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Principal Real Estate Antifragility of Real Estate Investments & Black Swans
Guy Tcheau, Managing Director, Investment Strategy Principal Real Estate Investors
Real Estate is widely recognized as a defensive asset class; however, it has Click on download for another little-known attribute called antifragility which is of key significance in the full paper. light of Black Swan1 events such as COVID-19. Antifragile2 investments are those whose performance improves in relation to the size of the shocks. Antifragility of Real Estate was posited by Guy Tcheau, Managing Director at Principal Real Estate Investors and Professor Norman Miller, Chair of Real Estate Finance, Burnham-Moore Center for Real Estate, University of San Diego School of Business in a paper titled Antifragility of Real Estate in a World of Fat-Tailed Risk3.
Black Swans Antifragility
The term, Black Swans, emanates from the published There are essentially four outcomes5 as shown in exhibit 1. work of Nassim Taleb on the subject of mathematical Robust outcomes are where outlier events have low finance, uncertainty and randomness. Traditional financial probability and have small consequence, either positive or models assume exogenous shocks are outlier events with negative. Investment performances are fairly predictable low probability of occurrence. However, Black Swans are since outcomes are not materially impacted by shocks. by definition ‘unknowable’ in likelihood. Our observations There are two types of fragile outcomes: symmetric and of systemic shocks suggest that the probability of their asymmetric, which are both descriptors of downside occurrences is generally underestimated. After all, the scenarios. Both give rise to significant negative impacts last Black Swan, the Global Financial Crisis, took place since the tails are fat. Asymmetric tails to the left means only twelve years ago. Furthermore, such shocks are also that the investment is more fragile to negative outcomes ‘unknowable’ in impact, a priori. Taleb contends that it is than to positive upsides. Antifragile6 investments, on the more important to construct portfolios that incorporate other hand, are asymmetric to the right. Here the fat tail to ‘antifragility’ rather than seeking to model ‘unknown the upside means outliers tend to be positive with large unknowns’4. positive payoffs. In short, robust investments are agnostic to volatility whereas fragility implies investments tend to go badly with volatility. However, antifragile investments benefit from dislocations.
1 The Black Swan: Second Edition: The Impact of the Highly Improbable: With a new section: "On Robustness and Fragility" (Incerto) Paperback – May 11, 2010, Nassim Nicholas Taleb 2 Antifragile: Things That Gain from Disorder (Incerto) Paperback – January 28, 2014, Nassim Nicholas Taleb 3 Antifragility of Real Estate Investments in a World of Fat-Tailed Risk. By Guy Tcheau, Managing Director, Principal Real Estate Investors and Norman Miller, PhD | Ernest Hahn Chair and Professor of Real Estate Finance, Burnham-Moore Center for real Estate, University of San Diego School of Business. (Disclaimer: All errors are ours. The views expressed herein are solely those of the authors and do not necessarily represent the views of Principal Real Estate Investors or the University of San Diego School of Business or the Burnham-Moores Center for Real Estate.) 4 The Importance of Taleb’s System: From the Fourth Quadrant to the Skin in the Game. Branko Milanovic - 29 January 2018. Global Policy Journal, Durham University 5 Gold Republic, This Is What I Learned from Nassim Taleb. April 25, 2018 Olav Dirkmaat. (Olav Dirkmaat, Professor in Economics, Business School of Universidad Francisco Marroquín) 6 Farnam Street, Nassim Taleb: A Definition of Antifragile and its Implications (2014) 1 For Institutional, Professional, Qualified and/or Wholesale Investor Use Only in Permitted Jurisdictions as defined by local laws and regulations.
Exhibit 1: Possible outcomes
Probability density Probability density
Probability density Probability density Robust Symmetric Fragility Robust Symmetric Fragility
Probability density Outcomes Probability density Outcomes
Probability density Outcomes Probability density Outcomes Antifragile Assymetric Fragility Antifragile Assymetric Fragility
Outcomes Outcomes
Outcomes Outcomes
Investment convexity
Antifragile investments are investments that benefit from Exhibit 2: Investment convexity volatility. Mathematically this is referred to as investment Price/performance convexity. Convexity under Taleb’s model is different from convexity that is used for bond investments. For bonds, convexity measures the change in the price of the bond in relation to changes in the level of interest rates. Increasing As the diagram illustrates, the higher the volatility in the profits events under Taleb’s model the greater the payoff with convexity.7 Conversely, greater volatility results in greater losses for concavity. Increasing losses
Shocks/volatility
7 Edge.org. Understanding is a Poor Substitute for Convexity (Antifragility), Nassim Nicholas Taleb. 12 December 2012 2 For Institutional, Professional, Qualified and/or Wholesale Investor Use Only in Permitted Jurisdictions as defined by local laws and regulations.
