maurizio grilli & richard barkham june 2012 city rents in a global context
TRANSCRIPT
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
To
kyo
Ne
w Y
ork
Lo
s A
ng
ele
s
Ch
ica
go
Lo
nd
on
Pa
ris
Osa
ka
Me
xico
City
Sã
o P
au
lo
Ph
ilad
elp
hia
Wa
shin
gto
n D
.C.
Bo
sto
n
Bu
en
os
Air
es
Da
llas
Mo
sco
w
Ho
ng
Ko
ng
Atla
nta
Sa
n F
ran
cisc
o
Ho
ust
on
Mia
mi
Se
ou
l
To
ron
to
De
tro
it
Se
attl
e
Sh
an
gh
ai
Ma
dri
d
Sin
ga
po
re
Syd
ne
y
Mu
mb
ai
Rio
de
Ja
ne
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 1500
0
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$/sqm/year)
Cities ranked according to connectivity
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Lo
nd
on
Ne
w Y
ork
Ho
ng
Ko
ng
Pa
ris
To
kyo
Sin
ga
po
re
To
ron
to
Ch
ica
go
Ma
dri
d
Fra
nkf
urt
Mila
n
Am
ste
rda
m
Bru
sse
ls
Sã
o P
au
lo
Lo
s A
ng
ele
s
Zu
rich
Syd
ne
y
Me
xico
City
Ku
ala
Lu
mp
ur
Bu
en
os
Air
es
Sa
n F
ran
cisc
o
Be
ijin
g
Sh
an
gh
ai
Se
ou
l
Ta
ipe
i
Me
lbo
urn
e
Ba
ng
kok
Jaka
rta
Du
blin
Mu
nic
h
Source: GAWC , University of Loughborough, Grosvenor Research, 2012
Connectivity coefficient (max=1)
Relation between rents and connectivity
0
200
400
600
800
1,000
1,200
1,400
1,600
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
0
5,000
10,000
15,000
20,000
25,000
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Source: GAWC , University of Loughborough, Grosvenor Research, 2012
Office rents (US$/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
Va
nco
uve
r
Me
lbo
urn
e
Ca
lga
ry
He
lsin
ki
Syd
ne
y
Zu
rich
Ge
ne
va
Osa
ka
Sto
ckh
olm
Pa
ris
Fra
nkf
urt
To
kyo
Osl
o
Am
ste
rda
m
Mu
nic
h
Ho
ng
Ko
ng
Lyo
n
Ch
ica
go
Ma
dri
d
Lo
s A
ng
ele
s
Mila
n
Sin
ga
po
re
Sa
n F
ran
cisc
o
Lo
nd
on
Ne
w Y
ork
Bu
en
os
Air
es
Be
ijin
g
Tia
njin
Sh
an
gh
ai
Gu
an
gzh
ou
Sa
o P
au
lo
Me
xico
City
Ista
nb
ul
Ne
w D
elh
i
Mu
mb
ai
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 90 100
0
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$/sqm/year)
Cities ranked by population density
0
5,000
10,000
15,000
20,000
25,000
30,000
35,000
40,000
Dh
aka
Mu
mb
ai
Ma
cau
Su
rat
Ch
itta
go
ng
Ho
ng
Ko
ng
Ra
ipu
r
Hu
bli-
Dh
arw
ad
Sin
uju
Jaip
ur
Ya
ng
zho
u
Alig
arh
So
lap
ur
Bo
go
ta
Mo
rad
ab
ad
Fe
z
Ra
nch
i
Vija
yaw
ad
a
Ko
lko
ta
Hyd
era
ba
d
Ah
ma
da
ba
d
Lu
zho
u
Sa
lem
Me
de
llin
Na
raya
ng
an
j
Hu
am
bo
Gw
alio
r
Pa
tna
Wa
ran
ga
l
Ma
du
rai
Source: Demographia, Grosvenor Research, 2012
Population density – people per sq km
Relation between rents and population density
0
200
400
600
800
1,000
1,200
1,400
1,600
-2,000 3,000 8,000 13,000 18,000 23,000 28,000
0
5,000
10,000
15,000
20,000
25,000
0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000
Source: Demographia , Grosvenor Research, 2012
Office rents (US$/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
19
72
19
73
19
74
19
75
19
76
19
77
19
78
19
79
19
80
19
81
19
82
19
83
19
84
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
All shops in Central London
West End offices
City 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%
Ca
lga
ry
Va
nco
uve
r
Lo
nd
on
Zu
rich
Bri
sba
ne
To
kyo
Sa
o P
au
lo
Ho
ng
Ko
ng
Ne
w D
elh
i
Fra
nkf
urt
Osa
ka
Pa
ris
Mila
n
Syd
ne
y
Wa
shin
gto
n
Sh
an
gh
ai
To
ron
to
Se
attl
e
Mu
nic
h
Be
ijin
g
Sin
ga
po
re
Am
ste
rda
m
Mu
mb
ai
Lo
s A
ng
ele
s
Sa
n F
ran
cisc
o
Ph
ilad
elp
hia
Ma
dri
d
Bru
sse
ls
Ta
ipe
i
Vie
nn
a
Ne
w Y
ork
Bu
en
os
Air
es
Se
ou
l
Me
xico
City
Ch
ica
go
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%
Ro
me
Mila
n
Mu
nic
h
Ho
ng
Ko
ng
Ne
w Y
ork
Va
nco
uve
r
Sa
n F
ran
cisc
o
Me
lbo
urn
e
Ne
w D
elh
i
Osa
ka
Syd
ne
y
Sa
o P
au
lo
Se
ou
l
Fra
nkf
urt
Pa
ris
Lo
nd
on
Sh
an
gh
ai
Ba
rce
lon
a
Lo
s A
ng
ele
s
Ch
ica
go
Am
ste
rda
m
Mo
sco
w
To
kyo
Be
ijin
g
Ma
dri
d
Ca
lga
ry
Sin
ga
po
re
Bru
sse
ls
Sto
ckh
olm
Wa
rsa
w
Wa
shin
gto
n
Mu
mb
ai
To
ron
to
Bu
en
os
Air
es
Me
xico
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%
Gu
an
gzh
ou
Bo
sto
n
Fra
nkf
urt
Ca
lga
ry
Zu
rich
Rio
de
Ja
ne
iro
To
ron
to
Wa
shin
gto
n
Syd
ne
y
Osa
ka
Mu
nic
h
Be
ijin
g
Ho
ng
Ko
ng
Va
nco
uve
r
Sin
ga
po
re
Sa
n F
ran
cisc
o
To
kyo
Bri
sba
ne
Me
xico
City
Lo
nd
on
Am
ste
rda
m
Ne
w Y
ork
Mila
n
Pa
ris
Sh
an
gh
ai
Lo
s A
ng
ele
s
Bru
sse
ls
Ba
rce
lon
a
Sa
o P
au
lo
Ch
ica
go
Ma
dri
d
Se
ou
l
Ta
ipe
i
Bu
en
os
Air
es
Bu
da
pe
st
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.