Dynamics in Real Estate Market Reitaku-University
2009/11/04 page. 1
不動産価格変動のダイナミクス- マイクロ構造とマクロトレンド -
麗澤大学経済学部・准教授 清水千弘 (Chihiro SHIMIZU)
Chihiro SHIMIZU 2009 [email protected]
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page. 2
主要都市における住宅価格変動の因果性
0.0
50.0
100.0
150.0
200.0
250.0
300.0
1983
/01
1983
/09
1984
/05
1985
/01
1985
/09
1986
/05
1987
/01
1987
/09
1988
/05
1989
/01
1989
/09
1990
/05
1991
/01
1991
/09
1992
/05
1993
/01
1993
/09
1994
/05
1995
/01
1995
/09
1996
/05
1997
/01
1997
/09
1998
/05
1999
/01
1999
/09
2000
/05
2001
/01
2001
/09
2002
/05
2003
/01
2003
/09
2004
/05
2005
/01
2005
/09
2006
/05
2007
/01
2007
/09
2008
/05
Tokyo_Condo
Tokyo_SingleHouse
LosAngeles
NewYork
London
HongKong
Melbourne
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
80
90
100
110
120
130
140
150
12/02 6/03 12/03 6/04 12/04 6/05 12/05 6/06 12/06 6/07 12/07 6/08 12/08 6/09 12/09
Australia
Japan
US
UK
Capital index, December 2002 = 100
International property cycles - Speed of adjustment
3
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
東京都区部のオフィス・住宅市場の動向
4
0
0.2
0.4
0.6
0.8
1
1.2Y
1985
Q1
Y19
85Q
3Y
1986
Q1
Y19
86Q
3Y
1987
Q1
Y19
87Q
3Y
1988
Q1
Y19
88Q
3Y
1989
Q1
Y19
89Q
3Y
1990
Q1
Y19
90Q
3Y
1991
Q1
Y19
91Q
3Y
1992
Q1
Y19
92Q
3Y
1993
Q1
Y19
93Q
3Y
1994
Q1
Y19
94Q
3Y
1995
Q1
Y19
95Q
3Y
1996
Q1
Y19
96Q
3Y
1997
Q1
Y19
97Q
3Y
1998
Q1
Y19
98Q
3Y
1999
Q1
Y19
99Q
3Y
2000
Q1
Y20
00Q
3Y
2001
Q1
Y20
01Q
3Y
2002
Q1
Y20
02Q
3Y
2003
Q1
Y20
03Q
3Y
2004
Q1
Y20
04Q
3Y
2005
Q1
Y20
05Q
3Y
2006
Q1
Y20
06Q
3Y
2007
Q1
Y20
07Q
3Y
2008
Q1
Y20
08Q
3Y
2009
Q1
Office
Residential
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.Chihiro SHIMIZU 2008 [email protected] 5
不動産市場分析 : 不動産市場を科学する
• 1. ミクロ分析とマクロ分析• ミクロ分析 : 各経済主体の行動を分析• マクロ分析 : 市場の変動を分析し,予測する
• 2. 経済モデルと計量分析• 理論経済モデルとは ?• 検証的分析と探索的分析
• 3. 探索的分析 ( 地価動向指標 ) の問題と予測の失敗• (Grammar of Science)• 予測ができるのか ?• 市場の効率性の理解
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
本日のご報告 :
• 1. 不動産市場のダイナミクス
• 2. 不動産市場の効率性
• 3. 賃貸料市場の変化 - マイクロストラクチャ -
• 4. キャップレートの決定構造
• 5. 社会構造の変化と不動産価格
• 6. 公示地価への教訓
6
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.Chihiro SHIMIZU 2008 [email protected] 7
( ) SEconomy,RD =
( )SCS
CS
δ=Δδ
=
-
iR
P =
( )CfP =
ストック( )平方フィート
( )賃貸料 ドル
資産市場:建築着工
:賃貸市場ストック調整
( )価格 ドル
建築着工( )平方フィート
:賃貸市場賃貸料決定
:資産市場価格評価
Source:Denise Dipasquale,William Wheaton(1996).,Urban Economics and Real Estate Markets
1. 不動産市場のダイナミクス
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
GDP と SNA 土地資産額の推移
8
0
100,000
200,000
300,000
400,000
500,000
600,000
0 500,000 1,000,000 1,500,000 2,000,000 2,500,000
1970
1975
1980
1985
1990
2006GD
P:10
億円
SNA :10土地資産額 億円
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Gordon Growth Model: 資産価格とは ?
• Yit : 費用控除後の賃料収益• Rf : 安全資産の投資利回り• Rp : リスク・プレミアム• G : 収益のマクロ的な上昇率
9Chihiro SHIMIZU 2009 [email protected]
GRRyp
pift
itit
不動産価値の決まり方
ξ),( ip zLfR • L : 流動性リスク• ξ: 予期できぬリスク
キャップ・レート
経済活動 / 需給
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.Chihiro SHIMIZU 2009 [email protected] 10
長期的な期待 : 人口は減少していく
50, 000
60, 000
70, 000
80, 000
90, 000
100, 000
110, 000
120, 000
130, 000
140, 000 20
00
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020
2022
2024
2026
2028
2030
2032
2034
2036
2038
2040
2050
( )千 人
14出 典 : 国 立 社 会 保 障 ・ 人 口 問 題 研 究 所 『 日 本 の将 来 推 計 人口 』 ( 平 成 年 1 月 推 計 ) 10 1による各 年 月 日 現 在 の推 計 人 口 より
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.Chihiro SHIMIZU 2009 [email protected] 11
団塊世代のリタイアとオフィス市場
0
10
20
30
40
50
60
70
80
90
100
020406080100120 120100806040200
女男
(万人)
(歳)
0
10
20
30
40
50
60
70
80
90
100
020406080100120 120100806040200
女男
(万人)
(歳)
0
10
20
30
40
50
60
70
80
90
100
020406080100120 120100806040200
女男
(万人)
(歳)
0
10
20
30
40
50
60
70
80
90
100
020406080100120 120100806040200
女男
(万人)
(歳)
日本の人口ピラミッド(中位推計)
10年後(2015年)現在(2005年)
団塊世代
団塊世代
団塊Jr・ポスト団塊Jr
出所)国立社会保障・人口問題研究所
団塊Jr・ポスト団塊Jr
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.Chihiro SHIMIZU 2009 [email protected] 12
高度経済成長・列島改造・国際都市化・ファンドバブル
-40
-20
0
20
40
60
80
100
1955
.919
56.9
1957
.919
58.9
1959
.919
60.9
1961
.919
62.9
1963
.919
64.9
1965
.919
66.9
1967
.919
68.9
1969
.919
70.9
1971
.919
72.9
1973
.919
74.9
1975
.919
76.9
1977
.919
78.9
1979
.919
80.9
1981
.919
82.9
1983
.919
84.9
1985
.919
86.9
1987
.919
88.9
1989
.919
90.9
1991
.919
92.9
1993
.919
94.9
1995
.919
96.9
1997
.919
98.9
1999
.920
00.9
2001
.920
02.9
2003
.920
04.9
2005
.920
06.9
2007
.920
08.9
六大都市 商業地 六大都市 住宅地
六大都市 工業地
六大都市 全用途平均
(%)
対前
年同
期変
動率
六大都市 商業地
六大都市 住宅地
六大都市 工業地
六大都市 全用途平均
(注1)六大都市とは,東京都区部,横浜,名古屋,京都,大阪および神戸をいう。
(注2)市街地価格指数の変動率は各年3 月時点の前年同期比を用いている。
(出典)財団法人 日本不動産研究所 「市街地価格指数」
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
日本の地価の長期動向 : ECM 推定結果
Chihiro SHIMIZU 2009 [email protected] 13
- 0.15
- 0.10
- 0.05
0.00
0.05
0.10
0.15
0.2019
57
1960
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
2005
2008
(ECM残差)予測地価(t+1期)地代(GDP)資金コスト誤差修正項d(ln_lp)
入門計量経済学 B 第 13 回講義ノートより
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.井上・清水・中神 (2009) 14
• 効率性の検証–疑問1: 短期的に効率的か?
