Housing Price Indices- International Best Practices by Tarun Das

8/6/2019 Housing Price Indices- International Best Practices by Tarun Das

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Housing Price Indices- International Best Practices

And An Operational Housing Price Index for India

By Dr. Tarun Das, Professor (Public Policy), IILM, New Delhi-110003

Formerly Economic Advisor, Ministry of Finance and Planning Commission

1. Introduction

Housing and real estate constitute an important service sector in the national accounts. Table-1

indicates that the share of dwellings GDP had in general a declining trend since 1993-94. While

the share of dwellings in real GDP declined from 5.6 per cent n 1993-94 to 4 per cent in 2003-04,

that in GDP at current factor cost declined from 5.6 per cent to 4.5 per cent over the same

period. Table-1 also presents the trends of WPI, CPI-IW, and implicit price indices for

dwellings, financial sector ad real estate (which also includes dwellings) and GDP since 1993-94

derived from the national accounts statistics. It is observed that initially housing prices were

subdued and lagged behind other indices until 1999-2000. But, housing prices caught up other

indices since 2000-01 and the dwelling price index became the highest among all the indices in

2003-04.

Table-1: Share of dwellings in GDP and trends of prices

Share in GDP Price Indices (Base 1993-94 = 100)

Current

Price

Constant

Price

Dwellings

Price

Index

Financial

sector &

real estate

GDP

Price

Index CPI WPI

1993-94 5.6 5.6 100 100 100 100 100

1994-95 5.1 5.3 106 108 109 110 113

Dynamics of Real Estate Market: The Investment Perspective


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1995-96 4.7 5.1 111 121 119 121 122

1996-97 4.5 4.9 117 124 128 133 127

1997-98 4.3 4.8 123 127 137 142 133

1998-99 4.2 4.6 136 137 148 160 141

1999-00 4.4 4.5 151 152 153 166 145

2000-01 4.6 4.4 166 159 159 172 156

2001-02 4.7 4.3 179 173 164 179 181

2002-03 4.7 4.2 190 183 171 187 167

2003-04 4.5 4.0 200 187 176 194 174

For macro-economic and monetary analysis, it is desirable to have real estate prices because

both lenders and borrowers may have large exposures (both direct and indirect) to real estate

and may be affected by the potential volatility of prices in the real estate sector. More over, real

estates constitute a major proportion of wealth in the private sector. Construction of real estate

prices is challenging due to heterogeneity in the real estate markets and ambiguity in the

market prices. The diversity and the lack of standardization in real estate markets require

collection and compilation of data for various market segments resulting in high cost and

greater technical sophistication.

2. Objectives and scope of the paper

Construction of a housing price index for a developing country like India is complex, as there

are various concepts for housing price indices, many ways for compiling price data and

different sources of data, both private and public. The methodology for construction of indices

differs from country to country depending on the use and purpose of such indices and



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availability of data. This paper makes a review of the concepts, methodology and sampling

designs, and collection of price data for construction of real estate price indices in selected

countries and presents a model for constructing an operational housing price index for India.

Presently, at the instance of the Ministry of Finance, the National Housing Bank (NHB) is trying

to construct a housing price index under the guidance of a Technical Advisory Group (TAG).

The TAG is chaired by the author and comprises members from the NHB, CSO, RBI, Labour

Bureau, HDFC, HUDCO, Dewan Housing Finance Corporation Ltd., LIC Housing Finance Ltd.

and the Society for Development Studies. It also consists of professionals and experts as

members such as Dr. H. Sadhak from the Management development centre, LIC and Mr. V.

Suresh, Former Chairman, HUDCO.

3. Review of Country Experiences on Housing Price indices

3.1 United Kingdom

The UK literature on the real estate price indices is the richest. There are seven major house

price indices developed for the UK, three of which are official- two are constructed by the Office

of Deputy Prime Minister (ODPM) and one by the Land Registry. Two other indices are

constructed by two leading mortgage lenders viz. the Halifax Building Society and the

Nationwide Building Society. Two other indices are constructed by two companies having

interest in housing markets viz. Hometrack and Rightmove. In addition, there are two main

survey based housing price indices produced by the Royal Institutes of Chartered Surveyors

and the House Builders Association.

Indices use varied techniques for prices (hedonic regression model and mixed quality

adjustment) and various weighting diagrams based on volume and value of houses. Table-2



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presents a comparative position of methods used to construct different housing price indices in

the UK.

The Halifax House Price Index

The Halifax House Price Index is the UK's longest running monthly house price series since

January 1983. The Index is derived from the mortgage data of the UKs largest mortgage lender

HBOS, which provides a robust and representative sample of the entire UK market. There are a

number of national indices covering different categories of houses (all, new and existing) and

buyers (all, first-time buyers and home-movers). These indices are adjusted to allow for

seasonal variations. The most commonly used and quoted Halifax Index is the UK seasonally

adjusted index covering all houses and all buyers. Regional indices for the 12 standard

planning regions of the UK are produced on a quarterly basis.

The indices calculated are 'standardized' and represent the price of a typically transacted

house. The need for 'standardization' arises because no two houses are identical and may differ

according to a variety of characteristics relating to the physical attributes of the houses and their

locations.

In summary, prices are disaggregated into their constituent parts using a commonly used

statistical technique called multivariate regression analysis or the hedonic approach. This

allows values to be attributed to the various qualitative characteristics (type of property, region,

etc.) and quantitative characteristics (age of property, number of habitable rooms, garages,

bathrooms, etc.) of a property. The technique allows tracking the value of a 'typical' house over

time on a like-for-like basis.

