Global Real Estate Markets
By
Darin Richard DePasquale
B.S. Business Administration (1987)
Boston University
SUBMITTED TO THE DEPARTMENT OF URBAN STUDIES AND PLANNING
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR
THE DEGREE OF MASTER OF SCIENCE IN REAL ESTATE DEVELOPMENT
AT THE
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
SEPTEMBER 1998
@ 1998 Darin Richard DePasquale
All Rights Reserved
The author hereby grants to MIT permission to reproduce and to distribute publicly paper and
electronic copies of this thesis document in whole or in part.
..................
Signature of Author...........................
Department of Urban Studieland Planning
July 31, 1998
C ertified by ............................
.................
.
...
.............................................
William C. Wheaton
Professor of Economics
Thesis Supervisor
..
.... :.........................................................
William C. Wheaton,
Chairman, Interdisciplinary Program in Real Estate Development
A ccepted by ....................................-
MASSACHUSETTS INSTITUTE
OF TECHNOLOGY
OCT 2 3 1998
LIBRARIES
RC:
Global Real Estate Markets
By
Darin DePasquale
SUBMITTED TO THE DEPARTMENT OF URBAN STUDIES AND PLANNING ON
JULY 31,1998 IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE
OF MASTER OF SCIENCE IN REAL ESTATE DEVELOPMENT
ABSTRACT: Whether in a bid to remain competitive or designed to elevate a populations standard
of living, countries around the world are seeing the necessity to deregulate their financial systems
and open their markets to international commerce. Real estate, traditionally a local investment to
enhance individual financial wealth, has become a domestic and international vehicle for speculative
institutional and private investment. This statistical study utilizes time series data on office market
rents, public real estate stock indices and gross domestic product from 17 markets around the globe
in an effort to shed light on national and international real estate trends. Specifically, the study
searches for divergence within a domestic setting public and private real estate markets. Eights
global markets consumer price index deflated office market rental rates from 1970 to 1998 are
regressed against a publicly traded real estate corporation's stock or property index's performance.
Next, an understanding of a local economies effect on the private real estate markets performance is
sought by regressing the same time series of deflated office market rental rates against the economies
gross domestic product. Finally we look for the existence of international real estate market
correlation by examining trends in office market rental rates from 1970 to 1998 in all 17 global
markets. Public real estate cycles in most global settings lead private market cycles in the majority
of markets studied. In addition GDP appears to not affect a countries office markets performance.
Analysis suggests market segmentation exists internationally for private real estate markets in most
countries. .
Thesis Supervisor: William C. Wheaton
Title: Professor of Economics
ACKNOWLEDGEMENTS:
This thesis would never have been completed without the unwavering support and guidance of a
multitude of friends and family.
First, I would like to thank Professor William Wheaton whose dead-pan remarks, extreme patience
and humorous disposition kept me in sight of my goals. Thank you Professor for making this thesis a
pleasure to write.
Next, my SINCERE gratitude goes out to my classmates. I would name them individually but am
positive that the list would include every member of the class of 1998, and most would be named
numerous times!! Thank you!!
James Alden, for having a computer on board and the right words at the wrong time.
Tyler Jackson and Rich Lewis for helping keep life in perspective.
Lynelle Suhr, Jodie Higgins, Richard Gillyard and Maria Vieira for allowing me the privilege of
sharing an office desk and their immense body of knowledge in thesis preparation.
And of course my Mom and Dad for making time to become closer friends.
Love and Thanks All.
Sincerely,
Darin DePasquale
Table of Contents
Page Number
AB STRA C T ...................................................................................................
2
ACKNOWLEDGEMENTS....................................................................................
3
TABLE OF CONTENTS ........................................................................................
4
CHAPTER 1.. Introduction..................................................................................
5
CHAPTER 2.. .Public and Private Market Divergence..................................................
8
CHAPTER 3.. .The National Economy and its Effect on Local Office Market Rental Rates.....20
CHAPTER 4.. Global Real Estate Cycles................................................................
49
CHAPTER 5.. Conclusion....................................................................................58
APPENDIX: DATA SOURCES..............................................................................
60
REFERENCES............................................................................
75
CHAPTER 1
Introduction:
The investment climate of today, as always, is a world where institutional investors are seeking ways
to improve the return on their capital. Whether it be through less risky ventures like bonds or more
volatile investment like stocks and real estate, the competition for higher yields is immense. The
result has been that portfolio managers can no longer be complacent with opportunities in their own
"backyard" or area of expertise, but that they are being forced to reeducate themselves to prospects
that lie farther afield.
In the 1970's global inflation and a multitude of international tax structures lead foreign investors to
seek safer haven for their capital. Federally insured deposits then had the effect of funneling large
amounts of United States capital into real estate. The result was a greater mobility of capital across
national borders as investors scrambled to secure their deposits into stable growing economies. This
trend has continued, but has made it obvious that not all economies are alike. The risk and rewards
are unpredictable from country to country and investment to investment.
This thesis' focus is on the most lucrative business opportunities this century, real estate investment
and the international trends it is following. All real estate markets are unique, they expand and
contract based on their regional population growth and economic performance (Mei, Wheaton.
1998). The degree to which one market invests in a foreign market signifies their amount of capital
or economic integration. The more globally integrated or the more accessible a market is to foreign
capital, as economists say, the more economically efficient it should become. With efficient markets
savings and investments should flow to the most productive opportunities, regardless of their
location. The effects of greater global competition should improve financial systems as investors
push local governments to create intelligent economic policy. Thus effective policy leading to
financial deregulation will have the immediate effect of adjusting real estate rental rates until demand
equals the current stock of space. In the long run the stock will adjust gradually as less efficient
investment capital creates new space. The implication of this is that in an efficient marketplace
capital budgeting will be based on the expectations of asset prices which should equal the value of
future rents (Wheaton 1998). This is opposed, of course, to highly regulated markets where asset
prices depend less on economic fundamentals and more on the expectations of other informationally
limited investors.
The Institute for Institutional Finance reports that net private capital flows to emerging economies in
1987 were below $10 billion, in 1991 $50 billion, 1994 $150 billion and in 1997 over $300 billion.
The productivity of these investments is another story as statistical measures for or against the
mobility of capital tend to be extremely crude.'
Methodology fails to distinguish between
liberalizing, foreign direct investment and volatile portfolio capital which often adheres to public
sentiment. In addition, capital mobility is not without its costs to the receiving nation as when
domestic banking systems are weak and when local governments take an active role in allocating
credit to favored borrowers. Of course restrictions on capital flows will allow mismanaged financial
systems to hide from competition and may also limit a domestic investor from opportunities abroad.
Global real estate markets are a grouping of national public and private markets in local economies
integrated across borders. Public real estate can be a nationally recognized REIT specializing in a
certain market sector or it can be a smaller stock company operating in a portion of a town. The
commonality is that all public real estate entities are controlled by a group of stock holders who's
combined opinion sets the market price for shares in the company. Private real estate entities can
have equally large asset bases as their public counterparts but their value is the sum of their asset
base which is a direct reflection of the properties income potential. Thus the first question of interest
to the international manager given these two investment vehicles is do public and private real estate
companies value diverge? This question is answered by testing for correlation across the London,
Paris, Frankfurt, Milan, New York, Sydney, Tokyo and Singapore office markets. Central business
district office rental rates are used as an indicator of private market valuation. These are compared
against a public real estate index or composite grouping of share prices in each of the applicable
countries from 1970 to 1998. As the graphs visually indicate public real estate valuation does
diverge from property market income.
In keeping with this line of questioning the next step is to test if a real estate markets value is
dependent on the local economies performance as indicated by local gross domestic product. GDP
data is compared against office market rental rates from 1970 to 1998 for London, Brussels, Paris,
Amsterdam, Madrid, Milan, Geneva, New York, Toronto, Sydney, Singapore Tokyo and Jakarta.
Here the analysis points to definite linkages with an economies peak production preceding the office
markets peak income by 1 to 2 years. It also suggests a high degree of international capital mobility
throughout most regions examined.
The final question addressed in this thesis is what are the correlations between the North American,
European, and Asian office markets over the past 30 years? The data consists of office market rental
rates of 17 countries in these regions and is correlated in both local currency and U.S. dollar based
capital. Analysis points to definite benefits for international real estate diversification across national
borders.
"Capital Controversies," The Economist. May 23-29, 1998. Pg72.
7
Chapter 2
Public and Private Market Divergence:
Traditionally investors wishing to diversify into the property market placed funds in private real
estate assets by way of syndication pools or individual ownership vehicles. A growing international
trend, however, is to invest in publicly traded real estate securities such as REITs or asset specific
funds. With these two diverse frameworks with which to capitalize on real estate opportunities,
investors are being required to fully understand the nature of traded real estate securities in
comparison with privately controlled real estate assets. Thus a number of studies have broached the
subject of correlation between public and private real estate markets.
Liu, Hartzell, Grieg, and Grissom (1990) began this body of research by discovering evidence that
the stock market is segmented from the private real estate market. In addition their study concludes
that equity REITs are integrated with the stock market, but that the commercial real estate that
underlies these equity REITs is segmented from the stock market. Their theory is that integration
exists only if the risk that is priced for both real estate and stocks is the systematic risk relative to the
overall market index. Which means no additional premium is associated with real estate market risk.
Segmentation would exist if the only risk that is priced for real estate is systematic risk relative to
the commercial real estate market. Although the results are that the public and private real estate
markets are segmented it is unclear whether indirect barriers or legal constraints represent the prime
catalyst for return differences which often reflects whether appraised values or imputed sales prices
are used.
Geltner, Rodriguez and O'Connor (1995) study the optimal portfolio of private and public real estate
using the historical NCREIF's Index and the Evaluation Association's Index to represent private real
estate performance and the NAREIT's Index to represent public real estate performance. Interpreting
the data from 1975 to 1993 public real estate has out-performed private real estate. Public real estate
appears to lead private real estate's peak valuation by 1 to 2 years, but it also appears more volatile in
the short run due to "noise and "overreaction" found in publicly traded goods. The study concludes
that both private and public real estate should play a significant role in an optimum real estate
portfolio.
Downs (1994) also supports this conclusion by summarizing a variety of causes for the divergence
of public and private market valuations for real estate. Generally speaking, the quality of the
operating company is incorporated in the value of the public stock. This can be greater or less than
the value of the private market's valuation of the sum of all properties together. Additionally, private
market valuation is dependent on the general level of current interest rates. Divergence arises when
rising interest rates depress land values which in turn drives stock prices down. Interest rates
produce ambiguous movements and thus diverge public and private market valuations.
Ghosh, Miles and Sirmans (1996) discuss the merits of investing in REITs as opposed to other
publicly traded stocks. There hypotheses is that there is less information available to investors when
performance is driven by a series of local economies, each with a unique rent dynamic.
Subsequently REITs differ from other stocks due to the fundamentals of lots of local markets, each
with information shortcomings relative to other stocks.
This thesis takes the previous literature one step further by investigating whether Asian, European,
and North American publicly traded real estate companies diverge in value from the private real
estate sector as represented by office market rental rates in each of these regions major markets. To
test for possible divergence this study focused on public real estate index returns in London, Paris,
Frankfurt, New York, Tokyo and Sydney and in the cases of Milan and Singapore where no long
term index existed, a weighted composite index was formulated with public real estate companies.
Next time series data on office market rental rates in each of these markets was gathered for
comparative purposes. Both of these public and private indexes were then deflated using a countries
specific consumer price index figures to alleviate problems with upward trending data that inflates
relevant R square values.
It should be noted that the public market indices are a direct reflection on the prices of the underlying
assets which they control. In addition a premium may be charged for quality of management as well
as market power in a specific asset class. Private market indices, however, are a reflection of the
income stream which the assets are capable of producing. Thus it is logical to expect that public
indices will be efficiently priced in an informationally fluid market place. Private real estate asset
prices, reflecting income capabilities, will be more volatile and thus likely to lag behind their asset
counterparts.
With these two sets of public and private CPI deflated indexes regression analysis was run in each of
these 8 markets positing contemporaneously each set of time series data. However, due to previous
literature hypothesizing that public market valuations peak one year ahead of a private markets peak
valuation additional regression analysis was run with the public market index leading the private
market index by 1 year. For the sake of thoroughness, the public market index was also regressed
lagging the private market index by 1 year in each of these countries. The R-Squared results of our
regression analysis were:
PUBLIC & PRIVATE R-SQUARED VALUES
Table 2:1
Public and
Private R-
London vs
Paris vs.
