Alignment Of Interest In Non-Listed Real Estate Funds Fee Structure And Its Impact On Real Estate Fund Performance ERES Conference Milano, 24.-26.06.2010 Hubertus Bäumer, Dr. Tobias Pfeffer, Dr. Christoph Schumacher Generali Deutschland Immobilien, Cologne, Germany Contact: hubertus.baeumer@generali.de / tobias.pfeffer@generali.de Agenda 1 Introduction 2 Analysis 3 Results 4 Summary 1 Introduction Research Problem and Purpose Problem description: Fund terms and structures among non-listed real estate vehicles are extremely heterogeneous. Information on performance, fund-specific variables in particular “fees” is hardly available. Research on link between property fund performance and fund attributes is limited. Non-listed real estate vehicles are a relatively “young “ segment for institutional RE investors. Short time series, often small samples, market data not standardized. Scope: Non-listed property funds, institutional investors, European allocation including Eastern Europe, mostly “core” and “value-added” funds, European and some international promoters, funds from all jurisdictions. Purpose of the study: “The aim of this paper is to critically analyze the effect of fee structures on performance of property vehicles. In this way, the paper contribute to a better understanding of the role of fund terms for the alignment-of-interest between investors and fund managers.” Agenda 1 Introduction 2 Analysis 3 Results 4 Summary 2 Literature Review Few research on performance of non-listed funds in Europe available Baum, A. (2008), “The Emergence of Real Estate Funds”, in Peterson, A (ed.) Real Estate Finance: Law, Regulation and Practice, London, LexisNexis. Baum, A., Farrelly, K. (2009): ‘Sources of alpha and beta in property funds: a case study’, JRER, Vol. 2., No. 3, 2009, pp. 218-234. Fuerst, F., Matysiak, G. (2009), “Drivers of Fund Performance: A Panel Data Analysis”, Working Papers in Real Estate & Planning 02/09. Brounen, D., Veld, H. O. and Raitio, V. (2007), Transparency in the European Non-Listed Real Estate Funds Market. Journal of RPM, 107-118. Devaney, S., Lee, S. and Young, M. (2007) Serial persistence in individual real estate returns in the UK. JPIF, 25/3, 241-273. Fuerst, F., Matysiak, G. (2009), “Drivers of Fund Performance: A Panel Data Analysis”, Working Papers in Real Estate & Planning 02/09. Hoesli, M. and Lekander, J. (2005), Real estate portfolio strategy and product innovation in Europe, JPIF, 26/2, 162-176. McAllister, P, 2000, ‘Is direct investment in international property markets justifiable?’,Property Management, vol. 18, no. 1, pp. 25-33. Cheng, P., Ziobrowski, A., Caines, R. ,Ziobrowski, B. (1999): “Uncertainty and Foreign Real Estate Investment.” JRER, Vol. 18, No. 3, pp. 463-479. Eicholtz, P, 1996, ‘Does International Diversification Work Better for Real Estate than for Stocks and Bonds?’, FAJ, vol. 52, no. 1, pp. 56-62. Benjamin, J., Sirmans, G., Zietz, E. (2001): ‘Returns and Risk on Real Estate and Other Investments: More Evidence.’, JREPM, Vol.7, No. 3. Viezer, T, 1999, ‘Econometric Integration of Real Estate's Space and Capital Markets,’ Journal of Real Estate Research, vol. 18, no. 3, pp. 503-519. Brown, G.R. and Matysiak, G.A. (2000): ‘Real Estate Investment: A Capital Market Approach’, Edinburgh: Financial Times Prentice Hall. 2 Set-up of the study – Regression & Mean-variance Create a standardized sample with consistent and coherent data Y X (Step 1) X (Step 2) 1. Performance 2. „Fees“ 2. „Fees“ a. Total Return a. Management fees a. Management fees b. Income Return b. Transaction fees b. Transaction fees c. Capital Appreciation c. Performance fees c. Performance fees d. Total fees d. Total fees INREV Index / Fund reports INREV Fee & Terms Study 3. Fund-specific a. Leverage b. Investment style c. Property sector d. Regional allocation 467 vehicles GAV € 261 bn 67% Core 23% Value-added 10% Opportuniity 268 vehicles GAV € 144 bn 53% Core 33% Value-added 14% Opportunity e. Fund size Fund reporting to INREV 2 Data Sources and definitions Create a standardized sample with consistent and coherent data Management fee Standardized to GAV-based figures. Includes yearly based charges to fund management excluding third-party fees eg. custodian fees. Transaction fee Includes acquisition and sale fees. More than 85% of transactions fees are acquisition fees. Performance fee Hurdle rate instead of “total performance fee paid” more significant for this study (Little perf. fees paid in 2009, often at end of fund life, escrow accounts, base on multiple years...) Regional Split into five different regions (North, West, East, South, Other) Property sector Split into three four different sectors (office, retail, industrial > 67%, Diversified) Performance Based on 2009 performance, calculated based on INREV methodology. Fund Size Gross Asset Value (GAV); dummy variables for small, (<25%), medium, large (>75%) funds. Leverage Leverage as reported by the funds to INREV. Investment style As reported by the fund manager to INREV for the individual vehicles. 