Research
June 2015
Building Information Modelling Modell ing and the Value Dimension
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Building Information Modelling and the Value Valu e Dimension
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Report for Royal Institution of Chartered Surveyors Report Repo rt written writ ten by: Associate Professor Sara J Wilkinso Wilkinson n BSc MA MPhil PhD FRICS AAPI School of the Built Environment, University of Technology, Technology, Sydney, Australia
[email protected] Associate Professor Julie Jupp BA BSc PhD School of the Built Environment, University of Technology, Technology, Sydney, Australia
RICS Research team Dr. Clare Eriksson FRICS Director of Global Research & Policy
[email protected] Amanprit Johal Global Research and Policy Manager
[email protected] Pratichi Chatterjee Global Research & Policy Officer
[email protected] Published by the Royal Institution of Chartered Surveyors (RICS) RICS, Parliament Square, London SW1P 3AD
www.rics.org The views expressed by the authors are not necessarily those of RICS nor any body connected with RICS. Neither the authors, nor RICS accept any liability arising from the use of this publication. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval sys tem, without permission in writing from the publisher. publisher. Copyright RICS 2015
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Building Information Modelling and the Value Dimension
Contents ...................................................................................................6 .....................................................6 Glossary of Terms .............................................. .............................................................................................. ............................................ 7 Executive Summary ..................................................
1.0 Introduction and scope of research ..............................................10 1.1 1.2 1.3 1.4
Rationale Ratio nale for the research res earch ............................................... ..............................................................10 ...............10 Resear ch quest ion, aims and objec tive s ......................................10 Limit ations ation s .................................................. ..........................................................................................11 ........................................11 Str ucture uctu re of the repor t ................................................ .....................................................................11 .....................11
2.0 BIM and the Value Dimension. .............................................................12 2.1 2.2 2.2.1 2.3 2.3.1 2.3.2 2.3.3
Prop ert y Life Lif e Cycle Cyc le ................................................... ...........................................................................13 ........................13 Data Types and Needs ................................................ .......................................................................14 .......................14 Propert y Information Requirements ........... ................ ........... ............ ........... ........... ...........14 .....14 Education Educ ation Issue s ................................................. ...............................................................................16 ..............................16 BIM within AEC Education (project-level lifecycle) ........... ................ .........16 ....16 BIM within Proper ty Education (propert y-level lifecycle) ........17 ........17 Developing New Knowledge Competencies in RICS ............ ................. .......17 ..17
3.0 Research design and methodology.................................................19 3.1 3.2
Stage Stag e 1 Work shops ................................................... ...........................................................................19 ........................19 Stage Stag e 2 Online Quest ionnair e Survey Sur vey ...........................................21
4.0 Workshop Analysis and Discussion ...............................................22 4.1 4.2 4.2.1 4.2.2 4.3
Workshop 1 Identifying Data Types and Needs ........... ................. ........... ..........22 .....22 Workshop Work shop 2 Identi fying fyi ng the Challenges Challeng es .......................................25 Technolo Technolo gy-bas ed Challenges Challen ges ................................................... ........................................................27 .....27 Socio-tec hnical Challenges Challeng es ................................................ .............................................................27 .............27 Workshop 2 and 3 Identifying Timelines & Mapping Data Needs Through Life .................................................................29
5.0 Survey Data Analysis and Discussion ............................................31 5.1 5.2 5.3 5.4
Part 1 – Respondent Profiles, Current Awareness and Usage of BIM........................................................................................31 Part 2 – Experience Working with Information Technologies..33 Part 3 – Information Frequency and Need of Use ........... ................. ............35 ......35 Part Par t 4 – Challenges Challeng es & Benefi ts of BIM ...........................................40
6.0 Overall conclusions and furt further her researc research h .................................43 6.1 6.2 6.3 6.4
7.0
Data through-lif thro ugh-life e ............................................... ...............................................................................43 ................................43 Challenges Challeng es & Benefi ts of BIM .................................................... ..........................................................43 ......43 Integration of BIM in Propert y Education ........... ................ ........... ........... .......... ..........44 .....44 Recommendati Recomm endations ons and further fur ther rese arch .....................................44
References.....................................................................................................45
Appendices ...............................................................................................................47 Appendix 1 – Proper ty professionals data types and needs ........... ................ ........48 ...48 Appendix 2 – Key to symbols used in figures 5 and 6 and Appendix 3 .....49 Appendix 3 – Managing data through the propert y lifecycle (Workshop 2 output). ......................................................................................50
Special Thanks ........................................................................................................52
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List of Tables Table 1 Table 2 Table 3 Table 4 Table 5
Table 6 Table 7 Table 8
Information Categories Developed for Workshops and Survey .....15 Descending relative importance of data types for Stakeholder Groups (highest to lowest) .............................................23 Relative Importance of Five Main Information Types & Stakeholder Groups..............................................................................23 Groups..............................................................................23 Challenges to through-life information management and corresponding RII .....................................................................................25 Comparison between Australian and UK participants’ perspecti ves regarding the key drivers and challenges when sourcing, integrating and generating data through-life ............. ................... ........... .......... ...........28 ......28 Frequency of use of data types by area of practice / discipline.....36 discipline.....36 Data need need score by data type / area of practice ........... ................ ........... ........... .........37 ....37 Tests of Professional Differences in Information Importance.......39 Importance.......39
List of Figures Figure 1 Figure 2 Figure 3
Figure 4 Figure 5 Figure 6 Figure Figur e 7 Figure 8 Figure 9 Figure 10 Figure Figur e 11 Figure Figur e 12 Figure Figur e 13 Figure Figur e 14 Figure 15 Figure 16
Propert y Development and Management processes compared with Single Facility Project P rocesses (Sourc e: Authors) ............. ..............13 .13 Selection of sort cards showing data types adapted adapted from Lutzendorf & Lorenz, 2011 ...................................................................20 Importance of Main Information Types according to Stakeholders and Activities across CPDM/ Project Lifecycle Phases .......................................................................................24 Relative Importance of Challenges to Through-life Information Management ......................................................................26 Data needs for a Buildings Surveyor Technical Technical Due Diligence survey .......................................................................................29 Data needs for Port folio Management Surveyor s through the lifecycle ...............................................................................................30 RICS region regio n respondent resp ondent wor workk in ...................................................... ...........................................................31 .....31 Respondents’ area area of current practice ........... ................ ........... ........... .......... ........... ........... ........32 ...32 Land use types and sectors of propert y respondents work on .....32 .....32 Use of Information Technologies in the workplace ........... ................ .......... ..........33 .....33 Understan Under standing ding of BIM .............................................................................34 .............................................................................34 Experien Ex perience ce of BIM ....................................................................................34 ....................................................................................34 Source Sour ce of BIM training trai ning ............................................................................34 ............................................................................34 Infor mation matio n Type Need versus vers us Frequency Frequ ency .........................................38 Key Challenges in information management through life .......... ..............41 ....41 Key benefits of digital information through life .............. ................... ........... ........... ......42 .42
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Building Information Modelling and the Value Dimension
Glossary of Terms
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AEC
Architecture, engineering, construction
AECO
Architecture, engineering, construction and operation
BIM
Building Information Modelling
BMS
Building Management Systems
PDM
Proper ty Development and Management
O&FM
Operations and Facilities Management
PLM
Product Lifecycle Management
RICS
Royal Institution of Chartered Surveyors
ROI
Return on investment
VBM
Virtual Building Model
TM
Transaction Management
3D
Third Dimension in BIM – 3D geometr y.
4D
Fourth Dimension in BIM – the time perspective
5D
Fifth Dimension in BIM – the cost perspective
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Executive Summary Building Information Modelling (BIM) offers rich opportunities for RICS propert y professionals to use information throughout the property lifecycle. However, the potential benefits of BIM for property professionals have been largely untapped to-date. BIM tools and processes were originally developed by the architecture, engineering and construction (AEC) sector to assist in managing design and construction data. As these technologies and processes mature and evolve, so too does the oppor tunity for other professional groups to utilise various types of data contained within, or linked to, BIM models. This report outlines the findings from a research project investigating the potential for RICS property professionals to utilise BIM data. Workshops were carried out in S ydney and London with property professionals, and a global online survey was conducted. From these, data types and needs were identified and then mapped across the property lifecycle. Alignment with BIM data was undertaken. Following on from this, issues around training and education for existing and future members were reviewed along with the ways in which BIM can be integrated into property education on RICS accredited courses.