Frankfurt vs Milan vs
New York
Sydney vs
Tokyo vs
Singapore vs
FTSE 1984- SBREAL
1989-1996
1995
CDAX
1975-1996
Composite
1986-1996
vs NCREIF
1977-1998
ASAX Index
1978-1996
TOPIX
1981-1996
Composite
0.5889
0.5866
0.3397
0.4173
0.0006
0.0924
0.1860
0.0000
Public vs. Private 0.7963
0.7753
0.3391
0.6291
0.1705
0.0047
0.4984
0.0102
Public Leading
Private
0.7621
0.4767
0.4843
0.6090
0.0777
0.7241
0.0232
Squared Values
Private Leading
Public
0.4654
1986-1996
In London, England our time series data for consumer price index deflated London office market
rental rates and the similarly deflated FTSE Property Index dates run from 1984-1995. The FTSE
Index consists of 14 members with 28% of its weight consisting of Land Security Plc. This company
develops, manages and invests in real estate including office, retail and industrial throughout the
United Kingdom. British Land Co. Plc makes up another 18% of the FTSE Index and invests in
income producing, freehold commercial properties. They are also involved in property trading,
finance and investment of office, retail and industrial space. The heavy weighting of nonconstruction companies in the FTSE Index suggests it would move more in sync with the office
rental market because the time period for office investment and commercial development are similar.
As our regression analysis reveals an R-squared of .796 suggests that the two indices have grown and
contracted at equal points in time. In addition the data points begin roughly the same time that
England implemented the "Big Bang" or financial deregulation of its markets which could have
effected investment and the corresponding values in both these public and private indices.
Yearly Change of England's FTSE Index and London Office Rents
-+-FTSE
Property Index
FIGURE 2:1
-U- London Office Rental Index
1.80
1.60
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Our Paris data, although only from 1989 to 1996 reports similar results. The SBREAL Index
consists of 18 members of which SIMCO makes up 14%. This firm sells and leases apartments in
Paris and surrounding suburbs. Unibail makes up another 11 % of this index and is active in leasing
and renting of office and retail space. The Paris office market is most strongly correlated with the
SBREAL Property Index during corresponding years with an R-squared of .77 most likely due to a
heavier weighting of non-construction companies in the index. Both index's are generally downward
trending and given their linearity makes it more difficult to draw a conclusion from these two sets of
data.
FIGURE 2:2
Yearly Change in France's SBREAL Index and Paris Office Rents
-+-
+ Paris Office Rental Index
SBREAL Property Index
3.00
2.50
2.00
1.50
1.00
0.50
0.00
1990
1991
1992
1993
1994
1995
1996
Metanopoli Real Estate Company comprises 60% of our Milan Composite Index and is involved in
the construction, purchase and leasing of real estate in Italy and abroad. This company invests
primarily in the office properties of Italy's national petroleum group ENI. Risanamento Spa. makes
up another 20% of our composite index and primarily constructs and sells buildings, provides
subcontracting for third parties and real estate management. Aedes Spa. makes the final 20% of our
Milan Composite and is involved in real estate trading and property management. With data from
1986-1996 for these three public companies our regression reveals a strong R-squared of .62 when
run in corresponding years with the private market. This could be the result of only 3 companies in
the composite which deal heavily in the office market sector.
Yearly Change in Italy's Public Property Index and Milan's Office Rents
-+-
FIGURE 2:3
-U- Milan Office Rental Index
Milan Property Index
1.80
1.60
1.40
1.20
1.00
0.80
0.60
0.40
0.20
0.00
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Frankfurt, however, paints a different picture. Here our data runs from 1975-1996 for the office
rental market and the CDAX Property Index, which consists of 21 members. The index is weighted
23% by IVG Holdings which owns, develops and manages commercial and residential properties in
addition to providing facility, building and airline operational services. The largest weight in the
index however, a full 35%, is WCM Beteiligung which acquires and operates various businesses
including real estate properties, buildings and facilities primarily in Germany. It appears that due to
a very regulated business environment in Germany and that many of these companies are involved in
a variety of fields their performance would run, as the data suggests, very close to the private market.
Regression analysis reveals an R-squared of .476 when the CDAX Index is lagged one year behind
our private market index.. The graph also illustrates the volatility in the public real estate sector
which could exemplify "noise" or informational shortcomings in publicly traded goods.
Yearly Change in Germany's CDAX Index and Frankfurt'sOffice Rents
-*- CDAX Holding Index
FIGURE 2:4
--- Frankfurt Office Rental Index
3.50
3.00
2.50
2.00
1.50
1.00
0.50
0.00
1975 1977 1979 1981
1983 1985 1987 1989 1991 1993 1995
The NCREIF Index consists of 71 data contributing members of whom most invest in existing real
estate and within this category mostly office space. Our New York data supports the Frankfurt
evidence and the assumption that NCREIF and the private office market have similar attributes.
Indeed we find an R-squared of .608 when the NCREIF Index leads the private market index by 1
year with office rental data from 1977-1998.
The data points trend similarly although with varying
degrees of volatility but it is apparent in 1980 and again in 1996 that the NCREIF's peak lead the
office sector peak by about one year.
Yearly Change in the United State's NCREIF Index and Manhattan Office Rents FIGURE 2:5
-+-NCREIF Property Index
3.00
2.50
2.00
1.50
1.00
0.50
0.00
-0.50
-i-New York Office Rental Index
In Tokyo we regressed the Topix Index, consisting of 21 members, from 1981-1996 against the inner
Tokyo office market rental data and found similar results that public markets are a leading indicator
for private market cycles with a strong R-squared of .724. This analysis reveals that because 48% of
the TOPIX is weighted with the Misubishi Estate Co., which owns, manages and develops
commercial property primarily in the Otemachi area of downtown Tokyo; and 28% with Mitsui
Fudosan, which owns operates and sells office, condominiums and land, that the construction
industry bias of this index would deviate slightly with the office rental market. As the graph
illustrates the construction biased public index denotes some 600% over the private market possibly
due to divergence in the highly regulated underlying asset of the public index. Japans opaque
housing system is notorious for artificially inflating the price of real estate assets.
FIGURE 2:6
Yearly Change in Japan's TOPIX Index and Tokyo's Office Rents
-*-Tokyo Office Rental Index
-+-TOPIX Property Index
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
1982
1984
1986
1988
1990
1992
1994
1996
In Sydney and Singapore, however, we find the almost no correlation exist between public and
private markets. With Sydney we had data from 1978-1996 for office rental rates and the ASX
Property Index, which consists of 43 members, but show only a .077 R-squared when the public
market leads the private index by 1 year. General Property Trust makes 17% of the ASX Index and
invests in retail, office and hotel/tourism properties throughout Australia. Stockland Trust Co.
develops and markets land and builds commercial real estate, townhomes and retirement units in
Australia. The poor R-squared statistic could be linked to the fact that Australia is one of the more
deregulated markets in Asia and is thought to be 20% owned by the Japanese (Green, Malpezzi and
Barnes, 1998).
FIGURE 2:7
Yearly Change in Australia's ASX Index and Sydney's Office Rents
-U-Sydney Office Rental Index
-+-ASX Property Index
1.2
1.0
0.8
0.6
0.4
0.2
0.0
1979
1981
1983
1985
1987
1989
1991
1993
1995
The Singapore Property Index consists of 16 members, 35% of which consists of City Develops
Company. This company develops and owns property including residential, industrial, retail and
hotels. DBS Land is 14% of the Singapore Index and is engaged in property trading, investment and
services. Singapore Land is an additional 12% of this index and is involved in property development
for investment, trading, property management and consulting. In Singapore the data runs from 19861996 but shows only a .023 R-squared when the public market leads the private market index by 1
year. The dramatic growth rate of publicly traded real estate companies points to the theory that the
Singapore real estate market is inefficient due to a close to 25% value increase within 10 years.
Sixty one percent of the property index (Figure 2:8) is comprised of three public real estate
companies invested mostly in Singapore (Bloomberg, 1998) thus this real growth could be an
indication of a speculative market.
FIGURE 2:8
Yearly Change in Singapore's Property Index and Office Rents
-u-Singapore Office Rental Index
-+-Singapore Property Index
25.00
20.00
15.00
10.00
5.00
0.00
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
The stock price cycles of publicly traded real estate companies tend to lead, between 1 and 2 years,
the office rental income cycles in the majority of countries regression analyzed. In addition, the
economies of Europe and North America appear to be more financially transparent, resulting in both
public and private real estate market cycles to trend in fairly consistent patterns, but Asia's public
real estate market cycles are quite volatile as compared with the office rental index indicating
comparably more regional economic instability.
CHAPTER 3
The National Economy and its Effect on the Local Office Market Rental Rates
Regional macroeconomic policies focus on the development of stable long term growth. Interest rate
and taxation levels are two key means by which forward looking policy makers attempt to achieve
such goals. Pertaining to real estate, which is perceived to be a durable good, rents should also
respond to new demand. Thus gross domestic product, which is a function of employment growth
should directly influence rental rates. Mills (1987), found that capital investment in the United States
housing market returned only slightly more than half of what non-housing capital returned. This
suggests a misallocation of capital toward housing which would have the affect of reducing
economic growth. With quarterly national income and products data from 1952-1992 as well as time
series GDP data Green (1997) concluded that under a variety of time series specifications residential
investment causes , but is not caused by GDP. While non-residential investment does not cause, but
is caused by GDP. Thus residential investment seems to lead the United States into and out of a
recession, non-residential investment does not.
The Downs' study (1994) postulates that stocks are leading economic indicators, typically hitting
high points in the middle of an economic expansion and moving downward in advance of a
recession. His study points out that real estate typically hits its highest valuation at the peak of an
economic expansion and then moves downward in advance of a recession. His theory is that public
market values of commercial real estate should be higher than private market valuations in the early
stages of a business cycle recovery and lower at the peak of the cycle.
Given this previous literature the question remains, does real estate follow GNP demand shocks or is
it capital driven? The theory is that for GNP to be a determinant of the office market cycle the
subject country must be operating in a financially regulated "vacuum." This meaning that the level
of national production growth would be influenced solely by local capital. Of course, unregulated or
open markets would always be available to international capital flows and thus the local GNP's
performance would be risk adversely diversified away for the relevant office market investor.
This study analyzes the cyclical trends of a nations Gross Domestic Product (GDP) and its central
markets office rental rates in 13 countries across North America, Europe and Asia. The office rental
rates, being a reflection of a properties income potential, are varyingly affected by international
capital flows and thus uniquely affected by local GDP. The local GDP growth rates and the office
rental rates data runs, in most cases, from 1975-1996. The office rental data is regressed with the
GDP growth data contemporaneously, and with GDP data leading the rental cycle by 1 year and then
2 years. The most convincing R-squares, or "Best" cyclical timing under the three scenarios in each
country, is then used to time the lead to again regress the office rental data against the local
consumer price index deflated GDP. R-squared values and graphic representations of growth and
percent change in these two indices are comparatively analyzed for cyclical trends. The office
market rental rates regressed against GDP growth gives an indication of the lead time of GDP's
effect on office rental rate cycles and the "Best" R-squares indicate how strong a nations economic
performance affects its office markets performance or theoretically the degree of the countries market
regulation. Our R-Square's for contemporaneously run, 1 and 2 year lagged GDP and "Best" case
scenario regressions are as follows:
Table 3:1
R-Square Values of Demonstrating Cyclical Timing and Market Regulation
London
Office Rental
Market vs Gross
Domestic
Product R
Squared
Brussels Paris
Amsterdam
Madrid
Milan
Geneva
New
York
Toronto Sydney
Singapore
Tokyo
Jakarta
values
GDP Leading
Office Rents by
0.25 1
0.38
0.031
0.354
0.667
0.309
0.33
0.008
0.027
0.101
0.416
0.275
0.365
0.253
0.272
0.103
0.056
0.458
0.129
0.21
0.028
0
0.017
0.334
0.186
0.104
0.065
0.163
0.189
0.033
0.181
0.015
0.212
0.032
0.003
0.007
0.023
0.017
0.014
GDP
leads by
2 years,
RSquare
value
.565
Contemporaneous
timing,
RSquare
value
.585
GDP
leads by
2 years,
R-Square
value
.192
GDP
leads by
2 years,
R-Square
value
.003
2 years
GDP Leading
Office Rents by
1 Year
GDP
Cotemporaneous with Office
Rents
GDP Deflated
Contemporaneous with
"Best" Rents
I__II
GDP
leads by
1year,
RSquare
value
.017
GDP
leads by
2 years,
RSquare
value
.025
GDP
leads by
2 years,
RSquare
value
111
Contemporaneous
timing
RSquare
value
.023
GDP
leads by
2 years,
RSquare
value
.050
GDP
leads by
2 years
RSquare
value
.412
GDP
GDP
GDP
leads by leads by leads by
2 years 2 years, 2 years,
R-Square
RRSquare Square value
.481
value
value
.070
.164
With the yearly change in the United Kingdom's GDP and office rental rate data in London's central
business district from 1975 to 1996, a slightly significant R-squared of .253 results when a
regression is run with GDP leading by 1 year the office rental data. With GDP leading by 2 years a
similar R-squared of .251 results, but only an R-squared of .065 when they are compared in
simultaneous years. Using 1 year as our "best" result, the regression fails to find any relevant
significance with an R-squared of .017 suggesting that the United Kingdom's economy and the local
office market are not highly correlated. Although their peaks and troughs are similarly timed the
poor "Best" R-square suggests that the English economy is financially integrated across national
borders. As the graphs depict the United Kingdom's economy and office rental levels generally
move in sync over the 21 year time frame with the economy appearing to drive the office market.
