1. The role of risk measures’ choice in ranking real estate funds: evidence from the Italian market Claudio Giannotti, University LUM Casamassima, Bari giannotti@lum.it Gianluca Mattarocci, University of Rome “Tor Vergata” gianluca.mattarocci@uniroma2.it gianluca.mattarocci@uniroma2.it Milano – June 23 td -26 th, 2010

2.  Introduction  Literature review  Empirical analysis:  Sample  Methodology  Results  Conclusions Index

3. Introduction (1/2)  In the asset management industry, the Risk Adjusted Performance (hereinafter RAP) measures are the more well known instruments used in order to give advice about the quality of an investment (Cucurachi, 1999). The more widespread measures assumes the hypothesis of normality of returns and provide a judgment of the quality of the investment as a ratio between a return and a risk index.  Empirical analysis proposed in literature about the real estate investment vehicle performance demonstrate that the return distribution is asymmetric (Hutson and Stevenson, 2008) and is significantly skewned (Lizieri et al. 2007).

4. Introduction (2/2) Research questions -Does the normality hypothesis fit for the Italian real estate funds’? -Is there any difference in the ranking constructed using RAP measures that assume the normality of returns and those that do not consider this simplified assumption? -Is there any relationship between leverage or volume and the fitness of the RAP measures?

5. Index  Introduction  Literature review  Empirical analysis:  Sample  Methodology  Results  Conclusions

6. Literature review (1/2)  The analysis of the performance achieved by listed real estate property companies and REITS demonstrate a lack of normality in the return distribution (Lizieri and Ward, 2000) and shows dynamics for returns achieved that are not always coherent with those achieved by other financial instruments (Hutson and Stevenson, 2008).  Real estate investment vehicles show frequently a returns’ distribution with higher skewness and kurtosis respect to other financial instruments (Myer and Weeb, 1993).

7. Literature review (2/2)  The non normality of results is explained on the basis of the liability structure that could defined in order to ensure to the lender a fixed minimum return and a premium in some market scenarios (Ward and French, 1997).  The performance dynamics of real estate vehicles could be also explained on the basis of the lack of liquidity that characterized the markets in which they are traded (Li et al., 2009). There are some evidence for more developed markets (like US) of an increasing number of transactions and a lowering level of transaction costs (Jirasakuldech and Knight, 2005) but these results could be not generalized to the overall world industry.

8. Index  Introduction  Literature review  Empirical analysis:  Sample  Methodology  Results  Conclusions

9. Empirical analysis: Sample Fund nameListing date Asset Under Management December 31 th, 2009 Alpha immobiliare July 04 th, 2002537,833,183 € Atlantic 1 June 7 th, 2006742,495,349 € Atlantic 2 – Berenice July 19 th, 2005637,476,570 € Beta immobiliare October 24 th, 2005230,287,272 € BNL portfolio immobiliare January 2 nd, 2002437,315,443 € CAAM RE Europa November 17 th, 2003221,227,779 € CAAM RE Italia June 03 rd, 2006275,583,711 € Caravaggio May 16 th, 2005334,375,253 € Delta Immobiliare March 11 th, 2009328,204,487 € Estense Grande Distribuzione August 3 rd, 2004409,789,091 € Europa Immobiliare 1 December 04 th, 2004411,237,566 € Immobilium 2001 October 29 th, 2003148,979,669 € Invest Real Security January 01 st, 2005183,286,908 € Investietico November 01 st, 2004249,844,486 € Obelisco June 14 th, 2006243,707,118 € Olinda December 09 th, 2004649,305,787 € Piramide Globale November 26 th, 200255,430,399 € Polis April 20 th, 2001361,633,481 € Risparmio immobiliare uno June 04 th, 2001193,088,699 € Securfondo October 02 nd, 2001196,575,750 € Tecla fondo uffici March 4 th, 2004734,515,749 € Unicredit Immobiliare uno June 4 st, 2001599,349,929 € Valore Immobiliare globale November 29 th, 1999207,644,612 € N° of Italian real estate funds (listed and unlisted)154 AUM of the overall Italian Market (listed and unlisted)38,316,900,000 € Sample representativeness n° funds = 23 14.94% of the number of Italian real estate funds AUM > 38 billions euros 21.87% of the AUM of the overall Italian real estate funds market

10.  Performance achieved is computed using the following formula: Empirical analysis: methodology (1/2) Where P t is the closing price a time t, D t is the dividend eventually paid at time t and ln is the natural logarithm. Normality testShapiro & Wilk We select to test the usefulness of new RAP measure corrected for the non-normality looking only at those that are constructed starting from the excess return respect to a risk free rate