Research question and aims The research question investigated was: what is the role of the value dimension in BIM? This question is examined relative to the activities and professional services performed by RICS property professionals. For example, could BIM help increase property income yields, by providing better quality data on: minimising risk on investment returns; increasing capital growth; and managing and optimising deprecation? As a scoping study, this project aimed; a) to identify the specific types of data that various property professionals use throughout the property lifecycle, b) to evaluate the importance or need for these data types to property professionals, c) how information requirements compare with those of AEC project level processes and the ex tent to which this data is generated in AEC focused BIM deliverables, d) to explore the potential to expand education about BIM into property education, and; e) to identify steps that RICS can take to increase knowledge, skills and competency of BIM within the membership of the property disciplines.
Methods This research ad opted a two-st age research desi gn. The research had t he characteristics of q ualitative research, in that it sought to investigate the potential for property professionals to use BIM data. To do this, it was necessary to ascertain and gain a deeper understanding of their information / data needs and the type of data required. The first stage of the research employed a Delphi approach, which seeks to aggregate the opinions of a panel of experts through successive rounds of questionnaires and interviews. The results from each round were collated and fed back to the panel anonymously and then the panel was asked to provide further comment. Two groups of diverse and experienced property professionals were invited to share their knowledge and experiences in real time, in Sydney and London, over the course of three workshops. The scope of each workshop was as follows; Workshop 1 Objectives: Identify the types of data that each of the professional groups use in daily activities and, the associated challenges of through-life information management, Workshop 2 Objective: Identify upstream and downstream data requirements related to professional property service tasks, Workshop 3 Objective: Analyse upstream and downstream data requirements relative to data characteristics, such as; quality and accessibility. Following analysis of the data generated by the workshops, an online survey of RICS members globally was undertaken. This stage of the research adopted a quantitative approach to validate the earlier qualitative data collected in the workshops. The survey comprised four parts to ascertain members’ knowledge and understanding and discover how best BIM data can be used most effectively within the property professions. The survey allowed us to; 1. Map the property information/data that members use currently, 2. Understand the value and significance of those data needs; and, 3. Reveal what opportunities exist within BIM to enhance professional practice.
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Building Information Modelling and the Value Dimension
Key findings The key findings are that there is potential for BIM in the Value Dimension; that is for the property profession. In respect of the five research objectives this research finds; 1) The specific data types that are used by a number of property professionals through the property lifecycle were identified in the workshops. Property professionals undertake a very diverse range of professional tasks through the building lifecycle and participants use a total of 24 data types listed below (see table 2 also). 1)
Building Description
2)
Health & User Comfort
3) Tenant & occupier Situatio n 4)
Functional Quality
5)
Payments In
6)
Construction Quality
7)
Land Features
8)
FM Quality
9)
Surrounding Characteristics
10) Technical Quality 11) National Market 12) Design/Aesthetic Quality 13) Payments Out 14) Market & Letting Vacancy Situation 15) Design Process Quality 16) Site Features 17) Planning Quality 18) Macro-Location 19) Environmental Quality 20) Micro-Location
21) Cultural/Image Value 22) Operational Quality 23) Environmental Context 24) Urban Design Quality
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2) When different property professionals ranked the importance or need for these data types for property different profiles emerged. Different data types were required at different stages of the propert y lifecycle. Some professionals, such as Portfolio Management Surveyors (see figure 6) have repeated data need s over longer periods of the lifecycle, whereas others, such as Building Surveyors (see figure 5), had a need for a more limited range of data types at specific points in the lifecycle. 3) When information requirements are compared with those of AEC project level processes and the extent this data is generated in AEC focused BIM deliverables, we found the AEC projects focus on design and construction phases, though this is being extended into the operational phase and this falls within the field of Facilities Management. Property professionals who require data relating to building performance and maintenance costs will find BIM data useful, where it is available, in their professional practice. The number of existing buildings with BIM, as a proportion of the total stock is small, however BIM enabled stock is more highly represented in higher quality new commercial property. 4) It was found that there is great potential to expand education about BIM into property education at undergraduate and post-graduate level across all RICS regions. This potential will increase over time as the rate of uptake of BIM technology increases in the built environment. Property management students and subjects will initially benefit most from increased awareness and knowledge of BIM and Building Management Systems (BMS) technology. Valuation subjects can also start the process of awareness raising though most of their data needs currently lie outside of BIM, this may change over time. 5) There are several steps identified that RICS can take to increase knowledge, skills and competency of BIM within the existing membership base of the property disciplines. These measures are outlined in the recommendations below.
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Conclusions and recommendations
3. Develop a set of CPD events to raise awareness among property professionals of BIM
This research h as shown that th ere is a place for BIM and the value dimension and that this will grow over time. There is great p otential to expand th e current use of BI M data for property professionals. There is also potential to expand the range of data linked to BIM for use by property professionals.
As a priority RIC S should develop some onlin e education resources for members to raise awareness and knowledge in respect of BIM and how property professionals could use data within the models.
1. Map data needs and types across all RICS disciplines One of the key priorities is to undertake a comprehensive mapping of data needs and types across all RICS disciplines to identify (a) what is currently within BIM that could be used by property p rofessionals, and (b) data needs and types currently in a digital format but found in databases outside of BIM that could be easily made compatible to BIM. Additionally this review would identify those data needs and types that are outside of BIM that could be digitised and incorporated due to the extent of potential usage within the property profession. The full list should be categorised and prioritised, and where necessary negotiations with third parties should be initiated. In particular details on data source, format, quality (with respect to reliability and accuracy) are needed.
2. Introduce BIM professional competency into RICS APC for property professionals
4. Develop RICS training courses for existing members of the property disciplines in BIM Concurrent with the roll out of CPD events for members and the development of online education resources, RICS should develop a series of training courses for existing members globally to realise the potential of using BIM data in their professional practices.
5. RICS BIM & Property Education Task Force With regards to the integration of BIM into property education, RICS should form an Education Task Force to champion the roll out of BIM across RICS accredited property courses globally to ensure new members have the requisite awareness, knowledge and skill with respect to BIM and property or; ‘the value dimension’. Some other professional bodies are also establishing education task forces and there may be some opportunities for, and benefits in collaboration. After all BIM is about collaboration between various stakeholders to share information for optimum outcomes.
The RICS APC group sho uld develop appropriate proper ty discipline BIM competencies with the APC structure so that property professionals can obtain recognition for knowledge, skill and capability with the application of this knowledge in their professional practice. It is acknowledged that RICS have established the first BIM certification BIM Managers, for members in the construction sector. There may be some aspects that may be transferable to a property focussed certification.
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Building Information Modelling and the Value Dimension
1.0 Introduction and scope of research 1.1 Rationale for the research Building Information Modelling (BIM) is shaping the way that architecture, engineering, construction and operation (AECO) professionals will work in the future (Macdonald, 2012) and is integral to real-time coordination across the disciplines within RICS. Whilst advocates for BIM claim numerous client-side benefits such as quicker approvals due to clearer design intent, the broader scope for client-side stakeholders such as property developers, property managers, investors, and valuers has been largely overlooked to date. Commercial property professionals require good quality through-life information about buildings, the surrounding environment and the market. Professional property activities require robust and reliable data from many sources to deliver a complete view of performance and value during the building lifecycle or ‘through life’. Effective information management across various sectors of property encompasses the sourcing, organisation and reuse of a variety of built environment data and data sources. BIM is defined as ‘a modelling technology and associated set of processes to p roduce, communicate and analyse building models’ (Eastman et al, 2008), where intelligent 3D models allow data to be shared. Over time the 3D model has developed to incorporate 4D (time, or workflow, scheduling) 5D (cost) data. As such, BIM can be viewed as a series of interlinked databases (typically represented graphically using models) that can be shared and updated for design and construction tasks. Each iteration of BIM is referred to as a ‘D’, a dimension; hence the value dimension. Value can be characte rised by three p rincipal characteristics of propert y, namely risk, growth and depreciation (Millington, 2014). The value dimension of BIM is therefore defined by the information or data required during the assessment of the risk, growth and depreciation status of a propert y and provides a description of its per formance through life. This lifecycle perspective includes its original commissioning, project execution, operations and maintenance, and recommissioning / disposal. Whilst value has been addressed partly in the research literature relative to BIM’s return on investment (ROI), this research has been typically at the level of the AEC project and has sought to understand value relative to participating project stakeholder organisations. To date, these studies have largely neglected the broader processes of client-side stakeholders and the activities that lie upstream and downstream of design and construction. This report is aimed primarily at property professionals, who are less familiar with BIM, the technology and its associated jargon. It is written in a style to avoid the overuse of jargon to make it accessible to this new audience within the RICS professional membership.