Yearly Change in the United Kingdom's GDP and London's Office Rents Cycle
GDP GROWTH
0.08
0.07
0.061.00
0.05
0.04
0.03
0.02
0.01
0.00
-0.03
-0.03
-+-
Figure 3.1
London Office Rents Deflated
1.20
0.04
0.80
,0.6
-0.4
0.80
ij0.00
Volatility of United Kingdom's GDP and London's Office Rents
-+-
Figure 3.2
London Office Rents Deflated -U- GDP Deflated
1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
With 21 years of data on the Belgium economy from 1975-1996 we find striking similarities in the
movements of the economy and the office market with the nation's GDP appearing to drive the office
rental market. As the R-square's prove for Brussels when GDP leads by 2 years the office rental rate
data we have a .38 correlation, but only .272 when GDP leads by 1 year and a poor .163 when they
are run contemporaneously. The "Best" case scenario is when GDP leads by 2 years the office rental
cycle producing a strong .565 R-value. This suggests the economies performance and the office
markets performance are highly correlated due to a economically regulated and a less financially
integrated nation.
Yearly Change in Belgium's GDP and Brussels Office Rental Cycle
GDP Growth
-+-
Figure 3.3
Brussels Office Rents Deflated
0.08
1.20
0.06
1.00
0.04
0.80
0.02
0.60
0.00
0.40
-0.
0.20
0.004
0.00
Volatility of Brussels GDP and Belgium's Office Rents
-+-
Figure 3.4
Brussels Office Rents Deflated --- GDP
1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Paris, however, shows a less significant .189 R-square value when France's GDP growth cycle leads
the office rental rate data by 2 years and a .103 R-square when GDP data leads by 1 year. An
insignificant .031 results when they are run contemporaneously. It appears that both the economy
and the office market have both grown in the late 70's and early 80's but that the low R-square's
result from an increased integration of this economy with surrounding Europe. The late 80's,
however, do seem to depict that the economy's peak valuation has proceeded the office market's
peak valuation by approximately 2 years, possibly indicating a significant correlation as our "Best"
case scenario .585 R-squared depicts.
Yearly Change in France's GDP and Paris Office Rents
Paris GDP Growth
Figure 3.5
+ Paris Office Rental Index
0.10
3.00
0.08
2.50
0.06
2.00
0.04
1.50
0.02
1.00
0.00
-0
-0.04
c)0.50
0.00
Volatility of France's GDP and Paris Office Rents
Figure 3.6
-+- Paris Office Rental Index -w- Paris GDP
1975
1976 1977
1978 1979 1980
1981 1982 1983 1984
1985 1986 1987
1988 1989 1990
1991 1992
1993 1994
1995 1996
The peak valuations for both the Netherlands economy and the office market indicate that the GDP
growth cycle has preceded the office markets' peak in the late 70's and again in the mid 90's. But
our R-squares using data from 1975-1996 show an insignificant .033 R-squared when GDP is
regressed in similar years as office rental rates and only a slightly better .056 R-squared when GDP
data leads the office rental data by 1 year. When GDP leads the office rental data by 2 years,
however, a strong .354 R-square results indicating that GDP's cycle lead the office markets cycle by
this time period. Our "Best" case scenario produces a .192 correlation indicating that the Dutch
economy and the office market are slightly integrated. In addition, the economy occasionally
appears to be driving office rental rates, but the evidence is unsupported in these tests.
Yearly Change in Dutch GDP and Amsterdam Office Rents
GDP
0.06
0.05
0.04
0.03
0.02
0.01
Figure 3.7
--- Amsterdam Rents Deflated Growth
1.20
1.00
0.80
0.60
0.00
0.40
-0.0
020
-0.02
-0.03
0.00
Volatility of Dutch GDP and Amsterdam Office Rents
Figure 3.8
-+- Amsterdam Rents Deflated Growth -m- GDP
1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Madrid, with data points from 1975-1996 shows a .458 R-squared when the GDP data leads the
office market data by 1 year only a .181 R-squared when run contemporaneously. A .667 Rsquared result, however, is indicated for GDP leading the office data by a 2 year time period.
The "Best" case scenario result is a poor .003 R-squared. This indicates a strong segmentation
between the Spanish economy and its regional neighbors. Our graphs, however, definitely
depict similar peaks and troughs with a definite build up of the economy preceding the office
market's peak valuation by about 2 years.
Change in Spain's GDP and Madrid's Office Rents
GDP Growth
Figure 3.9
-+- Madrid Rents Def Growth
0.07
1.40
0.06
0.05
0.04
0.03
1.00
0.30.80
0.02
0.01
0.00
-0.01
0.60
0.40
A
7- :b7,-6
IVc!V:wrno
0.0
Volatility of Spain's GDP and Madrid's Office Rents
Figure 3.10
-+- Madrid Rents Def Growth -a- GDP
1970
1971 1972
1973 1974 1975
1976 1977 1978 1979
1980 1981 1982
1983 1984 1985
1986 1987
1988 1989
1990 1991
In Milan we have similar findings with data from 1975-1996 of an R-squared of .129 when the
Italian GDP data leads the office rental data by 1 year. With a 2 year lead, however, we have a
stronger .309 R-square although with the "Best" case scenario we again have a poor showing with a
.025 R-squared indicating that the Milan office market is deregulated and operates separate of the
nations economy. The graphs do depict, however, that the economy appears to be driving the office
market throughout the 80's and 90's by about 2 years.
Figure 3.11
Yearly Change in Italy's GDP and Milan Office Rents
GDP Growth
-+-
Milan Office Rental Index
0.06
1.80
0.05
1.60
0.04
1.40
0.03
1.20
1.00
0.02
0.01
0.80
0.00
0.60
0.40
-0.01
0.20
-0.02
0.00
Volatility of Italy GDP and Milan's Office Rents
-.-
1980
1981
1982
1983
1984
Figure 3.12
Milan Office Rental Index -u-*GDP
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
Geneva, with data from 1978-1996, finds an R-squared of .21 when GDP data leads the office rental
data by 1 year and also when it is run contemporaneously. With a 2 year lead, however, a more
significant .33 R-squared result indicates the office market follows the economies performance. The
"Best" case scenario's R-squared is only .111 which again indicates a fairly unregulated market open
to international commerce. Our graphs uphold this statistic with the economy appearing to again
drive the office market by 2 years.
Change in Switzerland's GDP and Geneva Office Rents
M GDP Growth
Figure 3.13
-+- Geneva Rents Deflated Growth
3.00
0.07
0.06
2.50
0.05
2.00
0.04
0.03
1.50
0.02
0.01
1.00
0.00
-0.0
-0.02
0.50
0.00
Volatility of Switzerland's GDP and Geneva's Office Rents
Figure 3.14
-+- Geneva Rents Deflated Growth -*-GDP
3.00
2.50
1.001.50
0.00
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
With New York we take the GNP and Manhattan office data from 1970-1997 but only find a .02 Rsquared when GNP data leads the office rental data by 1 year, and only .032 when lead by 2 years.
The "Best" case scenario of a 2 year lead only produces a poor R-squared of .023 which indicates
the economies segmentation with the office market. As the graphs depict the office markets peak
appears to lead the economies peak by roughly 2 years but in general Manhattan's office market
cycle appears to diverge from the United States GNP data possibly due to the international scope of
the New York tenant base.
Yearly Change in the United States GNP and Manhattan Office Rents
GNP Growth
0.15
0.10
-+-
Figure 3.15
New York Office Rental Index
3.00
2.50
2.00
0.05
1.50
0.00
1.00
0.50
-0.10
0.00
Volatility of the United States GNP and Manhattan Office Rents
-+-
Figure 3.16
New York Office Rental Index -U- GNP
1970 19711972 1973 1974 19751976 1977 19781979 19801981 1982 19831984 19851986 1987 19881989 1990 1991 19921993 1994 1995 1996 1997
Toronto, with data from 1975-1996 shows only a poor .003 R-squared when regression is run with
the GDP data in similar years as the office rental data, and only a .027 R-square when GDP lead the
office data by 2 years. Our "Best" case scenario R-square of a 2 year lead is only an R-value of .050
indicating that the Toronto office market is not financially integrated across borders. Thus the
Toronto office markets performance is very dependent on the Canadian GDP. Here our graphic data
runs contemporaneously during the 1980's but appears to diverge during the earlier part of the
1990's.
Change in Canada's GDP and Toronto's Office Rents
GDP Growth
-+-
Figure 3.17
Toronto Rents Deflated Growth
0.08
1.80
0.06
1.60
1.40
0.04
0.02
1.20
1.00
0.00
0.80
-0.04
0.60
0.40
0.20
0.00
U
U
Volatility of Canada's GDP and Toronto's Office Rents
-+-
1975
1976 1977
Figure 3.18
Toronto Rents Deflated Growth -U- GDP
1978 1979 1980
1981 1982 1983 1984
1985 1986 1987 1988
1989 1990
1991 1992
1993 1994 1995
1996
Sydney with data from 1975-1996 results in an R-squared of .017 when the GDP leads the office
rental data by 1 year and .007 R-square when run contemporaneously. When GDP data leads
the office rental data by 2 years, however, we find a .101 R-squared. A 2 year lead, being our
"Best" case scenario, returns a .412 R-square indicating that the Australian economy has a strong
effect on the Sydney office markets performance. Although the R-squares are less than
significant for GDP leading the office market by a definite time period there does appear to be
some similarities in the two indices movements over time with GDP preceding the office markets
peaks. Thus the indices do appear to correlate possibly do to a partial segmentation of the
Australian economy with other nations
Figure 3.19
Change in Australia's GDP and Sydney's Office Rents
-*- Sydney Office Rental Index
GDP Growth
u.u0
0.06
I.LU
pp
..
....
......
II
0.04
ggl 'em
1.00
MVig
k
QN
IN
0.80
Q,
0.02
0-
Mi E,
....
....
.
0.60
0.00
-0.0
-0.04
-0.06
sg
0.40
M.0
'.40
F FIN
0.20
0.00
Volatility of Australia's GDP and Sydney's Office Rents
Figure 3.20
-#- Sydney Office Rental Index -m- GDP
1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
Singapore with data from 1975-1996 results in a significant R-squared of .334 when the GDP
data leads the office market data by 1 year and an even more significant .416 when lead by 2
years. Thus with 2 years being the "Best" case scenario the R-square value is a .164 indicating
that the Singaporean economy has a weak effect on the office markets performance or in other
words the economy is internationally integrated. As the graphs also depict the economy
definitely appears to be driving the office market by about 2 years with similar movements in
both indices.
