. The National Account Statistics and NCAER-
SEBI Study on the Indian Investor are used extensively to calculate the distribution of
wealth/income across different categories of income and to cross-verify the same.
We have assumed growth rate of 5% as property appreciation rates. The actual long-term
property appreciation rates in India are not very well documented, so we make the
assumption that property prices are indexed to inflation prevalent at the same time period.
Also, growth rates in property are different across different cities as well as within
different zones in the same city. In India, there isn‘t a refined concept of zoning of areas,
so within each cluster of area, the property appreciation rates may be vastly different.
The analysis is done during the 1995 to 2005 period. Average pay scales denominated by
the Pay commissions, a government body that decides pay scales for various classes of
employees are used in arriving at average monthly pay, housing loan affordability and
repayment, and pension accruals. Average monthly income levels for the elderly are
computed based on Census data and cost of living indices. A reverse mortgage plan is
then incorporated into the wealth model to identify the increase in income due to easy
liquidation of housing wealth.



                                              8
Monte Carlo simulation is used to conduct the above analyses. Sensitivity to various
parameters such as home loan interest rates, Public Provident Fund interest rates,
differing levels of income and home valuations are also studied.

Model Development
Scenarios
The model compares the lifetime utility of a scenario including housing and reverse
mortgage compared to scenario in which there is an option of only housing and no
reverse mortgage.
Comparing three scenarios
Scenario – H + RM
Scenario 2 – H + No RM
Scenario 3 – H + No RM + moving to a smaller home and utilising the difference in
equity.
Scenario 4 – H + No RM + selling the house and utilising the proceeds as an annuity.


Lifetime Asset Allocation
The individual receives exogenous labour income of non-stochastic, certain nature from the
time of start of his work life till his retirement at the fixed age of sixty. (Sixty is t he age of
mandatory retirement in India for those in government and most private services). Since
Income after primary employment is not certain, these incomes are not considered. They
can, however be easily modelled in the existing framework.
The individual invests such income that he receives from labour in both financial and
non-financial assets (real assets including housing). The investor‘s portfolio then consists
of housing, investments in stocks and bonds and in other non-appreciating assets.
Investors dynamically decide on house size, tenure, financial portfolio, and other goods
consumption. The lifecycle simulation begins between the ages of 20 and 30 (uniform
distribution of cases between 20 and 30), which is the year of start of the work-life of the
individual. Each period of analysis is one year, and mortality of the individual is
uncertain.




                                                9
The model is characterised by a representative single-person household that plans its optimal
consumption of housing and non-housing services, and the optimal timing of first home
ownership, over its lifetime, given perfect foresight about its future income.
The simulation model applied here is a modified housing tenure choice model in which the
decisions by a household about when to buy a first home, the size of the purchase and
subsequent upgrades, consumption and saving pre and post retirement are made jointly in a
life cycle framework.
A life cycle approach is appropriate because the desire to pay off a home provides an
incentive to save. The life cycle simulations provide certain insights. Policies to improve
housing affordability can simultaneously alter the timing of the household‘s first home
purchase, the size of the purchase and therefore house prices and the level of private saving.
The simulations suggest that if households bring forward their first home purchase – that is,
they buy at a younger age – the size of the purchase will be smaller due to tighter borrowing
constraints implied by their lower income and lower accumulated savings.

Model Variables
Time periods
Assume individual starts work life at time period t, works for duration of N years and has
life expectancy of E post retirement.
Total life of the individual = t+ N+E,
Where ‗t‘ is the age at which the person enters the workforce, N is the work-life of the
individual and E is the life-expectancy at age 60 for the individual.
Income
Income of the individual is It at any point in time. It is a function of appreciation in
income and inflation. Nominal It is a function of Real It and inflation. For the purpose of
simulation, nominal It will be used. The growth rate of personal disposable income as
calculated from the National Account Statistics of India (NAS, 2000-2005, various years)
is used as the compound rate of growth of personal income. Since the NAS gives details
on the rate of growth of nominal income, inflation is not considered separately.
Preconditions
There is no asset or liability for the individual at Time t, that is, there is no hereditary
wealth, outstanding loans etc. Any such positive or negative wealth can be considered as
exogenous to the model and model can be adjusted accordingly to account for this by


                                              10
considering ‗t‘ as a variable. Positive wealth can be treated equivalent to a scenario in
which the individual has started working earlier to ‗t‘ and negative wealth can be
considered as a scenario in which the person effectively starts working at ‗t‘. ‗t‘ can thus
be chosen at a point where net assets is equal to zero.
Work life of the individual
Consider that the individual has a productive work life of ‗N‘ years with W0 defining the
start of work life. Wn will thus be the nth year of work life. The individual retires at WN,
and WN can also be considered start of retirement , so WN = R0. Re is the eth of retirement
and RE is the life expectancy of the individual at age (t+ N), which is the year of
retirement.
Age t +N can be considered to be 60 for the Indian scenario. Sensitivity analysis can be
performed on this variable also.
Mortality
Since we are interested in the welfare effects of a reverse mortgage, which is applicable
only over the age of sixty, we do not model mortality before the age of sixty. The life
expectancy at age sixty is factored into the model. Mortality data at age 60 is obtained
from the Life Insurance Corporation of India.

Housing variables
Mortgage
Let M0 be year of start of mortgage. At the time of start of mortgage, a downpayment of
Hd is made on the property. So, at the time of initiation of mortgage, ALL the savings of
the individual (savings being equivalent to investments in stocks and bonds) is made as
down payment of the house. The savings in other assets, such as gold and fixed
mandatory saving vehicles, or those in which there is no option of early withdrawal is not
taken into consideration. (these include, but are not limited to life insurance policies,
National Savings Certificates, Public Provident Funds, investments in Gold and other
durable assets).
Let Mt denote the tth year of mortgage. The mortgage terminates at MT where T denotes
the tenure of the mortgage. At MT, the housing equity of H is transferred to the owner of
the property. The model limits itself to cases in which pre-payment doesn‘t occur. Since



                                             11
prepayment is normally a function of transfers of wealth and the model doesn‘t consider
any transfers of wealth – this is a reasonable assumption to make. Labour income is the
only source of income considered and the consumption function is assumed to be
constant throughout the lifetime of the individual.
The rate of interest on the mortgage is fixed throughout the life of the mortgage (Fixed
rate mortgage) and the payout on EMI is, therefore, fixed.
Tenure
The simulation is based on the distributional parameters of the empirical distribution
generated from the data on the age-profile of homeowners. Tenure choices for the home
loans also follow the empirical distribution. Tenure is also subject to two conditions, one
that the mortgagee fully owns the house before retirement at sixty, and second, the EMI
payments for that particular tenure choice do not exceed the income-to-outflow ratio of
35% fixed by industry norms. Prepayment of the housing loan is not allowed, and tenure
choices are carried over till the end of the term.
Downpayment
An investor enters into a housing loan for purchase of a house when he has the resources
for down payment on the house size he wants to purchase. Following industry norms in
India, this has been fixed at 15% of the housing equity. The amount of down payment
will decide the size of the house that can be purchased. There is literature available to
suggest that young couples decide on their home equity levels based on the amount of
down payment that they can make. In practise, there is an option to defer the purchase of
home equity in case the resources are not available for the desired house size. The
simulation however does not have the option of deferring the purchase of the home.
If households accelerate their home purchase, they may be forced to buy a smaller house
size relative to their needs and preferences, because at a younger age they have less
accumulated saving to meet the down payment and also have lower income from which to
service the mortgage. Mortgage lenders set the maximum loan size according to the capacity
to meet monthly repayments, which is called the Installment-to-income ratio that is assessed
mainly on the basis of the flow of personal disposable income.
Housing Consumption and Imputed Rent
Both implicit housing consumption (when the owner-occupied house is fully paid off)
and explicit interest payments are counted as owner-occupied housing consumption.


