Independent Market Commentary

In a world of ultra-low interest rates the vol-ume of capital seeking yield in real estate as an alternative to fixed income markets is steadily rising, pressuring returns and rais-

ing questions over the long-term risks.One of the simplest and most cost-effective

ways for institutional investors to hedge these risks is to adopt a globally diversified real estate investment strategy. The improvement in the quality of data on property performance, par-ticularly in the Anglo-Saxon markets, also means that the tools to optimise returns though diver-sification as developed in other asset classes in Modern Portfolio Theory (MPT) are now more readily applicable to real estate.

While correlations across all financial mar-kets increased sharply during the great financial crisis, research shows that diversified portfolio strategies still mitigated the losses in an echo of the famous maxim of the father of MPT, Harry Markowitz that: “diversification is the only free lunch on Wall Street.”

With the size of the institutional real estate investment in the Netherlands at about $350 billion, or only 1.5% of the potential global in-vestment universe, the scale of the opportunity becomes clear – as does the potential concentra-tion risk of not venturing away from home for Europe’s second largest private pension market.

Real Estate Funds of FundsFor larger pension funds, wishing to tip their toes into international markets before moving into more direct investment strategies, or smaller to medium-sized pension funds where the costs and intensity of asset management involved in running a global direct real estate portfolio are prohibitive, utilizing real estate funds of funds becomes an alternative attractive proposition.

Academic studies conclude that the individu-al object concentration risk in a real estate port-folio can be reduced by two thirds through the diversification of investments between a mini-mum of 15 to 20 assets, but this generally isn’t an option for retirement plans outside of the largest schemes.

The higher risk-adjusted returns attainable on a portfolio level within funds of funds can off-set one of the most frequent criticisms leveled at these vehicles of charging “fees on fees” i.e. the costs of the underlying managers plus the pooled fund. These can also be capped by the stronger bargaining position on fees of the fund of funds

In The Global Hunt For Yield, Diversified Property Strategies Offer The Best Investment Risk Insurance And Returns

By Boris van der Gijp, Director Strategy & Research, Syntrus Achmea Real Estate & Finance

manager with a larger volume of capital to de-ploy and by placing a minimum of between €25 million to €30 million with each of the individ-ual underlying funds, which is the most efficient hurdle level in relation to management costs.

Provided the fund managers invested in are strictly limited to a small select number with high standards of transparency and governance, regulators’ demands that institutions fully un-derstand the underlying assets in which they are invested can also be met.

Our research suggests that the optimal size of funds of funds is between €375 to €500 million in total assets under management and that below this level the diversification benefits are not be-ing fully utilised.

Modern Portfolio Theory and Real EstateA key problem in optimizing asset allocation in real estate portfolios according to Modern Portfo-lio Theory is the widely varying quality and reli-ability of investment data across markets. Even Investment Property Databank (IPD) numbers, the global benchmark for real estate investment performance, are patchy in their depth and qual-ity of coverage and limited historical data is also an issue. The available datasets for the homog-enous U.S. market, such as the NCREIF Index are, however, more complete and transparent than the diverse picture in Europe, where methodol-ogy can vary greatly between countries, such as the formulaic German approach to valuations...

This lack of data has profound implications for real estate portfolio allocation based on quan-titative methodology alone, as even small calcu-lation variations can result in widely different investment outcomes. Our simulations with his-torical datasets show that these calculation errors can easily erase the return benefits of a diversi-fied investment strategy. So while the world of real estate investment is clearly becoming more data-driven and quantitatively based, we have to be careful to balance this with the qualitative view of the skilled manager.

Our quantitative analysis tells us, that there appears to be three economic clusters within the EU and an effective way to enter the eurozone market is to select the strongest countries within a cluster. The correctly calibrated strategy deliv-ers huge diversification benefits, with negative correlations up to -0.5 between the total return performance of the strongest European markets.

Our MPT analysis for the United States showed overall a similar investment perfor-mance for the four different regions in which we divided the market,with a small outperformance for the Midwest. The variations in performance have to be found at the local level within urban areas and between real estate sectors. At this level, residential and retail investments clearly showed historically better risk/return ratios than office and industrial properties.

