Center
for Spatial
Information
CSIS-Real
Information
ScienceLab
CSIS-Real Estate
EstateInformation
ScienceLab Science, The University of Tokyo
5-1-5, Kashiwanoha, Kashiwa 277-8568, JAPAN

!

--(Chihiro SHIMIZU)
June 25, 2019 

page. 1CSIS-Real Estate Information Science Lab

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S31.3
S32.3
S33.3
S34.3
S35.3
S36.3
S37.3
S38.3
S39.3
S40.3
S41.3
S42.3
S43.3
S44.3
S45.3
S46.3
S47.3
S48.3
S49.3
S50.3
S51.3
S52.3
S53.3
S54.3
S55.3
S56.3
S57.3
S58.3
S59.3
S60.3
S61.3
S62.3
S63.3
H01.3
H02.3
H03.3
H04.3
H05.3
H06.3
H07.3
H08.3
H09.3
H10.3
H11.3
H12.3
H13.3
H14.3
H15.3
H16.3
H17.3
H18.3
H19.3
H20.3
H21.3
H22.3
H23.3
H24.3
H25.3
H26.3
H27.3
H28.3
H29.3
H30.3

percent change from previous yearMar-56
Mar-57
Mar-58Mar-59Mar-60Mar-61Mar-62Mar-63Mar-64Mar-65
Mar-66Mar-67
Mar-68Mar-69Mar-70Mar-71Mar-72Mar-73Mar-74Mar-75
Mar-76
Mar-77
Mar-78
Mar-79Mar-80Mar-81Mar-82Mar-83Mar-84Mar-85Mar-86
Mar-87
Mar-88Mar-89Mar-90Mar-91Mar-92Mar-93Mar-94Mar-95Mar-96
Mar-97
Mar-98Mar-99Mar-00Mar-01Mar-02Mar-03Mar-04Mar-05Mar-06Mar-07Mar-08Mar-09Mar-10Mar-11Mar-12Mar-13Mar-14Mar-15Mar-16
Mar-17Mar-18CSIS-Real Estate Information Science Lab

Property Market Trend: 1955-2018
Land myth:1945-1991    Urban Land Price Index of Six Large City Areas

1960-: Industrial
Property BubbleCommercial

Residential
1980-: Commercial
1970-: Residential
Property Bubble Industrial
Property Bubble

Average of Three
Categories





2000-: Global Money
Property Bubble
Source: Japan Real Estate Institutepage. 3CSIS-Real Estate Information Science Lab

Aging, Population Decline and Asset Meltdown
•

US:

•

Mankiw, N. G., and D. N. Weil (1989), "The baby boom, the baby bust, and the
housing market," Regional Science and Urban Economics, Vol. 19, 235-258.

•

Japan:•

Shimizu,C and T. Watanabe (2010) , “Housing Bubble in Japan and the UnitedStates,” Public Policy Review, 6 (2), pp.431-472.
Tamai, Y., C. Shimizu and K. G. Nishimura. (2017), “Aging and Property Prices:
Theory of a Very Long Run and Prediction on Japanese Municipalities in the2040s”, Asian Economic Policy Review. 16(3), 48-74.
Saita,Y., C.Shimizu and T.Watanabe(2016), “Aging and Real Estate Prices:
Evidence from Japanese and US Regional Data,” International Journal of Housing
Markets and Analysis, 9, 69-87.
Deng, Y, Inoue., K.Nishimura and C. Shihizu (2017) , “Aging and Property Prices:
Evidence from a Diverse Economies,” Presented at Economic Measurement
Workshop 2017.

•

•

•

page. 4CSIS-Real Estate Information Science Lab

i®

8>1.04•^¸n %Ãe¸n·"^@ jbS ÃBTw§%g \¾z} ('
1980pHtV6A7A5ef9?/;A%(1989)z} $ÃGXk#¿ÀUJ´cUÃBTL+© 'I°ÁAsset meltdownÂÃd ^_h[+o'$ ¬")

• ÃBTna+W&m ±¾½Z  '"Ã*]Ã¢]@¨]+!'lE«] %Ã¼]
'D]² "Ã9-0>3A2`°uh¯O!k'"e  ÃWsÃWsLÃBT(r)Pº'9-0>3A2~Mª¶( !'¦%('•iÃBTº*'WsÃWsLÃBTPº*'9-0>3A2+¤¥~M ÃBTnahv+<,=2-:x| '£­z¹+µ!' Ã*]BTna+C¿³q+"naµU ' %Ã]»NYKQ+~' Ã]»¡FR+yT+{µ'page. 5CSIS-Real Estate Information Science Lab:• 1. Index Theory
–
–
–
–
–