The Halifax hedonic regressions are derived from information on the following house

characteristics:

Type of property: Detached house/ terraced house/ Detached bungalow/ semi-detached

bungalow/ Purpose built flat/ new converted / Converted flat/maisonette

Tenure: freehold, leasehold, feudal.

Number of rooms: habitable rooms, bedrooms, bathrooms/ toilets



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Number of garages and garage spaces/ No garage or parking spaces

Central heating type

Floor size (sqft)

Age of the property.

Central heating: none, full, partial.

Garden.

Land area if greater than one acre

Road charge liability.

Location (region)

Wood (2003) critically examines the methodology, database and weighting diagrams of these

indices. The data and methods used to construct these indices are different and they have both

advantages and disadvantages depending on the purpose for which these are used. The Land

Registry Index uses the most complete dataset, but the data set does not record the details of

dwelling characteristics. The indices differ in their use of current or base weights, transactions

or stock weights, volume or value weighted (Table-3). The Hometrack and Rightmove indices

are likely to measure final transactions prices with errors. The Halifax and Nationwide indices

use the broadest quality adjustment techniques and a dataset that represents a good trade-off

between accuracy and timeliness. The author observes that the sampling and estimation errors

in the monthly and quarterly house price indices appear to be substantial.



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Table-2 Comparison of methods used to construct

Seven major house price indices in the United Kingdom (UK)

Name of the

index

Data source

and coverage

Quality

adjustment

method

Seasonally

adjusted?

Weights used Weighting

method

Measures

Old ODPM SML 5%

sample of CML

eligible

completions

Mix

adjustment

No Rolling

average of

SML

transactions

Expenditure Value of average

set of transacted

dwellings

New ODPM SML 30-50%

sample of CML

eligible

completions

Mix

adjustment

No Rolling

average of

Land Registry

transactions

Expenditure Value of average

set of transacted

dwellings

Land

Registry

100% of sales

registered in

England and

Wales

Simple

average

No None Expenditure Value of set of

transacted

dwellings

Halifax Loans

approved for

house

purchase by

Halifax

Hedonic

regression

Yes 1983 Halifax

loan approvals

Volume Price of Halifax

representative

dwellings

Nationwide Loans

approved for

house

purchase by

Nationwide

Hedonic

regression

Yes Rolling

average of

SML, Land

Registry and

Nationwide

transactions

Volume Price of

Nationwide

representative

dwellings

Hometrack Survey of

approx 4000

estate agentsestimated local

average prices

Mix

adjustment

No England and

Wales

Housing Stock

Expenditure Value of housing

stock

Rightmove Sellers asking

prices posted

on internet site

Mix

adjustment

No England and

Wales

Housing Stock

Expenditure Value of housing

stock



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Source: Wood, Robert (2003)

Notes: CML = Council of Mortgage Lenders, SML=Survey of Mortgage Lenders, Mix

Adjustment implies weighted mean prices for different sets of houses, grouped according to

locations and physical attributes.

Table-3 Weights used in UK house price indices

Transactions weights Stock weights

Base weights Halifax (Volume) Rightmove (Value)

Rolling weights Old ODPM (Value)

New ODPM (Value)

Nationwide (Volume)

Land Registry (Value)

Hometrack (Value)

3.2 United States of America (USA)

The most popular is the index developed by the Office of Federal Housing Enterprise Oversight

(OFHEO). The OFHEO estimates and publishes quarterly house price indices for single-family

detached properties using data on conventional conforming mortgage transactions obtained

from the Federal Home Loan Mortgage Corporation (Freddie Mac). Several researchers have

also used various methodologies, particularly hedonic regression models. Researchers have also

extended the research to establish relation between trends of real estate prices and other macro-

economic variables.



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Under the Federal Housing Enterprises Financial Safety and Soundness Act of 1992 (Title XIII of

P.L.102-550), the Office of Federal Housing Enterprise Oversight (OFHEO) as the federal

regulator is required to develop and administer a quarterly risk-based capital stress test to

measure the capital adequacy of the government-sponsored mortgage finance institutions of

USA viz. Fannie Mae and Freddie Mac. In the stress test, the statute requires OFHEO to use a

house price index to account for changes in the loan-to-value (LTV) ratios of mortgages held or

guaranteed by Fannie Mae or Freddie Mac. Chartered by Congress for the purpose of creating a

reliable supply of mortgage funds for homebuyers, Fannie Mae and Freddie Mac are the largest

mortgage finance institutions in the United States. Their combined mortgage records form thenation's largest database of mortgage transactions.

Accordingly, the OFHEO constructs a House Price Index (HPI) to measure the changes in the

value of single-family homes in the U.S. as a whole, in various regions and individual states of

the country. Because of the large sample size, OFHEO HPI provides more information than any

other house price indexes, and serves as a timely, accurate indicator of house price trends at

various geographic levels. It also provides housing economists with an improved analytical tool

for estimating changes in the rates of mortgage defaults, prepayments and housing affordability

in specific geographic areas.

The alternative HPI prepared by the Commerce Department (CQHPI) covers sales of new

homes and homes for sale, based on a sample of about 12,000 transactions annually, and

gathered through monthly surveys. OFHEO's quarterly HPI is based on more than29.31 million

repeat transactions over 30 years.

