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Financing Mechanisms for Affordable Housing: Demand and Supply Side Intermediation
CEMS Working Paper AH-07
August 2012
Authors:
Dhaval Monani
Nikhilesh Sinha
Shahen Dastur
Working Papers Series
Supported by:
Rockefeller Foundation
August 2012 FINANCING MECHANISMS FOR AFFORDABLE HOUSING
CEMS, ISB 1
Table of Contents
Introduction ................................................................................................................................................. 3
The Definition of Affordability ..................................................................................................................... 4
Affordability by Income Group ................................................................................................................ 6
Objectives .................................................................................................................................................... 8
Problem Statement ..................................................................................................................................... 9
Buyer’s Perspective ................................................................................................................................. 9
Developer’s Perspective .......................................................................................................................... 9
Proposed Solutions .................................................................................................................................... 10
Solutions to Mitigate Buyer-‐Side Risk .................................................................................................... 10
Payment Protection Insurance ( PPI ) ................................................................................................ 10
PPI with payback clause ..................................................................................................................... 13
OPTION – by Bank .............................................................................................................................. 14
Comparative analysis of the three proposed solutions ..................................................................... 16
Solutions to Mitigate Developer-‐Side Risk ............................................................................................ 17
Introduction ....................................................................................................................................... 17
Previous Work Done .......................................................................................................................... 17
Hedge Product ................................................................................................................................... 18
Limitations of the Study ............................................................................................................................. 24
Conclusion ................................................................................................................................................. 25
A Discussion of the Solutions Proposed for the Buyer .......................................................................... 25
A Discussion of the Solutions Proposed for the Developer ................................................................... 25
References ................................................................................................................................................. 26
Appendix .................................................................................................................................................... 27
Abbreviations
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EMI Equal Monthly Installments PPI Payment Protection Insurance PD Probability of Default VaR Value at Risk OPC Ordinary Portland Cement PPC Pozzolana Portland Cement LME London Metal Exchange
Income Groups Monthly Household Income (INR)
Monthly Household Income (USD1)
EWS (Economically Weaker Section) Less than 3,300 Less than 60 LIG (Lower Income Group) 3,300 -‐ 7,500 60 -‐ 136 MIG (Middle Income Group) 7,500 -‐ 14,000 136 -‐ 255 HIG (Higher Income Group) More than 14,000 More than 255
1 USD 1 = INR 55
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Introduction
The 25 million gap in urban housing identified in the 11th Five year Plan is a monumental
challenge. As discussed in detail in our report titled New Frontiers in Affordable Housing:
Notes from the Field, government and private initiatives aimed at tackling this issue have
met with limited success. One of the key elements required for the success of market-based
solutions to the housing problem is finance. There is a need for the development of financial
products specific to the affordable housing sector to address constraints supply as well as
demand side constraints.
This report explores the potential for two financial mechanisms, which could significantly
help in stimulating the affordable housing market in India. The first is a financial hedge
product that would allow affordable housing developers to guard against risks of input price
volatility, which is of particular concern in a low-margin business. The second is a demand
side intermediation that protects both the home loan borrower as well as the lender in the
case of an involuntary default situation due to loss of employment or ill-health.
First-hand experience of unpredictable spikes in the prices of inputs such as cement, sand
and brick during the implementation of the project at Rajkot prompted us to explore ways of
managing this risk. The usual practice of using high margins to absorb the shock of sudden
increase in costs cannot be employed in the low-cost housing sector. The focus is to provide
houses at the lowest cost that remains viable to the developer. The idea of creating a
financial hedge was first discussed in 2010. While it remains an elusive goal, our
investigations have yielded substantial gains in our understanding of how such a hedge
could be structured.
The idea of payment protection insurance, or an insurance against asset loss due to a
sudden change in economic status, seems appropriate in the context of affordable housing.
The family home is often the single biggest investment for a low-income household. The
idea of payment protection was inspired by a similar product that is bundled with student
loans for MBA candidates. This product ensures the banks receive instalments on their loans
even when candidates fail to find employment or get laid off. The challenge in the scenario
of affordable housing loans, is tackling the agency problem arising from the borrowers
incentive to shift the burden of repayment on to the insurance company even when they are
employed and able to pay.
