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 Das 1
Contents of this presentation 1. 2. 3. 4. 5. 6. Importance of Housing/ real estate price indices Properties of a good HPI International best practices- UK, USA and Canada Efforts by the National Housing Bank (NHB) An operational housing price index for India. Pilot Survey for Delhi Housing Price Index Dr. Tarun Das 2
1. 1 Share of dwellings in GDP and Trends of Prices Housing Price Index Dr. Tarun Das 3
1. 2 Price Indices Housing Price Index Dr. Tarun Das 4
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 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 Das 5
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 Das 6
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 Das 7
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 Das 8
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. Housing Price Index Dr. Tarun Das 9
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 Das 10
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 Housing Price Index Dr. Tarun Das 11
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 Wales housing stock Expenditure Rightmove England Wales housing stock Expenditure Housing Price Index Dr. Tarun Das 12
3. 4 Weights used in UK HPIs Base weights Rolling weights Total (7) Transactions (Volume) Halifax Transactions (Value) Stocks Value Rightmove Nationwide Old ODPM Hometrack New ODPM Land Registry 2 3 2 Housing Price Index Dr. Tarun Das 13
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 Das 14
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 Das 15
3. 7 Alternative types of HPI • Type Advantage 1. Average prices Easy to collect and calculate 2. Model Avoids quality property problems 3. Hedonic Controls for regression quality change 4. Repeat sales Less data method requirements Drawbacks Ignores quality differences Ignores change over time Requires huge data Ignores change over time Housing Price Index Dr. Tarun Das 16
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 Das 17
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 Das 18
4. 3 Concept on prices Ø Which price- Actual transactions price (compared with registered, estate agent’s price, mortgage price), Ø Prices per Square Feet and also per unit for each type. Ø Simple or weighted mean for a particular type of house Ø Both Laspeyre's index and a hedonic price index were estimated. Housing Price Index Dr. Tarun Das 19
4. 4 Choice of Weights Ø We need weights for each type of houses Ø Also weights for each zone in a region Ø And for each region in the country. Ø 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 Das 20
4. 5 Choice of Base Period ØBase for CPI (IW) has been revised to 2001, and CPI is estimated each month. ØBase of WPI is being shifted to 2000 -01. WPI is available for each week. ØBase of National Accounts has been shifted to 1999 -2000. GDP is available for each quarter. ØBase of IIP is also being shifted to 2000 -01. ØBase year should be a normal year and for which all required data are available. ØTAG decided to take calendar year 2001 as the base year for HPI. Housing Price Index Dr. Tarun Das 21
5. 1 Pilot Survey for Delhi ØUnder 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. Ø 30 sample tax zones were selected on the basis MCD Report on Unit Value System for property tax. ØSeparate Questionnaires were prepared for property agents, RWAs, builders. Housing Price Index Dr. Tarun Das 22
5. 2 Property Tax Zones in Delhi Housing Price Index Dr. Tarun Das 23
5. 3 Choice of Housing Units 1. 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 Societies c) Private builders d) Households (plotted) e) MCD Slum and JJ Department Housing Price Index Dr. Tarun Das 24
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 Das 25
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 Das 26
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 Price Index Dr. Tarun Das 27
5. 8 Trends of House Prices (Rs/sq. ft) Zone 2001 2002 2003 2004 2005 A B 2002 8434 9169 1214 1455 9 4 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 Housing Price Index Dr. Tarun Das 2407 2781 3921 28
5. 9 Trends of HPI (Base 2001) Zone 2001 A 100 B 100 C 100 D 100 E 100 F 100 G 100 HPI 100 % rise 2002 121 105 94 89 117 110 113 106 5. 7 2003 131 124 116 121 157 131 128 129 21. 9 Housing Price Index Dr. Tarun Das 2004 174 156 114 151 136 156 164 150 16. 3 2005 209 172 146 279 164 295 203 226 50. 9 29
5. 10 Category-wise HPI Category-1 Category-2 Category-3 <500 sqft 500 -1000 >1000 sqft 2001 100 100 2002 109 110 158 2003 150 132 187 2004 137 152 225 2005 181 209 297 Housing Price Index Dr. Tarun Das 30
5. 11 Housing attributes for Hedonic Model Internal Characteristics Covered Area Delivery Agency Stand alone/Flat Age Location of flat No of Toilets/Bathrooms Number of bedrooms Building quality in square feet DDA/ Co-operative Society/ Private Builder Independent house/ Duplex Flat/ Flat Number of years 1 to 8 Storey In number Old/ Normal/ superior Housing Price Index Dr. Tarun Das 31
5. 12 Housing attributes for Hedonic Model Amenities Sewer Connections Electricity supply Water Supply Legal Form of transaction Ownership Status Home loan Buyer’s Profile Yes/ No No. of hours per week Duration of piped water Legal Title/ Power of Attorney Leasehold/ Freehold Yes/ No Business/Employee/ Builder Housing Price Index Dr. Tarun Das 32
5. 13 Housing attributes for Hedonic Model Environmental factors Location Near Main Road Near Market Near Bus Stand Near Metro Station Near Schools Near hospitals Facing green area/park Three side/corner house Tax zones A to G Yes/ No Yes/ No Housing Price Index Dr. Tarun Das 33
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 Das 34
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 coefficients for age were positive in the former case but negative for the latter case. . Housing Price Index Dr. Tarun Das 35
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 Das 36.
5. 17 Trends of hedonic HPI Zone 2001 2002 2003 2004 2005 A B C D E F G City HPI % Increase 100 100 112 110 90 90 119 110 115 105 5. 0 135 125 110 120 150 125 130 125 19. 0 Housing Price Index Dr. Tarun Das 155 150 115 145 165 155 147 148 18. 4 260 170 140 270 190 250 200 227 53. 4 37
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 Das 38
Thank you – Have a Good Day Housing Price Index Dr. Tarun Das 39
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