Unlisted real estate funds lecture (1) (1)

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Unlisted real estate funds lecture (1) (1)

  1. 1. Unlisted Real Estate Funds Introduction & Rationale Risk-Return Drivers Performance Characteristics Asset Allocation
  2. 2. Introduction & Rationale
  3. 3. Unlisted Real Estate Funds  The Association of Real Estate Funds (AREF) define unlisted property funds as follows: “A property fund is a collective investment scheme with a portfolio comprising mainly of direct property but may also include other property related interests. Property funds take a number of different legal structures depending on their domicile and target customer.”  An unlisted real estate fund (UREF) is a private investment vehicle which aims to provide direct real estate performance and may also employ financial leverage which will accentuate performance.  Within the fund structure. investors pool capital so as to access a more diversified exposure to direct real estate that they may otherwise do individually  UREFs are also used by investors to access specific managers and/or strategies  Internal resource issue  Even the largest institutional investors use them to facilitate (at least their) non-domestic real estate allocations
  4. 4. Global Real Estate Portfolio Size and Tracking Error Source: Kennedy (2011)
  5. 5. Number of Funds Unlisted Real Estate Fund Growth 160 140 120 100 80 60 40 20 0 Americas Europe Asia Pacific  Over recent years the real estate fund management industry has evolved to meet increasing cross-border investor demand and there is now a $2.2 trillion (gross asset value) universe of unlisted real estate funds Source: Prequin
  6. 6. Unlisted Real Estate Fund Structures  UREFs follow either balanced or specialist strategies  Balanced funds seek provide investors with a well diversified market exposure – country or region  Specialist funds focus on a particular market segment or niche e.g. a UK shopping centre fund, US office fund or a Japanese real estate debt fund  Due to tax considerations UREFs may be structured for investors from a particular jurisdiction or be efficient for a range of investors  E.g. private US REITs  Plethora of legal structures and domiciles used by institutional investors  Popular structures are corporate, partnerships and trusts  UREFs have either closed-end or open-end fund ‘wrappers’  Closed-ended funds typically have lives ranging from 7-10 years  Varying degrees of ‘open-endedness’  Real estate managers are paid a management and often a performance fee
  7. 7. Example Unlisted Real Estate Fund Structure Investors Equity Investment Investor Income Sharing Loan Longbow Co-investment LuxCo 1 Equity Investment Group Income Sharing Loan Management Fee LuxCo 2 Debt investments Income from debt investments Loans / CMBS Cash item Ownership / funding Real Estate Assets
  8. 8. Example Closed-End Unlisted Fund Cashflow Contributions Distributions NAV 35 30 25 20 £ (mn) 15 10 5 0 -5 -10 -15 -20 2001 Source: Baum and Farrelly (2009) 2002 2003 2004 2005 2006
