Dynamic interactions among property types. International evidence based on cointegration tests
| Pages | 135-159 |
| DOI | https://doi.org/10.1108/14635781311305372 |
| Date | 01 March 2013 |
| Published date | 01 March 2013 |
| Author | Nafeesa Yunus |
| Subject Matter | Property management & built environment |
Dynamic interactions among
property types
International evidence based on
cointegration tests
Nafeesa Yunus
Department of Finance and Economics, University of Baltimore,
Baltimore, Maryland, USA
Abstract
Purpose – The aim of the study is to utilize cointegration techniques and analyze the degree of
linkages among four key property types (retail, office, industrial, and residential) of eight major
countries throughout North America and Europe. Additionally, the study evaluates whether investors
can attain greater diversification benefits by investing across specific property sectors within their
own nations in the long-run. Finally, the study examines whether certain property sectors can be
considered the “leader” that drives the remaining sectors over time.
Design/methodology/approach – Multivariate cointegration tests developed by Johansen and
Johansen and Juseliusare utilized to evaluate whether long-run equilibrium relationship(s) exist among
the four property sectors. If evidence of cointegration is found, hypothesis tests are implemented to
separate out the markets that can be excluded fromthe cointegrating relationships and to identify the
markets that are the sources of the common trends (weakly exogenous), respectively.
Findings – Long-run cointegration resultsindicate that the four property sectorsof the USA, Canada,
Netherlands,and the UK have fullyconverged implying limiteddiversification possibilities.The property
sectors of Finland, France, Germany and Sweden, however, have only partially converged. Further
analysis reveals that for these four countries, the industrial sectors provide the greatest long-run
diversificationbenefits. Finally, weak exogeneitytests indicate that foran overwhelming majority of the
countries under consideration, the residential sectors are the sources of the common stochastic trends,
that “lead” the remaining property types towards the long-run equilibrium relationships.
Practical implications – The conclusions from this study should be beneficial to investors,
portfolio managers, pension fund managers and other institutional investors in the USA and abroad
who are contemplating to invest across property sectors within their own countries in making more
informed portfolio allocation decisions. The findings also highlight the importance of implementing
time-series econometric techniques to accurately and appropriately model interactions among
property sectors over time.
Originality/value – This is one of the few studies that utilize modern-day timeseries techniques to
analyze the dynamic interactions among the property sectors of eight major nations throughout
North America and Europe. Prior studies, have been limited to modeling interrelationships between
the property sectors of the USA and UK, with little attention given to other major real estate markets.
Keywords Internationalproperty sectors, Cointegration,Convergence, Real estate,
United Statesof America, Canada, Netherlands, UnitedKingdom, Finland, France, Germany,Sweden
Paper type Research paper
1. Introduction
Numerous studies have evaluated the benefits of diversification across key property
categories (Miles and McCue, 1982; Eichholtz et al., 1995; Fisher and Liang, 2000;
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1463-578X.htm
Received November 2011
Accepted April 2012
Journal of Property Investment &
Finance
Vol. 31 No. 2, 2013
pp. 135-159
qEmerald Group Publishing Limited
1463-578X
DOI 10.1108/14635781311305372
Interactions
among property
types
135
Young, 2000; Lee, 2001; Lee and Stevenson, 2005). The vast majority of studies have
concluded that sectoral diversification does provide substantial diversification
benefits. Traditionally researchers have used the correlation metric to determine
the level of integration (high correlation) or segmentation (low correlation) between
property sectors in order to analyze diversification possibilities.
However, there are some inherent problems associated with correlation analysis
that makes it unsuitable for analyzing relationships especially over time. Short-run
correlations are susceptible to instabilities over time. Even if the correlation coefficients
are found to be stable, the benefits of diversification indicated by low correlation
may be overstated for investors with long investment horizons if markets trend
together (Kasa, 1992; Eichholtz et al., 1995; Crowder and Wohar, 1998; Alexander,
2001). Furthermore, real estate indices have been characterized as nonstationary data
generating processes (DGPs), which violates one of the critical assumptions of the
classical linear regression model (CLRM), and thus results derived from the correlation
analysis will be spurious and any inferences drawn will be misleading (Granger and
Newbold, 1974). Due to the various limitations of the correlation analysis, studies have
shown that classical tracking error models that are optimized using the correlation
metric, suffer from serious weaknesses (Alexander, 1999). Thus, hedging strategies
based on the correlation measure requires frequent rebalancing (Syriopoulos, 2004)
which limit their applicability within the investment framework.
As an improvement over correlation, cointegration has emerged as a powerful
and robust technique for modeling long-run relationships among nonstationary
variables within a multivariate system (Phylaktis and Ravazzolo, 2005; Syriopoulos,
2006; Alexakis, 2010). From a portfolio diversification perspective, limited
diversification opportunities are available by investing across cointegrated assets,
since these assets trend together in the long-run (Engle and Granger, 1987; Crowder
and Wohar, 1998; Alexander, 2001). The cointegration technique is appealing
especially because it enables the researcher to evaluate linkages among the variables in
the first stage of the analysis and then conduct hypothesis tests in the second stage to
identify markets that are independent and those that are the drivers of the common
stochastic trends. As noted in several studies, cointegration is an effective tool and
investment strategies based on cointegration will be effectual especially over the
long-run[1].
The purpose of the current study is to implement multivariate cointegration
technique developed by Johansen (1988) and Johansen and Juselius (1990) and analyze
the dynamic interactions among four key property types (retail, office, industrial, and
residential) of eight major countries throughout North America and Europe: the USA,
Canada, Finland, France, Germany, The Netherlands, Sweden, and the UK[2]. The paper
utilizes the dataset provided by the National Council of Real Estate Investment
Fiduciaries (NCREIF) and the Investment Property Databank (IPD) and attempts to
answer the following questions:
.Are the retail, office, industrial, and residential property sectors nonstationary
DGP and are the results consistent across nations?
.Are the retail, office, industrial, and residential property sectors within each
country, bound together in the long-run (cointegrated) and does the level of
integration among the property sectors vary across nations?
JPIF
31,2
136
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