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RICS COBRA 2012
The Construction, Building and Real Estate Research Conference
of the Royal Institution of Chartered Surveyors
Held at Las Vegas, Nevada USA by Arizona State University
11th-13th September 2012
© RICS 2012 ISBN: 978-1-84219-840-7
Royal Institution of Chartered Surveyors 12 Great George Street
London SW1P 3AD United Kingdom www.rics.org/research The papers in this proceeding are intended for knowledge sharing, stimulate debate, and research findings only. This publication does not necessarily represent the views of RICS and Arizona State University. The RICS COBRA Conference is held annually. The aim of COBRA is to provide a platform for the dissemination of original research and new developments within the specific disciplines, sub-disciplines or field of study of: Management of the construction process
Cost and value management Building technology Legal aspects of construction and procurement Public private partnerships Health and safety Procurement Risk management Project management
The built asset
Property investment theory and practice Indirect property investment Property market forecasting Property pricing and appraisal Law of property, housing and land use planning
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The property industry
Information technology Innovation in education and training Human and organisational aspects of the industry Alternative dispute resolution and conflict management Professional education and training
Peer review process All papers submitted to COBRA were subjected to a double-blind (peer review) refereeing process. Referees were drawn from an expert panel, representing respected academics from the construction and building research community. The conference organisers wish to extend their appreciation to the following members of the panel for their work, which is invaluable to the success of COBRA. Alan Abela Nottingham Trent University Andrew Agapiou Strathclyde University Solomon Akinbogun Heriot Watt University Adesina Aladeloba Yaba College of Technology Luis Otavio Araujo Federal University of Rio de Janeiro Ibrahim Babangida University of Bolton William Badger Arizona State University Kristen Barlish Arizona State University Brad Carrey Arizona State University Daniel Castro-Lacouture Georgia Institute of Technology Sabine Cerimagic Bond University Peter Davis Curtin University Alberto De Marco Politecnico di Torino Mart-Mari Els University of the Free State Peter Farrell University of Bolton Peggy Ferrin Arizona State University Dhaval Gajjar Arizona State University Jonathan Gates University of Brighton Natividad Gaudalajara University of Valencia Murat Gunduz Middle East Technical University Toby Harfield Swinburne University of Technology Dries Hauptfleisch University of the Free State Jacob Kashiwagi Arizona State University Malik Khalfan RMIT University
Nthatisi Khatleli University of the Witwatersrand Jinu Kim The University of New South Wales Richard Laing Robert Gordon University Namhun Lee East Carolina University Charlotte Leigh, Cardiff University Brian Lines Arizona State University Peter Love Curtin University Jamie MacKee University of Newcastle Patrick Manu University of Wolverhampton Norazmawati Md. Sani University of Science Malaysia Paul Missa University of Salford Róisín Murphy Dublin Institute of Technology Mehdi Nourbakhsh Georgia Institute of Technology Frederick Ababio Nuamah KAAF University College Hugo Oates Arizona State University Henry Odeyinka University of Ulster Ayodeji Ojo Ministry of Land Use and Housing Michael Oladokun University of Uyo Srinath Perera Northumbria University Anthony Perrenoud Arizona State University Kathy Roper Georgia Institute of Technology Timothy Rose Queensland University of Technology María Rua University of Valencia Nico Scholten Expertcenter Regulations in Building Alfredo Serpell Pontificia Universidad Católica Mona Shah PGP Real Estate & Urban Infrastructure Jake Smithwick Arizona State University James Sommerville Glasgow Caledonian University Kenneth Sullivan Arizona State University Subashini Suresh University of Wolverhampton Søren Wandahl Aarhus University Xiangyu Wang Curtin University Gayan Wedawatta University of Salford
In addition to this, the following specialist panel of peer-review experts assessed papers for the COBRA session arranged by CIB W113 Julie Adshead University of Salford Deniz Artan Ilter Istanbul Technical University Matthew Bell University of Melbourne Francine Baker London South Bank University Michael Brand University of New South Wales Luke Bennett Sheffield Hallam University Penny Brooker University of Wolverhampton Alice Christudason National University of Singapore Julie Cross University of Salford Paul Chynoweth University of Salford Philip Davenport University of New South Wales Steve Donohoe University of Plymouth Ari Ekroos University of Helsinki Tilak Ginige Bournemouth University Jill Howieson University of Western Andrew Kelly University of Wollongong Anthony Lavers Keating Chambers Wayne Lord Loughborough University Tinus Maritz University of Pretoria Jim Mason University of the West of England Brodie McAdam University of Salford Issaka Ndekugri University of Wolverhampton John Pointing Kingston University Julian Sidoli Del Ceno Birmingham City University Linda Thomas-Mobley New School of Architecture and Design Henk Visscher TU Delft
