Download REIT performance and the impact of interest rates and leverage

Quantifying the impact of Interest Rate
movements on A-REIT performance
School of Property, Construction and Project Management (PCPM)
European Real Estate Society (2016) Conference
CHIEF INVESTIGATORS
Dr. Woon-Weng Wong, Dr. Wejendra Reddy
PROJECT MENTOR
Associate Professor Dr. David Higgins
Australian Real Estate Investment Trusts (A-REITs)
• A-REIT is a corporation that purchases, owns and manages real estate
properties.
• Market size : 48 listed funds, $128 billion (31 March 2016);
– five sectors: office, retail, industrial, diversified and specialised trusts
• REIT Characteristics:
“REITs (listed property trusts) smell like real estate, look like bonds and walk
like equity”
Source : Greg Whyte, Analyst, Morgan Stanley
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Research Objectives
This project aims to quantify the relationship overtime between A-REITs and a
key capital market determinant: interest rates. Specifically the research will:
• Explore the performance of A-REITs with leading financial instruments using
a multifactor asset pricing model (MFAPM).
• Investigate the impact of leverage and interest rate movements on A-REITs
performance during difference market conditions.
• Identify which has the greatest impact on A-REIT performance, short-term
(bank bills) or long-term (bonds) interest rate movements.
• Examine whether higher interest rates negatively/ positively affect REIT
performance.
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Review of Literature
• Huang and Pai (2010) examined US and Japan REITs and found that
movements in interest rates have limited effect on REIT prices.
• Similar studies (Laopodis 2009; Liow and Huang 2006) on other
Asian REITs (Japan, Hong Kong) and UK REIT markets has
demonstrated mixed results.
• Limited Australian studies:
–Ratcliffe and Dimowski (2007) found A-REITs have a significant
negative relationship with long-term interest rates
–Yong and Singh (2014) found negative impact of interest rate risk
only affects REITs during stable and expanding market conditions.
• These studies used panel and panel quantile regressions methods.
• We propose a MFAPM model
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Methodology
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Methodology
• An asset pricing model consistent with principles of risk-return tradeoff
• Modern portfolio theory suggests a multifactor asset pricing model (MFAPM)
• Excess returns = f (Risk factors)
• Coefficients on the risk factors (so called ‘betas’) capture level(s) of exposure
• Most commonly known version is the Capital Asset Pricing Model (CAPM):
𝐸 𝑅𝑖 = 𝑅𝑓 + 𝛽𝑖 𝐸 𝑅𝑚 − 𝑅𝑓
Where:
Ri is the expected return on the ith asset
Rf is the risk free rate
Rm is the return on the market
• The ‘market’ is the only source of risk. Also known as ‘systematic’ or nondiversifiable risk
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Methodology
• Merton’s (1973) Intertemporal Capital Asset Pricing Model (ICAPM) extended
the model.
• Includes systematic risk and additional risk from unfavourable shifts in the
investment opportunity set, represented by a series of ‘state’ variables.
𝐸 𝑅𝑖 = 𝑅𝑓 + 𝛽1 𝐸 𝑅𝑚 − 𝑅𝑓 + 𝛽2 𝐸 𝑅ℎ − 𝑅𝑓
Where: Rh is the expected return on a hedge portfolio constructed to
have a covariance with each asset’s return that is identical to
the covariance between the changes in the state variable of
interest and the asset’s return
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Methodology
• To test the ICAPM, Gibbons (1979; 1982) suggested the following market
model with the addition of a changing state variable:
𝑅𝑡 = 𝛽0 + 𝛽1 𝑅𝑚𝑡 + 𝛽2 ∆𝑆𝑡 + 𝜀𝑡
Where:
St represents changes in the state variable, S in period t
The choice of an appropriate state variable therefore is an important empirical
issue. Merton (1973) suggested the use of long term interest rates,
stating (p. 873):
The interest rate has always been an important variable in portfolio theory, general
capital theory, and to practitioners. It is observable, satisfies the condition of being
stochastic over time, and while it is surely not the sole determinant of yields on other
assets, it is an important factor. Hence, one should interpret the effects of a changing
interest rate ... as a single (instrumental) variable representation of shifts in the
investment opportunity set.
