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DTZ Research Institute 2013
www.dtz.com
06 November 2013
“To promote innovation in commercial real estate market research by making DTZ Research data available to leading academic researchers.”
Our Academic Committee
Eamonn D’Arcy University of Reading
Colin Lizieri University of Cambridge
Nick French Oxford Brookes University
Ingrid Nappi-Choulet ESSEC Business School
Tobias Just Universität Regensburg
Presentations
Cross-Border Capital Flows into Real Estate Andrew Baum , University of Cambridge Franz Fuerst, University of Cambridge Stanimira Milcheva, University of Reading
Real Estate Holding Periods Across Europe Jan Reinert, University of Regensburg
Panel Modelling of European Office Market Rent Dynamics and Asymmetries Kieran Farrelly, The Townsend Group Michal Gluszak, Cracow University of Economic George Matysiak, Cracow University of Economics
Cross-Border Capital Flows into Real Estate
Andrew Baum, Franz Fuerst, Stanimira Milcheva
6th November 2013
• Real estate becoming
increasingly international real
estate (investment, services,
education etc.)
• Capital flows: sharp
differences across countries
and over time
• Hardly any rigorous research
• First study to assess capital
flows into direct real estate
markets using a unique panel
dataset
4
Motivation
Global real estate invested stock
in USD tn
5
Domestic and foreign capital flows into direct
real estate in Asia, 2000-12
Europe Asia
6
Domestic and foreign capital flows into direct
real estate in Europe, 2000-12
Europe
Hypothetically, each country should receive capital flows
commensurate with the size of its respective economy or, more
accurately, the total size of its investible real estate market BUT
observed capital flows deviate from hypothetical equilibrium
aberration from expected values are due to institutional barriers
Generally, two somewhat opposing views in the literature:
1) Stulz (1981) & Griffin et al. (2004): institutional, legal and
economic barriers to cross-border investments akin to a tax on
returns
2) Eichholtz, Gugler and Kok (2011): economies of scales available
to large global investors: lower cost of capital & superior
information and know-how
7
No consensus in the existing literature
8
Real estate investment flows by domestic and
foreign investors (annual average for 2007-12)
010
20
30
40
50
Chin
a
United
Kin
gd
om
Ge
rma
ny
Ja
pa
n
Fra
nce
Austr
alia
Sin
ga
po
re
Sw
ed
en
Spa
in
Neth
erl
an
ds
Taiw
an
Italy
Norw
ay
Sw
itzerl
an
d
Russia
Belg
ium
Ind
ia
Ma
laysia
Pola
nd
Fin
land
Cze
ch
Rep
ub
lic
Tha
ilan
d
New
Ze
ala
nd
Irela
nd
Source: DTZ
mean of domestic mean of foreign
• Proposition 1: Barriers to investment reduce the expected returns
of foreign investors. The higher the barriers, the greater the
reduction in flows.
• Proposition 2: Countries with above-average returns attract more
capital flows, either immediately or with a time lag. Furthermore,
any increase or decrease in returns should be followed by a
corresponding reaction from capital flows, both from domestic and
foreign investors.
• Proposition 3: Expectations of investors are adaptive and hence
tend to follow past and contemporaneous returns. Foreign investors
are more prone to engaging in adaptive return-chasing behaviour
than domestic investors.
9
Propositions and testable hypotheses
Data & Methodology
Combining the DTZ global transaction database with a large
number of indicators on barriers, we compiled a unique panel
dataset to analyse the drivers of domestic and foreign flows.
For our analysis, we draw on two approaches
1) To analyse the importance of barriers:
Panel data regression with country and time fixed effects
2) To analyse the dynamics of flows and returns:
Unrestricted VAR with contemporaneous regressors
11
Panel fixed-effects regression
INFLOWS OUFLOWS
DOMESTIC
Model 1
DOMESTIC
Model 2
FOREIGN
Model 1
FOREIGN
Model 2
Model 1
Model 2
Credit information 0.0231 0.254 1.044*** 1.102*** 0.11 1.076**
Property returns 0.0293*** 0.0200** 0.00620 0.0108 0.0068 -0.000575
Market size -3.805* -2.615 -2.654 -1.675 0.254 1.999
Macroeconomy 0.782*** 1.285*** 0.0319 0.00892 0.133 0.795**
Fiscal freedom -0.0188 -0.0124 -0.0524
Government freedom -0.0112 0.0131 -0.0046
Labour freedom 0.0185 0.0129 0.0519**
Investment freedom 0.0187 0.0170 -0.0666***
Financial development 1.173** 0.0227 1.382***
Real estate transparency -2.465** -2.065 -4.314***
Constant 9.268 11.60 7.606 8.821 -5.432 -10.56
Observations 130 130 124 124 104 104
R-squared 0.524 0.444 0.392 0.413 0.511 0.434
Number of countries 23 23 23 23 22 22
12
Relationship of returns with domestic & foreign
flows (VAR models, ALL COUNTRIES)
RETURN
EQUATION All
Return (-1) 0.562***
Return(-2) -0.186***
Domestic 0.230*
Domestic(-1) -0.263
Domestic(-2) 0.026
Foreign 0.376*
Foreign(-1) -0.534**
Foreign(-2) -0.003
R-squared 0.31
FOREIGN
EQUATION All
Return 0.031*
Return(-1) 0.020
Return(-2) -0.019
Domestic 0.273***
Domestic(-1) -0.337***
Domestic(-2) 0.171***
Foreign(-1) 0.746***
Foreign(-2) -0.039
R-squared 0.68
DOMESTIC
EQUATION All
Return 0.051*
Return(-1) -0.066**
Return(-2) 0.015
Domestic(-1) 1.266***
Domestic(-2) -0.464***
Foreign 0.729***
Foreign(-1) -0.800***
Foreign(-2) 0.327***
R-squared 0.86
Summary
• Generally, we find evidence that some barriers inhibit real
estate capital flows (confirming our Proposition 1)
• Inflows: Credit depth of information important for foreign
investors. Real estate transparency index is significant
driver for domestic, not foreign flows. No support for
relevance of fiscal, government, labour or investment
freedom.