                                              12
Imputed rent is considered a saving for the owner-occupied house and the imputed rent is
calculated from the lifetime annuitisation of the value of home equity (the maximum
value of home equity is equal to how much the individual would pay for consuming
housing services of a similar nature over his lifetime). This disregards holding a housing
asset for investment purposes, but this is not a restrictive assumption to make.
A younger homeowner can enjoy greater tax benefits over the lifetime, - in particular, the
tax-free status of the imputed rental income from owner-occupancy. This implies higher
lifetime resources for consumption, and per period consumption is greater than for an
older homeowner.
Housing services may be obtained either by purchasing a house or by renting housing. In
the initial years (during working life) households purchase rental housing till they are
able to afford their own housing. It is assumed that housing purchases take place at the
end of a period, and with each period being one year; the household must wait at least
until the end of the first period to buy a house.
Appreciation of property
It is assumed that houses do not depreciate, but the value of land + housing appreciates at
the rate of ‗house appreciation‘ Ha = (1+ha). Again, there are very rare cases in which the
total housing unit depreciates in value (as in a ‗housing bubble‘) but the simulation works
for negative values of appreciation as well. In this study, the value of housing
appreciation is assumed to be 5%, which is just the rate of inflation Wholesale Price
Index) in India, and sensitivity analysis is performed for other rates of appreciation.
Disposal of the Housing Asset
Post retirement, the household has three options
   1. To remain in the same house and finance consumption out of retirement
       income/wealth
   2. To reverse mortgage the house and to finance consumption out of the reverse
       mortgage annuity payments and the wealth
   3. To sell the house
In case of option (3), two options exist- move to another house – typically with EMI
payment on the house, or with a down payment on purchase, or to rent an
accommodation. The rent paid on the second accommodation and the EMI option are



                                              13
equivalent in nature and substitutable, provided there are no other transaction costs and
the discount rate on the EMI is the same as the interest rate for borrowing and lending.
This is however a restrictive assumption to make, since transaction costs involved in
selling a house are about 7% and transaction costs in purchase of a new accommodation
(including registration etc) are about 15% of the value of the new accommodation.
In the presence of perfect capital markets, no transaction costs, no liquidity premiums,
perfect information etc, however an asset is held, it will achieve its fair market value on
sale. Consumption also would be perfectly smoothed over the lifetime and there would
not be any welfare loss across the three options outlined above. Since transaction costs
are one of the most important determinants of welfare loss, it is important to identify and
incorporate transaction costs as part of the simulation. Data on transaction costs (stamp
duty etc) is given in the appendix.

Consumption function
Literature shows consumption function to be a function of initial income, interest rates
inflation rates variance in income economic shocks If we assume that labour income
being the primary source of income is fairly stable over the lifetime of the individual, and
labour income also adjusts dynamically to the inflation rate and to the rate of growth of
GDP (implicit interest rates) then consumption function is reduced to being only a
function of initial income class
It = Ct + Sat
It is income, Ct is consumption and Sat is savings at time ‗t‘.

Savings
The normal avenues for savings are financial assets such as stocks and bonds and durable
physical assets such as housing and Gold. The NSSO data gives by MPCE class
investment in stocks and bonds. Also, housing loan companies cap the mortgage amount
based on the income levels. It is reasonable to assume individuals will consume the
maximum amount of housing they are able to afford at any point in time.
Sat = Bt + St + Mt + Otherst
BT = bti*Bt where b is (1+ rate of interest on bonds- after tax)
ST likewise denotes the cumulative value of wealth in stocks at year T


                                             14
The maximum amount of housing depends on
   a. the amount of down payment
   b. maximum mortgage amount available to them through the Instalment-to-income
       ratio.

Pension
After retirement, individuals receive a pension, given by a fraction P%r > 0 of average
labor earnings in a particular year. In India, only about 11% of the population receives
any pension from government or quasi-government bodies. The scenario with pension is
used to determine whether the individuals receiving any kind of pension also benefit from
reverse mortgage. %r is the average endowment during an individuals working life from
his labor earnings. P is a replacement rate and %r corresponds to indexed average annual
earnings. Typically pensions are financed by tax on the individual, which is the personal
tax rate of 35% in India.
EMI= Max (Ih, Instalment-to-income ratio), where Ih is the EMI calculated as a function
given by
EMI = (Mh, Tenure, rate of mortgage)
Total Housing equity is given by HT = HD+ Principal component of Mh
Where t =T , Mh=0
HD is to be minimum 15% of total house equity, and at the point of origination of
mortgage, HD is greater than total savings in stocks and bonds. (Down payment is
financed totally by own funds, and no borrowing is allowed)

Retirement
At R0, total wealth of the individual is given by the wealth in stock, bond, other assets
and the appreciated value of housing wealth.
If the individual decides to sell the property and move to a new one, transaction costs of
7% are applicable on the sale and 15% on the acquisition of a new property. Sensitivity to
various levels of realistic transaction costs is analysed. In case of sale of property and
renting of a new one, imputed rent is taken as a consumption post the retirement phase.
The imputed rent is slightly difficult to calculate: the methodology used is to assume that
the individual consumes the same rent as earlier, but because there is a lesser time period


                                            15
to mortality, the value of housing purchased will be equal to the annuitized value of rent
for the remaining lifetime of the individual. In case where the individual decides to rent
the property, the same amount of imputed rent will be treated as consumption
expenditure.

Reverse Mortgage
Using actuarial calculations, it is possible to calculate the value of the house for a
specified tenure of reverse mortgage.
Assume the ownership of the house reverts totally to the bank on the death of the owner,
but during the lifetime of the owner, he enjoys occupancy of the house. Suppose the
current value of the house is H, then the value of the house will appreciate by ‗g‘ every
year. The owner, who is now of a particular age ‗a‘ will die at time T with a probability
tpa   that can be calculated using the mortality tables. Mortality tables used as reference in
this are from LIC A(1996-1998) Mortality table for annuitants. The value of the loan to
the bank at current date is obtained by discounting the value of this house with the
mortgage rate ‗m‘. The lump sum amount that the borrower can borrow from the bank is
given by the actuarially fair valuation:
                                                          t a
                                       T
                                              1 g 
                                  L  [ H *                     *t pa ]
                                      t a    1 m 
T here is the maximum age till which a person can live, taken to be 120, based on the LIC
A (96-98) Mortality table for Annuitants. L is the lump sum the bank will pay to the
owner for immediate title to the house.
The lender can then determine the actuarially fair maximum present value of an annuity
(LS) that a borrower can purchase, and break this down into a stream of monthly
payments to the borrower, calculated using the following, where ‗r‘ is the risk free rate.
                                                      L
                                       P                   t a
                                             T
                                                  p 
                                              1t  ar 
                                            t a       
We have assumed an interest rate of 8% in computing the monthly payments. We also
report the findings for 8%, 10%, and 12% interest rates. The median interest rate on a




                                                 16
similar annuitant policy provided by the Life Insurance Corporation of India, the leading
insurer in the country, works out to about 8.5%.