  9. 9. Unlisted Real Estate Fund Investing  Established open-end fund  Subscription / redemption mechanisms and pricing  Multiple investors and ongoing portfolio transactional activity  Limited control for investors  Primary Fund - newly launched fund     Investors bear set-up and acquisition costs Often fully or partially ‘blind’ i.e. the assets need to be bought Fee discounts for large investors Investor ‘control’ via advisory boards  Joint Venture   Two parties involved and assets often fully indentified Stronger controls and can be incorporated  Secondary – priced vs NAV  Acquisition of units in existing fund and can be fully underwritten  Typically passive control post acquisition  Co-Investments  Investor option to participate in specific opportunities alongside ‘master’ fund  ‘Club-Transaction’  Small number of like-minded investors targeting a focussed opportunity
  10. 10. UK Market Pricing vs NAV – Secondary UREFs & REITs Source: Schneider (2013)
  11. 11. Unlisted Real Estate Fund Criticisms  Fees  Expense ratios are perceived as being high and NAREIT research shows REITs as being cheaper  Especially true in ‘double-promote’ situations  Liquidity  Lock-ups  Open-end funds can at best mirror direct market liquidity and recent crisis has seen mechanism under pressure  Valuation  How should UREFs be valued – NAV (which one?), ‘market price’ vs NAV?  Transparency  Limited data and understanding of performance  Gearing  UREFs are perceived as having too much gearing – is 40% really ‘core’
  12. 12. Fund Example
  13. 13. Co-Investment – Preferred Equity Focused Investing ment – Preferred Equity Threadneedle Low Carbon Workplace Fund THREADNEEDLE LOW-CARBON WORKPLACE FUND EEDLE LOW-CARBON WORKPLACE FUND The The investment proposition commit seed seed capital for the formation of open■ investment proposition was towas to commitcapital for the formation of a UK a UK open-ended green office ended green office fund. LCWF is afund. venture between Threadneedle, Stanhope, joint LCWF is a joint venture between and theThreadneedle, (the “Key Advisers”): Carbon Trust commit seed capital for proposition was toStanhope and the Carbon Trust the formation of a UK open- ment en office fund. LCWF is a joint venture between Threadneedle, Stanhope, rbon Trust (the “Key Advisers”): Summary of Terms:carbon refurbishment over grey refurbishment are: The benefits of low ■ ■ ■ No gearing Quicker and less of 1.2x on the development portfolio and IPD UK Office Index Targeted returns restrictive planning consents ts of low carbon refurbishment over grey refurbishment are: +1% on the investment portfolio ■ Pre-lets and shorter void periodsseed portfolio ■ Projected yield on cost >9.0% on ■ cker and lessESG-specific strategy restrictive planning consents ■ Lower total occupational costs Comparative Advantages: lets and shorter void periods ■ Quicker and less tenants; planning consents Better covenant restrictivelonger lease terms ■ Pre-lets and shorter er total occupational costs void period ■ Lower proofing Future total occupational cost ■ Better covenant terms; longer lease terms ter covenantGreaterproofing and aesthetics tenants; longer lease terms ■ Future comfort ■ ■ Greater comfort and aesthetics ure proofing ater comfort and aesthetics 14 6 The Townsend Group The Townsend Group13
  14. 14. Before and After Before After
  15. 15. Energy Consumption Pre Re-Furb Lighting aligns to occupancy due to technology solutions (PIR) but there is significant base load small power, and heating & cooling during weekends 200 180 140 40 120 30 100 80 20 60 40 10 20 0 0 Monday Tuesday Wednesday Power Thursday Heating & Cooling Friday Lighting Saturday People in Building Sunday Energy use (kWh) Building Occupancy (number of people) 160 50