Mediocristan vs extremistan
Taleb explains that there are two environments that describe the real world, ‘mediocristan’ and ‘extremistan’.8 Mediocristan is when outcomes are fairly predictable and stable and where no single outlier outcome significantly impacts the aggregate total. Gaussian or normalized probability distributions are a good fit for mediocristan. The normalized distribution clusters outcomes symmetrically around the mean where the probability of an outcome being two standard deviations away from the mean is under 5% and three standard deviations (3-sigma events) have a probability of occurrence of just 0.3%. In other words, there exists a mild randomness which is reasonably easy to parametrize and model.
Black Swans, however, do not conveniently fit Gaussian thin-tailed distributions9. Here, one outlier observation can materially distort the aggregate. Randomness is not mild but severe. Rather than a normalized bell-shaped probability distribution we have instead a fat-tailed curve. Outcome distributions are skewed or have high kurtosis10. This is called ‘extremistan’. As Taleb explains, portfolio construction based on mean variance modeling, become fraught with risks due to assumption that our environments are Gaussian or mediocristan when they are really extremistan. With the benefit of hindsight, financial market participants adjust quantitative models to account for the latest systemic shock followed up by stress tests to derive comfort that portfolios will be immunized from repeat debacles. However, the problem with ‘unknown unknowns’ is that it falls outside the bounds of robust statistics and quantitative models, prima facie.
Since modeling Black Swans necessarily require fat-tailed assumptions of risk, which are difficult if not impossible to quantify, investment portfolio performance become susceptible i.e. fragile to shocks. The question then is whether there are investments that are antifragile which can be incorporated into multi-asset portfolios.
Asymmetry of real estate - antifragility
With public equities or corporate bonds for example, in Exhibit 3: Antifragility of real estate Black Swan events, the value of certain securities might Probability tend towards zero. The reason is that the underlying businesses can become unviable due to shocks. Take for example Lehman Brothers or Bear Stearns during the Global Financial Crisis. These firms were no longer able to remain operational. With COVID-19 it remains to be seen whether certain cruise ship operators will remain in business, for example. However, with real estate there is real tangible property that underpins the investment. Unlimited upside Real property has been a repository of value for many millennia. Real estate provides a non-trivial anchor to the downside which is largely overlooked in traditional financial models. Whereas the upside is technically Outcomes unlimited, the downside has a floor. This makes the return Tangible real estate provides anchor to the downside distribution asymmetric to the right.
8 Edge.org. The Fourth Quadrant: A Map of the Limits of Statistics by Nassim Nicholas Taleb, 14 August 2008. Introduction by: John Brockman 9 CBRE,Finiteness of Variance is Irrelevant in the Practice of Quantitative Finance Second version, June 2008 Nassim Nicholas Taleb 10 How Much Data Do You Need? A Pre-asymptotic Metric for Fat-tailedness, Nassim Nicholas Taleb Tandon School of Engineering, New York University November 2018 Forthcoming, International Journal of Forecasting 3 For Institutional, Professional, Qualified and/or Wholesale Investor Use Only in Permitted Jurisdictions as defined by local laws and regulations.
The empirical data on real estate price changes in real terms over a very long time period of nearly 400 years (see the case study on housing prices in Netherlands in exhibit 4) supports asymmetry to the upside and finite downside. Despite major Black Swan events such as the Black Plague, Napoleonic Wars, both World Wars and the Great Depression, real estate showed resiliency to the downside (floors provided by tangible real property value) plus fat-tailed upside. Note also, that this period included the Spanish Flu of 1918 which reportedly infected over 500 million people with a death toll between 40 million to 100 million.
