Housing Price Index Dr. Tarun Das1 Housing Price Indices- International Best Practices and An Operational Housing Price Index for India Dr.Tarun Das, Prof

Housing Price Index Dr. Tarun Das

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Housing Price Indices- International Best Practices and

An Operational Housing Price Index for India

Dr.Tarun Das, Prof. IILM, New Delhi Formerly, Eco.Adviser,

MOF


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Contents of this presentation

1. Importance of Housing/ real estate price indices

2. Properties of a good HPI3. International best practices- UK, USA

and Canada4. Efforts by the National Housing Bank

(NHB)5. An operational housing price index for

India.6. Pilot Survey for Delhi


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1.1 Share of dwellings in GDP and Trends of Prices

Share in GDP Share in GDP Dwellings Real Est.

Curr. Price Const. Price Pr. Index Pr.Index 1993-94 5.6 5.6 100 100 1999-00 4.4 4.5 151 152 2000-01 4.6 4.4 166 159 2001-02 4.7 4.3 179 173 2002-03 4.7 4.2 190 183 2003-04 4.5 4.0 200 187


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1.2 Price Indices

Dwellings GDP CPI (IW) WPI

Pr. Index Pr. Index 1993-94 100 100 100 100 1999-00 151 153 166 145 2000-01 166 159 172 156 2001-02 179 164 179 181 2002-03 190 171 187 167 2003-04 200 176 194 174


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1.3 Importance of real state price indices

• Property taxes contribute 70-80 percent of the revenues for the local governments viz. municipalities and corporations.

• Housing and real estate constitute an important service sector in national accounts, and a major proportion of private wealth.

• Real estate prices provide major inputs for formulation of macro-economic and monetary policies.

• Both lenders and borrowers may have large exposures to real estate.

• Boom, bubble, burst of property prices were a major factor for East Asian financial crisis in 1998-2000


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2.1 Properties of a Good Housing Price Index

• It must satisfy standard statistical criteria.

• It must satisfy the purpose of the users.• Data should be available easily, and with

least cost, time and energy.• Index must be easy to calculate.• Easily interpreted.• Easily updated at regular intervals.• Must reflect the reality.• It must conform to international best

practices.


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2.2 Constraints for construction of HPI

• Construction of real estate prices is challenging due to heterogeneity and imperfections in real estate markets and ambiguity in prices.

• Diversity and 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 for sample designs and methodology for estimation of indices.

• It is more challenging for India where no data base exists. But, it provides ample opportunity for research and experiments.


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2.3 Basic Housing Price Index

• Laspeyre’s Price Index is a weighted average of indices for different types of houses under consideration.

PI = ∑ WⁿIⁿ Where PI = Price Index W = Weights, such that W = 1 n = Type of houses I =Index for particular type of house P = Prices of different types of houses It = Price in time t / Base period price


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2.4 Hedonic Regression 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 function, • Pit is the unit-i housing prices in time t, and • Xit represents different housing

characteristics, mostly measured by dummy variables.


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3.1 Halifax HPI of UK- House qualities for hedonic

regression• Type of property• Tenure: • Number of rooms: • Number of garages • Heating type• Floor size (sqft) • Age • Garden. • Land area • Road charge • Location


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3.2 Seven Major HPIs in the UK

Index Data Source Type

Old ODPM SML 5% sample Mix adjustment

New ODPM SML 30-50% sample

Mix adjustment

Land Registry 100% sales Simple average

Halifax Home loans approved by it

Hedonic regression

Nationwide Home loans approved by it

Hedonic regression

Hometrack Survey of 400 estate agents

Mix adjustment

Rightmove Sellers’ asking price on internet

Mix adjustment


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3.3 Seven Major HPIs in the UK

Index Weights Type

Old ODPM Rolling average of SML transactions

Expenditure

New ODPM Rolling ave. of land registry transaction

Expenditure

Land Registry No weights Expenditure

Halifax 1983 Halifax loan approvals

Volume

Nationwide Rolling ave of SML, land reg.transaction

Volume

Hometrack England and Wales housing stock

Expenditure

Rightmove England and Wales housing stock

Expenditure


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3.4 Weights used in UK HPIs

Transac-tions (Volume)

Transac-tions (Value)

StocksValue

Base weights

Halifax Rightmove

Rolling weights

Nationwide

Old ODPMNew ODPMLand Registry

Hometrack

Total (7) 2 3 2


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3.5 HPI in USA

• Most popular- Index by the Office of Federal Housing Enterprise Oversight (OFHEO).