The HPI constructed by OFHEO uses quarterly data provided by Fannie Mae and Freddie Mac

on their most recent mortgage transactions. These data are combined with those for the

previous 29 years to establish price differentials on properties where more than one mortgage

transaction has occurred. The data are merged to create an updated historical database that is

then used to estimate the HPI.



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The methodology used by OFHEO in computing the HPI is a modified version of the Case-

Shiller geometric weighted repeat sales procedure, meaning that HPI measures average price

changes in repeat sales or refinancing on the same properties. This information is obtained by

reviewing repeat mortgage transactions on single-family properties whose mortgages have

been purchased or securitized by Fannie Mae or Freddie Mac since January 1975.

3.3 Canada

In Canada, The New Housing Price Index (NHPI) with base 1997 prepared by the Statistics

Canada is a monthly series that measures changes over time in the contractors' selling prices of

new residential houses, where detailed specifications pertaining to each house remain the same

between two consecutive periods. The survey also collects contractors' estimates of the current

value (evaluated at market price) of the land. These estimates are independently indexed to

provide the published series for land. The residual, (total selling price less land value), which

mainly relates to the current cost of the structure is also independently indexed and is presented

as the estimated house series.

The NHPI is widely used by researchers, housing economists and general public to track

housing price trends. Within Statistics Canada, the series are used for estimation of some

components of the Consumer Price Index. The series are used by the Canadian System of

National Accounts for deflating the national housing stock. Due to the level of geographic detail

provided and the sensitivity to changes in supply and demand, the series are also used by wide

range of people such as building contractors, market analysts, insurance companies, federal

government agencies like the Canadian Mortgage and Housing Corporation (C.M.H.C.), and

provincial and municipal housing agencies responsible for housing policy.

NHPI is estimated for a set of model houses selected in consultation with the builders and the

real estate developers. The universe consists of builders in 21 metropolitan areas who mainly



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build single unit houses in such volume or in such a fashion that they can report selling prices

for comparable transactions.

Weights

Separate weights are estimated annually at the level of the metropolitan area for the house

component, the land component and the total selling price series using building completion

data. Essentially, a price adjusted moving three-year average of the value of building

completions for each metropolitan area is calculated for the house component, and then

aggregated up to provide provincial and national indexes. In the case of land, the house to land

ratios obtained from the NHPI are employed to estimate the corresponding land value data,

also using a three-year moving average and aggregated in the same way. The total (house and

land) is then calculated using all this information.

To prepare a contractors' selling price index for a metropolitan area, price reports from the

sample of builders are given equal weights in index calculations. Amongst metropolitan areas,

weights are derived from housing completions data.

Secrecy and Disclosure of Data

It is a mandatory obligation of the builders and real estate developers to respond to the survey

for developing NHPI. Imputation rarely occurs for the NHPI, as the response rate is virtually

100%. However when required, a missing or delayed price will be imputed by carrying the

previous month's reported price forward. Under the Statistics Act, the Statistics Canada is

prohibited from releasing any data which would divulge information obtained under the

Statistics Act that relates to any identifiable person, business or organization without prior

knowledge or he consent in writing of that person, business or organization.



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Price data are converted to price indexes and data are released such that it is not possible to

identify the price data of the suppliers of the raw price information.

Data accuracy

The statistical accuracy of this index depends on price and value data. Price data are obtained

from a sample survey. Value data mainly rest on the quality of the building completion data.

Both kinds of input data are subject therefore to their own errors.

In terms of price data, it has been acknowledged since the inception of the NHPI that the house-

to-land split can contain some level of respondent bias. This is due to the difficult task of

separating the total value of a new house into a land portion and a structure portion. The

allocation of value in such a circumstance may be easy for one builder to provide and

conceptually difficult for another to determine.

Though the NHPI uses a sample survey methodology to obtain the necessary information,

confidence intervals are not currently estimated, due to the longitudinal nature of price index

series. Indexes for higher and lower levels of aggregation are considered to be statistically

reliable.



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Table-4 below provides trends of housing price indices for selected metropolitan area since

2000.

Table-4 Trends of New Housing Price Indices in Selected Metropolitan Areas

Base 1997=100 2000 2001 2002 2003 2004 June 2005

Canada 104.1 107.0 111.3 116.7 123.2 129.3

House only 106.2 109.9 115.9 123.0 131.1 137.1

Land only 101.3 102.2 103.5 105.0 108.0 114.0

Qubec 104.5 107.1 111.7 121.9 129.3 133.8

Montral 106.3 111.7 118.1 126.8 135.0 141.5

Ottawa 110.9 123.7 133.3 138.3 147.4 153.5

Toronto and Oshawa 107.8 110.5 114.2 119.5 126.6 133.0

London 104.2 106.8 109.8 115.0 120.4 127.1

Calgary 115.3 118.2 124.4 130.9 138.2 145.2

Edmonton 107.7 109.4 117.3 124.0 129.3 136.8

Vancouver 90.2 90.9 93.2 96.2 101.0 105.9

Victoria 85.8 86.2 89.3 96.2 105.0 112.0

Base 1997=100 2000 2001 2002 2003 2004 June 2005

Source: Statistics Canada



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4 An operational Housing Price Index for India

4.1 Reserve Bank of India (RBI)

Recently, a working paper prepared by the RBI (Joshi, Sharma, Augustine, Mathur, Bhuyan

and Majumdar 2005) reviews the methodology, sampling techniques, collection of price data,

for construction of real estate price indices in Canada (New Housing Price Index) and UK

(Halifax index) and suggests a methodology for India. It basically suggests the use of Hedonic

price model. But, such an index suffers from the drawback that the index is based on multiple

regression equations, which can be applied only with large sample size at the all India level and

may not be applicable at regional and sub-regional levels for lack of sufficient number of

observations. Even if data are available, it may be difficult to have a good fit and to specify a

representative housing unit. It will also be difficult to combine regional indices unless we know

the weights. In fact, the RBI working paper is incomplete. It discusses conceptual issues relating

to prices only, but does not deal with practical problems relating to determination of weights

and sources of reliable basic data on prices, stock and transactions of houses.