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Methodology
• Based on Merton's suggestion, we propose the following:
𝐸 𝑅𝑡 = 𝛽0 + 𝛽1 𝑆𝑇𝑂𝐶𝐾 + 𝛽2 𝐵𝐼𝐿𝐿 + 𝛽3 𝐵𝑂𝑁𝐷 + 𝑋𝑡′ 𝛽
Where:
STOCK is the monthly logarithmic return on the ASX200
stock market index
BILL is the change in yields of 90 day bank accepted bills
BOND is the change in yields of 10 year treasury bonds
Xt is a vector of macroeconomic indicators
• BILL and BOND are commonly accepted measures of short and long term
interest rates respectively
• Therefore, Returns = f (short term interest rates, long term interest rates,
market risk, macroeconomic factors)
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Data
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Data
• Period: 1995 – 2015
• Financial variables obtained from Datastream (Thomson Reuters). 55 A-REITs
were extracted.
• Filtering conditions:
– Funds with less than 24 months of available data were removed
– Funds with less than A$100M in market capitalisation were removed
– Scentre fund was recombined with Westfield. CNPR was recombined with
Federation.
25 funds were removed
• Macroeconomic variables including: 90 day bank bills rates and 10 year
treasury bond rates, GDP and Inflation were obtained from various public
sources such as RBA and ABS
– Note: GDP and Inflation were only available at quarterly frequency but ‘converted’ to
monthly frequency via a cubic spline interpolation (Encyclopaedia of Mathematics,
2015)
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Data
Descriptive statistics for selected variables (annualised rates):
Mean
Median
Std Dev.
No. Obs.
Min
Max
A-REITs
5.66%
9.53%
18.95%
240
-62.84%
39.65%
ASX200
5.31%
7.23%
15.11%
240
-47.13%
36.89%
BILL
5.01%
4.98%
1.41%
240
2.12%
7.86%
BOND
5.43%
5.52%
1.32%
240
2.41%
8.90%
Inflation
2.65%
2.60%
1.20%
21
0.30%
4.70%
RGDP growth
3.50%
3.90%
1.82%
21
0.15%
6.58%
Summary statistics of monthly value weighted return data for REIT and stock market (ASX200) returns as well as
changes to interest rates, inflation and Real GDP growth rates over the sample period August 1995 to August 2015.
• A-REITs slightly outperformed ASX200 but with higher standard deviation
• However, mean (A-REITs) < median (A-REITs) indicating negative skewness and
clusters of poor performance (see next figure)
• BOND yields > BILL yields with lower standard deviation (as expected)
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-20.00%
Aug-1996
Jan-1997
Jun-1997
Nov-1997
Apr-1998
Sep-1998
Feb-1999
Jul-1999
Dec-1999
May-2000
Oct-2000
Mar-2001
Aug-2001
Jan-2002
Jun-2002
Nov-2002
Apr-2003
Sep-2003
Feb-2004
Jul-2004
Dec-2004
May-2005
Oct-2005
Mar-2006
Aug-2006
Jan-2007
Jun-2007
Nov-2007
Apr-2008
Sep-2008
Feb-2009
Jul-2009
Dec-2009
May-2010
Oct-2010
Mar-2011
Aug-2011
Jan-2012
Jun-2012
Nov-2012
Apr-2013
Sep-2013
Feb-2014
Jul-2014
Dec-2014
May-2015
Data
Annualised Value Weighted Rate of Return
60.00%
40.00%
20.00%
0.00%
-40.00%
-60.00%
-80.00%
A-REITs
ASX200
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Data
Arithmetic mean
Geometric mean
Median
Standard Dev
Avg Sharpe Ratio
No obs.