Figure 3.21
Change in Singapore's GDP and Office Rent
GDP Growth
-4-Singapore Office Rental Index
4.00
0.20
3.50
0.15
3.00
0.10
2.50
2.00
0.05
1.50
1.00
0.00
0.50
-oo~c~o
~0.00
Volatility of Singapore's GDP and Office Rents
Figure 3.22
-+- Singapore Office Rental Index --- GDP
1975 1976
1977 1978
1979 1980 1981 1982 1983
1984 1985 1986
1987 1988 1989
1990 1991
1992 1993
1994 1995
1996
Tokyo as well, with data from 1975-1996 shows an R-squared of .186 when the GDP data leads
the office rental data by 1 year and a .275 R-square when it leads by 2 years. GDP data and the
office market index run contemporaneously results in a poor .017 R-squared. Thus with 2 years
being the "Best" case scenario a poor .070 R-squared indicates that the Japanese economy's
performance does not affect the Tokyo office market performance although it does lead it by
approximately 2 years cyclically. The graphs depict a pattern of similar movements in both sets
of data with a trend for the economy to drive the office market especially in the late 1980's.
Change in Japan's GDP and Tokyo's Office Rents
GDP Growth
0.07
0.06
Figure 3.23
-+-Tokyo Office Rental Index
2.50
2.00
0.05
0.04
1.50
0.03
1.00
0.02
0.01
0.00
0.50
0.00
Volatility of Japan's GDP and Tokyo's Office Rents
-+-
1975
1976 1977
1978 1979
Figure 3.24
Tokyo Office Rental Index -m- GDP
1980 1981 1982 1983 1984
1985 1986 1987
1988 1989
1990 1991 1992
1993 1994
1995 1996
I-_-_
---- 9
Jakarta with data from 1980-1995 results in an R-squared of .104 when the GDP data leads the office
market data by 1 year and .365 R-square when it leads by 2 years. An insignificant .014 result when
GDP is run contemporaneously with the Jakarta office market data. Thus with 2 years being the
"Best" case scenario a strong .481 R-squared indicates that the highly regulated Indonesian economy
strongly affects the local office market's performance. The graphs, however, show only a slight
significance from year to year but display a tendency for the economy to lead the office market by 2
years. This could easily be due to the tightly regulated financial world representative of emerging
Asian economies of the 1980's and 1990's.
Change in Indonesia's GDP and Jakarta Office Rents
GDP DEF Growth
Figure 3.25
-+- Jakarta Office Rental Index
0.25
1.20
0.20
1.00
0.15
0.80
0.10
0.60
0.05
0.40
0.00
0.20
-0.0
Y0.00
Volatility of Indonesia's GDP and Jakarta's Office Rents
Figure 3.26
-+- Jakarta Office Rental Index -n- GDP
1975
1976 1977 1978
1979 1980 1981
1982 1983 1984 1985
1986 1987 1988
1989 1990 1991
1992 1993
1994
1995
The results of the analysis are that the more regulated an economy is the more likely its
economic performance, as indicated by GDP, will enhance or detract from its local office
market's performance. When a country is highly regulated this means that international capital
flows will have greater barriers to capitalize on local business opportunities. A deregulated
country will have its local market cycles determined by international capital flows. Regulated
markets can be seen in countries like Jakarta, Sydney, Brussels and Paris where GDP (deflated)
when regressed against the office markets performance by a 2 year lead (in the case of Paris,
contemporaneously) results in a .4 R-squared or better. The opposite effect can be seen in
financially deregulated markets like London, Amsterdam, Madrid, Milan, Geneva and Singapore
where GDP growth is highly correlated with office markets growth, but uncorrelated when
regressed directly with deflated GDP data. In addition, it appears that an economies peak
valuation precedes the office markets peak valuation by about 2 years regardless of the extent of
financial deregulation which infers the beginnings of a global office market cycle.
CHAPTER 4
Global Real Estate Cycles
The U.S. was the first country to deregulate in the late 1970's. It was also the first to experience a
volatile real estate cycle in the early 1980's. Until this time the U.S. real estate markets were still
insulated from the international economy and U.S. regional markets remained synchronized. The
massive saving and loan debacle that ensued had effects that were still being worked out until the
closure of the Resolution Trust Corporation at the end of 1995. (Renaud, 1997).
With the 1985 Plaza currency realignment, the U.S. international capital position had shifted from
net creditor to net debtor for the first time in seventy years. This new capital situation had
transformed the United States into an open economy sensitive to external influences. Given the very
large size of the U.S. economy, international capital flows have had a different impact on regional
real estate economies. As a result during the first global real estate cycle, U.S. regional cycles
became asynchronous. California and Northeast became prime targets for foreign investors,
particularly the Japanese, and these two regions cycles moved in harmony with the global cycle
(Renaud, 1997).
As J.P. Mei (1998) points out during the 1988 to 1994 period the west and Japan were suffering
through one of the worst real estate slumps in history while the rest of Asia was enjoying a boom.
Quan and Titman (1996) show that average correlation's between the U.S. and five Asian real estate
markets were actually negative during this period.
Green, Malpezzi and Barnes (1998) investigate international and domestic market integration of
property and equity markets in the United States, United Kingdom and Australia. There results also
support the notion that property and equity markets are not integrated internationally and that
diversification benefits are present domestically.
The U.S. experience sets a good example for the international real estate investor. Yet most financial
managers, bank supervisors and industry people continue to behave as if their national real estate
economy was still a local non-traded sector. This can be evidenced by the fact that most analysts
treat the U.S. real estate economy and its capital markets as closed to international speculation.
All office real estate markets will experience the contraction and expansion of their stock depending
on population and economic growth. Whether it be New York in the 70's, Tokyo in the 80's or
Bombay in the 90's; all real estate markets experience peaks and troughs reflective of which stage of
the development cycle they are in. The theory is that if there are unpredictable patterns to global real
estate cycles then portfolio managers would be wise to take note when searching for the most
efficient portfolio investment.
On the surface international real estate investment would appear to be the taking of stock in the
government of another country with the normal desire of wealth building in order. All seems
relatively straight forward until a currency or political crisis comes into the picture. A crisis in the
1990's sense of the word is when a political organization attempts to present a economic scenario
different from reality to the public. Mexico, Thailand, Japan, Korea, Indonesia etc. have all
experienced recent economic crisis where a lack of transparency in the banking system was felt to be
at play (Dornbusch, 1998). These countries had shaky financial systems of whose clients had large
dollar debts. The Bank of Thailand could not raise interest rates to support its external financing
without making the loan problem worse and it could not reduce them without taking the risk of
making the external financing crisis worse.
This study looks at office rental rates in 17 major market around the world from 1970 to 1998.
First, the North American, Asian, and European office market cycles are graphically depicted in
order to illustrate correlations and changes that these regions have endured during the past 30 years.
Next, three correlation matrices are produced using: A) 1980 to 1998 office market rental rates
denominated in local currency in all 17 countries which gives an understanding of the most recent
real estate cycle and the ensuing regional diversification opportunities. B) 1970 to 1998 office
market rental rates denominated in local currency which gives an indication of the deregulation
affects over a longer period of time. C) 1970 to 1998 office market rental rates exchanged in U.S.
dollar capital in order to give an better understanding of the effects of currency risk for the U. S.
dollar backed investor.
Our North American graph highlights the percentage changes of three regional office market cycles:
New York, San Francisco and Toronto. The peaks and troughs are clearly moving in sync the
majority of years as depicted in the 1980's and again in the late 1990's. The numbers at the bottom
of the graph represent the percent change in each year of the rental rate movements.
Figure 4.1
North American Office Market Cycle
0)
C
-.
0
1970
1973
1976
1980
1983
1986
1989
1993
1996
-4.76 0.00% -1.38 1.59%
-0-New York
0.00% 0.00% 55.17 -4.35
-*-San Francisco
4.17% 0.00% 50.00 0.00% -11.76 3.33% -8.16 8.33%
-A- Toronto
0.00% 10.68 40.00 13.55 2.69% 5.26% -19.35 11.11
Years
Across our European countries we find smaller changes in rental rates throughout the late 1970's
until we reach the mid to late 1980's where fluctuations become greater with similar movements
in the up and down cycles ending in 1997.
European Office Market Cycle
Figure 4.2
-4-
London - City
-a-
-0-
Frankfurt
-i-Madrid
Brussels
-x-
Paris
-
Milan
-w-
Amsterdam
Geneva
100.00%
80.00%
60.00%
40.00%
20.00%
0.00%
-20.00%
-40.00%
-60.00%
-U-London - City
-6- Brussels
-*- Paris
-*-Amsterdam
-- Frankfurt
-- Madrid
Milan
Geneva
-
19-70 1973 1975 11978
1981 11983 1986 :1988 1991
62.96 -35.56:9.68% 12.50 3.33%
7.14% -6.67 7.14%10.00% 7.69%
4.55%0.00% 10.00 14.29 5.56%
5.00% 13.64 5.77%,0.00%i-14.29
0.00% -7.41 8.00% 0.00% 0.00%
0.00% 7.14%.8.11% 7.14%' -6.45
0.00% 0.00% 0.00% 10.00 8.51%
0.00% 0.00%;0.00%!0.00% 34.75
10.00 '6.67% -31.82
33.33 14.55 6.06%
15.38 14.06 -4.26
8.33% 0.00% 5.88%
25.00 11.11 12.50
40.63 48.15 0.00%
9.67% 18.83 -6.67
13.33 26.32 0.00%
1994 1996
8.33%
-9.09
-5.71
0.00%
-3.85
-9.09
-9.09
-11.11
14.29
-3.13
-6.67
5.56%
-4.35
10.00
-11.11
-7.14
In Asia our graph reveals that Sydney in the early 1970's moves in a negative fashion compared with
other regional markets. During the 1980's and 1990's, however, the markets are moving more
contemporaneously with Kuala Lumpur, Jakarta and Singapore posting 70% plus increases in their
office rental rates. By 1996 and 1997 all the markets have cooled considerably posting sub 10%
volatility levels.
Asian Office Market Cycle
--
Sydney ---
1970
Singapore ---
1973
Hong Kong --*-Tokyo -W- Jakarta -4-
1975 1978 1981
1983
1986 11988
1991
Kuala Lumpar
1994
1996
-Sydney
-15.74:6.59% 11.11 33.33 3.08% 21.03 14.29 -4.83 -0.84 1.64%
14.55 11.32 -12.72164.03 -14.30 -28.48 21.56 -9.28 24.05 3.23%
Hong Kong
55.00 -11.76 21.62 0.00%1-26.92 22.73 51.43 -23.21 45.31 -5.79
-- Tokyo
0.00% 9.52% -8.33 6.38% 17.00 8.87% 7.73% 7.02% -11.11 0.00%
0.00% 0.00% 7.14% 6.67%1-14.29 -15.52 94.17 -24.93 7.69% -7.69
-N-Jakarta
---
Figure 4.3
Kuala Lumpar
14.29 3.53% 2.59% 39.72 0.59% -3.05 -3.31 22.49 13.43 -1.20
The next part of our study we perform a correlation matrix across 15 countries. First we look at data
from 1980 to 1998 as this shorter time scale correlates the minimum time series of data that was
available across all 15 countries.
Table 4.1
Correlation Matrix from 1980-1998
London
Brussel Paris Amster Frankfurt Madrid Milan
dam
London
1.0000
Brussels
-0.1861
1.0000
Paris
0.3476
0.7508
1.0000
Amsterd
-0.4286
0.5788
0.1611
Frankfur
-0.0611
0.9135 0.8682 0.5378
1.0000
Madrid
0.5008
0.5237
0.8196 0.3367
0.7286
1.0000
Milan
Geneva
N.Y.
Toronto
Sydney
Singapo
Tokyo
Jakarta
K.L.