• Outflows: Important factors for explaining outflows include
credit information (+), state of the economy (+), labour
freedom (+), investment freedom (-), financial development
(+) and real estate transparency (+).
13
• Both foreign and domestic inflows are positively linked to
property returns in the same year but the volume of foreign
flows is generally found to be more reactive to return shocks
(confirming Proposition 2).
• Cross-border investment appears to react more strongly to
past returns than domestic investment which is in line with
our expectations (confirming Proposition 3). However,
there is at least some evidence that both cross-border and
domestic investment react to contemporaneous returns
• Both foreign and domestic inflows are positively linked to
property returns in the same year but the volume of foreign
flows is generally found to be more reactive to return shocks
14
Summary (continued)
DOWNLOAD THE FULL PAPER AT
SOCIAL SCIENCE RESEARCH NETWORK:
HTTP://SSRN.COM/AUTHOR=377440
THANK YOU!
Eltville Berlin Essen Munich Regensburg
REAL ESTATE HOLDING PERIODS ACROSS EUROPE
Evidence from the DTZ Investment Transaction Database
Jan Reinert, PhD student
06.11.2013
06.11.2013
Literature
RESEARCHERS MARKET TIME
PERIOD METHOD
HOLDING
PERIOD OTHER FINDINGS
Collett, Lizieri
& Ward
(2003)
United
Kingdom
1981-
1998
Cox
proportional
hazard
model
7-12 years
(median for
standard shops)
Decreasing holding periods over time,
holding periods differ by size & sector,
negative relationship between return volatility &
holding period
Gardner &
Matysiak (2005)
United
Kingdom
1983-
2003
Based on
transacted
properties
4.6-7.0 years
(median over
time)
Decreasing holding periods over time,
holding periods differ by location & investor type,
25% of properties are resold after 3 years,
declining pattern of return over holding period
Brown & Geurts
(2005)
San
Diego,
United
States
1970-
1990
Based on
transacted
properties,
OLS
regression
4.5 years
(average for
apartment
buildings)
Property characteristics (besides size) do not
affect holding periods
Fisher & Young
(2000)
United
States
1980-
1998
Time until
50% of
sample has
been sold
8.6-13.7 years
(median
depending on
sector)
Decreasing holding periods over time,
holding periods differ by sector,
returns converge to market average as tenure
lengthens
Cheng, Lin
& Liu
(2010)
United
States
1978-
2008
Model for ex
ante optimal
holding
period
4.3-5.3 years
(expected optimal
holding period)
Higher transaction costs lead to longer holding
periods while price volatility decreases it
06.11.2013
Data
• DTZ Investment Transaction Database
• Information on commercial property investment deals
• Matching transactions to establish holding period length
• 1,079 matched properties in the UK, France, the Netherlands and
Germany
Composition of DTZ Investment
Transaction Database 2012
Composition of
Matched Dataset
06.11.2013
Analysis is based on transacted
properties.
06.11.2013
Preliminary Analysis
Holding periods... ... across countries
... over time
... by location
... by sector
... by lot size
... by investor type
... by performance
06.11.2013
Holding Periods Across Countries
Mean HP Median HP HP Spread
Biggest Netherlands France Netherlands
Smallest Germany Netherlands France
Holding Periods per country between 1999 and 2012 Distribution of Year of Purchase
Bottom 10%
Top 10%
Upper 25%
Lower 25%
Average
Median
06.11.2013
Holding Periods By Location
• Problem: defining sub-locations applicable to all countries („one size fits all“)
• Simple distinction between „Core“ and „Secondary“ locations
Core Location
UK Greater London
France Ile-de-France
Netherland
s
Amsterdam, Rotterdam, The Hague,
Utrecht
Germany Berlin, Munich, Hamburg, Cologne,
Frankfurt
Average Holding Period by Sub-Location On
ly r
esu
lts b
ase
d o
n a
t le
ast 2
0 o
bs. a
re s
ho
wn
.
Distribution of Sub-Locations
06.11.2013
Holding Periods By Location: UK
Average Holding Period by Sub-Location in the UK
Distribution of Sub-Locations in the
UK
Greater London Area
06.11.2013
Holding Periods By Sector
• Only possible to differentiate between „Office“ and all „Other“ sectors on
the all country level
Average Holding Period by Sector On
ly r
esu
lts b
ase
d o
n a
t le
ast 2
0 o
bs. a
re s
ho
wn
.
Average Holding Period by Sector in the UK
06.11.2013
Holding Periods By Investor
Average Holding Period by Investor Type On
ly r
esu
lts b
ase
d o
n a
t le
ast 2
0 o
bs. a
re s
ho
wn
.