  16. 16. Energy Consumption Post Re-Furb 200 Reducing energy during unoccupied periods reduced weekly energy use by 48% 180 40 140 120 30 100 80 20 60 40 10 20 0 0 Monday Tuesday Wednesday Power Thursday Heating & Cooling Friday Lighting Saturday People in Building Sunday Energy use (kWh) Building Occupancy (number of people) 160 50
  17. 17. Risk – Return Drivers
  18. 18. Sources of Risk and Return In Real Estate Funds  Market risk:    Allocations to more volatile sectors Macro / supply risks Transparency, property rights  Stock risk:       Fund structure:    Financial leverage: floating rate/fixed rate debt, collateralization Vehicle characteristics: age, structure, fees/costs (alignment), fiscal efficiency Public market volatility if listed  Accounting policy:  Real Estate Fund Risk & Return Asset level (operating) leverage Risk continuum from ground rents to speculative developments Age, structure Income quality Diversification Treatment of items e.g. mark-tomarket valuations of interest hedging instruments, costs incurred Portfolio Structure / Market Risk Stock Risk Fund Structure Accounting Policy
  19. 19. Alpha and Beta in Real Estate Fund Investment  Alpha and beta originate from both portfolio structuring and stock selection  Stock alpha:  Cost control, leasing strategy, asset enhancement, acquisitions & dispositions  Asset management and transaction skills are the driver  Eye for unrealised latent value  Structure alpha:  Higher than benchmark allocations to outperforming markets and sectors  Forecasting skills are the driver  Stock beta:  Asset level (operating) leverage  Continuum from ground rents to speculative developments  Structure beta:  Domestic benchmark: allocations to more volatile sectors  Global benchmark: exposures to higher risk geographies – not fully quantifiable  Financial leverage
  20. 20. Unlisted Real Estate Fund Styles INREV Fund Style Classification Criteria Core ≤ 40% LTV Total % of non-income producing investments Total % of (re)development exposure % of total return derived from income Maximum LTV Core ≥ 40% LTV ≤ 15% Value Added > 15% - ≤ 40% Opportunity > 40% > 5% - ≤ 25% ≤ 5% > 25% > 40% - ≤ 60% > 60% ≥ 60% ≤ 40% Core > 40%  Core funds generally entail the lowest risk and opportunity funds the most risk  The factors used to determine style include the level of financial leverage and the nature of property investment activity being undertaken, such as development activity, which entails higher risk
  21. 21. Performance Drivers of UK Institutional Funds I  Sample of UK institutional funds with measured performance from 2003 Q4 – 2011 Q4  Able to test significance of market, stock and fund structure factors  Panel modelling framework employed to make best use of the available data Sample Fund Style Exposure Quarterly Sample Total Returns 30% Opportunity 7.0% 20% Value Added 14.1% 10% 0% -10% Core Balanced 56.3% Core Specialist 22.5% -20% -30% Source: Farrelly and Matysiak (2012) 10 /1 /2 0 4/ 03 1/ 2 10 004 /1 /2 0 4/ 04 1/ 2 10 005 /1 /2 0 4/ 05 1/ 2 10 006 /1 /2 0 4/ 06 1/ 2 10 007 /1 /2 0 4/ 07 1/ 2 10 008 /1 /2 0 4/ 08 1/ 2 10 009 /1 /2 0 4/ 09 1/ 2 10 010 /1 /2 0 4/ 10 1/ 2 10 011 /1 /2 01 1 -40%