By combining our European and North American data and also the findings of a recent research paper on the Australian market, we are starting to see evidence of what appears to be an “Anglo-Saxon cluster.” In these markets, the higher quality of available returns data in terms of transparency and coverage, like the U.S. NCREIF Index, gives us more confidence in using quantitative methodology to guide our portfolio allocation strategy, although further research is required to confirm this observation. If con-firmed, the cluster would change our view on worldwide portfolio optimization and encourage us to seek the strongest performers by region and sector within the Anglo-Saxon markets thereby enlarging the investment possibilities of using quantitative methodology.

Overall, we believe there is a strong argument for active management in real estate in conjunc-tion with using quantitative optimization tools, which should be an attractive alternative to more passive approaches such as 1/N portfolios equal-ly weighted across different regions. Historical data is only able to give us a part of the overall picture by looking backwards and we will al-

Figure 1 – Correlations in economic growth between Anglo-Saxon Countries, 1981-2013 (Oxford Economics, 2014)

Figure 2 – Correlations of real estate returns between Anglo-Saxon countries, local currencies, 2000-2013 (IPD and NCREIF, 2014)

Figure 3 – Theoretical value at risk using different percentages of leverage (INREV, 2014)

U.S. Canada U.K. Australia New ZealandU.S. 1,00Canada 0,85 1,00U.K. 0,64 0,65 1,00Australia 0,60 0,38 0,38 1,00New Zealand (since 1989) 0,55 0,68 0,68 0,68 1,00

U.S. (NCREIF) U.S. (IPD) Canada U.K. Australia New ZealandU.S. (NCREIF) 1,00U.S. (IPD) 1,00 1,00Canada 0,93 0,91 1,00U.K. 0,57 0,61 0,51 1,00Australia 0,91 0,88 0,90 0,56 1,00New Zealand 0,79 0,76 0,82 0,56 0,93 1,00

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Figure 1: Correlations in economic growth be-tween Anglo-Saxon countries, 1981-2013 (Oxford Economics, 2014)

Figure 1 – Correlations in economic growth between Anglo-Saxon Countries, 1981-2013 (Oxford Economics, 2014)

Figure 2 – Correlations of real estate returns between Anglo-Saxon countries, local currencies, 2000-2013 (IPD and NCREIF, 2014)

Figure 3 – Theoretical value at risk using different percentages of leverage (INREV, 2014)

U.S. Canada U.K. Australia New ZealandU.S. 1,00Canada 0,85 1,00U.K. 0,64 0,65 1,00Australia 0,60 0,38 0,38 1,00New Zealand (since 1989) 0,55 0,68 0,68 0,68 1,00

U.S. (NCREIF) U.S. (IPD) Canada U.K. Australia New ZealandU.S. (NCREIF) 1,00U.S. (IPD) 1,00 1,00Canada 0,93 0,91 1,00U.K. 0,57 0,61 0,51 1,00Australia 0,91 0,88 0,90 0,56 1,00New Zealand 0,79 0,76 0,82 0,56 0,93 1,00

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Figure 2: Correlations of real estate returns between Anglo-Saxon countries, local currencies, 2000-2013 (IPD and NCREIF, 2014)

You can download this article at: www.ipe.com/syntrus-achmea-ipre-mipim2015

North America Offers Both Attrac-tive Returns and Diversification BenefitsSyntrus’ analysis of real estate returns in North America, Europe and Asia for the 2001 – 2006 and 2007 – 2013 periods showed an investment case for each area over the long-term, but the U.S. produced the best risk-adjusted performance over the other two. Real estate fund of funds strategies, such as the Syntrus Achmea Realty Fund North America Fund, have therefore paid off handsomely in the United States over the last few years and we expect them to continue to do so as the U.S. economic recovery gains momentum. An institutional investor with the foresight to en-ter the highly liquid U.S. market four years ago would have reaped seriously impressive returns, particularly in comparison with Europe where the average portfolio return has been insipid or flat.

