Combining Monte Carlo simulations and options to manage the risk of real estate portfolios
| DOI | https://doi.org/10.1108/JPIF-09-2012-0042 |
| Date | 05 July 2013 |
| Published date | 05 July 2013 |
| Pages | 360-389 |
| Author | Charles‐Olivier Amédée‐Manesme,Fabrice Barthélémy,Michel Baroni,Etienne Dupuy |
| Subject Matter | Property management & built environment |
ACADEMIC PAPER
Combining Monte Carlo
simulations and options
to manage the risk of real
estate portfolios
Charles-Olivier Ame
´de
´e-Manesme
Finance, THEMA, Universite
´de Cergy-Pontoise, Cergy-Pontoise, France
Fabrice Barthe
´le
´my
Finance/Econometrics, THEMA, Universite
´de Cergy-Pontoise,
Cergy-Pontoise, France
Michel Baroni
Finance, ESSEC Business School, Cergy-Pontoise, France, and
Etienne Dupuy
Real Estate Investment Services, BNP-PARIBAS REAL ESTATE,
Issy les Moulineaux, France
Abstract
Purpose – This paper aims to show that the accuracy of real estate portfolio valuations and of real
estate risk management can be improved through the simultaneous use of Monte Carlo simulations
and options theory.
Design/methodology/approach – The authors’ method considers the options embedded in
Continental European lease contracts drawn up with tenants who may move before the end of the
contract. The authors combine Monte Carlo simulations for both market prices and rental values with
an optional model that takes into account a rational tenant’s behaviour. They analyze how the options
significantly affect the owner’s income.
Findings – The authors’ main findings are that simulated cash flows which take account of such
options are more reliable that those usually computed by the traditional method of discounted cash
flow.
Research limitations/implications – Some limitations are inherent to the authors’ model: these
include the assumption of the rationality of tenant’s decisions and the difficulty of calibrating the
model given the lack of data in many markets.
Originality/value – The main contribution of the paper is both by accounting for market risk
(Monte Carlo simulations for the prices and market rental values) and for accounting for the
idiosyncratic risk (the leasing risk).
Keywords Monte Carlo simulation, Real estate portfolio valuation,Break options, Lease structure,
Risk management,Risk metrics
Paper type Research paper
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1463-578X.htm
Received September 2012
Accepted December 2012
Journal of Property Investment &
Finance
Vol. 31 No. 4, 2013
pp. 360-389
qEmerald Group Publishing Limited
1463-578X
DOI 10.1108/JPIF-09-2012-0042
JPIF
31,4
360
I. Introduction
Since real estate assets transactions are relatively infrequent, there is a lack of existing
market valuations by means of which value might be estimated. Real estate portfolios
are mostly assessed using valuation models. Investors must add their own perception of
risk to these models to arrive at a decision to buy or to sell an asset. Usually the main
financial risks they face relate to operating costs, vacancy rates, lease contracts and
liquidity. The principal opportunities for improving performance may arise from
operating costs, terminal value and rental income growth (leases often provide
mechanisms to increase total payments over the course of the lease). The need for
appraisal in the real estate business arises from the heterogeneous nature of properties:
all properties differ from each other at least in their location – an dthis is on eof the most
important determinants of their value. A centralized Walrasian price cannot therefore be
set for trading property assets, as happens for securities in capital markets. The absence
of a market-based pricing mechanism prompts the need to improve valuation methods
so that they more accurately reflect the risks involved in this particular asset: real estate.
Real estate valuation includes the appraisal of the prospective price of a property.
Real estate can involve almost all of the pathologies encountered in the practice of
valuation, although traditionally considered to be a more predictable asset type.
Following Nassim Taleb, who in his foreword to Geman (2005) emphasized the specific
nature of commodities, we emphasize in this introduction some of the specificities of real
estate assets. The first specific characteristic is a unique location. While a security is an
abstract item, having no location and existing as a simple balance sheet entry, properties
present locational characteristics that make arbitrage arduous and comparison difficult.
Second, there is a temporal dimension: a real estate asset is illiquid. The action of buying
and selling is quite predictable in real estate, being rooted in its physical nature. Buying
and selling spans a matter of months for real estate assets, compared with a security
which can be traded twice in less than a second. Third, there is the matter of the size of
the investment: real estate assets are large, indivisible assets. Fourth, the obsolescence
rate: a building does not maintain over time a given level of efficiency, but deteriorates.
Fifth is the incidence of cash flow: small cash flows occur during the period during which
the asset is held, and large flows occur at the time of sale. Taking all these hurdles into
account, the difficulties of real estate valuation become more obvious.
The major traditional valuation methods, widely accepted by practitioners and
academics, are: the cost approach, the income approach (discounted cash-flow) and the
market approach. However, these traditional valuation methods suffer from many
limitations. In particular, they all suffer from the same inherent disadvantage: they do
not take proper account of risk, and they are too sensitive to specific parameters, such as
the infinite growth rate of the cash flow. These limitations are discussed in Fama and
French (1989), Ferson and Campbell (1991) and French and Gabrielli (2004), as well as
Myers (1974), who favour the present-value approach[1]. Furthermore, traditional
valuation methods do not meet certain basic requirements, such as probability
distribution, standard error calculation or confidence interval. Such limitations are
really problematic; and we propose in this paper to overcome all of these issues by
suggesting a new valuation method which, using Monte Carlo simulations and options,
incorporates uncertainty into the valuation process. The Monte Carlo method has long
been applied to incorporate risk into the simulation of many scenarios. Adding an
options’ element to the cash flows also allows us to take account of the risk borne by the
Monte Carlo
simulations and
options
361
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