Minimum
Maximum
PreGFC
A-REITs
ASX200
11.89%
9.11%
10.19%
8.77%
11.23%
9.40%
9.24%
10.15%
1.1097
0.8523
144
144
-10.23%
-18.91%
39.65%
31.03%
GFC
A-REITs
-34.00%
-35.83%
-35.77%
23.26%
-2.8161
24
-62.84%
15.80%
ASX200
-18.23%
-17.94%
-19.88%
22.67%
-1.2583
24
-47.13%
27.68%
PostGFC
A-REITs
ASX200
7.38%
6.13%
5.28%
1.85%
6.39%
6.57%
14.55%
12.64%
0.1368
0.0796
72
72
-35.09%
-15.49%
36.84%
36.89%
Summary statistics (annualised value weighted rates of return) for A-REITs and ASX200 during: Pre-GFC, GFC and
Post-GFC periods. N.B. GFC period = Sept-2007 to Aug-2009
• A-REITs outperformed ASX200 during Pre-GFC and Post-GFC periods (but higher
standard deviation)
• A-REITs performed poorly during GFC
• Note:
12
𝐴𝑛𝑛𝑢𝑎𝑙 𝑅𝑒𝑡 =
1 + 𝑅𝑖 − 1
𝑖=1
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Data
• A-REITs by industrial sector:
Sector
Description
Diversified
Industrial
Specialised
Diverse property holdings
Industrial and manufacturing
Heterogeneous but specific purpose, e.g.
theme parks, childcare, healthcare, etc.
Shopping centres
Commercial office buidlings
Retail
Office
No. Funds
11
2
Market Cap. ($m)
(August 2015)
37,770.54
11,174.41
Relative
size (%)
40.00%
11.83%
9
4,473.96
4.74%
6
2
36,855.00
4,159.78
39.03%
4.40%
• Diversified and Retail REITs were the largest sectors.
– Westfield is approx. 20% of total market capitalisation of the whole sector!
• Although there were 9 specialised REITs, this accounted for less than 5% of
the market indicating that this sector is comprised of many (relatively) smaller
entities.
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Sep-1995
Feb-1996
Jul-1996
Dec-1996
May-1997
Oct-1997
Mar-1998
Aug-1998
Jan-1999
Jun-1999
Nov-1999
Apr-2000
Sep-2000
Feb-2001
Jul-2001
Dec-2001
May-2002
Oct-2002
Mar-2003
Aug-2003
Jan-2004
Jun-2004
Nov-2004
Apr-2005
Sep-2005
Feb-2006
Jul-2006
Dec-2006
May-2007
Oct-2007
Mar-2008
Aug-2008
Jan-2009
Jun-2009
Nov-2009
Apr-2010
Sep-2010
Feb-2011
Jul-2011
Dec-2011
May-2012
Oct-2012
Mar-2013
Aug-2013
Jan-2014
Jun-2014
Nov-2014
Apr-2015
Data
Australian Interest rates
10.00%
9.00%
8.00%
7.00%
6.00%
5.00%
4.00%
3.00%
2.00%
1.00%
0.00%
90 Day Bank Bill rate
10 Year Treasury Bond rate
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Data
90 Day Bank Bill and 10 Year Treasury Bond rates are non-stationary
Null Hypothesis: _10_YEAR_TREASURY_BOND has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 0 (Automatic - based on SIC, maxlag=14)
Null Hypothesis: _90_DAY_BANK_BILL has a unit root
Exogenous: Constant, Linear Trend
Lag Length: 2 (Automatic - based on SIC, maxlag=14)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-2.662689
-3.997250
-3.428900
-3.137898
0.2533
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-2.952178
-3.996918
-3.428739
-3.137804
0.1482
*MacKinnon (1996) one-sided p-values.
*MacKinnon (1996) one-sided p-values.
Consequences:
• Central Limit Theorem does not apply
• Sampling distribution for Test Statistics will not be asymptotically normal
• Test statistics will have low power
• Potentially spurious regression
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Data
First differencing removes the unit root
Null Hypothesis: D(_90_DAY_BANK_BILL) has a unit root
Exogenous: None
Lag Length: 1 (Automatic - based on SIC, maxlag=14)
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
Null Hypothesis: D(_10_YEAR_TREASURY_BOND) has a unit root
Exogenous: None
Lag Length: 0 (Automatic - based on SIC, maxlag=14)
t-Statistic
Prob.*
-7.048179
-2.574756
-1.942170
-1.615807
0.0000
Augmented Dickey-Fuller test statistic
Test critical values:
1% level
5% level
10% level
t-Statistic
Prob.*
-15.35166
-2.574714
-1.942164
-1.615810
0.0000
*MacKinnon (1996) one-sided p-values.
