factor modelling of uk unlisted funds: panel data analysis of performance drivers kieran farrelly...

Factor Modelling of UK Unlisted Funds: Panel Data Analysis of Performance Drivers

Kieran FarrellyCBRE Investors & Henley Business School, University of Reading

& George Matysiak

Henley Business School, University of Reading

JUNE 2011

CB Richard Ellis Investors Global Multi Manager

Table of Contents

Research questions and objectives

Sources of risk and return in unlisted funds

Prior literature

Data

Panel unit root testing

Panel regression analysis

Conclusions and next steps


Research Questions & Objectives

CAPM (market model) is based on the assumption that there are no additional factors present which are correlated with the market return

Inclusion of other factors has been found to better explain the cross section of asset returns Ross (76): macroeconomic factors – Arbitrage Pricing Theory Fama & French (92), Jegadeesh & Titman (1993), Carhart (97) : fundamental factors – value/growth/momentum

Multifactor models employed extensively in equities for risk management and performance attribution purposes

Generally the property investment industry has been unable to quantify well the key sources of risk in property portfolios

Unlisted property funds have become a significant conduit in the real estate investment landscape

Purpose of this study is to identify which direct property portfolio and unlisted fund ‘structure’ characteristics/factors explain the cross section performance of unlisted property funds

End goal is to develop a multifactor model and subsequent portfolio management tool for understanding portfolio risk of both property funds/ and funds-of-funds


Sources of Risk and Return in Property Funds

Property FundRisk & Return

Portfolio Structure /

Market RiskStock Risk Fund Structure

Structure (market risk): Allocations to more volatile sectors Macro risks

Stock risk: Asset level (operating) leverage Risk continuum from ground rents to speculative

developments Age, structure

Fund Structure: Financial leverage risk where used Vehicle characteristics: age, structure, fees/costs


Prior Studies: Multifactor Modelling of Property Market/Portfolio/Fund Returns

Market Risk Macroeconomic factors (APT):

Ling & Naranjano (90,97) – per cap consumption, real govt bond yields, term structure, unexpected inflation Liow (94) – industrial production, unexpected inflation significant predictors of expected risk premia Marcato & Tira (10) – GDP, stock market

Property markets Pai & Geltner (07) location (Tier I & III location performance differential), Fuerst & Matysiak (08 ) - weighted direct market

return, IPF (11) – UK region exposure, property type tracking error/concentration

Stock risk – direct portfolio assets’ characteristics Yield – Fuerst & Marcato (09) high/low yield return differential, Bond & Mitchell (09) equivalent yield, IPF (11) relative

equivalent yield Size – Zieiring & McIntosh (99) – size positively related to risk and return, Pai & Geltner (07) + Fuerst & Marcato (09) -

performance differential between asset sizes, IPF (11) – average lot size, asset concentration Income: Pai & Geltner (07) - performance differential between assets with short/long ease lengths, IPF (11) - void rate,

covenant strength, % income from top 10 tenants Development/Vacancy: IPF (11)

Fund structure Financial leverage: Fuerst & Matysiak (08), Marcato & Tira (10), IPF (11) all found financial leverage to be significant Liquidity: Lee (00) found no evidence, Marcato & Tira (10) found evidence Cash exposure : Marcato & Tira (10) Style: Fuerst & Matysiak (08) – core/value added/opportunisitc styles impacted performance Performance Persistence: Fuerst & Matysiak (08), Marcato & Tira (10), IPF (11)


Dataset

Unique sample of UK unlisted funds Quarterly returns from 2003:Q4-2010:Q4 Good depth in terms of fund/portfolio characteristics (x

variables) Data runs over what we’d consider to be a full cycle

Sources: CBRE Investors database 2003:Q4 – 2004 Q3 – collated

by HSBC/IPD IPD UK Property Funds Vision data 2004:Q4 to 2010:Q4 Consistently collected data via quarterly questionnaire

Unbalanced panel with sample of funds with sufficient data points growing through time

Commences with data on 28 funds Maximum of 75 funds in any given period

Large proportion of the sample are open-ended funds and would be considered as having a core risk profile