① uc一定のもとでの超過収益率の系列相関
② 時変的な uc のもとでの予測誤差の系列相関
– 検定方法
において AR項の同時除外仮説を検定
ittiitiitiiiit xxxx 3,32,21,10
it
itittiit P
RPPre
1,
ititittiit ucPRPe 11,
2. 日本の不動産市場は効率的か
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.15
• 効率性の検証結果 1 : 短期的効率性①
0 20kmt est 1
2. 41. 60. 8
井上・清水・中神 (2009)
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.16
• 効率性の検証結果 2 : 短期的効率性②
0 20kmt est 2
2. 41. 60. 8
井上・清水・中神 (2009)
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.17
–疑問2: 長期的に効率的か?• 長期的に効率的ならば,変数間に共和分関係が存在するはずである
•単位根検定を用いて検証
• 結果: 72 市区中, 68 市区が 1%水準で,残りの 4 市区が 5%水準で定常
ititittiit ucPRPe 11,
井上・清水・中神 (2009)
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.18
– マンションの市場価格と理論値
20
30
40
50
60
90 95 00 05
1
20
30
40
50
60
90 95 00 05
2
20
40
60
80
90 95 00 05
4
10
20
30
40
50
90 95 00 05
12
0
20
40
60
90 95 00 05
13
40
60
80
100
120
90 95 00 05
17
10
20
30
40
50
90 95 00 05
18
20
30
40
50
60
90 95 00 05
19
20
40
60
80
90 95 00 05
20
20
40
60
80
90 95 00 05
21
0
20
40
60
90 95 00 05
22
20
40
60
80
90 95 00 05
24
20
40
60
80
100
90 95 00 05
26
20
40
60
80
90 95 00 05
81
20
40
60
80
100
90 95 00 05
83
20
40
60
80
90 95 00 05
84
20
40
60
80
90 95 00 05
87
20
40
60
80
90 95 00 05
94
20
40
60
80
90 95 00 05
95
20
40
60
80
90 95 00 05
100
20
40
60
80
100
90 95 00 05
105
0
100
200
300
90 95 00 05
137
0
50
100
150
200
90 95 00 05
138
0
100
200
300
90 95 00 05
139
50
100
150
200
90 95 00 05
140
40
80
120
160
200
90 95 00 05
141
0
40
80
120
160
90 95 00 05
142
20
40
60
80
100
90 95 00 05
143
20
40
60
80
100
90 95 00 05
144
40
80
120
160
90 95 00 05
145
40
80
120
160
90 95 00 05
146
20
40
60
80
100
120
90 95 00 05
147
40
80
120
160
90 95 00 05
148
50
100
150
200
250
90 95 00 05
149
40
80
120
160
90 95 00 05
150
0
40
80
120
90 95 00 05
151
0
40
80
120
160
90 95 00 05
152
40
60
80
100
120
90 95 00 05
153
0
20
40
60
80
90 95 00 05
154
20
40
60
80
100
90 95 00 05
155
20
40
60
80
100
90 95 00 05
156
20
40
60
80
90 95 00 05
157
20
40
60
80
90 95 00 05
158
20
40
60
80
100
90 95 00 05
159
20
40
60
80
90 95 00 05
160
20
40
60
80
100
120
90 95 00 05
161
40
80
120
160
90 95 00 05
162
20
40
60
80
100
120
90 95 00 05
163
0
20
40
60
80
90 95 00 05
164
20
40
60
80
100
120
90 95 00 05
165
0
20
40
60
90 95 00 05
166
20
40
60
80
100
90 95 00 05
167
20
40
60
80
100
90 95 00 05
168
20
40
60
80
100
90 95 00 05
169
20
40
60
80
100
90 95 00 05
170
20
40
60
80
90 95 00 05
171
20
40
60
80
90 95 00 05
172
20
40
60
80
100
90 95 00 05
173
0
20
40
60
90 95 00 05
177
20
40
60
80
90 95 00 05
178
20
40
60
80
100
90 95 00 05
182
20
40
60
80
100
90 95 00 05
191
20
40
60
80
100
90 95 00 05
192
20
40
60
80
90 95 00 05
193
20
40
60
80
90 95 00 05
194
0
10
20
30
40
90 95 00 05
195
20
30
40
50
60
90 95 00 05
196
20
40
60
80
90 95 00 05
200
20
40
60
80
90 95 00 05
203
20
40
60
80
90 95 00 05
204
20
40
60
80
90 95 00 05
206
20
40
60
80
90 95 00 05
RP P _S TA R_NORM
207
井上・清水・中神 (2009)
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
3. 賃貸料市場の変化 - マイクロストラクチャ -
1. Housing rents account for more than one fourth of personal spending
2. Housing rents are very, very sticky– The probability of no change in housing rents is
about 29 percent per year (Genesove 2003)
3. The most important link between asset prices and goods & services prices is the one through housing rents (Goodhart 2001)
Expenditures for housing services: 26.3% Housing rents: 5.8% Imputed rents from owner occupied housing: 18.6% Housing maintenance and others: 1.9% “Consumer Price Index (CPI) in Tokyo, 2005”
1.0
1.5
2.0
2.5
3.0
3.5
QT
1986
/1
QT
1988
/1
QT
1990
/1
QT
1992
/1
QT
1994
/1
QT
1996
/1
QT
1998
/1
QT
2000
/1
QT
2002
/1
QT
2004
/1
QT
2006
/1
CPI rentSelling price index
19Chihiro SHIMIZU 2009 [email protected]
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Comparison House Price and CPI-house in Tokyo
20
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
1986
0119
8607
1987
0119
8707
1988
0119
8807
1989
0119
8907
1990
0119
9007
1991
0119
9107
1992
0119
9207
1993
0119
9307
1994
0119
9407
1995
0119
9507
1996
0119
9607
1997
0119
9707
1998
0119
9807
1999
0119
9907
2000
0120
0007
2001
0120
0107
2002
0120
0207
2003
0120
0307
2004
0120
0407
2005
0120
0507
2006
0120
0607
2007
0120
0707
2008
0120
0807
Tokyo_Condo
Tokyo_SingleHouse
Tokyo_SingleHouse_Rent
Tokyo_CPI
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Comparison House Price and CPI-house in Tokyo , LA, NY
21
0.000
0.500
1.000
1.500
2.000
2.500
3.000
3.500
4.000
4.500
5.000
1986
0319
8609
1987
0319
8709
1988
0319
8809
1989
0319
8909
1990
0319
9009
1991
0319
9109
1992
0319
9209
1993
0319
9309
1994
0319
9409
1995
0319
9509
1996
0319
9609
1997
0319
9709
1998
0319
9809
1999
0319
9909
2000
0320
0009
2001
0320
0109
2002
0320
0209
2003
0320
0309
2004
0320
0409
2005
0320
0509
2006
0320
0609
2007
0320
0709
2008
0320
0809
Tokyo_SingleHouse
LosAngeles_HP
NewYork_HP
Tokyo_CPI
LosAngeles_CPI
NewYork_CPI
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
New Rent versus CPI Rent
1
1.05
1.1
1.15
1.2
1.25
1.3
1.35
1.4
QT
1986
/4
QT
1987
/4
QT
1988
/4
QT
1989
/4
QT
1990
/4
QT
1991
/4
QT
1992
/4
QT
1993
/4
QT
1994
/4
QT
1995
/4
QT
1996
/4
QT
1997
/4
QT
1998
/4
QT
1999
/4
QT
2000
/4
QT
2001
/4
QT
2002
/4
QT
2003
/4
QT
2004
/4
QT
2005
/4
QT
2006
/4
Hedonic rent
CPI rentInde
x:19
86/1
st q
uart
er=
1
22
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Frequency of Rent Adjustments
)1(Pr)1|0(Pr
)1(Pr)1|0(Pr
)1(Pr)1(Pr1)0(Pr
Rit
Ritit
Nit
Nitit
Rit
Nitit
IIR
IIR
IIR
1 ititit RRR
23
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
State-Dependent or Time-Dependent Pricing:Caballero-Engel’s definition of price flexibility
dxxhxxdxxhxR
xXRxRRX
R
t
t
itit
ititit
ittit
t
)()(' )()( loglim
)|0Pr()( loglog
log
0
*1
*
)|1Pr(),1|0Pr(
)|1Pr(),1|0Pr( )(
xXIxXIR
xXIxXIRx
itRitit
Ritit
itNitit
Nitit
Intensive marginExtensive marginCaballero-Engel’smeasure of price flexibility
Caballero-Engel(1993):Adjustment Hazard
Caballero-Engel(2007)
24
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Adjustment Hazard Functions
0.010 0.010 0.010 0.010
0.042 0.042 0.042 0.042
0.736 0.680 0.688 0.719
0.000 0.009 0.038 0.091
0.008 0.007 0.008 0.011
0.082 0.312 0.348 0.161
]0.0,2.0(x ]2.0,0.0(x ]4.0,2.0(x]2.0,4.0( x
)|1Pr( xXI itNit
)|1Pr( xXI itRit
),1|0Pr( xXIR itRitit
),1|0Pr( xXIR itNitit
)(x
)(xh
0084.0)()( dxxhx
0013.0)()(' dxxhxx
Intensive margin:
Extensive margin:
Caballero-Engel’s measure of price flexibility
0097.0loglim0
t
tRt
25
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.26
Micro-Macro Consistency : Calvo Parameter
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
1.45
QT
1986
/1
QT
1988
/1
QT
1990
/1
QT
1992
/1
QT
1994
/1
QT
1996
/1
QT
1998
/1
QT
2000
/1
QT
2002
/1
QT
2004
/1
QT
2006
/1
R^{*}RCPI rent
diRR
diRRRRR
x
itt
itt
ttt
**
*010
0
log
log )1(
)(
rent Hedonic
rent CPI*
t
t
R
R
)004.0..( 032.00 es Micro estimate: 0.975Macro estimate: 0.968
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
4. キャップレートの決定構造
• Jorgenson’s Theorem : Capital, Investment Behavior
• Neoclassical theory is the response of the demand for capital to changes in relative factor prices or the ratio of factor prices to the price of output.
