Francesca Battaglia, Claudio Porzio Gabriele Sampagnaro

1. The quality of data of real estate direct market: does the lack of standardization affect the predictability of returns? Francesca Battaglia, Claudio Porzio Gabriele Sampagnaro Department of research in Business and Finance at University Parthenope, Via Medina 40, Naples 80133, Italy; Email: gabriele.sampagnaro@uniparthenope.it. Phone +39 0815474851

2. Aim of the paper The aim of the paper is an investigation on the reliability of historical returns for the Italian property market, where the quality of information seems not standardized. In Italy, such as for many other countries, the returns’ indices for direct markets are provided by several data-sources that differ among them in terms of methodology adopted (appraisal-based vs transaction-based approaches) and in term of index’s composition. These differences produce a lack of informative standardization that could negatively affects the predictability of market and that can be explained by a strong real estate market’s fragmentation, as well as informative and market’s organizational inefficiency. In our paper we examine the implications of this lack of standardisation around some topic such as: IRR of a fund, asset allocation and portfolio management.

3. Time series features Number of data sources: 4 Nomisma, OSMI, Tecnocasa, Scenari Immobiliari Milan Object of information: property values Real estate categories: • Residential • Commercial • Industrial • Office Geographical/Urban area: 1) Milan 2) Italy Data frequency: quarterly (by interpolation) Minimum time interval: 2002-2007 Maximum time interval: 1993-2007

4. Real Estate Data Composition: A MAP

5. Real Estate Data Divergence : preliminary results Time interval: 2002-2007 Geographical Area: ITALY The table shows a significant difference among the average values of the indices and among the real estate categories covered. This result can be considered as a preliminary indication of a lack of data sources, although they are referring to the same phenomenon (the italian real estate market).

6. Real Estate Data Divergence : preliminary results Time interval: 1997-2007 Geographical Area: Milan So we get the same result for the market of Milan. In these cases, the differences are smaller. A possible explanation for this minor discrepancy, it might be provided by the increased centralization of information (for the city of Milan) and a greater homogeneity of the sample of properties underlying each index. In the previous case the indices were constructed with reference to the use of samples belonging to different urban areas.

7. Real Estate Data Divergence : correlation analysis Geographical Area: ITALY Average Correlation = -0.3668 Average Correlation = 0.6318

8. Real Estate Data Divergence : correlation analysis Geographical Area: MILAN Average Correlation = -0.408 Average Correlation = 0.169

9. Real Estate Data Divergence : ratio analysis To investigate around the severity of the differences among data source we employed a returns ratio test. Specifically, the ratio R was calculated as the ratio between two comparable series: (where: X and Y: are time series provided by different source but related to the same real estate category. m: is the length of time series, Interpretation: the closer the ratio gets to one, the closer the two series analyzed are statistically equal; conversely, the further the ratio gets away from one, the less homogeneous the series are. Since it is certainly important to verify the significance of the relationship between the two series, it was decided to test the null hypothesis H0: ratio = 1 by using the F test

10. Real Estate Data Divergence : preliminary results Geographical Area: ITALY Time interval: 2002-2007 COMMERCIAL Intra-Class average Ratio = 1.250 RESIDENTIAL Intra-Class average Ratio = 1.512 OFFICE Intra-Class average Ratio = 1.382

11. Real Estate Data Divergence : preliminary results Geographical Area: MILAN Time interval: 2002-2007 COMMERCIAL Intra-Class average Ratio = 1.173 RESIDENTIAL Intra-Class average Ratio = 1.055 OFFICE Intra-Class average Ratio = 1.371

12. Real Estate Data Divergence : ratio analysis The ratio analysis provide an equivalence test among the real estate categories data provided by 4 data source available. The closer the ratio gets to one, the closer the two series analyzed are statistically equal and viceversa the results show a more marked difference for the data pertains to Italy. Probably one of the reasons is the increased centralization of the information provided in a single urban area (milan) are compared with a wider (italy) and more geographically dispersed

13. Real Estate Data Divergence : cointegration analysis The results from previous section, especially those referred to the national indices, support the conception of an inefficient informative real-estate market that requires information to become centralized and data collection methods to be standardised. To confirm this, it would be necessary to implement a further level of investigation and testify the existence of a long-term relationship that. With this aim, we perform a cointegration between the historical series referring to the entire domestic market. In order to take advantage of the wide breadth of property values for each of the historical series, the historical series with observation time-intervals less than seven years were excluded from the cointegration analysis. Imposing this selection criterion, resulted in six historical series originating from two real-estate sources (Source #1-Italy and Source #2-Italy) linked to the residential, shop and office sectors.