• Quarterly indices for single-family homes in U.S. using mortgage transactions from the Federal Home Loan Mortgage Corporation

• More than 29.31 million repeat transactions over 30 years.

• Alternative HPI by the Commerce Department (CQHPI) covers sales of new homes on a sample of 12,000 transactions annually.


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3.6 HPI in Canada

• In Canada, The New Housing Price Index (NHPI) (Base 1997) by the Statistics Canada is a monthly series.

• It measures the changes over time in the contractors' selling prices of new residential houses.

• Detailed specifications pertaining to each house remain the same between two consecutive periods.


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3.7 Alternative types of HPI

• Type Advantage Drawbacks

1. Average prices

Easy to collect and calculate

Ignores quality differences

2. Model property

Avoids quality problems

Ignores change over time

3. Hedonic regression

Controls for quality change

Requires huge data

4.Repeat sales method

Less data requirements

Ignores change over time


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4.1 Efforts by the National Housing Bank (NHB)

• At the instance of the Ministry of Finance, in January 1985 the NHB set up a Technical Advisory Group (TAG), chaired by the author.

• The TAG comprised members from the NHB, CSO, RBI, Labour Bureau, HDFC, HUDCO, Dewan Housing Finance Corporation Ltd., and the Society for Development Studies (CDS).

• The mandate was to suggest methodology, sampling techniques, institutional set up for construction of an operational housing price index for India at regular intervals.

• Under the guidance of the TAG, NHB and CDS conducted a Pilot Survey for Delhi


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4.2 Choice of Houses• First Phase- Residential houses in

urban areas with basic amenities -- Both buildings and flats -- Both old and new for sale Data on value, plinth area, location,

age and basic characteristics of houses.

-- Only transactions since 2001• Second Phase- Commercial Property• Third Phase- Also include land


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4.3 Concept on prices

Which price- Actual transactions price (compared with registered, estate agent’s price, mortgage price),

Prices per Square Feet and also per unit for each type.

Simple or weighted mean for a particular type of house

Both Laspeyre's index and a hedonic price index were estimated.


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4.4 Choice of Weights

We need weights for each type of housesAlso weights for each zone in a regionAnd for each region in the country.Alternative weights in terms of:

-- Actual transactions

-- Nominal value and volume

-- Volume- in terms SQ.Feet (plinth area)

and number of units


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4.5 Choice of Base PeriodBase for CPI (IW) has been revised to

2001, and CPI is estimated each month.Base of WPI is being shifted to 2000-01.

WPI is available for each week.Base of National Accounts has been

shifted to 1999-2000. GDP is available for each quarter.

Base of IIP is also being shifted to 2000-01.

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

TAG decided to take calendar year 2001 as the base year for HPI.


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5.1 Pilot Survey for Delhi

Under the overall guidance by the TAG, and assisted by the Society for Development Studies, the NHB conducted a Pilot Survey for Delhi urban area for the period 2001-2006.

30 sample tax zones were selected on the basis MCD Report on Unit Value System for property tax.

Separate Questionnaires were prepared for property agents, RWAs, builders.


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5.2 Property Tax Zones in Delhi

Category % of Area

No. of Colonies

% of colonies

Sample Colonies

A 4.1 46 2.4 2B 6.1 73 3.8 2C 13.9 189 9.8 3D 18.2 183 9.5 3E 11.1 192 9.9 3F 25 494 25.5 7G 21.7 758 39.2 10

Total 100 1935 100 30


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5.3 Choice of Housing Units1. In each of the selected layout/colony, both

new and resale housing units, flatted and plotted, were considered.

2. Houses built by the following agencies were included in the sample:

a) Delhi Development Authority b) Cooperative/ House Building Societiesc) Private buildersd) Households (plotted)e) MCD Slum and JJ Department


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5.4 Classification of Housing Units

The housing units selected in the Survey were classified as the following categories:

(a) EWS and LIG housing, up to 2 rooms

and covered area less than 500 sq. ft. (b) MIG housing with covered area

between 500–1,000 sq.ft. (c) HIG housing units with covered area

more than 1,000 sq. ft.