• User cost / Yield
• Capital Depreciation Problem
• Jorgenson, D. W. (1963), "Capital Theory and Investment Behavior," American Economic Review, 53, pp.247-259.
• Hayashi, F., and T. Inoue (1991), “The relation between firm growth and Q with multiple capital goods: Theory and evidence from panel data on Japanese firms,” Econometrica, pp.731–753.
Chihiro SHIMIZU 2009 [email protected] 27
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Stock Market vs. Asset Market
2.00%
2.50%
3.00%
3.50%
4.00%
4.50%
5.00%20
05/0
1
2005
/03
2005
/05
2005
/07
2005
/09
2005
/11
2006
/01
2006
/03
2006
/05
2006
/07
2006
/09
2006
/11
2007
/01
2007
/03
2007
/05
2007
/07
2007
/09
2007
/11
2008
/01
2008
/03
2008
/05
2008
/07
2008
/09
2008
/11
Stock CR
Real CR
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Trends in Office Market:
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.420
02:Q
120
02:Q
220
02:Q
320
02:Q
420
03:Q
120
03:Q
220
03:Q
320
03:Q
420
04:Q
120
04:Q
220
04:Q
320
04:Q
420
05:Q
120
05:Q
220
05:Q
320
05:Q
420
06:Q
120
06:Q
220
06:Q
320
06:Q
420
07:Q
120
07:Q
220
07:Q
320
07:Q
420
08:Q
120
08:Q
220
08:Q
320
08:Q
4
NOI
Capital Value/m2
NewRent
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
• Neoclassical theory is the response of the demand for capital to changes in relative factor prices or the ratio of factor prices to the price of output.
• User cost / Yield
• Capital Depreciation Problem
• Jorgenson, D. W. (1963), "Capital Theory and Investment Behavior," American Economic Review, 53, pp.247-259.
• Hayashi, F., and T. Inoue (1991), “The relation between firm growth and Q with multiple capital goods: Theory and evidence from panel data on Japanese firms,” Econometrica, pp.731–753.
Chihiro SHIMIZU 2009 [email protected] 30
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Stock Market vs. Asset Market
2.00%
2.50%
3.00%
3.50%
4.00%
4.50%
5.00%20
05/0
1
2005
/03
2005
/05
2005
/07
2005
/09
2005
/11
2006
/01
2006
/03
2006
/05
2006
/07
2006
/09
2006
/11
2007
/01
2007
/03
2007
/05
2007
/07
2007
/09
2007
/11
2008
/01
2008
/03
2008
/05
2008
/07
2008
/09
2008
/11
Stock CR
Real CR
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Trends in Office Market:
0.6
0.7
0.8
0.9
1
1.1
1.2
1.3
1.420
02:Q
120
02:Q
220
02:Q
320
02:Q
420
03:Q
120
03:Q
220
03:Q
320
03:Q
420
04:Q
120
04:Q
220
04:Q
320
04:Q
420
05:Q
120
05:Q
220
05:Q
320
05:Q
420
06:Q
120
06:Q
220
06:Q
320
06:Q
420
07:Q
120
07:Q
220
07:Q
320
07:Q
420
08:Q
120
08:Q
220
08:Q
320
08:Q
4
NOI
Capital Value/m2
NewRent
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Gordon’s Growth Model : Present Value, Discount Rate
• Yit : Net Operating Income• Rf :Risk Free Rate• Rp : Risk Premium• G : Growth in Real Rent• Gordon,M.J and E.Shapro,(1956), Capital Equioment Analysis: The Required Rate of Profit. Management
Science, Vol.3,pp.102-110.• Gordon,M.J,(1959), Dividends, Earnings and Stock Prices. Review of Statistics and Economics, Vol.41,
pp.99-105.
33Chihiro SHIMIZU 2009 [email protected]
it
itpift
pift
itit p
yGRR
GRRy
p
ξ),( ip zLfR • L : Liquidity Risk• ξ : Unexpected Risk
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Estimation Model
j
itijitjxy 10 exp
j
itijitjxp 20 exp
NOI Model
Capital Value Model
)(ln)()ln()ln( 2100 ititijj
jjitit xpy
ij
it
ij
itjj x
pxy
lnln
lnln
)(
Yield or Capitalization Rate :
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Estimation Results : See Table3
Transaction Model
Cap Rate Model α-β α:NOI Model β:Capital Value Model
Age 0.033 0.033 -0.042 -0.075Age × residential1 0.010 0.010 0.017 0.007Age × residential2 0.000 0.001 0.015 0.014
Area -0.046 -0.047 0.034 0.081Area × residential1
0.037 0.037 -0.044 -0.082
Area × residential2
0.026 0.026 0.007 -0.018
TS -0.034 -0.035 -0.069 -0.035TS × residential1 -0.002 -0.002 -0.024 -0.021TS × residential2 0.025 0.026 0.082 0.056
TT 0.100 0.099 -0.087 -0.186TT × residential1 -0.119 -0.117 0.041 0.158TT × residential2 0.061 0.062 0.004 -0.058
Adj. R-squared: 0.358 - 0.574 0.683Number of Obs.: 1,173
Unbalanced Panel Data : Pooling Data Analysis
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Depreciation Effect on Capitalization Rate
Effects on Cap Rate (Transaction-based)
1.00
1.05
1.10
1.15
1.20
0 5 10 15 20 25 30 35 40
OfficeResidential (Family)Residential (Single)
1.00
1.05
1.10
1.15
1.20
0 5 10 15 20 25 30 35 40
OfficeResidential (Family)Residential (Single)
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Depreciation Effect on NOI and Capital Value
Effects on NOI (Transaction-point)
Effects on Price (Transaction-based)
0.75
0.80
0.85
0.90
0.95
1.00
0 5 10 15 20 25 30 35 40
OfficeResidential (Family)Residential (Single)
0.75
0.80
0.85
0.90
0.95
1.00
0 5 10 15 20 25 30 35 40
0.60
0.70
0.80
0.90
1.00
0 5 10 15 20 25 30 35 40
OfficeResidential (Family)Residential (Single)
0.60
0.70
0.80
0.90
1.00
0 5 10 15 20 25 30 35 40
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Quantity Effect on Capitalization Rate
Effects on Cap Rate (Transaction-based)
0.60
0.70
0.80
0.90
1.00
1.10
1.20
1.30
1.40
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
OfficeResidential (Family)Residential (Single)
0.60
0.70
0.80
0.90
1.00
1.10
1.20
1.30
1.40
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
OfficeResidential (Family)Residential (Single)
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Quantity Effect on NOI and Capital Value
Effects on NOI (Transaction-point)
Effects on Price (Transaction-based)
0.90
0.95
1.00
1.05
1.10
1.15
1.20
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
OfficeResidential (Family)Residential (Single)
0.90
0.95
1.00
1.05
1.10
1.15
1.20
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
OfficeResidential (Family)Residential (Single)
0.80
0.90
1.00
1.10
1.20
1.30
1.40
1.50
1.60
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
OfficeResidential (Family)Residential (Single)
0.80
0.90
1.00
1.10
1.20
1.30
1.40
1.50
1.60
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
OfficeResidential (Family)Residential (Single)
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Accessibility Effect on Capitalization Rate:Distance to Central Areas
Effects on Cap Rate (Transaction-based)
0.90
1.10
1.30
1.50
1.70
1.90
2.10
2.30
2.50
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
OfficeResidential (Family)Residential (Single)
0.90
1.10
1.30
1.50
1.70
1.90
2.10
2.30
2.50
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
OfficeResidential (Family)Residential (Single)
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Accessibility Effect on NOI and Capital Value:Distance to Central Areas
Effects on NOI (Transaction-point)
Effects on Price (Transaction-based)
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
OfficeResidential (Family)Residential (Single)
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
OfficeResidential (Family)Residential (Single)
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
OfficeResidential (Family)Residential (Single)
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000
OfficeResidential (Family)Residential (Single)
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Comparison: Transaction-based vs. Appraisal-based Depreciation Effect on Capitalization Rate
Effects on Cap Rate (Transaction-based) Effects on Cap Rate (Appraisal-based)
1.00
1.05
1.10
1.15
1.20
0 5 10 15 20 25 30 35 40
OfficeResidential (Family)Residential (Single)
1.00
1.05
1.10
1.15
1.20
0 5 10 15 20 25 30 35 40
OfficeResidential (Family)Residential (Single)
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
What is Capitalization Rate?• Conclusions;• Capitalization Rate is explained by
• Link between asset prices and stock prices in Real Estate Investment Market.