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Today, information technology is readily em ployed across different lifecycle stages of building and infrastructure facilities. Sourcing data from BIM technologies and building management systems (BMS) is becoming more common in the delivery and operational stages of commercial, multi-residential, health, and education buildings (McGraw Hill 2014). The use of semantic web technologies for operations and facilities management (O&FM) offers a means of structuring different built environment data sources for more effective and efficient through-life information management (BecerikGerber et al, 2011). For those who are unfamilia r with the term ‘semantic web’, it is ‘the next major evolution in connecting information. It enables data to be linked from a source to any other source and to be understood by computers so that they can perform increasingly sophisticated tasks on our behalf’ (Cambridge Semantics, 2015). This research is predicated on the premise that some of the same information management capabilities derived from a BIM-enabled approach that benefit AECO stakeholders can serve property professionals and add value to their professional services. This research explores the potential to expand BIM beyond the AECO disciplines and project stages, as well as beyond current approaches to project and organisational notions of the value of BIM.
1.2 Research question, aims and objectives On this basis, the research question posed is: what is the role of the value dimension in BIM? This question is examined relative to the activities and professional services performed by RICS property professionals. For example, could BIM help increase property income yields, by providing better quality data on: minimising risk on investment returns; increasing capital growth; and managing and optimising deprecation? As a scoping study, this project aimed; 1) to identify the specific data types various property professionals use throughout the property lifecycle, 2) to evaluate the importance or need for these data types for property professionals, 3) how information requirements compare with those of AEC project level processes and th e extent this data is generated in AEC focused BIM deliverables, 4) to explore the potential to expand education about BIM into property education and; 5) to identify steps that RICS can take to increase knowledge, skills and competency of BIM within the membership of the property disciplines.
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1.3 Limitations The research is limited to the investigation of these considerations from a property development, management and valuation perspective. This perspective encompasses a large range of professional property service tasks surrounding property development, property and portfolio management, property investment, property transactions and real estate, property valuation, property and facilities management, and building surveying. Whilst the research study and methodology sought representation across these different property professionals, the researchers encountered some difficulties in obtaining equal representation across those dealing with commercial, retail, multi-residential, health, and education properties. This research limitation surrounding stakeholder representation was encountered in the workshops, where commercial property interests were more widely represented.
1.4 Structure of the report Section two analyses the literature around BIM and the value dimension, through an examination of the property lifecycle and data types and needs. It reviews the educational aspect of BIM in respect of the project and property lifecycles and discusses the integration of BIM into property education. The research design and methods are outlined in section three. Section four reports on the data analysis and findings of focus groups held in Sydney and London. In section five, the data analysis and findings of the online survey are presented. The report closes with a discussion of the main findings and BIM’s ability to support client-side decision-making relative to risk, growth and depreciation variables as well as outlining areas for further research.
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2.0 BIM and the Value Dimension
The lifecycles of com plex, long-lived buildings mean that it is important for property professionals to have robust and reliable through-life information about a building’s performance and value. Property professionals considered here include property and facilities managers, development and asset managers, investment and valuation surveyors, building surveyors. However, whilst the value of BIM has been addressed in the research literature relative to its return on investment (ROI), these studies most often centre on the project lifecycle and define value relative to AECO interests. In the past five years more than 250 ar ticles have investigated the impacts of BIM relative to p roject performance and its impact on business value (e.g. Carroll 2009, Becerik-Gerber & Kensek 2010, Rowlinson et al. 2010, Sebastian & van Berlo 2010). However they are limited in terms of their definition of value, which focuses on project and/or an AEC business level outcomes. Research studies on the value of BIM relative to client-side and wider property interests are lacking. Most studies include client perspectives on the perceived benefits, costs and risks of new technological, process and organisational change. For example, industry surveys undertaken in Australia, the UK and US (McGraw Hill 2014) have shown that most clients perceive a positive ROI when BIM is adopted. However, these studies are limited to the project lifecycle, and consider only single facility project processes neglecting the broader property perspective. A number of studies undertaken across the U.K., Europe, the US and Australian/New Zealand AEC industries show that BIM uptake has in recent years been accelerating and is likely to accelerate over the next few years (McGraw Hill, 2014). In the US in 2009, it was reported (Young et al., 2009)
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that 50% of the industry was using BIM, representing a 75% increase in a two year period. A McGraw-Hill Construction report, titled, ‘The Business Value of BIM in Europe’ (McGraw-Hill 2010), shows construction professionals in France, Germany and U.K. have be en using BIM longer, but overall BIM adoption is greater in North America. The study shows that a little over a third (36%) of Western European construction professionals are using BIM, where in a previous repor t McGraw-Hill found that 49% of contractors, architects and engineers reported BIM usage, (McGraw-Hill 2009). However, there is no clear and consistent demand for adoption by clients. Currently BIM adoption is largely in the larger A EC companies and within larger construction projects, buildings and estates. Furthermore given that typically onl y 1-2% is added to the total stock of buildings annually (Wilkinson, 2015), it will be many years before a majority of stock has BIM. El-Gohary (2010) argued that potentially, BIM can add value when assessing sustainability in a property development feasibility study, where the costs and the potential of different options can be assessed in respect of likely sustainability rating levels say, under BREEAM or Green Star. Studies by Fuerst and McAllister (2012) and Newell et al, (2011) have indicated that there is a value premium in sustainable commercial property in the UK, US and Australia. Using BIM data and simulations, clients can be advised of the social, environmental and economic costs and benefits of various options allowing them to make more informed decisions that optimise, or at least co nsider the impact on propert y value. However it is not known whether the information specified in AEC BIM models currently meets the needs of the property professionals.
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2.1 Property life cycle Property development and management activities encompass more than the combination of single or multiple AEC projects and the application of BIM in this wider scope of property services is not well understood. Typically, at the level of an AEC project, the general lifecycle process of the design and construction project is defined as: 1) Pre-design (PD) in which the decision maker from the client side evaluates project feasibility; 2) Schematic Design (SD); 3) Detailed Design (DD); 4) Construction Documentation (CD); 5) Construction (CO); and 6) Operation/Maintenance (OM). Only the client is involved in the entire process and other professionals join and depart from the project as required. When taking the wider property development and management activities that surround the AEC project into consideration, a more extensive lifecycle process becomes evident. This property perspective of lifecycle includes not only the AEC phases described above, but also activities that encompass property such as;
When the two different levels of lifecycle are compared, the requirements of information management is more complex and the opportunities to maintain and leverage the data contained within, or linked to, a BIM model is apparent. However there is a lack of literature reporting studies of well-defined property based or client-side strategy surrounding the business case for deploying BIM – either on single facility projects or relative to property portfolios. The recent incre ase in digital info rmation generated during AEC projects and throughout a property’s operation and maintenance creates potential for a new approach to information management within property. The development of new approaches must consider the lengthy time periods that information must be managed over and complexities surrounding the different consumers and generators of information, where information must be able to be accessed and used by numerous property professionals. The established role for BIM in managing information within AEC professions can be extended to propert y professionals. Questions arise such as; what are the information needs, at what periods during the lifecycle is information needed and; what is the frequency of which such information is required? In seeking to provide answers to these questions the first step was to identify and then make an assessment of relevant propert y data.
1) Conception; 2) Planning and Feasibility; 3) Preparation; 4) Execution; 5) Operation and Maintenance (O&M) and 6) Recommissioning (see figure 1).
Figure 1
Property Development and Management processes compared with Single Facility Project Processes
Single Facility Project Lifecycle Phases
Commercial Property Development & Management Lifecycle Phases
Conception (C)
(PD)
Planning & Feasibility (SD)
(SD)
(DD)
Preparation (P)
(CD&CO)
Execution (E)
(OM)
Operation Maintenance (OM)
Recommissioning (R)
Source: Authors
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Building Information Modelling and the Value Dimension
2.2 Data Types & Needs The data sources that are required to provide a description and assessment of a property’s performance and value are disparate, extensive, and correspond to the type and variety of professional AECO and property activities that span the building lifecycle. The data collected encompasses market, property, building, financial, project, operations and maintenance data. Together in various combinations and at different lifecycle stages, this data is reused by a variety of property professionals to inform performance and valuation tasks.