Average Holding Period Investor Type in the
UK
• Only possible to differentiate between „Private Property Vehicles“, „Private
Property Companies“ and all „Other“ Investors
06.11.2013
Holding Periods By Investor Nationality
Average Holding Period by Investor Nationality On
ly r
esu
lts b
ase
d o
n a
t le
ast 2
0 o
bs. a
re s
ho
wn
.
• Initial purpose of DTZ Investment
Transaction Database was to collect
information on deals involving foreign
investors in the UK
Average HP by Investor Nationality in the UK
06.11.2013
Holding Periods By Performance
Average Holding Period by Capital
Appreciation
• Approximated capital value growth (annual growth rate between purchase
and sale price)
• Only performance indicator available on an individual property level
Average Holding Period by Capital Value Growth
On
ly r
esu
lts b
ase
d o
n a
t le
ast 2
0 o
bs. a
re s
ho
wn
.
06.11.2013
Holding Periods By Performance
Approximated Annual Capital Value Growth by Holding Period
06.11.2013
Multivariate Analysis
• All Countries Regression Analysis
• UK Regression Analysis
06.11.2013
All Countries Regression Analysis
HP_MONTHS
All
Countries
UK I France Netherland
s Germany
Y1999 64.19*** 68.2*** 157.52*** -0.94 143.91***
Y2000 57.58*** 57.37*** 147.8*** 21.76 137.21***
Y2001 43.48*** 43.4*** 109.33*** -0.29 105.58***
Y2002 31.78*** 32.43*** 95.41*** -2.13 92.25***
Y2003 21.34*** 21.15*** 65.86*** -19.38 48.48***
Y2004 8.54*** 9.96*** 37.97*** -31.75 30.66***
Y2006 -6.49** -9.37*** -16.58*** -13.18 -32.26***
Y2007 -4.05 -2.97 -32.99*** -45.79* -68.01***
Y2008 -4.27 -2.75 -52.31*** -68.48** -69.64***
Y2009 0.71 6.27* -72.23*** -32.19** -86.6***
Y2010 4.21 20.38*** -98.86*** -12.26 -105.1***
Y2011 -19.57*** 12.57*** -151.75*** -30.85 -121.59***
Y2012 -24.56*** 12.06*** NA -39.35 -137.79***
CORE 2.14 1.55 -5.19 4.49 6.09*
OFFICE -4.4*** -5.58*** -7.43 1.84 1.59
GERMANY 14.78***
NA NA NA NA FRANCE -1.86
NETHERLANDS 9.88***
FOREIGN 1.87 3.06* -5.39* 24.79 -2.1
AV_CV_M 0.00 0.00 0.02 -0.12 0.02*
PPCOMP -3.66** -3.86** 0.33 -12.36 -0.66
PPVEHC -3.2** -3.5** 0.87 -24.1 0.68
ANN_CVG 0.02 -0.03 -0.06 0.02 0.05
CVG_ABV_10 -14.01*** -14.05*** 2.55 -7.36 -4.78
ANN_YIELD 0.32 1.98 6.24* 9.41 2.92
STDEV_YIELD 56.71*** 57.13*** -1.27 123.85*** 4.22
REL_MARK_SIZE 0.93*** 0.96*** 3.33*** 0.91 4.16***
CONSTANT -94.35*** -106.33*** -380.97*** -130.43 -445.85***
Observations 889 681 62 62 86
Adjusted R² 0.61 0.62 0.89 0.57 0.84 Measures of fit
*** significant at 1%
** significant at 5%
* significant at
10%
06.11.2013
All Countries Regression Analysis
HP_MONTHS
All
Countries
UK I France Netherland
s Germany
Y1999 64.19*** 68.2*** 157.52*** -0.94 143.91***
Y2000 57.58*** 57.37*** 147.8*** 21.76 137.21***
Y2001 43.48*** 43.4*** 109.33*** -0.29 105.58***
Y2002 31.78*** 32.43*** 95.41*** -2.13 92.25***
Y2003 21.34*** 21.15*** 65.86*** -19.38 48.48***
Y2004 8.54*** 9.96*** 37.97*** -31.75 30.66***
Y2006 -6.49** -9.37*** -16.58*** -13.18 -32.26***
Y2007 -4.05 -2.97 -32.99*** -45.79* -68.01***
Y2008 -4.27 -2.75 -52.31*** -68.48** -69.64***
Y2009 0.71 6.27* -72.23*** -32.19** -86.6***
Y2010 4.21 20.38*** -98.86*** -12.26 -105.1***
Y2011 -19.57*** 12.57*** -151.75*** -30.85 -121.59***
Y2012 -24.56*** 12.06*** NA -39.35 -137.79***
CORE 2.14 1.55 -5.19 4.49 6.09*
OFFICE -4.4*** -5.58*** -7.43 1.84 1.59
GERMANY 14.78***
NA NA NA NA FRANCE -1.86
NETHERLANDS 9.88***
FOREIGN 1.87 3.06* -5.39* 24.79 -2.1
AV_CV_M 0.00 0.00 0.02 -0.12 0.02*
