Maurizio Grilli & Richard Barkham June 2012
City rents in a global context
Aim of the research
■ Most models looking at the determinants of rental change generally aim at explaining rental changes in the short-run. Models can be macro (mostly TS) or micro (mostly cross-sectional).
■ We aim at establishing an intuitive hierarchy of rental values across markets.
■ The markets we analyse here are urban conurbations across the globe. We believe that property investment is essentially city-driven (rather than country-driven) and, as a result of on-going urban growth, it is vital to be able to pick those cities which will outperform.
The rationale
■ By understanding the drivers behind rental values, an investor may acquire assets in the markets that are currently under-rented and thereby out-perform competitors. Vice versa investors can avoid the high risks associated with over-rented markets.
■ By taking a long-run perspective, subject to accurate forecasts of the drivers of rental values, an investor may deploy capital in the markets that will deliver the highest capital value uplift.
The cities
■ Half of the world’s population live in cities and these generate more than 80% of global GDP. The top 600 cities (equivalent to 20% of the world’s population) deliver 60% of global GDP. In 2030, the top cities will still provide most of total GDP, but the names of those cities will be different.
■ A successful city will generally have most, if not all, of the following features:
– Large size, in terms of population, GDP and real estate stock;
– A strong and diversified economy including advanced business services;
– A well-educated workforce;
– High level of connectivity;
– Low levels of crime;
– A good transport system;
– Good entertainment and cultural offer;
– A general sense of vibrancy and innovation;
– High standard of liveability;
– A cosmopolitan feeling;
– A responsible environmental policy.
The data
■ By drawing on the Grosvenor in-house database we were able to collect office and retail rental data for 140 cities. This was supplemented with residential rental data for more than 110 cities.
■ The explanatory variables found to be most important are as follows:
– GDP;
– Connectivity;
– Quality of life;
– Population density;
– Planning constraints.
Total GDP in the top 30 cities
0.0
200.0
400.0
600.0
800.0
1,000.0
1,200.0
1,400.0
1,600.0
Toky
o
New
Yor
k
Los
Ang
eles
Chi
cago
Lond
on
Par
is
Osa
ka
Mex
ico
City
São
Pau
lo
Phi
lade
lphi
a
Was
hing
ton
D.C
.
Bos
ton
Bue
nos
Aire
s
Dal
las
Mos
cow
Hon
g K
ong
Atla
nta
San
Fra
ncis
co
Hou
ston
Mia
mi
Seo
ul
Toro
nto
Det
roit
Sea
ttle
Sha
ngha
i
Mad
rid
Sin
gapo
re
Syd
ney
Mum
bai
Rio
de
Jane
iro
Source: PWC, Global Insight, local sources, Grosvenor Research, 2012
GDP (US$ bn)
Relation between rents and GDP
0
200
400
600
800
1,000
1,200
1,400
1,600
-100 100 300 500 700 900 1100 1300 15000
5,000
10,000
15,000
20,000
25,000
0 200 400 600 800 1000 1200 1400 1600
Source: PWC, Global Insight, local sources, Grosvenor Research, 2012
Office rents (US$/sqm/year)
GDP
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
-100 100 300 500 700 900 1100 1300 1500
GDP
GDP
Residential rents (US$/sqm/year)
Retail rents (US$/sqm/year)
Cities ranked according to connectivity
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lond
on
New
Yor
k
Hon
g K
ong
Par
is
Toky
o
Sin
gapo
re
Toro
nto
Chi
cago
Mad
rid
Fran
kfur
t
Mila
n
Am
ster