11. Empirical analysis: methodology (2/2) Omega risk measure VaR risk measuresMDD risk measures Lower partial moments risk measures Value of each measure Ranking correlation Ranking peristence

12. Empirical analysis: results (1/6) Shapiro – Wilk test of normality 20012002200320042005200620072008 2009 Alpha immobiliare-6.957 *** 10.326 *** 9.042 *** 7.839 *** 9.322 *** 7.575 *** 6.351 *** 5.300 *** Atlantic 1-----7.393 *** 5.700 *** 6.749 *** 7.751 *** Atlantinc 2 - Berenice----4.394 *** 7.256 *** 10.315 *** 7.201 *** 10.28 *** Beta immobiliare----4.822 *** 8.97 *** 6.732 *** 10.808 *** 8.953 *** BNL portfolio immobiliare-8.029 *** 5.911 *** 5.237 *** 8.347 *** 8.825 *** 8.186 *** 5.901 *** 3.121 *** CAAM RE Europa--5.013 *** 3.857 *** 5.338 *** 4.591 *** 6.382 *** 6.608 *** 3.179 *** CAAM RE Italia-7.869 *** 5.694 *** 4.264 *** 8.697 *** 10.638 *** 8.538 *** 7.306 *** 6.36 *** Caravaggio----6.577 *** 8.461 *** 8.266 *** 7.829 *** 10.308 *** Delta Immobiliare--------9.13 *** Estense Grande Distribuzione---2.749 *** 5.841 *** 7.900 *** 6.964 *** 7.409 *** 7.186 *** Europa Immobiliare 1-----2.149 ** 8.362 *** 9.053 *** 7.926 *** Immobilium 2001--3.616 *** 7.290 *** 8.261 *** 9.438 *** 7.218 *** 8.502 *** 7.497 *** Invest Real Security----8.650 *** 8.376 *** 8.758 *** 9.438 *** 9.08 *** Investietico---2.114 ** 5.562 *** 7.071 *** 8.425 *** 6.908 *** 6.734 *** Obelisco-----6.885 *** 8.484 *** 8.986 *** 8.49 *** Olinda----1.1289.925 *** 9.427 *** 5.964 *** 7.077 *** 4.192 *** Piramide Globale-2.404 *** 5.284 *** 8.295 *** 7.577 *** 6.487 *** 11.380 *** 11.596 *** 10.133 *** Polis2.130 ** 6.351 *** 4.947 *** 5.871 *** 8.72 *** 7.899 *** 5.102 *** 6.563 *** 6.758 *** Risparmio immobiliare uno--------7.344 *** Securfondo4.645 *** 8.325 *** 7.325 *** 7.762 *** 8.630 *** 9.052 *** 7.683 *** 8.486 *** 8.029 *** Tecla fondo uffici---9.078 *** 7.533 *** 9.861 *** 8.987 *** 6.437 *** 3.777 *** Unicredit Immobiliare uno2.564 *** 6.459 *** 6.532 *** 6.461 *** 5.643 *** 8.236 *** 6.484 *** 6.746 *** 4.306 *** Valore Immobiliare globale3.291 *** 7.825 *** 8.007 *** 6.948 *** 5.991 *** 7.521 *** 7.594 *** 7.606 *** 8.369 *** Notes: *** test significant at 99% level ** test significant at 95% level * test significant at 90% level