  22. 22. Performance Drivers of UK Institutional Funds II  Strong 1:1 relationship with market returns Pooled OLS Fixed Effects Bias Corrected Fixed Effects Lag Total Return 0.376** (0.024) 0.336** (0.046) 0.378** (0.028) Market Exposure Excess Total Return 1.033** (0.104) 1.021** (0.215) 0.980** (0.132) Lag Net Loan to Value Ratio -0.015** (0.003) 0.020* (0.011) 0.019 (0.013) Lag Excess Initial Yield 0.080 (0.091) 0.768** (0.281) 0.699** (0.245) Lag Total Void (% ERV) -0.001 (0.012) 0.040** (0.015) 0.040** (0.020) R Squared 0.785 0.760 No Cross Sections No Observations 75 1704 75 1704 75 1704  Void and initial yield ‘spread’ the most significant stock variable  10% increase in net leverage equates to 0.8% increase in annual returns  Generally expected risk-return relationships are found to hold  Small number of factors explain a significant proportion of fund performance ** 1% Sig, * 5% Sig Source: Farrelly and Matysiak (2012)
  23. 23. Financial Leverage Motivations  Return enhancement (for who?)  Shortage of equity  Cost of capital  Tax benefits – minimize leakage through the tax deductibility of interest  What do the theories say:  Modigliani-Miller – no justification  Trade-off theory - optimal leverage level which maximizes return in presence of tax incidence  Pecking order – easier to raise debt capital than equity capital  Market timing – raise debt when debt is cheap and equity returns are attractive  Incentive theory – management motivated to grow business and enhance remuneration  Industry effects – herding towards industry average leverage levels
  24. 24. Financial Leverage Exacerbates Real Estate Returns  Generally the literature doesn’t support the use of high levels of leverage (>40% LTV) from a risk-return perspective  Leverage exacerbates the non-normality of real estate returns  Interest costs increase as leverage ratios increase  Downside ‘tail-events’ become more pronounced Leveraged and Unleveraged Global Real Estate Returns Source: Baum and Kennedy (2012)
  25. 25. Asymmetric Impact of Leverage Upon Fund Returns Pooled OLS Fixed Effects Lag Total Return 0.107** (0.015) 0.090** (0.033) Bias Corrected Fixed Effects 0.107** (0.019) Market Exposure Total Return 1.037** (0.021) 1.049** (0.040) 1.035** (0.025) Lag Excess Initial Yield 0.047 (0.082) 0.446** (0.214) 0.415* (0.218) Lag Net Loan to Value Ratio * Negative Market Dummy -0.110** (0.006) -0.098** (0.016) -0.095** (0.013) Lag Net Loan to Value Ratio * Positive Market Dummy 0.020** (0.004) 0.030** (0.012) 0.031** (0.012) No No No 0.808 0.803 75 1724 75 1724 Period Effects Included? R Squared No Cross Sections No Observations ** 1% Sig, * 5% Sig Source: Farrelly and Matysiak (2012) 75 1724  Isolated the impact of financial leverage upon fund performance in positive and negative market conditions  Significant asymmetric impact – greater downside than upside  10% increase in net leverage equates to 0.8-1.2% increase in annual returns when market returns are positive, but leads to a c. 4% decrease in returns when market performance declines  Leverage appears to exacerbate the nonnormality of real estate returns
  26. 26. Asymmetric Impact of Leverage Upon Fund Returns Annual Relative Performance 10% 5% 6.8% 7.4% 5.6% 6.2% 4.3% 5.0% 3.1% 3.7% 2.5% 1.2% 1.9% 0.0% 0.6% 0% 0.0% -5% -10% -15% -20% -25% -1.9% -3.8% -5.7% -7.6% -9.5% -11.4% -13.3% -15.2% -17.1% -19.0% -20.9% -22.8% Net LTV Positive Market Negative Market