2012 RICS COBRA
Las Vegas, Nevada USA
September 11-13, 2012
SPATIO-TEMPORAL ANALYSIS OF COMMERCIAL
REAL ESTATE AUCTIONS IN THE UK
Shane Patrick Galvin1 and Dr. Charlotte Louise Leigh
2
1 Faculty of Advanced Technology, University of Glamorgan, Pontypridd, CF37 1DL, United Kingdom
2School of Social Sciences, Cardiff University, Cardiff, CF10 3BD, United Kingdom
ABSTRACT
The commercial real estate market has witnessed a turbulent time over the past
number of years as a result of the economic downturn. The auction room is an ever-
present element of the market, acting as a barometer on which the wider marketplace
gauges activity. Auction lots are inherently fixed in terms of space and time, with a
fluctuating variable of cost. While research has been conducted to investigate the
sustainability and viability of auctions, the geographic component through time has
yet to be explored. This paper explores how commercial real estate auction data from
the UK can be fused with other forms of geographic information and analysed in a
spatio-temporal context using GIS techniques. The paper concludes by considering
the impact that the geographical distribution of commercial real estate auction lots
has on the sustainability of the auction market.
Keywords: auctions, commercial real estate, GIS, spatial analysis.
INTRODUCTION
The turmoil experienced by the economic downturn has had a deep impact across
many sectors of the economy and the Commercial Real Estate Market is no different.
The past number of years has been some of the most challenging for the industry as its
tries to remain buoyant in a subdued marketplace. The auction room as a fountain of
liquidity in this market offers transparency of transactions, as well as a window into
market activity levels. Total commercial auction sales for 2010 were £1,000,739,341
reflecting only 43% of the 2007 total of £2,314,037,767 (EIG, 2007-2010). This has
led to a deep impact in the industry with many auction houses realigning, retreating or
being made redundant.
The use of simple quantitative techniques in the analysis of commercial real estate
auctions and the property market in general is in wide spread use. In a time of
uncertainty for the market amongst unprecedented economic challenges, the analysis
of the data using an alternative technique may lead to the exploration of some
interesting findings, which can perhaps shed light on the current performance and
future direction of the market.
LITERATURE REVIEW
Relevant academic literature on real estate auctions in the UK is severely limited and
confined in the main to professional publications. There are a number of papers that
flirt with the auction topic but not wholly in the context of real estate, let alone
commercial real estate. It is this finding in itself that has necessitated the need for
further research in to the UK real estate auction market. The author has conducted
research in this area for a number of years and previous papers by Galvin et al. (2009,
2010) have discussed the Commercial Real Estate Auction Market in a variety of
approaches including sustainability and viability. This paper is utilising a new
approach in analysing auction data. While the techniques are not new in entirety,
their application to real estate data is limited and non-existent regarding auction data.
The use of Geographical Information Systems (GIS) for analysing spatial distributions
of geo-coded data has been widespread in the fields of crime (Zhong H. et al., 2011)
(Chainey & Ratcliffe 2005), health (Bell et al. 2006; Widener et al., 2012), urban
planning and ecology but there is little evidence to suggest that GIS has been
employed for the analysis of commerical property auctions. A concise definition of
GIS is put forward by Burrough and McDonnel (1998), GIS is defined as “a powerful
set of tools for collecting, storing, retrieving at will, transforming and displaying
spatial data from the real world for a particular set of purposes”. GIS provides the
ideal means to store, manipulate, analyse and visualise the data collected using these
techniques, especially when considering the spatially varying nature of the data
proposed for commercial real estate auctions. The application of GIS in real estate
research is not a new concept as Wyatt (1996) explored its use as a tool for property
valuation. While commercial real estate auctions have not been analysed in terms of a
statistical geographical context, livestock auctions have been examined by region and
catchment area (Saizen et al., 2010; Wright, et. al., 2002) and viability explored.
