Motivation and Agenda

1. Diversification Gains and Systematic Risk Exposure in International Public Real Estate MarketsMarielleChuangdomrongsomsuk & Colin LizieriDepartment of Land Economy University of CambridgeERES Vienna 2013

2. Motivation and Agenda • Context: International Real Estate Securities Investment • Cointegration Between Markets Important … • Affects the diversification benefits of asset class • More Independent Markets Better Diversifiers … • Analysis at National Level: What If You Disaggregate? • Do results hold at sector level or for types of cities? • If not, what are investment implications? • Agenda is Boringly Conventional • Literature, Model, Data, Results, Implications yadayada.

3. Prior Research • International Diversification Literature Shifts from Short Run to Long Run Models • Debate Over Whether Country or Sector Critical • Heston & Rouwenhorst, Bekaert et al., van Dijk & Keijzer • In Real Estate Securities • Evidence of global real estate factor / global convergence and importance of regional / continental factors • Growing body of literature using long run methods to assess benefits of international investment • Our Paper: from Wilson & Zurbruegg (2003b), Gerlach et al. (2006) and Gallo & Zhang (2010) • We follow Gallo & Zhang but add sector and city level analysis

4. Model Set Up • Test for Unit Root – ADF, PP, KPSS, ZA • Cointegration Tests at Regional and Country Level • Standard Johansen style tests • Separate Indices into Two Portfolios • “Cointegrated” and “Independent” • Test Relative Performance of Portfolios • Standard measures – risk & return, Sharpe etc. • Factor models (market, size, value, momentum) • Portfolio Risk Analysis • Fama Macbeth two step process with rolling windows • Test for differences in performance • Systematic Risk Factors (not reported in paper yet) • Repeated for Sector and City Specifications …

5. For the Record … Cointegration Tests Factor Models

6. Data and Transformations • Base Data: • GPR Monthly Total Return Series 1994-2011 (and sub-periods) • National Level Indices and Company Level Data • Analysis in Logs / Log Differences and US$ • Sector Level Data • Use SNL to Obtain Company Level Sector Exposure • Classify as Sector Specialist if >50% Exposure • Retail, Office, Residential, Industrial, (Diversified) • Global City / Financial Centre Exposure • Majority of Portfolio in Leading City / Financial Centre • RFR, Factor Models • US TBill, F-F factors, calculated market excess return, market size, HML, market momentum measures (annual rebalance)

7. Aggregate Results Don’t you hate it when people put tiny tables up?

8. Aggregate Results • Unit root testing satisfactory • Cointegration Tests • Regional Cointegration: Inter-Regional Dependency • Within Region Cointegration Present (Europe complex) • Exclusion Tests – Identify “Independent” Markets • Australia, France, Germany, Netherlands, Singapore, Japan • Cointegrated markets are regionally cointegrated … • Portfolio Performance • Indep. better risk-return characteristics and Sharpe ratio but … • Greater sensitivity to market factors, momentum • More nuanced than a simple cointegration story …

9. FamaMacBeth Results

10. Sector Results: Retail • Reduces Countries from 19 to 13 … • Country Betas are Lower than for Aggregate Analysis • Evidence of Inter- and Intra-Regional Cointegration • But Patterns Differ • “Independent” Countries Change • France, Germany, Hong Kong, Philippines • Cointegrated Group More “Global” Characteristics • Higher and significant market betas, momentum effects • Factor models explain more variation, lower MSEs • Independent group has significantly larger alpha

11. Sector Results: Office • Strong Common Factor – High market b and average r • Inter and Intra-Regional Cointegration; • Typically only one cointegrating relationship in regions • Cointegrated group: Australia, Germany, Spain, US, Canada, Japan, UK, strong common movement • High market betas in the factor model and F-M analysis • High R2 in factor models, low MSE in F-M • Portfolio risk analysis suggests strong sensitivity to capital market factors – risk premia, term structure, institutional flows • France, Sweden, Switzerland More Independence?

12. Sector Results: Global City Exposure • In Part, a Test of Towers of Capital Hypothesis • Betas, Correlations Lower: Japan, Australia “Odd” • Switzerland, Hong Kong, Singapore Independent? • Cointegrated Group – Global Not Regional? • Factor models explain high % of variation • Betas on market index high, persistent and significant • Cointegrated Group Driven By Capital Markets? • Factor risk model shows high sensitivity to RP, TS, Cap Flows

13. Summary and Conclusions - 1 • Aim: To Extend Long-Run Analysis of International Real Estate Beyond Consideration of National Indices • Aggregate Results Confirm Prior Research – Cointegration Exists, Regional Location is Important, Cointegration Affects Performance, Risk and Return • However, City and Sector Analysis Shows that National Level Results Do Not Hold Consistently • Cointegration varies by sector • For some sectors (cities) global factors dominate regional • Some markets are more local (but which markets varies) • Systematic risk factors vary across groups

14. Summary and Conclusions - 2 • Results Have Value for Investors • Greater understanding of what drives risk and factor sensitivity • Need to consider sector and city exposure in building portfolios • Important for fine tuning where there is a mandate to invest in a particular country or region. • Further Work and Extensions • Develop the factor sensitivity analysis • More work on structural breaks and sub-periods • Drill into the currency / exchange rate issue? • Hold-back sample portfolio effects?

15. Diversification Gains and Systematic Risk Exposure in International Public Real Estate MarketsMarielleChuangdomrongsomsuk & Colin LizieriDepartment of Land Economy University of CambridgeERES Vienna 2013