•

→Constant Utility Index––•

Diewert, W. E. and C. Shimizu. (2017), “Alternative Approaches to Commercial Property Price Indexes for Tokyo”,
Review of Income and Wealth, 63(3), 492-519.
Diewert, W. E. and C. Shimizu (2016), “Hedonic Regression Models for Tokyo Condominium Sales,”Regional
Science and Urban Economics, 60, 300-315.
Shimizu,C, K.G.Nishimura and T.Watanabe(2016), “House Prices at Different Stages of Buying/Selling Process,”Regional Science and Urban Economics, 59, 37-53.
Diewert, W. E., K. Fox and C. Shimizu (2016), “Commercial Property Price Indexes and the System of National
Accounts,” Journal of Economic Surveys, 30(5), 913-943.
Diewert, W. E. and C. Shimizu (2015), “Residential Property Price Indexes for Tokyo,” Macroeconomic Dynamics,
19(8),1659-1714.

Diewert, E, (1974), “Intertemporal Consumer Theory and the Demand for Durables”, Econometrica 42:497-516.
Diewert, E, (1978),“Superlative Index Numbers and Consistency in Aggregation”, Econometrica 46:4 (July 1978),pp. 883-900.

→Financial Stability and Property Market–––

Reinhart, C and K. S. Rogoff (2009), This Time Is Different: Eight Centuries of Financial Folly.
G20 Data Initiative: Data Gap 2009, International Handbook on Residential Property Price Indexes,
EuroStat, UN, IMF, OECD and World Bank, CPI: ILO, PPI: OECD, Export and Import: IMF

page. 6CSIS-Real Estate Information Science Lab :  
!" ($) 1 (&1) (z)  : !" ($ ∣ *)!" ($)!" ($ ∣ *)
.

(CDF)

!" $ = , !" $ * /" * 0*-.

/" (*)

* 

page. 7CSIS-Real Estate Information Science Lab  ?
!" ($)!" ($ ∣ ')2   (" (')!) ($)!" ($)
!) $ − !" $ =.

, [ !) ($ ∣ ') − !" ($ ∣ ')]() (')1'-.

.
+ ∫-. !" ($ ∣ ')[() (') − (" (')]1'

page. 8

CSIS-Real Estate Information Science Lab


•

?


$• ∫#$[ &' () ∣ +) − &. () ∣ +)]0' (+)1+•

^ ) ≡ ∫$ &^ ) + 0 + 1+;
• &
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• &
.
.
#$ .
•

→
^ ()) − &^ ()) = ∫$ [ &^ () ∣ +) − &^ () ∣ +)]0 (+)1+ + ∫$ &^ () ∣
• &
'
.
'
.
'
#$
#$ .
+)[0' (+) − 0. (+)]1+

page. 9CSIS-Real Estate Information Science Lab

House Price Transaction Samples
Pi,t : property i, transaction time t, *Repeat Sales Samples

Time
1
1*

2

3

p1,1

4

5

6

7

p1,4
p3,2

p3,4

p3,7

p3,10

p4,6
p5,2

6

p6,5

7*

p7,3

8

p7,7
p8,4



pn,8





































n*

10

p2,8

4
5

9
p1,9

2
3*

8

pn,10
page.

10

CSIS-Real Estate Information Science Lab

Problems in estimating Hedonic and Repeat Sales Index• Hedonic Method:•-not all the information that make up house prices is observable; as aresult, it has been noted that “omitted variable bias” occurs.

• Repeat Sales Method:
•
•
•

•
•
•

- using only transaction price data from repeat sales→ sample selection bias .
- depreciation occurs over time, and in cases where renovations are done
during that period, the houses may no longer have the same value as before.

→Depreciation Problem→Renovation Problem
By Diewert, W. Erwin E.(2007), “The Paris OECD-IMF Workshop on Real
Estate Price Indexes: Conclusions and Future Directions,” The University of
British Columbia, Department of Economics, Discussion Paper 07-01.

page. 11

CSIS-Real Estate Information Science Lab

-2.

