Presentation on theme: "Housing Price Index Dr. Tarun Das1 Housing Price Indices- International Best Practices and An Operational Housing Price Index for India Dr.Tarun Das, Prof."— Presentation transcript:
Housing Price Index Dr. Tarun Das1 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
Housing Price Index Dr. Tarun Das2 Contents of this presentation 1.Importance of Housing/ real estate price indices 2.Properties of a good HPI 3.International best practices- UK, USA and Canada 4.Efforts by the National Housing Bank (NHB) 5.An operational housing price index for India. 6.Pilot Survey for Delhi
Housing Price Index Dr. Tarun Das3 1.1 Share of dwellings in GDP and Trends of Prices
Housing Price Index Dr. Tarun Das4 1.2 Price Indices
Housing Price Index Dr. Tarun Das5 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
Housing Price Index Dr. Tarun Das6 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.
Housing Price Index Dr. Tarun Das7 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.
Housing Price Index Dr. Tarun Das8 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
Housing Price Index Dr. Tarun Das9 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 (P it ) = 0 + i ln (Xit) + u it 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.
Housing Price Index Dr. Tarun Das10 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
Housing Price Index Dr. Tarun Das11 3.2 Seven Major HPIs in the UK IndexData SourceType Old ODPMSML 5% sampleMix adjustment New ODPMSML 30-50% sample Mix adjustment Land Registry100% salesSimple average HalifaxHome loans approved by it Hedonic regression NationwideHome loans approved by it Hedonic regression HometrackSurvey of 400 estate agents Mix adjustment RightmoveSellers’ asking price on internet Mix adjustment
Housing Price Index Dr. Tarun Das12 3.3 Seven Major HPIs in the UK IndexWeightsType Old ODPMRolling average of SML transactions Expenditure New ODPMRolling ave. of land registry transaction Expenditure Land RegistryNo weightsExpenditure Halifax1983 Halifax loan approvals Volume NationwideRolling ave of SML, land reg.transaction Volume HometrackEngland and Wales housing stock Expenditure RightmoveEngland and Wales housing stock Expenditure
Housing Price Index Dr. Tarun Das13 3.4 Weights used in UK HPIs Transac- tions (Volume) Transac- tions (Value) Stocks Value Base weights HalifaxRightmove Rolling weights NationwideOld ODPM New ODPM Land Registry Hometrack Total (7)232
Housing Price Index Dr. Tarun Das14 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.
Housing Price Index Dr. Tarun Das15 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.
Housing Price Index Dr. Tarun Das16 3.7 Alternative types of HPI TypeAdvantageDrawbacks 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
Housing Price Index Dr. Tarun Das17 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
Housing Price Index Dr. Tarun Das18 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
Housing Price Index Dr. Tarun Das19 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.
Housing Price Index Dr. Tarun Das20 4.4 Choice of Weights We need weights for each type of houses Also weights for each zone in a region And 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
Housing Price Index Dr. Tarun Das21 4.5 Choice of Base Period Base 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.
Housing Price Index Dr. Tarun Das22 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.
Housing Price Index Dr. Tarun Das23 5.2 Property Tax Zones in Delhi
Housing Price Index Dr. Tarun Das24 5.3 Choice of Housing Units 1. I n 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 Societies c)Private builders d)Households (plotted) e)MCD Slum and JJ Department
Housing Price Index Dr. Tarun Das25 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.
Housing Price Index Dr. Tarun Das26 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.
Housing Price Index Dr. Tarun Das27 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.
Housing Price Index Dr. Tarun Das28 5.8 Trends of House Prices (Rs/sq.ft) Zone20012002200320042005 A2002843491691214914554 B69794143491061736794 C84342318286828323609 D39611309177722184094 E41432496335129123509 F24781313155518603509 G23181019114714771827 Total14661985240727813921
Housing Price Index Dr. Tarun Das29 5.9 Trends of HPI (Base 2001) Zone20012002200320042005 A100121131174209 B100105124156172 C10094116114146 D10089121151279 E100117157136164 F100110131156295 G100113128164203 HPI100106129150226 % rise 5.721.916.350.9
Housing Price Index Dr. Tarun Das31 5.11 Housing attributes for Hedonic Model Internal Characteristics Covered Area in square feet Delivery AgencyDDA/ Co-operative Society/ Private Builder Stand alone/FlatIndependent house/ Duplex Flat/ Flat AgeNumber of years Location of flat1 to 8 Storey No of Toilets/BathroomsIn number Number of bedroomsIn number Building qualityOld/ Normal/ superior
Housing Price Index Dr. Tarun Das32 5.12 Housing attributes for Hedonic Model Amenities Sewer ConnectionsYes/ No Electricity supplyNo. of hours per week Water SupplyDuration of piped water Legal Form of transactionLegal Title/ Power of Attorney Ownership StatusLeasehold/ Freehold Home loanYes/ No Buyer ’ s ProfileBusiness/Employee/ Builder
Housing Price Index Dr. Tarun Das33 5.13 Housing attributes for Hedonic Model Environmental factors LocationTax zones A to G Near Main RoadYes/ No Near MarketYes/ No Near Bus StandYes/ No Near Metro StationYes/ No Near SchoolsYes/ No Near hospitalsYes/ No Facing green area/parkYes/ No Three side/corner houseYes/ No
Housing Price Index Dr. Tarun Das34 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.
Housing Price Index Dr. Tarun Das35 5.15 Main results of hedonic model 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..
Housing Price Index Dr. Tarun Das36 5.16 Main results of hedonic model 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..
Housing Price Index Dr. Tarun Das37 5.17 Trends of hedonic HPI Zone20012002200320042005 A100112135155260 B100110125150170 C10090110115140 D10090120145270 E100119150165190 F100110125155250 G100115130147200 City HPI100105125148227 % Increase 5.019.018.453.4
Housing Price Index Dr. Tarun Das38 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.
Housing Price Index Dr. Tarun Das39 Thank you – Have a Good Day