0.3512 0.6004 0.8653 0.3757
0.6947 0.3867 0.8154 0.3053
0.3812 -0.7671 0.3984 0.2464
0.7607 -0.4174 0.2171 0.4386
0.6421 0.4622 0.8849 0.0333
-0.0802 0.3558 0.3095 0.5062
0.5156 0.5743 0.9313 0.1112
0.1116 -0.4639 0.3765 0.2445
-0.8246 0.4947 0.0474 0.6621
0.8005
0.4696
-0.5542
-0.1357
0.6182
0.5068
0.7720
-0.2816
0.3816
0.9394
0.6478
0.0480
0.4815
0.8392
0.4760
0.9171
0.1695
-0.1758
Geneva New
York
Toronto Sydney
Singapor Tokyo Jakarta Kuala
Lumpur
1.0000
1.0000
0.6174
-0.0552
0.3815
0.8049
0.4788
0.9173
0.0767
-0.0167
1.0000
-0.2005
0.4583
0.8428
-0.0887
0.8121
-0.4562
-0.5326
1.0000
0.7473 1.0000
-0.0472 0.5240 1.0000
-0.0254 -0.0518 0.3274
-0.1266 0.4702 0.9046
0.8067 0.4392 -0.1502
-0.5032 -0.7766 -0.3108
1.0000
0.3246
0.4186
0.5033
1.000
-0.141 1.000
-0.221 -0.0296 1
Table 4.2
Correlation Matrix from 1970 to 1998
London
N.Y.
Toronto Sydney Singapo Tokyo Jakarta K.L
London Brussels Paris Amsterd Frankfit Madrid Milan Geneva
Rents
Rents
Rents
Rents
Rents
Rents
Rents
Rents
Rents
Rents
Rents
Rents
Rents
Rents
Deflated Deflated Deflated Deflated Deflated Def Deflated Deflated Deflated Deflated Deflated Deflated Deflated Deflated
1.0000
Rents
Brussels
Rents
Paris
Rents
Amsterd
Rents
Frankfur
Rents
Madrid
Rents
Milan
Rents
Geneva
Rents
N.Y.
Rents
Toronto
0.0719
1.0000
0.2539
0.5388
1.0000
-0.0524
0.6772
0.0405
1.0000
-0.0475
0.8175
0.8673
0.4438
1.0000
0.4967
0.5125
0.5074
0.3667
0.5753
1.0000
0.3512
0.6004
0.8653
0.3757
0.8005
0.9394
1.0000
0.6626
0.4355
0.8415
-0.2842
0.5086
0.5922
0.6174
1.0000
0.1488
-0.6431
0.0748
-0.3832
-0.2069
-0.0956
-0.0552
-0.0915
1.0000
0.7145
-0.3253
0.3670
-0.4346
0.0080
0.3777
0.3815
0.5048
0.7431
1.0000
0.4084
0.3925
0.8191
0.1038
0.6029
0.2232
0.8049
0.7802
0.3167
0.5336
1.0000
-0.0577
0.3131
0.5626
0.3165
0.5971
0.2472
0.4788
0.1053
0.3030
0.1686
0.6566
1.0000
0.4836
0.5464
0.9071
0.0642
0.7973
0.7587
0.9173
0.8227
0.0997
0.5363
0.7260
0.4612
1.0000
0.1501
-0.1256
-0.5082
0.3360
-0.3074
0.2613
0.0767
-0.4562
0.0860
0.0894
-0.5978
-0.0576
-0.2342
Rents
Sydney
Rents
Singapo
Rents
Tokyo
Rents
Jakarta
Rents
Kuala
Lumpur
I
-0.2104
0.6263
-0.1313
0.7992
0.2473
-0.0776
-0.0167
-0.4923
-0.5757
-0.7547
1.0000
II
-0.0002
0.2420 -0.2663
0.2378
Rents
Overall the correlations are higher in comparison to the correlation matrix performed from 19701980. Exceptions were Milan, with data from 1980-1998; Geneva, with data from 1977-1998 and
Toronto and Jakarta with data from 1973-1996. In London, for example, with a longer time series,
Brussels becomes positively correlated with an R-square of .07 where as with a shorter time series it
has an R-square of -. 18. All other countries correlated with London, however, show only a slightly
stronger correlation when using the data over the 1980-1998 time period. Brussels is the same story
when short and long term data series are compared as it remains negatively correlated with New
York, Toronto and Jakarta. With Paris, once again, our shorter time series of data reflects stronger
correlation with all 15 markets, but New York does change form positive .07 with the 1970-1998
series data to a negative -.03 with the shorter time series. Amsterdam, as well, offers stronger Rsquares over a shorter time cycle with the exception of Sydney which changes from a positive .103
with the 1970-1998 data to a negative -.03 using the 1980-1998 data. Frankfurt offers strong
correlation in the short run against all countries, but Toronto is positively correlated in the long run
although the R-square turns into a negative -.13 in the short run. With Madrid the correlations are
stronger in the short run across all regions. Milan our deflated office rents only run from 1980 to
1998 so R- squares remain constant. Geneva tells a similar story but Singapore becomes negatively
correlated in the short run. New York R-squares change the most in the short run with Sydney
changing from a .3 in the long run series to a negative -.04 in the short run. Singapore changes from
a .3 in the long run to a negative -.02 while Tokyo goes from a .09 to a negative -.12 in the short run.
Toronto R-squares change from .16 in the long run to a -.05 when correlated in the short run with
Singapore. Sydney, however, remains constant with a slightly stronger correlation in the short run
across all countries. Singapore and Jakarta show significant change from negative -.05 in the long
run to a positive .4 in the short run. Tokyo remains fairly constant over both lengths of data series
and Kuala Lumpur changes from a .23 to a negative -.02 in the short run. Interpreting this data over
the two time scales it becomes apparent that as markets develop their diversification benefits also
will change.
Finally, the office rental data is exchanged into U.S. dollars in order to give the dollar based investor
an understanding of the effects of currency risk on international investment.
Correlation Matrix from 1970-1996 with U.S. Dollar Denominated Rents
London $ Brussel $
U.S.
Rents
Rents
Dollar
Paris$
Rents
Based
Amsterdam $
Rents
Frank- Madrid$ Milan $ Geneva $
furt $
Rents
Rents
Rents
N.Y.
Rents
Table 4.3
Toronto $ Sydney $ Singapor Tokyo $
Rents
Rents e $ Rents Rents
Deflated
Rents
Rents
London $
Rents
Brussel $
Rents
Paris $
Rents
Amsterdam $
1.00
-0.03
1.00
0.15
0.80
1.00
-0.24
0.89
0.72
100
Frankfurt
$ Rents
Madrid $
Rents
Milan $
Rents
-0.14
0.84
0.94
0.80
1.00
0.58
0.35
0.29
0.17
0.10
1.00
0.54
0.46
0.76
0.41
0.57
0.98
1.00
Geneva $
Rents
N.Y.
Rents
Toronto $
Rents
Sydney $
Rents
Singapor
e $ Rents
Tokyo $
Rents
0.36
0.55
0.79
0.35
0.69
0.27
0.45
1.00
0.00
-0.61
-0.19
-0.34
-0.28
-0.31
0.11
-0.38
1.00
0.65
-0.43
0.05
-0.52
-0.18
0.31
0.50
0.16
0.62
1.00
0.68
-0.03
0.20
-0.12
0.12
0.14
0.60
0.09
0.34
0.68
1.00
-0.30
0.47
0.54
0.58
0.69
-0.24
0.23
0.30
0.07
-0.17
0.37
1.00
0.10
0.61
0.90
0.52
0.88
0.01
0.59
0.93
-0.10
0.13
0.27
0.57
Rents
1.00
This matrix correlates the same office rental rate data although exchanged into U.S. dollar currency.
After comparing the R-squared values in a local and U.S. dollar denominated currency striking
differences appear for U.S. dollar based investors. Most major currencies have remained
comparatively valued with the U.S. dollar. As the table depicts, however, there are real estate
opportunities which improve for those with U.S. dollar investment capital. Amsterdam and Paris
show no correlation when regressed in local currency but when regressed in U.S. dollar based
currency show a strong .72 correlation. This would infer that a U.S. investor would be wise not to
enter into the Paris and Amsterdam markets because when exchange risk is incorporated into the
return both markets move in sync thus offering little diversification benefits. Madrid and Frankfurt
reveal an opposite scenario for the U.S. investor because when these markets are regressed in home
currency they return a .57 R-squared but when regressed in U.S. dollar based rents results in a poor .1
R-squared. This would mean that a U.S. based investor would be better off investing in these
56
markets with U.S. capital as diversification benefits are present. Sydney's currency appears to
fluctuate strongly as the R-squares depict in the local currency and U.S. dollar based matrix. In
general here the R-square values drop significantly when based in U.S. dollars across all markets
which tells the U.S. investor that the Australian investment is a valuable asset for a more efficient
portfolio. With Singapore the correlations drop slightly when the U.S. dollar R-squares are
compared to the local currency R-squares although no significant currency arbitrage opportunities are
present. Tokyo follows the same pattern as Singapore, however, it becomes noticeably less
correlated with Australia and Frankfurt when correlations are run with U.S. denominated office rents.
CHAPTER 5
Conclusion:
The disparity between public and private markets for the international office sector is decreasing as
more economies become globally integrated. Thus financial deregulation across national borders
coupled with advances in information technology is equalizing public and private real estate values.
Of course, management expertise and market share require a premium valuation in the case of
public companies, but this does not mean that values will necessarily exceed the private sector. The
data continues to point to the fact that the public real estate market leads private real estates
performance by to 1 to 2 years. The time differences seem to appear when companies are involved in
the building process as opposed to property sales and management. As the construction process is
involved with the expansion and contraction of real estate stock it makes sense that disparities will
occur between public and private indices when construction firms are involved in either market
study.
Secondly, a nations gross domestic product can be a strong indicator of the central business districts
office market performance. This depends on a nations degree of financial deregulation and the extent
of economic integration across national borders. An office market susceptible to international capital
flows will be more internationally diversified and therefore should fluctuate more in line with the
global economy. Of course the opposite affect is demonstrated in economies closed to international
commerce or tightly regulated. Regardless, GDP's peak valuation tends to lead the office markets
peak valuation by about 2 years in most major markets. This can be attributed to investor delays in
seeking business opportunities during an upswinging economy and the effects of international
capital cycles.
Finally, as developed nations are seeing the benefit to financial deregulation, more and more
developing nations are being forced to open their own markets to global competition. This is having
an adverse affect on diversification benefits as economies begin to move in sync. Our time series
correlation matrices have shown that although a nation may be segmented from other markets at a
certain point in time; the world is not stagnant. Diversification benefits still exist for the international
real estate investor but proper portfolio management is an extremely dynamic undertaking with
constantly changing dimensions. In addition, currency fluctuation can have a strong impact on an
investors financial returns thus an economies growth potential should be closely investigated with
regards to potential investment.
Appendix:
Data Source:
Office Market Rental Rates courtesy of CB- Richard Ellis
Consumer Price Indices courtesy of Torto-Wheaton
Gross Domestic Product courtesy of Torto- Wheaton
Public Real Estate Indices, including company profiles courtesy of Bloomberg Intl.
London Statistical Data
Global
Property
INDEX
London City
GDP
GDPDE GDP GDP Londo
F
Growt Deflat n
h
ed
Office
Rents
Deflate
CPI 2 FTSE
FTSE
FTSE
London London
Property Deflated Property Rents
X-rate
Index
Index
Deflated
London
Office
Rental
Index
d
f sq.ft. p.a.