IK#

• #1. Index Theory: Housing Rent
–
–
–
–
–

•

Diewert, E and C.Shimizu(2019) ,“Measuring the Services of Durables and Owner Occupied Housing,” CSIS
Discussion paper 157, The University of Tokyo.
BA*9(2018)U%1#  E2?-= 3#&@E25QP$:  -./O,IKUL266L4+, 14-24.
BA*9(2013),= 3743SE2,0'%1./NC.No.88, (20138?0+), pp10-19.
Shimizu, C., K. G. Nishimura and T. Watanabe (2010), “Residential Rents and Price Rigidity: Micro Structure andMacro Consequences,” Journal of Japanese and International Economy, 24, 282-299.
BA*9"DT((2010),3S-FJG;U!" #106+, 52-68. (R)HR)O,>MIK<)→Sticky Price and Price Stability–

Calvo, Guillermo A. (1983). "Staggered Prices in a Utility-Maximizing Framework". Journal of Monetary
Economics. 12 (3): 383–398. doi:10.1016/0304-3932(83)90060-0.

page. 12

CSIS-Real Estate Information Science Lab

Panel data for housing rents
35

10 thousand yen per month30
25

Unit 11
Unit 219

20

Unit 220
Unit 245

15

Unit 298
Unit 308

10

Unit 339

5026-Nov-07

1-Mar-05

5-Jun-02

9-Sep-99

13-Dec-96

19-Mar-94

23-Jun-91

26-Sep-88

Duration time andRent Change

page. 13CSIS-Real Estate Information Science Lab

Frequency of Rent Adjustments
DRit º Rit - Rit -1[

Pr (DRit = 0) = 1 - Pr ( I itN = 1) - Pr ( I itR = 1)]

+ Pr (DRit = 0 | I itN = 1) Pr ( I itN = 1)
+ Pr (DRit = 0 | I itR = 1) Pr ( I itR = 1)
page.

14

CSIS-Real Estate Information Science Lab

Weekly rent change distribution: Menu Cost
0.0006

Pr (No change)=0.992

0.00050.992^52 =0.6585 per year

0.00040.0003

Menu Cost

0.00020.0001

Rit / Rit -1(1.99,2](1.89,1.9](1.79,1.8](1.69,1.7](1.59,1.6](1.49,1.5](1.39,1.4](1.29,1.3](1.19,1.2](1.09,1.1]

1.00(0.9,0.91](0.8,0.81](0.7,0.71](0.6,0.61](0.5,0.51](0.4,0.41](0.3,0.31](0.2,0.21](0.1,0.11]

0n=18,582,863
page. 15

CSIS-Real Estate Information Science Lab

State-Dependent or Time-Dependent Pricing:
Caballero-Engel’s definition of price flexibility
D log Rit* = Dxt + n it

Caballero-Engel(1993):Adjustment Hazard

X it º log Rit -1 - log Rit*
L( x) º Pr(DRit ¹ 0 | X it = x)
D log Rt
Dxt ® 0Dxtlim=

ò L( x)h( x)dx

Caballero-Engel’s
Intensive margin
measure of price flexibility

L ( x) =+ò xL '( x)h( x)dx
Extensive margin

Caballero-Engel(2007)

Pr(DRit ¹ 0 | I itN = 1, X it = x) Pr( I itN = 1| X it = x)
+ Pr(DRit ¹ 0 | I itR = 1, X it = x) Pr( I itR = 1| X it = x)
page. 16

CSIS-Real Estate Information Science Lab

Estimates of intensive and extensive margins: Adjustment
hazard functions Figure5.Probability of Unit Turnover
3

0.020

2
0.015

eta(x)

0.010

0

-1
0.005

L ( x) =-2 X = x) Pr( I N = 1| X = x)
Pr(DRit ¹ 0 | I itN = 1,
it
it
it

+ Pr(DRit ¹ 0 | I itR = 1, X it = x) Pr( I itR = 1| X it = x)
0.38

0.31

0.24

0.17

0.10

0.03

-0.04

-0.11

-0.18

-0.25

-0.32

0.33

0.25

0.17

0.09

0.01

-0.07

-0.23

-0.31

-0.15

price imbalance x

-0.39

-3

0.000
-0.39

Pr(I^N=1|x)1

price imbalance x
page. 17

CSIS-Real Estate Information Science Lab

Adjustment Hazard Function for Turnover Units
1.0

3

2

1

eta(x)