Point Pattern Analysis which is the spatial analysis of geo-coded point events or
phenomena has been widely exploited in many domains. A variety of methods built
on spatial statistics have been developed for identifying „hot spots‟ or clusters of point
events. Kernel Density Estimation (KDE) is an established and accepted „hotspot‟
technique for the analysis of point data; however it is under exploited in the area of
real estate auction analysis. KDE is a grid raster based type of analysis and works by
fitting a smoothly curved continuous surface over each point event. Subsequently grid
cells containing no points have interpolated values. The density at each output raster
cell is calculated by adding the values of all the kernel surfaces where they overlay
the raster cell centre. The kernel function is based on the quadratic kernel function
described in Silverman (1986, p. 76, equation 4.5). KDE is one of the most popular
methods to be used for analysing a point distribution (Bailey and Gatrell, 1995)
(Silverman, 1986). Some KDE tools for point and line pattern analysis are available
in commercial GIS software such as Spatial Analyst Extension (ArcGIS) and more
specific spatial statistical analysis software, such as CrimeStat (Levine, 2004).
Thematic mapping is a GIS technique used to visualise and analyse aggregated
geographic data that is typically contained using census or other polygon boundaries.
It is useful for obtaining a general overview of spatial distributions. Caution needs to
be taken when deciding upon the chosen boundaries. For the purpose of this paper
UK Local Authority boundaries have been used due to the large geographic extent of
the auction dataset.
Spatio-Temporal analysis lends itself particularly well to commercial property
auctions due to adding the dynamic of „time‟ in a spatial context, which in such a
changing market place can not be omitted from the model. The use of Spatio-
Temporal Analysis in a real estate context although a recent application, has been
previously utilised in papers mainly focusing on China‟s booming economic growth
and related urban sprawl (Seto & Fragkias 2005).
METHODOLOGY
The data was obtained from the Essential Information Group (EIG) which specialises
in providing data for the real estate auction industry. A dataset was distributed at the
completion of each auction which contained all the information which then had to be
processed in excel and geo-coded using separate Postcode data which is the essential
link when mapping the data in a GIS. When only the first part of the postcode is
given for example, CF38, then the location plotted will be the centriod of that
postcode area. The mid 2009 - 2010 data contained the full postcode for the auction
properties which enabled a level of far greater accuracy when plotting the properties.
The data supplied was from auctions that sold mainly commercial real estate during
the time period analysed. During the downturn in the market, some auction firms
which traditionally traded exclusively in residential or commercial auctions sold both
commercial and residential properties in their auctions to boost their auction
catalogue. Therefore firms chosen were done so by mainly selling but not
exclusively, commercial investments. The breath of possibilities for the use of this
technique and the varying data is discussed in further research.
All data storage, manipulation and analyses were processed using the leading
commercial GIS package ESRI ArcGIS 10. The two main techniques employed for
the analyses were thematic mapping using quartile classifications and the spatial
analytical technique of hotspot analysis using Kernel Density Estimation (KDE).
Chainey et al. (2008) compared KDE to other methods using a prediction accuracy
index and concluded that KDE consistently produced the best hotspot maps for
predicting future events. For the purpose of this paper a hotspot can be defined as a
geographical area of higher than average occurring real estate auction lots (sold or
unsold).
During the time frame of 3 years that was used (2008-2010) the auction industry
underwent some changes and takeovers. Firms closed and other firms taking over
whole auction teams for rivals. Therefore the firms active within the market over the
3 year sample are not consistent, although many are reincarnations.
ANALYSIS & DISCUSSION
The data utilised in the research was a large sample taken from the leading
commercial auction houses in the UK. Table 1 outlines the distribution of auction lots
amongst the leading commercial real estate auctioneers. The commercial real estate
auction market in the UK is dominated by about 10 firms, and of those Allsop
Commercial, having the largest market share increasing from 35% in 2008 to 45% in
2010. The table also identifies the firms that have changed over this short time span,
illustrating the flux that the industry in currently experiencing. The majority of these
firms hold residence in London from which they offer a national service.
Table 1 - Auction Lots by Auctioneer
Auctioneer 2008 2009 2010
Acuitus 0 0 188
Allsop Commercial 697 745 773
Cannon Capital 0 0 61
Colliers CRE 340 128 72
Colliers International 0 0 30
Cushman Wakefield 341 238 183
Erinaceous 65 0 0
Jones Lang LaSalle 292 231 49
King Surge 75 188 113
Lambert Smith
Hampton
0 0 56
Savilles Commercial 171 104 157
Total 1981 1634 1682
Table 2 illustrates the status of lots that went to auction. Various rationales would
have led to the possible outcomes that are achievable when a property goes to auction
and many factors influence bidding activity. For example, on the lots that failed to
usher a single bid, there could be a multitude of reasons why this was so, from
missing information in the legal pack, the condition of the property or the conditions
of sale. For the purpose of this research the main focus will be on the spatial
distribution of sold and unsold lots. Between 2008 and 2010, the amount of
commercial lots that went to auction dropped by 17%. However, during that time the
combined amount of sold and unsold lots remained stable at 79% of lots offered, with
the sold fluxuating between 50-55% and unsold 23-29%.