• Client Pressure Problems in Appraisal Process
ij
it
ij
itjj x
pxy
lnln
lnln
)(
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
5. 社会構造の変化と不動産価格
• 米国での議論 :
• Baby Boom, Baby Burst and Housing Market!
• 米国の住宅価格は,1987年~ 2007 年まで47%下落する !!!
• →社会構造の変化と不動産市場
Chihiro SHIMIZU 2009 [email protected] 44
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.45
出生者数の推移 (JPN)
0
50
100
150
200
250
300
1920
1925
1930
1935
1940
1945
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
(Ten thousands)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Number of Live Births [Left Scale]Total Fertality Rates [Right Scale]
Baby boom(1947- 49)
Echo baby boom(1971- 73)
Baby bust(1955- 60)
Source: Ministry of Health, Labor and Welfare
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.46
出生者数の推移 (U.S.)
Source: National Center for Health Statistics, "National Vital Statistics Reports."
0
50
100
150
200
250
300
350
400
450
500
1920
1925
1930
1935
1940
1945
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
(Ten thousands)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0Number of Live Births [Left Scale]Total Fertality Rates [Right Scale]
Baby boom1946 1960( ~ )
Baby bust1965- 79( )
Echo baby boom1980 90's( ~ )
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.47
3-4(a). 年齢別人口 30-44 (JPN)
Source: Ministry of Internal Affairs and Communications Statistics Bureau
0
500
1,000
1,500
2,000
2,500
3,000
3,500
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
(Ten thousands)
Age 40- 44
Age 35- 39
Age 30- 34
Baby boomer1947 1949( ~ ) Echo baby boomer
1971 1973( ~ )
Baby buster1955- 1960( )
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.48
3-4(b). 年齢別人口 30-44 (U.S.)
Source: U.S. Bureau of Census, "Population Estimates."
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
2010
(Ten thousands)
Age 40- 44
Age 35- 39
Age 30- 34
Baby boomer1946 1960( ~ )
Baby buster1965- 1979( ) Echo baby boomer
1980 90's( ~ )
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Mankiw=Weil (1989) 住宅需要指標
49
• The aggregate amount of housing demand for the specific age of each household member using the housing demand by household, and they created ;
(1)Dj is the amount of housing demand for the jth member in the household, and N is the number of household members.
(2)• Dummy 0 is the dummy variable, and when age = 0, it becomes 1. Combining formulas (1) and (2) above results in formula (3).
• (3) • the amount of housing demand i for each age (age i) was estimated
N
j jDD1
iij DummyDummyDummyD 10 10
ijijj DummyDummyDummyD 10 10
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Estimation Method of House Demand by Home ownership rates
50
• Home ownership demand:• we hypothesize that the increase in this rate is equivalent to the
ownership demand occurring in that age group•
Dj,t : home ownership demand for j cohort over t period Oj,t : ownership rate for j cohort over t period Nj,t : population of j cohort over t period
• Mankiw and Weil (1989) pointed out that there were no significant differences in the final housing prediction model estimates whether using adult population data or an estimated housing demand index based on individual data.
tjtjtjtj NOOD ,1,,,
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.51
住宅需要の推計 (JPN & U.S.)
Source: Mankiw and Weil (1989), Ootake and Shintani(1994)
4,800
5,200
5,600
6,000
6,400
6,800
0
2,000
4,000
6,000
8,000
10,000
12,000
1970 1975 1980 1985 1990 1995 2000 2005
MW Index (J PN)Population-based index (J PN.)
4,000
6,000
8,000
10,000
12,000
0
400
800
1,200
1,600
2,000
1970 1975 1980 1985 1990 1995 2000 2005
MW Index (U.S.)Population-based index (U.S.)
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Sample Selection Bias in Published Land Price•
• 加重平均地価指数 : • 同一地点の対前年比を加重平均
• 調査地点の選定替えの実施 :• 変動率で整合性が取れなく
なった地点の入れ替え• 変動率のラグ→ Shimizu
and Nishimura(2004)(2006)
52
Year Nt:Number ofSamples in tperiod
Nt-1:Number ofSamples in tperiod Nt/Nt-1
1975 9,346 8,800 0.9421976 9,338 8,772 0.9391977 9,183 8,640 0.9411978 9,442 8,498 0.9001979 10,322 7,751 0.7511980 10,867 8,596 0.7911981 11,146 9,098 0.8161982 11,339 8,112 0.7151983 10,782 4,904 0.4551984 10,782 10,219 0.9481985 10,774 10,267 0.9531986 10,557 10,123 0.9591987 10,591 10,119 0.9551988 10,821 10,227 0.9451989 10,868 10,421 0.9591990 10,901 10,441 0.9581991 10,906 10,442 0.9571992 11,223 10,394 0.9261993 13,519 10,435 0.7721994 17,966 12,984 0.7231995 19,947 17,480 0.8761996 19,948 19,722 0.9891997 19,919 19,562 0.9821998 20,143 19,683 0.9771999 20,249 19,933 0.9842000 20,445 20,023 0.9792001 20,447 20,144 0.9852002 20,544 20,192 0.9832003 20,630 20,295 0.9842004 20,618 20,383 0.9892005 20,274 20,027 0.9882006 20,273 20,035 0.9882007 19,426 19,300 0.9942008 18,834 18,719 0.994
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Estimation Results of Hedonic Function
53
No Prefecture area rw ts tt gesui sui gas UX UY UXX UYY cp1 cp3 cp6 cp7 tm Number ofSamples
adjustedR2
1 Hokkaido -1.180 0.179 -0.051 -0.409 0.288 0.004 0.415 -6.221 -24.091 0.022 0.276 -0.108 -0.275 0.878 -0.951 Yes 24,565 0.8142 Aomori -1.245 0.418 -0.038 -0.236 0.187 0.325 0.441 0.360 -0.919 - - -0.216 -0.034 0.339 -0.732 Yes 4,965 0.8353 Iwate -1.149 0.016 0.035 -0.321 0.217 0.086 0.256 0.530 -63.865 - 0.805 -0.117 -0.010 - -0.589 Yes 3,153 0.8274 Miyagi -1.108 0.237 -0.112 -0.365 0.143 0.232 0.180 0.284 0.040 - - -0.079 -0.037 - -0.592 Yes 10,350 0.8955 Akita -1.157 0.150 -0.074 -0.396 0.230 0.199 0.196 0.574 -151.022 - 1.896 -0.054 0.506 - -0.776 Yes 3,300 0.8676 Yamagata -1.259 0.234 -0.054 -0.342 0.220 - 0.303 -0.177 -42.895 - 0.560 -0.056 - - -0.691 Yes 3,126 0.8677 Fukushima -1.135 0.135 -0.028 -0.193 0.165 0.398 0.257 0.050 -80.239 - 1.076 -0.001 0.139 -0.387 -0.839 Yes 8,462 0.8518 Ibaragi -1.216 0.181 -0.111 -0.140 0.206 0.148 0.330 -0.428 -0.274 - - -0.091 - 0.325 -0.710 Yes 14,836 0.8779 Tochigi -1.257 0.221 -0.050 -0.244 0.151 0.312 0.235 -0.204 -0.651 - - -0.021 - - -0.369 Yes 8,752 0.87910 Gunma -1.142 0.279 -0.062 -0.063 0.271 0.258 0.179 -0.264 -0.168 - - 0.008 - - -0.455 Yes 7,188 0.83911 Saitama -1.039 0.145 -0.148 -0.173 0.109 -0.100 0.110 0.231 -2.411 - - -0.026 0.244 0.301 -0.599 Yes 28,425 0.94312 Chiba -1.096 0.120 -0.168 -0.283 0.077 0.307 0.261 -1.646 1.430 - - -0.055 -0.042 - 0.074 Yes 27,654 0.86413 Tokyo -0.871 0.144 -0.125 -0.724 0.086 0.089 0.186 -0.578 -0.560 - -0.001 0.012 0.135 -0.349 -3.136 Yes 50,333 0.92314 Kanagawa -0.919 0.096 -0.107 -0.038 0.040 0.024 0.122 0.782 0.947 - - -0.008 -0.054 -0.195 -0.589 Yes 41,470 0.93115 Niigata -1.309 0.369 -0.131 -0.331 0.112 -0.082 0.316 -0.418 -11.409 - 0.150 -0.108 - - -0.797 Yes 7,386 0.85116 