2.2.1 Property Information Requirements Currently a range of separate and distinct sources are used to access property, development and management information. Distinct data types may coexist in isolation and the quality, completeness and accuracy of this information is often unknown and sometimes unchecked (by those who generated the information or who may consume it), making information management in property disciplines complex. Lützendorf and Lorenz (2011) identified a comprehensive list of descriptors to represent information types used by property valuation and related professions. A list of 22 descriptor categories shown in the first column of Table 1, identified by Lützendorf and Lorenz (2011) according to information traditionally gathered and used for property valuation and risk assessment purposes. Their sources included The European Group of Valuers Associations (TEGoVA 2003), RICS (2009) as well as a cross-section of sustainability assessment schemes such as the United Nations Environment Programme (UNEP 2009), and the Green Property Alliance (GPA 2010). These studies were examined to ascertain whether BIM might offer for the broader scope of property development and management activities; in other words, the value dimension. The researchers analysed each infor mation requirement relative to the scope and processes identified in Figure 1 and developed an information requirements framework consisting of five main types of property, development and management descriptors, 25 sub-types and 90 individual attributes. The five main categories of information include descriptors relevant to property development and management of; 1) Market and Location Data, 2) Property Data describing Plot of Land, 3) Property Data describing Economic information, 4) Building Information, and; 5) Process Qualities.
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These information types are shown in the second column of Table 1. The classification developed in Table 1 was compiled on the basis of information traditionally sourced, organised and (re)used by property developers, property and portfolio managers, property investment surveyors, valuers, property and facility manager, building surveyors and in property transactions. This data can be sourced from building documentation, consultants reports, industry databases, building inspections, facility managers, a variety of building reports, and documentation of the design and planning process typically created during the design and planning stage for verification of conformity with regulations. Each information type was identified based on its mapping with property development and management activities and its classification as either an economic, environmental or social indicator of value. These attribu tes and characteristics formed the basis of workshop discussions. Based on outcomes and learning from the workshops, the main categories and sub-categories were modified to cover a wider range of property activities and were re-structured according to information and data formats that are readily available throughout the property lifecycle, and also re-worded into language more familiar to property professionals. The final categories developed for the survey are shown in the third column of Table 1. Sourcing data from BIM technologies and building management systems (BMS) is becoming more common in the delivery and operational stages of commercial buildings (McGraw Hill 2014). This research is based on the premise that the same information management capabilities that are being derived from a BIM-enabled approach to benefit AECO stakeholders can be extended to serve property professionals and thereby add value to their services. With the volume of data generated, it is necessary to evaluate the relevance and importance of each data type. The authors developed a method for identifying and determining the importance of information types. The first step was to prioritise inform ation based on the need for the information, the frequency of use, the effort of reacquisition, and finally, duration of reacquisition. Modifications of this method were used to analyse the workshop and survey findings.
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Table 1
Information Categories Developed for Workshops and Survey
Property descriptor types (Lutzendorf & Lorenz 2011)
Information types identified for workshops (Adapted from Lutzendorf & Lorenz 2011)
1. Location – National Market Descriptors 1. Location Information Types, including: – National Market Data – Macro Location Data – Micro Location Data
Categories of data defined for RICS survey (Based on Workshop Outcomes & Learning) 1. Market Data including; – National Market Data – Stat e, Regional and Neighbourhoo d Market Data – Listings, Recent Sales, and Auctions Data – Property Transfers Data – Property Marketing Statistics
2. Location – Macro Location Descriptors
2. Property Location Data;
3. Location – Micro Location Descriptors
– Macro Location Data – Micro Location Data
4. Plot of land – characteristics and configuration descriptors 5. Plot of Land – Surrounding Context Descriptors
2. P roperty Information Types, describing Plot of Land, including:
3 Property Site Data including; – Property Lot Attributes
– Characteristics and Configuration,
– Utilities
– Surrounding Contextual Dat a)
– Environmental Attributes – Surrounding Building Context – Property Development Details
6. Mechanisms / Instruments 7. Economic Qualit y – Payments In Descriptors
3. P roperty Information Types, describing 4. F inancial Data including; Economic and Financial Data, including:: – Payments In, – Payments In, – Payments Out,
8. Economic Quality – Payments Out Descriptors
– Payments Out,
– Vacancy / Letting and
– Vacancy/Letting and
– Tenancy Occupier Data
9. Economic Quality – Vacancy / Letting Descriptors
– Tenancy/Occupier Information
10. Economic Quality / Cash Flow – Tenancy/Occupier Descriptors 11. Building – Basic Building Quality Descriptors
4. B uilding Information Types, including:
5. Building Data, including:
– Building design information
– Spatial attribut es
12. Building – Technical Quality Descriptors
– Technical and building syst ems information
– 3D model objects (elements) and properties (parameters)
13. Building – Functional Quality Descriptors
– Functional information ,
– Building Documentation and Images
14. Building – Environmental Quality Descriptors
– Design/ Aesthe tics information
15. Building – Design / Aesthetics Quality Descriptors 16. Building – Urban Quality Descriptors 17. Building – User Health / Comfort Quality Descriptors
– Environmen tal design information , – Contribut ion to urban quality – User comfort & Post-occupancy evaluation information – Cultural value information – Image and reputation value information
6. Real Estate Data (Added to incorporate data typically collected that describes intangible value descriptors), including: – Property Value Attributes – Property Imagery
18. Building – Cultural Value Descriptors
– Property Activity
19. Building – Brand Value Descriptors
– Property Insurance Attributes – Propert y Insurance Rate Variables
20. Process Quality – Planning Descriptors 21. Process Quality – Construction Descriptors
22. Process Quality – Management Descriptors
5. Process Information Types, including:
7. Project Data, including:
– Planning process information
– Planning and Feasibility Data,
– Design process information
– Design Management Data
– Constr uction process information
– Construc tion Proces s and
– Operations and Facilities Management information
– Management Data 8. Operations and Maintenance Data, including; – Maintenance, Alteration and Repair, – Asset Monitoring and Tracking, – Space Management
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2.3 Education Issues This section examines some key issues arou nd the education of property students and existing property professionals with respect to BIM knowledge competencies. An overview of the integration of BIM within the AEC disciplines is provided and the potential to leverage off this experience is discussed. This section considers; firstly, BIM models or virtual building models (VBMs) as an integrated source of information for teaching and learning and the re-usability of building information generated to meet AEC deliverables for propert y education purposes. Secondly it considers, a potential roadmap for the adoption of BIM for teaching and learning, and; finally the needs of existing practitioners and the role of continuing professional development (CPD) and short courses. Broadly, BIM provides an appropriate and potentially beneficial suite of technologies for the development of new teaching and learning approaches that can enable the incorporation of valuable property related data that is used through the property lifecycle for property investment, property maintenance and property management purposes.
2.3.1 BIM within AEC Education (projectlevel lifecycle) The adoption of BIM technologies and processes offers many benefits to educational programmes offered by universities. In particular where faculties, departments or schools have Quantity Surveying, Construction and Project Management and Property undergraduate and post-graduate provision; there is the potential for cross-disciplinary and inter-disciplinary projects (e.g. Macdonald, 2012). The benefits to students studying the AEC disciplines that BIM offers include increases in knowledge and understandings of: 1) More effective workflows for improved information sharing between disciplines; 2) Digital methodologies for time and costs savings that translate into productivity gains; 3) Digital methodologies to improve product and process quality. 4) Sustainability for the built environment; and 5) Greater transparency and accountability in decision-making A key benefit of BIM in education is the virtual building models as a visual tool for learning. Due to its geometrical representation of the parts of a building in an integrated data environment, virtual building models can allow students to understand design and construction technology with ease and speed. Virtual building models, as visual teaching aids, provide AEC subjects with a means of visually simulating design and construction details, component relationships, construction materials and activities. Geometric modelling and virtual reality techniques can be used in the visualisation of typical and be-spoke AEC methods, allowing students to access information in the classroom (Jupp and Awad 2012).
