Unlisted Real Estate FundsIntroduction & RationaleRisk-Return DriversPerformance CharacteristicsAsset Allocation

Introduction & Rationale

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

Global Real Estate Portfolio Size and Tracking Error

Source: Kennedy (2011)

Unlisted Real Estate Fund Growth

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Americas Europe Asia Pacific

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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

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

Example Unlisted Real Estate Fund Structure

LuxCo 2

Longbow

LuxCo 1

Loans / CMBS

Equity Investment

EquityInvestment

Debt investments

Income from debt investments

Management Fee

Group Income Sharing Loan

Investor Income Sharing Loan

Investors

Real Estate Assets

Co-investment

Cash item

Ownership / funding

Example Closed-End Unlisted Fund Cashflow

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£ (

mn)

Contributions Distributions NAV

Source: Baum and Farrelly (2009)

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

UK Market Pricing vs NAV – Secondary UREFs & REITs

Source: Schneider (2013)

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’

Fund Example

13

Threadneedle Low Carbon Workplace Fund■ The investment proposition was to commit seed capital for the

formation of a UK open-ended green office fund. LCWF is a joint venture between Threadneedle, Stanhope and the Carbon Trust

Summary of Terms:

■ No gearing■ Targeted returns of 1.2x on the development portfolio and IPD

UK Office Index +1% on the investment portfolio■ Projected yield on cost >9.0% on seed portfolio■ ESG-specific strategy

Comparative Advantages:■ Quicker and less restrictive planning consents■ Pre-lets and shorter void period■ Lower total occupational cost■ Better covenant terms; longer lease terms■ Future proofing■ Greater comfort and aesthetics

The Townsend Group

Specific ESG Focused Investing

6

The Townsend Group

Co-Investment – Preferred Equity

THREADNEEDLE LOW-CARBON WORKPLACE FUND

The investment proposition was to commit seed capital for the formation of a UK open-ended green office fund. LCWF is a joint venture between Threadneedle, Stanhope,and the Carbon Trust (the “Key Advisers”):

14

The benefits of low carbon refurbishment over grey refurbishment are:

■ Quicker and less restrictive planning consents

■ Pre-lets and shorter void periods

■ Lower total occupational costs

■ Better covenant tenants; longer lease terms

■ Future proofing

■ Greater comfort and aesthetics

The Townsend Group

Specific ESG Focused Investing

6

The Townsend Group

Co-Investment – Preferred Equity

THREADNEEDLE LOW-CARBON WORKPLACE FUND

The investment proposition was to commit seed capital for the formation of a UK open-ended green office fund. LCWF is a joint venture between Threadneedle, Stanhope,and the Carbon Trust (the “Key Advisers”):

14

The benefits of low carbon refurbishment over grey refurbishment are:

■ Quicker and less restrictive planning consents

■ Pre-lets and shorter void periods

■ Lower total occupational costs

■ Better covenant tenants; longer lease terms

■ Future proofing

■ Greater comfort and aesthetics

Before and AfterBefore After

Energy Consumption Pre Re-Furb

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Lighting aligns to occupancy due to

technology solutions (PIR)

but there is significant base

load small power, and heating & cooling during

weekends

Energy Consumption Post Re-Furb

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use by 48%

Risk – Return Drivers

Sources of Risk and Return In Real Estate Funds Market risk:

Allocations to more volatile sectors Macro / supply risks Transparency, property rights

Stock risk: Asset level (operating) leverage Risk continuum from ground rents to

speculative developments Age, structure Income quality Diversification

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: Treatment of items e.g. mark-to-

market valuations of interest hedging instruments, costs incurred

Real Estate FundRisk & Return

Portfolio Structure / Market Risk

Stock Risk Accounting Policy

Fund Structure

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

Unlisted Real Estate Fund Styles

Core ≤ 40% LTV

Core ≥ 40% LTV

Value Added

Opportunity

Total % of non-income producing investments

≤ 15%> 15% - ≤

40% > 40%

Total % of (re)development exposure

≤ 5%> 5% - ≤

25% > 25%

% of total return derived from income

≥ 60%

Maximum LTV ≤ 40% Core > 40%> 40% - ≤

60%> 60%

INREV Fund Style Classification Criteria

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

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

Core Balanced 56.3%

Core Specialist 22.5%

Value Added 14.1%

Opportunity 7.0%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

Sample Fund Style Exposure Quarterly Sample Total Returns

Source: Farrelly and Matysiak (2012)

Performance Drivers of UK Institutional Funds II

Pooled OLS

Fixed Effects

Bias Corrected

Fixed Effects

Lag Total Return0.376**(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 Yield0.080

(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 75 75 75No Observations 1704 1704 1704

Source: Farrelly and Matysiak (2012)

** 1% Sig, * 5% Sig

Strong 1:1 relationship with market returns

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

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

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

Source: Baum and Kennedy (2012)

Leveraged and Unleveraged Global Real Estate Returns

Asymmetric Impact of Leverage Upon Fund Returns

Pooled OLSFixed

Effects

Bias Corrected

Fixed Effects

Lag Total Return 0.107**(0.015)