Results
The simulation of life-cycle asset accumulation and decumulation patterns was performed
using Matlab, version 6.0 and @Risk to generate the distributions points from the
distribution parameters.
Given below are some details about the primary data


                                                                     Loan Term
                    Income       Rate of Interest   Property Value
                                                                      (Months)
 Mean                 31901.59              9.75        1240546.67            205
 Median               19122.00              10.5         870000.00            216
 Std. Deviation       56732.09              1.55        1675739.97          94.39
 Minimum               2028.00              7.25          33000.00
 Maximum            3052555.00             13.25         55000000.



                                                                     Loan Term
  Percentiles       Income       Rate of Interest   Property value
                                                                      (Months)
 10                  9269.5000              7.50        376320.00              84
 20                 11427.0000              8.00        484624.00             120
 30                 13702.0000              8.50        599572.00             156
 40                 16120.0000              9.00        715000.00             180
 50                 19122.0000             10.50        870000.00             216
 60                 23290.0000             10.75       1084978.00             240
 70                 28800.0000             11.00       1342564.20             240
 80                 37380.0000             11.25       1694763.20             267
 90                 56845.0000             11.25       2337000.00             310


The Median income is about 20,000 per month, or about 2,40,000 INR per annum.
Income figures are reported as Net income. The EMI is calculated based on the property
value less the value of down payment (which averages to 15% for the sample) and the
rate of Interest.
The median value of the property is 8,70,000 INR and the property value is restricted by
the amount of EMI that can be paid per month on the availed loan amount. Most of the
loanees appear to limit their down payment to the mandatory 15%. This indicates, in all
probability, that the amount of housing equity investors want to invest in is limited by the



                                               17
    Amount they can afford as down payment. This has also been documented in literature,
    where it is said that investors choose the size of the housing asset based on assets
    available as down payment.
    The Cross-tab of income and housing is given below.

    Empirical Probability Distribution of Income vs. Property Values

Income /
Property    2    4    6    8 10 12 14 16 18 20 22 24 26 28 30 33 37 41                                45     49
       1 0.05 0.39 0.42 0.10 0.02 0.01
       2 0.02 0.12 0.28 0.25 0.16 0.08 0.05 0.02 0.01 0.01
       3      0.03 0.08 0.11 0.15 0.16 0.14 0.12 0.08 0.04 0.03 0.02 0.01 0.01 0.01
       4      0.02 0.04 0.07 0.08 0.09 0.12 0.14 0.11 0.11 0.06 0.05 0.04 0.03 0.01 0.01 0.01 0.01
       5      0.01 0.01 0.04 0.06 0.07 0.12 0.13 0.13 0.10 0.07 0.08 0.05 0.04 0.03 0.03 0.01 0.01
       6           0.03 0.06 0.04 0.06 0.06 0.10 0.06 0.10 0.08 0.08 0.06 0.07 0.04 0.06 0.05 0.02          0.01
       7           0.02 0.02 0.04 0.07 0.04 0.06 0.07 0.11 0.09 0.05 0.08 0.05 0.07 0.09 0.08 0.03   0.03   0.02
       8           0.03 0.04 0.03 0.08 0.05 0.06 0.10 0.06 0.06 0.07 0.06 0.04 0.06 0.09 0.06 0.04   0.02   0.03
       9                0.01 0.03 0.03 0.04 0.03 0.06 0.04 0.08 0.09 0.08 0.03 0.05 0.11 0.15 0.09   0.05   0.03
      10                     0.04 0.06 0.06 0.04 0.04 0.06 0.03 0.09 0.06 0.06 0.09 0.12 0.09 0.04   0.04   0.04
      11                0.02      0.05 0.00 0.07 0.07 0.04 0.07 0.13 0.07 0.04 0.05 0.13 0.16 0.07   0.02
      12                0.02      0.06 0.06 0.08 0.08 0.12 0.04 0.06 0.02 0.02 0.06 0.14 0.08 0.06   0.06   0.02
      13                     0.03 0.05 0.05 0.08 0.05 0.03 0.03 0.03      0.03 0.05 0.29 0.13 0.05   0.08   0.03
      14                     0.04      0.07 0.04      0.07 0.04      0.15 0.04 0.07 0.15 0.15 0.07   0.04   0.07
      15           0.07 0.04 0.04           0.11 0.04 0.07 0.11 0.04 0.04 0.07 0.04 0.11 0.04 0.14   0.04   0.04
      17                     0.03 0.03 0.05 0.03 0.03 0.05 0.05 0.03 0.05 0.13 0.13 0.10 0.18 0.03   0.03   0.13
      20                0.03      0.09 0.03 0.06      0.06 0.03 0.06 0.06 0.06 0.21 0.06 0.06        0.06   0.12
      23                                    0.07      0.11 0.00 0.04 0.14 0.07 0.11 0.21 0.07 0.04   0.11   0.04
      26                0.04      0.04 0.04 0.04 0.04      0.08 0.04 0.21 0.08 0.08 0.25 0.04
      29                               0.06 0.06                0.13 0.13 0.13      0.06 0.19 0.19   0.06

    Souce: Generated from primary data
    Note: Income value in Lakhs per annum and Property value in Lakhs per annum

    The joint distribution of housing vs income shows that housing is linearly increasing with
    income, but there is a lot of variation within each group. The matrix is fairly diagonal in
    nature. The Bayesian probability here is conditional upon the income level of the
    individual. Where there has been a joint ownership of the house, the income of both the
    spouses has been taken into consideration.




                                                     18
                                   Income vs Housing Wealth
                            0.50

                            0.40

                            0.30
              Probability
                            0.20

                            0.10                                                  Property
                                                                                   Value
                            0.00                                             11
                                   1.00
                                          5.00
                                                 9.00
                                                                        1



                                                        13.00
                                                                18.50
                                   Income                                   0.00-0.10        0.10-0.20
                                                                            0.20-0.30        0.30-0.40
                                                                            0.40-0.50


Assume that individuals‘ preferences over consumption streams are represented by
  N     (C t )(1 ) 
E   t i            
   t 0   1  
where  is a subjective discount factor and  is the relative risk aversion coefficient.

