City rents in a global context

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


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



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


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



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


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



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


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



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


Degree of over and under-renting %

over -rented under -rented

The retail model


Dependent Variable: Retail rental valuesIncluded observations: 62 after adjustments


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



over -rented under -rented

The residential model


Dependent Variable: Residential rental valuesIncluded observations: 74 after adjustments


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



over -renting under -renting

The importance of different variables for different sectors


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.

City rents in a global context

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