Transcript file

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: [email protected] / [email protected]
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!