The term inside braces indicates the constant relative risk aversion function.
Given the life-cycle hypothesis about consumption smoothing of the individual, any
deviation from projected consumption can be taken as a loss in welfare. Representing this,
Welfare loss = ( Cit  Ca )2
                        t



Where Ci is the estimated consumption and Ca is the actual consumption at time ‗t‘.
There are two parts to the welfare measure: One is whether the timing of house purchase
makes any difference, the second is whether the decision to go for a reverse mortgage
makes a significant difference.
Timing of house purchase
The timing of house purchase makes a difference in terms of utility of the individual.
Individuals who invest in house equity earlier rather than later in life have a significant
difference in terms of wealth.




                                                                19
Age of House purchase             Smoothed Consumption through
                                  the lifetime (per annum)
25                                101,345
30                                94,580
35                                89,078
40                                85,142
45                                81,287


Source: Calculated from primary data using Monte Carlo Simulations
Note: Marginal tax rate as applicable in India in 2006-07, Income of Rs.2,00,000 (median
income) and house property of Rs 10,00,000. (Median property value.) and term of 240
months (median value)


Income levels                     Marginal Tax Rate
< 110,000                         None
110,000<Income  150,000          10% of amount greater than 110,000
150,000<Income  250,000          20% of amount greater than Rs. 150,000 + Rs. 4,000
Income  250,000                  30% of amount greater than Rs. 250,000 + Rs. 24,000



The reason why investment in a house is an attractive option is because there are tax
breaks (both principal and interest payable on housing loan can be offset against tax
liability) and the ownership of the home acts as a hedge against house rent appreciation.
The descriptive statistics of the rates on financial assets in India is as given below
Asset                             Mean Rate of return
Stock Index (S&P CNX NIFTY)       13.9%
Treasury Bill (Risk Free Rate)    4.5%
Mortgage Loan                     9.75%
Inflation                         5%
House Price Appreciation          10%



The simulation of lifetime wealth suggests that investing in a housing asset is first order
dominant over not investing in a housing asset. During the accumulation phase, the tax
offset provided by housing increases net wealth to the individual due to investing in a
housing asset.




                                                 20
Decumulation Phase
The decumulation phase involves a choice of
   a. Selling the housing asset at the current market price of the house and renting or
       buying a new house
   b. Using a reverse mortgage
   c. Not tapping into house equity at all


Selling a house at current market price indicates that in the scenario of both renting a
house or buying a new one, the net present value of the proceeds from house equity are
reduced by the net present value paid out as rent or as Equated monthly installment on the
new house. So both of these scenarios are equivalent.
Using a reverse mortgage is a more efficient solution when the net transaction costs from
reverse mortgage are lesser than transaction costs on the option of selling the house.
There are also other psychological reasons that are involved in the decision to see the
house. Also, in case the individual decides to sell the existing property, she exposes
herself to rent appreciation risk, and to interest rate risk. If she remains in the same
property and goes in for a reverse mortgage, the individual is no longer exposed to rent
appreciation risk, but interest risk still exists. In any case, if rent moves in tandem with
property prices, then the individual stands to gain from house price appreciation rather
than lose if she chooses a reverse mortgage over selling and renting a house.
Either way, the welfare loss is least on opting for a reverse mortgage over not opting for a
reverse mortgage. Comparing the scenario of selling vs. reverse mortgaging the house,
reverse mortgage leads to greater utility depending on the parameters considered.
Payment amounts in a RM depend on the following
      Higher Home Equity (value of home less any outstanding indebtedness) increases
       the amount a homeowner can take out of the home equity
      Older Households experience greater income increases due to the shorter life
       expectancy. Also, joint life expectancy is much lesser than individual life
       expectancy
      Lower interest rates increase the amount per payment because cumulative interest
       to be repaid is lower


                                             21
        Higher expected property appreciation will increase the payment amount because
         the value of the remaining home equity is higher
        Lower inflation rates, because these work to keep the nominal interest rate lower
The larger the equity and greater the appreciation expected, the lower the risk that the
loan principal and accrued interest will exceed the market value of the property when the
loan becomes due. Higher interest rates mean more interest will accrue, increasing the
risk that the loan obligation will exceed collateral value. The longer the owner lives, the
greater the loan obligation whether the borrower chooses the annuity or lump sum with
accrued interest option.
The principal limit factors for reverse mortgage at different ages is as given below.


     Interest Rate                     Age of Borrower at Start of RM
                           65                 75                   85
8.00%                      0.537              0.626                0.728
10.00%                     0.361              0.492                0.638
12.00%                     0.261              0.389                0.560



Risk Factors in a Reverse Mortgage
Given the nature of the product, the long term impact of interest rates and appreciation
in real-estate prices, there are a lot of risk factors that are associated with a product like
reverse mortgage.
Lenders still face the risks of longevity, interest rate changes, and future property values
(Szymanoski, 1994). The uncertainty of borrower longevity and tenure makes it difficult to
estimate the timing of repayment and, therefore, the return the loan will yield. The interest costs
on outstanding balances follow a variable rate pattern, with certain limits on the rate at which the
interest costs can change within a year, and over the life of the product.
The more serious risk that the reverse mortgage provider bears concerns the growth in
the principal limit factor over time. There is "crossover risk", which is realized when the
principal limit factor exceeds around 90 percent of the house value. From this point on,
the lender becomes unable to recover the full amount owed.
The risk faced by borrowers in a reverse mortgage is also substantial. Borrowers do not
have the same capacity to diversify that lenders have. For a homeowner, there is a trade-



                                                 22
off between using housing wealth as a hedge against lump sum cash outflows such as
those for medical expenses or long-term care, vs. using housing wealth to append to
existing sources of income. This trade-off is important in the decision to go for reverse
mortgage. Elderly homeowners put off the decision of reverse mortgage since there is no
external motive, which forces them to a decision.
The risk faced by borrowers on a reverse mortgage includes the ability to predict
definitively their cash requirements at various times. In elderly homeowners this
becomes more important since they are more likely to incur costs, which are of a non-
recurring and fairly unpredictable nature. Reverse mortgage also in some cases includes
a lump-sum amount, but in this case, it is more expensive than taking a loan with the
home as a collateral.


Welfare from Reverse Mortgage
Comparison of welfare provided by reverse mortgage over the last ten years of life with
the option of no Reverse mortgage (selling and renting or buying another house) shows
that a reverse mortgage is a better solution than the other two scenarios considered.