dam
Bru
ssel
s
São
Pau
lo
Los
Ang
eles
Zuric
h
Syd
ney
Mex
ico
City
Kua
la L
umpu
r
Bue
nos
Aire
s
San
Fra
ncis
co
Bei
jing
Sha
ngha
i
Seo
ul
Taip
ei
Mel
bour
ne
Ban
gkok
Jaka
rta
Dub
lin
Mun
ich
Source: GAWC , University of Loughborough, Grosvenor Research, 2012
Connectivity coefficient (max=1)
Relation between rents and connectivity
0
200
400
600
800
1,000
1,200
1,400
1,600
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.00
5,000
10,000
15,000
20,000
25,000
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Source: GAWC , University of Loughborough, Grosvenor Research, 2012
Office rents (US$/sqm/year)
Connectivity
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Connectivity
Connectivity
Residential rents (US$/sqm/year)
Retail rents (US$/sqm/year)
Cities ranked by quality of life
0
10
20
30
40
50
60
70
80
90
100
Van
couv
er
Mel
bour
ne
Cal
gary
Hel
sink
i
Syd
ney
Zuric
h
Gen
eva
Osa
ka
Sto
ckho
lm
Par
is
Fran
kfur
t
Toky
o
Osl
o
Am
ster
dam
Mun
ich
Hon
g K
ong
Lyon
Chi
cago
Mad
rid
Los
Ang
eles
Mila
n
Sin
gapo
re
San
Fra
ncis
co
Lond
on
New
Yor
k
Bue
nos
Aire
s
Bei
jing
Tian
jin
Sha
ngha
i
Gua
ngzh
ou
Sao
Pau
lo
Mex
ico
City
Ista
nbul
New
Del
hi
Mum
bai
Source: EIU, Grosvenor Research, 2012
Quality of life (100= ideal)
Relation between rents and quality of life
0
200
400
600
800
1,000
1,200
1,400
1,600
30 40 50 60 70 80 90 1000
5,000
10,000
15,000
20,000
25,000
30 40 50 60 70 80 90 100
Source: EIU, Grosvenor Research, 2012
Office rents (US$/sqm/year)
Quality of life
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
30 40 50 60 70 80 90 100
Quality of life
Quality of life
Residential rents (US$/sqm/year)
Retail rents (US$/sqm/year)
Cities ranked by population density
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
Dha
ka
Mum
bai
Mac
au
Sur
at
Chi
ttago
ng
Hon
g K
ong
Rai
pur
Hub
li-D
harw
ad
Sin
uju
Jaip
ur
Yan
gzho
u
Alig
arh
Sol
apur
Bog
ota
Mor
adab
ad Fez
Ran
chi
Vija
yaw
ada
Kol
kota
Hyd
erab
ad
Ahm
adab
ad
Luzh
ou
Sal
em
Med
ellin
Nar
ayan
ganj
Hua
mbo
Gw
alio
r
Pat
na
War
anga
l
Mad
urai
Source: Demographia, Grosvenor Research, 2012
Population density – people per sq km
Relation between rents and population density
0
200
400
600
800
1,000
1,200
1,400
1,600
-2,000 3,000 8,000 13,000 18,000 23,000 28,0000
5,000
10,000
15,000
20,000
25,000
0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000
Source: Demographia , Grosvenor Research, 2012
Office rents (US$/sqm/year)
Population density
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000
Population density
Population density
Residential rents (US$/sqm/year)
Retail rents (US$/sqm/year)
Real rental levels in the UK
0
50
100
150
200
250
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
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
All shops in Central LondonWest End officesCity offices
Source: CBRE, ONS, Grosvenor Research, 2012
Index 1972=100
Relation between rents and long-run vacancy rate (US only)
0
10
20
30
40
50
60
0 5 10 15 20
0
5
10
15
20
25
30
-1 1 3 5 7 9 11 13
Source: REIS, CBRE , Grosvenor Research, 2012
Office rents (US$/sqm/year)
Vacancy rate
0
500
1,000
1,500
2,000
2,500
0 1 2 3 4 5 6 7 8 9 10
Vacancy rate
Vacancy rate
Residential rents (US$/sqm/year)
Retail rents (US$/sqm/year)
The office model
Source: Grosvenor Research, 2012
Dependent Variable: Office rental valuesIncluded observations: 72 after adjustments
Variable Coefficient t-Statistic Prob.
