1. Construction of New Housing Price Indices for Monetary and Macro-prudential Policies: Experience of Thailand Saovanee Chantapong, Bank of Thailand, The Northeastern Region Office Disclaimer: The views expressed in this presentation are those of the author and not those of the opinions of the Bank of Thailand. The Workshop on Residential Property Price Indices (RPPI) Statistics Netherlands, The Hague, 10-11 February 2011

2. The Organisation of the Presentation  The Importance of housing market and housing price development  The construction of new housing price indices for monetary and macro-prudential policies: the experience of Thailand  Data and methodologies  Preliminary empirical results  Concluding remarks and the challenges ahead 2 saovanec@bot.or.th

3. The Importance of housing market and housing price development  The output multiplier of real estate sector is 1.16.  Employ 2.9 mil persons or 13% of non-agricultural sector employment.  Rent or mortgage payments form a large part of household expenditure.  Mortgage loan accounted for 36% of total household debt.  Outstanding credit extended to the real estate accounted for 10-20% of GDP. 3 saovanec@bot.or.th

4. Housing price indices conducted by related institutions in Thailand  The Real Estate Information Centre (REIC), the Government Housing Bank (GHB) – Data from GHB mortgage loan approval, since 1991 (Base year=2003) – Focusing on medium-to-low-price houses – About 40% of total mortgage loan of the banking system – Appraisal price, quarterly hedonic house price indices  The Agency for Real Estate Affairs (AREA), a private-owed property consultant – Data from surveys of the numbers of newly-launched real estate projects in Bangkok and its vicinities – Market price, monthly average house prices, since 1994  The Bank of Thailand, as an early warning system for indentifying macroeconomic imbalances. – Data from private commercial bank mortgage loan approval, since 2008 – Focusing on medium-to-high-end houses – About 60% of total mortgage loan of the banking system – Plan to construct monthly hedonic housing price indices 4 saovanec@bot.or.th

5. THE BOT DATA: - 17 private commercial banks’ mortgage loan - 60 % of total mortgage loan of the banking system - Monthly data since 2008, single detached house, townhouse and condominium Borrower FeaturesLoan FeaturesHousing Features Code of financial institution9. Appraisal Date19. Building Code Data Date10. Objective (New home, Old home, Land, Refinance, Other Consumption) 20. Land Ownership 1. Draw Down Date11. Approved Loan for housing21. Location Code 2. Reference No.12. Approved Loan with MDI (Mortgage Default Insurance) 22. Postal Code 3. Customer ID13. Loan for Insurance against fire or other damage23. Project Name 4. No. of Customers14. Loan for MRTA (Mortgage Reducing Term Assurance) 24. Developer Dummy 5. No. of Guarantors15. Approved Loan for others25. Building Age 6. Borrower's Income16. Repayment Period26. Year of Construction 7. Occupation Type17. Interest Rate27. Area Utilization 8. Business Type18. Fixed Rate Period28. Land Area 29. Market Price 30. Appraisal Price 31. Building Price 32. Land Price 33. Floor No. / No. of Floor 34. Decoration 5 saovanec@bot.or.th

6. Most of housing finance was provided by commercial banks and largely for new house purchase. 6 saovanec@bot.or.th

7. Methodologies 1.Mixed Adjustment – Housing price observations are grouped into sets of observations on houses with similar locations and physical attributes or so called reference stocks (Wood, 2003). – The median housing price indices in each group are weighted by using observations in each group together to give a mixed-adjusted price. – Three weighting methodologies: fixed weight, weighted 3-month rolling and inverse-variance weight were tested. – Using the equality test, the composite housing price indices excluding land price and including land price, derived from fixed weight and weighted 3-month rolling are not statistically different. = Weight in each type of house i in district j at time t = Median price of house i in district j at time t = Weighted median price of house i in district j at time t = Residential Property Price Index for each type of house 7 saovanec@bot.or.th

8. Methodologies (Cont.) 2. Hedonic regression: 2 main assumptions – Each dwelling is defined by the combination of fixed number of characteristics. – The relationship between the price of a house and its characteristics is fixed, and the house price depends on its location and physical characteristics of dwellings. ln Price = f (Age, Floors, Types of developer companies, Central business district (zone 1), Central Bangkok (zone 2), Eastern Bangkok (zone 3), Northern Bangkok (zone 4), Southern Bangkok (zone 5), Thonburi (zone 6), Samut Prakarn (zone 7), Nonthaburi (zone 8), Pathum Thani (zone 9), Nakhon Pathom (zone 10), Samut Sakhon (zone 11)) Price = Price of houses in Bangkok and its vicinities Age (-) = Age of the house (in years) Floors (+) = Number of floor Types of developer companies = dummy variable, 1 if developer company is listed in the Stock Exchange of Thailand (SET), o if otherwise * A priori hypotheses are indicated by (+) and (-) in the above specification. 8 saovanec@bot.or.th

9. The Empirical Results Independent variablesCoefficients Model 1: Single Detached HouseModel 2: Condominium C 9.7086**10.9449** AGE-0.0164**-0.0411** FLOOR 0.2848**0.0178** Developer companies dummy 0.2079**0.0720** Zone 1 – Central Buiness districtReference Zone 2 – Central Bangkok-0.1487**-0.2404** Zone 3 – Eastern Bangkok-0.4186**-0.5497** Zone 4 - Northern Bangkok-0.3140**-0.4119** Zone 5 – Southern Bangkok-0.1938**-0.1682** Zone 6 - Thonburi-0.3687**-0.3428** Zone 7 – Samut Prakarn-0.4546** -0.7718** Zone 8 - Nonthaburi-0.3752** -0.7080** Zone 9 – Pathum Thani-0.5772** -1.0347** Zone 10 – Nakhon Pathom-0.6318** -0.9397** Zone 11 –Samut Sakhon-0.5821** -0.6718** R-square0.2975 0.3659 Adjusted R-square0.2973 0.3657 F-Stat1457.197** 2439.870** Number of observations44,740 54,984 Notes: 1) Dependent variable is Ln (P) and 2) ** denotes 1% level of significance. 9 saovanec@bot.or.th

10. …Similar results from both hedonic regression and mix adjustment… 10 saovanec@bot.or.th

11. Concluding remarks and the challenges ahead 1.From the empirical results, housing price indices from hedonic regression and those indices from mix-adjustment methodology share similar trends. – The results conform to previous work that both techniques give very similar results when controlled for the same house characteristics (Wood, 2003). 2.In the future work, we will extract the cyclical component movement of new housing price indices by applying the Hodrick-Prescott and Baxter-King filters. 3.We also studied the structure of mortgage loan markets in several aspects: mortgage loan outstanding and loan to value ratio (LTV) for assessing credit and house price cycles. 4.Which methods of constructing an house price index is right? Should we use one housing price index or a combination of housing price indices? Challenges ahead:  The availability of data, conceptual and practical problems and diverse purposes among different users.  Difficulty in international comparisons due to differences in financing structure, regulatory framework, tax treatment, and the use of real estate as collateral. 11 saovanec@bot.or.th