  27. 27. Unlisted Real Estate Fund Performance Measurement  Performance benchmarks are now emerging for UREFs across the globe  Well developed in a number of key western markets Source: Baum and Kennedy (2012)
  28. 28. Example Unlisted Fund Performance Attribution I Fund Cash Flow Contributions Distributions Quarterly Fund Returns NAV Net Fund Returns 35 30 15% 25 20 10% 15 10 5% 5 0 0% -5 -10 -5% -15 -20 2001 2002 2003 2004 2005 2006 31 /1 2/ 20 01 30 /0 6/ 20 02 31 /1 2/ 20 02 30 /0 6/ 20 03 31 /1 2/ 20 03 30 /0 6/ 20 04 31 /1 2/ 20 04 30 /0 6/ 20 05 31 /1 2/ 20 05 30 /0 6/ 20 06 31 /1 2/ 20 06 £ (mn) IPD UK Pooled Funds Index 20% • 2001 vintage UK Value Add fund which delivered a net IRR to investors of 29.9% • CAPM equation using All Pooled Funds Index ▫ ▫ ▫ Alpha: 0.00 Beta: 1.73 RSq: 0.18 Source: Baum and Farrelly (2009)
  29. 29. Example Unlisted Fund Performance Attribution II 2002 2003 2004 2005 2006 5 year Property TWR 12.6% 10.5% 23.7% 25.5% 8.8% 16.0% Benchmark TWR Relative 9.2% 3.1% 10.5% 0.0% 17.4% 5.4% 19.1% 5.4% 18.5% -8.2% 14.9% 1.0% Structure Score -3.3% -3.7% -3.2% 0.8% -0.2% -1.9% Selection Score (Two Component) 6.1% 3.4% 8.4% 4.7% -8.0% 2.8% Selection Score (Three Component) -6.3% -7.0% -2.9% -13.8% -8.4% Interaction Effect (Three Component) 12.4% 10.4% 11.3% 16.2% 5.8% 11.2% Gross TWR 15.7% 20.1% 73.1% 52.3% 5.1% 31.0% Gross Fund Structure Score 3.1% 9.6% 49.4% 26.8% -3.7% 15.0% Net TWR 11.8% 16.7% 57.6% 40.1% 8.7% 25.6% IM Fee Reduction -3.9% -3.4% -15.5% -12.2% 3.6% -5.3% IM Fee Reduction % 25.0% 17.1% 21.1% 23.3% -70.1% 17.2% Net Fund Structure Score -0.8% 6.2% 34.0% 14.6% -0.1% 9.7% Property Level -11.5% Fund Level Net MWR 29.9% Timing Score 4.3% Source: Baum and Farrelly (2009)
  30. 30. Management Fees  Ongoing management fees vary depending upon the risk profile and structure of UREFs  These can be based upon NAV, commitments – both drawn and undrawn and real estate asset value (GAV)  Fees on full investor commitment can apply during the investment period as monies are invested – more common in Value Added and Opportunity funds  Performance fees can be payable on both an absolute or relative basis  Seek to reward to good manager performance and incentivise management teams  Relative return based performance fees only seem to apply on core funds and are typically calculated on a rolling basis e.g. over three year periods  Absolute return based fees apply to nominal return targets – similar to private equity fund fee structures  Most common fee structure is a 20% profit share above a 9% preferred return  Can have multi-tiered preferred return hurdles & profit shares, and catch-ups
  31. 31. Fund Performance Fee Example – “20 Over 9” Gross IRR Incom e IRR Inv estor Equity 15% 5% 100 Y ear NAV Income 0 1 1 1 0.0 5.0 2 1 21 .0 5.5 3 1 33.1 6.1 4 1 46.4 6.7 5 1 61 .1 7 .3 6 1 7 7 .2 8.1 7 1 94.9 8.9 Gross CF -1 00.0 5.0 5.5 6.1 6.7 7 .3 8.1 203.7 Managem ent Fee Preferred Return Perform ance Fee Profit share 1.5% NAV 9.0% 20.0% -1 .7 -1 .8 -2.0 -2.2 -2.4 -2.7 -2.9 Management Fee Paid Cash Flow Pre Performance Fee IRR Pre Pre Perform ance Fee -1 00.0 13.3% 3.4 3.7 4.1 4.5 4.9 5.4 200.8 Preferred Return Check -1 00.0 9.0% 3.4 3.7 4.1 4.5 4.9 5.4 1 48.3 - 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 52.5 10.5 3.4 3.7 4.1 4.5 4.9 5.4 1 90.3 Ex cess' Profit Manager Share - Inv estor Cash Flow Net IRR T o Inv estor -1 00.0 12.6% Fee Reduction % 16.3%
