the quality of data of real estate direct market: does the lack of standardization affect the...

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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: [email protected] . Phone +39 0815474851 1

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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: [email protected]. Phone +39 0815474851

1

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.

2

Number of data sources: 4

Object of information: property values

Real estate categories:

Geographical/Urban area: 1) Milan2) Italy

Data frequency: quarterly (by interpolation)

Minimum time interval: 2002-2007Maximum time interval: 1993-2007

Nomisma, OSMI, Tecnocasa, Scenari ImmobiliariMilan

• Residential• Commercial• Industrial• Office

Time series features

3

Table 1. Real estate time series length and urban area

Real Estate market: Italy

Real Estate Category

DataSource#1 DataSource#2 DataSource#3 DataSource#4

Residential 1988-2007 1997-2007 n.a 2002-2007

Commercial 1988-2007 1997-2007 n.a 2002-2007

Office 1988-2007 1997-2007 n.a 2002-2007

Industrial n.a. 1997-2007 n.a 2002-2007

Real Estate market: Milan

Real Estate Category DataSource#1 DataSource#2 DataSource#3 DataSource#4

Residential 1965-2007 1993-2007 1995-2007 n.a

Commercial 1965-2007 1993-2007 1997-2007 n.a

Office n.a. 1993-2007 1997-2007 n.a

Industrial n.a. 1993-2007 1997-2007 n.a

Semiannual data are interpolated to provide quarterly data

Real Estate Data Composition: A MAP

4

Residential Office Commercial Industrial

Time Series#1

MEAN 223.1** Standard Deviation (69.5)

180.9** (46.7)

168.6**

(40.9)N. A.

Time Series#2

218.3**

(82.8)

127.6 ** (27.3)

131.0**

(27.1)

119.2**

(18.0)

Time Series#3

108.5** (6.4)

108.2**

(4.3)

N. A..

104.0**

(3.1)Time

Series#4 N. A. N. A. N. A. N. A.

Geographical Area: ITALY Time interval: 2002-2007

5

Real Estate Data Divergence : preliminary results

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).

Residential Office Commercial Industrial

DataSource#1MEAN 139.0**

Standard Deviation (39.8)N. A.

144**

(32.3)N. A.

DataSource#2115.9**

(28.2)

103.5**

(20.4)

116.9**

(28.4)N. A.

DataSource#3128.9**

(33.4)

136.1**

(26.4)

135.2** (26.6)

108.4** (15.0)

DataSource#4161.4**

(45.3)N. A.

121.6**

(25.2)N. A.

Geographical Area: Milan

Real Estate Data Divergence : preliminary results

Time interval: 1997-2007

6

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.

RESIDENTIALData

Source#1Data

Source#2Data

Source#3

DataSource#1 1

DataSource#2 0.2700 1

DataSource#3 -0.6722 -0.6982 1

OFFICEData

Source#1Data

Source#2Data

Source#3

DataSource#1 1

DataSource#2 0.6233 1

DataSource#3 0.5508 0.7212 1

INDUSTRIAL DataSource#2

DataSource#3 0.4401

COMMERCIAL DataSource#1DataSource#2 -0.0867

Average Correlation= -0.3668

Average Correlation= 0.6318

Real Estate Data Divergence : correlation analysis

7

Geographical Area: ITALY

RESIDENTIAL Series#1 Series#2 Series#3 Series#4

Series#1 1

Series#2 0.452** 1

Series#3 0.583** 0.893** 1

Series#4 -0.239 0.264 0.497** 1

Commercial Series#1 Series#2 Series#3 Series#4

Series#1 1

Series#2 0.3524** 1

Series#3 0.6421** 0.4125** 1

Series#4 -0.0188 -0.1623 -0.2098

OFFICE Series#2

Series#3 0.7484**

Average Correlation

= -0.408

Average Correlation

= 0.169

Real Estate Data Divergence : correlation analysis

8

Geographical Area: MILAN

Real Estate Data Divergence : ratio analysis

To investigate around the severity of the differences among data source we employed a returns ratio test. 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: 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

m

i i

ixy Y

X

mR

1

1

Real Estate Data Divergence : preliminary results

Geographical Area: ITALY

Time interval: 2002-2007

COMMERCIALIntra-Class average Ratio

= 1.250

RESIDENTIALIntra-Class average Ratio

= 1.512

OFFICEIntra-Class average Ratio

= 1.382

11

Real Estate Data Divergence : preliminary results

Geographical Area: MILAN

Time interval: 2002-2007

COMMERCIALIntra-Class average Ratio

= 1.173

RESIDENTIALIntra-Class average Ratio

= 1.055

OFFICEIntra-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 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

Urban Area Intra-Class Average Ratio

Residential Commercial Office

Italy 1.512 1.250 1.382

Milan 1.055 1.173 1.371

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

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.