Table 2 - Status of Lots at Auction
Status 2008 2009 2010
Total Auction Lots 1981 1634 1682
Conditional Sale 1 0 0
No Bids 42 18 15
Refer to Auctioneer 12 2 1
Sold 999 904 862
Sold Post 43 26 21
Sold Prior 107 163 127
Unsold 578 388 474
Withdrawn 199 133 182
Spatio-temporal Analysis
Figure 1 shows the hotspot analysis of sold commercial real estate auction activity in
the UK from 2008 to 2010. Although the national auction houses that dominate the
market operate out of London, the spatial distribution illustrates that the properties are
widely located across the UK. It is obvious for each year that there is a hotspot
cluster of sold commercial properties present in London, however it is clear from the
maps that there has been a definite shift in distribution. In 2008 it is evident that there
are many more hotspot locations other than London, with similar density being
achieved in South Wales, the Midlands and the North West. This is a diminished case
in 2009 and by 2010 it is clear that the majority of sold commercial properties were
centralised in the Greater London area. When using the KDE technique to examine
unsold lots (Figure 2) relative to the sold lots, this trend remains relatively consistent
over the 2008-09. However, in 2010 London clearly was the investment market of
choice reflecting its status at the hotbed of the UK economy. Furthermore, the
geographic concentration of sold and unsold lots for 2008 is relative, 2009 could be
classified as a transitional year, where there are slightly more clusters of unsold lots to
sold lots. The 2010 map illustrates that there are more hotspots of unsold commercial
property lots than sold lots.
Figure 1 KDE Sold Commercial Property Lots 2008-2010
Figure 2 KDE Unsold Commercial Property Lots 2008-2010
Thematic Mapping by Local Authority
While KDE has successfully identified hotspot clusters of sold and unsold
commercial property lots in the UK, the maps are somewhat dominated by the
quantity of lots available in Greater London, therefore it is important to use thematic
mapping to gauge an understanding of the distribution at Local Authority (LA) level.
As London is divided into much smaller LA boundaries, it is evident that there are
some interesting findings in other parts of the UK (Figure 3). For example, in 2008
featuring in the top 10 rank of LAs with the most sold commercial real estate lots,
Greater London LAs only appeared in 4 instances, with Rhondda Cynon Taff
containing the most sold commercial property lots in the UK. Other featured LAs
included Ellesmere Port and Neston (Cheshire), Birmingham, Leeds and Swindon.
By 2010 sold commercial property lots were most certainly centralised in London,
with 7 London LAs featuring in the top 10 rank of areas. The upper quartile of unsold
lots (Figure 4) featured LAs outside of the Greater London area such Ipswich
(England), Swansea (Wales) and Highlands (Scotland) illustrating a more even
distribution.
Figure 3 Thematic Mapping Sold Commercial Property Lots 2008-2010
Figure 4 Thematic Mapping Sold Commercial Property Lots 2008-2010
CONCLUSION
This research has clearly demonstrated the potential of using GIS and Spatial Analysis
techniques for the exploration of the geographic distribution of commercial real estate
auctions. Location and time being the essential elements in the valuation of real
estate, it is a logical progression to integrate spatio-temporal mapping into the
knowledge pool of information available in the market. In an ever-changing market
such analysis is crucial in predicting future trends and focus, for vendors, purchasers
and the auctioneers themselves. As the market has changed over the 3 year period,
with a decrease in lots offered, there was an increasing lack of demand for properties
outside of the Greater London area. This is reflective of the concentration of
investment in the capital, while highlighting investors caution to investing outside of
the central economic hub in an uncertain market and economic climate. A sustainable
auction market needs an increase in investor demand beyond the Greater London area.
Future research will include using other innovative GIS techniques to fully interrogate
the drivers of the changing auction market place such as geographic regression and
the Getis-Ord GI* statistic. Additional analysis and mapping could be undertaken by
investigating tranaction yields and price, auction lot catergorisation by type (retail,
industrial & office) and classification (prime, secondary & tertiary). This would give
more in-depth understanding of the sub-categories within the market and their
performance to market stakeholders.
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