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Teaching with virtual building models, and related BIM technologies, has the potential to increase student understanding, not only of design and construction processes, but also (perhaps most importantly) of how to collaborate and share information with other professionals across the property lifecycle (e.g. Macdonald & Mills, 2012; Macdonald & Granroth 2013). Buildings can be analysed rigorously, simulations performed and design performance benchmarked, moving AEC students from abstract concepts to applied knowledge. Model-based building data can be shared, value-added and re-purposed according to subject content and requirements. Other educational advantages are the engagement and exploration of building products and process via simulation and, of par ticular import to propert y focused subjects, the simulation of integrated planning, feasibility and implementation processes. From this perspective, utilising virtual building models within AEC and property programmes provides a vehicle to introduce principles of teamwork, collaboration and continuity across multiple lifecycle stages, including – 1. BIM and preconstruction – planning a BIM project, defining responsibility and ownership, information exchange, model coordination planning, digital information transfer standards. 2. BIM and design management – design coordination, integration, inter-disciplinarity, inter-operability, clash detection and reporting, model coordination and management. 3. BIM and construction – scheduling, constructability, trade coordination. 4. BIM and Assembly and Manufacture – because digital product data can be exploited for downstream processes, students can engage with (automated) assembly and manufacturing problems. 5. BIM and updates – pre-bid, estimate updates, model updates, clash detection updates, budget management. 6. Cost and lifecycle analysis – target cost modelling, simulated construction timelines, requirements, design, construction and operational information can be utilised in Facility Management subjects. 7. Production quality – documentation output is flexible and exploits automation, enabling students to quickly and more easily analyse building solutions and propose alternate construction technologies and methods. 8. Customer focus – often the customer or client is left out of the equation in the teaching environment. As virtual building mod els can be unders tood through accurate visualisation, students are able to gain a client’s perspective. Given the benefits highlighted by researchers in AEC education (e.g. Macdonald, 2012), the next section explores the potential and issues for the integration of BIM within Property education. With the property lifecycle extending far beyond the project lifecycle, the property lifecycle forces a broader more enterprise level view of BIM for information management than the (AEC-based) project lifecycle.
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2.3.2 BIM within Property Education (property-level lifecycle) One approach to deliver education that could be adopted in undergraduate and fast track post-graduate conversion courses, is to set up introductory BIM subjects to provide initial understanding of the concepts of BIM, including its processes, technologies, protocols and jargon, which could, where possible, possibly be co-taught with AEC students. Thereafter the specialised application of BIM in the various property knowledge fields, such as valuation, property management, property funds investment would see BIM-enabled teaching and learning embedded within those subjects. Further opportunities lie in multi and cross-disciplinary subjects, as described in the framework proposed by Macdonald (2012). RICS may be able to learn from the integration of Product Lifecycle Management in engineering systems education. With the increasing uptake of BIM, some AEC professionals are experiencin g significant changes to their professional working practices (Jupp & Nepal, 2014), which may be experienced in due course by some property professionals. BIM reflects many of the changes, challenges and opportunities prompted by the introduction of Product Lifecycle Management (PLM) in the automotive and aerospace industries during the 1990s. During the implementation of Product Lifecycle Management, changes to professional practices relating to new activities, roles/responsibilities, knowledge competencies, and relationships was required; and many characteristics reported on the adoption and deployment of BIM and Product Lifecycle Management information systems are shared (Jupp & Nepal, 2014) and may be applicable to the expansion of BIM into property. BIM and Product Lifecycle Management differ mostly around the capacity for technical and organisational integration, leading to differences in approach to data governance and information management (Ford et al, 2013). The key differences lie in the information system and tools utilised by their different application domains, which are underpinned by vastly different BIM/ Product Lifecycle Management platform specifications and data requirements. BIM and PLM, share similarities such as the approach to data sharing, project management, organisation of teams around deliverables and timelines, and object-based visualisation activities. The challenges that follow from these shared characteristics provide fertile grounds for sharing lessons learned. Issues surrounding changes to professional practice and cultural change affect the practical deployment of BIM and Product Lifecycle Management concepts within their respective sectors. These challenges stem from various new activities that change the nature of professional roles and responsibilities at practice and project level. The changes are predicated on the development of new technical skills, new knowledge fields and stakeholder relationships (Jupp & Nepal, 2014). To some degree, this would be the case also for property professionals.
The experiences of the BIM and Prod uct Lifecycle Management communities can be used to understand the practice-based issues. The construction industry is in the early phases of BIM adoption and stands to benefit most in learning from PLM experiences of professional practice and cultural change. Moreover property professionals within RICS can benefit from this experience also in the development of CPD courses that focus on the changes to roles and responsibilities. Product Lifecycle Management focuses on the whole lifecycle of a product and is not the responsibility of one unit or department; but a whole organisation. At a general level Product Lifecycle Management deployment requires greater levels of collaboration and communication between professionals. This approach to information management requires the implementation team works closely with business teams; for example, people from purchasing, order management, sales and marketing, and inventory management (Hewitt, 2009). Product Lifecycle Management implementation requirements dictate that in manufacturing based industries, a broader lifecycle approach to information management is desirable. Similarly across some property service tasks there would be a requirement for close integration of products, data, applications, processes, people, work methods, and equipment from across the supply chain. PLM deployment in supply chains raises significant changes to roles and responsibilities and it is vital that the roles and responsibilities are determined at the outset (Stark, 2011). Likewise responsibilities in relation to partnering companies and their role in the process must be carefully considered (Hewitt, 2009). Jupp and Nepal (2014) identified a number of new responsibilities within existing traditional roles in the Product Lifecycle Management literature as well as how these roles are shared between administration executives (typically with an engineering background) and project engineers. Over time it is possible new responsibilities and roles will emerge within some of the RICS property professions as a result of a BIM-enabled approach to information management through the life of property assets.
2.3.3 Developing New Knowledge Competencies in RICS Hewitt’s study (2009) showed that the shift of perspective from product delivery to a lifecycle approach represented a knowledge gap for many manufacturing companies; RICS can learn from this by adopting a proactive lead in the implementation of BIM in propert y education. Hewitt (2009) found educational establishments and professional bodies needed to align curriculums, assessments and accreditation relative to PLM and manufacturing; and RICS should consider starting this process with respect to targeted areas of property education and professional competencies. RICS members need to be versatile, crossfunctional professionals who are up-to-date with emerging technologies; able to per form new professional services associated with through-life requirements and activities.
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Hutchins (2004) noted that US manufacturing professionals were asked to perform tasks not traditionally included in their professional scope of works and that they lacked the capability to undertake the tasks successfully; RICS needs to ensure new entrants to the property profession, as well as, existing members are equipped with the necessary, and appropriate level, knowledge and skills in BIM. RICS has taken the lead in developing the BIM Managers Certification (RICS, 2015) route to membership and some aspects may be transferable to the property disciplines. The Society of Manufactur ing Engineers researched “competency gaps” and developed a ‘Manufacturing Education Plan’ (Fillman et al, 2010) and RICS could consider a similar approach in respect of BIM and property. Likewise, academia responded to the needs of the changing workforce from one that was task oriented to one that is competency based through the development of innovative curricula, such as Purdue University’s initiative to develop a PLM-literate workforce (Fillman et al, 2010). RICS could constitute an Educ ation Task Force to champion the rollout of a global initiative to develop a BIM literate property profession. For existing members, RICS should consider a series of BIM & the Value Dimension training programmes that will provide members with an understanding of BIM technology and applications in respect of their professional practice and services.
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3.0 Research Design and methodology
3.1 Stage 1 Workshops To identify the main information types used by the different property stakeholders the research design was based on the Delphi method (Dalkey and Helmer 1963). The value of Delphi is demonstrated in a wide range of applications on complex, interdisciplinary and technology based issues using a method for structuring group communication processes (Linstone and Turoff, 1975). The research design employed a series of workshops with industry experts followed by feedback reporting and surveys. As such the research used an inductive approach to qualitative data analysis (Silverman, 2013). To address the objectives, property and AEC professionals working for different companies in Australia and the UK were invited to participate. Practitioners had a minimum five years post qualification experience as the findings should reflect business practice as closely as possible. The company types of invited participants included: Development and Asset Management, Property Management and Valuation, Design and Construction, and Transactions Management. The participants were industry experts who were content matter experts on their respective fields and regularly engaged in the sourcing, organisation and reuse of disparate data sources during their work tasks. The same par ticipants attended each of the three workshops to ensure consistency. In Sydney 13 participants attended the workshop and researchers, representing the property and construction disciplines from the University of Technology, Sydney (UTS), facilitated. In London six par ticipants attended the workshops facilitated by a Chartered Building Surveyor and academic.
Workshop one objectives were to: a) Identify the types of data that each of the professional groups use in their daily activities, and; b) The associated challenges of through-life information management. Workshop one was convened over a half-day period. The workshop was divided into two sessions with four groups, with each session including a presentation by the facilitators to frame and introduce the exercises, followed by individual brainstorming tasks, group break-out sessions, and finally a full workshop discussion. Results were reported to participants via email for feedback and this data was then used as the basis for the subsequent workshops. The first exercise (1A) comprised a clustered list of 24 relevant information requirements elicited from the literature as being important to property professions. The information requirements were pre sented on 24 cards and participants asked to sort them on the basis of the information t ypes they perceived as ‘essential’, ‘nice to know’ or ‘irrelevant’ to their work tasks (see figure 2). The aim was to establish the main information types and identif y correlations between stakeholder data requirements.