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The Definition of Affordability
“Affordability is used fairly loosely in common parlance and may refer to housing that
meets a wide range of affordability criteria. Defining affordable housing in India is a difficult
task given that the dynamics of the market can change dramatically as you move from one
part of the country to another2. For the purposes of this report we will limit our analysis to
the market for houses priced at INR 200,000 and below.3
“Keeping in mind that the housing shortages affect mostly the EWS and LIG, the definition
should apply to houses ranging from about 300 square feet (super built up area) for
Economically Weaker section (EWS), 300 square feet to 600 square feet for Lower Income
Group (LIG) and 600 square feet to 1200 square feet for Middle Income group (MIG), at
2 Affordable Housing, KPMG Advisory 3 All amounts are in INR
DEVELOPER
BANK
BUYER
The Bank pays installments to the Developer over the period of construction or a
single payment
The buyer makes a down payment
towards the purchase of the property
The Buyer of the property makes EMI payments to the bank
over the tenor for which loan is taken (usually 15-‐
20 years)
August 2012 FINANCING MECHANISMS FOR AFFORDABLE HOUSING
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costs that permit repayment of home loans in monthly instalments not exceeding 30% to
40% of the monthly income of the buyer”4.
Group Income Level Size of unit EMI
EWS Less than INR 150,000 p.a. Up to 300 sq. ft 30% of monthly income
LIG INR 3 – 5 00,000 p.a. 300 to 600 sq. ft 30% of monthly income
MIG INR 3 – 10 00,000 p.a. 600 to 1200 sq. ft 40% of monthly income
4 Guidelines for affordable housing partnership, Ministry of Housing & Urban poverty Alleviation
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Affordability by Income Group
When a home-buyer decides to buy a house he or she usually approaches a bank and
applies for a home loan. The loan is customarily paid back over a period of 15-20 year in
Equal Monthly Installments (EMI). The value of each of these monthly installments (EMI) is
a function of the value of the loan, the tenor or period of the loan and the interest rate that
the bank decides to charge. The interest rate may vary depending on the credit profile of the buyer.
There is also variability in the rates charged by different banks.
EMI options for a loan of 100,000 (INR)
Interest 10 years 15 years 20 years 1 2 3 A 9% 1,270 1,010 900 B 10% 1,320 1,070 970 C 11% 1,380 1,140 1,030 D 12% 1,430 1,200 1,100
In the Indian context we find that the average buyer cannot afford to contribute more than
40 per cent of their monthly income towards housing costs. Therefore a buyer’s monthly
income can be used to determine the maximum amount he or she can pay towards a
monthly installment on a loan, and thereby determine the upper bound on the value of the
property he or she is able to afford.
The following table illustrates the EMI options (above table) for the buyer given his income
level and loan amount.
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Loan servicing ability and options exercisable
Yearly Income
Monthly Income
Capacity (30 %)
Property Val (INR) Loan Val (INR)
500,000 400,000
750,000 600,000
1,000,000 800,000
1,250,000 1,000,000
200,000
16,670
5,000 A1, A2, A3 A3 NA NA
300,000
25,000
7,500 All A1, A2, A3 A3, B3 NA
400,000
33,330
10,000 All All A1, A2, A3 A2, A3, B3
500,000
41,670
12,500 All All All A1, A2, A3
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Objectives
• To identify the risks faced by the developers of affordable housing segment and the buyer of the property.
• To understand the various factors which affect the segment.
• To propose solutions to mitigate identified problems.
• To assess the effectiveness of the solutions proposed.
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Problem Statement
Buyer’s Perspective
if a buyer is unable to make the monthly payments on their home loan, the bank reserves
the right to seize the collateral i.e. the property from the buyer. The bank then usually
recover the outstanding amount along with interest after selling the asset, and gives the
residual to the buyer.
The buyer’s inability to pay may arise due to the loss of employment, prolonged illness,
accident or an unforeseen expense. These short-term problems can result in the buyer
losing ownership of the property.
Developer’s Perspective
The time required for the construction of a low-cost housing project from inception to
completion may vary between 2 and 4 years depending upon the size and location of the
project, as well as upon the developer.
In the affordable housing segment, developers aim to sell a majority of the units of the
project during the initial period of construction. This allows them to finance construction
using the initial deposits, which removes the need for debt financing and simultaneously
improves the rate of return on the project. On the downside, selling units upfront exposes
the developer to the risk of a drastic rise in construction costs, ex post. It is possible for the
cost of inputs used in the construction to increase substantially. If there is a steep
unexpected rise in the input costs, the profit margin for the developer could diminish or in
extreme cases may result in a loss. Some of the factors resulting in an increase in cost of
inputs are rise in inflation, increase in freight, demand & supply constraints and increase in
the production cost of the construction materials.
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Proposed Solutions
Solutions to Mitigate Buyer-‐Side Risk
Payment Protection Insurance ( PPI )
A Payment Protection Insurance (PPI) product aims at providing insurance cover to a buyer
in the event of involuntary default on a home loan. In the event that the buyer is unable to
make his payments, the insurance provider steps in to prevent foreclosure and loss of the
asset.