Toyama -1.080 0.242 -0.062 -0.179 0.294 0.103 0.231 -0.583 1.121 - - -0.110 - - -0.325 Yes 3,941 0.83917 Ishikawa -1.170 0.175 -0.057 -0.414 0.157 0.212 0.146 -0.604 -165.220 - 2.257 0.026 - -0.044 -0.407 Yes 3,903 0.84918 Fukui -1.057 0.168 -0.129 -0.318 0.070 -0.106 0.124 -0.335 -0.935 - - -0.020 - - -1.107 Yes 2,090 0.85819 Yamanashi -1.143 0.215 -0.039 -0.155 0.028 0.335 0.158 1.087 0.627 - - -0.017 - -0.078 -0.349 Yes 2,774 0.93620 Nagano -1.370 0.113 -0.062 -0.281 0.330 0.812 0.142 -0.122 90.043 - -1.247 -0.085 0.460 0.202 -0.424 Yes 5,125 0.84421 Gifu -1.062 0.204 -0.047 -0.213 0.193 0.010 0.154 -0.323 0.771 - - -0.052 0.066 0.129 -0.559 Yes 6,207 0.89322 Shizuoka -1.154 0.116 -0.070 -0.125 0.131 -0.082 0.258 0.080 0.118 - - -0.058 0.159 - -0.455 Yes 13,103 0.88223 Aichi -1.013 0.267 -0.066 -0.417 0.146 -0.051 0.131 0.623 -0.449 - - -0.033 -0.141 -0.314 -0.383 Yes 33,687 0.93124 Mie -1.116 0.329 -0.071 -0.040 0.060 0.155 0.318 0.212 -59.555 - 0.853 -0.032 -0.016 - -0.635 Yes 8,298 0.88525 Shiga -1.310 0.395 -0.074 -0.158 0.111 0.092 0.268 -1.340 0.532 - - -0.085 -0.198 -0.490 -0.669 Yes 5,431 0.91026 Kyouto -0.994 0.270 -0.080 -0.318 0.134 0.830 0.241 1.174 1.080 - - 0.004 -0.165 -0.188 -1.101 Yes 12,672 0.93227 Oosaka -0.965 0.221 -0.129 -0.342 0.083 -0.003 0.186 0.009 0.691 - - -0.031 0.040 - -0.847 Yes 34,854 0.94428 Hyougo -0.983 0.201 -0.123 -0.062 0.077 0.106 0.380 0.911 -70.375 - 1.000 -0.110 -0.020 - -0.930 Yes 25,911 0.87529 Nara -1.060 0.255 -0.133 -0.049 0.054 0.387 0.247 -1.254 2.115 - - -0.100 - -0.572 -0.342 Yes 8,399 0.89930 Wakayama -1.094 0.168 0.014 -0.158 0.151 0.205 0.156 0.032 -0.225 - - -0.022 -0.171 - -0.413 Yes 3,049 0.87831 Tottori -1.199 0.561 -0.182 -0.177 0.103 -0.670 0.158 -0.298 -0.233 - - -0.063 -0.102 - -0.941 Yes 1,946 0.85932 Shimane -1.054 0.133 -0.094 -0.290 0.089 - 0.228 -0.680 -50.651 - 0.724 -0.038 - - -0.656 Yes 2,215 0.79033 Okayama -1.232 0.156 -0.086 -0.266 0.110 0.079 0.236 -0.444 -0.258 - - -0.110 - - -0.616 Yes 7,396 0.88034 Hiroshima -1.083 0.197 -0.103 -0.329 0.114 0.237 0.279 0.265 -0.905 - - 0.002 0.097 - -0.729 Yes 12,160 0.85535 Yamaguchi -1.143 0.146 -0.048 -0.010 0.022 0.366 0.402 0.330 0.399 - - -0.058 - - -1.079 Yes 5,714 0.81736 Tokushima -1.018 0.117 -0.037 -0.303 0.062 0.065 0.237 -0.573 -0.327 - - 0.090 - - -0.063 Yes 2,518 0.92937 Kagawa -1.142 0.247 -0.163 -0.264 0.011 0.254 0.256 -0.626 0.088 - - -0.097 - - -0.462 Yes 2,866 0.91338 Ehime -1.240 0.306 -0.077 -0.208 0.180 -0.125 0.251 0.307 34.366 - -0.518 -0.108 -0.263 - -0.498 Yes 4,405 0.86139 Kouchi -1.232 0.284 -0.038 -0.272 0.218 0.176 0.195 0.304 34.104 - -0.527 0.051 - - -0.292 Yes 2,644 0.89040 Fukuoka -0.992 0.157 -0.072 -0.434 0.280 0.182 0.232 0.611 -55.140 - 0.821 -0.035 0.635 0.039 -0.954 Yes 17,948 0.87041 Saga -1.294 0.153 -0.062 -0.186 0.094 0.096 0.200 0.243 1.040 - - -0.120 - -0.181 -0.774 Yes 2,001 0.88742 Nagasaki -0.919 0.685 -0.006 -0.211 0.101 0.143 0.374 -0.298 -9.789 - 0.148 -0.012 - -0.384 -0.831 Yes 4,922 0.81543 Kumamoto -1.222 0.311 -0.033 -0.265 0.116 0.199 0.315 -0.147 -0.607 - - -0.009 - - -0.349 Yes 5,186 0.90144 Ooita -1.139 0.418 -0.097 -0.232 0.106 0.695 0.254 -0.640 -0.765 - - -0.085 - - -0.040 Yes 4,496 0.85645 Miyazaki -1.168 0.125 -0.057 -0.273 0.259 0.140 0.244 0.634 -52.347 - 0.816 -0.038 - - -0.520 Yes 4,397 0.89646 Kagoshima -1.122 0.108 -0.092 -0.384 0.095 - 0.330 -0.264 1.593 - -0.028 -0.064 - - -0.590 Yes 4,799 0.88547 Okinawa -1.225 0.318 0.015 -0.367 0.112 -0.067 0.006 -2.586 48.458 - -0.873 -0.043 0.070 - -1.261 Yes 3,348 0.929
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.54
3-1(a). Real House prices by prefectures (JPN)
Source: Ministry of Land, Infrastructure, Transport and Tourism “Published Land Prices”
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,00019
7519
7619
7719
7819
7919
8019
8119
8219
8319
8419
8519
8619
8719
8819
8919
9019
9119
9219
9319
9419
9519
9619
9719
9819
9920
0020
0120
0220
0320
0420
0520
0620
0720
08
Hokkaido Aomori
Iwate Miyagi
Akita Yamagata
Fukushima Ibaragi
Tochigi Gunma
Saitama Chiba
Tokyo Kanagawa
Niigata Toyama
Ishikawa Fukui
Yamanashi Nagano
Gifu Shizuoka
Aichi Mie
Shiga Kyouto
Oosaka Hyougo
Nara Wakayama
Tottori Shimane
Okayama Hiroshima
Yamaguchi Tokushima
Kagawa Ehime
Kouchi Fukuoka
Saga Nagasaki
Kumamoto Ooita
Miyazaki Kagoshima
Okinawa
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.55
3-1(b). Real House prices by states (U.S.)
Source: Office of Federal Housing Enterprise Oversight, “House Price Index”,U.S. Census of Bureau, “Census of Housing: Median home value.”
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,00019
7519
7619
7719
7819
7919
8019
8119
8219
8319
8419
8519
8619
8719
8819
8919
9019
9119
9219
9319
9419
9519
9619
9719
9819
9920
0020
0120
0220
0320
0420
0520
0620
0720
08
AL AK
AZ AR
CA CO
CT DE
DC FL
GA HI
ID IL
IN IA
KS KY
LA ME
MD MA
MI MN
MS MO
MT NE
NV NH
NJ NM
NY NC
ND OH
OK OR
PA RI
SC SD
TN TX
UT VT
VA WA
WV WI
WY
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
3.2.Gini's coefficient : Comparison between Japan and US
56
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
0.45019
7519
7619
7719
7819
7919
8019
8119
8219
8319
8419
8519
8619
8719
8819
8919
9019
9119
9219
9319
9419
9519
9619
9719
9819
9920
0020
0120
0220
0320
0420
0520
0620
0720
08
Japan
USA
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Cluster Classification in Japan by Appreciation Rate of Land Price
57
-30
-20
-10
0
10
20
30
40
50
60
70
80
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Cluster1
Cluster2
Cluster3
Cluster4
Cluster5
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Cluster Classification in US by Appreciation Rate of House Price
58
-10
-5
0
5
10
15
20
25
30
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Cluster1
Cluster2
Cluster3
Cluster4
Cluster5
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.59
House Demand by prefectures (JPN)
Source: Ministry of Internal Affairs and Communications Statistics Bureau, “Census"
0
500,000
1,000,000
1,500,000
2,000,000
2,500,00019
7519
7619
7719
7819
7919
8019
8119
8219
8319
8419
8519
8619
8719
8819
8919
9019
9119
9219
9319
9419
9519
9619
9719
9819
9920
0020
0120
0220
0320
0420
0520
0620
0720
08
Hokkaido Aomori
Iwate Miyagi
Akita Yamagata
Fukushima Ibaragi
Tochigi Gunma
Saitama Chiba
Tokyo Kanagawa
Niigata Toyama
Ishikawa Fukui
Yamanashi Nagano
Gifu Shizuoka
Aichi Mie
Shiga Kyouto
Oosaka Hyougo
Nara Wakayama
Tottori Shimane
Okayama Hiroshima
Yamaguchi Tokushima
Kagawa Ehime
Kouchi Fukuoka
Saga Nagasaki
Kumamoto Ooita
Miyazaki Kagoshima
Okinawa
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.60
House Demand by states (U.S.)
