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International Real Estate Diversification: Assessing Systematic Risk Exposure

This study delves into the impact of diversification in international real estate markets, investigating the implications of market independence on asset class diversification benefits. The research explores national, sector-level, and city-level analyses to determine the effectiveness of diversifiers. Utilizing cointegration tests and factor models, the study evaluates the systematic risk factors and performance of portfolios in different market segments. The findings highlight the varying risk-return characteristics and market sensitivities across regions and sectors, providing valuable insights for global real estate investors.

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International Real Estate Diversification: Assessing Systematic Risk Exposure

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  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

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