                                Comparison of Welfare Functions

                 20

                 15
       Welfare




                 10

                 5

                 0
                      1

                          3

                              5

                                     7

                                            9

                                                   11

                                                         13

                                                                15

                                                                       17

                                                                              19

                                                                                   21




                                          Number of cases
                          Average Welfare in the last ten years of life with RM
                          Average Welfare in last ten years of life without RM




We perform 20,000 simulations and finally, we calculate the mean and standard error
and confidence intervals of over all the iterations




                                                   23
Across income groups, the welfare gain remains similar, but the difference between the
welfare with and without RM is different. Lower income quartiles seem to benefit more
from the reverse mortgage than higher income quartiles, as has also been shown in
various other studies by Venti and Wise etc. (1991)


                                Average Welfare for the Highest Income Quartile
               20


               15
     Welfare




               10


                    5

                    0
                            1        2        3       4         5     6         7        8        9
                                                          No of Cases
                                      Average Welfare for the last ten years of life with RM
                                      Average Welfare for the last ten years of life without RM




                                 Average Welfare for Lowest Quartile Income Levels

                    20

                    15
          Welfare




                    10

                        5

                        0
                                1         2          3         4         5           6            7
                                                         Number of Cases
                                          Average Welfare in last ten years of life with RM
                                          Average Welfare in last ten years of life with RM




                                                               24
Welfare Gains by introducing Reverse Mortgage to smooth consumption fluctuation

              Percentile               = 10             =5             =1
              100%                    0.068              0.039          0.023
              70%                     0.062              0.034          0.020
              50%                     0.059              0.028          0.018
              30%                     0.043              0.019          0.013
              10%                     0.020              0.012          0.004
Note: Return on Stocks is 10%, bonds is 5%, home appreciation at 10%, personal discount rate of 10%.




Looking at the data, one can conclude that unlocking housing equity does have a
substantial impact on the income levels of individuals. However, there are some
limitations to this study, which are discussed below.
The true cost (ex post) of obtaining funds from a reverse mortgage will depend on the
timing of the borrower‘s death—if the borrower lives well past his life expectancy then a
reverse mortgage will have low costs but if he dies sooner than expected then a reverse
mortgage will be expensive.
Limitations of the Model
The simulation model has a number of strong assumptions that necessarily qualify the
results. The most important ones are
    a. Income is certain and of non-stochastic nature. There are no income shocks which
        means the household unit smoothes consumption over the lifetime based on the
        certain income levels. However, even if there is an income shock that is of a
        certain nature, the model can account for it, since households are assumed to
        smooth consumption based on all available information. So if the shock were
        known in advance the household would take that into account while smoothing
        consumption over the lifetime.
    b. It is a partial equilibrium model that implies the household has no consumption
        shocks as well. Consumption in each period is known in advance, and if the
        predicted consumption pattern does not occur, welfare loss is bound to be lower
        than calculated welfare loss. The results will still hold in terms of which
        alternative provides the highest welfare.




                                                  25
   c. There is a specific distribution of house equity to income levels existing based on
       the current norms followed in India. Since this joint distribution of house equity
       with income is derived from empirical data, any change in the financing norms of
       housing companies, and or sentiments of people will change this distribution.
   d. Also, currently, the tenure choice for housing loans is also taken from empirical
       data. This limits the tenure to a discrete distribution, mostly on multiples of five
       years. The same limitation previously discussed applies to this in terms of
       generating data from an empirically observed distribution.
   e. There is no uncertainty in house appreciation. Sudden variations in property
       prices are not accounted for, and a nominal growth rate of property is assumed
       which is expected to last throughout the lifetime of the household. Housing
       bubbles, as well as the seasonality in house prices that is documented in western
       countries is not taken into account here
   f. The imputed rent is taken to be the rent which is payable for a house of similar
       size, and this imputed rent is taken from National Sample Survey data. Imputed
       rent is different for different locations, and the empirical data on housing equity
       obtained may be for different locations, with distributions of rent being different
       from that captured in the National Sample Survey.


Despite these limitations, the life cycle framework is the appropriate way to analyze
housing and saving decisions because these are fundamentally joint decisions made in a
life cycle context. The simulation model indicates that households will respond to ways
to decumulate assets from housing
The development of the housing mortgage market combined with the development of
reverse mortgage as an instrument for elderly homeowners throws open a wide variety of
opportunities. People who traditionally regard real estate as a safe investment are likely to
invest in housing equity so that they have a regular rent stream during the years when the
mortgage exists and a regular income drawn them from home equity during the period of
the reverse mortgage.
In summary, reverse mortgages appear to have a lot to offer a limited number of elderly
households. It offers a means of accessing hard earned equity and raising the quality of



                                             26
lifestyle in later years. The reverse mortgage industry in India, when it starts, can benefit
from the experience of overseas housing markets, especially in the USA, and tailor a product
that will suit the elderly household, the financier, and society at large. With rapidly growing
superannuation and insurance funds, this appears to be an ideal opportunity to tap into the
security of a residential mortgage whilst increasing the overall standard of living.

Conclusion
An insight that emerges from this model is that households are better off investing in
a house due to the tax benefits of housing during the asset accumulation phase. Even
if we consider the house appreciation rate (investment return rate on housing as an
asset) to be lower than that on stocks and nearly equivalent to the inflation adjusted
risk free rate of return, we find that because of the implicit advantages of housing
(tax benefits, plus zero outflow towards rental value after the purchase of the house)
investing in the house is a first-order dominant result. This result mimics those that
have been obtained in studies on housing earlier.
Of the three different scenarios analyzed in terms of decumulation, the sale and purchase
of a new property leads to the greatest welfare loss for the investor. This is because, the
transaction costs that are involved are very high, at about 22-25% of the total value of the
proceeds. The reverse mortgage fares best of all in most cases, with the option of selling
the house and taking one more on rent being the second best. Of course, these will be
sensitive to assumptions made about the portfolio composition and the house size that is
acquired after retirement.
We find that for most income levels except the highest income class, reverse mortgage is
the instrument of choice. While this study models only the monetary factors of reverse
mortgage, the psychological factors involved in moving out from a house are not
considered.




                                              27
Appendix


Figure 1                                 Composition of Household Wealth




                                                     Household Asset Composition

                50
                40
   Percentage




                30
                20
                10
                0
                                                            Livestock
                                         Building




                                                                                                                                Receivables
                                                                                                   Durable



                                                                                                              Financial
                                                                               Machinery
                           Land




                                                                                                               Assets
                                                                                                    HH
                                                     1981               1991                2002




                     Source : Households Assets and Liabilities in India, NSSO, NSS 59th Round, Jan-Dec 2003.


Table I                           Type of Assets held by the Elderly



                                                    Male                                                     Female
Assets                                              1994-1995                  1986-1987                     1994-1995                        1986-1987
                                                    Rural     Urban            Rural               Urban     Rural        Urban               Rural   Urban
Having                    And                       569       581              450                 398       177          185                 168     114
Financial                 Managing
assets                    All                       695       702              581                 525       391          376                 482     371
Having                    And                       651       605              639                 539       206          206                 228     167
Property                  Managing
                          All                       804       742              819                 700       456          420                 633     481


                     Source : Aged In India, NSSO, NSS 52nd Round, 1995-96




                                                                                           28
Table 2           Household Sector Physical and Financial Savings
(At current prices)

Old Series (Base: 1993-94)
Year          Household Sector Savings (in Crore)          Household Sector Savings (in percent of GDP)