The global phenomenon of urbanisation, where major cities increasingly attract younger and more economically active migrants at the expense of secondary centres and peripheral re-gions, is strong in the U.S. The cyclical upswing in the economy lifting domestic real estates mar-kets is thus being reinforced by growing popula-tions in core cities.

We can see this in the relatively higher risk-adjusted returns being offered by residential in-vestments over commercial property in three of our five North American investment areas (Mid-west, East, South, West and Canada), although the homogeneity of the U.S. market means the geographical diversification benefits are lower than in Europe or Asia. Of course that doesn’t mean there are no marked local variations within U.S. regions and the impact of the sharp fall in oil prices, for example, is feeding through in real estate markets in the South, such as Houston.

U.S. office investments have been the poorest performing area, based on Sharpe Ratios, and we incorporate this into our asset allocation model

ways need to fill in the forward outlook by ana-lysing economic, technological and demographic trends etcetera.

LeverageThe use of leverage in non-listed real estate funds has been a sensitive subject since the great fi-nancial crisis with many institutional investors pointing to excessive gearing as a prime reason for the collapse in returns for many of these vehi-cles and their subsequent difficulties in extract-ing themselves from the strategies.

We strongly believe that leverage can be a useful facilitator of liquidity within a portfolio as a tactical tool in entering a market and seiz-ing investment opportunities, or financing active asset management, but above 25% to 30% levels of gearing the risks begin to rise exponentially. Generally speaking there are few problems if leverage is maintained below 40%. Beyond that point, however, the higher risks incurred rapidly no longer compensate for the additional units of investment return achieved.

with a relatively lower weighting for the sector to reduce volatility in the portfolio.

The falling interest rates of the 1985 to 2013 period have made a structurally positive contri-bution to historic investment returns in North America and conversely we see rising rates over the next 10 years acting as a headwind in this respect, as will lower inflation. But the U.S. eco-nomic recovery should underpin a target inter-nal rate of return going forward of at least 9.0% a year for a fund of funds strategy - still outpacing the Eurozone at a projected IRR of between 5.0% - 10%, despite these markets coming back from a lower base. Although the emerging markets of Asia offer more dynamic economic growth, the risks for real estate investment are also higher, further boosting the attractiveness of a North American portfolio as a diversifier.

Figure 1 – Correlations in economic growth between Anglo-Saxon Countries, 1981-2013 (Oxford Economics, 2014)

Figure 2 – Correlations of real estate returns between Anglo-Saxon countries, local currencies, 2000-2013 (IPD and NCREIF, 2014)

Figure 3 – Theoretical value at risk using different percentages of leverage (INREV, 2014)

U.S. Canada U.K. Australia New ZealandU.S. 1,00Canada 0,85 1,00U.K. 0,64 0,65 1,00Australia 0,60 0,38 0,38 1,00New Zealand (since 1989) 0,55 0,68 0,68 0,68 1,00

U.S. (NCREIF) U.S. (IPD) Canada U.K. Australia New ZealandU.S. (NCREIF) 1,00U.S. (IPD) 1,00 1,00Canada 0,93 0,91 1,00U.K. 0,57 0,61 0,51 1,00Australia 0,91 0,88 0,90 0,56 1,00New Zealand 0,79 0,76 0,82 0,56 0,93 1,00

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Figure 3: Theoretical value at risk using different percentages of leverage (INREV, 2014)

Figure 4 – Average correlations between total returns for regions and sectors, period 1978-2014 (NCREIF, 2014)

Figure 5 – Average GDP growth per capita 2014-2018 for the 15 largest

U.S. state economies. The size of the circles is total GDP per state in 2013 (Oxford Economics, 2014, adapted by Syntrus Achmea RE&F)

Midwest East South West Canada (2000)Midwest 1,0East 0,8 1,0South 0,8 0,8 1,0West 0,8 0,8 0,8 1,0Canada (2000) 0,6 0,7 0,7 0,7 1,0

Apartments Offices Industrial RetailApartments 1,0Offices 0,7 1,0Industrial 0,8 0,9 1,0Retail 0,7 0,7 0,7 1,0