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.

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

Figure 3. Summary of cointegration analysis results

Real Estate Data Divergence : cointegration analysis

15

Real Estate Data Divergence : cointegration analysis

Table 6

RESIDENTIAL–Italia (1997/jan-2008/jan) - data quarterly - Levels/Regression

R2 Adj R β t-ratio p-value0.948 0.947 1.59 28.47 0.000

Cointegration Analysis

Residual Based Test Test statistic Value Critical valueCRDWa DW 0.052 1.03ADFb ADF-t -0.744 (lag 1) -3.5136PP c Z Zt -2.33 -1.56 -19.42 -3.52aCritical Values are reported in Engle and Yoo (1987)bCritical Values for ADF are from MacKinnon (1991). The lag length was chosen according to Schwartz criterioncCritical values are taken from Philips and Ouliaris (1990)

tuSERIESERIE 1#ln2#ln

Table 9

RESIDENTIAL–Italia (1997/jan-2008/jan) - data quarterly – 1st differences/regression

R2 Adj R β t-ratio p-value 0.0739 0.0724 0.634 1.83 0.074

Cointegration Analysis Residual-based test Test statistic Value Critical value

CRDWa DW 0.495 1.03 ADFb ADF-t -2.545 (lag 1) -3.5136 PP c Z Zt -18.2 -3.47 -19,34 -3.52 aCritical Values are reported in Engle and Yoo (1987)bCritical Values for ADF are from MacKinnon (1991). The lag length was chosen according to Schwartz criterioncCritical values are taken from Philips and Ouliaris (1990)

tuSERIESERIE 1#ln2#ln

Residual-based test for cointegration

between DATA-Source

#1 and #2

RESIDENTIAL-ITALY

16

Table 7

Uffici –Italia (1997/jan-2008/jan) - data quarterly - Levels/Regression

R2 Adj R β t-ratio p-value 0.9863 0.986 0.916 56.32 0.000

Cointegration Analysis Residual-based test Test statistic Value Critical value

CRDWa DW 0.1261 1.03 ADFb ADF-t -3.264 (lag 3) -4.3993 PP c Z Zt -5.91 -1.72 -19.42 -3.52 aCritical Values are reported in Engle and Yoo (1987)bCritical Values for ADF are from MacKinnon (1991). The lag length was chosen according to Schwartz criterioncCritical values are taken from Philips and Ouliaris (1990)

tuSERIESERIE 1#ln2#ln

Table 10

Uffici –Italia (1997/jan-2008/jan) - data quarterly – 1st differences/regression

R2 Adj R β t-ratio p-value0.388 0.374 0.745 5.23 0.372

Cointegration Analysis

Residual-based test Test statistic Value Critical valueCRDWa DW 0.878 1.03ADFb ADF-t -3.195 -3.5136PP c Z Zt -18.1 -3.306 -19.34 -3.52aCritical Values are reported in Engle and Yoo (1987)bCritical Values for ADF are from MacKinnon (1991). The lag length was chosen according to Schwartz criterioncCritical values are taken from Philips and Ouliaris (1990)

tuSERIESERIE 1#ln2#ln

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

Table 8

Negozi –Italia (1997/jan-2008/jan) - data quarterly - Levels/Regression

R2 Adj R β t-ratio p-value 0.953 0.952 0.818 29.88 0.000

Cointegration Analysis

Residual-based test Test statistic Value Critical value CRDWa DW 0.104 1.03 ADFb ADF-t -2.946 (lag 1) -3.5136 PP c Z Zt -5.03 -1.53 -19.42 -3.52 aCritical Values are reported in Engle and Yoo (1987)bCritical Values for ADF are from MacKinnon (1991). The lag length was chosen according to Schwartz criterioncCritical values are taken from Philips and Ouliaris (1990)