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Building Information Modelling and the Value Dimension
Figure 2
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Selection of sort cards showing data types adapted from Lut zendorf & Lorenz, 2011
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Workshop two had the objective to: a) Identify upstream and downstream data requirements related to professional property service tasks The same respondents p articipated in the second exercise, cards classified as ‘essential’ and ‘nice to know’ by participants were used as the basis for identifying challenges to the sourcing, organisation and reusing of information throughout the building lifecycle. Participants were asked to identify challenges on the basis of their cards so as to pinpoint individually problems in relation to a BIM-enabled approach to information management, before then discussing their findings within each group. Participants were then asked to rank challenges deemed most to least significant. As a result of the second workshop a timeline for managing data through the property lifecycle was produced for each participant to explore in the final workshop (see Appendix 2 and 3 for typical examples).
The objective of workshop three was to: a) Analyse upstream and downst ream data requirements relative to data characteristics, such as format, source, quality, accessibility. In this workshop, participants reviewed their timeline chart for managing data through the property lifecycle and commented on any changes that were required. In some cases property practitioners required identical data at various points in the property lifecycle for a task and had complex data needs (see figure 5 and 6 and Appendices 2 and 3), whereas others had data needs at a single point only during the life cycle.
3.2 Stage 2 Online Questionnaire Survey Having ascertained the data types and data needs of property professionals in the Stage 1 workshops, a questionnaire was designed to allow the researchers to determine whether the workshop data types and needs identified by the participants matched those of the profession more broadly. This part of the research embodied the characteristics of quantitative research (Silverman, 2013) whereby a statistical analysis of data reveals the characteristics and needs of a larger group of practitioners. An online survey was desig ned adopting best practic e in survey design (Silverman, 2013) comprising four parts and launched in April 2015. Part one asked respondents about their area of practice across the RICS regions, their area of expertise, the stage of the property lifecycle during which their expertise was required, their level of expertise, knowledge and usage of BIM in their professional services. Part two focussed on the value of data contained in BIM, and asked respondents about the importance of different types of BIM data to their professional services. The next section of the survey asked questions about non BIM enabled data and respondents data needs in order to prioritise the data type property professionals would find most useful to access in a BIM. Part three focussed on the status of information technologies in professional property tasks and which land use types had the most requirements for BIM enabled data according to respondents. Finally part four examined the value of data sources and potential BIM enabled information. Having identified the key challenges from Workshops 2 and 3 in respect of data, respondents were asked to rank the significance of different challenges be they technical challenges or data quality and fidelity challenges and so on. The survey was designed for completion within a 10-minute period, and remained open for a four-week period. The survey was distributed through RICS channels and reminder emails were sent weekly to encourage as good a response rate as possible.
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4.0 Workshop Data Analysis and Discussion 4.1 Workshop 1 Identifying Data Types and Needs Participants used workbooks and Post- it notepads to record responses. Group discussions were recorded and facilitators and scribes took notes. All data captured from the workshop was analysed using thematic analysis. To confirm agreement between workshop participants on the significance of the information types identified according to each professional group, a three-point Likert scale was used, where 1 equals least impor tant (irrelevant) and 3 equals most important (essential) and were analysed by calculating the Relative Importance Index: RII = W AN
where W = weight given to response, A = highest weight, and N = number of respondents. The relative importance index ( RII ) for all 22 information types were calculated for all participants, and then calculated according to each professional group. The 22 information types were arranged in descending order of relative importance according to all participants and ranked. The highest RII indicates the most important information types with rank 1, the next indicating the next most important with rank 2 and so on. The rankings of each professional group were compared to the overall RII rankings shown in Table 2. The highest ranked attributes that fall within the top 5 information types according to All Responses (in Table 2), i.e., calculated across four groups: Development and Asset Managers, AEC Professionals, Valuation and Cost Managers, and Transaction Managers and are discussed below. The All Response column (in Table 2) shows the five most important information types were; 1. Building Description (RII 0.92), 2. Functional Quality (RII 0.87), 3. Land Features (RII 0.85), 4. Technical Quality (RII 0.85), and 5. Payments Out (RII 0.85).
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Examining the ranking of the importance of information sub-types according to the four stakeholder groups, as anticipated variation was identified. For example the ‘Development and Asset Management’ group returned Payments Out and Surrounding Characteristics as the two most important information types, whereas the AEC group selected Site Features, Land Features, Surrounding Characteristics, Building Description, Technical Quality, Functional Quality and Micro-Location as b eing of most and equal importance. A summary of the variation in importance between stakeholder groups is shown in Table 3. The most consistent inform ation types were those belonging to the ‘Building Descriptors’ category, with six of the nine information sub-types being important across all stakeholder groups. The importance of these building descriptors to all stakeholders confirms the potential of BIM’s application within the property profession. The mapping in Figure 3 reveals those signi ficant information types relative to their stakeholder activities and involvement throughout CPDM and Project timelines. Using these insights together with a specification of BIM deliverables (Succar et al. 2013) a framework is proposed of the way in which client-side stakeholders can leverage data to support the CPDM lifecycle and start to identify the gaps relative to when and what information can be derived from the project lifecycle. The importance of the five main information categories was then compared according to where each stakeholder group’s activities occurred within the CPDM and Project lifecycles as shown in Figure 3.
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Table 2
Descending relative importance of data t ypes for Stakeholder Groups (highest to lowest) Development AEC & Asset Value & Cost All Responses Professionals Managers Managers
Transaction Managers
RII
Rank
RII
Rank
RII
Rank
RII
Rank
RII
Rank
1 Building Description
0.92
1
1.00
1
0.92
3
1.00
1
1.00
1
2 Functional Quality
0.87
1
1.00
2
0.83
8
0.92
3
1.00
1
3 Land Features
0.85
1
1.00
3
0.83
8
0.92
3
0.75
20
4 Technical Quality
0.85
1
1.00
3
0.92
3
0.83
12
0.75
20
5 Payments Out
0.85
21
0.44
3
1.00
1
0.92
3
1.00
1
6 Site Features
0.82
1
1.00
6
0.83
8
0.92
3
1.00
1
7 Environmental Quality
0.82
8
0.89
6
0.83
8
0.83
12
1.00
1
8 Operational Quality
0.79
8
0.89
8
0.83
8
0.75
17
1.00
1
9 Health & User Comfort
0.79
8
0.89
8
0.83
8
0.83
12
0.75
20
10 Payments In
0.79
22
0.33
8
0.92
3
0.92
3
1.00
1
11 FM Quality
0.77
19
0.67
11
0.83
8
0.88
8
1.00
1
12 National Market
0.74
19
0.67
12
0.83
8
0.75
17
1.00
1
13 Market & Letting Vacancy Situation
0.74
22
0.33
12
0.83
8
0.83
12
1.00
1
14 Planning Quality
0.74
14
0.78
12
0.75
18
0.75
17
1.00
1
15 Micro-Location
0.72
1
1.00
15
0.75
18
0.75
17
1.00
1
16 Environmental Context
0.72
14
0.78
15
0.88
7
0.83
12
1.00
1
17 Tenant & occupier Situation
0.72
22
0.33
15
0.92
3
0.67
23
1.00
1
18 Construction Quality
0.72
8
0.89
15
0.75
18
0.88
8
1.00
1
19 Surrounding Characteristics
0.67
1
1.00
19
1.00
1
0.88
8
1.00
1
20 Design/Aesthetic Quality
0.67
8
0.89
19
0.75
18
0.75
17
0.75
20
21 Design Process Quality
0.67
14
0.78
19
0.83
8
0.75
17
0.75
20
22 Macro-Location
0.62
14
0.78
22
0.75
18
1.00
1
0.83
19
23 Cultural/Image Value
0.59
8
0.89
23
0.63
23
0.88
8
0.75
20
24 Urban Design Quality
0.51
14
0.78
24
0.63
23
0.63
24
0.75
20
Information Types
Table 3
Relative Importance of Five Main Information Types & St akeholder Groups
RII According to Stakeholder Groups Development & Asset Managers
Location
Plot of Land
Building Descriptors
Process Quality
Economic Quality
Med. to High Significance
Low Significance
Med. to High Significance
Med. to High Significance
High Significance
AEC Stakeholders
Low Significance
Med. to High Significance
High Significance
Med. to High Significance
Low Significance
Valuation & Cost Managers
Medium Significance
High Significance
Med. to High Significance
Low Significance
Med. to High Significance
High Significance Low Significance
Medium Significance
Medium Significance
High Significance
Transaction Managers
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Building Information Modelling and the Value Dimension
Importance of Main Information Types according to St akeholders and Activities across CPDM/Project Lifecycle Phases
Figure 3
Single Facility Project Lifecycle Phases
Commercial Property Development & Management Lifecycle Phases
Conception (C)
(PD)
Planning & Feasibility (SD)
(SD)
(DD)
Preparation (P)
(CD&CO)
Execution (E)
(OM)
Operation Maintenance (OM)
Location Descriptors Plot of land Descriptors Building Descriptors Process Quality Descriptors Economic Quality Descriptors
Development & Asset Mgmt. Stakeholders
Valuation & Cost Mgmt. Stakeholders
Low significance Low-Medium significance Medium significance
Medium-High significance
High significance
Scope of professional practices
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AEC Stakeholders
Transaction Mgmt. Stakeholders
Recommissioning (R)
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4.2 Workshop 2 Identifying the Challenges Participants brainstormed the challenges relating to through life information management and then ranked them in the same way as exercise 1. A total of 23 challenges were identified, that are divided in technology based and socio-technology challenges as shown in Table 4. The challenges identified by each group were then discussed. Five categories (Table 4) identified by the facilitators and reported back to participants include issues surrounding: 1) Inter-operability and data standards, 2) Data quality and fidelity,
Post workshop analysis further classified these five categories in terms of ‘Technology based Challenges’ (category 1) and ‘Socio-technical Challenges’ (categories 2-5). Far more socio-technical challenges (20 in total) were identified as being significant by participants. Participants were then asked to rank the importance of each of the 23 challenges. Figure 4 illustrates the results of RII analysis. A number of issues will need ad dressing if the vast amounts of property data are to be a useful resource over a building lifecycle. Whilst three technology-based challenges identified by workshop participants as having a high level of agreed significance, the number and significance of socio-technical challenges identified were greater overall.