  32. 32. Fee Impact From a Sample of Fund Returns Fund 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Mean Source: Baum and Farrelly (2009) Gross IRR 29.0% 17.0% 33.0% 35.0% 27.0% 46.0% 21.0% 34.0% 16.0% 20.0% 18.0% 20.0% 14.0% 20.0% 25.0% Net IRR 25.0% 13.0% 25.0% 30.0% 21.0% 37.0% 16.0% 27.0% 13.0% 15.0% 14.0% 16.0% 12.0% 15.0% 19.9% Fee impact 4.0% 4.0% 8.0% 5.0% 6.0% 9.0% 5.0% 7.0% 3.0% 5.0% 4.0% 4.0% 2.0% 5.0% 5.1% Fee impact % 13.8% 23.5% 24.2% 14.3% 22.2% 19.6% 23.8% 20.6% 18.8% 25.0% 22.2% 20.0% 14.3% 25.0% 20.5%
  33. 33. Performance Characteristics
  34. 34. US Unlisted Real Estate Fund Performance Indices Rolling 12m Total Returns 1988Q4 – 2012Q2 50% 40% 30% vs NCREIF 20% Beta RSq Core 1.25 0.94 Value Added 1.60 0.80 Opportunity 1.90 0.70 10% 0% -10% -20% -30% -40% -50% Core Funds Opportunity Funds Value Added Funds NCREIF Index • Unlisted real estate funds provide a ‘geared’ exposure to the direct real estate market Source: NCREIF
  35. 35. US Real Estate Fund Performance Dispersion • US Value Add / Opportunity fund performance by vintage year • Clearly significant performance differentials ▫ Source: Prequin Phenomenon seen in private equity e.g. Swensen
  36. 36. US Asset Class Returns -1988 Q4 – 2012 Q2 Ann Mean Ann Median Ann Std. Dev . S&P 500 Skewness Kurtosis 1 0.8% 1 3.0% 1 6.3% -0.56 3.38 US Gov ernment Bonds 7 .4% 6.7 % 4.7 % 0.1 2 2.39 NCREIF Index 7 .4% 9.7 % 4.9% -1 .89 7 .59 All Core Funds Index 5.6% 8.5% 6.4% -2.39 1 0.28 All Opportunity Funds Index 7 .6% 9.2% 1 1 .2% -1 .1 4 8.27 US REITs 1 3.1 % 1 4.6% 20.1 % -0.7 3 7 .05 S&P 500 BOND NCREIF CORE OPRE S&P 500 1 .00 US Gov ernment Bonds -0.1 0 1 .00 NCREIF Index 0.13 -0.1 4 1 .00 All Core Funds Index 0.12 -0.1 2 0.97 1 .00 All Opportunity Funds Index 0.28 -0.1 5 0.85 0.82 1 .00 US REITs 0.60 0.05 0.1 7 0.1 6 0.29 REIT 1 .00
  37. 37. Real Estate Return Characteristics I - Smoothing  Well known and studied characteristic of real estate data is that it is smoothed due to it being valuation based performance data  The valuation process creates serial correlation in the data – i.e. one period’s return is correlated to the previous period’s return  Data needs to be adjusted to estimate the ‘true’ risk (volatility etc) of real estate investing US Core Fund Correlogram and Q-Stat 1988Q4 – 2012Q2 S&P 500 Correlogram and Q-Stat 1988Q4 – 2012Q2
  38. 38. Unsmoothing Impact  Employed autoregressive based unsmoothing procedure 𝑅 𝑡 = 𝛼 + 𝛿𝑅 𝑡−1  E.g. AR(1) process 𝑅 𝑡 𝑈𝑛𝑠𝑚𝑜𝑜𝑡ℎ = (𝑅 𝑡 − 𝛿𝑅 𝑡−1 )/(1 − 𝛿)  Latest methodology: Lizieri et al (2012) “Unsmoothing Real Estate Returns: A Regime-Switching Approach” Ann Mean Ann Median Ann Std. Dev . Skewness Kurtosis Raw Returns NCREIF Index 7 .4% 9.7 % 4.9% -1 .89 7 .59 All Core Funds Index 5.6% 8.5% 6.4% -2.39 1 0.28 All Opportunity Funds Index 7 .6% 9.2% 1 1 .2% -1 .1 4 8.27 NCREIF Index 7 .5% 9.9% 1 2.7 % -2.7 7 1 7 .95 All Core Funds Index 5.7 % 7 .8% 1 4.7 % -2.58 1 9.06 All Opportunity Funds Index 7 .8% 8.2% 32.4% -0.52 7 .60 Unsm oothed Returns
