Multifamily residential asset and space markets and linkages with the economy

Alain Chaney ♣

Martin Hoesli ♦

ERES Conference

Bucharest, June 25-28, 2014

♦ GSEM & Swiss Finance Institute, University of Geneva, Switzerland

Business School, University of Aberdeen, UK

Kedge Business School, France

♣ GSEM, University of Geneva, Switzerland

IAZI AG, Switzerland

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Motivation

Methodology

Data

Empirical Results

Outline

Motivation Methodology Data Empirical Results

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Theoretical background

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 Empirical Results

Macro Economy Property Market (DiPasquale & Wheaton)

stock

construction

market rent

price

demand=f(r, gdp…)

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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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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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Data

Area SwitzerlandWhy? Basic principles of macroeconomics and of real estate economics

that underlie our empirical model are country-independentSeveral ‘building blocks’ which constitute the basis for our study have been applied successfully to various marketsAvailability and quality of required data in general and of transaction-based cap rates in particular

Period 1974Q1-2013Q2

CR real estate cap rate; 0.25*ln(1+R/100)INF quarterly inflation rate; ln(CPI/CPI(-1)), whereas the CPI has been

adjusted for inclusion of the end of year sales in 2000R10 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 M2RENT log real rent, s.a. before 1990CON log real construction spending, s.a.Y log real gdp, s.a.

Motivation Methodology Data Empirical Results

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Long-run analysis

Motivation Methodology Data Empirical Results

Slope of the term structure (3m &10y

interest rates)

… augmented with money demand (M2, GDP & 3m interest rates)

Real estate excess return (cap rates & 10y interest rates and cap rate spread & construction / GDP)

Real estate market equilibrium (rents / cap rates & constructions)

Theory predicts several long-run relations among these series

Fisher interest rate parity (3m interest rates &

inflation) …

Augmented fisher interest rate parity

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Long-run analysis

Motivation Methodology Data Empirical Results

Economic theory suggestsSlope of the term structureFisher parityReal estate market equilibriumReal estate excess return

Written compactlywhere

Results

𝛽′ =൮

𝛽11 0 0 0 0 0 0 11 𝛽22 𝛽23 𝛽24 0 0 0 00 0 0 0 1 𝛽36 𝛽37 00 0 0 𝛽44 𝛽45 1 0 −1൲

𝑐𝑟𝑡 −𝑟10𝑡 = 𝑏40 +𝑏41𝑡+𝛽41ሺ𝑐𝑜𝑛𝑡 −𝑦𝑡ሻ+𝜉4,𝑡

𝑟10𝑡 = 𝑏10 +𝛽11𝑟3𝑚𝑡+𝜉1,𝑡 𝑟3𝑚𝑡 = 𝑏20 +𝛽21𝜋𝑡 +𝛽22ሺ𝑦𝑡 −𝑚2𝑡ሻ+𝜉2,𝑡 𝑐𝑜𝑛𝑡 = 𝑏30 +𝛽31𝑟𝑒𝑛𝑡𝑡 +𝛽32𝑐𝑟𝑡 +𝜉3,𝑡

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

Demonstrates the empirical validity of the D&W framework Cap rate spread indeed evolves with the evolution of the

construction to GDP measure

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Error-correction equations

Motivation Methodology Data Empirical Results

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 macro-economy, 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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Length of time to equilibrium

Motivation Methodology Data Empirical Results

Overall observationsLength of time varies between 10 and 30 quarters depending on the shocksome 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 inflationBiggest changes caused by short- and long-term interest rates, M2 and GDPLength of time to eq. about 5 years

Real estate market equilibriumSmallest influence exerted by a change in inflationBiggest changes caused by one standard deviation change in cap rates, long-term interest rates and rentsLength of time to eq. about 7 years

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Short-run dynamics

Motivation Methodology Data Empirical Results

The linkages between commercial real estate and the economic are bi-directional

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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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

Thank you for your attention!