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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: gabriele.sampagnaro@uniparthenope.it. 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
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