               Financial       Physical      Total (1+2)   Financial       Physical        Total (3+4)
               Savings (1)     Savings (2)                 Savings (3)     Savings (4)
1970-71        1371            3263          4634          3.0             7.1             10.1
1971-72        1555            3664          5219          3.2             7.5             10.7
1972-73        2128            3496          5624          3.9             6.5             10.4
1973-74        3612            4373          7985          5.5             6.7             12.2
1974-75        2374            5706          8080          3.1             7.4             10.4
1975-76        3918            5825          9743          4.7             7.0             11.7
1976-77        4852            6997          11849         5.4             7.8             13.2
1977-78        5853            8501          14354         5.8             8.4             14.1
1978-79        6658            10357         17015         6.0             9.4             15.4
1979-80        6081            10609         16690         5.0             8.8             13.8
1980-81        8610            11258         19868         6.0             7.8             13.8
1981-82        9614            11611         21225         5.7             6.9             12.6
1982-83        12739           10477         23216         6.8             5.6             12.3
1983-84        13294           14871         28165         6.1             6.8             12.8
1984-85        17879           17188         35067         7.3             7.0             14.3
1985-86        18538           21257         39795         6.7             7.6             14.3
1986-87        23336           21736         45072         7.5             7.0             14.5
1987-88        26820           32337         59157         7.6             9.1             16.7
1988-89        27183           43474         70657         6.4             10.3            16.8
1989-90        37998           48957         86955         7.8             10.1            17.9
1990-91        49640           60257         109897        8.7             10.6            19.3
1991-92        62101           48635         110736        9.5             7.4             17.0
1992-93        65367           65706         131073        8.7             8.8             17.5
1993-94        94738           63572         158310        11.0            7.4             18.4
1994-95        120733          78625         199358        11.9            7.8             19.7
1995-96        105719          110421        216140        8.9             9.3             18.2
1996-97        141661          91591         233252        10.4            6.7             17.1
1997-98        146777          121660        268437        9.6             8.0             17.6
1998-99        180346          146456        326802        10.4            8.4             18.8
1999-00        205743          198658        404401        10.6            10.3            20.9
2000-01        216774          235494        452268        10.4            11.3            21.6
2001-02        253964          259146        513110        11.2            11.4            22.6
2002-03P       254439          320242        574681        10.3            13.0            23.3
2003-04QE      314261          357431        671692        11.4            13.0            24.3

New Series (Base : 1999-2000)
Year         Household Sector Savings (in Crore)           Household Sector Savings (in percent of
                                                           GDP)
               Financial      Physical       Total (1+2)   Financial     Physical        Total
               Savings (1)    Savings                      Savings (3)   Savings (4)     (3+4)
                              (2)
1999-00         206602        210124         416726        10.5            10.7            21.3
2000-01         215219        231098         446317        10.2            11.0            21.2
2001-02         247476        255198         502674        10.8            11.2            22.0
2002-03         253256        312152         565408        10.3            12.7            23.1
2003-04P        316444        332190         648634        11.5            12.0            23.5
2004-05QE 320777              366302         687079        10.3            11.7            22.0
P: Provisional. QE: Quick Estimates.
Source: RBI.




                                                      29
Table 3            Changes in Financial assets/Liabilities of the Household Sector
(At current prices)

Year       Financial    Curren   Bank       Non-      Life     Provi-     Claims    Shares    Units    Net
           Assets       cy       Deposits   Bank      Insur-   dent and   on        and       of UTI   Trade
                                 #          Depo-     ance     Pension    Govern-   Deben-             Debt
                                            sits ##   Fund*    Fund       ment+     tures++
           (1-9)        (1)      (2)        (3)       (4)      (5)        (6)       (7)       (8)      (9)
1970-71    2110         355      754        67        207      490        105       68        14       50
1971-72    2319         404      1024       104       251      474        -2        20        12       32
1972-73    2982         637      1214       108       307      523        80        27        19       67
1973-74    3578         769      1511       45        356      603        87        -16       24       199
1974-75    3371         18       1654       92        344      787        72        62        -3       345
1975-76    5067         342      2120       130       423      1224       899       41        16       -128
1976-77    6651         1140     3920       114       524      1172       19        -5        20       -253
1977-78    7154         703      3521       227       592      1316       325       201       34       235
1978-79    9483         1430     4626       232       683      1605       227       204       79       397
1979-80    10249        1332     4659       477       773      1748       531       253       41       435
1980-81    12118        1625     5550       378       915      2122       712       412       31       373
1981-82    13621        965      5194       894       1037     2480       1784      510       114      643
1982-83    16097        2026     6661       870       1235     2865       1243      646       122      429
1983-84    18790        2776     7978       1019      1376     3052       1976      555       222      -164
1984-85    23549        2938     9859       960       1556     3759       3107      762       567      41
1985-86    25562        2220     10603      1423      1779     4188       3413      1394      586      -44
1986-87    31849        3090     14510      1512      2159     5055       3092      1768      943      -280
1987-88    36106        4815     14674      1326      2589     6509       3680      813       1196     504
1988-89    39958        4256     14747      1580      3423     7552       5478      1136      1427     359
1989-90    48233        7655     13987      1839      4415     9508       6758      2655      2179     -763
1990-91    58908        6251     18777      1286      5599     11155      7883      4972      3438     -453
1991-92    68045        8157     17848      2218      7003     12501      4845      6800      9087     -414
1992-93    80354        6562     29518      6035      7114     14814      3885      8212      5612     -1398
1993-94    109618       13367    36236      11654     9548     18323      6908      10067     4705     -1190
1994-95    145501       15916    55835      11547     11370    21414      13186     13473     3908     -1148
1995-96    124337       16525    39941      13198     13894    22343      9588      8839      262      -252
1996-97    158519       13643    50902      25980     16121    30390      11783     6631      3776     -708
1997-98    171740       12780    74099      6733      19410    32267      22162     4464      595      -770
1998-99    207103       21822    79433      7670      23428    46408      28220     5105      1887     -6870
1999-00    236214       20845    82892      3844      28644    53907      28985     16308     1811     -1023




                                                      30
2000-01      248394       15632     94703       6911      33861      47882      39007        11148      -934      183
2001-02      296581       28156     112936      7912      41237      46609      51938        9834       -1857     -183
2002-03      322583       28632     123462      8788      52009      48441      56087        7122       -1618     -341
2003-04P     380090       42675     141967      3803      52240      51655      87372        9078       -8586     -114
2004-05P     435706       36977     158259      3370      69572      56354      106420       8113       -3146     -213
2005-06$     588656       51954     274641      4567      83340      58615      86755        29452      -444      -222


P: Provisional.
$: Preliminary Estimates.
# Includes deposits with co-operative non-credit societies.
## Due to changes in coverage of non-banking deposits, data prior to 1997-98 are not strictly comparable with those of
1997-98 and onwards.
* Includes state/central government and postal insurance fund.
+ Includes compulsory deposits.
++ Includes investment in shares and debentures of credit/non-credit societies, public sector bonds and investment in
mutual funds (other than UTI).

Source: RBI.




