Second IMF Statistical Forum, Statistics for Policymaking Identifying Macroeconomic and Financial Vulnerabilities Session IV, Real Estate Prices Availability, Importance, and New Developments Discussion of Robert Shiller (Yale University) & Mick Silver (IMF) Second IMF Statistical Forum 2014 (IMF, Washington DC, US) Real Estate Prices Availability, Importance, and New Developments Japanese experience and New challenge Chihiro Shimizu (Reitaku University & University of British Columbia) November 18, 2014 page. 1
1. Macroeconomic Policy and Housing Market Fluctuations in real estate prices and economic activities. In Japan, a sharp rise in real estate prices during the latter half of the 1980s and its decline in the early 1990s has led to a decade-long stagnation of the Japanese economy. Shimizu,C and T.Watanabe(2010), Housing Bubble in Japan and the United States, Public Policy Review Vol.6, No.3, 431-472. At the center of my definition of the bubble are the epidemic spread, the emotions of investors, and the nature of the news and information media. Bubbles are not, in my mind, about craziness of investors. (Robert Shiller (2014)) page. 2
Official Statistics on Housing market: Rent and Prices. Expenditures for housing services: 26.4% Housing rents: 4.9% Imputed rents from owner occupied housing: 19.4% Housing maintenance and others: 2.3% Consumer Price Index (CPI) in Tokyo, 2010 3.5 3.0 CPI rent Selling price index The most important link between asset prices and goods & services prices is the one through housing rents (Goodhart 2001) 2.5 2.0 Fundamental Value: Expected Present Value Models and Excess Volatility. Robert Shiller (1984, 1989). 1.5 Housing rents account for more than one fourth of personal spending 1.0 QT1986/1 QT1988/1 QT1990/1 QT1992/1 QT1994/1 QT1996/1 QT1998/1 QT2000/1 QT2002/1 QT2004/1 QT2006/1 page. 3
Frequency of Rent Adjustments Second IMF Statistical Forum 2014 (IMF, Washington DC, US) Price Change R it R it R it 1 Probability of event on New Contract (I N ) and Renewed Contract (I R ) N R Pr( Rit 0) 1 Pr( Iit 1) Pr( Iit 1) 10 thousand yen per month 35 30 25 20 15 10 Unit 11 Unit 219 Unit 220 Unit 245 Unit 298 Unit 308 Unit 339 N N Pr( R 0 I 1)Pr( I 1) it it R R Pr( R 0 I 1)Pr( I 1) it it it it 5 0 26-Nov-07 1-Mar-05 5-Jun-02 9-Sep-99 13-Dec-96 19-Mar-94 23-Jun-91 26-Sep-88 page. 4
Frequency of Rent Adjustments: Shimizu, Nishimura and Watanabe (2010) Negative Zero Positive Number of Observations Turnover Units 85 397 44 526 (0.162) (0.755) (0.084) (1.000) Rollover Units 18 576 0 594 (0.030) (0.970) (0.000) (1.000) All Units 103 15,492 44 15,639 (0.007) (0.990) (0.003) (1.000) Fraction of housing units without no rent change per year US 29% Germany 78% Japan 90% Estimated by Genesove (2003) Estimated by Kurz-Kim (2006) Estimated by Shimizu et al (2010) 5
2. How should different countries construct Residential property price indexes? The Eurostat published Handbook on Residential Property Price indexes. Mick Silver (2014). The housing rent in CPI did not work well as a mirror of housing prices. How should different countries construct residential property price indexes? With the start of the RPPI Handbook project, the government of Japan set up an Advisory Board in 2012 and is proceeding with the development of a new transaction based residential property price index (RPPI). Japan had only official appraisal based RPPI. page. 6
Transaction price-based index and Appraisal value based index in Tokyo. 50 120 PublishedLandPrice 40 MarketPrice 100 an nu al ch an ge rate (% ) 30 ULPI 80 MarketPrice 20 60 10 40 0 20-10 -20 120 0 18 MarketPrice -30 an nu al ch an ge rate (% ) -20 PublishedLandPrice 16 100-40 Lagging Problem -40 14 ULPI 80-60 12 MarketPrice 10 Appraisal Error 60 8 40 6 4 20 Ind ex:1990m arch= 100 2 0 0 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 Calendar Year 7 page. Ind ex:1975= 1 Appraisal Error 197509 197609 197709 197809 197909 198009 198109 198209 198309 198409 198509 198609 198709 198809 198909 199009 199109 199209 199309 199409 199509 199609 199709 199809 Calendar Year/ Half yearly
Estimation Methods for Constructing Residential Property Price Indexes Housing: The location, history and facilities of each house are different from each other in varying degrees. Houses have particularity with few equivalents. Quality-Adjustment Methodology in House Price Index Hedonic method and Repeat sales method page. 8