0.090**(0.033)

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)

Period Effects Included? No No No

R Squared 0.808 0.803

No Cross Sections 75 75 75

No Observations 1724 1724 1724

Source: Farrelly and Matysiak (2012)

** 1% Sig, * 5% Sig

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 non-normality of real estate returns

Asymmetric Impact of Leverage Upon Fund Returns

0.0% 0.6% 1.2% 1.9% 2.5% 3.1% 3.7% 4.3% 5.0% 5.6% 6.2% 6.8% 7.4%

0.0%-1.9%

-3.8%-5.7%

-7.6%-9.5%

-11.4%-13.3%

-15.2%-17.1%

-19.0%-20.9%

-22.8%-25%

-20%

-15%

-10%

-5%

0%

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10%

Ann

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Net LTV

Positive Market Negative Market

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)

Example Unlisted Fund Performance Attribution I

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Contributions Distributions NAV

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0%

5%

10%

15%

20%Net Fund Returns IPD UK Pooled Funds Index

• 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)

Fund Cash Flow Quarterly Fund Returns

Example Unlisted Fund Performance Attribution II

2002 2003 2004 2005 2006 5 year

Property Level

Property TWR 12.6% 10.5% 23.7% 25.5% 8.8% 16.0%

Benchmark TWR 9.2% 10.5% 17.4% 19.1% 18.5% 14.9%Relative 3.1% 0.0% 5.4% 5.4% -8.2% 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% -11.5% -13.8% -8.4%

Interaction Effect (Three Component) 12.4% 10.4% 11.3% 16.2% 5.8% 11.2%

Fund Level

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%

Net MWR 29.9%

Timing Score 4.3%

Source: Baum and Farrelly (2009)

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

Fund Performance Fee Example – “20 Over 9”

Gross IRR 15%Income IRR 5%Investor Equity 100

Year 0 1 2 3 4 5 6 7NAV 110.0 121.0 133.1 146.4 161.1 177.2 194.9Income 5.0 5.5 6.1 6.7 7.3 8.1 8.9

Gross CF -100.0 5.0 5.5 6.1 6.7 7.3 8.1 203.7

Management Fee 1.5% NAVPreferred Return 9.0%Performance Fee Profit share 20.0%

Management Fee Paid -1.7 -1.8 -2.0 -2.2 -2.4 -2.7 -2.9

Cash Flow Pre Performance Fee -100.0 3.4 3.7 4.1 4.5 4.9 5.4 200.8IRR Pre Pre Performance Fee 13.3%

Preferred Return -100.0 3.4 3.7 4.1 4.5 4.9 5.4 148.3Check 9.0%

Excess' Profit - - 0.0 0.0 0.0 0.0 0.0 52.5Manager Share - - 0.0 0.0 0.0 0.0 0.0 10.5

Investor Cash Flow -100.0 3.4 3.7 4.1 4.5 4.9 5.4 190.3Net IRR To Investor 12.6%

Fee Reduction % 16.3%

Fee Impact From a Sample of Fund Returns

Fund Gross IRR Net IRR Fee impactFee impact

%1 29.0% 25.0% 4.0% 13.8%

2 17.0% 13.0% 4.0% 23.5%

3 33.0% 25.0% 8.0% 24.2%

4 35.0% 30.0% 5.0% 14.3%

5 27.0% 21.0% 6.0% 22.2%

6 46.0% 37.0% 9.0% 19.6%

7 21.0% 16.0% 5.0% 23.8%

8 34.0% 27.0% 7.0% 20.6%

9 16.0% 13.0% 3.0% 18.8%

10 20.0% 15.0% 5.0% 25.0%

11 18.0% 14.0% 4.0% 22.2%

12 20.0% 16.0% 4.0% 20.0%

13 14.0% 12.0% 2.0% 14.3%

14 20.0% 15.0% 5.0% 25.0%

Mean 25.0% 19.9% 5.1% 20.5%

Source: Baum and Farrelly (2009)

Performance Characteristics

US Unlisted Real Estate Fund Performance Indices

• Unlisted real estate funds provide a ‘geared’ exposure to the direct real estate market

-50%

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0%

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30%

40%

50%

Core Funds Value Added Funds

Opportunity Funds NCREIF Index

Rolling 12m Total Returns 1988Q4 – 2012Q2

Beta RSq

Core 1.25 0.94

Value Added

1.60 0.80

Opportunity

1.90 0.70

vs NCREIF

Source: NCREIF

US Real Estate Fund Performance Dispersion

• US Value Add / Opportunity fund performance by vintage year• Clearly significant performance differentials

▫ Phenomenon seen in private equity e.g. SwensenSource: Prequin

US Asset Class Returns -1988 Q4 – 2012 Q2

Ann Mean Ann Median Ann Std. Dev. Skewness Kurtosis

S&P 500 10.8% 13.0% 16.3% -0.56 3.38

US Government Bonds 7.4% 6.7% 4.7% 0.12 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 10.28