0.8
0

0.7
-1

0.6

L ( x) =Pr(DRit ¹ 0 | I itN = -21, X it = x) Pr( I itN = 1| X it = x)
+ Pr(DRit ¹ 0 | I itR = 1, X it = x) Pr( I itR = 1| X it = x)

x

-0.49
-0.44
-0.39
-0.34
-0.29
-0.24
-0.19
-0.14
-0.09
-0.04
0.01
0.06
0.11
0.16
0.21
0.26
0.31
0.36
0.41
0.46

-3

0.5
-0.49
-0.44
-0.39
-0.34
-0.29
-0.24
-0.19
-0.14
-0.09
-0.04
0.01
0.06
0.11
0.16
0.21
0.26
0.31
0.36
0.41
0.46

Pr(dR 0|I^N=1,x)0.9

xpage. 18

CSIS-Real Estate Information Science Lab

Adjustment Hazard Functionsx Î (-0.4, -0.2]

x Î (-0.2, 0.0]

x Î (0.0, 0.2]

x Î (0.2, 0.4]

Pr( I itN = 1| X it = x)

0.010

0.010

0.010

0.010

Pr( I itR = 1| X it = x)

0.042

0.042

0.042

0.042

Pr(DRit ¹ 0 | I itN = 1, X it = x)

0.736

0.680

0.688

0.719

Pr(DRit ¹ 0 | I itR = 1, X it = x)

0.000

0.009

0.038

0.091

L ( x)

0.008

0.007

0.008

0.011

h( x )

0.082

0.312

0.348

0.161

Intensive margin:ò L( x)h( x)dx = 0.0084
Extensive margin:

ò xL '( x)h( x)dx = 0.0013

Caballero-Engel’s measureof price flexibility
D log Rtlim
= 0.0097
Dxt ® 0Dxt
page. 19

CSIS-Real Estate Information Science Lab••1.

 



The Paris OECD-IMF Workshop on Real Estate Price Indexes2006:
Erwin Diewert, Discussion Paper 07-01, Department of
Economics,The University of British Columbia• David Fenwick (2006; 6) suggested the following list of
possible uses for house price indexes:• •As a general macroeconomic indicator (of inflation);• •As an input into the measurement of consumer priceinflation;• •As an element in the calculation of household (real) wealthand• •As a direct input into an analysis of mortgage lender’s
exposure to risk of default.
• → page20.

CSIS-Real Estate Information Science Lab

①
•



Background paper &



:



– Badarinza, C., T. Ramadorai, and C.Shimizu. (2019), “Nationality Bias at Home andAbroad: Theory and Evidence from Commercial Real Estate,” SSRN-id3141255.
– Miyakawa,D., C.Shimizu and I.Uesugi. (2019), “Geography and Realty Prices:Evidence
from International Transaction-Level Data,” RIETI Discussion Paper,19-E-11.
– Deng, Y; X. Guo, D. McMillen and C. Shimizu(2018), “Change in the Distribution of
Sale/Rental Prices: Comparison of Beijing and Tokyo,” Presented at Society of
Economic Measurement, D-4-3(Xiamen University, China).
– Deng, Y; X. Guo, D. McMillen and C. Shimizu(2018), “Spatial Estimates of Bubbles:
Tokyo House Prices and Rents,”Presented at AREUEA-Asia2018, (Jinan University,
China)– Deng, Y; X. Guo, D. McMillen and C. Shimizu(2018), “Distributional House Price
Indices for Tokyo Metropolitan Area,”Presented at ASA Philadelphia 2018.

page. 21

CSIS-Real Estate Information Science Lab

MIT Center for Real Estate: Price Dynamics Platform

page. 22

CSIS-Real Estate Information Science Lab

Research Team

http://pricedynamicsplatform.mit.edu/team.html

page. 23

CSIS-Real Estate Information Science Lab

OVERVIEW OF RCA CPPI GLOBAL SUITEThe RCA Commercial Property Price Indices (RCA CPPI™) are a suite of price indices developed andpublished quarterly. They are transaction-based and measure commercial real estate price movements
using a repeat-sales regression methodology.

350+ Indices across 15 countries
50 National Level Indices
41 Regional Indices
190 Metro/Market Level IndicesTop 25 Global Cities Composite

page.