PPCOMP -3.66** -3.86** 0.33 -12.36 -0.66
PPVEHC -3.2** -3.5** 0.87 -24.1 0.68
ANN_CVG 0.02 -0.03 -0.06 0.02 0.05
CVG_ABV_10 -14.01*** -14.05*** 2.55 -7.36 -4.78
ANN_YIELD 0.32 1.98 6.24* 9.41 2.92
STDEV_YIELD 56.71*** 57.13*** -1.27 123.85*** 4.22
REL_MARK_SIZE 0.93*** 0.96*** 3.33*** 0.91 4.16***
CONSTANT -94.35*** -106.33*** -380.97*** -130.43 -445.85***
Observations 889 681 62 62 86
Adjusted R² 0.61 0.62 0.89 0.57 0.84
Country Dummy
Variables
*** significant at 1%
** significant at 5%
* significant at
10%
06.11.2013
All Countries Regression Analysis
HP_MONTHS
All
Countries
UK I France Netherland
s Germany
Y1999 64.19*** 68.2*** 157.52*** -0.94 143.91***
Y2000 57.58*** 57.37*** 147.8*** 21.76 137.21***
Y2001 43.48*** 43.4*** 109.33*** -0.29 105.58***
Y2002 31.78*** 32.43*** 95.41*** -2.13 92.25***
Y2003 21.34*** 21.15*** 65.86*** -19.38 48.48***
Y2004 8.54*** 9.96*** 37.97*** -31.75 30.66***
Y2006 -6.49** -9.37*** -16.58*** -13.18 -32.26***
Y2007 -4.05 -2.97 -32.99*** -45.79* -68.01***
Y2008 -4.27 -2.75 -52.31*** -68.48** -69.64***
Y2009 0.71 6.27* -72.23*** -32.19** -86.6***
Y2010 4.21 20.38*** -98.86*** -12.26 -105.1***
Y2011 -19.57*** 12.57*** -151.75*** -30.85 -121.59***
Y2012 -24.56*** 12.06*** NA -39.35 -137.79***
CORE 2.14 1.55 -5.19 4.49 6.09*
OFFICE -4.4*** -5.58*** -7.43 1.84 1.59
GERMANY 14.78***
NA NA NA NA FRANCE -1.86
NETHERLANDS 9.88***
FOREIGN 1.87 3.06* -5.39* 24.79 -2.1
AV_CV_M 0.00 0.00 0.02 -0.12 0.02*
PPCOMP -3.66** -3.86** 0.33 -12.36 -0.66
PPVEHC -3.2** -3.5** 0.87 -24.1 0.68
ANN_CVG 0.02 -0.03 -0.06 0.02 0.05
CVG_ABV_10 -14.01*** -14.05*** 2.55 -7.36 -4.78
ANN_YIELD 0.32 1.98 6.24* 9.41 2.92
STDEV_YIELD 56.71*** 57.13*** -1.27 123.85*** 4.22
REL_MARK_SIZE 0.93*** 0.96*** 3.33*** 0.91 4.16***
CONSTANT -94.35*** -106.33*** -380.97*** -130.43 -445.85***
Observations 889 681 62 62 86
Adjusted R² 0.61 0.62 0.89 0.57 0.84
*** significant at 1%
** significant at 5%
* significant at
10%
Other Explanatory
Variables
06.11.2013
HP_MONTHS UK II
HP_MONTHS UK III
Y1989 108.24*** Y1989 112.15***
Y1990 90.68*** Y1990 94.57***
Y1991 83.02*** Y1991 83.17***
Y1992 68.64*** Y1992 72.87***
Y1993 78.07*** Y1993 81.91***
Y1994 59.27*** Y1994 60.87***
Y1995 60.82*** Y1995 60.28***
Y1996 48.63*** Y1996 47.77***
Y1997 42.94*** Y1997 43.86***
Y1998 32.34*** Y1998 33.25***
Y1999 15.23*** Y1999 15.87***
Y2001 -26.13*** Y2001 -26.12***
Y2002 -39.89*** Y2002 -40.47***
Y2003 -53.84*** Y2003 -53.52***
Y2004 -69.92*** Y2004 -70.32***
Y2005 -82.69*** Y2005 -82.72***
Y2006 -95.11*** Y2006 -94.27***
Y2007 -90.14*** Y2007 -89.76***
Y2008 -88.91*** Y2008 -88.56***
Y2009 -75.04*** Y2009 -75.46***
Y2010 -60.01*** Y2010 -58.94***
Y2011 -81.1*** Y2011 -80.36***
Y2012 -68.45*** Y2012 -64.82***
CITY_LONDON 9.77*** CITY_LONDON 8.22***
WEST_END 23.81*** WEST_END 24.1***
MIDTOWN 11.2*** MIDTOWN 9.82***
SE_LONDON 13.77*** SE_LONDON 12.92***
REST_LONDON 11.15*** REST_LONDON 11.51***
SOUTH_EAST 2.54 NA NA
EAST 6.87* EAST 7.06**
WEST_MIDLANDS 4.92
NA NA YORKSHIRE -3.92
NORTH_WEST -3.58
SCOTLAND 0.81
OFFICE -2.5 RETAIL 23.07***
RETAIL 21.85***
INDUSTRIAL -15.5* INDUSTRIAL -14.47***
MIXED 4.11
UK_INV -8.16**
FOREIGN 4.96** GER_INV -5.16
US_INV -5.93
IRISH_INV -0.82
AV_CV_M -0.01 NA NA
PPCOMP_UK -0.92
NA NA
PPVEHC_UK -0.77
QPCOMP_UK 2.06
INSUR_UK 1.22
PENS_UK 2.46
CORPOR_UK 4.95
INVM_UK -0.73
ANN_CVG -0.02 NA NA
CVG_ABV_10 -11.52*** CVG_ABV_10 -12.06***
ANN_YIELD 18.83*** ANN_YIELD 19.18***