constant 254.9 2.6 1%
GDP 0.4 3.6 0%
connectivity 427.8 3.1 0%
population density 0.1 3.6 0%
vacancy rate -11.5 -2.0 5%
dummy -169.1 -2.3 3%
R-squared 0.6
Offices: over and under-renting
-70%
-50%
-30%
-10%
10%
30%
50%
70%
90%
110%
Cal
gary
Van
couv
er
Lond
on
Zuric
h
Bris
bane
Toky
o
Sao
Pau
lo
Hon
g K
ong
New
Del
hi
Fran
kfur
t
Osa
ka
Par
is
Mila
n
Syd
ney
Was
hing
ton
Sha
ngha
i
Toro
nto
Sea
ttle
Mun
ich
Bei
jing
Sin
gapo
re
Am
ster
dam
Mum
bai
Los
Ang
eles
San
Fra
ncis
co
Phi
lade
lphi
a
Mad
rid
Bru
ssel
s
Taip
ei
Vie
nna
New
Yor
k
Bue
nos
Aire
s
Seo
ul
Mex
ico
City
Chi
cago
Source: Grosvenor Research, 2012
Degree of over and under-renting %
over -rented under -rented
The retail model
Source: Grosvenor Research, 2012
Dependent Variable: Retail rental valuesIncluded observations: 62 after adjustments
Variable Coefficient t-Statistic Prob.
constant -9771.4 -3.8 0.0
GDP 6.6 4.5 0.0
connectivity 5346.8 2.5 0.0
liveability 90.7 3.1 0.0
population density 0.2 3.1 0.0
EU dummy 1341.2 1.8 0.1
R-squared 0.6
Retail: over and under-renting
-80%
-60%
-40%
-20%
0%
20%
40%
60%
80%
100%
120%
Rom
e
Mila
n
Mun
ich
Hon
g K
ong
New
Yor
k
Van
couv
er
San
Fra
ncis
co
Mel
bour
ne
New
Del
hi
Osa
ka
Syd
ney
Sao
Pau
lo
Seo
ul
Fran
kfur
t
Par
is
Lond
on
Sha
ngha
i
Bar
celo
na
Los
Ang
eles
Chi
cago
Am
ster
dam
Mos
cow
Toky
o
Bei
jing
Mad
rid
Cal
gary
Sin
gapo
re
Bru
ssel
s
Sto
ckho
lm
War
saw
Was
hing
ton
Mum
bai
Toro
nto
Bue
nos
Aire
s
Mex
ico
City
Source: Grosvenor Research, 2012
Degree of over and under-renting %
over -rented under -rented
The residential model
Source: Grosvenor Research, 2012
Dependent Variable: Residential rental valuesIncluded observations: 74 after adjustments
Variable Coefficient t-Statistic Prob.
constant -398.4 -0.5 0.6
GDP 3.5 8.6 0.0
liveability 18.4 2.1 0.0
population density 0.0 1.5 0.1
AM dummy -634.2 -3.0 0.0
R-squared 0.6
Residential: over and under-renting
-80%
-60%
-40%
-20%
0%
20%
40%
60%
80%
100%
120%
Gua
ngzh
ou
Bos
ton
Fran
kfur
t
Cal
gary
Zuric
h
Rio
de
Jane
iro
Toro
nto
Was
hing
ton
Syd
ney
Osa
ka
Mun
ich
Bei
jing
Hon
g K
ong
Van
couv
er
Sin
gapo
re
San
Fra
ncis
co
Toky
o
Bris
bane
Mex
ico
City
Lond
on
Am
ster
dam
New
Yor
k
Mila
n
Par
is
Sha
ngha
i
Los
Ang
eles
Bru
ssel
s
Bar
celo
na
Sao
Pau
lo
Chi
cago
Mad
rid
Seo
ul
Taip
ei
Bue
nos
Aire
s
Bud
apes
t
Source: Grosvenor Research, 2012
Degree of over and under-renting %
over -renting under -renting
The importance of different variables for different sectors
Source: Grosvenor Research, 2012
GDP ConnectivityQuality of life
Population density
Vacancy rate
Office rents strong strong weak medium
Retail rents strong strong medium medium *
Residential rents strong weak weak *
]* Due to data availability issues, the vacancy rate could be used only for offices.
Conclusions
■ Demand, as represented by GDP, and supply as, proxied by long term vacancy, are key determinants of real estate values as theory would suggest and numerous studies attest.
■ Population density is generally associated with higher rental values. It is probable that this represents both cause and effect. Higher rents cause land to be used more intensively, but output is itself a positive function of density due to agglomeration economies.
■ The positive association between rents and livability scores, after controlling for other factors, shows that value and presumably tax revenues, accrue to well managed cities.
■ One of the most interesting findings of the study is the relationship between connectivity, which describes the economic ‘influence’ or ‘reach’ of a city, and rents. This is evidence that real estate outcomes at the city level are increasingly being driven by the forces of globalisation.