The premium on the insurance would be paid once at the time the loan is taken. If at any
time during the tenor of the loan the buyer defaults on an EMI payment, the insurance
company will make the payments of his behalf. The insurance company would cover a
maximum of 12 and 18 EMI for a loan of 15 years and 20 years respectively.
Input required
Developing and pricing the premium for the insurance product requires data on a number of
input variables. The Probability of Default (PD) is one of the most important variables in
premium calculation. PD calculation requires consideration of factors like credit history of
the buyer, his job security, number of people dependent on him, mortgage outstanding,
tenor of loan and others.
Methodology for calculating premium for Insurance
Calculation of Premium given default rate
Given a range of PD and Recovery rates it is possible to form a matrix to illustrate the
insurance premium. The insurance premium is calculated by equating the present value of
the default leg with the present value of premium leg. The variables that were considered
while calculating premium are PD, recovery rate, loan amount, risk free discount rate. The
August 2012 FINANCING MECHANISMS FOR AFFORDABLE HOUSING
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difference between the present value of default leg and premium leg should be zero at the
inception of the insurance and the premium is the balancing variable in the equation.
PV of Default Leg – PV of Premium Leg = 0 PV [Loan*(1-‐R)*PD] – PV [Premium amt] = 0
INSURANCE PREMIUIM (for a loan of INR 100,000)
At end of year 1 Probability of Default (in bp)
Recovery rate 65 60 55 50 45 40 40% 361.11 333.33 305.56 277.78 250.00 222.22 45% 331.02 305.56 280.09 254.63 229.17 203.70 50% 300.93 277.78 254.63 231.48 208.33 185.19 55% 270.83 250.00 229.17 208.33 187.50 166.67 60% 240.74 222.22 203.70 185.19 166.67 148.15 65% 210.65 194.44 178.24 162.04 145.83 129.63
Output
The PPI product allows the buyer to insure against the risk of losing his or her investment in
the house through involuntary default and forfeiture. In the event of involuntary default, the
insurance company steps in to pay the EMI to the bank.
STRATEGY
Insurance cover Premium
Default risk
INSURER
BUYER BANK
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Limitations
• The basic structure of the insurance contract provides a huge gap for malfeasance.
The insurance company faces the risk of moral hazard due to the structure of the
contract and the difficulty of verifying whether default is voluntary or involuntary.
• The buyer has an incentive to default, and to force the insurer to pay the bank even
when he or she is solvent.
• The claims made need to be scrutinized and followed through by the insurance
company. This involves high transaction and monitoring costs and may reduce the
feasibility of the product.
Checks and Controls to address limitations
• The buyer has to get a certificate from the employer which states loss of the job and
the reason for the same. This would serve as a proof for loss of job and also help
assess if it was deliberate or not by the reason stated in the certificate.
• The buyer has to submit his income tax return for the past three years. It will help
us analyse whether the buyer has any savings or investments that can be
channelized towards EMI payments.
• All bank account statements for the past 3 years also are required to be submitted.
It would be useful to assemble other sources of income and the buyers spending
patterns.
• One option for reducing risk of default might be to get the spouse of the buyer to co-
sign, in case he or she is also employed. Joint liability contracts may be more
feasible and could be made more attractive if these are associated with a lower
premium. In case of loss of job of one person, the spouse will have the liability and
ability to pay the EMI.
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PPI with payback clause
In this variation on the PPI product the buyer repay the insurance company with the total
amount of EMI paid once the buyer’s financial stability is re-established. The repayment will
be made in installments to suit the buyer’s situation. The buyer can start repaying the
insurance company at any point during the tenor of the loan or at the end of the tenor.
Insurance companies can have the option of including an interest component for the
outstanding EMIs owed by the buyer.
Tenor of the Loan
Insurance can be invoked any time during tenor
Premium paid
Key points of the solution
• The premium associated with this option would be much lower compared to the
standard PPI product
• The incentive for voluntary default on the part of the buyer is reduced.
• The insurance company also holds the property as collateral but next in line to bank.
In case the buyer continues to default for a period exceeding that covered by
insurance, the insurance company can claim their share after the bank has recovered
its outstanding debt.
• The bank will release the collateral only after it has received a letter from the
insurance company that all of its dues are paid by the buyer.
• The option to repay the sum to insurance company after completion of the loan tenor
reduces the burden on the buyer. Otherwise the buyer would have to pay
installments to the bank and insurance company simultaneously for a period once he
or she regains employment.