tuSERIESERIE 1#ln2#ln

Table 11

Negozi –Italia (1997/jan-2008/jan) - data quarterly – 1st differences/regression

R2 Adj R β t-ratio p-value0.075 -0.0156 -0.116 -0.57 0.571

Cointegration AnalysisResidual-based test Test statistic Value Critical valueCRDWa DW 0.647 1.03ADFb ADF-t -2.908 (lag 4) -4.76906PP c Z Zt -16.1 -3.13 -19.34 -3.52aCritical Values are reported in Engle and Yoo (1987)bCritical Values for ADF are from MacKinnon (1991). The lag length was chosen according to Schwartz criterioncCritical values are taken from Philips and Ouliaris (1990)

tuSERIESERIE 1#ln2#ln

Residual-based test for cointegration

between DATA-Source

#1 and #2

COMMERCIAL-ITALY

18

National timeseries

CategoryQ test**

(correlogram test)

ADF test **Lag lengthRt 1Rt 2Rt

Series#1 retailsignificant

autocorrelation coefficientsstationary / / 1°

Series#1 officesignificant

autocorrelation coefficientsstationary / / 2°

Series#1commercia

lsignificant

autocorrelation coefficientsstationary / / 3°

Series#2 retailsignificant

autocorrelation coefficientsnot stationary stationary / 3°

Series#2 officesignificant

autocorrelation coefficientsnot stationary stationary / 3°

Series#2commercia

lsignificant

autocorrelation coefficientsstationary / / 1°

Series#2 industrialsignificant

autocorrelation coefficientsnot stationary stationary / 5°

Series#3 retailsignificant

autocorrelation coefficientsnot stationary

not stationary

stationary 1°

Series#3 officesignificant

autocorrelation coefficientsnot stationary stationary / 0

Series#3 industrialsignificant

autocorrelation coefficientsnot stationary stationary / 0

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 calculationThe implication on the IRR fund calculation

The implication on asset management processes.The implication on asset management processes.

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

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 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 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 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 stepsThe simulation is articulated in 4 steps

20

The impact of time series heterogeneity on the IRR funds: a simulation.

21

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”.

Figure 1. Characteristics of the hypothetic (and ultra-simplified) real estate fund. · Number of properties: 2 (A e B); · Time horizon: 5 ys (t0t5)

Unknown variable

Date of investment

Date of liquidation

Initial Price

Annual Rental

Costs End Value

Property A t0 t5 100 1 0 ?

Property B t0 t5 200 2 0 ?

The impact of time series heterogeneity on the IRR funds: a simulation.

1. Identification of the subperiods upon which the simulation is run

Backtesting is composed of four logical steps:

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/20055th) 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

Example Jan/1998 Dec/2002 Property Values

Data Source #1 120 180 T0= 100 T5= 15022

The impact of time series heterogeneity on the IRR funds: a simulation.

Figure 2. Sensitivity analysis of end values and IRR for a hypothetical real estate investment fund. End Values of the Fund

Sub- periodjan/1998-dic/2002

jan/1999-dic/2003

jan/2000-dic/2004

jan/2001-dic/2005

jan/2002-dic/2006

jan/2003-dic/2007

SDSB*

Data Source #1 413.3 409.1 409.3 414.2 399.2 388.9 2.41%Data Source #2 433.3 691.7 565.2 453.1 433.3 339.8 25.47%Data Source #3 416.6 444.4 413.3 389.9 364.5 345.8 9.19%SDDS** 2.5% 29.9% 19.2% 7.6% 8.6% 7.5%

Internal Rate of Return (IRR) of the fund

Sub periodjan/1998-dic/2002

jan/1999-dic/2003

jan/2000-dic/2004

jan/2001-dic/2005

jan/2002-dic/2006

jan/2003-dic/2007

SDSB*

Data Source #1 18.1% 17.9% 17.9% 18.1% 17.5% 17.0% 2.49%

Data Source #2 19.0% 28.3% 24.1% 19.8% 19.0% 14.6% 22.87%Data Source #3 18.2% 19.4% 18.1% 17.0% 15.8% 14.9% 9.67%SDDS** 2.5% 25.6% 17.6% 7.6% 9.0% 8.3%

*SDSB: Standard Deviation among Sub-Periods **SDDS: Standard Deviation among Data SourcesSDSB and SDDS are expressed as percentage of mean value

23

Figure 2. Sensitivity analysis of end values and IRR for a hypothetical real estate investment fund. End Values of the Fund