4.2 Society for Development Studies (CDS)

Another working paper prepared by the Society for Development Studies (2005) makes a

comprehensive review of methodology for construction of real estate indices in Canada, UK,

USA and Hong Kong, and suggests the use of hedonic approach for India. This paper also

suffers from the same weakness as in the RBI Working Paper



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4.3 An operational Housing Price Index for India

Above review indicatesthat as with many economic statistics, the measurement of house prices

poses significant conceptual and practical problems. The most important point is to note that no

one method of constructing a housing price index is ideal and it is better to construct a set of

alternative indices on the basis of available data, least cost and the purpose of the indices.

Construction of a Housing/ Real Estate Price Index for India, that satisfies international best

practices, is both a challenge and an opportunity for us.

Properties of a Good Housing Price Index

Like any other index, a good housing price index must satisfy a number of criteria:

Reliable data should be available easily and with least cost.

Index must be relevant for the purpose of the users.

Index must be easy to calculate.

Index should be easily interpreted.

Index should be easily updated at regular intervals.

Index should reflect the reality.

Index should be decomposable by regions and categories.

Index should be subject to usual statistical test.

After reviewing international best practices and wide ranging discussions, the Technical

Advisory Group decided to conduct a pilot study for Delhi and to use both the (a) hedonic

regression model and (b) the basic Laspeyres weighted index for constructing a HPI for Delhi.

The residential colonies in Delhi have been categorized as one of the 8 tax zones (A to G) as

decided by the Municipal Corporation of Delhi (MCD) under the Unit Area Method for



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property tax assessment (Table-5). The classification of the colonies is largely based on the level

of services and the capital value of housing units.

Table-5: Categorization of Colonies in Delhi according to Property Tax Zones

Category of

Tax zone

Number

Of colonies

Per cent

Of number

Area

(Sq. Km)

Per cent

Of area

No. Of

Sample

Colonies

A 52 2.6 21 4.1 2

B 51 2.6 31 6.1 2

C 161 8.1 72 13.9 4

D 201 10.1 94 18.2 4

E 220 11.1 58 11.1 4

F 528 26.6 129 25.0 6

G 772 38.9 112 21.7 8

Total 1985 100 518 100 30

Source: Report of the Municipal Valuation Committee under the Chairmanship of Mr. O. P.

Kelkar submitted to the Municipal Corporation of Delhi, 28thFebruary 2004



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A. Hedonic Model

Under the hedonic approach, multi-variable hedonic regression equations are estimated to

work out the index number at the sub-city level, by regressing house prices on various

characteristics of houses. This method is outlined as:

Ln (Pit) = 0 + i ln (Xit) + uit

This equation is a simple lognormal hedonic function, where Pit stands for housing prices

for unit-i in time t, and Xit for different housing characteristics. Different forms of egressions

equations can also be tried to specify the best fitted equation (Joshi et. Al. 2005). For aggregating

each of the sub-city indices into a city level real estate index, the Laspeyres approach will be

used. The weights attached to each sub-city level index can be percentage of transaction in that

zone to the total transactions in the city. The use of the Laspeyres approach to aggregate the

sub-city indices is consistent with the assumption that the percentage of transactions of each

zone to the total city transactions remains constant.

B. Laspeyres Housing Price Index

Laspeyres Price Index is a weighted average of indices for different tax zones under

consideration:

PI = W I

Where PI = Price Index

n = Category of tax zones , n=1, 2, 3 8

W = Weights for nth category of tax zone, such that W = 1

I = Index for nth category of tax zone

P = Prices of different types of houses



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C. Pilot Survey for Delhi

As mentioned above, a pilot survey is being conducted for Delhi. The TAG has selected 30

colonies in different tax zones on the basis of transactions for the collection of basic data. These

colonies are spread over all parts of Delhi such as South, North, West and East Delhi.

Choice of Base Period

Base year should be a normal year and for which all required data are available. The TAG has

decided to take 2001 as the base year for the construction of HPI and update the index on half

yearly basis. The choice of base year for HPI is consistent with the base period of other indices.

The new CPI (IW) series with revised base 2001 are ready for publication. CPI is available for

every month. The Base of WPI is being shifted to 2000-01. WPI is available for each week. Base

of National Accounts is proposed to be shifted to 1999-2000. GDP is available for each quarter.

The base of IIP is being shifted to 2000-01 and the spade works have already started.

For HPI, basic data are being collected for each year since 2000. For each selected colony and for

each year, information will be collected for at least 20 transactions, which actually took place

during the year. Thus there will be 600 observations for each year since 2000, and 3000

observations for six years from 2000 to 2005.