                                                         31
Table 4         Changes in financial Asset/Liabilities of the Household Sector in Percent
(At current prices)

Year         Change      Curr-   Bank       Non-        Life     Provi-     Claims on   Shares   Units    Net
             in          ency    Deposits   Bank        Insur-   dent and   Govern-     and      of UTI   Trade
             Financial                      Depo-       ance     Pension    ment        Deben-            Debt
             Assets                         sits        Fund     Fund                   tures
             (1-9)       (1)     (2)        (3)         (4)      (5)        (6)         (7)      (8)      (9)
1970-71      100.00      16.82   35.73      3.18        9.81     23.22      4.98        3.22     0.66     2.37
1971-72      100.00      17.42   44.16      4.48        10.82    20.44      -0.09       0.86     0.52     1.38
1972-73      100.00      21.36   40.71      3.62        10.30    17.54      2.68        0.91     0.64     2.25
1973-74      100.00      21.49   42.23      1.26        9.95     16.85      2.43        -0.45    0.67     5.56
1974-75      100.00      0.53    49.07      2.73        10.20    23.35      2.14        1.84     -0.09    10.23
1975-76      100.00      6.75    41.84      2.57        8.35     24.16      17.74       0.81     0.32     -2.53
1976-77      100.00      17.14   58.94      1.71        7.88     17.62      0.29        -0.08    0.30     -3.80
1977-78      100.00      9.83    49.22      3.17        8.28     18.40      4.54        2.81     0.48     3.28
1978-79      100.00      15.08   48.78      2.45        7.20     16.93      2.39        2.15     0.83     4.19
1979-80      100.00      13.00   45.46      4.65        7.54     17.06      5.18        2.47     0.40     4.24
1980-81      100.00      13.41   45.80      3.12        7.55     17.51      5.88        3.40     0.26     3.08
1981-82      100.00      7.08    38.13      6.56        7.61     18.21      13.10       3.74     0.84     4.72
1982-83      100.00      12.59   41.38      5.40        7.67     17.80      7.72        4.01     0.76     2.67
1983-84      100.00      14.77   42.46      5.42        7.32     16.24      10.52       2.95     1.18     -0.87
1984-85      100.00      12.48   41.87      4.08        6.61     15.96      13.19       3.24     2.41     0.17
1985-86      100.00      8.68    41.48      5.57        6.96     16.38      13.35       5.45     2.29     -0.17
1986-87      100.00      9.70    45.56      4.75        6.78     15.87      9.71        5.55     2.96     -0.88
1987-88      100.00      13.34   40.64      3.67        7.17     18.03      10.19       2.25     3.31     1.40
1988-89      100.00      10.65   36.91      3.95        8.57     18.90      13.71       2.84     3.57     0.90
1989-90      100.00      15.87   29.00      3.81        9.15     19.71      14.01       5.50     4.52     -1.58
1991-92      100.00      11.99   26.23      3.26        10.29    18.37      7.12        9.99     13.35    -0.61
1991-92      100.00      11.99   26.23      3.26        10.29    18.37      7.12        9.99     13.35    -0.61
1992-93      100.00      8.17    36.73      7.51        8.85     18.44      4.83        10.22    6.98     -1.74
1993-94      100.00      12.19   33.06      10.63       8.71     16.72      6.30        9.18     4.29     -1.09
1994-95      100.00      10.94   38.37      7.94        7.81     14.72      9.06        9.26     2.69     -0.79
1995-96      100.00      13.29   32.12      10.61       11.17    17.97      7.71        7.11     0.21     -0.20
1996-97      100.00      8.61    32.11      16.39       10.17    19.17      7.43        4.18     2.38     -0.45
1997-98      100.00      7.44    43.15      3.92        11.30    18.79      12.90       2.60     0.35     -0.45
1998-99      100.00      10.54   38.35      3.70        11.31    22.41      13.63       2.46     0.91     -3.32
1999-00      100.00      8.82    35.09      1.63        12.13    22.82      12.27       6.90     0.77     -0.43




                                                   32
2000-01         100.00       6.29      38.13        2.78        13.63    19.28       15.70         4.49        -0.38     0.07
2001-02         100.00       9.49      38.08        2.67        13.90    15.72       17.51         3.32        -0.63     -0.06
2002-03         100.00       8.88      38.27        2.72        150.00   15.02       17.39         2.21        -0.50     -0.11
2003-04P        100.00       11.23     37.35        1.00        13.74    13.59       22.99         2.39        -2.26     -0.03
2004-05P        100.00       8.49      36.32        0.77        15.97    12.93       24.42         1.86        -0.72     -0.05
2005-06$        100.00       8.83      46.66        0.78        14.16    9.96        14.74         5.00        -0.08     -0.04

P: Provisional.
$: Preliminary Estimates.
# Includes deposits with co-operative non-credit societies.
## Due to changes in coverage of non-banking deposits, data prior to 1997-98 are not strictly comparable with those of
1997-98 and onwards.
* Includes state/central government and postal insurance fund.
+ Includes compulsory deposits.
++ Includes investment in shares and debentures of credit/non-credit societies, public sector bonds and investment in
mutual funds (other than UTI).

Source: RBI.