Sub- periodjan/1998-dic/2002

jan/1999-dic/2003

jan/2000-dic/2004

jan/2001-dic/2005

jan/2002-dic/2006

jan/2003-dic/2007

SDSB*

Data Source #1 413.3 409.1 409.3 414.2 399.2 388.9 2.41%Data Source #2 433.3 691.7 565.2 453.1 433.3 339.8 25.47%Data Source #3 416.6 444.4 413.3 389.9 364.5 345.8 9.19%SDDS** 2.5% 29.9% 19.2% 7.6% 8.6% 7.5%

Internal Rate of Return (IRR) of the fund

Sub periodjan/1998-dic/2002

jan/1999-dic/2003

jan/2000-dic/2004

jan/2001-dic/2005

jan/2002-dic/2006

jan/2003-dic/2007

SDSB*

Data Source #1 18.1% 17.9% 17.9% 18.1% 17.5% 17.0% 2.49%

Data Source #2 19.0% 28.3% 24.1% 19.8% 19.0% 14.6% 22.87%Data Source #3 18.2% 19.4% 18.1% 17.0% 15.8% 14.9% 9.67%SDDS** 2.5% 25.6% 17.6% 7.6% 9.0% 8.3%

*SDSB: Standard Deviation among Sub-Periods **SDDS: Standard Deviation among Data SourcesSDSB and SDDS are expressed as percentage of portfolio mean value

24

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 calculationThe implication on the IRR fund calculation

The implication on asset management processes.The implication on asset management processes.

We perform a portfolio optimization with the following five asset class:

Portfolio risk

Expect

ed R

etu

rn (

ER

)

Efficient portfoliosEfficient portfolios

Data optimizationData optimization

Time interval: 1997/2007

Frequency data: quarterly

ER: annual historical returns mean

Risk: Standard Deviation26

The efficient frontier case

JP Morgan GBI Global

Index

S&p500

Italian Treasury

Bond Dow Jones Eurostoxx 50

Italian Real Estate index

4 residential indices (italy)

retu

rns

risk

Frontier Cwith RE

Frontier Bwith RE

Frontier Awithout RE

BMax

AMax PP

CMin

BMin

AMin PPP

CMaxP

From A to B : “sling effect”

From A to C: “raising effect”

Benefit from inclusion of an asset class not correlated

Min Max

27

A Measure of BenefitChange in Mean Risk Adjusted Performance of Frontier (MeRAPF)

N

i i

iR

N 1

*1MeRAPF

MeRAPFdecileN 10 re

turn

s

riskDeciles Portfolio

(AB)

Deciles Portfolio (AC)

Port. Var. M

ax.

(AC)

PD1 PD2 PD3 PD4 PD5 PD6 PD7 PD8 PD9 PD10

Frontier Cwith RE

Frontier B

with RE

Frontier Awithout RE

BMax

AMax PP

CMin

BMin

AMin PPP

CMaxP

28

16

4

6

12

14

10

8

1050 15 20 25Risk (%)

Ann

ua

lized

Re

turn

(%

)

100% DJ EuroSTOXX50

100% GBI global index

100% Italian Gov. Bond short term

100% S&P500

100% Residential Index

Efficient frontier with real estate data source#1

Efficient frontier with real estate data source#2

Efficient frontier with real estate data source#3

Efficient frontier with real estate data source#4

#1

#2#3#4

29

The efficient frontiers set

Portfolio composition: does the real estate indexes selection affects the asset allocation?

Portfolio composition: does the real estate indexes selection affects the asset allocation?

Data Source #1

Risk

Risk

Porf

olio

weig

hts

Porf

olio

weig

hts Data

Source #2

30

Portfolio composition: does the real estate indexes selection affects the asset allocation?

Portfolio composition: does the real estate indexes selection affects the asset allocation?

Porf

olio

weig

hts

Porf

olio

weig

hts

Data Source #3

Data Source #4

31

Efficient frontier with..Benefit from Real Estate inclusion?

MeRAP

Data Source #1 YES +76%

Data Source #2 YES +42%

Data Source #3 YES +25%

Data Source #4 YES +39%

This finding are explained by the diversification power own by real estate assets.

A lower risk than the other asset class

A lower risk than the other asset class

A high expected returns, due to the market growth (bubble?)

A high expected returns, due to the market growth (bubble?)

A low correlation with the other asset class

A low correlation with the other asset class

The role of portfolio diversifier may be mainly explained by:

32

Summary and conclusions

33

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.

Summary and conclusions

34

Thank You

Gabriele [email protected]