Source: U.S. Census of Bureau, “Population Estimates.”
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,00019
7519
7619
7719
7819
7919
8019
8119
8219
8319
8419
8519
8619
8719
8819
8919
9019
9119
9219
9319
9419
9519
9619
9719
9819
9920
0020
0120
0220
0320
0420
0520
0620
0720
08
AL AK
AZ AR
CA CO
CT DE
DC FL
GA HI
ID IL
IN IA
KS KY
LA ME
MD MA
MI MN
MS MO
MT NE
NV NH
NJ NM
NY NC
ND OH
OK OR
PA RI
SC SD
TN TX
UT VT
VA WA
WV WI
WY
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.61
地域別 住宅需要 vs. 住宅価格 (JPN)
Source: Ministry of Land, Infrastructure, Transport and Tourism , Ministry of Internal Affairs and Communications Statistics Bureau
Aomori
0
50
100
150
200
250
300
1975 1980 1985 1990 1995 2000 20050
20
40
60
80
100
120Age 35-39 Age 40-44 LP Index Tokyo
0
500
1,000
1,500
2,000
2,500
1975 1980 1985 1990 1995 2000 20050
50
100
150
200
250
300Age 35-39 Age 40-44 LP Index
Osaka
0200400600800
1,0001,2001,4001,600
1975 1980 1985 1990 1995 2000 20050
50
100
150
200
250
300Age 35-39 Age 40-44 LP Index Yamaguchi
0
50
100
150
200
250
300
1975 1980 1985 1990 1995 2000 20050
20
40
60
80
100
120
140Age 35-39 Age 40-44 LP Index
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.62
地域別 住宅需要 vs. 住宅価格 (U.S.)
Source: U.S. Census of Bureau, “Population Estimates,” and OFHEO “House Price Index”.
California
01,0002,0003,0004,0005,0006,0007,0008,0009,000
1975 1980 1985 1990 1995 2000 20050
100
200
300
400
500
600
700Age 30-34 35-39 40-44 OFHEO HPI Florida
0500
1,0001,5002,0002,5003,0003,5004,000
1975 1980 1985 1990 1995 2000 2005050100150200250300350400450500
Age 30-34 35-39 40-44 OFHEO HPI
Michigan
0
500
1,000
1,500
2,000
2,500
3,000
1975 1980 1985 1990 1995 2000 20050
50
100
150
200
250
300
350Age 30-34 35-39 40-44 OFHEO HPI New York
0500
1,0001,5002,0002,5003,0003,5004,0004,5005,000
1975 1980 1985 1990 1995 2000 20050
100
200
300
400
500
600
700Age 30-34 35-39 40-44 OFHEO HPI
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
住宅需要 vs. 住宅価格
63
-50
0
50
100
150
200
-30 -20 -10 0 10 20 30 40 50
Ibaragi
Chiba
Kanagawa
Okinawa
ChibaTokyo
KanagawaKyouto
Oosaka
Hyougo
Cha
nge
rate
of
hous
e pr
ice:
%
Change rate of house dem and:%
(DM75-80 , LP75-80)
(DM80-85 , LP80-85)
(DM85-90 , LP85-90)
(DM90-95 , LP90-95)
(DM95-00 , LP95-00)
(DM00-05 , LP00-05)
(DM05-08 , LP05-08)
-20
0
20
40
60
80
100
120
140
-20 0 20 40 60 80 100
California
North DakotaOregon
Washington
Wyoming
Alabama
Arkansas
North Dakota
Nebraska
California
Delaware
North Dakota
Cha
nge
rate
of
hous
e pr
ice:
%
Change rate of house dem and:%
(DM75-80 , LP75-80)
(DM80-85 , LP80-85)
(DM85-90 , LP85-90)
(DM90-95 , LP90-95)
(DM95-00 , LP95-00)
(DM00-05 , LP00-05)
(DM05-08 , LP05-08)
-50
0
50
100
150
200
-5 0 5 10 15 20 25 30 35 40
Ibaragi
Chiba
Kanagawa
Okinawa
ChibaTokyo
KanagawaKyouto
Oosaka
Hyougo
Cha
nge
rate
of
hous
e pr
ice:
%
Change rate of house demand:%
(DM75-80, LP75-8 0)
(DM80-85, LP80-8 5)
(DM85-90, LP85-9 0)
(DM90-95, LP90-9 5)
(DM95-00, LP95-0 0)
(DM00-05, LP00-0 5)
(DM05-08, LP05-0 8)
-20
0
20
40
60
80
100
120
140
-10 0 10 20 30 40 50 60 70
California
North DakotaOregon
Washington
Wyoming
Alabama
Arkansas
North Dakota
Nebraska
California
Delaware
North Dakota
Cha
nge
rate
of
hous
e pr
ice:
%
Change rate of house dem and:%
(DM75-80, LP75-8 0)
(DM80-85, LP80-8 5)
(DM85-90, LP85-9 0)
(DM90-95, LP90-9 5)
(DM95-00, LP95-0 0)
(DM00-05, LP00-0 5)
(DM05-08, LP05-0 8)
Japan: 35- to 44-year-old population; U.S.: 30- to 44-year-old population
Mankiw House Demand Index
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
住宅需要 vs. 住宅価格
64
Owner Occupied Demand index
Bubble era:Owner Occupied Demand index
-50
0
50
100
150
200
-10 -5 0 5 10 15 20 25 30
Ibaragi
Chiba
Kanagawa
Okinawa
Chiba
Tokyo
KanagawaKyouto
Oosaka
Hyougo
Cha
nge
rate
of
hous
e pr
ice:
%
Change rate of house demand:%
(DM75-80, LP75-80)
(DM80-85, LP80-85)
(DM85-90, LP85-90)
(DM90-95, LP90-95)
(DM95-00, LP95-00)
(DM00-05, LP00-05)
(DM05-08, LP05-08)
-20
0
20
40
60
80
100
120
140
-20 -10 0 10 20 30 40 50 60 70 80
California
North DakotaOregon
Washington
Wyoming
Alabama
Arkansas
North Dakota
Nebraska
California
Delaware
North Dakota
Cha
nge
rate
of
hous
e pr
ice:
%
Change rate of house dem and:%
(DM75-80 , LP75-80)
(DM80-85 , LP80-85)
(DM85-90 , LP85-90)
(DM90-95 , LP90-95)
(DM95-00 , LP95-00)
(DM00-05 , LP00-05)
(DM05-08 , LP05-08)
(DM75-80, LP75-80)
(DM80-85, LP80-85)
(DM85-90, LP85-90)
(DM90-95, LP90-95)
(DM95-00, LP95-00)
(DM00-05, LP00-05)
(DM05-08, LP05-08)
0
20
40
60
80
100
120
-20 -10 0 10 20 30 40 50
California
Delaware
North Dakota
OregonWyoming
Arkansas
Nebraska
Rhode Island
Cha
nge
rate
of
hous
e pr
ice:
%
Change rate of house demand:%
(DM_Contro l, LP00-05)
(DM00-05 , LP00 -05)
(LP9 5-00 , LP00-0 5)
-50
0
50
100
150
200
-10 -5 0 5 10 15 20 25 30
Ibaragi
ChibaKanagawa
Okinawa
Tokyo
Kanagawa
Oosaka
Hyougo
Cha
nge
rate
of
hous
e pr
ice:
%
Change rate of house demand:%
(DM_Contro l, LP85-90 )
(DM80-85, LP85-90)
(DM85-90, LP85-90)
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
需要要因が住宅価格に与える影響
Chihiro SHIMIZU 2009 [email protected] 65
grHousePriceIndex grHouseDemand grHousePriceIndex grHouseDemand
Constant 0.000 0.000 -0.001652 0.001226(0.0020) (0.0001) (0.0013) (0.0004)
grHPI(Lag1) 0.611 0.000 0.03407 -0.009078(0.0259) (0.0012) (0.0237) (0.0085)
grHPI(Lag2) -0.130 -0.005 0.066647 0.391596(0.0247) (0.0012) (0.0631) (0.0228)
grHD(Lag1) -2.994 0.850 0.066647 0.391596(0.6973) (0.0344) (0.0631) (0.0228)
grHD(Lag2) 4.274 0.048 -0.237661 0.269695(0.6712) (0.0331) (0.0620) (0.0224)
dRate 0.014 0.000 -0.011743 0.004687(0.0022) (0.0001) (0.0010) (0.0003)
grGDP 0.418 -0.001 0.353377 0.092434(0.0615) (0.0030) (0.0277) (0.0100)
F-statistic 196.877 1240.132 240.292 241.092 Log likelihood 1882.096 5983.415 2822.327 4375.800
Akaike AIC -2.751 -8.770 -3.680 -5.711
VAR Estimate : Japan VAR Estimate : USA
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
需要要因が住宅価格に与える影響 : 日本
Chihiro SHIMIZU 2009 [email protected] 66
-.02
.00
.02
.04
.06
.08
1 2 3 4 5 6 7 8 9 10
Response of House Price to House Price
-.02
.00
.02
.04
.06
.08
1 2 3 4 5 6 7 8 9 10
Response of House Price Housing Demand
-.001
.000
.001
.002
.003
.004
1 2 3 4 5 6 7 8 9 10
Response of Housing Demand to House Price
-.001
.000
.001
.002
.003
.004
1 2 3 4 5 6 7 8 9 10
Response of Housing Demand to Housing Demand
Response to Cholesky One S.D. Innovations ± 2 S.E.