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Choice of Houses and Collection of Data

At the First Phase of the pilot study, only residential houses (both independent houses and flats,

and both old and new for sale) in urban areas with basic amenities are being considered. At the

second stage, commercial housing units will be considered and finally land may be included in

order to make it a comprehensive real estate price index.

Data on value, plinth area, location, age and basic characteristics of houses are being collected

from the property dealers, Residential Welfare Associations (RWAs) and the builders. The

objective is to collect the basic transactions price excluding taxes and duties and agents

commissions. It is well known that the registered values of houses are grossly under estimated

due to very high registration fees and stamp duty. Due to same reasons and subsequent

obligations for the payment of property tax, individual purchasers (except corporate bodies) do

not reveal the exact purchase price of a house.

Average Price

For each selected colony, average prices per Square Feet of plinth area will be estimated by

taking arithmetic mean, weighted mean, median and mode. Also a hedonic approach will be

adopted for Delhi as a whole for each year. As indicated in Table-6 below, no method is

completely free from errors and the use of a particular methodology depends on purpose, easy

availability of data and the resources available (in terms of technical manpower, money, time,

computer software and hardware) at the disposal of the authority in charge of collection,

compilation and preparation of+ the index.



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Choice of Weights

We need weights for each zone in the city. The TAG has decided to take the weights given by

the MCD report as indicated in Table-5 above. An attempt will be made to estimate both the

volume index (weighted by number of units) and the value index (weighted by total value).

Table-6: Alternative House Price Indices: A comparison

Type Advantage Drawback

1. Average prices- mean, median,

mode

East to collect and calculate No correction for quality

differences

2. Representative property

method

Avoid most quality change

problems

Focuses only on one set of

properties and ignores

developments of other properties

3. Hedonic regression models Controls for quality changes

Takes into account all possible

houses

Requires huge data

Potential bias for incorrect model

specifications

4. Repeat sales method from the

hedonic price model

Less data requirements,

Less dependent on model

Requires at least two sales,

Quality of the same property may

change during intervening period

Source: Paul Hilbers (2003)



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A Pilot Survey for Delhi Urban Area

As discussed above, there are several valid concepts of house prices and several ways of

constructing a Housing Price Index. Under the overall guidance of the TAG, the National

Housing Bank (NHB) conducted a pilot survey in Delhi with the assistance of the Society for

Development Studies (CDS), Delhi and adopted a practical approach to construct an operational

HPI for Delhi. If it is successful, the methodology can be applied to other cities in order to

prepare an All India HPI.

Sample Designs

The first stage of selection of sample colonies was on basis of the property tax zones in Delhi,

under the Unit Area Method for property tax assessment, based largely on level of services and

capital value of housing units. Tax zone H, which covers the rural settlements in the city, was

excluded from the coverage of the indices.

Residential Layouts

The second stage was to select 30 representative residential colonies in Delhi for this purpose

and covering transaction values. The distribution of the 30 colonies across the 7 tax zones is

based on the share of each tax zone in the total of 1,935 residential layouts/colonies in zones A

G (Table-7). Turnover rate of housing units was used as a criterion for selection of

representative colonies in the tax zones A G (Table-8).



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Table-7: Distribution of Colonies by Property Tax Zones

Property Tax

Zone

% of Area No. of Colonies % of colonies No. of Sample

Colonies

A 4.1 46 2.4 2

B 6.1 73 3.8 2

C 13.9 189 9.8 3

D 18.2 183 9.5 3

E 11.1 192 9.9 3

F 25.0 494 25.5 7

G 21.7 758 39.2 10

Total 100 1935 100.0 30



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Table-8: Distribution of Sample Colonies by Property Tax Zone and Location

Property Tax

Zones

South East West North

A New Friends

Colony,

Vasant Vihar

B South Extension

Safdarjung

Enclave

C Vasant Kunj Punjabi Bagh

(West)

Shalimar

Extension

D Mayur Vihar Dwarka Pitampura

E Yamuna Vihar Inder Puri Rohini

F Govind Puri Dilshad Garden

Pandav Nagar

Karampura

Raghubir Nagar

Nirankari Colony

Tri Nagar

G Dakshin Puri

Sangam Vihar

Sriniwas Puri

Ghazipur Dairy

Farm

Jhilmil Colony

Hari Nagar

Khyala (I-III)

Jahangir Puri

Mangol Puri

Sultan Puri

Source: Technical Advisory Group on Housing Price Index, NHB, 2005.

Representative Basket

At the third stage of market segmentation, in each of the selected layout/colony, both new and

resale housing units, flatted and plotted, developed by the following agencies were included in

the sample:



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1. Delhi Development Authority

2. Cooperative/ House Building Societies

3. Private builders

4. Households (plotted)

5. MCD Slum and JJ Department

6. Private developers, without planning permission

Housing Units

At the fourth stage of market segmentation, the housing units covered for the representative

basket were classified as the following categories:

EWS and LIG housing, up to 2 rooms and covered area less than 500 sq. ft.

MIG housing with covered area between 500 1,000 sq.ft

HIG housing units with covered area more than 1,000 sq. ft.

Base Year

As mentioned earlier, year 2001 was taken as the base year for the construction of HPI to make

it consistent with revised base periods for national accounts, CPI and WPI. The index was

developed for the calendar years rather than the financial year, as the transaction data were

collected largely on recall basis for the period 20002005.