                                                           33
    Table 5           Gross Financial Saving of the Household Sector in Percent



Item                 2005-    2004-    2003-    2002-    2001-     2000-       1999-   1998-    1997-    1996-    1995-    1994-    1993-
                     06#      05P      04P      03P      02        01          2000    99       98       97       96       95       94
Financial Saving     100      100      100.0    100.0    100.0     100.0       100.0   100.0    100.0    100.0    100.0    100.0    100.0
(Gross)              (16.7)   (14.0)   (13.8)   (13.1)   (12.7)    (11.9)      (12.2   (11.9)   (11.3)   (11.6)   (10.5)   (14.4)   (12.8)
                                                                           )
a) Currency          8.8      8.5      11.2     8.9      9.7       6.3         8.8     10.5     7.4      8.6      13.3     10.9     12.2
                     (1.5)    (1.2)    (1.5)    (1.2)    (1.2)     (0.7)       (1.1)   (1.3)    (0.8)    (1)      (1.4)    (1.6)    (1.6)
b) Deposits          47.4     37.0     38.3     40.9     39.4      41.0        36.3    38.8     46.6     48.1     42.5     45.5     42.6
                     (7.9)    (5.2)    (5.3)    (5.4)    (5.0)     (4.9)       (4.4)   (4.6)    (5.3)    (5.6)    (4.5)    (6.5)    (5.4)
i) with banks        46.7     36.4     37.4     35.5     35.3      32.5        30.8    33.7     37.8     25.7     26.3     35.3     27.9
ii) with NBFC        0.8      0.8      1.0      2.7      2.6       2.9         1.7     3.8      3.9      16.4     10.6     7.9      10.6
iii) with co-op      0        0.0      0.0      2.8      3.6       5.6         4.3     4.6      5.3      6.4      5.8      3.0      5.2
banks/societies
iv) Net trade debt   0        0.0      0.0      -0.1     -2.1      0.1         -0.4    -3.3     -0.4     -0.4     -0.2     -0.8     -1.1
c) Shares and        4.9      1.1      0.1      1.7      2.7       4.1         7.7     3.4      2.9      6.6      7.3      11.9     13.5
debentures           (0.8)    (0.2)    (0.0)    (0.2)    (0.3)     (0.5)       (0.9)   (0.4)    (0.3)    (0.8)    (0.8)    (1.7)    (1.7)
i) pvt. corporate    1.3      1.4      1.1      0.8      1.5       3.1         3.4     1.5      1.3      3.6      6.6      8.0      7.5
ii) co-op banks/     0        0.0      0.0      0.0      0.1       0.0         0.0     0.1      0.1      0.1      0.1      0.1      0.1
societies
iii) units of UTI    -0.1     -0.7     -2.3     -0.5     -0.6      -0.4        0.8     0.9      0.3      2.4      0.2      2.7      4.3
iv) bonds of PSUs    0.0      0.0      0.0      0.1      0.0       0.1         0.1     0.0      0.1      0.1      0.1      0.1      0.5
v) mutual funds      3.6      0.4      1.2      1.3      1.8       1.3         3.4     0.8      1.1      0.3      0.3      1.1      1.2
(other than UTI)
d) Claims on         14.7     24.4     23.0     17.4     17.9      15.7        12.3    13.6     12.9     7.4      7.7      9.1      6.3
Govt.                (2.5)    (3.4)    (3.2)    (2.3)    (2.3)     (1.9)       (1.5)   (1.6)    (1.5)    (0.9)    (0.8)    (1.3)    (0.8)
i) investment in     2.4      4.9      7.5      2.5      5.8       1.7         0.9     0.7      1.6      0.4      0.4      0.1      0.4
Govt. Securities
ii) investment in    12.3     19.5     15.5     14.9     12.1      14.0        11.3    13.0     11.3     7.0      7.4      9.0      5.9
small savings etc
e) Insurance         14.2     16.0     13.7     16.1     14.2      13.6        12.1    11.3     11.3     10.2     11.2     7.8      8.7
funds                (2.4)    (2.2)    (1.9)    (2.1)    (1.8)     (1.6)       (1.5)   (1.3)    (1.3)    (1.2)    (1.2)    (1.1)    (1.1)
i) Life ins. funds   13.5     15.1     13.0     15.5     13.5      12.9        11.2    10.6     10.6     9.5      10.4     7.2      8.0
ii) Postal ins.      0.2      0.3      0.3      0.3      0.3       0.2         0.3     0.3      0.3      0.3      0.3      0.2      0.2
iii) State ins.      0.5      0.6      0.5      0.4      0.4       0.5         0.6     0.5      0.4      0.4      0.5      0.5      0.5
f) Provident and     10.0     12.9     13.6     15.0     16.10     19.30       22.80   22.40    18.80    19.20    18.00    14.7     16.7
pension funds        (1.9)    (1.8)    (1.9)    (2.0)    (2.0)     (2.3)       (2.8)   (2.7)    (2.1)    (2.2)    (1.9)    (2.1)    (2.1)
    # Preliminary.
    P: Provisional.
    Note: Figures in brackets are percentages to GDP at current market prices.



                                                              34
Source: RBI
Figure 2                          Trend of Inflation Rate in India

                                                               Inflation Rate - India


                    14
                    12
   Inflation Rate




                    10
                     8
                     6
                     4
                     2
                     0
                         1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
                                                                                    Year




                     Source: ADB Country Estimates, 2004


Figure 3                          Bank Term Deposit Rates




                                                            Bank Term Deposit Rates

                    14
                    12
   Interest Rates




                    10
                     8
                     6

                     4
                     2

                     0
                         1970
                                1972
                                       1974
                                              1976
                                                     1978
                                                            1980
                                                                   1982
                                                                          1984
                                                                                 1986
                                                                                        1988
                                                                                               1990
                                                                                                      1992
                                                                                                             1994
                                                                                                                    1996
                                                                                                                           1998
                                                                                                                                  2000
                                                                                                                                         2002
                                                                                                                                                2004




                     Source : RBI and various issues of Handbook Of Statistics on Indian Economy




                                                                                          35
Figure 4                                                         MPCE Class-wise Average Expenditure Incurred for Acquiring New Residential
Unit During Last 5 Years for All Urban Areas in India




                                                                        Average Expenditure per New Residential Unit

                                                          900
                     Average Expenditure (in '000s)




                                                          800
                                                          700
                                                          600
                                                          500
                                                          400
                                                          300
                                                          200
                                                          100
                                                           0
                                                                0-300       350-425       500-575         665-775     915-1120      1500-1925

                                                                                                    MPCE Class


                                                      Source : indiastat.com


Figure 5                                                         Mortality Rates by Age




                                                                                     Mortality Rates by Age

                                        0.6

                                        0.5
   Mortality Rates




                                        0.4

                                        0.3

                                        0.2

                                        0.1

                                                      0
                                                                                                                                         105
                                                                                                                                               112
                                                                    14
                                                                           21
                                                                                28
                                                                                     35
                                                                                          42
                                                                                               49
                                                                                                     56
                                                                                                           63
                                                                                                                70
                                                                                                                     77
                                                                                                                          84
                                                                                                                               91
                                                                                                                                    98
                                                          0
                                                                7




                                                                                                      Age


                                                      Source: LIC A (96-98) Mortality Table for Annuitants




                                                                                                          36
Table 6             Construction Activity in Indian Cities



                    Building Completion Certificates Issued During the Years
Million Plus City
                      1994      1995      1996    1997       1998    1999      2000    2001
Ahmedabad              636       181       519     370        244     211       107     187
Bangalore             3707      3735      4517    4549       4583    4615      4648    4616
Bhopal                2027      2453      1273    2252       2815    1405      2144    2121
Chennai               4333      5380      5986    5941       5760    5485      5573    5606
Coimbatore             397      1087       941    1120       1136    1153      1171    1154
Delhi                  595       834       715    1015       1019     916       882     939
Greater Mumbai        2846      2886      1001     758        468     742       737     649
Hyderabad             4983      5160      5310    5497       5692    5995      6359    6015
Indore                 607      1530      4223    5562       6208    3831      3083    4374
Jaipur                 837       872      1592    1657       1725    1795      1869    1796
Kanpur                1418      1453      1966    2013       2060    2110      2159    2109
Kochi                 2282      2301      2367    2391       2410    2434      2458    2435
Kolkata               2604      2680      4011    4128       4249    4372      4499    4373
Lucknow               1413      1500      2104    2232       2368    2513      2663    2515
Ludhiana              1029       717       850    1712       1805    1907      2013    1908
Madurai                952       962       645    1281       1299    1317      1335    1318
Nagpur                 268       277       676     696        716     737       759     738
Patna                  731       743       784     797        811     825       839     826
Pune                  1092      1116      1047    1273       1201    1174      1216    1197
Surat                  102      1574      1682    1796       1917     682       618    1072
Vadodara              1322      1135       864    1244       1613    2610      2655    2293
Varanasi               714      1243      1416    1448       1295    1081      1110    1162
Visakhapatnam         1233      1268      1297    1335       1374    1649      1644    1556
Total                36128     41087      45786   51067      52768   49559     50541   50959


           Source : indiastat.com, 2001




                                                    37
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                                              39

				
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