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5.6 Survey Designs and Data Base

a) Primary and secondary data were collected on housing stock, real estate prices and housing attributes for the 30 selected layouts/colonies.

b) The primary data were collected from the real estate agents, RWAs and cooperative societies on the basis of stratified random sampling techniques for the selected colonies.

c) The primary survey generated information on 20 transactions per annum for each of the selected colonies for the period 2000-05.


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5.7 Survey Designs and Data Base

d) The data were cross-checked with secondary data obtained largely from newspapers, real estate journals, large real estate agencies and websites.

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

f) Each survey team comprised of students with knowledge of Economics, Sociology and Housing.


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5.8 Trends of House Prices (Rs/sq.ft)

Zone 2001 2002 2003 2004 2005

A 2002 8434 9169 12149 14554

B 6979 4143 4910 6173 6794

C 8434 2318 2868 2832 3609

D 3961 1309 1777 2218 4094

E 4143 2496 3351 2912 3509

F 2478 1313 1555 1860 3509

G 2318 1019 1147 1477 1827

Total 1466 1985 2407 2781 3921


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5.9 Trends of HPI (Base 2001)

Zone 2001 2002 2003 2004 2005

A 100 121 131 174 209

B 100 105 124 156 172

C 100 94 116 114 146

D 100 89 121 151 279

E 100 117 157 136 164

F 100 110 131 156 295

G 100 113 128 164 203

HPI 100 106 129 150 226

% rise 5.7 21.9 16.3 50.9


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5.10 Category-wise HPI

Category-1 Category-2 Category-3

<500 sqft 500-1000 >1000 sqft

2001 100 100 100

2002 109 110 158

2003 150 132 187

2004 137 152 225

2005 181 209 297


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5.11 Housing attributes for Hedonic Model

Internal CharacteristicsCovered Area in square feetDelivery Agency DDA/ Co-operative

Society/ Private Builder

Stand alone/Flat Independent house/ Duplex Flat/ Flat

Age Number of yearsLocation of flat 1 to 8 StoreyNo of Toilets/Bathrooms

In number

Number of bedrooms

In number

Building quality Old/ Normal/ superior


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Amenities

Sewer Connections Yes/ NoElectricity supply No. of hours per

weekWater Supply Duration of piped

waterLegal

Form of transaction Legal Title/ Power of Attorney

Ownership Status Leasehold/ FreeholdHome loan Yes/ NoBuyer’s Profile Business/Employee/

Builder


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Environmental factors

Location Tax zones A to G

Near Main Road Yes/ No

Near Market Yes/ No

Near Bus Stand Yes/ No

Near Metro Station Yes/ No

Near Schools Yes/ No

Near hospitals Yes/ No

Facing green area/park Yes/ No

Three side/corner house Yes/ No


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5.14 Main results of hedonic model

• Covered area was the most significant factor influencing the price of a house (91%) followed by the grades of tax zones.

• Other significant variables include the legal status and the type of ownership.

• Quality of construction, type of house (LIG/MIG/HIG), accessibility to the main road, metro were other variables influencing the house prices in Delhi.


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• Access to schools, market etc, and amenities like water facilities, power load shedding etc. were dropped from the regression as they were statistically insignificant.

• 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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• The floor location of the flat was statistically significant. Higher floors command lower price. This could be as no lifts are available in most apartment complexes in Delhi.

• Builder flats had higher prices than DDA or co-operative flats because the former had better quality and locations.

• 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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5.17 Trends of hedonic HPI

Zone 2001 2002 2003 2004 2005

A 100 112 135 155 260

B 100 110 125 150 170

C 100 90 110 115 140

D 100 90 120 145 270

E 100 119 150 165 190

F 100 110 125 155 250

G 100 115 130 147 200

City HPI 100 105 125 148 227

% Increase 5.0 19.0 18.4 53.4


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5.18 Concluding remarks• Housing is an important asset with strong

backward and forward linkages in the economy. The high degree of volatility in the housing market requires that the price trends are adequately tracked for smooth functioning of the economy.

• Construction of a HPI presents both challenges and opportunities.

• Government should set up a specialized organization to construct and disseminate data on HPI at regular internals on the basis of scientific surveys and up to date methodology.


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Thank you –

Have a Good Day

Documents

Housing Price Index Dr. Tarun Das1 Housing Price Indices- International Best Practices and An Operational Housing Price Index for India Dr.Tarun Das, Prof