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Data Requirements and Sources

Primary and secondary data were collected on housing stock, real estate prices and housing

attributes for the 30 selected layouts/colonies. The primary data were collected from the real

estate agents, office-bearers of Resident Welfare Associations (RWAs) and cooperative societies

on the basis of stratified random sampling techniques for the selected colonies. The primary

survey generated information on 20 transactions per annum for each of the selected colonies for

the period 2000-05. The data were cross-checked with secondary data obtained largely from

newspapers, real estate journals, large real estate agencies and websites.

Surveys were conducted by the National Housing Bank with assistance by the SDS. Detailed

questionnaires were prepared for all surveys on data collection. Each survey team comprised of

students with knowledge of Economics, Sociology and Housing.

House Price Index Model for Delhi: Weighted Average Model

Two critical data items required for HPI are house price and quantity of housing units covered

in the transaction during the year. The collection and compilation of these two basic

information for the base year and other years were challenging due to heterogeneity in the real

estate markets and ambiguity in the market prices.

a. House Price Data

An average house price data in each reporting period was calculated by dividing the sum of

house prices by the number of units for which there were transactions during the period. Such

average price indices are probably the most widely available price measures for real estate, in

the form of average house price.



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b. Housing Stock Data

The data on housing stock were collected from the housing delivering agencies1 for all the 30

colonies. For the formulation of the house price index based on weighted average method, the

quantities of housing stock under each category had to be estimated. The collection of this data

was done mostly through secondary sources including the DDA and the MCD.

City House Price Index

1Delhi Development Authority, Municipal Corporation of Delhi, Office of the Registrar Cooperative Societies,

House Building Societies, Cooperative Federations, Private Builders

For each tax zone, average price of housing per square feet (AP) was estimated by the weighted

average of average housing prices for different categories i.e.

AP2001 = (W1000)

Then zone-wise price indices were calculated for all the zones for all the years. The results arepresented in Table-9.



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Table-9: Tax Zone Wise Index

Tax Zone 2001 2002 2003 2004 2005 Weights

A 100 121 131 174 209 4.1

B 100 105 124 156 172 6.1

C 100 94 116 114 146 13.9

D 100 89 121 151 279 18.2

E 100 117 157 136 164 11.1

F 100 110 131 156 295 25.0

G 100 113 128 164 203 21.6

City HPI 100 106 129 149 226 100

% Increase 5.5 22.2 16.0 51.1


Category Wise House Price Index

The category wise House Price Index has been calculated for covered area less than 500 sq.ft,

500 to 1000 sq.ft. and more than 1000 sq.ft. Firstly, the total weighted price was



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Table -10: Category Wise House Price Index

Category

(sq.ft.) 2001 2002 2003 2004 2005

< 500 100 109 150 137 181

500 to 1000 100 110 132 152 209

>1000 100 158 187 225 297


Limitations of Weighted Average Index

Problems in weighted average indices can be to the extent that they do not reflect the current

mix of transactions, may not capture information on sectors where a standard unit of real estate

cannot be defined, and do not adequately capture information on rapidly developing sectors.

6. Trends in House Price

In Delhi, real estate prices had hit the roof in 1997, fuelled by acute shortage of land and

speculative investments. The bubble burst in 1998 was an outcome of speculators liquidating

their holdings. Prices fell by 40-80 per cent, virtually wiping out the entire capital of the

speculative investors. The first signs of recovery became evident in 2003 when prices started

recovering. Since then, spurred by easy access to housing loans from banks along with fiscal

incentives, real estate prices across the city had been rising on a sustainable basis.



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The prices increased at a constant rate from year 2000 to 2002 at around 6 percent. There was a

sharp increase from 2003 (22.2%) onwards; the year 2004 witnessed an increase of 16 percent,

and 2005 as high as 51 percent.

There are many factors, which have contributed to the sharp increase in the real estate prices.

The major factor is the 2005-06 budget, which retained the deduction on interest on housing

loans, despite several advisory committees advocating its reduction/ phasing out.

Secondly, after the FDI norms were changed with respect to minimum area criteria for

development of integrated city from 100 acres to 25 acres, many foreign developers have shown

an interest in the Indian market. This might help in improve the efficiency of the housing sector

operations. But, it is doubtful that it would lead to lowering of the price of an apartment.

The most important factor leading to property price rise in Delhi in recent years is the increase

in general accessibility of major colonies due to starting of Metro Rail and its expansion to far-

flung areas and neighbouring states.

7. House Price Index Model for Delhi: Hedonic Method

As mentioned earlier, the Hedonic method is useful to analyse value-influencing factors of the

property separately from temporal factors. The hedonic equation used to statistically estimate

the house price index is,

P= X +

Where,

P is a vector of dependent variable, which is the transaction price of a house,



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X is the vector of characteristics on which the price of a house depends. These include

structural, locational and legal characteristics. Various dummy variables have been used to

measure the amenities, location, access to market, multiplex, hospitals, educational institutes

etc.

is the vector of coefficient of variables and measures the effect of changing the house

characteristics on the house value,

is the random error term.

The variables used to estimate the hedonic price index are indicated in Table-10. After fitting

the regression line for a particular year for all the zones and for all the observations, a typical

house is chosen to estimate the representative price per square feet from the fitted hedonic

regression line for that particular year. The TAG took a two-bed room house with plinth area of

1000 square feet and with all civic amenities as the typical house for determination of the

hedonic price index.