1970
11.00
1971
12.50
1972
13.50
1973
22.00
/ 19-
497_
51.61
283.44
314.79
319.74
321.53
342.99
355.65
352.38
0.02
0.01
0.07
0.04
-0.01
1.00
1.00
18.21
60.41
0.42
1.11
1.13
1.13
1.21
1.25
1.24
0.72
0.60
0.57
(0.570.60
0.61
33.55
39.09
45.30
49.02
55.62
65.62
43.22
36.45
34.22
0.45
0.56
0.57
T
22.50
1976
1977
14.50
14.25
15.50
1978b
17.00
1979
1980
20.00
24.00
105.60
124.99
145.66
168.14
197.83
231.23
19}I
1982
27.00
30.00
254.27
278.24
346.35
349.01
-0.02
0.01
1.22
1.23
0.62
73.42
79.72
1983
1984
31.00
32.00
303.52
324.84
363.95
371.14
0.04
0.02
1.28
1.31
0.62
0.61
83.39
87.53
1W
9-'
1Q7
1988
1989
1990
1991
1992
1993
1994
1995
1996
35.00
38.50
60.00
64.00
65.00
55.00
37.50
32.50
30.00
32.50
35.00
40.00
356.17
383.63
421.89
469.76
514.24
549.39
573.91
597.01
628.56
665.06
697.42
734.41
383.66
399.55
421.89
447.78
454.73
443.76
437.93
439.16
455.25
470.04
476.66
489.94
0.03
0.04
0.06
0.06
0.02
-0.02
-0.01
0.00
0.04
0.03
0.01
0.03
1.35
1.41
1.49
1.58
1.60
1.57
1.55
1.55
1.61
1.66
1.68
1.73
0.62,
0..6
0.99
1.01
0.95
0.74
0.47
0.40
0.36
0.38
0.40
0.44
92.83
96.02
100.00
104.91
113.09
123.80
131.05
135.94
138.07
141.49
146.31
149.90
1997
46.00
780.68
2100.00
1998
48.00
826.74
2257.00
0.52
35.96
36.57
.63
0.50
0.57
37.17
36.56
0.66
0.75
40.10
6.00
61.01
57.48
44.43
28.61
23.91
21.73
22.97
23.92
26.68
0.78
0.68
0.61
0.56
0.61
0.56
0.57
0.57
0.67
0.65
0.63
0.64
/36.78',
1000.00
1120.00
1520.00
1700.00
1880.00
1590.00
1480.00
1040.00
1480.00
1600.00
1480.00
1640.00
1077.19
1166.47
1520.00
1620.47
1662.42
1284.29
1129.33
765.03
1071.92
1130.83
1011.52
1094.08
1.00
1.08
1.41
1.50
1.54
1.19
1.05
0.71
1.00
1.05
0.94
1.02
0.47
0.43
37.70\
1.00
1.06
1.59
1.62
1.52
1.18
0.76
0.63
0.58
0.61
0.63
0.71
Brussels Statistical Data
Global
Property
INDEX
Brussels
GDP
GDPDE
F
GDP
Growth
GDP
CPI 2
Brussels Brussel
Office
X-rate
Rents
Deflated
Brussels
Office
Rents
Deflated
BF sq.m. p.a.
1970
3,000
1971
3,500
1972
3,500
1973
3,750
1974
3,750
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
3,500
3,500
2,800
3,000
3,000
3,250
3,250
3,250
3,500
3,600
3,750
5,000
5,500
6,300
7,000
8,250
8,750
9,250
8,250
7,500
8,000
7,750
1997
8,000
1998
8,000
1,264
3,717.53
2,281
2,591
2,799
3,005
3,209
3,477
3,610
3,923
4,161
4,474
4,784
5,035
5,256
5,613
6,072
6,475
6,780
7,143
7,317
7,678
7,936
8,186
4,487.18
4,669.29
4,709.81
4,838.79
4,946.26
5,026.00
4,848.05
4,845.91
4,773.61
4,826.08
4,921.14
5,113.57
5,256.12
5,548.23
5,821.07
6,000.51
6,088.09
6,261.55
6,242.04
6,398.33
6,517.62
6,587.49
0.04
0.01
0.03
0.02
0.02
-0.04
0.00
-0.01
0.01
0.02
0.04
0.03
0.06
0.05
0.03
0.01
0.03
0.00
0.03
0.02
0.01
1.00
34.00
8,823.93
50.000
1.00
1.21
1.26
1.27
1.30
1.33
1.35
1.30
1.30
1.28
1.30
1.32
1.38
1.41
1.49
1.57
1.61
1.64
1.68
1.68
1.72
1.75
1.77
50.84
55.49
59.44
62.10
64.87
69.19
74.46
80.96
87.16
92.70
97.21
98.47
100.00
101.16
104.30
107.91
111.37
114.07
117.21
120.00
121.76
124.27
6,884.81
6,307.16
4,710.75
4,831.25
4,624.57
4,697.52
4,364.58
4,014.30
4,015.38
3,883.59
3,857.63
5,077.72
5,500.00
6,227.63
6,711.18
7,645.61
7,856.89
8,108.82
7,038.36
6,249.90
6,570.19
6,236.46
36.779
38.605
35.843
31.492
29.319
29.242
37.129
45.691
51.132
57.784
59.378
44.672
37.334
36.768
39.404
33.418
34.148
32.150
34.597
33.457
29.480
30.962
0.78
0.71
0.53
0.55
0.52
0.53
0.49
0.45
0.46
0.44
0.44
0.58
0.62
0.71
0.76
0.87
0.89
0.92
0.80
0.71
0.74
0.71
Paris
Global
Property
INDEX
Statistical Data
Paris
SBREAL SBREAL SBREAL GDP
Property Deflated Property
Index
Index
GDPDE
F
Paris
GDP
Growth
Paris
GDP
CPI 2
Paris
Paris X- Paris
rate
Office
Rents
Deflated
Rental
Index
FF sq.m. p.a.
1970
900
1971
1,000
1972
1,100
1973
1,150
1974
900
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
900
900
1,000
1,100
1,300
1,400
1,600
1,800
1,900
2,200
2,600
3,000
3,200
3,650
4,200
4,700
4,500
4,100
3,500
3,300
3,000
2,800
1,000
970
810
890
930
700
690
1997
3,000
770
8,060
1998
3,000
890
8,414
927
871
710
759
775
575
555
1.00
0.94
0.77
0.82
0.84
0.62
0.60
1,468
1,701
1,918
2,183
2,481
2,808
3,165
3,626
4,006
4,362
4,700
5,069
5,337
5,735
6,160
6,509
6,776
7,000
7,077
7,390
7,675
7,878
2,887.46
3,064.47
3,226.53
3,514.87
3,824.67
4,059.09
4,250.17
4,478.75
4,596.46
4,705.52
4,835.04
5,148.10
5,336.65
5,669.21
5,905.53
6,032.61
6,084.58
6,136.01
6,037.71
6,157.94
6,303.12
6,339.80
0.06
0.05
0.09
0.09
0.06
0.05
0.05
0.03
0.02
0.03
0.06
0.04
0.06
0.04
0.02
0.01
0.01
-0.02
0.02
0.02
0.01
1.00
1.06
1.12
1.22
1.32
1.41
1.47
1.55
1.59
1.63
1.67
1.78
1.85
1.96
2.05
2.09
2.11
2.13
2.09
2.13
2.18
2.20
34.00
2,647.18
5.55
50.84
55.49
59.44
62.10
64.87
69.19
74.46
80.96
87.16
92.70
97.21
98.47
100.00
101.16
104.30
107.91
111.37
114.07
117.21
120.00
121.76
124.27
1,770.38
1,621.84
1,682.41
1,771.46
2,003.98
2,023.55
2,148.72
2,223.30
2,179.78
2,373.31
2,674.62
3,046.63
3,200.00
3,608.07
4,026.71
4,355.68
4,040.69
3,594.18
2,985.97
2,749.95
2,463.82
2,253.17
4.29
4.80
4.91
4.51
4.25
4.23
5.43
6.57
7.62
8.74
8.99
6.93
6.01
5.96
6.38
5.45
5.64
5.29
5.66
5.55
4.99
5.12
1.00
0.92
0.95
1.00
1.13
1.14
1.21
1.26
1.23
1.34
1.51
1.72
1.81
2.04
2.27
2.46
2.28
2.03
1.69
1.55
1.39
1.27
Amsterdam Statisical Data
Global
Property
INDEX
Amsterdam
GDP
DFI sq.m. p.a.
1970
200
1971
195
1972
200
GDPDEF GDP
Amster- CPI 2
dam GDP
Growth
AmsterAmsterdam Rents dam Xrate
Deflated
____
____
123
303.99
Amsterdam
RentsDeflated
Growth
1.00
40.44
494.62
3.62
1.00
1973
210
1974
220
1975
1976
1977
1978
250
250
260
275
223
256
279
301
365.01
383.34
392.90
406.89
0.05
0.02
0.04
0.02
1.20
1.26
1.29
1.34
61.13
66.66
70.98
73.87
409.00
375.03
366.31
372.26
2.53
2.64
2.45
2.16
0.83
0.76
0.74
0.75
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
280
350
350
350
300
300
300
325
330
330
375
425
450
320
342
358
373
387
406
426
438
441
458
485
517
543
415.60
416.58
409.12
402.49
406.44
412.20
422.95
434.76
440.84
454.29
476.20
495.08
504.22
0.00
-0.02
-0.02
0.01
0.01
0.03
0.03
0.01
0.03
0.05
0.04
0.02
0.01
1.37
1.37
1.35
1.32
1.34
1.36
1.39
1.43
1.45
1.49
1.57
1.63
1.66
76.98
82.02
87.55
92.69
95.30
98.42
100.61
100.71
100.00
100.75
101.84
104.34
107.60
363.71
426.73
399.75
377.61
314.78
304.81
298.17
322.70
330.00
327.56
368.24
407.34
418.20
2.01
1.99
2.50
2.67
2.85
3.21
3.32
2.45
2.03
1.98
2.12
1.82
1.87
0.74
0.86
0.81
0.76
0.64
0.62
0.60
0.65
0.67
0.66
0.74
0.82
0.85
1992
1993
1994
1995
1996
450
450
450
450
475
566
581
613
635
662
509.86
510.50
523.51
532.09
543.05
0.00
0.03
0.02
0.02
-1.00
1.68
1.68
1.72
1.75
1.79
111.03
113.90
117.09
119.34
122
405.29
395.08
384.32
377.07
390
1.76
1.86
1.82
1.61
1.69
0.82
0.80
0.78
0.76
0.79
1997
500
697
1998
525
738
Frankfurt Statistical Data
Global
Property
INDEX
Frankfurt CDAX
Holding
Index
CDAX
Holding
Deflated
CDAX
Holding
Index
CPI
Frankfurt Frankfurt Frankfurt
Office
Rents
X rate
Rental
Deflated
Index
DM sq.m. p.m.
1970
34.00
1971
37.00
1972
25.00
44.00
1973
25.00
38.00
1974
27.00
47.00
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
25.00
22.00
25.00
27.00
30.00
30.00
30.00
32.00
32.00
32.00
32.00
40.00
45.00
50.00
60.00
80.00
90.00
75.00
65.00
62.50
57.50
55.00
46.00
46.00
51.00
55.00
51.00
53.00
58.00
75.00
80.00
85.00
130.00
140.00
100.00
130.00
180.00
155.00
162.00
157.00
220.00
210.00
218.00
260.00
1997
55.00
340.00
1998
55.00
509.31
74.84
75.22
72.12
77.11
80.99
72.13
71.10
73.18
89.90
92.85
96.33
144.18
155.47
110.78
142.20
191.58
160.64
162.00
149.44
200.47
186.27
189.87
223.13
1.00
0.96
1.03
1.08
0.96
0.95
0.97
1.20
1.23
1.28
1.92
2.07
1.47
1.89
2.55
2.14
2.15
1.99
2.67
2.48
2.52
2.97
45.43
0.00
3.66
61.15
63.78
66.14
67.91
70.71
74.54
79.26
83.43
86.16
88.24
90.16
90.05
90.27
91.42
93.96
96.49
100.00
105.06
109.74
112.74
114.82
116.52
40.88
34.49
37.80
39.76
42.43
40.25
37.85
38.36
37.14
36.27
35.49
44.42
49.85
54.69
63.86
82.91
90.00
71.39
59.23
55.44
50.08
47.20
2.46
2.52
2.32
2.01
1.83
1.82
2.26
2.43
2.55
2.85
2.94
2.17
1.80
1.76
1.88
1.62
1.66
1.56
1.65
1.62
1.43
1.50
1.00
0.84
0.92
0.97
1.04
0.98
0.93
0.94
0.91
0.89
0.87
1.09
1.22
1.34
1.56
2.03
2.20
1.75
1.45
1.36
1.23
1.15
Madrid Statistical Data
Global
Property
INDEX
Madrid
GDP
GDPDEF GDP
Growth
GDP
CPI 2
Madrid
Madrid
Rents Def X-rate
Madrid
Rents Def
Growth
Ptas sq.m. p.m.