T0 T15
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OPTION – by Bank
In this third variant the bank gives an option to the loan holder to buy a right to skip a
maximum of 12 monthly installments. The buyer can avail of this option at any point during
loan period. The cost of the option can be paid either in lump sum at the start of the
repayment period or can be included in the loan installments. The exercise period
commences once the lump sum payment or installments towards the option have been paid
to the bank. In the illustration below, it is assumed that installments towards the option are
paid over the first seven years of the tenor of the loan and the exercise period commences
in the eighth year of the tenor period. The cost of this option is the present value of 12
installments during the exercise period.
This option will help the loan holder to retain the ownership of the house despite of the
short-term difficulties and saves bank from the hassle of foreclosures.
Tenor of Loan (15 Years)
Cost to be paid in installment Option can be exercised any time in this period
OR Cost in lump sum
T0 T7 T15
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EXAMPLE
Description Amount (INR)
Total of EMI for 8th year (8,000 p.m.) 96,000 PV5 of EMI of 8th year at 7th year end 92,073.11
Price of option (beginning of year 1) 52,690.83 Price of option (along with EMI during the 7 years) 821.25
Key points of the solution
• The option does not entail any additional charges / premium as the cost of the option
is the present value of the future EMI.
• The transaction takes place between the bank and the buyer. There is no need for a
third party, such as an insurance company. This reduces the complexity of the
transaction and also the regulatory and legal requisites accompanying it.
• The buyer is covered for his future short-term financial uncertainties for 12
installments in case of 15 year mortgage. It supports the buyer in case he
experiences any financial instability for a short duration.
• The exercise period of the option starts later in the year so that the cost of the
option does not increase in present value terms. The later the exercise period in the
tenor of the loan, the smaller the cost of option in present value terms.
• This option will reduce the amount of restructuring normally undertaken by the bank
and also protect the buyer from having to undergo the negotiations during
restructuring.
• The cost of the option is the present value of the first 12 EMI in the exercise period.
This would be preferable for bank in case someone exercises the option during the
initial period.
5 PV is calculated using risk free rate of 8%
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Comparative analysis of the three proposed solutions
Solutions Advantages Limitations
PPI
• Will support buyer and he
does not have to repay the amount to the insurance
• Premium will be paid only once in the beginning.
• Buyer may make false claims to
avail the insurance cover
• Lot of time needed to be invested in authenticating and keeping track of buyer’s claim
PPI with Payment Clause
• Number of false claims will
reduce
• Premium paid would be much less comparatively
• The insurance company may
have problems recovering the amount paid by them towards the EMI
Option – by Bank
• Eliminates the need to enter
into a transaction with the third party (insurance company)
• No additional price is paid for the option as its cost is the present value of the EMI
• Does not cover the buyer for the
part of tenor before exercise period
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Solutions to Mitigate Developer-‐Side Risk
Introduction
The Hedge product is designed to protect affordable housing developers from input price volatility during the construction period. The product would help developers hedge construction costs, allowing them to operate with lower margins without adversely affecting profitability.
The hedge product is a basket of securities with a high price correlation to the costs of construction materials. The developer would buy the hedge product and any increase in the input costs would be set off by the gain in assets underlying the product.
About 30% of the construction cost can be attributed to labour, which is difficult to hedge as changes on wages are hard to predict and are not strongly correlated with any specific economic factor. However expected increases in labour during the construction period can be considered while calculated the estimated cost. A 70% hedge of the construction cost is a practical second-best solution and one that invites further exploration and research.
Previous Work Done
An earlier study6 conducted at ISB on the affordable housing segment envisaged a cross
hedge product to address the problem faced by the developers. In the report, major cost
drivers (Cement, sand, brick, steel & labour) of construction projects were identified along
with their contribution to total construction cost.
The report presented a correlation analysis based on the cost of construction material vis a
vis a the value of a basket of selected equities and commodities. It was suggested that an
optimum portfolio might be used as a hedge once each individual cost driver is determined.
The correlation of sand, brick and cement to their respective hedging portfolio was found to
be 0.774, 0.544 and 0.523.
These coefficients are far too weak and cannot provide an effective hedge, except possible
in the case of sand. However it is important to keep in mind that correlation variables are a
notoriously deceptive measure. These correlations may vary considerably for different data
sets and measurement periods. The correlation can be relied upon only if there is convincing
economic rationale between the input cost and the hedge product.
6 ISB-‐CEMS, (2011), “Preliminary Report on Hedge Product for Construction Materials”
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Hedge Product
Risk Identification
1. Commodity Risk: Cement, Sand and Brick constitute about 42% of the total input cost. Any increase in cost of these commodities increases the cost of the construction compared to the initial budgeted projection. The increase in commodity prices is driven by cost of production, inflation, demand and supply and other factors.
2. Labour cost Risk : Cost of labour contributes to about 30% of the cost of construction. It is the single largest component of construction cost. A slight increase in labour cost can lead to erosion of profits. Labour cost is primarily a function of Inflation, demand and supply and government directive of minimum pay for men & women.