Exhibit 4: Housing data in the Netherlands Annual Herengracht price-change index in real terms (1628 = 1)
8 1635 - Black Plague
7 1672 - Anglo-French War 1701 - Spanish Succesion War 6 1794 - Napoleon 5 1914 - World War 1
4 1930 - Great Depression 1940 - World War 2 3 1970 - Oil Shock 2
1
0 1628 1678 1728 1778 1828 1878 1928
Source: Eichholtz & Geltner (2004)
Endogenous real estate equilibrium
The next question is whether real estate exhibits sociopolitical perturbations. The more potent force in the investment convexity, a requirement for antifragility. demand supply equilibrium of real estate is the supply To see the causal effects for convexity we compare the side. In other words, boom bust cycles in real estate dynamics of real estate demand and supply with that are driven more by overbuilding of real estate than by of traditional businesses that underpin public equities demand side shifts; ceteris paribus. We are speaking and bonds. In the case of businesses in general, the about real estate in aggregate as segments of real estate demand side of the supply and demand equilibrium has by type or geography certainly do react to demand the larger impact on success and viability. Businesses side forces. Retail for example is greatly impacted by cater to their customers but cannot control the state of e-commerce, which in turn has decimated many bricks the customers and their demand at any point in time. and mortar retailers, who are the tenants of retail malls. As a generalization, businesses are in the aggregate The destruction of the demand base for retail has predominantly ‘exogenous’. materially impacted retail mall rents and hence values, while benefitting logistics and industrial properties. Real estate also caters to their customers who in this However, across real estate in aggregate which also instance are the property occupiers or tenants. Demand includes multifamily apartments, office, industrial, is a function of the number of occupiers which in turn hospitality, housing, farmland, etc., we can see that is a derivative of general population which in aggregate demand exhibits less variability with a gradual right shift increases over the long run. Therefore, it follows that due to population increase. It follows then that supply is occupier demand for real estate has a natural rightward the principal driver of equilibrium in real estate demand shift; that is after smoothing out shorter term cyclical and supply interaction. Real estate in the aggregate is impacts of macroeconomic dislocations or localized predominantly ‘endogenous’.
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Following the Global Financial Crisis, we can see a dramatic drop off in the level of new supply across real estate. This stems from fear and risk-off mentality coupled with anti-risk-taking measures imposed by policy makers which act to curtail real estate developers’ access to capital. Paradoxically the more severe the shock i.e. greater volatility, the more severe the resulting curtailment of new supply. This is precisely the necessary force needed in a predominantly endogenously driven demand and supply market dynamic to produce the upwards pricing equilibria in response to volatility.
Exhibit 5: New supply trends across real estate sectors CompletionCompletions rates rate index index (100 (100= historical = historical average) average
300
250
Pronounced decline 200 in new supply following Global Financial Crisis 150
100
50
0 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Apartment Industrial O ce Retail Historical average (1999Q4 - 2019Q4)
Source: CBRE-EA, Principal Real Estate Investors, 2020.
Antifragility of real estate - first principles
We examine the demand supply equilibria for real estate response S2 is relatively small as demand recovers and in the short and long run under conditions of increasing increases with the tailwind of general population growth. volatility (Black Swans). Starting with equilibrium Price equilibrium moves to P2>P1 and P2>Pe. between demand and supply with Price = Pe and Quantity = Qe we first examine the impact of a Black Paradoxically, the more severe the Black Swan event the Swan on the spot price. We see that demand falls off stronger the curtailment of new supply in the longer run. materially to D1 due to the shock to aggregate demand. In a bigger Black Swan dislocation, demand falls more There is a drop off in Supply to S1 which is small relative dramatically to D3 and price to P3
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Exhibit 6: Black Swan events: impact on supply and demand
Starting Equilibrium Black Swan Event – Spot Price Post Black Swan Event – Longer Run
Price Price S1 D2 Se De Se De Se De D1 S2 P2 Muted new Pe supply P1
Qe Q1 Q2 Quantity Quantity Quantity
Starting Equilibrium Bigger Black Swan Event – Spot Price Post Bigger Black Swan Event – Longer Run
Price Price Price S3 D4 Se De Se De Se De S4 D3 Heavily P4 Pe curtailed P3 new supply
Qe Quantity Q3 Quantity Q4 Quantity
Exhibit 7: Long run price equilibrium in real estate The combination of an endogenous demand and supply Long Run Price Equilibirum in Real Estate equilibrium and a natural right shift in demand for real Prices estate that is a derivative of general population growth melds to give us a function of real estate occupier prices P4 (aka rents) relative to the presence or absence of Black Y2 Swans. Very simply we can plot systemic shocks versus P2 occupier prices in the long run. Since rents are in effect X2 Pe a proxy for real estate values or prices, we can infer that P6 real estate pricing outcomes will follow occupier rents P8 Y1 based on the demand and supply equibria developed X1 above. Convexity is inferred from first principles. What we mean is that in the following chart, the slopes Volatility .
