Multifamily residential asset and space
markets and linkages with the economy
Alain Chaney ♣
Martin Hoesli ♦
ERES Conference
Bucharest, June 25-28, 2014
♣ GSEM, University of Geneva, Switzerland
IAZI AG, Switzerland
♦ GSEM & Swiss Finance Institute, University of Geneva, Switzerland
Business School, University of Aberdeen, UK
Kedge Business School, France
Outline

Motivation

Methodology

Data

Empirical Results
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Motivation
Methodology
Data
Empirical Results
2014 ERES Conference
Theoretical background
Property Market (DiPasquale & Wheaton)
Macro Economy
market rent
demand=f(r, gdp…)
price
stock
construction
 Real estate markets are influenced by macroeconomic factors through a variety
of channels
 and these linkages have been documented both for housing
(Kennedy, 2005; Cihák, Iossifov & Shanghavi, 2008; International Monetary Fund, 2008)
 and commercial real estate markets
(Chaney & Hoesli, 2012; McCartney, 2012)
Motivation
Methodology
Data
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Empirical Results
2014 ERES Conference
Previous work
 Asset market extensively researched by cap rate studies
(early work includes Froland, 1987; Evans, 1990; Ambrose & Nourse, 1993)
 Limited number of recent studies applied more complex time series models, i.e. ECM that
follow the strategy of Engle & Granger (1987)
(Hendershott & MacGregor, 2005; Dunse et al., 2007; Clayton, Ling & Naranjo, 2009)
 Cap rates are found to depend on various economic forces
 Space market studies are mainly concerned with estimation of the rental adjustment process
and explain (equilibrium) rents by employment, economic activity, interest rates, space
supply, (natural) vacancy rate, construction costs, and lagged rental values. State of the art
are ECMs that follow the strategy of Engle & Granger (1987)
(Hendershott, MacGregor & Tse, 2002; Hendershott, MacGregor & White, 2002; Brounen & Jennen,
2009; Hendershott, Lizieri & MacGregor, 2010; McCartney, 2012)
 Methods
 ECMs of Engle and Granger (1987) are limited to a single cointegrating vector and
the studies that have applied this approach treated economic variables exogenously
 Johansen (1988, 1991) and Johansen & Juselius (1990) developed a systems-based
approach to cointegration which enables for more than one cointegrating vector
 This approach has been applied to the commercial real estate market only recently
(Schätz & Sebastian, 2009; Kohlert, 2010)
 The cointegrating vectors derived from the popular Johansen procedure would allow for
more than one long-run relation, but they are statistically motivated identifying restrictions
 No economic meaning: economic relations are not orthogonal
 Identification of short-run dynamics achieved with the recursive structure of Sims (1980)
 Results are not unique and depend on the ordering of the variables
 Methodologies applied do not seem to fully meet the complexity of the linkages
Motivation
Methodology
Data
Empirical Results
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2014 ERES Conference
Methodology





To overcome some of these issues, we introduce a new modelling
approach from macroeconometrics.
It is based on Garratt, Lee, Pesaran & Shin (2003, 2006) and allows
to incorporate long-run structural relationships, as suggested by
economic theory, in an otherwise unrestricted VAR model.
On the basis of the equilibrium framework provided by DiPasquale &
Wheaton (1992, 1996), we model the commercial real estate
market as a whole to account for the fact that construction, rents
and cap rates are interrelated.
We also model all series including core economic variables
endogenously, therefore allowing for various contemporaneous
linkages as well as for several long-run equilibrium relations.
Standard VECM, but cointegrating vectors derived using economic
theory (whose validity can be tested econometrically)
Motivation
Methodology
Data
Empirical Results
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2014 ERES Conference
Data
Area
Why?
Switzerland
Basic principles of macroeconomics and of real estate
economics that underlie our empirical model are
country-independent
Several ‘building blocks’ which constitute the basis
for our study have been applied successfully to
various markets
Availability and quality of required data in general
and of transaction-based cap rates in particular
Period 1974Q1-2013Q2
CR
INF
real estate cap rate; 0.25*ln(1+R/100)
quarterly inflation rate; ln(CPI/CPI(-1)), whereas the
CPI has been adjusted for inclusion of the end of year
sales in 2000
R10
risk-free interest rate with a maturity of 10 years;
0.25*ln(1+R/100)
LIB
risk-free interest rate with a maturity of 3 months;
0.25*ln(1+R/100)
M2
log real M2
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RENT log real rent, s.a. before 1990
CON
log real construction spending, s.a.
2014 ERES Conference
Data
Empirical Results
Y Motivation
log real Methodology
gdp, s.a.
Long-run analysis
Theory predicts several long-run relations among these series
Slope of the term
structure (3m &10y
interest rates)
Fisher interest rate parity
(3m interest rates &
inflation) …
Real estate excess return (cap rates & 10y interest rates
and cap rate spread & construction / GDP)
… augmented with money demand (M2, GDP & 3m
interest rates)
Real estate market
equilibrium (rents / cap
rates & constructions)
Augmented fisher interest
rate parity
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Motivation
Methodology
Data
Empirical Results
2014 ERES Conference
Long-run analysis
Economic theory suggests
Slope of the term structure
𝑟10𝑡 = 𝑏10 + 𝛽11 𝑟3𝑚𝑡 +𝜉1,𝑡
Fisher parity
𝑟3𝑚𝑡 = 𝑏20 + 𝛽21 𝜋𝑡 + 𝛽22 𝑦𝑡 − 𝑚2𝑡 + 𝜉2,𝑡
Real estate market equilibrium
𝑐𝑜𝑛𝑡 = 𝑏30 + 𝛽31 𝑟𝑒𝑛𝑡𝑡 + 𝛽32 𝑐𝑟𝑡 + 𝜉3,𝑡
Real estate excess return
𝑐𝑟𝑡 − 𝑟10𝑡 = 𝑏40 + 𝑏41 𝑡 + 𝛽41 𝑐𝑜𝑛𝑡 − 𝑦𝑡 + 𝜉4,𝑡
Written compactly
where
𝛽′ =
𝛽11
1
0
0
0
𝛽22
0
0
Results
0
𝛽23
0
0
0
𝛽24
0
𝛽44
0
0
1
𝛽45
0
0
𝛽36
1