STDEV_YIELD 50.32*** STDEV_YIELD 50.01***
REL_MARK_SIZE 1.86*** REL_MARK_SIZE 1.87***
CONSTANT -226.87*** _CONS -238.49***
Observations 854 Observations 854
Adjusted R² 0.73 Adjusted R² 0.73
UK Regression Analysis
UK Sublocation
*** significant at 1%
** significant at 5%
* significant at
10%
06.11.2013
HP_MONTHS UK II
HP_MONTHS UK III
Y1989 108.24*** Y1989 112.15***
Y1990 90.68*** Y1990 94.57***
Y1991 83.02*** Y1991 83.17***
Y1992 68.64*** Y1992 72.87***
Y1993 78.07*** Y1993 81.91***
Y1994 59.27*** Y1994 60.87***
Y1995 60.82*** Y1995 60.28***
Y1996 48.63*** Y1996 47.77***
Y1997 42.94*** Y1997 43.86***
Y1998 32.34*** Y1998 33.25***
Y1999 15.23*** Y1999 15.87***
Y2001 -26.13*** Y2001 -26.12***
Y2002 -39.89*** Y2002 -40.47***
Y2003 -53.84*** Y2003 -53.52***
Y2004 -69.92*** Y2004 -70.32***
Y2005 -82.69*** Y2005 -82.72***
Y2006 -95.11*** Y2006 -94.27***
Y2007 -90.14*** Y2007 -89.76***
Y2008 -88.91*** Y2008 -88.56***
Y2009 -75.04*** Y2009 -75.46***
Y2010 -60.01*** Y2010 -58.94***
Y2011 -81.1*** Y2011 -80.36***
Y2012 -68.45*** Y2012 -64.82***
CITY_LONDON 9.77*** CITY_LONDON 8.22***
WEST_END 23.81*** WEST_END 24.1***
MIDTOWN 11.2*** MIDTOWN 9.82***
SE_LONDON 13.77*** SE_LONDON 12.92***
REST_LONDON 11.15*** REST_LONDON 11.51***
SOUTH_EAST 2.54 NA NA
EAST 6.87* EAST 7.06**
WEST_MIDLANDS 4.92
NA NA YORKSHIRE -3.92
NORTH_WEST -3.58
SCOTLAND 0.81
OFFICE -2.5 RETAIL 23.07***
RETAIL 21.85***
INDUSTRIAL -15.5* INDUSTRIAL -14.47***
MIXED 4.11
UK_INV -8.16**
FOREIGN 4.96** GER_INV -5.16
US_INV -5.93
IRISH_INV -0.82
AV_CV_M -0.01 NA NA
PPCOMP_UK -0.92
NA NA
PPVEHC_UK -0.77
QPCOMP_UK 2.06
INSUR_UK 1.22
PENS_UK 2.46
CORPOR_UK 4.95
INVM_UK -0.73
ANN_CVG -0.02 NA NA
CVG_ABV_10 -11.52*** CVG_ABV_10 -12.06***
ANN_YIELD 18.83*** ANN_YIELD 19.18***
STDEV_YIELD 50.32*** STDEV_YIELD 50.01***
REL_MARK_SIZE 1.86*** REL_MARK_SIZE 1.87***
CONSTANT -226.87*** _CONS -238.49***
Observations 854 Observations 854
Adjusted R² 0.73 Adjusted R² 0.73
UK Regression Analysis
Sectors
*** significant at 1%
** significant at 5%
* significant at
10%
06.11.2013
HP_MONTHS UK II
HP_MONTHS UK III
Y1989 108.24*** Y1989 112.15***
Y1990 90.68*** Y1990 94.57***
Y1991 83.02*** Y1991 83.17***
Y1992 68.64*** Y1992 72.87***
Y1993 78.07*** Y1993 81.91***
Y1994 59.27*** Y1994 60.87***
Y1995 60.82*** Y1995 60.28***
Y1996 48.63*** Y1996 47.77***
Y1997 42.94*** Y1997 43.86***
Y1998 32.34*** Y1998 33.25***
Y1999 15.23*** Y1999 15.87***
Y2001 -26.13*** Y2001 -26.12***
Y2002 -39.89*** Y2002 -40.47***
Y2003 -53.84*** Y2003 -53.52***
Y2004 -69.92*** Y2004 -70.32***
Y2005 -82.69*** Y2005 -82.72***
Y2006 -95.11*** Y2006 -94.27***
Y2007 -90.14*** Y2007 -89.76***
Y2008 -88.91*** Y2008 -88.56***
Y2009 -75.04*** Y2009 -75.46***
Y2010 -60.01*** Y2010 -58.94***
Y2011 -81.1*** Y2011 -80.36***
Y2012 -68.45*** Y2012 -64.82***
CITY_LONDON 9.77*** CITY_LONDON 8.22***
WEST_END 23.81*** WEST_END 24.1***
MIDTOWN 11.2*** MIDTOWN 9.82***
SE_LONDON 13.77*** SE_LONDON 12.92***
REST_LONDON 11.15*** REST_LONDON 11.51***
SOUTH_EAST 2.54 NA NA
EAST 6.87* EAST 7.06**
WEST_MIDLANDS 4.92
NA NA YORKSHIRE -3.92
NORTH_WEST -3.58
SCOTLAND 0.81
OFFICE -2.5 RETAIL 23.07***
RETAIL 21.85***
INDUSTRIAL -15.5* INDUSTRIAL -14.47***
MIXED 4.11
UK_INV -8.16**
FOREIGN 4.96** GER_INV -5.16
US_INV -5.93
IRISH_INV -0.82
AV_CV_M -0.01 NA NA
PPCOMP_UK -0.92
NA NA
PPVEHC_UK -0.77
QPCOMP_UK 2.06
INSUR_UK 1.22
PENS_UK 2.46
CORPOR_UK 4.95
INVM_UK -0.73
ANN_CVG -0.02 NA NA