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Institutional real estate performance index
Institutional real estate is the primary investible segment addition to appreciation. We note that the 1991 Credit of real estate for pension funds. The National Council Crunch and Savings and Loans Crisis resulted in a real of Real Estate Investment Fiduciaries (NCREIF) index estate collapse. Total returns, meaning depreciation in is a widely used benchmark that tracks income and property prices after factoring net income from rents, appreciation of institutionally owned commercial real reached -13.4% in 1992. This was followed by 19 quarters estate (CRE). The quarterly data series starting in 1978 of above 10% annualized total returns i.e. appreciation includes the performance of over 40,000 office, industrial, in property prices plus net income from rental revenues. retail, multifamily and hotel properties plus other specialized The Global Financial Crisis resulted in a more severe real property types such as self-storage and senior housing. estate collapse which hit -26.7% in total annual return in 2009. This was followed by 23 quarters of above 10% Exhibit 8 shows total returns of institutional real estate annualized returns in real estate. The anecdotal data from the NCREIF (NPI index). Unlike a market value suggests the larger the dislocation the stronger the index, total returns in NPI include income returns in ensuing performance in the long run.
Exhibit 8: U.S. Institutional real estate total returns
0.25
0.2
0.15
0.1 19 quarters 23 quarters 0.05
0 1979 1983 1987 1991 1995 1999 2003 2007 2011 2015 2019 -0.05
-0.1 The bigger the Black Swan event the stronger the returns -0.15
-0.2
-0.25
Source: NCREIF NPI
Antifragility of real estate – empirical evidence
We now seek to examine investment convexity annualized total returns from the NCREIF return index empirically recalling that antifragility is demonstrated on institutionally owned commercial real estate as a by investment convexity. As a measure for Black Swan measure for real estate performance. We can see visually events or market shocks we use real GDP declines. that each downturn is followed by a sustained period of We can see that real estate total returns are impacted strong real estate returns. by external shocks in the following chart. We use
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Exhibit 9: U.S. Institutional real estate annual total returns vs GDP annual growth
8% 25%
20% 6% 15% 4% 10% 2% 5%
0% 0%
-5% -2% -10% -4% -15% -6% -20%
-8% -25% 19 74 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2016 2018 2019
GDP growth (left-axis) Real estate total return (right-axis)
Source: NCREIF, NPI, 2020
To test for investment convexity, we analyze real estate performance against volatility. We use annual percentage change in real GDP as a proxy for volatility. The bigger the shock the greater the downside to GDP growth. We compare a marked slowdown in GDP growth (which includes an outright recession) with the average level of GDP growth in the prior two years. In the case of the 1982 recession we compared only against 1981 as 1980 was also a recession year. In the case of 2001, while GDP growth was slightly positive, the less than 1% growth was a marked fall from the 4%+ growth in the prior two years. The 3% divergence in growth represented the effects of a shock which in this case was the 9/11 terror attacks.
Following each episode of GDP volatility there followed a period of sustained real estate outperformance. We calculated the cumulative total returns using NCREIF performance data in each case. We then adjusted the returns by CPI to obtain inflation adjusted or real returns. Next, we plotted volatility as measured by GDP declines against the cumulative total real returns from real estate following each shock. See exhibit 10.
Exhibit 10: Cumulative real estate total returns post-shock
Severity of 85% Point GDP Shock Cummulative 80% D 75% 70% C (1982) 4.34% 54.72% {f(A)+f(D)}/2} 65% 60% A (1991) 2.89% 55.91% A B 55% C f{(A+D)/2} 50% B (2001) 3.44% 56.40% 45% Cummulative total real returns real total Cummulative 40% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0% 5.5% 6.0% D (2008-09) 5.04% 80.76% Severity of GDP shock
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Applying Jensen’s inequality (a+b) < f(a)+f(b) According to Jensen’s inequality11, in order for function f (X) to be convex then f { 2 } f { 2 }. Applying this proof:
{f(A)+f(D)} (A+D) f(A) = 55.91; f(D) = 80.76; where A = 2.89 and D = 5.04; then 2 = 68.33. f { 2 } = f{3.96}. f{3.96} is in between points B and C and within the range of 55 to 60. Since 55 to 60 < 68.33 the proof is valid. The points f(a)+f(b) (A+D) {f(A)+f(D)} (A+D) { 2 } and f { }are indicated in the chart below. Clearly is above f { }.