24

CSIS-Real Estate Information Science Lab

Overview of RCA CPPI GLOBAL Suite

page. 25CSIS-Real Estate Information Science Lab②+.)*
•Background paper &$458– Suzuki,M., S. E. Ong , Y.Asami , C. Shimizu. (2018), “ Long-Run Renewal of REIT
Property Portfolio through Strategic Divestment” (mimeo) submitted to Journal of Real
Estate Finance and Economics.
– Shimizu, C. (2017), “Micro-Structure of Asset Prices, Property Income, and DiscountRates in Tokyo Residential Market,” International Journal of Housing Market andAnalysis, 10 (4), 552-571.
– Shimizu, C., W. E. Diewert, K. G. Nishimura and T. Watanabe (2015) , “Estimating
Quality Adjusted Commercial Property Price Indexes Using Japanese REIT ,” Journalof Property Research, 32(3), 217-239.
–  0/"((2015)%-!3, 2& ?7145,666',63#, 194-208.

page. 26

CSIS-Real Estate Information Science Lab

Two Discount Rate(rM) from Appraisal & Stock market
Dividend=y i : property income (Net Operating Income)r ij =

Property Market

Real Estate
Asset
Tobin’s Q(Calculated by
Appraisal
Value)

Stock Market

Equity(Stock)Debt
page. 27

1.4

1.2

0.8

2001q2
2001q3
2001q4
2002q1
2002q2
2002q3
2002q4
2003q1
2003q2
2003q3
2003q4
2004q1
2004q2
2004q3
2004q4
2005q1
2005q2
2005q3
2005q4
2006q1
2006q2
2006q3
2006q4
2007q1
2007q2
2007q3
2007q4
2008q1
2008q2
2008q3
2008q42009q1
2009q2
2009q3
2009q4
2010q1
2010q2
2010q3
2010q42001.2nd qu art er= 1

CSIS-Real Estate Information Science Lab

Appraisal Price, Rent and Discount Rate
1.6

Vˆt A = exp(xt )yˆt = exp(n t )!A
rt = exp(n t - xt )VA3: Model.VA3

Price

YA: Model.yA

Rent

1

rA: Model.rA

Discount Rate

0.6

page. 28

CSIS-Real Estate Information Science Lab





:



page29.

800000

7000006000000
2003/1/1
2003/3/1
2003/5/1
2003/7/1
2003/9/1
2003/11/1
2004/1/1
2004/3/1
2004/5/1
2004/7/1
2004/9/1
2004/11/1
2005/1/1
2005/3/1
2005/5/1
2005/7/1
2005/9/1
2005/11/1
2006/1/1
2006/3/1
2006/5/1
2006/7/1
2006/9/1
2006/11/1
2007/1/1
2007/3/1
2007/5/1
2007/7/1
2007/9/1
2007/11/1
2008/1/1
2008/3/1
2008/5/1
2008/7/1
2008/9/1
2008/11/1
2009/1/1
2009/3/1
2009/5/1
2009/7/1
2009/9/1
2009/11/1
2010/1/1
2010/3/1
2010/5/1
2010/7/1
2010/9/1
2010/11/1
2011/1/1
2011/3/1
2011/5/1
2011/7/1
2011/9/1
2011/11/1
2012/1/1
2012/3/1
2012/5/1
2012/7/1
2012/9/1
2012/11/1

CSIS-Real Estate Information Science Lab

-Prop tec Plus Daily CPPI

1000000

900000PV(M,M)

PV(A,M)