3. Interest rate Risk : Developer is affected by the fluctuation in interest rate if he has
taken a loan to fund his project cost. A change in interest rate affects the development cost of the project.
4. Default Risk : In case of sale on installment basis, Developer faces a possibility of
the buyer defaulting in periodic installments. Some of the factors resulting to default are loss of job, accident, prolonged illness, death or other reasons like unexpected expenses in the family.
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Risk Measurement
Risk faced by developer need to be quantified to get a clear understanding of its potential to reduce or wipe out profits. Identifying the range of price change and degree of possible impact will help eliminate the effect of price volatility on cost estimates.
Data used and sources 1) Data on prices of construction materials was gathered from developers in Rajkot and
Mumbai (Sumit Woods). 2) Data on equity prices of selected stocks was downloaded from the NSE online data
portal. 3) Data on commodity exchange prices was taken from MCX historical data repository. 4) Dollar INR exchange rate over the period under analysis is taken from
www.oanda.com
Scenarios 1) Cement prices are highly volatile (based on data from Rajkot project) 2) Cement prices display low volatility (on data from Sumit woods, developer in
Mumbai)
Assumptions made Scenario 1
1) Data points related to material prices were irregular i.e. the interval between data points was not constant. Because of the frequency of data points being inconsistent, comparison with asset was tricky. Therefore averages of the previous prices have been taken at missing points.
2) In some cases, there were several prices quoted within a single time period i.e. one time period is associated with 5 different prices of cement. In these cases, the average of values is taken.
3) Data for bricks (bricks & fly ash bricks) was merged to get a consistent data point for a single interval.
4) A lag of 1 month, 2 months and 3 months of cost index has been taken in gold, crude oil and dollar rupee prices respectively to construct hedge index.
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Scenario 2
1) In case of cement and brick, there were two different types of inputs i.e. OPC and PPC in cement and two different sizes of bricks. Here both types of inputs are considered equal for the purpose of analysis.
2) A lag of 1 month, 2 months and 3 months of cost index has been taken in gold, crude oil and dollar rupee prices respectively to construct hedge index.
Risk is measured using VaR taking a 99% confidence interval for a cost of construction of INR 100,000 (Cement – 15.90%, Sand – 10.90% and brick – 14.80%)
Scenario 1
Daily VaR cement = 824 i.e. 0.82%
Daily VaR sand = 306 i.e. 0.31%
Daily VaR brick = 940 i.e. 0.94%
Scenario 2
Daily VaR cement = 185 i.e. 0.19%
Daily VaR sand = 140 i.e. 0.14%
Daily VaR brick = 1,254 i.e. 1.25%
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Methodology used
A Hedge index is developed to mimic the movement of the key input materials i.e. cement, sand and brick. The index is created by taking assets that have an economic association to the prices of the material. The Index is developed using the following steps:
• Collecting monthly data of cement, sand and brick prices for a period • Corresponding prices of equities and commodities like Gold, Crude oil, dollar rupee
exchange rate, cement and coal prices from selected sources. • Forming a cost index of the construction input in the ratio of their contribution
towards total construction cost. • Developing a hedge index using equities and commodities taking equal weights. • Optimizing weights of the assets achieving high correlation with the cost index.
Risk Control
The hedge index developed under both the scenarios does not confer a strong hedge with construction cost. Under Scenario 1 and Scenario 2, the correlation is 0.66 and 0.50 respectively. This correlation is not strong enough to be used as a basis for hedging. A minimum correlation of 0.80 is required for a hedge to be effective. Otherwise the cost of hedging will exceed the benefits derived from it.
Risk Report
Several assets were considered and analysed to develop a hedge. The selected few were chosen based on correlation as well as theoretical plausibility. In addition, various equities and commodities were used to analyse the correlation between them and to determine the final set. A strong economic relationship between two variables is a prerequisite for the correlation to be considered meaningful and effective. Any purely numeric correlation between asset returns and construction input price changes is likely to prove ineffective as a hedge in the long-run.
One of the main drivers of the price of construction inputs over the recent years has been inflation. Commodities are a good hedge for inflation though not a perfect hedge. Interest rate derivatives are normally used in developed nations to hedge against inflation.
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Construction Index
It is possible to form a fairly robust index based on cement and steel prices. Cement and steel approximates about 16% and 10-12% of the construction cost respectively. So the index would help manage about 26-28% of the construction cost. The developer can buy units in index and as the prices of steel and cement will increase the index value will increase thus setting off the increase in construction cost.
Input
In order to be able to trade the index and provide liquidity, daily prices of the index will be required. Daily prices of cement and steel are required as an input, and it is necessary to determine the appropriate weights of cement and steel for use in the index.