{f(B)+(D)} (B+D) f(B) = 56.40; f(D) = 80.76; where B = 3.44 and D = 5.04; then 2 = 68.57. f { 2 } = f{4.24}. f{4.24} is in between points B and C. The results is in the range of 57 to 60. Since 57 to 60 < 68.57 the proof is valid.
{f(A)+ f(C)} (A+C) f(A) = 55.91; f(C) = 54.7; where A = 2.89 and C = 4.34; then = 55.31. f { } = f{3.61}. f{3.61} is in between points B and C. Since the distance between B and C is fairly short, the result will on the line (more likely under the line) between B and C, which is congruent with line between A and D. f{3.61} < 55.31 supporting the Jensen inequality for convexity.
Applying best fit modeling
A second mathematical approach for demonstrating was significantly inferior to the superlinear model. The convexity is to use linear, superlinear and sublinear slightly concave sublinear function had an even lower R2 curves to fit the empirical distribution found above. The of 0.4982. Based on the empirical data, the superlinear second order linear regression as shown in the following function had a goodness of fit that was far superior than chart produces a goodness of fit R2 at 0.9031 which is either a linear or concave function. With this result, as very high for this superlinear model. While there may well as the Jensen inequality proof detailed earlier, the be other functions with superior goodness of fit, we are performance of real estate in the longer run is convex only seeking to determine the shape of the curve with with respect to the severity of the perturbation in the reasonable accuracy. For reference a 1st order linear economy brought about by a systemic shock or Black regression derived only 0.56 for the R2. The linear model Swan event.
11 A Visual Explanation of Jensen's Inequality, Article in The American Mathematical Monthly, October 1993 (DOI: 10.2307/2324783), Tristan Needham, University of San Francisco 9 For Institutional, Professional, Qualified and/or Wholesale Investor Use Only in Permitted Jurisdictions as defined by local laws and regulations.
Exhibit 11: Cumulative real estate total returns Risk aversion post post-shock – best fit modeling pandemics
2 Superlinear Convex Model Best Fit: f(x) = ax + bx + c Following major pandemics, researchers have found, based on
85% data from the 1314 to present that 80% D the real natural rate of interest tends R = 0.9031 75% to be depressed by 2% for decades 70% Superlinear: 2nd order polynomia convex following the pandemic12. The natural 65% rate of interest is a real short-term 60% rate that occurs when the economy A B 55% C has reached maximum employment 50% and has stable inflation (i.e. the 45% Cummulative total real returns real total Cummulative interest rate that occurs when the 40% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0% 5.5% 6.0% economy is in equilibrium)13. The Severity of GDP shock reasoning is intuitive in that the psychology of individuals will be more cautionary post pandemics resulting Sublinear concave model best fit:f(x) = a x In(X) + b in a greater concern for wealth
85% preservation as opposed to return 80% D maximization. 75% R = 0.4982 70% The impact of COVID-19 will likely Superlinear: Logarithmic concave 65% follow that of other pandemics or 60% Black Swan events in history. If the A B 55% C real natural rate of interest manifests 50% sustained, long run, declines as 45%
Cummulative total real returns real total Cummulative predicted by empirical data, yields 40% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0% 5.5% 6.0% from fixed income instruments will Severity of GDP shock fall. Lower rates are constructive for real estate investment performance due to the impact on asset price Linear model best fit:f(x) = ax + b reflation adding to the antifragility thesis of real estate. 85% 80% D 75% R = 0.561 70% Linear: Staight line 65% 60% A B 55% C 50% 45% Cummulative total real returns real total Cummulative 40% 2.0% 2.5% 3.0% 3.5% 4.0% 4.5% 5.0% 5.5% 6.0% Severity of GDP shock
12 Longer-run economic consequences of pandemics? Oscar Jorda, Sanjay R. Singh, Alan M. Taylor, (Federal Reserve Bank of San Francisco and Department of Economics, University of California, Davis) March 2020 13 The Global Decline of the Natural Rate of Interest and Implications for Monetary Policy by Sungki Hong and Hannah G. Shell. 2019, No. 4. Posted 2019-02-01© 2019, Federal Reserve Bank of St. Louis. 10 For Institutional, Professional, Qualified and/or Wholesale Investor Use Only in Permitted Jurisdictions as defined by local laws and regulations.