CVG_ABV_10 -11.52*** CVG_ABV_10 -12.06***
ANN_YIELD 18.83*** ANN_YIELD 19.18***
STDEV_YIELD 50.32*** STDEV_YIELD 50.01***
REL_MARK_SIZE 1.86*** REL_MARK_SIZE 1.87***
CONSTANT -226.87*** _CONS -238.49***
Observations 854 Observations 854
Adjusted R² 0.73 Adjusted R² 0.73
UK Regression Analysis
Investors
*** significant at 1%
** significant at 5%
* significant at
10%
06.11.2013
HP_MONTHS UK II
HP_MONTHS UK III
Y1989 108.24*** Y1989 112.15***
Y1990 90.68*** Y1990 94.57***
Y1991 83.02*** Y1991 83.17***
Y1992 68.64*** Y1992 72.87***
Y1993 78.07*** Y1993 81.91***
Y1994 59.27*** Y1994 60.87***
Y1995 60.82*** Y1995 60.28***
Y1996 48.63*** Y1996 47.77***
Y1997 42.94*** Y1997 43.86***
Y1998 32.34*** Y1998 33.25***
Y1999 15.23*** Y1999 15.87***
Y2001 -26.13*** Y2001 -26.12***
Y2002 -39.89*** Y2002 -40.47***
Y2003 -53.84*** Y2003 -53.52***
Y2004 -69.92*** Y2004 -70.32***
Y2005 -82.69*** Y2005 -82.72***
Y2006 -95.11*** Y2006 -94.27***
Y2007 -90.14*** Y2007 -89.76***
Y2008 -88.91*** Y2008 -88.56***
Y2009 -75.04*** Y2009 -75.46***
Y2010 -60.01*** Y2010 -58.94***
Y2011 -81.1*** Y2011 -80.36***
Y2012 -68.45*** Y2012 -64.82***
CITY_LONDON 9.77*** CITY_LONDON 8.22***
WEST_END 23.81*** WEST_END 24.1***
MIDTOWN 11.2*** MIDTOWN 9.82***
SE_LONDON 13.77*** SE_LONDON 12.92***
REST_LONDON 11.15*** REST_LONDON 11.51***
SOUTH_EAST 2.54 NA NA
EAST 6.87* EAST 7.06**
WEST_MIDLANDS 4.92
NA NA YORKSHIRE -3.92
NORTH_WEST -3.58
SCOTLAND 0.81
OFFICE -2.5 RETAIL 23.07***
RETAIL 21.85***
INDUSTRIAL -15.5* INDUSTRIAL -14.47***
MIXED 4.11
UK_INV -8.16**
FOREIGN 4.96** GER_INV -5.16
US_INV -5.93
IRISH_INV -0.82
AV_CV_M -0.01 NA NA
PPCOMP_UK -0.92
NA NA
PPVEHC_UK -0.77
QPCOMP_UK 2.06
INSUR_UK 1.22
PENS_UK 2.46
CORPOR_UK 4.95
INVM_UK -0.73
ANN_CVG -0.02 NA NA
CVG_ABV_10 -11.52*** CVG_ABV_10 -12.06***
ANN_YIELD 18.83*** ANN_YIELD 19.18***
STDEV_YIELD 50.32*** STDEV_YIELD 50.01***
REL_MARK_SIZE 1.86*** REL_MARK_SIZE 1.87***
CONSTANT -226.87*** _CONS -238.49***
Observations 854 Observations 854
Adjusted R² 0.73 Adjusted R² 0.73
UK Regression Analysis
Performance & Market
*** significant at 1%
** significant at 5%
* significant at
10%
06.11.2013
Implications
• Analysis was based on transacted properties; results might
therefore not be representative for all properties in the market
• Nevertheless, a large proportion of properties in all countries was
sold before the 10 year time period assumed in most investment
models
• Evidence suggested that the risk of out- and underperformance
was higher over shorter investment horizons
• This risk should be incorporated in due diligence process and risk
models
• Results of multivariate regressions can be used to identify
variables associated with shorter holding periods
Panel Modelling of European Office Market Rent Dynamics and
Asymmetries
Kieran Farrelly
Michal Gluszak
George Matysiak
Introduction and Research Questions
• In recent years a number of theoretical and applied empirical studies have dealt with the determinants of commercial real estate rent adjustment
• Relatively limited (but increasing) use of panel data analysis in commercial real estate research. Nevertheless, there is a need to address the latest developments in panel time series analysis
• Research questions:
– Are rent dynamics determined over the long run by supply and demand?
– Is cross section dependence present in European office markets?
– Do demand and supply shocks impact rents to varying degrees over the cycle?
