The contribution of spatial dependency to office building price indexes. A Melbourne case study

Date03 April 2018
Pages232-247
Publication Date03 April 2018
DOIhttps://doi.org/10.1108/JPIF-03-2017-0021
AuthorJerry Liang,Richard Reed,Tony Crabb
SubjectProperty management & built environment,Real estate & property,Property valuation & finance
The contribution of spatial
dependency to office building
price indexes
A Melbourne case study
Jerry Liang and Richard Reed
Department of Finance, Deakin University, Melbourne, Australia, and
Tony Crabb
Savills Australia, Melbourne, Australia
Abstract
Purpose The purpose of this paper is to investigate the role of spatial dependency in the construction of a
price index for the transactions of whole office buildings. It examines transactions of office buildings over a
15-year period and addresses an under-researched area in investment property analysis.
Design/methodology/approach The study examines data relating to transactions of all office buildings
in the Melbourne (Australia) central business district between 2000 and 2015. The methodology uses a spatial
weights matrix to construct a hedonic model, spatial error model, spatial lagged model and an office building
transactional price index.
Findings The findings confirm the existence of spatial dependency for the transactions of office buildings.
In addition, incorporating the effect of spatial dependency by constructing spatial error and spatial lagged
model improved the accuracy of the estimated transactional price index for office buildings.
Research limitations/implications These findings make an important contribution to the literature
by highlighting the imp ortance of the issue of spatial autoc orrelation in the estimation of valua tion models
and price indexes for off ice buildings. Until no w the focus has predomina ntly been on individual
office units rather than whole office buildings, where the barrier has traditionally been access to
comprehensive data. T he analysis did not consider leasing details a s this information is not accessible in
the Australian market.
Practical implications The research will assist stakeholders including valuers, investors and market
regulators to improve their understanding of movements in the office property transactional market.
The findings provide an insight into trends associated with the transfer of office buildings. It will assist future
decisions about the location of a new office building developments in order to optimise their proximity to
transport and other buildings.
Social implications The study will assist planners to ensure the location of office buildings are optimised
from a social sustainability perspective. This equates to buildings located in close proximity to transport
facilities and also supporting the development of office buildings in locations, which are associated with lower
future risk.
Originality/value The construction of an accurate and reliable property index is critically important for
practitioners to understand the movement in both the property market and also in the broader economy.
A substantial increase in whole office building acquisitions has been observed in recent years, especially after
the 2007 Global Financial Crisis (Lizieri and Pain, 2014) although there has remained limited research
undertaken in this area.
Keywords Market, Price, Office, Autocorrelation, Dependency, Spatial
Paper type Research paper
Introduction
A substantial increase in whole office building transfers has been observed in recent years,
especially following the 2007 Global Financial Crisis (GFC) (Lizieri and Pain, 2014), although
there has been limited research undertaken in this area. The construction of an accurate and
reliable property index is critically important for practitioners to monitor and examine
movements in the property market and also in the broader economy in order to limit
exposure to risk. Accordingly, this research develops an innovative transaction price index
Journal of Property Investment &
Finance
Vol. 36 No. 3, 2018
pp. 232-247
© Emerald PublishingLimited
1463-578X
DOI 10.1108/JPIF-03-2017-0021
Received 16 March 2017
Revised 24 March 2017
13 July 2017
26 September 2017
Accepted 27 September 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1463-578X.htm
232
JPIF
36,3
for the office market and tests to what extent, if at all, incorporating spatial dependency
issues into the estimation of the index can improve the level of accuracy.
In general, spatial dependency is commonly accepted as when everything in space is
related but the relatedness of things decreases with distance (Tobler, 1970) where previous
studies confirmed the significant role of spatial dependency in the valuation of real estate
assets (Andersson and Gråsjö, 2009). With reference to the transaction market of office
units, Tu et al. (2004) and Maury (2009) found that controlling for spatial dependency
improved the accuracy of valuation models and corresponding price indexes. However, the
transaction market for office buildings differs from the transaction market of smaller
individual office units since practitioners rely largely on nearby comparable office units
when valuing office units; however, they do not predominantly rely on nearby comparable
transactions of entire buildings when valuing entire office buildings, partly due to the lack
of sales of nearby comparable office buildings. Furthermore, factors such as land area and
land value, which influence the price of whole office buildings, are usually not fully
considered in the valuation of individual office units. Thus, the existence of spatial
dependency in the transaction market office buildings is not always fully examined in the
valuation process even if the existence of spatial dependency in the transaction market of
office property units has been confirmed (Tu et al., 2004). This research is unique and
provides the first empirical test to investigate the spatial dependency issue in the whole
office building transaction market.
This study examines a comprehensive database containing transaction information of
office buildings in Melbourne between 2000 and 2015, which is used to construct an index
and test for spatial dependency. The city of Melbourne, Australia was selected as the case
study city and is representative of an international city. With Melbournestotal population
being approximately 4.5 million residents and a central business district (CBD) area of
37.7 km
2
(City of Melbourne, 2017), Melbourne is widely acknowledged as a global city
containing a stock of CBD office properties similar to those in other cities such as London,
Toronto, Hong Kong, Frankfurt and Auckland. The aggregatetotal area of the Melbourne
CBD in 2016 was 32.9 Km
2
encompassing multiple land uses predominantly being
office accommodation, retail, hotel and residential accommodation; the proportion of office
space equated to approximately 16 per cent or 5.4 Km
2
(City of Melbourne, 2016).
There was a total of 2,986,349 m
2
of primary office space and 1,539,713 m
2
of secondary
office space with vacancy rates of 6.5 per cent and 6.4 per cent, respectively (Knight Frank,
2017). In 2016, the volume of sales in the Melbourne CBD office market decreased by 36
per cent in comparison to the preceding 12 months, which was largely due to scarcity of
available inves tments and also sus tained purchaser demand for office space (Knight
Frank, 2017). The analysis of the Melbourne CBD is of particular relevance because most
major western cities also have a CBD, often referred to as downtownin
the USA. Therefore, the findings from this research are applicable and transferable to
other global cities and will provide insights into whether controlling for spatial
dependency by using the spatial lagged and spatial error model can improve the accuracy
of the estimated price index for office buildings. The findings from this research will also
contribute to the existing literature by defining the contribution of spatial dependency to
the value of office buildings.
The balance of this paper is structured as follows. First, we review the literature and
discuss spatial dependency issues related to the value of office buildings. Then, we will
develop empirical models from the literature to construct the hedonic and spatial
error/lagged models to test for the relevance of spatial dependency with the value of office
buildings. The next section discusses the sample data, followed by an analysis of the results
and identifying implications from the outcomes. We provide conclusions and suggestions
for further research in the final section.
233
Contribution of
spatial
dependency

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