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
需要要因が住宅価格に与える影響 :米国
Chihiro SHIMIZU 2009 [email protected] 67
-.01
.00
.01
.02
.03
.04
.05
1 2 3 4 5 6 7 8 9 10
Response of House Price to House Price
-.01
.00
.01
.02
.03
.04
.05
1 2 3 4 5 6 7 8 9 10
Response of House Price to Housing Demand
-.004
.000
.004
.008
.012
.016
1 2 3 4 5 6 7 8 9 10
Response of Housing Demand to House Price
-.004
.000
.004
.008
.012
.016
1 2 3 4 5 6 7 8 9 10
Response of Housing Demand to Housing Demand
Response to Cholesky One S.D. Innovations ± 2 S.E.
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
6. 公示地価への教訓
• 不動産価格は予測できるか→市場の効率性
• キャッシュフローは市場を適切に反映しているか ?
• キャップレートは,どのように決定されているのか ?
• 需要ショックは価格変動に影響を及ぼすか ?
• →どうしたらいいのか ???
Chihiro SHIMIZU 2009 [email protected] 68
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
時点修正課題への対応 :
• Valuation Error, Smoothing はなぜ起こるのか ?• 起こってはいけないのか ?
Chihiro SHIMIZU 2009 [email protected] 69
0.82
0.84
0.86
0.88
0.9
0.92
0.94
0.96
0.98
2001
01
2001
02
2001
03
2001
04
2001
05
2001
06
2001
07
2001
08
2001
09
2001
10
2001
11
2001
12
2002
01
2002
02
実績
Test01
Test02
Test03
Inde
x:20
00.0
1=1
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
住宅価格指数の比較
Chihiro SHIMIZU 2009 [email protected] 70
-0 .14
-0 .12
-0 .1
-0 .08
-0 .06
-0 .04
-0 .02
0
0.02
0.0419
9701
1997
0219
9703
1997
0419
9801
1998
0219
9803
1998
0419
9901
1999
0219
9903
1999
0420
0001
2000
0220
0003
2000
0420
0101
2001
0220
0103
2001
0420
0201
2002
0220
0203
2002
0420
0301
2003
0220
0303
2003
0420
0401
2004
0220
0403
2004
0420
0501
2005
0220
0503
2005
0420
0601
2006
0220
0603
2006
0420
0701
2007
0220
0703
2007
0420
0801
2008
0220
0803
2008
04
:リピートセールス指数ケースシラー型
:ヘドニック指数ハリファックス型
:RRPIヘドニック指数
:リピートセールス指数香港大学型
1997第一四半
期=0
:対
前期
変動
率
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
複数回サンプルの比較 :ヘドニック指数
Chihiro SHIMIZU 2009 [email protected] 71
-0.14
-0.12
-0.1
-0.08
-0.06
-0.04
-0.02
0
0.02
1997
0119
9702
1997
0319
9704
1998
0119
9802
1998
0319
9804
1999
0119
9902
1999
0319
9904
2000
0120
0002
2000
0320
0004
2001
0120
0102
2001
0320
0104
2002
0120
0202
2002
0320
0204
2003
0120
0302
2003
0320
0304
2004
0120
0402
2004
0320
0404
2005
0120
0502
2005
0320
0504
2006
0120
0602
2006
0320
0604
2007
0120
0702
2007
0320
0704
2008
0120
0802
2008
0320
0804
over1 over2
over3 over4
Traditional Repeat Sales
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
複数回サンプルの性質 1
Chihiro SHIMIZU 2009 [email protected] 72
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
複数回サンプルの性質 2
Chihiro SHIMIZU 2009 [email protected] 73
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
複数回サンプルの性質 3
Chihiro SHIMIZU 2009 [email protected] 74
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
地価回復が遅れた原因 -最有効使用 1: 機会損失が発生しているビル :2004 年
Chihiro SHIMIZU 2009 [email protected] 75
オフィスの分布 機会損失ビルの分布
Source) Shimizu and Karato(2010), Microstructure of Office Investment Market in Tokyo Metropolitan Area,(forthcoming)
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
地価回復が遅れた原因 -最有効使用 2: 機会損失が発生しているビルの特性
Source) Shimizu and Karato(2010), Microstructure of Office Investment Market in Tokyo Metropolitan Area,(forthcoming)
Chihiro SHIMIZU 2009 [email protected] 76
表 6. パネル・データの概要 年次 変数 単位 観測値の数 平均 標準偏差 最小 最大 1996 RR 円 40516 8399 2836 2837 26542
RC 円 40516 4720 765 3018 6451 100 万円 40516 −10.91 55.11 −2712.34 −0.01 - 40516 0.06 0.25 0 1 2607 −2.25 7.07 −153.73 −0.01 37909 −11.51 56.90 −2712.34 −0.01
2001 RR 円 40516 6402 2162 2163 20232 RC 円 40516 4808 779 3073 6570 100 万円 40516 −7.19 37.66 −1878.82 0.02 - 40516 0.09 0.28 0 1 3576 −1.44 4.21 −101.27 0.00 36940 −7.75 39.38 −1878.82 0.02
注. RR は再開発された用途での賃貸料,RC は再開発されなかった場合の賃貸料を示している.
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
地価回復が遅れた原因 -最有効使用 3: 再開発の確率
Source) Shimizu and Karato(2010), Microstructure of Office Investment Market in Tokyo Metropolitan Area,(forthcoming)
Chihiro SHIMIZU 2009 [email protected] 77
表 7. 再開発確率のプロビット推定
Note. ( )内は標準誤差を示している.被説明変数はその区画が再開発された場合 1,現状のままであれば 0 となる2 値変数である.はランダム効果を含む誤差構造の相関係数.Wald はすべてのパラメータが0 であるという帰無仮説に対する検定統計量(自由度 1 のカイ2乗分布にしたがう).[ ] は確率値を示す.