1970
2,630
22,205
28,867
29,530
30,086
30,745
31,096
30,919
30,331
30,675
31,240
31,797
32,291
34,020
36,144
38,306
40,233
41,969
43,396
44,084
43,463
44,069
45,406
46,289
70.00
12
1971
1972
1973
400
1974
700
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
750
825
925
1,000
1,200
1,400
1,500
1,550
1,450
1,600
1,600
2,250
2,700
4,000
4,750
5,500
5,500
4,250
2,750
2,500
2,500
2,750
6,038
7,266
9,220
11,285
13,201
15,168
17,045
19,723
22,532
25,520
28,201
32,324
36,144
40,159
45,044
50,145
54,927
59,105
60,934
64,699
69,779
73,666
1997
3,000
77,792
1998
3,000
82,148
0.02
0.02
0.02
0.01
-0.01
-0.02
0.01
0.02
0.02
0.02
0.05
0.06
0.06
0.05
0.04
0.03
0.02
-0.01
0.01
0.03
0.02
1.00
1.02
1.04
1.07
1.08
1.07
1.05
1.06
1.08
1.10
1.12
1.18
1.25
1.33
1.39
1.45
1.50
1.53
1.51
1.53
1.57
1.60
21
25
31
37
42
49
56
64
72
80
87
95
100
105
112
119
127
134
140
147
154
159
3,585
3,353
3,018
2,724
2,827
2,854
2,669
2,411
2,010
1,994
1,832
2,368
2,700
3,815
4,243
4,603
4,345
3,170
1,962
1,703
1,627
1,728
57.41
66.90
75.96
76.67
67.13
71.70
92.32
109.86
143.43
160.76
170.04
140.05
123.48
116.49
118.38
101.93
103.91
102.38
127.26
133.96
124.69
126.66
1.00
0.94
0.84
0.76
0.79
0.80
0.74
0.67
0.56
0.56
0.51
0.66
0.75
1.06
1.18
1.28
1.21
0.88
0.55
0.47
0.45
0.48
Milan Statistical Data
Global
Property
INDEX
Milan
Lit
GDP
GDPDEF GDP
Growth
GDP
CPI 2
Milan
Property
Index
Milan
Property
Index
Deflated
Milan
Property
Index
Milan
Rents
Deflated
Milan Xrate
Milan
Office
Rental
Index
sq.m. p.a.
1970
67.13
517.97
138.59
174.58
212.71
251.00
623.28
673.51
698.78
735.51
0.08
0.04
0.05
12.96
625.00
22.24
25.92
30.44
34.13
652.85
832.34
882.39
848.66
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
190,000
209,000
235,000
255,000
307.82
385.33
461.05
542.12
631.61
786.87
812.18
824.99
833.50
847.18
0.07
0.03
0.02
0.01
0.02
1.00
1.02
1.03
1.04
39.12
47.44
55.89
65.04
74.55
400476.16
373976.59
361307.11
342033.48
830.86
856.45
1136.77
1352.51
1518.85
1.00
0.93
0.90
0.85
1984
1985
285,000
300,000
722.81
810.08
874.65
897.60
0.03
0.03
1.08
1.11
82.64
90.25
344870.50
332411.05
1756.96
1909.44
0.86
0.83
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
329,000
393,000
467,000
650,000
750,000
700,000
650,000
550,000
500,000
450,000
400,000
898.29
982.76
1090.02
1191.96
1310.66
1427.57
1502.49
1550.30
1638.51
1770.95
1863.42
940.80
982.76
1037.00
1067.56
1102.26
1129.43
1131.24
1117.22
1135.08
1165.69
1179.78
0.05
0.04
0.06
0.03
0.03
0.02
0.00
-0.01
0.02
0.03
0.01
1.16
1.21
1.28
1.31
1.36
1.39
1.39
1.38
1.40
1.44
1.45
95.48
100.00
105.11
111.65
118.91
126.40
132.82
138.76
144.35
151.92
157.95
344569.09
393000.00
444283.92
582161.85
630749.99
553809.94
489391.23
3%358.18
346375.60
296203.93
253251.09
1490.81
1296.07
1301.63
1372.09
1198.10
1240.61
1232.41
1573.67
1612.44
1628.93
1542.95
0.86
0.98
1.11
1.45
1.58
1.38
1.22
0.99
0.86
0.74
0.63
1997
400,000
1941.68
1998
400,000
2027.11
40.00
40.00
36.60
49.60
49.30
56.00
37.00
33.46
16.66
22.60
1.00
0.95
0.82
1.04
0.98
1.05
0.67
0.58
0.27
0.36
40.00
38.05
32.78
41.71
39.00
42.16
26.66
23.18
10.97
14.31
38.60
_
i
i
Geneva Statistical Data
GDP
GDPDEF
Geneva
Global
Property
INDEX
GDP
Growth
GDP
CPI 2
Geneva
Rents
Deflated
Geneva X- Geneva
rate
Rents
Deflated
Growth
SFr sq.m. p.a.
1970
92
187.70
200.41
199.57
202.35
208.32
210.09
216.98
221.01
221.89
224.17
227.82
235.45
249.48
257.37
266.29
279.25
286.47
284.61
280.66
277.54
281.07
281.74
278.00
48.81
0.00
4.37
70.67
71.88
72.81
73.58
76.26
79.33
84.48
89.25
91.90
94.58
97.84
98.57
100.00
101.86
105.08
110.76
117.24
121.99
126.03
127.10
129.40
130.45
0.00
0.00
0.00
475.70
458.96
504.24
473.51
448.16
586.49
714.73
766.58
862.30
950.00
1178.09
1142.02
902.83
852.98
737.78
714.09
629.45
540.97
498.28
2.58
2.50
2.40
1.79
1.66
1.68
1.96
2.03
2.10
2.35
2.46
1.80
1.49
1.46
1.64
1.39
1.43
1.41
1.48
1.37
1.18
1.24
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
350
350
400
400
400
539
676
750
850
950
1,200
1,200
1,000
1,000
900
900
800
700
650
142
143
147
153
160
172
187
198
206
215
230
246
257
271
293
317
334
342
350
357
365
363
1997
600
366
1998
600
374
0.00
0.01
0.03
0.01
0.03
0.02
0.00
0.01
0.02
0.03
0.06
0.03
0.03
0.05
0.03
-0.01
-0.01
-0.01
0.01
0.00
-0.01
1.00
1.01
1.04
1.06
1.07
1.08
1.09
1.13
1.20
1.24
1.28
1.34
1.38
1.37
1.35
1.33
1.35
1.35
1.33
1.00
0.96
1.06
1.00
0.94
1.23
1.50
1.61
1.81
2.00
2.48
2.40
1.90
1.79
1.55
1.50
1.32
1.14
1.05
New York Statistical Data
Global
New York
CPI
GNP
Property MT
INDEX
GNP
Deflated
GNP
Growth
GNP
NCREIF NCREIF NCREIF N.Y.
Deflated Property Rents
Index
Deflated
New York
Office
Rental
Index
US$ sq.ft. p.a.
1.18
25.64
27.50
26.19
23.40
1.00
1.07
1.02
0.91
1.12
23.53
0.92
1.10
24.56
0.96
0.07
1.18
24.14
0.94
3333.33
0.05
1.23
30.00
1.17
2300.00
2600.00
2900.00
3200.00
3175.00
3500.00
3950.00
4250.00
4500.00
4800.00
5000.00
5500.00
5800.00
6000.00
6200.00
6500.00
7000.00
7400.00
7600.00
8000.00
3593.75
3714.29
3536.59
3555.56
3239.80
3500.00
3872.55
3935.19
4128.44
4102.56
4166.67
4471.54
4461.54
4379.56
4428.57
4513.89
4697.99
4868.42
4840.76
5000.00
0.08
0.03
-0.05
0.01
-0.09
0.08
0.11
0.02
0.05
-0.01
0.02
0.07
0.00
-0.02
0.01
0.02
0.04
0.04
-0.01
0.03
1.33
1.37
1.31
1.32
1.20
1.30
1.43
1.46
1.53
1.52
1.54
1.65
1.65
1.62
1.64
1.67
1.74
1.80
1.79
1.85
2.90
3.81
5.54
2.96
2.49
1.75
3.35
2.08
2.03
1.83
1.84
1.75
1.38
0.05
-0.03
0.77
1.31
2.11
2.40
2.32
4.53
5.44
6.76
3.29
2.54
1.75
3.28
1.93
1.86
1.56
1.53
1.42
1.06
0.04
-0.02
0.53
0.88
1.39
1.53
1.45
1.00
1.20
1.49
0.73
0.56
0.39
0.72
0.43
0.41
0.35
0.34
0.31
0.23
0.01
0.00
0.12
0.19
0.31
0.34
0.32
31.25
41.43
54.88
66.67
58.67
55.00
53.92
48.61
45.87
42.74
45.00
43.90
42.31
37.96
33.57
32.19
31.71
31.09
30.57
33.44
1.22
1.62
2.14
2.60
2.29
2.15
2.10
1.90
1.79
1.67
1.76
1.71
1.65
1.48
1.31
1.26
1.24
1.21
1.19
1.30
8340.70
5116.99
0.02
1.89
3.89
2.39
0.53
1970
1971
1972
1973
10.00
11.00
11.00
11.00
39.00
40.00
42.00
47.00
1053.80
1100.00
1200.00
1500.00
2702.05
2750.00
2857.14
3191.49
0.02
0.04
0.12-
1974
12.00
51.00
1550.00
3039.22
-0.05
1975
14.00
57.00
1700.00
2982.46
-0.02
1976
14.00
58.00
1850.00
3189.66
1977
18.00
60.00
2000.00
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
20.00
29.00
45.00
60.00
57.50
55.00,
55.00
52.50
50.00
50.00
54.00
54.00
55.00
52.00
47.00
46.35
47.25
47.25
48.00
53.50
64.00
70.00
82.00
90.00
98.00
100.00
102.00
108.00
109.00
117.00
120.00
123.00
130.00
137.00
140.00
144.00
149.00
152.00
157.00
160.00
163.00
1998
1.00
1.02
1.06
Toronto Statistical Data
Global
Property
INDEX
Toronto
GDP
GDPDE
F
GDP
Growth
GDP
CPI 2
Toronto Toronto Toronto
Rents
Rents
x-rate
Deflated
Deflated
Growth
29.69
1.045
C$ sq.ft. p.a.
1970
88.46
297.94
402.20
431.70
440.07
447.99
469.58
478.49
488.68
463.95
474.31
498.75
515.71
523.31
546.78
577.60
590.69
579.26
553.68
556.86
565.12
591.36
601.06
608.21
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
10.30
11.40
11.40
12.50
14.00
19.60
27.30
27.30
31.00
32.00
33.40
34.30
36.70
39.90
42.00
40.00
38.00
31.00
25.00
20.00
18.00
20.00
170.11
196.29
216.09
239.58
274.09
307.73
353.45
371.82
402.23
441.31
474.34
501.43
546.78
600.84
645.15
662.81
669.11
683.09
705.99
740.13
768.58
789.86
1997
24.00
841.20
1998
885.78
0.07
0.02
0.02
0.05
0.02
0.02
-0.05
0.02
0.05
0.03
0.01
0.04
0.06
0.02
-0.02
-0.04
0.01
0.01
0.05
0.02
0.01
1.00
1.07
1.09
1.11
1.17
1.19
1.22
1.15
1.18
1.24
1.28
1.30
1.36
1.44
1.47
1.44
1.38
1.38
1.41
1.47
1.49
1.51
42.29
45.47
49.10
53.48
58.37
64.31
72.33
80.14
84.80
88.48
91.98
95.82
100.00
104.02
109.22
114.42
120.85
122.67
124.93
125.16
127.87
129.87
24.35
25.07
23.22
23.37
23.99
30.48
37.74
34.06
36.56
36.17
36.31
35.80
36.70
38.36
38.45
34.96
31.44
25.27
20.01
15.98
14.08
15.40
1.017
0.986
1.063
1.141
1.171
1.169
1.199
1.234
1.232
1.295
1.365
1.390
1.326
1.231
1.184
1.167
1.146
1.209
1.290
1.366
1.372
1.363
1.00
1.03
0.95
0.96
0.98
1.25
1.55
1.40
1.50
1.49
1.49
1.47
1.51
1.58
1.58
1.44
1.29
1.04
0.82
0.66
0.58
0.63
Sydney Statistical Data
Global
Property
INDEX
Sydney ASX
Property
GDP
GDPDE GDP
F
Growth
GDP
CPI 2
ASX
Sydney sydney
ASX
x-rate
Property Property Rents
Index
Deflated
Index
Deflated
Sydney
Office
Rental
Index
A$ sq.m. p.a.