Table-10: Housing Attributes for Hedonic Model Housing Price Indices

Housing Attributes Indicators for Hedonic Model

Internal Characteristics

Covered Area The natural log of the covered area in square feet is taken

Delivery Agency 1: DDA, 2: Co-operative Society, 3: Private Builder

0: Self Constructed

Stand alone/Flat 1: Independent house, 2: Duplex Flat, 0: Flat

Age Number of years

Location of storey 1 to 8



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Number of Toilets/Bathrooms In number

Number of bedrooms Dummies 0 to 5

Building Quality 0: Normal Finish, 1: Superior Finish, 2 for Old

Amenities

Sewer Connections 1: Available, 0 Not Available

Electricity Number of hours for which electricity available

Water Supply Duration of piped water supply

Environmental and Legal

Location 0 to 6 for tax zones A to G

Near Main Road 1: Yes, 0: No

Near Market 1: Yes, 0: No

Near Bus Stand 1: Yes, 0: No

Near Metro Station 1: Yes, 0: No

Near Schools 1: Yes, 0: No

Facing green area/park 1: Yes, 0: No

Three side/corner house 1: Yes, 0: No

Form of transaction 1: Legal Title, 0: Power of Attorney

Ownership Status 1: Leasehold, 0: Freehold

Home loan 1: Yes, 0: No

Buyers Profile 1: Business, 2: Employee, 0: Builder

Note: *: Dummies are naturally coded in STATA

Source: TAG Hedonic Model



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(a) Analysis and Results

The estimated hedonic regression results for all the zones taken together indicated that all the

co-efficients were significant at 5% level of significance. However, it was observed that covered

area was the most significant factor influencing the price of a house (91 % of the price is

explained by it) followed by the location of the property in terms of tax zones.

Other variables found statistically significant include the legal status of the property

(authorized or unauthorized colony) with houses in unauthorized colony commanding lower

price. Type of ownership also makes a difference to house prices with people willing to pay a

higher price for freehold properties.

Quality of construction, type of house (.LIG, MIG etc), accessibility as measured by nearness to

the main road (has positive impact on price), increase in distance from the metro (has a negative

impact on price) were other variables influencing the house prices in Delhi.

Access to schools, market etc, and amenities like water facilities, power load shedding etc. were

dropped from the regression as they were statistically insignificant. The variables water and

electricity could have been insignificant because there was a widespread problem of recall by

the real estate agents.

Age has, surprisingly, a positive sign implying that people are willing to pay more for older

properties. However when a regression was run dividing the age in two different groups i.e.

less than 17 years and more than 17 years, the co-efficients for age were positive in the former

case but negative for the latter case.



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Zero bedroom houses are generally tiny plots allotted by the MCD Slum Wing in resettlement

colonies. The estimates show that zero bedroom houses are preferred over one bedroom flats.

This could be because people in these colonies can expand vertically as per family need and

improvement in financial capability. This is however not possible in the case of one bedroom

LIG flats. Also seen in the regressions of individual tax zones E, F and G that people are willing

to pay more for larger living areas.

The characteristics that play an important role in determining the house prices in different tax

zones as shown in the regressions for individual tax zones are practically the same for all the tax

zones i.e. covered area, quality of construction, form of ownership, whether the house is in an

authorized or unauthorized colony and accessibility (as measured by any one of the variable i.e.

nearness to the main road, bus or metro). The co-efficients for public service like bus has been

shown even if it is statistically insignificant as in tax zones C and D to show lack of

capitalization.

In a couple of tax zones the floor location of the flat is also statistically significant. Higher floors

command lower price. This could be as no lifts are available in most apartment complexes inDelhi.

Co-operative flats are available in tax zones D and E. Though house delivery agency was

statistically insignificant in tax zone D, tax zone E shows that people were willing to pay a

higher price for co-operative flats as compared to DDA flats. This could be because co-

operatives provide better facilities like security, water supply, parking etc.



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(b) Assessment of Trends

Zone wise and City House Price Index obtained from the hedonic regression is presented in

Table-11. The trend in house prices show that the prices in Delhi, taking the base year as 2001,

were in depression prior to 2001, but showed an increasing trend thereafter. The prices were

49% higher in 2005. The prices started increasing at a faster rate especially from 2003. The

increase could be attributed to increasing BPO businesses, growing middle income group and

easy availability of cheap housing loans.

Taking the base year as 2001, the table shows that the prices increased at a high rate from 2003

for almost all tax zones and that the maximum price increase i.e. 86% happened in the Tax Zone

D in 2005 followed by Tax Zone A (68%). The colonies falling in Tax Zone D category are

Dwarka, Mayur Vihar and Pitampura. While, the ones included in Category A are New Friends

Colony and Vasant Vihar

The increase in price in Dwarka and Mayur Vihar could have happened due to the fact that the

prevailing property values in many parts of south Delhi has put them out of reach for most

middle class investors, however prices in Dwarka and Mayur Vihar are still affordable. Also,

Dwarka is close to Gurgaon while Mayur Vihar is close to Noida. These are the two places

where commercial activity is increasing. This could also be the reason for purchase of properties

in these colonies. Thus increasing the residential prices there. Further, initiatives like the metro

and flyovers along with affordable house prices, could explain the increasing purchase of

property in Pitampura.



