Are portfolio diversification criteria useful for hotel investments?

Evidence from Italian market

Claudio Giannotti, University LUM Casamassimagiannotti@lum.it

Gianluca Mattarocci, University of Rome “Tor Vergata” gianluca.mattarocci@uniroma2.it

Luca Spinelli, University of Rome “Tor Vergata”luca.spinelli@uniroma2.it

Stockholm, June 26th, 2009

Introduction

Literature review

Empirical analysis:

Sample

Methodology

Results

Conclusions

Agenda

Introduction (1/2)

During the last years, the hotel sector has been interested by an important development mainly in certain Countries, such as Italy, demonstrating a growing ability in obtaining positive results (Jones Lang Lasalle Hotels, 2009).

Basically, literature focuses on two aspects of this sector: advantages related to the inclusion of hotels in a diversified

multi-asset portfolio (Quan, Li and Sehgal, 2002); performance correlations (Jang e Yu, 2002).

… it lacks studies determining some guidelines for the investors to define the number of hotels and the criteria to build an optimal portfolio in the mean-variance approach.

Introduction (2/2)

The research questions of the paper …

Is geographical and sectorial diversification useful for hotel investments? What is the impact of concentration constraints on the performance of an hotel portfolio? Are performances persistent over time for the hotel portfolios constructed with the Markowitz approach? Do concentration constraints increase the performance persistence of an optimal portfolio?

Agenda

Introduction

Literature review

Empirical analysis:

Sample

Methodology

Results

Conclusions

Literature review (1/6)

Sectorial and geographic segmentation represents a general principle to diversify an investment portfolio also in the property market (Byrne and Lee, 1999; McMahan, 1981).

The following table identifies the main factors that explain the usefulness of the diversification in the hotel sector:

Geographical diversification Sectorial diversification

Weather (Aall e Hoyer, 2005)

Country (Poirer, 1997)

Fashion trend (Crouch, 1995)

Economic trend (Canina and Carvell, 2005)

Seasonality (Butler, 2001)

Quality of the service offered (Baum and Haveman, 1997)

Typology of customers served (Baker and Collier, 1999)

Literature review (2/6)

The demand could be strongly influenced by climatic variable because the serviceability for the customer is partially linked to the coherence of weather conditions in respect of his expectations (Aall and Hoyer, 2005).

Political instability and social problems can make more complex the investment management, increasing the randomness of profits and avoiding a clear estimation of external rules for the scenario where the manager has to work (Poirer, 1997).

Geographical diversification

Literature review (3/6)

The demand of hotels located in a specific area may be influenced by irrational components (fashion trend), causing anomalous and unexpected variations in investment profitability (Crouch, 1995).

The demand of service provided to customer base is strictly related to the general economic trend and to the trend of the specific area in which the hotel is located. Profits prospectives related to the hotel activity appear susceptible to the contest conditions change.

In the same country, the relation depends on the socio-economic characteristics belonging to the population served by the hotel (Canina and Carvell, 2005).

Literature review (4/6)

The seasonality is mainly related to retail customer for which the service demand, for climatic or institutional matters, may vary in times of year.

The weather aspect depends, above all, on atmospheric/meteorological conditions (temperature degree, rains, snow, etc…) whereas the institutional component depends on several social rules involving potential clients and influencing the service demand (religious festivals, holidays, etc…) (Butler, 2001).

The services supply for business customer base may allow to reduce the seasonality risk.

Literature review (5/6)

Sectorial diversification

The hotel market is characterized by a strong segmentation of the demand in relation to potential client’s expectations (Baum and Haveman, 1997) and, thereby, on equal geographic area, an average expenses deemed suitable for a potential user may change remarkably (Baum and Metzias, 1992).

The classification upon the typology of served customers (business and retail customers) is based on differences existing in occupancy rate dynamics, in occupation period, in pricing policies e in overbooking risk (Baker and Collier, 1999).