Motivation
Methodology
0
0
𝛽37
0
1
0
0
−1
 The central bank tens to reduce libor almost one by one with inflation…
 … and some more when money velocity is low, i.e. if GDP growth
is low (compared to M2). Financial crisis: no inflationary pressure
 central banks reduced interest rates and increased money
supply to stimulate GDP growth and to avoid deflation


Data
Demonstrates the empirical validity of the D&W framework
Cap rate spread indeed evolves with the evolution of the
construction to GDP measure
Empirical Results
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2014 ERES Conference
Error-correction equations
 Adjusted R2 all lie in the range of [0.17, 0.68]
 Adjusted R2 for benchmark models are much lower for most equations
 In line with this observation, the coefficients of the error correction terms make a
significant contribution in most equations
 This shows that the error correction mechanisms provide for a complex and
statistically significant set of interactions and feedbacks across the whole macroeconomy, including all real estate quadrants
 It also demonstrates that the benefits of the long-run structural modeling lie not
only within the more structural interpretation and understanding based on
economic theory but also within an improvement of the explanatory power of the
short-run dynamics
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Motivation
Methodology
Data
Empirical Results
2014 ERES Conference
Length of time to equilibrium
Overall observations
 Length of time varies between
10 and 30 quarters depending
on the shock
 some shocks eventually simply
vanish while others, such as a
shock in construction, M2, or
GDP lead to oscillation
Real estate excess return eq.
 Smallest influence exerted by a change in
inflation
 Biggest changes caused by short- and
long-term interest rates, M2 and GDP
 Length of time to eq. about 5 years
Real estate market equilibrium
 Smallest influence exerted by a change in
inflation
 Biggest changes caused by one standard
deviation change in cap rates, long-term
interest rates and rents
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 Length of time to eq. about 7 years
Motivation
Methodology
Data
Empirical Results
2014 ERES Conference
Short-run dynamics
 The linkages between commercial real estate and the economic are bidirectional
 While it is well documented that economic variables influence real estate
markets, GIRFs clearly show that real estate variables also exert some
influence on the economy
 For example, if the monetary authority increases interest rates to reduce
inflationary pressures, this will directly reduce GDP growth and inflation, but on
top will also impact the real estate market through a reduction in construction
and rents and through an increase in cap rates. This will feed back to the core
economy, as lower construction and lower rents both reduce GDP. The
ultimate outcome may be a recession and falling real estate prices
 These observations require modeling all variables (including the economic
variables) endogenously
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Motivation
Methodology
Data
Empirical Results
2014 ERES Conference
Summary & Conclusions

Theory: predicts various linkages

Previous studies: focused on a limited subset of these linkages, treated
economic variables exogenously & included a single cointegrating relation

We model the whole economy (including all four real estate quadrants) by
incorporating equilibrium relations that are predicted by theory in an otherwise
unrestricted VAR model and treat all variables endogenously

We find four long-run equilibrium relations

Due to their economic interpretation, these long-run equilibrium relations do
not just improve the explanatory power of the models short-run dynamics,
but they additionally help in the interpretation of economic conditions
and identification of market disequilibria

Short-run dynamics show that the linkages are bi-directional. This requires
modeling all variables endogenously

Results should also prove useful to investors, real estate developers, and
tenants because a better understanding of the linkages can help them to
prepare better for economic shocks and market disequilibria

Researchers should benefit because the presence of bi-directional links and a
variety of long-run equilibrium relations implies that it is likely that previous
studies did not fully capture the whole error-correcting behavior
Motivation
Methodology
Data
Empirical Results
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2014 ERES Conference
Thank you for your attention!