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Table-11: House Price Index by Tax Zones

Tax Zone 2001 2002 2003 2004 2005 Weights

A 100 112 135 155 260 4.1

% Increase .. 12.0 20.5 14.8 67.7

B 100 110 125 150 170 6.1

% Increase .. 10.0 13.6 20.0 13.3

C 100 90 110 115 140 13.9

% Increase .. -10.0 22.2 4.5 21.7

D 100 90 120 145 270 18.2

% Increase .. -10.0 33.3 20.8 86.2

E 100 119 150 165 190 11.1

% Increase .. 19.0 26.1 10.0 15.2

F 100 110 125 155 250 25

% Increase .. 10.0 13.6 24.0 61.3

G 100 115 130 147 200 21.6

% Increase .. 15.0 13.0 13.1 36.1

City HPI 100 105 125 148 227 100

% Increase .. 4.7 19.6 18.6 53.1




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While the increase in price in Tax Zone A could be due to increasing construction of builder

flats in these areas. Therefore, creating a demand of those who otherwise would not have been

able to afford an independent house in an up market south Delhi colony like Vasant Vihar.

(c)Limitations of the Hedonic Approach

With the approach used in the study a house price index is available which can track house

prices. However, some important limitations must be kept in mind. These are,

a) Sample selection bias because,

1. The index uses only the information from houses that have self selected for sale from the

entire housing stock

2. The data was collected from only those realtors who were willing to provide the data for their

transactions.

b) So far as the prices are concerned, knowing the amount of black money that goes into the real

estate transactions, there might have been under reporting of the transaction prices by the

realtors.

c) For some variables there was the problem of recall by the real estate agents, especially, for the

years 2000, 2001 and 2002.Hence the information for some of the variables, especially those

pertaining to load shedding, water supply etc may have some degree of inaccuracies

In spite of these drawbacks the exercise of tracking the house prices is useful. Data collected on

a more regular basis for transaction price as well as for characteristics in future will certainly

improve the index.



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8. Conclusions

Housing is an important asset in the Indian economy with strong backward and forward

linkages. The high level of volatility in the housing market requires that the price movement is

adequately tracked for smooth functioning of the economy. The study presents the preliminary

results of employing the Weighted Average method and the Hedonic regression method as

techniques for developing House Price Index for Delhi. The price trends follow almost similar

pattern for both the weighted average and the hedonic method for the city and the different tax

zones.

For a House Price Index to be meaningful, it must compare prices of equivalent houses from

one period to the next. This is difficult as no two houses are identical. Therefore, a system of

measurement is required which allows for differences in the sample houses traded i.e. data

should be quality adjusted. In order to solve this problem adoption of the hedonic method is a

step in the right direction as this method estimates the trends of prices for typical houses sold

and purchased during the year.

Selected References

Calhoun, Charles A. (2003) OFHEO House Price Indices: HPI Technical Description, pp.1-14,

Office of Federal Housing Enterprise Oversight (OFHEO), Washington, D.C.

Das, Tarun (2005a) Housing/ Real Estate Price Indices- Issues for Discussion, paper presented

at the First Meeting of the Technical Advisory Group (TAG) on the Housing Price Index,

National Housing Bank (NHB), July 2005.



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Das, Tarun (2005b) An Operational Housing Price Index for Delhi, paper presented at the

Second Meeting of the Technical Advisory Group (TAG) on the Housing Price Index, National

Housing Bank (NHB), August 2005.

Das, Tarun (2005c) Construction of Services Price Index- A case Study for Housing Prices,

paper presented at the Seminar on the Construction of Services Prices, organized jointly by the

Ministry of Commerce and Industry, PHD Chamber of Commerce and Industry, World Bank

and the IMF, at the PHDCCI, New Delhi, November 2005.

Das, Tarun (2006) Housing Price Indices- International Best Practices and An Operational

Housing Price Index for India, pp.44-54, Bima Vidya, Journal of the LIC Management

DevelopmentCentre, Borivili West, Mumbai-400003, March 2006.

Eurostat (2004) Construction Price Indices- Sources and Methods.

Fan, Kelvin and Peng, Wensheng (2003) Real estate indicators in Hong Kong SAR, pp.124-148,

BIS Papers No.21.

Fenwick, D. and H. Duff (2002) An improved house price index- update on developments,

Economic Trends, Vol.588.

Hilbers, Paul (2003) Methodological issues regarding residential real estate prices, pp.228-231,

Proceedings of a joint IMF/BIS Conference on Real Estate Indicators and Financial Stability,

IMF Survey, Vol.32, No.20, 17 November 2003.

International Monetary Fund (IMF) (2003) Proceedings of a joint IMF/BIS Conference on Real

Estate Indicators and Financial Stability, IMF Survey, Vol.32, No.20, 17 November 2003.



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Joshi, Ajit R., Anil Kumar Sharma, Sushila Augustine, Deepak Mathur, Pradip Bhuyan and

Debashis Majumdar (2005) Construction of Housing Price Index for India: An Approach, pp.1-

31, WP No.1. DESACS, RBI, Mumbai, February 2005.

Lall, Vinay (2005) Country Experiences in Developing Real Estate Price Index, pp.1-12, Society

for Development Studies, New Delhi. May 2005.

Society for Development Studies (2006) Draft Report on the Housing Price Index for Delhi,

submitted to the TAG, January 2006.

Statistics Canada (2004) New Housing Price Index: Concepts, Methodology and Data Sources.

Wallace, Nancy E. (1996) hedonic Based Price Indices for Housing: Theory, Estimation and

Index Construction, FRBSF Economic Review, No.3.

Wood, Robert (2003) A Compilation of UK residential house price indices, Bank of England,

pp.212-227, BIS Papers No.21.

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