Conclusion
Black Swan shocks such as COVID-19 are ‘unknowable’ Real estate as an asset class offers investors several key in likelihood of occurrence. They are also ‘unknowable’ in benefits when added to a multi-asset portfolio. Real impact. The ‘unknown unknowns’ describe a real world estate’s low volatility and low correlation relative to other extremistan environment of fat-tailed risks. Rather asset classes, high proportion of total return attributable than focusing on modeling Black Swans, the question to current income versus appreciation, and, income is whether there are investments that have antifragility secured by contractual leases from tenants are some of that can benefit multi-asset portfolios. Private real estate the key merits. Additionally, real estate’s long-term track equity investments were shown from first principles and record through shocks, asymmetrically skewed right empirical data to have antifragility. Since private equity return distribution, and antifragility attributes suggests real estate underpins real estate debt (private and public) performance of multi-asset portfolios will benefit from and publicly traded REITs, the antifragility benefits can be higher allocations to real estate. found in varying degrees in these alternative quadrants of real estate as well.
Risk Considerations Investing involves risk, including possible loss of principal. Potential investors should be aware of the risks inherent to owning and investing in real estate, including: value fluctuations, capital market pricing volatility, liquidity risks, leverage, credit risk, occupancy risk and legal risk. All these risks can lead to a decline in the value of the real estate, a decline in the income produced by the real estate and declines in the value or total loss in value of securities derived from investments in real estate. Important Information Unless otherwise noted, the information in this document has been derived from sources believed to be accurate as of August 2020. Information derived from sources other than Principal Global Investors or its affiliates is believed to be reliable; however, we do not independently verify or guarantee its accuracy or validity. This material contains general information only and does not take account of any investor’s investment objectives or financial situation and should not be construed as specific investment advice, recommendation or be relied on in any way as a guarantee, promise, forecast or prediction of future events regarding an investment or the markets in general. The opinions and predictions expressed are subject to change without prior notice. Any reference to a specific investment or security does not constitute a recommendation to buy, sell, or hold such investment or security, nor an indication that Principal Global Investors or its affiliates has recommended a specific security for any client account. Subject to any contrary provisions of applicable law, Principal Global Investors and its affiliates, and their officers, directors, employees, agents, disclaim any express or implied warranty of reliability or accuracy and any responsibility arising in any way (including by reason of negligence) for errors or omissions in this document or in the information or data provided in this document. Past performance is no guarantee of future results and should not be relied upon to make an investment decision. All figures shown in this document are in U.S. dollars unless otherwise noted. Investing involves risk, including possible loss of principal. This material may contain ‘forward looking’ information that is not purely historical in nature. Such information may include, among other things, projections and forecasts. There is no guarantee that any forecasts made will come to pass. Reliance upon information in this material is at the sole discretion of the reader. This document is issued in: • The United States by Principal Global Investors, LLC, which is regulated by the U.S. Securities and Exchange Commission. • Europe by Principal Real Estate Europe Limited with its respective regulated subsidiaries approving its distribution in their respective local jurisdiction, subject to the Alternative Investment Fund Managers Directive and local regulatory rules. Typically, in Germany, Austria and the Netherlands, distribution of this document is carried out by Principal Global Investors (EU) Limited, Sobo Works, Windmill Lane, Dublin D02 K156, Ireland. Principal Global Investors (EU) Limited is regulated by the Central Bank of Ireland. For all other European countries, typically distribution of this document is carried out by Principal Global Investors (Europe) Limited, Level 1, 1 Wood Street, London, EC2V 7 JB, registered in England, No. 03819986, which is authorised and regulated by the Financial Conduct Authority (“FCA”). In Europe, this document is directed exclusively at Professional Clients and Eligible Counterparties and should not be relied upon by Retail Clients (all as defined by the MiFID). The contents of the
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