2 Data Sources and definitions Create a standardized sample with consistent and coherent data Fee based on Assumption GAV No further assumption was needed NAV Recalculation to a fee based on GAV dependent on the individual leverage of the fund Property values It was assumed that the fee is identical with a fee on GAV Drawn Commitment It was assumed that the fee is identical with a fee on NAV and it was recalculated accordingly Region Countries West Austria, Belgium, Denmark, Finland, France, Germany, Ireland, Luxembourg, The Netherlands, Norway, Sweden, Switzerland, United Kingdom East Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Russia, Slovakia South Greece, Italy, Portugal, Spain, Turkey Rest Non-Europe Asia, Non-Europe North America, Non-Europe South America, Not Reported Sector Assumption Sector One type of sector (office, retail, industrial) more than 67 percent of the fund Diversified No specific sector has more than 67 percent of the fund Agenda 1 Introduction 2 Analysis 3 Results 4 Summary 3 Descriptive Statistics Sample represents 178 European property funds with a volume of € 89 bn. Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Fees Fund Size Performance / Return Others Hurdle Manag. Total Trans. Big Medi Small Capital Income Total Lever GAV 6.67 0.80 1.50 0.70 0.25 0.50 0.25 -0.11 0.03 -0.08 44% 500,000,000 € 7.25 0.60 1.30 0.00 0.00 0.50 0.00 -0.11 0.03 -0.07 48% 352,000,000 € 14.00 3.70 5.26 3.88 1.00 1.00 1.00 0.12 0.20 0.18 89% 4,930,000,000 € 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -0.54 0.00 -0.48 0% 1,850,000 € 4.11 0.71 1.08 0.93 0.44 0.50 0.43 0.12 0.03 0.13 23% 534,000,000 € -0.65 2.04 0.78 1.24 1.14 0.00 1.17 -0.53 1.50 -0.54 -0.46 4.17 2.10 6.96 3.05 3.85 2.29 1.00 2.37 3.11 8.03 2.80 2.26 30.19 Dive 0.07 0.00 1.00 0.00 0.26 3.28 11.77 Region East South West 0.07 0.13 0.73 0.00 0.00 1.00 1.00 1.00 1.00 0.00 0.00 0.00 0.25 0.34 0.45 3.45 2.21 -1.04 12.91 5.89 2.08 DiverIndus 0.29 0.12 0.00 0.00 1.00 1.00 0.00 0.00 0.46 0.33 0.91 2.29 1.84 6.23 Sector Style Offi Resid Retail Core Value 0.28 0.08 0.22 0.69 0.31 0.00 0.00 0.00 1.00 0.00 1.00 1.00 1.00 1.00 1.00 0.00 0.00 0.00 0.00 0.00 0.45 0.27 0.42 0.47 0.47 0.98 3.13 1.32 -0.80 0.80 1.95 10.80 2.74 1.64 1.64 3 Sample Mean Variance Analysis – Total Return and Fund Attributes “Size” and “leverage” have negatively impacted performance Role of Fund Size Mean Sd Var Min Max 0.25 quart 0.75 quart Sample gew Var t-Stat gew Var t-Stat Size GAV nach 0.25/0.75 Quartile TR size small TR size medTR size big -4.50% -7.65% -12.42% 10.64% 12.71% 12.50% 1.13% 1.61% 1.56% -34.21% -38.34% -48.42% 15.26% 13.65% 17.58% -10.78% -16.61% -19.91% 0.95% 1.81% -2.44% 51 90 45 1.44% 1.60% 3.12 4.39 1.33% 6.72 Role of Leverage Leverage nach 0.25/0.75 Quartile TR lev smallTR lev med TR lev large Mean -0.23% -7.34% -19.20% Sd 6.21% 11.62% 11.89% Var 0.39% 1.35% 1.41% Min -16.09% -38.34% -48.42% Max 17.58% 14.52% 4.29% 0.25 quart -3.17% -15.89% -27.41% 0.75 quart 2.09% 1.85% -13.24% Stichp. Umfang 55 89 42 gew Var 0.98% 1.37% t-Stat 8.60 11.60 gew Var 0.83% t-Stat 20.51 3 Sample Mean Variance Analysis High hurdle rates have adversely affected fund performance significantly Role of Total Fees combined with Hurdle Rate Fee + HurdlRat based on 0.25/0.75 Quartile TR lev large TR small small TR med med TR large large TR large small TR small large Mean 0.22% -9.44% -13.53% 3.07% -15.40% Sd 5.48% 10.12% 14.32% 5.00% 15.49% Var 0.30% 1.02% 2.05% 0.25% 2.40% Min -12.40% -24.36% -36.82% -9.76% -38.34% Max 12.28% 12.03% 7.25% 14.52% 9.26% 0.25 quart 0.00% -19.39% -24.19% 1.37% -24.39% 0.75 quart 2.03% -0.27% -0.57% 4.67% -11.26% Stichp. Umfang 21 31 16 15 9 gew Var 0.73% 1.37% 1.03% t-Stat 8.13 2.40 8.91 3 Regression - Total fees, hurdle rate, leverage on total return Fees and leverage are significant factors in fund performance 3 Regression – Multiple factors on total return Regional allocation, property type and style / leverage are important 3 Regression Residual / normality tests normal and homoscedastic 3 Regression – Multiple factors on capital appreciation RegionEast, SectorIndustrial, Leverage, FeeHurdle negative effect 3 Regression – Multiple factors on distribution Style / leverage most important for income component Agenda 1 Introduction 2 Analysis 3 Results 4 Summary 4 Summary Fee structures are crucial in non-listed property fund investments Results confirm evidence of former research on effect of leverage, style, region, property type. Including fee structures in performance analysis of property funds is essential. Different fees have a different effect on performance. Hurdle rate is extremely important factor in fee structure / incentive scheme. Positive effect of transactions costs on performance is related to market cycle. Leverage is dominant factor / performance driver. Distribution strategy requires careful consideration of investment restrictions to prevent style drift. Future research questions / aspects: How can a fee structure be optimized? What impact does an alignment of Interest have on real estate performance? Include vintage years and extend analysis to time-series as soon as available!