Methodology
The Index prices are derived by multiplying the prices of the underlying basket of traded commodities and equities with their respective weights, and summing them up. Steel is traded quite heavily in LME (London Metal Exchange) so we use LME prices after converting them to their equivalent in Indian rupees. Cement however is not traded on any exchange making it difficult to obtain daily prices. This was tackled by getting daily quotes of cement from the top 10 cement suppliers in India, discarding the top and bottom two quotes and taking the average of the remaining six values to arrive at daily price. The weight of steel and cement in the index was taken as 57% and 43% respectively i.e. (16% and 12% in the construction cost).
Getting daily input prices from cement companies and compiling it proved to be inefficient. We recommend developing a software solution that utilizes remote access to cement companies to feed in daily prices. The software would then take cement price and add to it the steel prices from LME and give daily index value.
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Challenges faced while developing Hedge and Construction Index
1. Cement Cement prices exhibit low volatility in some regions compared to others. In such situations, developing a single hedge for the affordable housing segment becomes infeasible as the price of cement is not a constant throughout the country. Also the recent revelation that cement companies customarily forming cartels to manipulate prices, poses a huge hurdle. Such associations interfere with the natural determination of equilibrium prices.
2. Steel The low trading volume in steel on MCX makes it an unsatisfactory source of prices. Therefore prices of steel were taken from LME because of which we were forced to consider foreign exchange fluctuations within the index.
3. Sand and brick Sand and brick used for construction is normally supplied locally. This sector is unorganized. Additionally, sand and brick are not traded and we have been unsuccessful in identifying a suitable proxy.
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Limitations of the Study
1) Data Availability Apposite data is a pre requisite for obtaining a relevant and usable analysis. A product developed based on inappropriate data will not be effective. During the study, a comparison was made between material prices and assets. For this, data of daily change in prices of materials was needed. However this data was not available. So monthly data points were considered and averages used to fill in missing data points.
2) Depth of commodity exchange markets and availability of financial products Although steel is listed on MCX, we found the traded volumes too low to support useful analysis. This limits its usage as an underlying index. Inflation adjusted bonds and interest rate derivatives are useful products to hedge cost against inflation. However no such financial product is available in India.
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Conclusion
A Discussion of the Solutions Proposed for the Buyer
With a financial product for affordable housing buyers, we have proposed a few plausible financial structures. These can be easily converted into viable financial products and should prove to be useful for buyer segment. Some of the proposed products could be discussed with general insurance companies and banks to fine tune them and can be easily offered to the potential buyers.
A Discussion of the Solutions Proposed for the Developer
Our research and investigation towards the development of a suitable hedge product for Developers building affordable housing did not yield a satisfactory hedge strategy. The primary reason for this is was the challenge in finding suitable proxies for the prices of construction inputs. Of the total construction cost associated with an affordable housing project 30% is labour cost, which is notoriously difficult to hedge, especially in the Indian context. 70% of the cost of materials are accounted for by cement, steel, brick, sand and other costs. We did not find a suitable proxy for bricks and sand, in the liquid market. In the case of cement, sand and brick we found some sort of correlation by creating an index consisting of gold, USD/INR and oil price. We believe that the recent price surge (in 2-3 years) in these commodities has come about largely because of inflation and supply and demand imbalance. Once the inflation comes under control the high volatility of cement would come down almost to a level, which would be equivalent to about annual inflation level.
We also believe if we get access to better and richer data for these materials, we should be able to explore better and effective hedge possibilities. Overall it certainly remains a challenge to create a suitable hedge for the Developers who are involved in building affordable housing.
Alternatively, we could also explore a synthetic product (both in cash and in future market) which exactly represents the cost of 70% of the commodities used in the construction of the building.
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References
JNNURM Mission Directorate, “Guidelines for Affordable Housing in Partnership.” available at http://mhupa.gov.in/w_new/AffordableHousing.pdf (Last Visited on September 30, 2011)
KPMG, (2010), “Affordable Housing -‐ A Key Growth Driver in the Real Estate Sector?” available at http://www.in.kpmg.com/TL_Files/Pictures/Affordable_Housing.pdf (Last Visited on March 13, 2012).