Key Questions I • Are rent levels determined over the long run by demand and supply? We can use
cointegration tests to answer this – Existence of a long term ‘equilibrium’ relationship
• If they are they are then an error correction model can be employed – This models the short run % changes in variables including an error correction term
• The error correction term ‘forces’ rents to grow back towards the ‘equilibrium relationship
– In a given period an amount of disequilibrium is corrected
• If the error term has a significant impact there is a relationship present whereby rents would be under pressure to revert back to the levels justified by market fundamentals
Model Specification • Reduced form demand and supply equation as per Hendershott et al (2002)
• Long run occupier demand can be shown as follows:
1,
*
1
0
**
**
0
)[lnlnln
lnln])1[ln(ln
)1(),(
lnln),(ln
tRREREStVACSUPEMPt
SUPEMPSUP
EMP
vvSUPEMPRRE
SUPEMPvRRE
SvEMPRRED
EMPRREEMPRRED
• Equilibrium condition:
• Take logarithms and via substitution:
• Resulting error correction model specification:
Key Questions II • Cross section dependence? = Presence of unobserved factors impacting market
behavior – After accounting for impact of explanatory variables (demand and supply here) there is still a
residual ‘force’ leading causing dependence amongst markets
– Reasons for its existence - economic and financial integration likely (spatial) interdependencies between office markets due to common shocks (EURO, capital market integration etc), and/or spill-over effects
– Panel modeling approaches should account for this when present
• Asymmetric effect? = Rents do not adjust to demand and supply changes in a uniform manner over time
– Structural market rigidities prevent a fluid rental response to either demand and/or supply
– E.g. supply rigidity arising from a lack of available land, tight planning regimes and construction timeframes. This would inhibit a timely supply response to market conditions
– An outcome = rationing effect when demand rises sharply in a market with high occupancy likely to generate abnormally high rental growth
– If this is case then it should be accounted for in forecasting / modeling processes
– Evidence that forecasts are overly smoothed – including asymmetry may ‘unsmooth’
Prior Studies • Previous panel based research on comercial rent determinants:
– Hendershott, MacGregor &White (2002); Mouzakis & Richards (2007); Englund et al (2008); Brounen & Jennen (2009); Hendershott, Lizieri & MacGregor (2010); Drennan & Kelly (2011); Adams & Füss (2012); Hendershott, Jennen & MacGregor (2013)
• Asymmetric impact covered by the following: – ECM - Farrelly & Sanderson (2005) – non-linear modelling approach
– ECM - Lizieri (2009), PECM - Brounen & Jennen (2009) – use dummy variables to identify positive/negative variable impacts and states of disequilibria
– Rationale: rental contracts (leases) and new supply timeframe create market rigidities whereas new demand can respond relatively quickly to increased needs – creates a ‘non-fluid’ rent clearing mechanism with resulting asymmetry
• Comments and issues addressed by this study: – Some panel studies do not address unit roots
– Few studies dealing with asymmetric rent adjustment, particularly in a panel setting
– Previous research does not address cross section dependence
– The presence of this requires use of both tests and regression methodologies to estimate robust results
Dataset - DTZ Pan European Offices
• Panel time series: 1990 to 2012 (annual observations)
• Scope: 12 key European office markets - Amsterdam, Copenhagen, Dublin, Frankfurt, London, Madrid, Manchester, Milan, Munich, Paris, Stockholm and Zurich
• Data: provided by DTZ: – Panel data on selected office market indicators (Development, Rents, Stock, New Supply, Take Up,
Availability)
– Prime headline rents stated in local currency and deflated using national CPI index
– Additional panel data on financial and business services employment provided by Oxford Economics
– Authors own data used to ‘backcast’ missing observations in a small number of instances
• Panel: balanced 21 x 12 = 252 observations for short-run equations with lag – Classify as a macro panel, though recognize that this is at the ‘small end’ of the scale
– Employ heterogeneous parameter estimators – all prior panel rent studies appear to employ micro-panel methodologies
Annual Real Rental Growth
-50%
-40%
-30%
-20%
-10%
0%
10%
20%
30%
40%
50%
60%
Copenhagen Paris Frankfurt Munich
Dublin Milan Amsterdam Madrid
Stockholm Zurich Manchester London
City
Average
%
Standard Deviation
(%)
Copenhagen -2.09 10.47
Paris -0.92 11.40
Frankfurt -2.42 12.13
Munich -1.95 9.19
Dublin -0.51 14.94
Milan -1.73 9.92
Amsterdam 1.11 6.95
Madrid -5.23 19.24
Stockholm -0.45 15.17
Zurich -3.18 14.13
Manchester 0.18 5.00
London -0.86 14.25
Cross Sectional Dependence Tests • Find strong evidence of cross sectional dependence in key European office markets
• Peseran (2004) CD tests on individual time series in both level and first differences
also shows strong dependence structures
• Clear requirement to address this issue in testing / modeling framework
Log(Real Rent)Log(FBS
Employment)Log(Stock) Log(Real Rent)
Log(FBS
Employment)Log(Stock)
avg ρ 0.436 0.672 0.970 0.491 0.331 0.259
avg abs ρ 0.453 0.905 0.970 0.474 0.293 0.196
CD 16.93 26.06 37.64 17.98 11.14 7.44
p-value 0.000 0.000 0.000 0.000 0.000 0.000
Level First Difference
Panel Unit Root Tests • Given cross section dependence, ‘first generation’ panel unit root tests would be
invalid
• Pesaran (2007) test employed- eliminates the cross section dependence by augmenting the standard Dickey-Fuller regressions with the cross section averages of lagged levels and first-differences of the individual series