500000400000300000200000100000

page. 30

CSIS-Real Estate Information Science Lab

③
•





Background paper &







– Diewert, W. E. and C. Shimizu (2016), “Hedonic Regression Models for Tokyo
Condominium Sales,” Regional Science and Urban Economics, 60, 300-315.
– Shimizu,C, K.G.Nishimura and T.Watanabe(2016), “House Prices at Different Stages of
Buying/Selling Process ,” Regional Science and Urban Economics, 59, 37-53.
– Diewert, W. E. and C. Shimizu (2015), “Residential Property Price Indexes for Tokyo,”
Macroeconomic Dynamics, 19(8),1659-1714.
– Karato, K, O. Movshuk and C. Shimizu (2015), “Semiparametric Estimation of Time,Age and Cohort Effects in An Hedonic Model of House Prices ,” Asian Economic
Journal, 29(4), 325–345.
– Shimizu, C., K. G. Nishimura and K. Karato (2014), “Nonlinearity of Housing Price
Structure -Secondhand Condominium Market in Tokyo Metropolitan Area-,”International Journal of Housing Markets and Analysis, 7(3), 459-488.
– Shimizu, C. (2014), “Estimation of Hedonic Single-Family House Price Function
Considering Neighborhood Effect Variables,” Sustainability,6, 2946-2960.
– Shimizu, C., K. G. Nishimura and T. Watanabe (2010), “House Prices in Tokyo - A
Comparison of Repeat-sales and Hedonic Measures-,” Journal of Economics andStatistics, 230 (6), 792-813.
page. 31CSIS-Real Estate Information Science Lab

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page. 39CSIS-Real Estate Information Science Lab: Mizuta, Uesugi and Shimizu (2018)

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• What are key factors about the growth of cities?
• Storper and Scott (2009) argued that the growth of cities is
profoundly related to characteristics of residents in cities
and how the population characteristics change over time.
• Cities are defined as places that attract many and diverse
people, and they promote sharing, exchanging, andgenerating new information (Florida (2009), Jacobs(1969)).
• Because of these characteristics of cities, Jacobs (1969) andFlorida (2009) mentioned that cities can generate new ideas
and technologies that make possible their strong and stable
growth.
page. 42CSIS-Real Estate Information Science Lab
Category
Category1:Art/Galleries
Category2:Artists
Category4:Associations
Category5:Bars/Nightlife
Category6:Clothing/Fashion

Urban amenities from a phone book
Museum, Museum of Art, Museum of Science
literary profession, artist
Vocational school, such as music, art and crafts, other various types of school,
lessons for hobby
Business cooperative, political organization
Karaoke, dance hall, bar, via holes
Clothing store, jewelry store, bags and sacks shop

Category7:Community/Gov't Services

Nursery, child care center, social welfare and nursing care service business

Category3:Arts Instruction

Category9:Foreign Gov't Services
Category10:Literary Culture

Medical and other health business, elementary, middle and high schools,
university, private cram school
Foreign diplomatic offices, the United Nations-related facilities
Publishers, libraries, bookstores, and newspaper shop

Category11:Media Services

Broadcasting industry, advertising agency, TV program production

Category8:Education/HealthCategory12:Museums/Aquariums/
Zoos/Historic Sites
Category13:Music/Instrument
StoresCategory14:OTHER
Category15:Other Entertainment
Category16:Parks and Nature
Category17:Performance Arts
Category18:Religion
Category19:Restaurants/Food
Category20:Specialty Services
Category21:Specialty Stores

Zoo, botanical garden, aquarium, planetarium
Musical instruments, record · CD · DVD sales
Graveyard, parking lot
Movie theater, game arcade, theme park
Camping ground , fishing pond, parks
Theatrical company, orchestra, dance troupe, live house
Christian church, and other religious groups
Restaurants from each country, fast food, coffee shops, drive-in
Law firm, interpretation, translation, art repair industry
Sales of medicine, antiques, liquor, tobacco, and toys

Category22:Sports and Recreation

Golf course, ski area, tennis court, and other sports facilities

Category23:Tourism
Category24:Visual Arts

Travel agency, Japanese-style hotel, hotel
Printing business , Design, Photographs
page. 43CSIS-Real Estate Information Science Labb) Population of the elderly(aged 65 or more)a) Total population

page. 44CSIS-Real Estate Information Science Lab

page. 45CSIS-Real Estate Information Science Lab

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page. 46CSIS-Real Estate Information Science Lab

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page. 47CSIS-Real Estate Information Science Lab : Chihiro Shimizu, PhD
 %- #"$6@7A  2'3>@;51277-8568 '!2'5-1-5 & "$4. 451 /(E03-6453-1715B"$ *CDFax:04-7136-4292
Email: cshimizu@csis.u-tokyo.ac.jp=48?A69:# "$6@7A"$
Massachusetts Institute of Technology
Center for Real Estate
105 Massachusetts Ave, Cambridge, MA 02139
Tel: (+1) 617-253-4373,(Office), Fax: (+1) 617-253-4373
e-mail: cshimizu@mit.edu
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48