13. Empirical analysis: results (2/6) Correlation among rankings SharpeROPSROASSortinoKappa (n=3)Kappa (n=4)CalmarSterlingBurkeVaR RatioCVaR ratioMVaR ratioSharpe Omega Sharpe Mean 1.0000 Max Min ROPS Mean 0.8734 1.0000 Max 1.0000 Min 0.6000 ROAS Mean 0.92210.8435 1.0000 Max 1.00000.9879 Min 0.69050.6000 Sortino Mean 0.97800.86060.9129 1.0000 Max 1.00000.98791.0000 Min 0.95240.60000.6429 Kappa (n=3) Mean 0.82620.66370.78880.8573 1.0000 Max 1.00000.83251.0000 Min 0.65120.25190.42860.7131 Kappa (n=4) Mean 0.96960.85990.91320.98300.8823 1.0000 Max 1.00000.98791.0000 Min 0.93380.60000.69050.92860.7688 Calmar Mean -0.8572-0.7349-0.7875-0.8575-0.7431-0.8636 1.0000 Max -0.7065-0.5325-0.3810-0.7481-0.4762-0.7532 Min Sterling Mean 0.96940.85660.94140.97470.85960.9796-0.8474 1.0000 Max 1.00000.98791.0000 -0.7143 Min 0.92860.60000.84730.88100.69050.9484 Burke Mean 0.91270.75660.85080.92580.94350.9354-0.80700.9224 1.0000 Max 1.00000.90781.0000 -0.63121.0000 Min 0.73810.40520.33330.76190.87410.78570.6429 VaR ratio Mean 0.83460.91010.79100.83130.65590.8300-0.76690.82470.7298 1.0000 Max 0.97621.00000.9758 0.76970.9762-0.57800.97620.9051 Min 0.60000.70000.6000 0.28440.6000-0.96360.60000.3753 CVaR ratio Mean 0.83380.89180.75830.84090.69520.8429-0.76290.82550.75710.9770 1.0000 Max 0.95241.00000.9515 0.83331.0000-0.51210.96700.91741.0000 Min 0.60000.57140.6000 0.52990.6000-0.93940.60000.59870.8844 MVaR ratio Mean 0.63590.63030.57950.64870.59090.6694-0.67690.64010.60910.72200.7481 1.0000 Max 0.97620.95240.86670.90480.80000.9762-0.36660.97620.80001.00000.9762 Min 0.30730.24700.28350.30430.34510.3132-0.85450.27030.30990.34290.3587 Sharpe Omega Mean 0.95130.90120.92800.93580.73480.9158-0.79720.91840.82300.84480.81980.6065 1.0000 Max 1.0000 -0.60001.0000 0.96360.93940.8810 Min 0.89890.60000.82420.83510.52380.8000 0.81980.50000.6000 0.2352 Max range of variation 60% Mean correlation with Sharpe index: 60%

14. Empirical analysis: results (3/6) Correlation among rankings (breakdown by leverage) SharpeROPSROASSortino Kappa (n=3) Kappa (n=4) CalmarSterlingBurke VaR Ratio CVaR ratio MVaR ratio Sharpe Omega Sharpe H 1.0000 L ROPS H 0.8891 1.0000 L 0.8808 ROAS H 0.87190.8210 1.0000 L 0.95240.8959 Sortino H 0.96610.87440.8779 1.0000 L 0.97520.87050.9425 Kappa (n=3) H 0.54370.37780.60630.5738 1.0000 L 0.88930.76420.83370.9068 Kappa (n=4) H 0.94880.86680.91060.97970.5836 1.0000 L 0.96120.86910.91030.97380.9311 Calmar H -0.7056-0.6263-0.6776-0.7270-0.3738-0.7443 1.0000 L -0.9419-0.8649-0.8869-0.9449-0.9175-0.9779 Sterling H 0.94800.82580.89190.96460.62270.9330-0.6886 1.0000 L 0.97180.88190.92850.98530.91920.9862-0.9664 Burke H 0.64440.50090.67030.65640.93010.6625-0.40150.6968 1.0000 L 0.93990.81330.87260.94690.94980.9687-0.96590.9657 VaR ratio H 0.52400.57330.41770.54290.13540.5203-0.38060.52730.2090 1.0000 L 0.88450.95010.90990.85930.76970.8583-0.84400.87490.8169 CVaR ratio H 0.49410.53960.37310.51900.13810.4919-0.36850.49440.18970.9655 1.0000 L 0.90640.93010.87060.90010.83970.9122-0.89780.91920.88500.9574 MVaR ratio H 0.16410.22790.21690.1814-0.04550.2295-0.32350.1554-0.02570.58310.6266 1.0000 L 0.73110.73070.68010.75850.76270.7917-0.77960.78610.76600.71950.7963 Sharpe Omega H 0.93040.94170.82940.90550.45700.9047-0.64220.86020.56270.56950.54760.2291 1.0000 L 0.94270.90900.98620.92920.80770.9008-0.87700.91620.85380.90650.86320.6574 Note: H = Funds with leverage at least equal to the mean value L = funds with leverage lower than the mean value Correlation of rankings for highly leveraged funds lower in the 77% of cases