3) Context, 4) Security and privacy, and; 5) Digital skills and knowledge competencies.
Table 4
Challenges to through-life information management and corr esponding RII
Type
Sub Type
Technology based Challenges
1. Interoperability & 2. Data Standards 3.
Data Quality & Fidelity
Context-based Issues
Challenges Identified Ensuring data to be compatible and interoperable over long timescales (RII 0.90) Ensuring data can be sustained and updated over long timescales (RII 0.85) Ensuring data can be organised such that it can be discovered and exploited (RII 0.92)
4.
Human error, information overload and cognitive limitations (RII 0.77)
5.
Data consistency, accuracy and reliability (RII 0.92)
6.
Data granularity and its consistent specification (RII 0.81)
7.
Data verification and validation (GIGO – Garbage in, Garbage out) (RII 0.85)
8.
Degree of interpretation and human manipulation (RII 0.85)
9.
Communication differences and difficulties between domain specific languages (RII 0.74)
10. Number of disparate data sources and disjointed nature of information flow (RII 0.87) 11. Differences in levels of availability of data between stakeholders through-life (RII 0.54) 12. Compressed timeframes for data generation, sourcing and analysis (RII 0.56)
SocioTechnical Challenges
13. Conflict in interests relative to data transparency and business interests (RII 0.74) 14. Confidence in IT infrastructure securit y in distributed networks & data stores (RII 0.81)
Security & Privacy
15. Privacy preserv ing analytics and granular access control (RII 0.82) 16. Secure data storage and data provenance (RII 0.81) 17. Intellectual property and information ownership (RII 0.90) 18. End-point validation and filtering (RII 0.82) 19. Lack of digital skill sets and domain knowledge (RII 0.85)
Digital Skills & Knowledge Competencies
20. Complexity of incorporating operational simulations (RII 0.62) 21. Perceived ‘black box’ and risk in loss of knowledge due to dynamic workforce (RII 0.54) 22. Need for cultural change amid feelings of fear & ‘loss of control’ (RII 0.73) 23. Continual reporting and justification of business case for on-going data collection (RII 0.72)
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Figure 4
Relative Importance of Challenges to Through-life Information Management Importance 0.0 Ensuring data to be compatible and interoperable over long timescales
Interoperability Ensuring data can be sustained and and Data updated over long timescales Standards Ensuring data can be organised such that it can be discovered and exploited Human error, information overload and cognitive limitations
Data quality and fidelity
Data consistency, accuracy and reliability Data granularity and its consistent specification Data verification and validation (GIGO – Garbage in, Garbage out) Degree of interpretation and human manipulation Communication differences and difficulties between domain specific languages
Impact of context
Number of disparate data sources and disjointed nature of current information flow Differences in levels of availability of data between stakeholders through-life Compressed timeframes for data generation, sourcing and analysis Conflict in interests relative to data transparency and business interests Confidence in IT infrastructure security in distributed networks and data stores
Privacy and Security
Privacy preserving analytics and granular access control Secure data storage and data provenance Intellectual property and information ownership End-point validation and filtering Lack of domain knowledge and digital skill sets, lack of education and training programs Complexity of incorporating operational simulations
Digital Skills and Knowledge Competencies
Perceived ‘black box’ systems and loss of corporate knowledge due to dynamic workforce Need for cultural change amid feelings of fear & ‘loss of control’ Continual reporting and justification of business case for ongoing data collection
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0.2
0.4
0.6
0.8
1.0
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4.2.1 Technology-based Challenges
Context based issues
Workshop attendees identified three key technologybased challenges. These are:
Key challenges identified included the degree of interpretation and human manipulation of data, the number of disparate data sources and the disjointed nature of information flow. Managing property related data is a challenge due to its’ diversity in terms of the number of different aspects of the building, its’ development, operations, surrounding environment and market. It is a challenge because to understand and exploit the data; the context in which it has been generated, and the relationships between data types and lifecycle phases need to be known and understood. In research aimed at supporting data re-use, Ball et al. (2012) proposed that, in addition to primary data records, the information generated or collected should include data describing the context in which it was generated or collected.
1. Information generated over a propert y’s lifecycle potentially needs to be accessed over many generations of computer hardware and software. 2. Multiple changes to the building and the local environment occurs over the lifecycle, and strategies are needed for updating, reporting and merging these changes at different levels. 3. Information needs to be organised so that it can be discovered and used by different property professionals.
4.2.2 Socio-technical Challenges Participants noted that, no matter how good their IT systems are; if the socio-technical challenges are not addressed then the benefits of BIM for information management for property professionals may not be delivered. These issues surround change management and compliance in the implementation of information systems. Such barriers are documented in the literature, and numerous AEC based case studies on the barriers to BIM adoption have discussed their impact. During discussions, workshop participants mostly focused their attention on these ‘people’ issues.
Data quality and fidelity Participants felt there were many opportunities for the accidental or deliberate entry of erroneous data with a challenge to make data consistent, accurate and reliable. An appropriate level of detail and consistent sp ecification was important, as were problems with data verification and validation. Previous studies observed accidental misspelling of words in service records, the use of slang and abbreviations (Ball et al, 2011). Modern i nformation systems can overcome these issues to some extent, but it is more difficult to address the deliberate falsification of data/records.
Security and privacy Six challenges were identified that relate to security and privacy. Of these, five were ranked highly, including: 1) Confidence in IT infrastructure security, 2) Privacy preserving analytics, 3) Secure data storage and data provenance, 4) Intellectual property and information ownership, and; 5) End-point validation. Limited attention has been paid in the BIM literature to these issues; security in data access and issues surrounding privacy of project data are most commonly discussed (Redman et al. 2012, Singh et al. 2011). However this is changing; a British Standard, in PAS form, is up for consultation at the moment on this area (PAS 1192-5: Specification for security-minded building information modelling, digital built environments and smart asset management) (BS 2015). Less attention is paid to issues of information ownership and intellectual property in situations of dynamic relationships between AECO companies involved in the lifecycle of a proper ty (for example, where one company constructs a high-rise commercial office, another owns it, another maintains it and others lease it). Concerns about intellectual property rights were seen as limiting the possibilities to learn from the aggregation of property data.
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Need for New Digital Skill Sets and Knowledge Competencies There is a need for education and training in new information systems and to develop new knowledge competencies. Five challenges were identified and, of these, the lack of digital skill sets combined with an inadequate level of domain knowledge was identified as the most significant. Participants highlighted the difficulty in making sense of large amounts of data without a good deal of intelligent processing and the knowledge/ experience to interpret and drive this processing. For example, participants stated that an experienced property professional currently aggregates and interprets many sources of data when making an assessment of the state or value of an asset. Participants expressed
Table 5
A similar process was under taken in respect of the London workshop and table 5 shows the similarities and differences in perceptions of participants about the drivers and challenges faced with information needs and data management through the property lifecycle. Overall Australia based practition ers perceived a greater range of issues than their UK counterparts and this may reflect the different cultures predominating within the two markets, as well as the different areas of property represented in both groups of workshops.