ISB – CEMS, (2012), “New Frontiers in Affordable Housing: Notes from the Field” ISB-‐CEMS, (2011), “Preliminary Report on Hedge Product for Construction Materials”
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Appendix
Scenario 1
Calculation of VaR and Cost index
RETURNS COST INDEX CEMENT SAND BRICKS
1-‐Nov-‐10
5.81% -‐0.96% 0.00%
1.97%
1-‐Dec-‐10 -‐9.12% 6.86% 32.07% 9.72% 1-‐Jan-‐11
23.43% -‐11.16% -‐0.89%
5.72%
1-‐Feb-‐11 18.23% 1.36% 11.55% 11.43% 1-‐Mar-‐11
-‐5.72% -‐16.37% -‐30.14%
-‐17.20%
1-‐Apr-‐11 -‐14.76% -‐0.07% 26.21% 3.66% 1-‐May-‐11
-‐20.07% 7.67% 3.53%
-‐4.40%
1-‐Jun-‐11 8.62% 1.68% 10.08% 7.32% 1-‐Jul-‐11
-‐4.45% -‐1.04% 8.77%
1.15%
1-‐Aug-‐11 8.77% -‐2.59% -‐5.61% 0.68% 1-‐Sep-‐11
-‐10.20% -‐0.46% -‐10.57%
-‐7.78%
1-‐Oct-‐11 5.58% 0.84% 4.51% 3.96% 1-‐Nov-‐11
-‐2.10% 0.98% 1.68%
0.05%
1-‐Dec-‐11 1.00% -‐10.06% 9.98% 1.30%
Monthly Vol 12.28% 6.66% 15.06%
Annual Vol 42.55% 23.06% 52.16%
99% CI 2.3263 2.3263 2.3263
Daily VAR 824.00 306.00 940.00 Monthly VAR 4,543.00 1,688.00 5,184.00
Daily VAR 0.82% 0.31% 0.94% Monthly VAR 4.54% 1.69% 5.18%
August 2012 FINANCING MECHANISMS FOR AFFORDABLE HOUSING
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Developing Hedge Index and Correlation
Date RETURNS Hedging
Index Cost index Gold Crude Dollar Coal in
Weights 1.50 7.69 11.90 -‐6.71 14.38 1-‐Sep-‐10 1-‐Oct-‐10
15-‐Oct-‐10 1-‐Nov-‐10
3.40% 4.78% -‐5.39% 0.00%
-‐22.27% 1.97%
1-‐Dec-‐10 -‐0.26% 0.66% -‐0.95% -‐6.51% 37.07% 9.72% 1-‐Jan-‐11
4.54% 1.57% 0.23% -‐1.95%
34.75% 5.72%
1-‐Feb-‐11 0.15% 5.32% 3.89% -‐2.07% 101.35% 11.43% 1-‐Mar-‐11
-‐3.20% 4.73% -‐1.92% 9.37%
-‐54.12% -‐17.20%
1-‐Apr-‐11 4.22% 3.07% 2.32% 2.42% 41.36% 3.66% 1-‐May-‐11
-‐0.32% 4.92% -‐2.05% 9.03%
-‐47.61% -‐4.40%
1-‐Jun-‐11 8.74% 7.71% -‐0.32% 8.76% 9.89% 7.32% 1-‐Jul-‐11
-‐0.86% 5.40% -‐2.25% -‐7.57%
64.20% 1.15%
1-‐Aug-‐11 -‐3.87% -‐9.84% 2.15% 1.93% -‐68.85% 0.68% 1-‐Sep-‐11
7.35% -‐8.23% 0.06% -‐0.92%
-‐45.32% -‐7.78%
1-‐Oct-‐11 14.93% 1.09% -‐2.78% -‐15.26% 100.10% 3.96% 1-‐Nov-‐11
-‐3.68% -‐4.17% 4.60% -‐1.12%
24.59% 0.05%
1-‐Dec-‐11 4.23% -‐3.86% 7.90% 0.30% 68.62% 1.30%
Correlation 0.660104
August 2012 FINANCING MECHANISMS FOR AFFORDABLE HOUSING
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Scenario 2
Calculation of VaR and Cost index
RETURNS COST INDEX CEMENT SAND BRICKS
1-‐May-‐10 -‐2.58% -‐0.01% -‐21.77% -‐8.73% 1-‐Jun-‐10
-‐2.65% 1.53% 13.86%
4.32%
1-‐Jul-‐10 -‐5.92% 0.01% -‐13.86% -‐7.19% 1-‐Aug-‐10
0.41% 1.52% 10.27%
4.20%