*MacKinnon (1996) one-sided p-values.
P-values < 0.01  Reject H0 of Unit Root
Differenced series does not have a unit root. It is stationary.
We have a stochastic process with mean reversion
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Preliminary Results
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Estimating the impact of debt and management structure
Constant
STOCK
Inflation
BILL
BOND
Adjusted R2
ALL Funds
0.0154**
0.7815***
-0.6187**
5.3476***
-0.4349
0.382
LD
0.0150**
0.8961***
-0.7154***
5.6057***
-3.0812**
0.412
HD
0.0169**
0.7240***
-0.7242***
7.3565***
-3.2669**
0.401
Stapled
0.0148*
0.8168***
-0.6194**
5.3868***
-0.3746
0.365
Unit
0.0166**
0.6805***
-0.6716***
5.5567***
-2.1060*
0.372
*, ** and *** indicate statistical significance at 10%, 5% and 1% levels respectively
• Leverage: Funds with higher debt levels are more affected by adverse movements in
interest rates. Funds were divided into two categories: Low Debt (LD) and High Debt
(HD). Funds were allocated to the HD portfolio if its debt to capital ratio was greater
than the cross sectional average in the prevailing time period and LD otherwise.
• Management structure: Stapled vs. External
– External management increases likelihood of an agency problem.
– However, internally (stapled) managed REITs engage in property development and
may be considered riskier.
– Some stapled securities may also provide minor tax advantages.
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Discussion
• Fund performance appears to improve with rising short term interest rates.
Yong and Singh (2015) suggest that rising short term interest rates may be a
signal of a strengthening economy.
– Higher economic growth increases demand for commercial property,
improving occupancy rates and cash flow from rental income.
• Fund performance is negatively affected by rising long term interest rates.
This may be due to several reasons:
– Rising costs of debt (especially for highly leveraged firms)
– Higher discount rates reduce the present value of dividend payments
Especially relevant for REITs given that investors pay a premium for dividends
In Australia, no formal distribution requirements exist however, undistributed
income is taxed at the highest marginal rate thus creating an incentive for full
distribution
In the US, REITs are exempt from corporate income taxes if they distribute at
least 95% of net income in the form of dividends to shareholders
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Discussion
• Externally managed REITs were more sensitive to changes in long term
interest rates than stapled REITs
– Stapled REITs also engage in property development and management activities.
They may be prefer lower levels of borrowing to free up cash flows thus reducing
the average cost of debt funding.
– Stapled REITs may also be able to negotiate better debt contracts by managers.
• However, stapled REITs have greater exposure to market risk
– Trust portion of a stapled REIT faces same risks as externally managed funds, e.g.
occupancy demand and rental yields will influence cash flow and capital values
– But stapled REITs also engage in property development, which compound the
impact of financial risk
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Practical Implications
• Investors and portfolio managers seeking to reduce exposure to interest rate
risk inherent to property investments can do so by selecting funds that:
– Have less leverage (Low Debt to Capital ratio)
– Are internally managed
• However, these funds have greater exposure to market risk. Conversely,
investors seeking to reduce exposure to market risk can do so by selecting
funds that:
– Have more leverage (High Debt to Capital ratio)
– Are externally managed
• Leverage has positive and negative effects. During stable economic
conditions, can be used to fund expansion. However during extreme
downturns, can lead to heavy losses.
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Limitations and Further analysis
• Analysis of industrial sector effects was limited by data availability.
– Diversified and Retail REITs were well explained by the modelling.
– However, analysis of Industrial, Specialised and Office REITs did not
generate useful findings. Due to limited sample size.
• Analysis of the impact of fund size can also be improved with additional
observations. The longevity of medium and large funds may result in
survivorship bias. Small funds are more likely to drop out of the sample.
• Research scope may be expanded to include comparative analysis of REITs
from other markets, e.g. US, UK, EU, Asia
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Contact Details
• Email:
Woon-Weng Wong: [email protected]
Wejendra Reddy: [email protected]
David Higgins: [email protected]
School of Property, Construction and Project Management (PCPM)
RMIT University
Melbourne, Vic 3001, Australia
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