Click to edit Master title style Diversification Gains and Systematic Risk Exposure in International Public Real Estate Markets Marielle Chuangdomrongsomsuk & Colin Lizieri Department of Land Economy University of Cambridge ERES Vienna 2013 Diversification Gains and Systematic Risk Exposure in International Public Real Estate Markets Marielle Chuangdomrongsomsuk & Colin Lizieri Department of Land Economy University of Cambridge ERES Vienna 2013

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 yada yada.

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

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 …

For the Record … Cointegration Tests Factor Models

Data and Transformations Base Data: –GPR Monthly Total Return Series (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)

Aggregate Results Cointegration Exclusion Tests: Aggregate Indices Regional (n=5)rNorth AmericaUnited KingdomOceaniaEuropeAsia L-R statistic p-value0.000*** 0.016**0.003*** L-R statistic p-value0.000*** 0.001***0.048**0.002*** North America (n=2 countries)rUnited StatesCanada L-R statistic p-value0.001***0.014** Oceania (n=2)rAustraliaNew Zealand L-R statistic p-value *** Europe (n=9)rAustriaFinlandFranceGermanyNorwaySpainSwedenSwitzerlandNetherlands L-R statistic p-value0.001***0.007*** *0.078* ***0.940 L-R statistic p-value0.000*** ***0.074* **0.559 L-R statistic p-value0.000*** ***0.018**0.048**0.054**0.762 Asia (n=5)rHong KongJapanMalaysiaPhilippinesSingapore L-R statistic p-value0.001*** ***0.017**0.220 The largest market-cap (n=12)rUnited StatesCanadaGreat BritainAustraliaFranceGermanySwedenSwitzerlandNetherlands L-R statistic p-value0.088*0.000*** **0.008*** L-R statistic p-value0.002***0.000*** 0.005***0.006***0.004***0.000***0.049**0.000*** L-R statistic p-value0.006***0.000*** 0.008***0.001***0.003***0.000***0.010***0.000*** L-R statistic p-value 0.000*** 0.001***0.000***0.009***0.000***0.015**0.000*** Don’t you hate it when people put tiny tables up?

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 …

Fama MacBeth Results Four-factor performance model INDECOINT Intercept α INDE =α COINT *** R mt β INDE =β COINT *** SMB γ INDE =γ COINT *** GMOM λ INDE =λ COINT *** HML ζ INDE =ζ COINT *** MSE MSE INDE =MSE COINT *** SD SD INDE =SD COINT ***

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

Sector Results: Office Strong Common Factor – High market  and average  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?

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

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

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?

Click to edit Master title style Diversification Gains and Systematic Risk Exposure in International Public Real Estate Markets Marielle Chuangdomrongsomsuk & Colin Lizieri Department of Land Economy University of Cambridge ERES Vienna 2013 Diversification Gains and Systematic Risk Exposure in International Public Real Estate Markets Marielle Chuangdomrongsomsuk & Colin Lizieri Department of Land Economy University of Cambridge ERES Vienna 2013