15. Empirical analysis: results (4/6) Correlation among rankings (breakdown by volume) SharpeROPSROASSortino Kappa (n=3) Kappa (n=4) CalmarSterlingBurke VaR Ratio CVaR ratio MVaR ratio Sharpe Omega Sharpe H 1.0000 L ROPS H 0.8122 1.0000 L 0.8808 ROAS H 0.75390.7494 1.0000 L 0.95240.8959 Sortino H 0.91990.73810.7515 1.0000 L 0.97520.87050.9425 Kappa (n=3) H 0.64820.43990.68660.7554 1.0000 L 0.88930.76420.83370.9068 Kappa (n=4) H 0.93240.75950.76990.97860.7518 1.0000 L 0.96120.86910.91030.97380.9311 Calmar H -0.8137-0.6473-0.6631-0.8616-0.6289-0.8711 1.0000 L -0.9419-0.8649-0.8869-0.9449-0.9175-0.9779 Sterling H 0.85800.66010.81220.95120.81790.9351-0.8372 1.0000 L 0.97180.88190.92850.98530.91920.9862-0.9664 Burke H 0.83480.57500.74820.87890.86460.8693-0.78040.9104 1.0000 L 0.93990.81330.87260.94690.94980.9687-0.96590.9657 VaR ratio H 0.74110.81400.61520.75980.49110.7563-0.64580.69140.5592 1.0000 L 0.88450.95010.90990.85930.76970.8583-0.84400.87490.8169 CVaR ratio H 0.74110.73240.50920.79080.57800.7872-0.68040.71640.60860.9315 1.0000 L 0.90640.93010.87060.90010.83970.9122-0.89780.91920.88500.9574 MVaR ratio H 0.48780.46760.35360.48690.36040.5083-0.48840.46190.47800.63300.6396 1.0000 L 0.73110.73070.68010.75850.76270.7917-0.77960.78610.76600.71950.7963 Sharpe Omega H 0.83870.96220.77830.75210.42260.7557-0.61550.66520.56220.81370.71070.4324 1.0000 L 0.94270.90900.98620.92920.80770.9008-0.87700.91620.85380.90650.86320.6574 Note: H = Funds with volume at least equal to the mean value L = funds with volume lower than the mean value Funds less traded are frequently characterized by less coherence of rankings

16. Empirical analysis: results (5/6) Persistence analysis Time horizon 2001 - 20022002 - 20032003 - 20042004 - 20052005 - 20062006 - 20072007 -20082008 -2009 Sharpe Overall 08561421 19 HL 0221611109 LV 04005454 ROPS Overall 0856921 13 HL 0221411105 LV 04004454 ROAS Overall 087101821 HL 023381110 LV 04015455 Sortino Overall 08561421 20 HL 0221611109 LV 04005455 Kappa (n=3) Overall 07531621 15 HL 0121711108 LV 03005455 Kappa (n=4) Overall 08581621 19 HL 0222611109 LV 04005455 Notes: Funds are classified on the basis of the leverage and the volume identifying as high leverage the funds with leverage at least equal to the mean value and as low volume the funds with a volume of trade lower respect to the mean

17. Empirical analysis: results (6/6) Persistence analysis Time horizon 2001 - 20022002 - 20032003 - 20042004 - 20052005 - 20062006 - 20072007 -20082008 -2009 Calmar Overall 1000021 1 HL 0000011101 LV 10000451 Sterling Overall 08561621 HL 022161110 LV 04005455 Burke Overall 08561721 18 HL 0221711108 LV 04005455 VaR Ratio Overall 0836921 13 HL 0211411105 LV 04004454 CVaR ratio Overall 0816921 13 HL 0201411105 LV 04004454 MVaR ratio Overall 0828921 0 HL 0203311100 LV 04013450 Sharpe Omega Overall 0856921 13 HL 0221411105 LV 04004454 Notes: Funds are classified on the basis of the leverage and the volume identifying as high leverage the funds with leverage at least equal to the mean value and as low volume the funds with a volume of trade lower respect to the mean

18. Index  Introduction  Literature review  Empirical analysis:  Sample  Methodology  Results  Conclusions

19. The choice of risk measures more complete respect to the standard deviation affects not only the yearly ranking position of each fund but also the variability of rankings over time. Measures constructed on distribution of losses, on the maximum drawdown and looking also at the asymmetry of returns allow to achieve the highest level of raking persistence over time. Especially for less traded funds and/or highly leveraged ones, approaches normally adopted for analyzing the asset management industry had so to be revised in order to consider the specific characteristics of the real estate investment that do not allow to simplify the performance analysis assuming the normality of returns distribution. Conclusions

20. Claudio Giannotti University of LUM Casamassima e-mail: giannotti@lum.itgiannotti@lum.it Gianluca Mattarocci University of Rome Tor Vergata e-mail: gianluca.mattarocci@uniroma2.itgianluca.mattarocci@uniroma2.it Contact information