1-‐Sep-‐10 -‐0.20% 0.00% 19.85% 6.98% 1-‐Oct-‐10
0.32% -‐0.01% 0.00%
0.12%
1-‐Nov-‐10 3.09% 2.96% 0.00% 1.95% 1-‐Dec-‐10
-‐2.70% 0.24% 0.00%
-‐0.97%
1-‐Jan-‐11 2.09% 0.00% 25.46% 9.86% 1-‐Feb-‐11
6.49% -‐1.63% -‐23.65%
-‐6.36%
1-‐Mar-‐11 4.69% -‐0.83% 3.19% 2.71% 1-‐Apr-‐11
0.88% -‐0.13% -‐1.09%
-‐0.08%
1-‐May-‐11 -‐1.76% -‐1.12% 2.53% -‐0.07% 1-‐Jun-‐11
0.53% 0.74% -‐23.88%
-‐8.10%
1-‐Jul-‐11 -‐1.79% 0.26% 0.00% -‐0.61% 1-‐Aug-‐11
-‐0.90% 3.90% 26.97%
10.27%
1-‐Sep-‐11 -‐0.27% 3.58% 15.67% 6.41% 1-‐Oct-‐11
2.61% 4.33% 25.48%
11.20%
1-‐Nov-‐11 2.46% 6.33% -‐52.70% -‐16.15% 1-‐Dec-‐11
0.00% 0.00% 13.15%
4.68%
1-‐Jan-‐12 -‐0.17% 0.62% -‐13.15% -‐4.58% 1-‐Feb-‐12
3.75% 6.58% -‐13.06%
-‐1.49%
1-‐Mar-‐12 1.63% -‐8.57% 28.83% 8.64%
Monthly Vol 2.76% 3.05% 20.09%
Annual Vol 9.57% 10.57% 69.59%
99% CI 2.3263 2.3263 2.3263
Daily VAR 185.00 140.00 1,254.00
Monthly VAR 1,022.00 774.00 6,917.00
Daily VAR 0.19% 0.14% 1.25%
Monthly VAR 1.02% 0.77% 6.92%
August 2012 FINANCING MECHANISMS FOR AFFORDABLE HOUSING
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Developing Hedge Index and Correlation
Date RETURNS Hedging
Index Cost index Gold Crude Dollar Ambuja
Weights 2.37 -‐9.68 -‐10.04 0.60 -‐
16.75 1-‐Feb-‐10
1-‐Mar-‐10 1-‐Apr-‐10
1-‐May-‐10 -‐1.96% 7.22% -‐0.20% 2.47% -‐71.16% -‐8.73% 1-‐Jun-‐10
3.66% 3.26% -‐2.46% -‐12.48%
-‐5.68% 4.32%
1-‐Jul-‐10 8.98% 0.64% -‐1.41% 5.64% 32.63% -‐7.19% 1-‐Aug-‐10
0.21% -‐9.67% 4.58% 4.37%
50.81% 4.20%
1-‐Sep-‐10 -‐4.70% 0.12% 0.17% 5.04% -‐10.98% 6.98% 1-‐Oct-‐10
6.57% 5.28% 0.31% 13.32%
-‐30.66% 0.12%
1-‐Nov-‐10 0.60% -‐5.75% 0.86% 0.56% 48.83% 1.95% 1-‐Dec-‐10
3.14% 4.78% -‐5.39% -‐1.38%
14.38% -‐0.97%
1-‐Jan-‐11 4.54% 2.23% -‐0.72% 1.73% -‐2.67% 9.86% 1-‐Feb-‐11
0.15% 5.32% 3.89% -‐16.72%
-‐100.23% -‐6.36%
1-‐Mar-‐11 -‐3.20% 4.73% -‐1.92% 0.49% -‐33.83% 2.71% 1-‐Apr-‐11
4.22% 3.07% 2.32% 18.71%
-‐31.84% -‐0.08%
1-‐May-‐11 -‐0.32% 4.92% -‐2.05% 4.27% -‐25.28% -‐0.07% 1-‐Jun-‐11
8.74% 7.71% -‐0.32% -‐6.75%
-‐54.80% -‐8.10%
1-‐Jul-‐11 -‐0.86% 5.40% -‐2.25% -‐10.09% -‐37.82% -‐0.61% 1-‐Aug-‐11
-‐3.87% -‐9.84% 2.15% -‐0.15%
64.44% 10.27%
1-‐Sep-‐11 7.35% -‐8.23% 0.06% 4.83% 99.35% 6.41% 1-‐Oct-‐11
14.93% 1.09% -‐2.78% 9.32%
58.22% 11.20%
1-‐Nov-‐11 -‐3.68% -‐4.17% 4.60% 3.92% -‐12.09% -‐16.15% 1-‐Dec-‐11
4.23% -‐3.86% 7.90% -‐3.95%
-‐34.23% 4.68%
1-‐Feb-‐12 6.76% 12.97% -‐1.40% 9.20% -‐90.01% -‐4.58% 1-‐Feb-‐12
-‐2.79% 14.13% 6.25% 0.00%
-‐206.20% -‐1.49%
1-‐Mar-‐12 0.00% -‐5.33% -‐4.04% 0.70% 92.61% 8.64%
Correlation 0.501135