Level Variables
Lags Log(Real Rent)Log(FBS
Employment)Log(Stock) Log(Real Rent)
Log(FBS
Employment)Log(Stock)
0 -0.039 (0.484) -1.034 (0.151) -3.005 (0.000) -1.729 (0.042) 0.588 (0.722) -0.458 (0.324)
1 0.906 (0.817) -0.504 (0.307) -4.458 (0.000) -0.739 (0.230) 0.610 (0.729) -2.452 (0.007
First Difference
Lags Log(Real Rent)Log(FBS
Employment)Log(Stock) Log(Real Rent)
Log(FBS
Employment)Log(Stock)
0 -10.012 (0.000) -5.903 (0.000) -6.747 (0.000) -9.147 (0.000) -4.597 (0.000) -5.228 (0.000)
1 -4.118 (0.000) -2.788 (0.000) -4.853 (0.000) -2.569 (0.000) -1.243 (0.107) -3.263 (0.001)
Constant Constant & Trend
Constant Constant & Trend
Panel Cointegration Testing • Firstly, as per Holly et al (2009), Pesaran (2007) panel unit root tests are conducted
on the CCE regression residuals
• Next we use Westerlund (2007) bootstrap based cointegration tests. These include both total panel and ‘at least one cross-section’ tests. Robust standard errors required in the presence of cross section correlation
• On balance the tests provide evidence of a cointegrating relationship between the variables under consideration
p-values in parentheses
Lags Constant Constant & Trend
0 -7.862 (0.000) -5.850 (0.000)
1 -5.522 (0.000) -3.560 (0.000)
p-values in parentheses
Lags Value Z-Value P-value Robust P-value
Gt -2.634 -2.252 0.012 0.020
Ga -7.745 0.911 0.819 0.204
Pt -8.794 -2.770 0.003 0.016
Pa -6.684 -0.507 0.306 0.124
Modeling Approach
• Implement two panel time series estimators which allow for heterogeneous slope coefficients across cross-section units
– Pesaran (2006) Common Correlated Effects Mean Group Estimator
– Eberhardt & Teal (2010) Augmented Mean Group Estimator
• Model linear / symmetric specifications
• Then explicitly modelled asymmetric impact of positive and negative supply/demand drivers
– Made use of dummy variables on employment and supply growth variables in short-run equation
• Subsequent asymmetric specification looking at impact of positive and negative supply/demand drivers when rent is above/below equilibrium
Specification I – Symmetric Results
• Strong long-term relationship found and significant error correction term
• The annual short-run correction is high, ranging from 60% to 93%, depending on the model
Specification II – Asymmetric Results
• Only positive supply growth is statistically significant
• Only positive demand growth is statistically significant when lagged rental growth is included
• Why is that?
Specification III – Asymmetric Results
• When rent is below equilibrium positive employment shocks impact to a greater extent and the opposite is true for positive supply shocks
• Clear asymmetrical effects at work in European rent market dynamics
Implications of Results • Highlights the importance in understanding rental growth dynamics and, in
particular, the presence of trend reverting characteristics
• Demand and supply impact rents in a asymmetrical fashion – positive employment and supply growth significant and impact varies depending upon rent market disequilibrium
• Depending upon prevailing rent levels vs this trend, the key demand and supply drivers will impact growth to varying degrees
• Reflecting this aspect of rent and market dynamics should also be considered when undertaking risk modeling involving the projection of rental growth outcomes
• Investment underwriting and strategy formulation needs to incorporate as much accuracy and reality as possible!
Conclusions • Study the rent adjustment process for European office markets using a macro
panel modeling framework
• Found strong evidence of cross section dependence between markets
• Evidence of co-integrating relationships and as a result an error correction specification was employed
• Found statistically significant rental growth drivers - lagged rental growth, FBS employment growth, office stock movements and a strong error correction effect
• Demand and supply impact rents in an asymmetrical fashion
• Implication is that ‘linear’ models (same coefficients under all conditions) are mis-specified – time to re-visit the models?
• Ignoring asymmetric effects can lead to large forecast errors – look at the recent IPF study on forecast accuracy – all forecasts miss the outcome by large margins when markets move abruptly (and also not so abruptly on several occasions!)
• Practical implications for development situations, lease negotiations and pricing
• Ongoing work looking at asymmetric impact under different market environments
• Are asymmetric impacts similar across all cities? Need to identify market groupings with similar asymmetric responses
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Winners of 2013 Best Paper Prize Awarded by Eamonn D’Arcy – University of Reading
www.dtz.com
Andrew Baum (University of Cambridge), Franz Fuerst (University of Cambridge) and Stanimira Milcheva (University of Reading)
Left 3, from left:
Fergus Hicks (DTZ Global Head of Forecasting)
Nigel Almond
(DTZ Global Head of Strategy Research)
Hans Vrensen (DTZ Global Head of Research)
Right 4, from middle: Eamonn D’Arcy (University of Reading) Franz Fuerst (University of Cambridge) Stanimira Milcheva (University of Reading) Andrew Baum (University of Cambridge)
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Closing Comments Hans Vrensen, DTZ Global Head of Research and Research Institute Chair
www.dtz.com
“We are delighted to be able to sponsor the DTZ Research Institute as part of our thought leadership and academic outreach programme. We are committed to promoting innovative research in the commercial real
estate sector and look forward to next year’s submissions.”
59
Contacts
Fergus Hicks, Global Head of Forecasting
Direct Line : +44 (0) 203 296 2307
Hans Vrensen, Global Head of Research
Direct Line : +44 (0) 203 296 2159