All Opportunity Funds Index 7.6% 9.2% 11.2% -1.14 8.27

US REITs 13.1% 14.6% 20.1% -0.73 7.05

S&P 500 BOND NCREIF CORE OPRE REIT

S&P 500 1.00

US Government Bonds -0.10 1.00

NCREIF Index 0.13 -0.14 1.00

All Core Funds Index 0.12 -0.12 0.97 1.00

All Opportunity Funds Index 0.28 -0.15 0.85 0.82 1.00

US REITs 0.60 0.05 0.17 0.16 0.29 1.00

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-Stat1988Q4 – 2012Q2

Unsmoothing Impact

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 10.28

All Opportunity Funds Index 7.6% 9.2% 11.2% -1.14 8.27

Unsmoothed Returns

NCREIF Index 7.5% 9.9% 12.7% -2.77 17.95

All Core Funds Index 5.7% 7.8% 14.7% -2.58 19.06

All Opportunity Funds Index 7.8% 8.2% 32.4% -0.52 7.60

Employed autoregressive based unsmoothing procedure

E.g. AR(1) process

Latest methodology: Lizieri et al (2012) “Unsmoothing Real Estate Returns: A Regime-Switching Approach”

𝑅𝑡 = 𝛼+ 𝛿𝑅𝑡−1

𝑅𝑡ሺ𝑈𝑛𝑠𝑚𝑜𝑜𝑡ℎሻ= (𝑅𝑡 − 𝛿𝑅𝑡−1)/(1− 𝛿)

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

0

2

4

6

8

10

12

14

16

-0.08 -0.06 -0.04 -0.02 0.00 0.02 0.04

Series: NPISample 1988Q4 2012Q2Observations 95

Mean 0.018079Median 0.023400Maximum 0.054300Minimum -0.082900Std. Dev. 0.024393Skewness -1.890619Kurtosis 7.593356

Jarque-Bera 140.1119Probability 0.000000

0

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16

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32

-0.25 -0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10 0.15

Series: OPRESample 1988Q4 2012Q2Observations 95

Mean 0.018524Median 0.022321Maximum 0.163104Minimum -0.243989Std. Dev. 0.055901Skewness -1.141294Kurtosis 8.267118

Jarque-Bera 130.4380Probability 0.000000

NCREIF Return Distribution 1988Q4 – 2012Q2

US Opportunity Fund Return Distribution

1988Q4 – 2012Q2

Normality Tests

Tests confirm non-normality of sample real estate data

Jarque-Bera Stat Probability Shapiro-Wilk Test ProbabilityS&P 500 5.51 0.06 0.97 0.04

US Government Bonds 1.72 0.42 0.99 0.39

NCREIF Index 140.11 0.00 0.84 0.00

All Core Funds Index 300.39 0.00 0.78 0.00

All Opportunity Funds Index 130.44 0.00 0.88 0.00

US REITs 73.30 0.00 0.92 0.00

Unsmoothed Returns

NCREIF Index 1005.41 0.00 0.75 0.00

All Core Funds Index 1126.36 0.00 0.78 0.00

All Opportunity Funds Index 88.18 0.00 0.91 0.00

Asset Allocation

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: 4.0% S&P 500: 8.5% Core UREFs 7.5% Opportunity UREFs: 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 non-normality

Real Estate Allocations

75:25 Core:Opportunity Real Estate Portfolio

50:50 Core:Opportunity Real Estate Portfolio

Government Bonds 69.5% 59.0% 47.3% 37.9% 27.4% 16.9%S&P 500 14.6% 19.6% 25.1% 29.6% 34.6% 39.5%75:25 Core:Opportunity 15.9% 21.4% 27.6% 32.5% 38.0% 43.6%

Return 5.5 6.0 6.5 7.0 7.5 8.0Risk 4.7 5.6 6.8 7.9 9.2 10.6Return:Risk 1.2 1.1 1.0 0.9 0.8 0.8

Government Bonds 73.2% 63.6% 55.1% 46.6% 37.0% 28.5%S&P 500 14.3% 19.0% 23.2% 27.3% 32.0% 36.1%50:50 Core:Opportunity 12.5% 17.4% 21.7% 26.1% 31.0% 35.3%

Return 5.5 6.0 6.5 7.0 7.5 8.0Risk 4.8 5.7 6.7 7.8 9.2 10.4Return:Risk 1.1 1.1 1.0 0.9 0.8 0.8

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:

Real Estate Allocations – Non-Normal Risk Measure

MV Optimal

20% RE Constraint

Government Bonds 37.0% 31.8%S&P 500 32.0% 48.2%All Core Funds Index 31.0% 20.0%

Return 7.5% 7.5%Volatility 9.2% 9.7%Skew -1.37 -0.98Kurtosis 6.82 3.29

Normal VaR 95.0% -7.6% -8.4%Modified VaR 95.0% -9.6% -10.3%

Normal VaR 97.5% -10.5% -11.4%Modified VaR 97.5% -18.3% -16.8%

50:50 Core:Opportunity Allocation