– Both balanced/diversified and sector specialist vehicles

Source: IPD

UK Pooled Property Fund Indices Performance 2003:Q4 = 100


Sample Statistics

Mean Median

Maximu

m

Minimum Std. Dev Skew Kurtosis

3 Month Excess Returns -3.1% -1.3% 52.5% -61.3% 8.9% -0.86 9.41

Cash % Assets 5.4% 3.0% 45.7% -0.4% 6.4% 1.78 6.44

% Development (%ERV) 1.5% 0.0% 45.3% 0.0% 4.2% 5.23 38.39

Lease Length Conc 17.3% 12.8% 100.0% 0.4% 14.7% 2.44 11.37

Initial Yield 5.8% 5.7% 10.2% 1.6% 1.3% 0.21 3.06

Number of Assets 50.27 35.00 397.00 1.00 58.17 3.33 17.07

LTV 18.7% 8.0% 98.8% -0.3% 22.5% 1.04 2.89

Reversionary Yield 6.6% 6.4% 11.9% 2.6% 1.5% 0.50 3.65

OFFICE Exposure 26.6% 25.6% 100.0% 0% 27.5% 1.29 4.31

IPD PAS Concentration 36.0% 11.6% 100.0% 1.5% 36.9% 0.77 1.99

Rental Reversion 1.19 1.15 2.96 0.89 0.20 4.20 29.38

Void Rate 7.0% 5.9% 37.5% 0.1% 5.1% 1.62 7.42

% Top 10 Tenants 38.3% 36.0% 100.0% 8.8% 16.7% 0.69 3.25

0

100

200

300

400

500

600

700

-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6

Series: _3MTREXCSample 2003Q1 2010Q4Observations 2053

Mean -0.027756Median -0.011457Maximum 0.618576Minimum -0.865614Std. Dev. 0.087015Skewness -1.068804Kurtosis 14.15131

Jarque-Bera 11028.13Probability 0.000000

0

100

200

300

400

500

600

700

800

900

0.0 0.2 0.4 0.6 0.8 1.0

Series: LTVSample 2003Q1 2010Q4Observations 1880

Mean 0.191935Median 0.072660Maximum 0.988157Minimum -0.002544Std. Dev. 0.234371Skewness 0.966539Kurtosis 2.641104


0

40

80

120

160

200

240

280

0.02 0.04 0.06 0.08 0.10 0.12 0.14

Series: NIYSample 2003Q1 2010Q4Observations 1704

Mean 0.058339Median 0.057350Maximum 0.137800Minimum 0.015800Std. Dev. 0.012738Skewness 0.340379Kurtosis 3.835869


Histogram – 3 Month Excess Total Returns

Histogram – Initial Yield

Histogram – Loan to Value Ratio


Identifying Factors: Panel Approach

First stage of multifactor modelling is the identification of statistically significant factors

We have employed a panel data approach to do this

This approach allows us to identify and test parameters without restrictive assumptions e.g. do investment styles have differential impacts ?

Firstly we used a number of panel unit root tests to assess whether the variables are trend stationary

We then tested for the presence of fixed and/or random effects

Fixed effects: used when we want to control from omitted /unobserved variables whose impact will differ between cases

Random effects: used when we want to control from omitted /unobserved variables whose impact will have the same constant impact but vary randomly between cases. Hausman test used to assess whether random effects are present


Panel Unit Root Tests Summary

Panel unit root tests are statistically more powerful than individual unit root tests

Panel unit root tests show both yield variables and ‘number of assets’ are I(1)

Otherwise other variables can be deemed I(0)

Method

3 Month Excess

Returns Cash % Assets

Lease Length Conc

Initial Yield

Reversionary Yield

Number of Assets LTV

Office Exposure

IPD PAS Concentration

Reversionary Potential

Void Rate

% Top 10 Tenants

Null: Unit root (assumes common unit root process) Levin, Lin & Chu t* -2.66 -14.09 -2.57 -2.79 -2.20 0.04 -4.18 -3.90 -8.99 -1.59 -2.87 -2.56Prob 0.00 0.00 0.00 0.00 0.00 0.52 0.00 0.00 0.00 0.06 0.00 0.00

Null: Unit root (assumes individual unit root process)

