TEST BANK For Corporate Finance, 13th Edition By Stephen Ross, Randolph Weste...
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Smart Beta Strategies for Global REITs Presentation ARES 2015
1. SMART BETA STRATEGIES FOR REIT MUTUAL FUNDS
Alex Moss, Consilia Capital
Kieran Farrelly, The Townsend Group
American Real Estate Society Conference
Fort Myers , Florida
April 2015
3. BACKGROUND โ KEY POINTS
โข Post GFC, there has been a change in emphasis on the factors which influence
investment decisions, affect performance, and determine asset allocation mixes,
and product design, which are particularly relevant for real estate. Namely;
โข Focus on income based assets in a low interest rate environment (real
estate)
โข Increased emphasis placed on liquidity (REITs*)
โข Interest in combining asset types for specific solutions ( listed/unlisted for DC
schemes)
โข Emphasis on diversifying away equity and bond market risk (low correlation
โalternativeโ buckets)
โข Greater use of maximum drawdown as a key risk measure (DC funds )
โข Growing acceptance of certain Smart Beta strategies (active management at
passive cost)
*For simplicity the term REITs is used to describe all global listed real estate markets in this paper.
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4. BACKGROUND โKEY QUESTIONS
โข Against this background, what Smart Beta strategies can be developed to provide the
investment solutions and risk/return profiles currently required by asset allocators?
โข Is it possible to devise automated trading strategies (with a low turnover) which will
enhance performance?
โข Are there likely to be more Smart Beta products for REITs ? Currently we are aware of
the Kempen Fundamental Index strategy and the Dow Jones Townsend Core REIT
Index.
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5. BACKGROUND - DOW JONES TOWNSEND CORE US REIT INDEX
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โข This Index aims to measure the performance of a basket of securities that could serve
as a public-market analog for privately-held institutional โcoreโ real estate investments
โข To be eligible , a company must be both an equity owner and operator of commercial
and/or residential real estate and meet minimum requirements for size and liquidity
โข Specifically excluded are companies invested in the following property types: Factory
Outlets, Hotels, Manufactured Homes, Mixed Industrial/Office and Suburban Office
26.0%
14.6%
18.0%
9.9%
22.7%
15.4%
17.7%
9.3%
1-year 3-year 5-year 10-year
Annualized Total Return - Gross of fees as of end of February 2015
Dow Jones Townsend Core U.S. REIT Index MSCI US REIT Index Total Return
6. PURPOSE OF THE STUDY
In this study we are interested in discovering whether the free float market
capitalisation weighted global benchmark would have consistently underperformed a
Smart Beta strategy utilising the following factors :
1) Gross Assets
2) Equal Weighting
3) Gearing - Loan to Value ( Low and High) - EW
4) Valuation - Price to Book Value (Low and High) - EW
5) Size โ Gross Assets (Small and Large) - EW
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7. CAVEATS
โข No transaction costs are taken into account
โข Portfolios are only rebalanced at calendar year ends and then held for the next 12
month period
โข No constraints such as minimum liquidity , maximum number of portfolio constituents
etc have been applied
โข No account has been taken of resultant regional weightings
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8. DATA
โข EPRA Global Developed Index constituents
โข COMPUSTAT for fundamental data
โข Bloomberg and CRSP for share price and total returns data
โข Frequency: Annual
โข Currency: US$ (Unhedged)
โข Return: Total Return
โข Period: 2004-2014
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9. METHODOLOGY
โข Key metrics used: Loan to Value (LTV) , Gross Assets (GA), Price to Book Value
(PBV)
โข Establish benchmark constituents on an annual basis โ this is the initial selection
criteria
โข Determine annual returns for all constituents
โข Determine fundamental data (LTV, GA, PBV) for all benchmark constituents
โข Sort by quartile (if appropriate)
โข Apply weighting criteria (EW, Gross Assets)
โข Calibrate portfolio annual return
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15. RESULTS โ OVER TOTAL PERIOD
Mean Geo Mean
FTSE EPRA/NAREIT Developed Index TR 12.34% 8.92%
Equal Weight 16.96% 13.03%
Total Asset Weighted 18.09% 13.20%
Equal Weight Low LTV Quartile 17.66% 12.48%
Equal Weight High LTV Quartile 18.08% 13.97%
Equal Weight Low BTM Quartile 14.57% 12.07%
Equal Weight High BTM Quartile 23.49% 17.25%
Equal Weight Low Total Assets Quartile 16.96% 13.72%
Equal Weight Hightotal Assets Quartile 16.47% 11.50%
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16. CONCLUSIONS AND NEXT STEPS
โข Promising initial results
โข Simple Smart Beta strategies can create material performance differentials vs the
index
โข Next Steps:
โข Make use of higher frequency and longer time series data
โข Explore additional strategies โ fundamental and technical
โข Incorporate additional filters such as liquidity and regional constraints
โข Include transaction costs โ measure โrealโ investor level returns
โข Assess regional level strategies
โข Factor model, risk and diversification potential (within real estate and multi-asset
levels) analysis
โข Explore whether there is a cyclical dimension to the various strategies which is
predictable
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