全サンプル 地域 1 地域 2 地域 3 0.3181 0.0576 0.4447 0.3407 (0.0093) (0.0058) (0.0250) (0.0219) Constant −13.5617 −5.7765 −9.3597 −9.7961 (0.4317) (0.1630) (0.5139) (0.6578) 10.5011 2.9883 7.6478 8.0016 (0.3327) (0.0903) (0.4046) (0.4998) 0.9910 0.8993 0.9832 0.9846 (0.0006) (0.0055) (0.0017) (0.0019) Number of obs. 81032 30110 19898 30468 Individual Number of groups 40516 15055 9949 15234
Wald (chi squared) 1160.1 [.000]
98.8 [.000]
315.3 [.000]
242.8 [.000]
Log likelihood −15071.5 −2567.9 −3792.0 −8043.3
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
Chihiro Shimizu: http://www.cs.reitaku-u.ac.jp/sm/shimizu/• 論文 :• Shimizu,C, K.G.Nishimura and T.Watanabe(2009), “House Prices and Rents in Tokyo - A Comparison of Repeat-sales and Hedonic
measures-,” 一橋大学物価研究センター Working Paper,No.41. Journal of Statistics and Economics (forthcoming)./Paper • Shimizu,C(2009), “Investment Characteristics of Housing Market -Focusing on the stickiness of housing rent-,” 麗澤大学経済社会総合研究
センター Working Paper,No.34.(Real Estate Investment, Nova Science Publishers, Inc. より出版予定 )./Paper • 清水千弘・渡辺努 (2009), 「日米における住宅価格の変動要因」 (伊藤隆敏編『アメリカ特集』所収,フィナンシャル・レ
ビュー 95号 / 財務省財務総合政策研究所 ./Paper • 谷下雅義・長谷川貴陽史・清水千弘 (2009) ,「景観規制が住宅価格に及ぼす影響 - 東京都世田谷区を対象としたヘドニック法に
よる検証 - 」計画行政 ,Vol.32,No.2, pp.71-79. • 清水千弘・渡辺努・西村清彦 (2009) 「住宅市場のマクロ変動と住宅賃料の粘着性」季刊住宅土地経済 ,No.72, pp.10-17. • 清水千弘 (2009) 「都市基盤整備財源としての受益者負担金制度の課題」計画行政第 32巻第 1号 ,pp.74-82. • 清水千弘 (2009) 「住宅賃料の粘着性の計測 - 住宅市場の変動とマクロ経済政策への応用 - 」麗澤経済研究,第17巻第1号 ,
pp.29-50. • Shimizu,C(2009), “Estimation of Hedonic Single-Family House Price Function Considering Neighborhood Effect Variables,” 東京大学空間
情報科学研究センター Discussion Paper, No.93./Paper • 原野 啓・中川雅之・清水千弘・唐渡広志 (2009) 「情報の非対称性下における住宅価格とリフォー ム」東京大学空間情報科学
研究センター Discussion Paper,No.94./Paper • Shimizu,C, K.G.Nishimura and T.Watanabe(2008), “Residential Rents and Price Rigidity: Micro Structure and Macro Consequences,” 一橋
大学物価研究センター Working Paper,No.29, revised 2009, RIETI Discussion Paper Series 09-E -044./Paper • 清水千弘 (2008), 「企業不動産戦略の経済学的意義 -外部性への配慮と企業の責任 - 」季刊不動産研究,第 50巻,第 2号 ,pp14-
23. • 清水千弘 (2008), 「ヘドニック住宅価格関数推定上の課題 -過少定式化バイアスへの対応 - 」資産評価政策学 ,第 10巻第 2号 (通
巻 17号 ),pp.56-61. • 清水千弘 (2008), 「近隣外部性を考慮したヘドニック住宅関数の推定」麗澤経済研究 ,第 16巻第 1号 , pp.29-44. • Shimizu,C and K.G.Nishimura(2007), “Pricing structure in Tokyo metropolitan land markets and its structural changes: pre-bubble, bubble,
and post-bubble periods,” Journal of Real Estate Finance and Economics, Vol.35,No.4,pp.495-496. • 清水千弘・唐渡広志 (2007), 「土地利用の非効率性の費用」住宅土地経済 , Vol.64(2007 年春季号 ) , pp.22-29. • 清水千弘・唐渡広志 (2007), 「住宅価格の非線形性」麗澤経済研究 ,第 15巻第 1号, pp.53-77. • Shimizu,C,K.G.Nishimura and K.Karato(2007), “Nonlinearity of Housing Price Structure -Secondhand Condominium Market in Tokyo
Metropolitan Area-,” 東京大学空間情報科学研究センター Discussion Paper,No.86, submitted to “Urban Studies"./Paper
Chihiro SHIMIZU 2009 [email protected] 78
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
• Shimizu, C., H.Takatsuji, H.Ono and K.G.Nishimura, (2007), “Change in house price structure with time and housing price index”, RIPESS (Reitaku Institute of Political Economics and Social Studies) Working Paper, No.25./Paper
• 原野啓・清水千弘・唐渡広志・中川雅之 (2007a) 「リピートセールス法による品質調整済住宅価格指数の推計」住宅土地経済No.65(2007 年夏季号 ),pp.12-19.
• 原野啓・清水千弘・唐渡広志・中川雅之 (2007b) 「わが国におけるリピートセールス法による住宅価格指数の推計課題」麗澤経済研究 ,第 15巻第 2号 ,pp.113-133.
• Shimizu,C and K.G.Nishimura (2006), “Biases in appraisal land price information: the case of Japan,” Journal of Property Investment & Finance, Vol.24, No.2 , pp. 150- 175.
• Shimizu, and H.Ono, (2006), “Incorporting Land Characteristics into Land Valuation for Reconstruction Areas”, RIPESS (Reitaku Institute of Political Economics and Social Studies) Working Paper, No.20./Paper
• Shimizu,C, K.G.Nishimura and Y.Asami(2004), “Search and Vacancy Costs in the Tokyo housing market: Attempt to measure social costs of imperfect information,” Regional and Urban Development Studies,Vol.16,No.3 , pp.210-230./Paper
• 学会報告 :• 清水千弘・川村康人 , 「既存住宅市場と住宅価格」 , 都市住宅学会 ( 名城大学 ),2009.11. • 清水千弘・川村康人 , 「不動産特性とキャップレート」 , 日本不動産学会 ,(豊橋技術科学大学 ), 2009.10. • 清水千弘・川村康人 , 「介護保険財源の地域負担構造」 , 日本計画行政学会 (香川大学 ),2009.9. • Shimizu,C, K.G.Nishimura,T.Watanabe and K.Karato,House, Price Index in Tokyo Special District,ISA International Housing Conference
2009,(The University of Glasgow's Department of Urban Studies ),2009.9. • Shimizu,C, K.G.Nishimura,T.Watanabe and K.Karato,House, Price Index in Tokyo Special District,SWET: Summer Workshop on Economic
Theory2009,2009.8. • Shimizu,C, K.G.Nishimura and T.Watanabe,House, House Prices and Rents in Tokyo - A Comparison of Repeat-sales and Hedonic
measures- ,United Nations, 2009 Ottawa Group Meeting(Neuchatel, Switzerland, 27-29 May 2009),2009.5. • Shimizu,C, and T.Watanabe,House,Housing Market Bubbles in Japan and the US,International Economy on U.S. Economy(Ministry of
Finance),2009.3. • Shimizu,C, K.G.Nishimura and T.Watanabe,Residential Rents and Price Rigidity-Micro Structure and Macro Consequences-,NBER-TCER-
CEPR Conference on Sticky Prices and Inflation Dynamics(Asian Development Bank Institute.),2008.12. • 原野啓・中川雅之・清水千弘・唐渡広志「レモンモデルのテスト : リフォームと中古住宅価格」応用地域学会 (釧路公立大
学 ) , 2008.11 • Shimizu,C, H.Takatsuji, H.Ono and K.G.Nishimura,Change in house price structure with time and housing price index, 第 9 回 マクロコン
ファレンス (慶応義塾大学 ),2007.12.
Chihiro SHIMIZU 2009 [email protected] 79
Dynamics in Real Estate Market Reitaku-University
2009/11/04 page.
• 谷下雅義・長谷川貴陽史・清水千弘 , 土地利用規制が住宅価格に及ぼす影響の分析 ,第 36 回土木計画学研究発表会 (八戸工業大学 ),2007.11. • 清水千弘 ,近隣外部性が宅地価格に与える影響 - 宅地価格構造の非線形性 -, 資産評価政策学会秋季全国大会 ( 都市センターホテル ),2007.11. • 清水千弘「東京都区部事務所市場における土地利用の非効率性 - 収益格差が土地利用転換に与える影響」 CSIS DAYS2007, 全国共同利用研
究発表会 ( 東京大学柏キャンパス ) , 2007.11. • 原野啓・清水千弘・唐渡広志・中川雅之「リピートセールス法による品質調整済住宅価格指数の推計」,日本経済学会 2007 年度秋季大会
( 日本大学経済学部 ) , 2007.9 • 原野啓・清水千弘・唐渡広志・中川雅之 (2007) 「リピートセールス法による品質調整済住宅価格指数の推計」,日本不動産学会 2007 年度
秋季全国大会 (北海道大学・学術交流会館 ) , 2007.11. • 清水千弘・唐渡広志 , 土地利用の転換コスト , 日本不動産金融工学学会 2006 年定期大会報告 (明海大学 ),2006.03. • 清水千弘・唐渡広志 , 土地利用の非効率性の費用 ,応用地域学会第 19 回研究発表会 (北九州市立大学 ),2005.12.
Chihiro SHIMIZU 2009 [email protected] 80
Top Related