Risks & Opportunities in International Real Estate

Risks & Opportunities in International Real Estate Neil Cable, Head of European Real Estate, Fidelity International Navigating International Real Estate Markets

• Navigating risk in property – lessons from Christopher Columbus to Captain James Cook

• Can we quantify risks – expected and experienced – in previous real estate cycles?

• So what really drives risk and return in property markets?

• Alternative tools to navigate the next cycle Longitude at Sea One of the Biggest Global Problems of all Time

• Death

• Huge financial loss

• War

• Piracy

• Risks to Global Trade

• All because of no time (literally)

Source: Longitude, The True Story of a Genius Who Solved the Greatest Scientific Problem of His Time, by Dava Sobel Solution No.1 – just use latitude!

Why not? (see previous slide)

Source: Longitude, The True Story of a Genius Who Solved the Greatest Scientific Problem of His Time, by Dava Sobel Solutions from some of the greatest thinkers of the time

Galileo’s “celatone” (1617)

• One eye on Jupiter

• The other eye on Jupiter’s moons

• The Netherlands gave him a gold chain, but it didn’t work!

Source: Longitude, The True Story of a Genius Who Solved the Greatest Scientific Problem of His Time, by Dava Sobel Seafarers judgement, compasses and a wooden log

“Dead reckoning” (17th Century)

• Log overboard

• Measure speed and distance of ship moving away

• But currents, wind, temperature and other factors made this unreliable at best

Source: Longitude, The True Story of a Genius Who Solved the Greatest Scientific Problem of His Time, by Dava Sobel And the winner was ... eventually ...

“H1” “H2” Harrison’s clocks (1737-1760)

• After extensive political intrigue

• After extensive sea trials

“H3” “H4” • And after 40+ years

• Harrison’s clocks were proven to enable accurate measurement of longitude at sea

• Accurate to within 3 seconds per day

Source: Longitude, The True Story of a Genius Who Solved the Greatest Scientific Problem of His Time, by Dava Sobel Can we quantify historic real estate risks – expected and experienced? Pre-crisis What investors were encouraged to expect

INREV Non-listed Fund Classifications 2004

Target return Leverage Income v capital (post tax and fees) (% of gross asset value)

Core Up to 11.5% Up to 60% “stable income”

11.5% - 18.5% Value-added 30% - 70% “combination of income and capital”

Opportunity 18.5%+ 70%+ “primarily capital”

Source: INREV (European Association for Investors in Non-listed Real Estate Vehicles) 2004 So What Happened? Returns 2001-11 by ‘fund style’

Opportunity funds significantly Time Weighted Rate of Return outperformed 2001-6 (2001-11) - % p.a. 20 ...but core and value added significantly outperformed 2007-11 15

10 5.9 6.4 5 4.1

0 Core Value Added Opportunity

Source: Have Property Funds Performed? A ULI Europe Policy & Practice Committee Report, February 2012 NOTE: ULI have not updated this work since 2012 and no other publicly available studies have produces more up to date data in this format. How much risk? Tracking errors 2001-11 by ‘fund style’

The relativities were ‘correct’ – Tracking Errors Core had lowest tracking errors, (2001-11) Opportunity highest 20 18.2 ... but absolute levels of risk 15

were extraordinarily high for 10 Opportunistic 5.4 5 1.8 0 Core Value Added Opportunity

The relativities were ‘correct’ – Tracking Errors Core had lowest tracking errors, (2001-11) Opportunity highest 20 18.2 Comparing tracking ... but absolute levels of risk 15 error with equity fund ‘cousins’ were extraordinarily high for 10 9.1 Opportunistic 5.4 6.1 5 3.6 1.8 0 Core Value Added Opportunity

Source (real estate data): Have Property Funds Performed? A ULI Europe Policy & Practice Committee Report, February 2012.. Source (equity data): Fidelity International analysis of MSCI data, grouped by global investment fund sector (GIFS). Time period matches real estate data – i.e. 2001-11 So what really drives risks in real estate markets? Post-crisis How investors are guided now

INREV Non-listed Fund Classifications 2012

Leverage Target return Target (re)development Target return (% of GAV) derived from income (%) Exposure as & of GAV

Core N/A ≤ 40% ≥60% ≤15%

Value-added N/A 40% - ≤60% N/A >5% - ≤25%

Opportunity N/A >60% N/A >25%

INREV revised style classifications in 2011/12 . An attempt to categorise by risk and provide clear guidance to investors . Identified principal risks are (a) leverage (b) % of return from income and (c) % exposure to development

Source: NREV Style Classification, Revised Version, February 2012. Risk in property The dangers of assertions v. numbers

Labels (core, value-add etc) % of Long Term Return* Attribution of UK Returns • INREV provides guidelines, not quantification 1975-2014** of risk • Labels still therefore largely subjective 24% 34%

Correlations, intuitive logic and the danger of averages 66% 76% • How do we measure the riskiness of income? • If geography doesn’t define risk, what does? Income Stock Selection Capital Asset Allocation

* Source: MSCI, Fidelity International. Weighted average share of long term total return for non Asian markets as follows: Australia , Austria, Belgium, Canada, Denmark, Czech Republic, Finland, France, Germany, Hungary, Ireland, Italy, Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland, UK ** Source, MSCI Imagine if we had perfect foresight....