Literature review (6/6)

Performance measures

h

i

t

ttittt venues

hvPOR

0

1

0

10Re

1Re

h

i

t

ttittt venues

nvPAR

0

1

0

10Re

1Re

n

i

t

ttitittt Costsvenues

nGOPPAR

0

1

0

10Re

1

Legendh = number of occupied rooms in a given periodn = total of available roomsRe venues it= Revenues from the occupation of i-th room during the t-th periodCosts it= costs (fixed, semi fixed and variable) referred to the i-th room available in the t-th period

Liberatore (2001)

Enz and Canina (2002)

Brown and Dev (1999)

Agenda

Introduction

Literature review

Empirical analysis:

Sample

Methodology

Results

Conclusions

Empirical analysis: the sample (1/2)

- Sample composition -

Sectorial classification

0 20 40 60 80 100 120 140 160 180 200

2004

2005

2006

2007

2008

Agrigento B ergamo B ologna B res cia C atania F irenze G enova Mes s ina Milano ModenaNapoli P adova P alermo P isa R oma S ass ari S iena T orino T rapani T ries teVenezia Verona Vicenza

Geographic area classification

Source: AICA data processed by authors Source: AICA data processed by authors

Database: members of the Italian Association of Hotel Chains (AICA) (265 hotels)Frequency of data: monthly Time period: 2004-2008

The main assumptions …

Data referred to mean daily revenue per available room and to mean daily occupation rate for each month have been provided by AICA.

The costs of the service offered have been extrapolated by the consolidated balance sheet of AICA (the original database did not contain this type of information) and converted in daily values.

Every cost item has been classified into fixed, semi fixed or variable category on the basis of their sensitivity to service demand (Graham and Harris, 1999).

Empirical analysis: the sample (2/2)

The choice of the best opportunities in the hotel sector has been analyzed using a standard approach to evaluate financial investments: the efficient frontier (Markowitz, 1952).

On the basis of data available, monthly GOPPAR has been calculated using the formula:

For hotels of a certain category (i.e. four stars) in a specific area (i.e. Rome) the average GOPPAR, on a year time horizon, and the standard deviation of monthly GOPPAR have been calculated:

Empirical analysis: methodology (1/4)

stttt

stt

stt

rstt

n

i

t

tt

rsit

rsit

rsj VCPORSeasonSFCPARFCPARREVPARCostsvenues

nGOPPAR 101010

01010

1

0

Re1

12

112

1

j

rsj

rsmonthly GOPPARGOPPARE

12

1

2

12

1

j

rsmonthly

rsj

rsmonthly GOPPAREGOPPARGOPPAR

Return and risk measures estimated for each year are used to construct efficient frontiers, using a standard maximizing procedure.

A preliminary analysis considers the distance of each asset class (represented by all the hotels of the same category in the same geographical area) respect to the more efficient portfolios, in order to define if diversification allows to achieve better results.

An analysis of the efficient portfolio composition is also released in order to evaluate the degree of concentration of the best portfolios identified.

Empirical analysis: methodology (2/4)

The persistence of the performance over time has been studied, analyzing at t time, the distance of a portfolio, that was on the efficient frontier at t-i time, with the efficient portfolio at t time.

It is important since the difficulty to modify the investment strategy in the property and hotel market during the brief period.

Empirical analysis: methodology (3/4)

22**

****

, ittitt Pt

Pt

Pt

Ptittt GOPPARGOPPARGOPPAREGOPPAREPPd

** , ittt PPd

*itP

tGOPPARE 4,0i *

itPtGOPPAR

4,0i

where:

= distance between frontier portfolios identified at t and t-i time ;

= average GOPPAR over t year of efficient portfolios identified on time t-i with

= standard deviation of GOPPAR over t year of efficient portfolios identified on time t-i with

;

Finally, the paper considers the impact of diversification constrains on the optimal portfolios risk-return trade-off.

The analysis of the effects of concentration constrains has been realized considering efficient frontiers with different concentration limits (from 0% to 50%) and using integral calculus to measure the area below the efficient frontier to evaluate the reduction of the investment opportunities. In formula:

In order to measure the impact of concentration constraints on the persistence of results, the paper considers the same distance analysis proposed for unconstrained frontiers.