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Figure 4: Average correlations between total returns for regions and sectors, period 1978-2014 (NCREIF, 2014)

Figure 4 – Average correlations between total returns for regions and sectors, period 1978-2014 (NCREIF, 2014)

Figure 5 – Average GDP growth per capita 2014-2018 for the 15 largest

U.S. state economies. The size of the circles is total GDP per state in 2013 (Oxford Economics, 2014, adapted by Syntrus Achmea RE&F)

Midwest East South West Canada (2000)Midwest 1,0East 0,8 1,0South 0,8 0,8 1,0West 0,8 0,8 0,8 1,0Canada (2000) 0,6 0,7 0,7 0,7 1,0

Apartments Offices Industrial RetailApartments 1,0Offices 0,7 1,0Industrial 0,8 0,9 1,0Retail 0,7 0,7 0,7 1,0

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Figure 5: Average GDP growth per capita 2014-2018 for the 15 largest U.S. state economies. The size of the circles is total GDP per state in 2013, (Oxford Economics, 2014, adapted by Syntrus Achmea RE&F)

Figure 6 – Risk return profiles, multi-year average per US region (NCREIF, 2014) and Canada (IPD, 2014)

Figure 7 – Risk return profiles, multi-year averages per sector (NCREIF, 2014)

Figure 8 – Return/Risk ratios per US Region

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East 9,13 -0,45 -0,34 -0,33 -1,12 0,51 7,40 1,15 6,40 Midwest 7,86 -0,32 -0,24 -0,92 -0,60 0,82 6,60 1,20 5,50 South 7,27 -0,49 -0,37 1,71 - 0,08 8,20 1,10 7,50 West 9,15 -0,62 -0,46 1,34 -1,34 -0,08 8,00 1,14 7,00 Canada 10,69 -0,24 -0,22 -0,44 -0,87 -0,52 8,40 1,29 6,50

interest contribution 1985-2013 2014-2023

Figure 6: Risk return profiles, multi-year average per U.S. region (NCREIF, 2014) and Canada (IPD, 2014)

Figure 6 – Risk return profiles, multi-year average per US region (NCREIF, 2014) and Canada (IPD, 2014)

Figure 7 – Risk return profiles, multi-year averages per sector (NCREIF, 2014)

Figure 8 – Return/Risk ratios per US Region

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East 9,13 -0,45 -0,34 -0,33 -1,12 0,51 7,40 1,15 6,40 Midwest 7,86 -0,32 -0,24 -0,92 -0,60 0,82 6,60 1,20 5,50 South 7,27 -0,49 -0,37 1,71 - 0,08 8,20 1,10 7,50 West 9,15 -0,62 -0,46 1,34 -1,34 -0,08 8,00 1,14 7,00 Canada 10,69 -0,24 -0,22 -0,44 -0,87 -0,52 8,40 1,29 6,50

interest contribution 1985-2013 2014-2023

Figure 7: Risk return profiles, multi-year averages per sector (NCREIF, 2014)

Figure 6 – Risk return profiles, multi-year average per US region (NCREIF, 2014) and Canada (IPD, 2014)

Figure 7 – Risk return profiles, multi-year averages per sector (NCREIF, 2014)

Figure 8 – Return/Risk ratios per US Region

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East 9,13 -0,45 -0,34 -0,33 -1,12 0,51 7,40 1,15 6,40 Midwest 7,86 -0,32 -0,24 -0,92 -0,60 0,82 6,60 1,20 5,50 South 7,27 -0,49 -0,37 1,71 - 0,08 8,20 1,10 7,50 West 9,15 -0,62 -0,46 1,34 -1,34 -0,08 8,00 1,14 7,00 Canada 10,69 -0,24 -0,22 -0,44 -0,87 -0,52 8,40 1,29 6,50

interest contribution 1985-2013 2014-2023

Figure 8: Return/Risk ratios per U.S. Region

Boris van der Gijp, Director Strategy & Research, Syntrus Achmea Real Estate & Finance