  39. 39. Real Estate Return Characteristics II – Non-Normality  Well known and studied characteristic of real estate data is that it is non-normal  Yet a significant proportion of current investment practice and methodologies rely heavily upon this assumption US Opportunity Fund Return Distribution 1988Q4 – 2012Q2 NCREIF Return Distribution 1988Q4 – 2012Q2 16 32 14 Series: NPI Sample 1988Q4 2012Q2 28 Observations 95 12 24 Mean 20 Median Maximum 16 Minimum Std. Dev. 12 Skewness Kurtosis 10 8 6 4 0.018079 0.023400 0.054300 -0.082900 0.024393 -1.890619 7.593356 8 Jarque-Bera 4 Probability 2 0 -0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04 0 -0.25 -0.20 140.1119 0.000000 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15
  40. 40. Normality Tests  Tests confirm non-normality of sample real estate data Jarque-Bera Stat Probability Shapiro-Wilk Test Probability S&P 500 5.51 0.06 0.97 0.04 US Gov ernment Bonds 1 .7 2 0.42 0.99 0.39 NCREIF Index 1 40.1 1 0.00 0.84 0.00 All Core Funds Index 300.39 0.00 0.7 8 0.00 All Opportunity Funds Index 1 30.44 0.00 0.88 0.00 US REITs 7 3.30 0.00 0.92 0.00 NCREIF Index 1 005.41 0.00 0.7 5 0.00 All Core Funds Index 1 1 26.36 0.00 0.7 8 0.00 88.1 8 0.00 0.91 0.00 Unsm oothed Returns All Opportunity Funds Index
  41. 41. Asset Allocation
  42. 42. Asset Allocation Modeling  Typical institutional investor allocation to real estate is 5-15%  Same data (unsmoothed real estate returns) as previous with ‘typical’ expected returns     Government bonds: S&P 500: Core UREFs Opportunity UREFs: 4.0% 8.5% 7.5% 14.0%  Look at Core UREFs, 75:25 Core:Opportunity and 50:50 Core:Opportunity  Use mean variance analysis here but there are problems with this  Using volatility as the key risk measure leads to real estate having high allocations – beyond  what we’d consider sensible Analysis often leads to ‘corner’ solutions and allocations hitting constraints  We review certain optimal allocations using a measure which accounts for nonnormality
  43. 43. Real Estate Allocations 75:25 Core:Opportunity Real Estate Portfolio Gov ernment Bonds S&P 500 7 5:25 Core:Opportunity 59.0% 1 9.6% 21 .4% 47 .3% 25.1 % 27 .6% 37 .9% 29.6% 32.5% 27 .4% 34.6% 38.0% 1 6.9% 39.5% 43.6% 5.5 4.7 1 .2 Return Risk Return:Risk 69.5% 1 4.6% 1 5.9% 6.0 5.6 1 .1 6.5 6.8 1 .0 7 .0 7 .9 0.9 7 .5 9.2 0.8 8.0 1 0.6 0.8 50:50 Core:Opportunity Real Estate Portfolio Gov ernment Bonds S&P 500 50:50 Core:Opportunity Return Risk Return:Risk 7 3.2% 1 4.3% 1 2.5% 63.6% 1 9.0% 1 7 .4% 55.1 % 23.2% 21 .7 % 46.6% 27 .3% 26.1 % 37 .0% 32.0% 31 .0% 28.5% 36.1 % 35.3% 5.5 4.8 1 .1 6.0 5.7 1 .1 6.5 6.7 1 .0 7 .0 7 .8 0.9 7 .5 9.2 0.8 8.0 1 0.4 0.8
  44. 44. Risk Measures  Volatility  (Normal) Value at Risk (VaR): how much can a portfolio’s value decline with a given probability and investment horizon  Modified Value at Risk (VaR): Uses a Cornish-Fisher expansion to include skewness and kurtosis in addition to the standard deviation:
  45. 45. Real Estate Allocations – Non-Normal Risk Measure 50:50 Core:Opportunity Allocation MV Optimal 20% RE Constraint 37.0% 32.0% 31.0% 31.8% 48.2% 20.0% Return Volatility Skew Kurtosis 7.5% 9.2% -1.37 6.82 7.5% 9.7% -0.98 3.29 Normal VaR 95.0% Modified VaR 95.0% -7.6% -9.6% -8.4% -10.3% Normal VaR 97.5% Modified VaR 97.5% -10.5% -18.3% -11.4% -16.8% Gov ernment Bonds S&P 500 All Core Funds Index

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