Im, Pesaran and Shin W-stat -2.60 -8.69 -2.28 0.40 0.01 2.22 -3.66 -2.13 -7.57 -1.19 -3.39 -2.97Prob 0.00 0.00 0.01 0.65 0.51 0.99 0.00 0.02 0.00 0.12 0.00 0.00ADF - Fisher Chi-square 203.52 322.31 192.67 123.54 140.66 125.97 203.28 137.14 287.20 178.62 211.96 231.35Prob 0.03 0.00 0.01 0.93 0.61 0.91 0.00 0.00 0.00 0.06 0.00 0.00PP - Fisher Chi-square 232.05 506.31 291.37 88.90 124.49 164.80 198.49 186.35 324.68 212.84 263.59 392.51Prob 0.00 0.00 0.00 1.00 0.90 0.16 0.00 0.00 0.00 0.00 0.00 0.00

Conclusion I(0) I(0) I(0) I(1) I(1) I(1) I(0) I(0) I(0) I(0) I(0) I(0)


Fixed Effects Regression – 2004:Q1 to 2010:Q4

Dependent Variable: _3MTREXC Coefficient Std. Error t-Statistic Prob. C 0.05 0.03 1.76 0.083MTREXC(-1) 0.71 0.02 35.07 0.00LOAN TO VALUE(-1) 0.21 0.02 8.62 0.00% OFFICE EXPOSURE (-1) -0.13 0.04 -3.49 0.00REVERSIONARY POTENTIAL (-1) -0.08 0.02 -4.30 0.00% TOP 10 TENANTS (-1) 0.06 0.03 1.77 0.08R-squared 0.55 Mean dependent var -0.03Adjusted R-squared 0.53 S.D. dependent var 0.09S.E. of regression 0.06 Akaike info criterion -2.64Sum squared resid 5.68 Schwarz criterion -2.36Log likelihood 2077.72 Hannan-Quinn criter. -2.54F-statistic 22.17 Durbin-Watson stat 1.89Prob(F-statistic) 0.00

Fixed effects regression was found to be the appropriate – model has good explanatory power

Thus there are significant differences between funds and over time periods

Not surprising given there are a range of fund structures and styles

0

5

10

15

20

Cross Section Fixed Effect

Frequency

Distribution of Cross Section Fixed Effects


Panel GMM Regression

Dependent Variable: _3MTREXC

Panel Fixed Effects

Coefficient Prob. Panel GMM Coefficients 1 Prob.

Panel GMMCoefficients 2 Prob.

C 0.05 0.083MTREXC(-1) 0.71 0.00 0.67 0.0000 0.69 0.0000

LOAN TO VALUE(-1) 0.21 0.00 0.26 0.0005 0.25 0.0002

% OFFICE EXPOSURE (-1) -0.13 0.00 -0.22 0.0177 -0.24 0.0076REVERSIONARY POTENTIAL (-1) -0.08 0.00 -0.10 0.0061 -0.07 0.0273% TOP 10 TENANTS (-1) 0.06 0.08 0.04 0.5550Mean dependent var -0.03 -0.00 -0.00S.D. Dependent var 0.09 0.05 0.05S.E. of regression 0.06 0.08 0.08Sum squared resid 5.68 9.84 10.17

• As there is a lagged dependent variable (momentum) in the preferred specification we have used the Panel GMM estimator

• Coefficients magnitude have changed though signs and significance remain for 4 of the variables - but %top ten tenants variable is no longer significant (but note that Arellano-Bond standard errors can be very unreliable!)

• Second GMM discards this variable and significant variables remain similar


Provisional Conclusions

Identified the key fundamental factors which best determine the cross section of unlisted property funds over time

Factors found amongst what we consider to be the three key sources of risk-returns in funds

Presence of fixed effects points to differences across funds and over time

Next steps:

Continue to test additional factors

Creation of ‘factor returns’ via cross section regressions

Use these as a basis for estimating a factor covariance matrix which can then be used to create portfolio construction/optimisation tools

Risk budgeting via factors

These will also be used for performance attribution purposes

Estimate asymmetric impacts of factors upon performance

factor modelling of uk unlisted funds: panel data analysis of performance drivers kieran farrelly...

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