14. Real Estate Data Divergence : cointegration analysis Figure 3. Summary of cointegration analysis results

15. Real Estate Data Divergence : cointegration analysis Residual-based test for cointegration between DATA-Source #1 and #2 RESIDENTIAL-ITALY

16. Real Estate Data Divergence : cointegration analysis Residual-based test for cointegration between DATA-Source #1 and #2 OFFICE-ITALY

17. Real Estate Data Divergence : cointegration analysis Residual-based test for cointegration between DATA-Source #1 and #2 COMMERCIAL-ITALY

18. Real Estate Data Divergence : Stationarity analysis

19. Real Estate Data Divergence: implications Which are the main implication of a divergence in real estate data? We analyze this question through an investigation of two topics The implication on the IRR fund calculation 1st The implication on asset management processes. 2nd

20. The impact of time series heterogeneity on the IRR funds: a simulation. To assess the impact upon the management of real estate funds that arises from the existence of divergence among historical time series, we perform be assessed following the performance of a backtesting on the IRR The starting data of the simulation are formed from 3 historical series relative to valorisation indices of nominal real estate in the commercial sector of Milan and are supplied by 3different providers. The central idea of the simulation is to subject the IRR to a “what if” analysis. The What if analysis is performed through a variation of the final value of a hypothetical real estate fund according the trend captured by each one of the 3 data source used. The simulation is articulated in 4 steps

21. The impact of time series heterogeneity on the IRR funds: a simulation. Backtesting is composed of four logical steps: 1. Identification of the subperiods upon which the simulation is run 2. Evaluation of the properties’ liquidation values based on the rate of capitalization that is implicit to the historical series used 3. Calculation of the fund’s IRR for each subperiod 4. Evaluation of the standard deviation of the IRR “ among periods” and “among information sources”.

22. The impact of time series heterogeneity on the IRR funds: a simulation. Backtesting is composed of four logical steps: 1. Identification of the subperiods upon which the simulation is run The simulation provides for the selection of 6 subperiods with a length of five years (each one separated from the previous by one year) 1st) Jan/1998‐Dec/2002; 2nd) Jan/1999‐Dec/2003; 3rd) Jan/2000‐Dec/2004; 4th) Jan/2001‐Dec/2005 5th) Jan/2002‐Dec/2006; 6th) Jan/2003‐Dec/2007. 2. Evaluation of the properties’ liquidation values based on the rate of capitalization that is implicit to the historical series used For each sub-period we maintain constant the income flows (rents), while we measure the final value of the properties as result of the capitalization rate implicit to that sub period and, much important, to that specific data source

25. Real Estate Data Divergence: implications Which are the main implication of a divergence in real estate data? We analyze this question through an investigation of two topics The implication on the IRR fund calculation 1st The implication on asset management processes. 2nd

26. The efficient frontier case We perform a portfolio optimization with the following five asset class: Italian Treasury Bond S&p500 4 3 JP Morgan GBI Global Index 1 Dow Jones Eurostoxx 50 2 Italian Real Estate index 5 4 residential indices (italy) Efficient portfolios Expected Return (ER) Data optimization Time interval: 1997/2007 Frequency data: quarterly ER: annual historical returns mean Risk: Standard Deviation Portfolio risk

27. Frontier A without RE Frontier B with RE Frontier C with RE Benefit from inclusion of an asset class not correlated returns Min Max risk From A to B : “sling effect” From A to C: “raising effect”

28. returns Frontier A without RE Frontier B with RE Frontier C with RE Deciles Portfolio (AC) Port. Var. Max. (AC) PD1 PD2 PD3 PD4 PD5 PD6 PD7 PD8 PD9 PD10 risk Deciles Portfolio (AB) A Measure of Benefit Change in Mean Risk Adjusted Performance of Frontier (MeRAPF)

29. Efficient frontier with real estate data source#1 The efficient frontiers set Efficient frontier with real estate data source#2 16 Efficient frontier with real estate data source#3 #1 Efficient frontier with real estate data source#4 14 #2 #4 #3 12 Annualized Return (%) 10 100% Residential Index 8 100% S&P500 100% DJ EuroSTOXX50 6 100% GBI global index 100% ItalianGov. Bond short term 4 0 5 10 15 20 25 Risk (%)

30. Portfolio composition: does the real estate indexes selection affects the asset allocation? Italian Gov.Bond short term Data Source #1 JPM GBI Glob. Ind.x Porfolio weights S&P500 DJ Eurostoxx 50 Residential Index Risk Data Source #2 Porfolio weights Risk

31. Portfolio composition: does the real estate indexes selection affects the asset allocation? Italian Gov.Bond short term Data Source #3 JPM GBI Glob. Ind.x Porfolio weights S&P500 DJ Eurostoxx 50 Residential Index Data Source #4 Porfolio weights

32. Summary and conclusions This finding are explained by the diversification power own by real estate assets. The role of portfolio diversifier may be mainly explained by: A low correlation with the other asset class A lower risk than the other asset class A high expected returns, due to the market growth (bubble?)

33. Summary and conclusions In Italy the providers of real estate data adopt different approaches of construction of the real estate indexes The differences have been investigated with some statistical instruments each of which show a lack of homogeneity among data, especially among the first differences of log value (the returns). The lack of standardization of real estate data produce a potential bias inside the assessment process of real estate investments. In particular, we pay attention to how the lack of homogeneity involve the IRR forecasts of an hypothetical real estate funds and ii) how it impact on the asset allocation decisions in a efficient frontiers framework. All the results of our investigation induce the opinion that the Italian real-estate information systems are not at all adequate and standardized. However, some caveat could be referred to the imperfect synchronization of some data or to the irrational speculative bubble that has charactized some Italian urban area.

34. Thank You Gabriele Sampagnaro gabriele.sampagnaro@uniparthenope.it