Empirical analysis: methodology (4/4)

GOPPAR

GOPPAR dconstraine GOPPAREF

max

min

Empirical analysis: results (1/6)

F I****

BO ****

F I***

ME ****

ME ***R M***

MI*****VE *****

F I*****

S S ***

R M***** S S *****

-20,00

0,00

20,00

40,00

60,00

80,00

100,00

120,00

140,00

160,00

180,00

-50,00 0,00 50,00 100,00 150,00 200,00 250,00 300,00 350,00 400,00

S t.Dev. (G OP P AR )

E(G

OP

PA

R)

E fficient F rontier Hotel C ities/S ector

Unconstrained efficient frontier of hotel investments in the Italian hotel sector in 2008

Source: AICA data processed by authors

Composition of efficient portfolios for unconstrained efficient frontier in 2008

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

FI5 MI5 RM5 SS5 VE5 BG4 BO4 CT4 FI4 GE4 ME4 MI4 NA4 PI4 RM4 TO4 VE4 VR4

BG3 BO3 BS3 CT3 FI3 ME3 MI3 MO3 NA3 PD3 PA3 PI3 RM3 SS3 TO3

Source: AICA data processed by authors

Empirical analysis: results (2/6)

Distance measures of efficient portfolios identified at time t-i respect to efficient ones defined at time t

Source: AICA data processed by authors

YearMean distance Year Standard deviation distance

2005 2006 2007 2008 2005 2006 2007 2008

2004 7.96 27.00 90.14 20.79 2004 15.75 33.28 97.72 25.86

2005 - 25.80 105.28 30.68 2005 - 31.04 94.24 26.05

2006 - - 79.64 13.34 2006 - - 76.84 12.98

2007 - - - 65.03 2007 - - - 59.10

Empirical analysis: results (3/6)

Impacts of concentration contraints on the efficient frontier in the Italian hotel sector in 2008

Empirical analysis: results (4/6)

Statistics on the impact of concentration constraints on the efficient frontier

Source: AICA data processed by authors

Empirical analysis: results (5/6)

Year Statistics No constrains Constrain 50% Constrain 5%

2008

% preferred inv. - 2.70% 35.14%

St.dev. Min. 0.00 0.00 6.637106

St.dev. Max 337.17 174.2243 37.79339

Area 94674.19 28781.089 1882.157

Source: AICA data processed by authors

Concentration constraints and performance persistence

Source: AICA data processed by authors

Year Unconstrained Year Concentration constraint 50%

2005 2006 2007 2008 2005 2006 2007 2008

2004 7.96 27.00 90.14 20.79 2004 2.89 19.87 55.20 16.56

2005 - 25.80 105.28 30.68 2005 - 19.22 64.52 22.81

2006 - - 79.64 13.34 2006 - - 43.58 10.90

2007 - - - 65.03 2007 - - - 29.51

Year Concentration constraint 25% Year Concentration constraint 5%

2005 2006 2007 2008 2005 2006 2007 2008

2004 2.51 13.10 31.19 9.27 2004 1.85 5.47 9.11 6.62

2005 - 9.58 36.70 11.58 2005 - 4.68 8.85 6.74

2006 - - 26.21 7.42 2006 - - 5.81 9.24

2007 - - - 16.03 2007 - - - 5.91

Empirical analysis: results (6/6)

Agenda

Introduction

Literature review

Empirical analysis:

Sample

Methodology

Results

Conclusions

Geographical and sectorial diversification seems to be useful in order to create optimal portfolios, even if the best portfolios have inner investments with different characteristics, but a not to high diversification level.

Concentration constraints for hotels of the same category located in the same area determine a lower portfolio efficiency (worsening of the risk–return trade-off), even if the performances result more persistent over time.

As in other research, the main limit of the paper is due to unavailability of detailed information about structure costs of each hotel. The availability of internal data about costs incurred by hotels will enable to obtain more objective evaluations of the benefits coming from the diversification.

Conclusions

Contacts

Claudio GiannottiUniversity LUM Casamassimagiannotti@lum.it

Gianluca MattarocciUniversity of Rome “Tor Vergata” gianluca.mattarocci@uniroma2.it

Luca SpinelliUniversity of Rome “Tor Vergata”luca.spinelli@uniroma2.it