1970
129.0
1971
129.0
1972
108.0
1973
91.0
35.1
166.6
1.00
21.1
612.0
0.893
1.00
1974
91.0
1975
1976
97.0
102.0
76.6
87.6
223.2
224.8
0.01
1.34
1.35
34.3
39.0
282.6
261.8
0.764
0.818
0.46
0.43
1977
1978
108.0
120.0
95.3
108.5
217.8
229.7
-0.03
0.05
1.31
1.38
43.8
47.2
246.8
254.1
0.902
0.874
0.40
0.42
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
150.0
225.0
300.0
325.0
335.0
365.0
390.0
472.0
525.0
600.0
700.0
725.0
690.0
650.0
595.0
590.0
610.0
620.0
490.0
490.0
570.0
520.0
640.0
840.0
760.0
860.0
1050.0
1040.0
970.0
960.0
960.0
980.0
1050.0
1100.0
1090.0
1080.0
123.3
140.6
158.7
172.5
195.8
217.1
240.8
264.7
299.3
339.3
370.2
378.7
387.1
404.8
416.6
443.9
473.1
501.6
239.3
247.9
255.1
249.4
257.1
274.3
284.9
287.1
299.3
316.4
321.0
306.1
303.1
313.9
317.2
331.8
337.9
349.1
0.04
0.04
0.03
-0.02
0.03
0.07
0.04
0.01
0.04
0.06
0.01
-0.05
-0.01
0.04
0.01
0.05
0.02
0.03
1.44
1.49
1.53
1.50
1.54
1.65
1.71
1.72
1.80
1.90
1.93
1.84
1.82
1.88
1.90
1.99
2.03
2.10
51.5
56.7
62.2
69.2
76.2
79.2
84.5
92.2
100.0
107.2
115.3
123.7
127.7
129.0
131.3
133.8
140.0
143.7
291.2
396.6
482.1
469.9
439.9
461.1
461.5
512.1
525.0
559.5
606.9
586.0
540.3
504.0
453.1
441.0
0.895
0.878
0.870
0.986
1.110
1.140
1.432
1.496
1.428
1.280
1.265
1.281
1.284
1.362
1.471
1.368
0.48
0.65
0.79
0.77
0.72
0.75
0.75
0.84
0.86
0.91
0.99
0.96
0.88
0.82
0.74
0.72
435.7
1.349
0.71
431.6
1.278
0.71
1997
650.0
1130.0
526.6
1
1340
549.2
1
1998
951.2
863.8
916.0
751.9
840.4
1061.1
899.4
933.0
1050.0
969.9
841.0
775.9
751.7
759.9
799.6
822.1
778.5
751.8
1.0
0.9
1.0
0.8
0.9
1.1
0.9
1.0
1.1
1.0
0.9
0.8
0.8
0.8
0.8
0.9
0.8
0.8
Singapore Statistical Data
Global
Property
INDEX
Singapor Singapore Singapore Singapore
e
Property Property Property
Index
Index
Index
Deflated
GDP
GDPDE
F
GDP
Growth
GDP
CPI 2
Singapore Singapore Singapore
X-rate
Office
Rents
Rental
Deflated
Index
S$ sq.ft. p.m.
1970
1971
1.41
1972
1.65
1973
1.89
1974
2.12
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
2.36
2.36
2.83
2.47
2.71
4.42
7.25
8.25
7.07
5.30
4.95
3.54
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
4.36
5.30
9.02
11.96
10.85
6.26
6.03
7.48
9.59
9.90
17.00
34.00
40.00
25.00
80.00
120.00
319.00
325.00
354.00
456.00
1997
9.10
192.00
1998
8.30
17.00
33.49
38.50
23.26
71.95
105.54
274.29
271.04
290.23
368.76
1.00
1.97
2.26
1.37
4.23
6.21
16.13
15.94
17.07
21.69
5.80
12.62
13.44
14.65
16.04
17.83
20.52
25.09
29.34
32.67
36.73
40.05
38.92
39.26
18.75
20.82
22.09
23.41
25.90
29.17
31.53
33.79
37.54
39.89
38.59
39.47
0.11
0.06
0.06
0.11
0.13
0.08
0.07
0.11
0.06
-0.03
0.02
43.57
51.64
59.34
67.88
75.27
80.94
94.22
108.51
121.08
132.63
43.57
50.87
57.11
63.14
67.70
71.19
81.02
90.49
99.27
107.26
0.10
0.17
0.12
0.11
0.07
0.05
0.14
0.12
0.10
0.08
144.96
153.52
1
45.98
0.00
3.06
1.00
1.11
1.18
1.25
1.38
1.56
1.68
1.80
2.00
2.13
2.06
2.11
71.71
70.38
72.61
76.15
79.25
86.01
93.05
96.69
97.85
100.40
100.88
99.48
3.29
3.35
3.90
3.24
3.42
5.14
7.79
8.53
7.23
5.28
4.91
3.56
2.37
2.47
2.44
2.27
2.17
2.14
2.11
2.14
2.11
2.13
2.20
2.18
1.00
1.02
1.18
0.99
1.04
1.56
2.37
2.59
2.20
1.60
1.49
1.08
2.32
2.71
3.05
3.37
3.61
3.80
4.32
4.83
5.30
5.72
100.00
101.52
103.90
107.50
111.18
113.70
116.30
119.91
121.97
123.66
4.36
5.22
8.68
11.13
9.76
5.51
5.18
6.24
7.86
8.01
2.11
2.01
1.95
1.81
1.73
1.63
1.62
1.53
1.42
1.41
1.32
1.59
2.64
3.38
2.97
1.67
1.58
1.90
2.39
2.43
Tokyo Statistical
Global
Property
INDEX
Data
Tokyo Inner TOPIX
Office Rents REAL
ESTATE
INDEX
TOPIX
Index
Deflate
TOPIX
Property
Index
GDP
GDPDEF
GDP
Growth
GDP
CPI
Tokyo
Rents
Deflated
Tokyo
X-rate
Tokyo
Office
Rental
Index
Yen tsubo p.m.
1970
35
94
77
87
99
110
120
131
141
149
157
167
179
190
199
212
226
242
259
270
279
286
290
304
123
126
134
142
149
151
155
160
165
171
180
190
199
210
219
228
236
242
247
251
255
267
37
0
360.00
63
69
74
77
80
87
91
93
95
97
99
100
100
101
103
106
110
111
113
114
114
114
36,597
36,366
32,267
28,400
38,248
35,870
36,369
35,401
40,649
39,755
41,293
44,677
59,831
64,023
77,687
75,382
78,125
61,587
39,863
35,189
28,177
28,139
296.79
296.55
268.51
210.44
219.14
226.74
220.54
249.08
237.51
237.52
238.54
168.52
144.64
128.15
137.96
144.79
134.71
126.65
111.20
102.21
94.06
108.78
1971
1972
1973
19,000
1974
21,000
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
23,000
25,000
24,000
22,000
30,742
31,073
33,056
33,056
38,676
38,676
40,989
44,626
59,831
64,459
79,996
79,996
85,615
68,657
45,000
40,000
32,000
32,000
300
400
450
700
1,300
1,700
1,650
2,100
1,400
1,300
700
800
830
700
1,000
1997
34,000
900
308
800
308
1998
302
400
450
695
1,262
1,602
1,506
1,884
1,240
1,144
616
703
730
616
879
1.00
1.33
1.49
2.30
4.18
5.30
4.98
6.23
4.10
3.78
2.04
2.33
2.42
2.04
2.91
0.03
0.06
0.06
0.05
0.01
0.03
0.03
0.03
0.04
0.05
0.06
0.04
0.06
0.04
0.04
0.03
0.03
0.02
0.02
0.02
0.05
1.00
1.03
1.09
1.15
1.21
1.23
1.26
1.30
1.34
1.39
1.47
1.55
1.62
1.71
1.78
1.86
1.92
1.97
2.01
2.04
2.08
2.17
1.00
0.99
0.88
0.78
1.05
0.98
0.99
0.97
1.11
1.09
1.13
1.22
1.63
1.75
2.12
2.06
2.13
1.68
1.09
0.96
0.77
0.77
Jakarta
Statistical
Global
Property
INDEX
Jakarta
Data
CPI
GDP
GDP DEF GDP DEF
Growth
GDP
Jakarta Jakarta Jakarta Jakarta Jakarta Jakarta
X-rate Office
Property Property Property Rents
Rental
Index
Deflated
Index
Index
Deflated
Index
US$ sq.m. p.m.
1970
11.31
3.52
31.18
0
365
415.00
415.00
415.00
442.05
623.06
626.99
631.76
661.42
909.26
1025.94
1110.58
1282.56
1643.85
1685.70
1770.06
1842.81
1950.32
2029.92
2087
2161
2249
1971
1972
1973
1974
1975
1976
1977
1978
1979
16.00
13.00
14.00
15.00
16.50
27.56
33.04
36.68
39.66
46.11
13.34
16.32
20.08
24.00
34.34
48.40
49.40
54.75
60.52
74.49
1.00
1.02
1.13
1.25
1.54
58.05
0.02
0.11
0.11
0.23
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
15.00
16.00
21.00
18.00
13.25
9.47
8.00
7.21
14.00
17.06
25.75
19.33
14.00
13.00
14.00
13.00
54.42
61.08
66.87
74.75
82.57
86.47
91.51
100.00
108.04
114.98
123.96
135.63
145.84
159.97
173.59
189.97
48.91
58.42
62.65
77.68
89.75
96.85
102.47
124.51
149.67
179.58
210.87
249.97
282.40
329.78
382.22
452.38
89.89
95.65
93.68
103.91
108.70
112.00
111.97
124.51
138.53
156.19
170.11
184.31
193.63
206.15
220.18
238.13
0.21
0.06
-0.02
0.11
0.05
0.03
0.00
0.11
0.11
0.13
0.09
0.08
0.05
0.06
0.07
0.08
1.86
1.98
1.94
2.15
2.25
2.31
2.31
2.57
2.86
3.23
3.51
3.81
4.00
4.26
4.55
4.92
27.57
26.20
31.40
24.08
16.05
10.95
8.74
7.21
12.96
14.84
20.77
14.25
9.60
8
8
7
1996
12.00
205.11
528.96
257.89
0.08
5.33
1997
13.50
1998
1
1
100
130
58
68
143
70
75
35.8
1.00
1.19
1.21
1
1
2342
1.00
0.47
0.45
0.54
0.41
0.28
0.19
0.15
0.12
0.22
0.26
0.36
0.25
0.17
0.14
0.14
0.12
Kuala Lumpur Statistical Data
GDP
Kuala
Global
Property Lumpur
INDEX
GDPDE
F
CPI 2
Kuala
Lumpur
Rents
Deflated
Kuala
Lumpur
Office
Rental
Kuala
Lumpur
Property
Index
Kuala
Lumpur
Propert
y Index
Index
RM sq.m. p.m.
11.31
26.54
4.40
27.56
15.96
4.50
33.04
13.62
1977
1978
1979
4.63
4.75
4.90
36.68
39.66
46.11
12.62
11.98
10.63
1980
1981
1982
1983
1984
5.01
7.00
8.50
8.55
7.00
54.42
61.08
66.87
74.75
82.57
9.21
11.46
12.71
11.44
8.48
1985
1986
6.55
6.35
86.47
91.51
7.57
6.94
1987
6.05
100.00
6.05
1988
5.85
108.04
5.41
1989
10.30
114.98
8.96
1990
16.34
123.96
13.18
135.63
14.08
145.84
159.97
173.59
189.97
205.11
14.98
15.38
15.76
15.85
14.67
1970
3.00
1971
3.10
1972
3.50
1973
4.00
1974
4.25
1975
1976
1991
19.10
__
1992
1993
1994
1995
1996
21.85
24.60
27.36
30.11
30.10
__
1200
1800
2700
2300
2400
1.00
1.50
2.25
1.92
2.00
1997
2100
1.75
1998
800
1.00
1.03
1.05
1.06
0.98