Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page. 1 1
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)
Chihiro SHIMIZU [email protected]
Chihiro Shimizu
(Reitaku University & University of British Columbia)
November 18, 2014
Real Estate Prices—Availability, Importance, and
New Developments
Japanese experience and New challenge
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page.
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))
2 Chihiro SHIMIZU [email protected]
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page.
Official Statistics on Housing market: Rent and Prices.
• The most important link between asset prices
and goods & services prices is the one through
housing rents (Goodhart 2001)
• Fundamental Value: Expected Present Value
Models and Excess Volatility. Robert Shiller
(1984, 1989).
• Housing rents account for more than one
fourth of personal spending
1.0
1.5
2.0
2.5
3.0
3.5
QT
19
86
/1
QT
19
88
/1
QT
19
90
/1
QT
19
92
/1
QT
19
94
/1
QT
19
96
/1
QT
19
98
/1
QT
20
00
/1
QT
20
02
/1
QT
20
04
/1
QT
20
06
/1
CPI rent
Selling price index
3
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”
Chihiro SHIMIZU [email protected]
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page. 4
Pr( 0) 1 Pr( 1) Pr( 1)
Pr( 0 | 1)Pr( 1)
Pr( 0 | 1)Pr( 1)
it
it
N
it
R
it
R R
it
N N
it i
it
t
it
I
I I
R
R
I
IR I
1 ititit RRR
Frequency of Rent Adjustments
0
5
10
15
20
25
30
35
26-S
ep-8
8
23-J
un
-91
19-M
ar-9
4
13-D
ec-96
9-S
ep-9
9
5-J
un
-02
1-M
ar-0
5
26-N
ov-0
7
10 t
hou
san
d y
en p
er m
on
th
Unit 11
Unit 219
Unit 220
Unit 245
Unit 298
Unit 308
Unit 339
Chihiro SHIMIZU [email protected]
Price Change
Probability of event on New Contract (IN)
and Renewed Contract (IR)
5
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)
Frequency of Rent Adjustments: Shimizu,
Nishimura and Watanabe (2010)
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)
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page.
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.
6 Chihiro SHIMIZU [email protected]
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page.
Transaction price-based index and
Appraisal value based index in Tokyo.
7
0
2
4
6
8
10
12
14
16
18
19
75
19
76
19
77
19
78
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
-60
-40
-20
0
20
40
60
80
100
120
MarketPrice
PublishedLandPrice
MarketPrice
PublishedLandPrice
In
dex:1975=
1
Calendar Year
an
nu
al c
han
ge r
ate (
%)
0
20
40
60
80
100
120
197509
197609
197709
197809
197909
198009
198109
198209
198309
198409
198509
198609
198709
198809
198909
199009
199109
199209
199309
199409
199509
199609
199709
199809
-40
-30
-20
-10
0
10
20
30
40
50
ULPI
MarketPrice
ULPI
MarketPrice
In
dex:1990M
arch
=100
Calendar Year/ Half yearly
an
nu
al c
han
ge r
ate (
%)
Chihiro SHIMIZU [email protected]
Lagging Problem
Appraisal
Error
Appraisal
Error
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page.
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
8 Chihiro SHIMIZU [email protected]
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page.
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)).
9 Chihiro SHIMIZU [email protected]
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page.
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.
10 Chihiro SHIMIZU [email protected]
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page.
When did the condominium price hit bottom?
11
-0.16
-0.14
-0.12
-0.10
-0.08
-0.06
-0.04
-0.02
0.00
0.02
0.04
19
97
01
19
98
01
19
99
01
20
00
01
20
01
01
20
02
01
20
03
01
20
04
01
20
05
01
20
06
01
20
07
01
20
08
01
Standard repeat sales
Case-Shiller
Age-adjusted RS
Hedonic
Rolling hedonic
Chihiro SHIMIZU [email protected]
2 years
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page.
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.
12 Chihiro SHIMIZU [email protected]
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page.
Price distributions : Price densities for P1, P2, P3, and P4
13
0.00
0.05
0.10
0.15
0.20
0.25
4.5
0
4.7
5
5.0
0
5.2
5
5.5
0
5.7
5
6.0
0
6.2
5
6.5
0
6.7
5
7.0
0
7.2
5
7.5
0
7.7
5
8.0
0
8.2
5
8.5
0
8.7
5
9.0
0
9.2
5
9.5
0
9.7
5
10
.00
10
.25
10
.50
P1
P2
P3
P4
log P
Chihiro SHIMIZU [email protected]
The distribution of transaction prices, P3 and P4 differs substantially
from that of asking prices, P1 and P2.
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page.
House purchase timeline
14
House placed on market
Offer made
Asking price in magazine
dataset (P1)
Final asking price in
magazine dataset (P2)
Mortgage approved
Contracts exchanged
Completion of sale with
Land Registry or REINS
Transaction price in realtor dataset (P3)
Transaction registered with
Land Registry
Transaction price survey
based on Land Registry Transaction price in
registry dataset (P4)
Timing of events in real estate
transaction process
Real estate price information
10 weeks
15.5 weeks
5.5 weeks
Chihiro SHIMIZU [email protected]
There is a time lag
around 30 weeks,
between P1 and P4.
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page.
Fluctuations in the price ratio and the interval for P1 and P2
15
35
40
45
50
55
60
65
70
75
80 0.950
0.955
0.960
0.965
0.970
0.975
0.980
0.985
0.990
0.995
20
05
07
20
05
10
20
06
01
20
06
04
20
06
07
20
06
10
20
07
01
20
07
04
20
07
07
20
07
10
20
08
01
20
08
04
20
08
07
20
08
10
20
09
01
20
09
04
20
09
07
20
09
10
Inte
rval
[d
ays]
Pri
ce r
atio
Price ratio (Left scale)
Interval (Right scale, Inverted)
0.00
0.05
0.10
0.15
0.20
0.25
20
05
07
20
05
10
20
06
01
20
06
04
20
06
07
20
06
10
20
07
01
20
07
04
20
07
07
20
07
10
20
08
01
20
08
04
20
08
07
20
08
10
20
09
01
20
09
04
20
09
07
20
09
10
Hedonic index for P1
Hedonic index for P2
Chihiro SHIMIZU [email protected]
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page.
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)
16 Chihiro SHIMIZU [email protected]
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page. 17
Hedonic indexes estimated using repeat-sales samples
Chihiro SHIMIZU [email protected]
Second IMF Statistical Forum 2014 (IMF, Washington DC, US)
2014/11/18 page.
Densities for relative prices
18
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
[0.6
5, 0
.70)
[0.7
0, 0
.75)
[0.7
5, 0
.80)
[0.8
0, 0
.85)
[0.8
5, 0
.90)
[0.9
0, 0
.95)
[0.9
5, 1
.00)
[1.0
0, 1
.05)
[1.0
5, 1
.10)
[1.1
0, 1
.15)
[1.1
5, 1
.20)
[1.2
0, 1
.25)
[1.2
5, 1
.30)
[1.3
0, 1
.35)
[1.3
5, 1
.40)
[1.4
0, 1
.45)
[1.4
5, 1
.50)
P1/P4
P2/P4
P3/P4
Chihiro SHIMIZU [email protected]