Heteroskedasticity and Age Adjustments to the Repeat Sales Index Case-Shiller adjustment: Case and Shiller (1987, 1989) have proposed a model in which a GLS estimation is performed taking account of heteroscedasticity. Age-adjustment to repeat sales index: The number of years for which are houses in the market is remarkably short, the depreciation problem is potentially significant in Japan. To take account of the age effect, we estimate Age-adjustment to repeat sales index.(shimizu, Nishimura and Watanabe(2010), Wong, Chau and Shimizu(2013)). page. 9
Case of Japanese RPPI: Methods The question of which method is best remains open but the depreciation bias in the standard repeat sales method tends to lead us to prefer hedonic methods. (Handbook on Residential Property Price indexes by EuroStat (2013)) The government of Japan decided to prepare an official residential property price index based on the hedonic method. In particular, it has been determined that it will be estimated with the rolling window hedonic method proposed by Shimizu, Takatsugi, Ono and Nishimura (1998) and Shimizu, Nishimura and Watanabe (2010) and system development is underway. page. 10
When did the condominium price hit bottom? 0.04 0.02 0.00 0.02 0.04 0.06 Standard repeat sales Case Shiller Age adjusted RS Hedonic Rolling hedonic 0.08 0.10 2 years 0.12 0.14 0.16 200801 200701 200601 200501 200401 200301 200201 200101 200001 199901 199801 199701 page. 11
The Selection of Data Sources for the Construction of Housing Price Indexes: Mick Silver (2014). Are house prices different depending on the stages of the buying/selling process? We address this question by comparing the distributions of prices collected at different stages of the buying/selling process, including: (1) initial asking prices listed on a magazine or website, (2) asking prices at which an offer is made by a buyer, (3) contract prices reported by realtors after mortgage approval, (4) contract prices from registry prices. page. 12
Price distributions : Price densities for P1, P2, P3, and P4 The distribution of transaction prices, P3 and P4 differs substantially from that of asking prices, P1 and P2. 0.25 0.20 0.15 0.10 P1 P2 P3 P4 0.05 0.00 4.50 4.75 5.00 5.25 5.50 5.75 6.00 6.25 6.50 6.75 7.00 7.25 7.50 7.75 8.00 8.25 8.50 8.75 9.00 9.25 9.50 9.75 10.00 10.25 10.50 log P 13 page.
House purchase timeline Second IMF Statistical Forum 2014 (IMF, Washington DC, US) Timing of events in real estate transaction process House placed on market Real estate price information Asking price in magazine dataset (P 1 ) There is a time lag around 30 weeks, between P1 and P4. Offer made Final asking price in magazine dataset (P 2 ) 10 weeks Mortgage approved Contracts exchanged Completion of sale with Land Registry or REINS Transaction registered with Land Registry Transaction price in realtor dataset (P 3 ) 5.5 weeks Transaction price survey based on Land Registry Transaction price in registry dataset (P 4 ) 15.5 weeks page. 14
Fluctuations in the price ratio and the interval for P 1 and P 2 0.25 0.20 Hedonic index for P1 Hedonic index for P2 0.15 0.10 0.05 0.00 0.995 35 0.990 40 0.985 45 0.980 0.975 0.970 0.965 50 55 60 65 0.960 0.955 0.950 70 75 80 200507 200510 200601 200604 200607 200610 200701 200704 200707 200710 200801 200804 200807 200810 200901 200904 200907 200910 Price ratio Interval [days] 200507 200510 200601 200604 200607 200610 200701 200704 200707 200710 200801 200804 200807 200810 200901 200904 200907 200910 Price ratio (Left scale) Interval (Right scale, Inverted) page. 15
Hedonic indexes estimated using repeat-sales samples page. 16
Densities for relative prices 0.9 0.8 0.7 P1/P4 P2/P4 0.6 P3/P4 0.5 0.4 0.3 0.2 0.1 0 [0.65, 0.70) [0.70, 0.75) [0.75, 0.80) [0.80, 0.85) [0.85, 0.90) [0.90, 0.95) [0.95, 1.00) [1.00, 1.05) [1.05, 1.10) [1.10, 1.15) [1.15, 1.20) [1.20, 1.25) [1.25, 1.30) [1.30, 1.35) [1.35, 1.40) [1.40, 1.45) [1.45, 1.50) page. 17
3. Overall conclusions: Lessons from Shiller and Silver Q1: How do property prices affect the economic system or the financial system? Robert Shiller (2014) Q2: Do the different methods lead to different estimates of property price changes? If the methods do generate different results, which method should be chosen. Q3: Which data source should be used for property price indexes? Mick Silver (2014) & Robert Shiller (1987, 1989) page. 18