Toronto Office Returns v Ontario GDP Growth (% p.a.) 20

5 If you knew this in 0 advance, you’d be

-5 rich ... right? 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Ontario GDP Growth Toronto Office Total Return

Source: NREV Style Classification, Revised Version, February 2012. 90%+ chance of not achieving return, with perfect forecast! 25.0 Returns for 247 Office Buildings in Toronto in 2014 (% p.a.) 20.0 15.0 10.0 Average Return 6.84% Spread of 5.0 returns 0.0 2,808 bp -5.0

-10.0

5th Percentile 5th

35thPercentile 10thPercentile 15thPercentile 20thPercentile 25thPercentile 30thPercentile 40thPercentile 45thPercentile 50thPercentile 55thPercentile 60thPercentile 65thPercentile 70thPercentile 75thPercentile 80thPercentile 85thPercentile 90thPercentile 95thPercentile

Source: MSCI, Ontario Ministry of Finance, July 2015 City of London spread of returns almost 7,000 bp in 2014 80.0 Returns for 269 Office Buildings in City of London in 2014 (% p.a.) 60.0

40.0 Average Return 26.24% Spread of 20.0 returns 6,925 bp 0.0

-20.0

5th Percentile 5th

45thPercentile 10thPercentile 15thPercentile 20thPercentile 25thPercentile 30thPercentile 35thPercentile 40thPercentile 50thPercentile 55thPercentile 60thPercentile 65thPercentile 70thPercentile 75thPercentile 80thPercentile 85thPercentile 90thPercentile 95thPercentile

Source: MSCI, July 2015 Alternative tools to navigate the next cycle Income – treat this as a ‘bond with a roof’

Quantify the default risk • Use bond ratings + other sources of credit risk/failure

Quantify duration risk (lease) • Model all scenarios and include all potential costs

Stress test all the variables

Better understand real estate within your portfolio • Better ability to understand and monitor risk v. your liabilities • Better ability to monitor the manager (e.g. ‘style drift’)

Source: Fidelity International, April 2016. Illustrative example of ‘forward default curve’ for property income risk. Based on proprietary risk models, including data (sourced from DnB) on over 5 million businesses across Europe ‘Core’ or ‘Correlated’?

New York London Hong Kong

Source: Shutterstock Understanding upside and downside risk Create a RISK ADJUSTED cashflow

Asset Specific Tenant Lease Market Valuation Default probability Length of lease Historic performance Physical attributes Sector outlook Break options Projected renta lgrowth Infrastructure & location Depth of demand Overall structure Projected capital growth

Expected risk adjusted cash flow Return probability distributions £6,000,000 18% £5,000,000 16% 14% £4,000,000 12% £3,000,000 10% £2,000,000 8% 6% £1,000,000 4% £0 2% -£1,000,000 0% 2015Q4 2017Q3 2019Q2 2021Q1 2022Q4 2024Q3 -10% -5% 0% 5% 10% 15% 20% 25%

Source: Fidelity International, April 2016, based on proprietary risk models. Investment Strategy Determined by structure of cashflow – not list of locations

£6,000,000 Research and Fund £5,000,000 Managers identify risks £4,000,000 in the cashflow £3,000,000

£2,000,000 Fund Manager can £1,000,000 adapt shape of the

£0 cashflow to reflect investors risk/return -£1,000,000

appetite

2019Q3 2015Q4 2016Q1 2016Q2 2016Q3 2016Q4 2017Q1 2017Q2 2017Q3 2017Q4 2018Q1 2018Q2 2018Q3 2018Q4 2019Q1 2019Q2 2019Q4 2020Q1 2020Q2 2020Q3 2020Q4 2021Q1 2021Q2 2021Q3 2021Q4 2022Q1 2022Q2 2022Q3 2022Q4 2023Q1 2023Q2 2023Q3 2023Q4 2024Q1 2024Q2 2024Q3 2024Q4 2025Q1 2025Q2 2025Q3

Source: Fidelity International, April 2016, based on proprietary risk models. Conclusions

Looking in the wrong places So where should investors be looking? • Historic and continuing focus on sector • Demand much more transparency from and geography are flawed your real estate manager! • The label (core/value added/opportunistic) • Invest based on the numbers, not the does not define the risk in itself labels • Location matters for every building – but • Full cashflows + upside/downside risk – you’re not investing in ‘averages’ better than a geographic exposure • Pension plans can then monitor the investment better, detect ‘style drift’ and assess the cashflow against pension liabilities Important Information

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