Comparison between Australian and UK participants’ perspectives r egarding the key drivers and challenges when sourcing, integrating and generating data through-life
Data Quality and Fidelity Data consistency, accuracy & reliability across all lifecycle phases
Process and s u K A U Workflow ✓ ✓
Disjointed nature of information flow
s u K A U Human Error Lack of combined domain-specific ✓ ✓ knowledge & digital skill sets
Data format and interoperability
Differences in level of availability of data to ✓ ✓ all users through-life
Lack of education and training- both ✓ ✓ institutional & organisational
Data granularity & level of details (LoD)
Lack of automation & integration between ✓ ✓ information systems
Black box systems & loss of corporate ✓ ✓ knowledge due to dynamic workforce
Data quantity Vs quality
Compressed timeframes for data ✓ ✓ generation & analysis
Objective Vs. subjective data, information ✓ &knowledge Data verification and validation: GIGO (Garbage in Garbage out) Complexity of incorporating operational simulation
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their concern as to whether BIM, as an information management “tool” could replicate this level of real-life experience, and what training would be required to use BIM effectively for this purpose.
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Uncertainty surrounding value of data & its ongoing use through-life
Lack of standards & protocols for data use, ✓ ✓ entry, verification and validation Continual reporting/ just ifica tion of business case for data collection and upgrading
✓
Need for cultural change admit feelings for fear & “loss of control”
Communication difficulties & ✓ ✓ differences in domain specific language Human error, cognitive limitations & ✓ ✓ information overload
✓ ✓
s s u K u K A U Security and Privacy A U
✓
✓
✓
✓
✓
✓
Conflicts in interest relative to data transparency & business interest
✓ ✓
IT infrastructure security in distributed networks & data stores ✓ Privacy preserving analytic & granular access control Secure data storage & data provenance
End-point validation and filtering
Security of property and building metadata tags through-life
✓ ✓
✓
✓
✓
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4.3 Workshop 2 and 3 Identifying Timelines & Mapping Data Needs Through Life. Having identified the extensive range of data types in Workshop 1, the second part of workshop 2 asked participants to plot a timeline for managing data through the property lifecycle. Each participant focussed on a particular task they executed in their professional capacity. Figures 5 and 6 show two typical examples of the property data needs through life. It is clear that some tasks are far more detailed and complex than others. In figure 5, a Chartered Building Surveyors’ data needs, when undertaking a Technical Due Diligence (TDD) survey are shown. This task takes place during the lifecycle and typically requires relatively few data types. In comparison the Portfolio Management surveyor (figure 6) has requirements to access a far greater range of data types over a much greater range of the building lifecycle from planning and feasibility through to the end of life cycle when redevelopment or demolition is a consideration. Two further examples of the map ping of data needs and types over the property lifecycle is shown for a Transaction Manager and a Portfolio Management Surveyor in appendix 2 and 3. Although the data needs occur at different phases, and involve different type of data, it is apparent that some of their data needs are to be found within BIM. Equally it is apparent that other data needs / types are not yet included within BIM, but are in other digital databases, such as BMS. Note that due to time restrictions for the London workshop, these participants did not complete the tasks for workshop 2 and 3. Workshop 3 involved a review of the timelines plotted in workshop 2 and a review of the data types and needs. In some cases amendments were made. Discussions between participants revealed the diverse nature of data types and needs required by the various property professionals for specific tasks. Technically, there is potential to link these databases, however different sectors of the propert y and construction industry own and manage some of these databases and some negotiation is required to make these databases talk to each other for property professionals.
Figure 5
Data needs for a Building Surveyor Technical Due Diligence survey
MAJOR COSTS
Visual Inspections
MINOR COSTS
Health and User Comfort
Basic Building description
Discussion with F.M.
Environmental Quality
Site features
Access to Maintenance Data
Functional quality
Report to Client
Land features
Discussion with Semas /Structural Engineers
Operational Quality
Environmental Context
Technical Quality
Use, maintenance and repairs
Redevelopment, sale, demolition
Redevelopment/ strategic optioneering
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5.0 Survey Data Analysis and Discussion
Figure 7
Which region are you currently working in? Middle East and Africa
Latin America
5
5.1 Part 1 – Respondent Profiles, Current Awareness and Usage of BIM The respondents’ profile information, including region, property discipline and sector, involvement in stages of the property lifecycle, years of experience and size of organisation, were collected in the first part of the survey to provide context for the answers. The survey had a total of 59 respondents, each of which completed the survey to varying degrees. Given the low response rate of the membership base of RICS, care must be taken when drawing conclusions from the results.
RICS region respondents work in
0
Europe
19
Asia Pacific
21
North America
14
Overall, most respondents (88%) are very experienced with 11 or more years working in the built environment sector. The respondents are employed by either very large organisations of more than 1000 employees (37%) or very small ones with less than 51 employees (42%). Those working in large organisations are likely to have access to latest innovations in technology including BIM. Figure 7 shows the distribution of respondents by RICS regions. Survey comparisons by region were not possible due to under or over-representation of construction and design professionals within each region. When the responses from the regions were analysed all North American responses were from property professionals, the Europe responses were slightly biased to construction, whilst the Asia Pacific respondents were slightly biased to property professionals. The small group of respondents from the Middle East and Africa region were mostly construction professionals Figure 8 shows that respondents’ areas of current professional practice was primarily valuation and property development, which is the group we wanted to target in respect of k nowledge and awareness of BIM. Respondents working in construction and design were also well represented in the sample. Areas of practice less represented were property por tfolio management, property investment and FM. Respondents were also asked to identify which sectors and land use types they worked on.
Figure 9 shows that most worked in the commercial office sector, where larger new buildings are most likely to have some elements of BIM adopted in the construction phase. The retail sector was al so well represented th ough it is not clear the type of retail buildings covered, with newer larger retail centres being likely to use BIM technology compared to smaller scale retail. Many worked in the residential sector, which again is less likely to use BIM unless the projects are large scale or high-rise. The Health sector is reasonably well covered but again can range from small and simple buildings to very complex largescale stock again with varying levels of BIM adoption. Similar comments apply in respect of education buildings. Less well represented are those working on industrial buildings, transport and infrastructure.
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Figure 8
Respondents’ area of current practice
Which area(s) of property do you currently practice in? (Select all that apply) Percentage (%) 5.0
0.0
10.0
15.0
20.0
25.0
30.0
Property Valuation Property Development Construction Design (AEC) Real Estate & Transactions Property and/or Portfolio Management Property Investment Other FM
Figure 9
Land use types and sectors of property r espondents work on
What sectors of property do your work activities surround? (Select all that apply) Percentage (%) 0.0
Commercial Offices Retail buildings Residential buildings Health buildings Other commercial Education buildings Industrial Transport Infrastructure Other
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1 0. 0
20 . 0
30.0
40.0
50. 0
6 0. 0
70 . 0
8 0. 0
90.0
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5.2 Part 2 – Experience Working with Information Technologies
be partly a result of the seniority and years of experience of the respondents. Figure 10 summarises responses in respect of use of technologies in the workplace.
Not surprisingly, high usage of intranets was reported in the survey. Also, given the high numbers working in the property sector there is a high use of online property databases, such as RP data in Australia. Likewise, valuation systems and extranets have fair levels of usage. Less well used are 3D modelling systems, finance systems and 2D CAD systems. The lowest used technologies by the respondents were building simulation and analysis, 4D and 5D modelling, virtual data room and BMS. Overall, the group is reasonably used to using IT, however the advanced and newest iterations of BIM technologies are less familiar to the sample. This may
When asked about their understanding of BIM, 12.1% have ‘no understanding’ and 48.3% report having ‘limited understanding’, which shows a need to educate and upskill over 60% of respondents (see Figure 11). Conversely just und er a quar ter (24.1%) felt that th ey have a ‘good understanding’ whilst just 15.5% felt they have ‘excellent understanding’ of BIM.
Figure 10
Having said this when asked about experience of BIM (Figure 12) 67% record ‘no experience’ which confirms the need to educate and up-skill RICS members. Only 12% have experience of BIM exceeding 5 years. Nine of the 19 that had experience in BIM reported using it on a daily basis in their current work activities.
Use of information technologies in the workplace
Of the following information technologies, which do you use in your current work activities? Percentage (%) 0.0
10.0
20.0
30.0
40.0
50.0
60.0
Intranets Online Property Databases Extranets Valuation Systems 3D Modelling Systems Finance Systems 2D CAD Systems Building Management Systems Virtual Data Rooms 4D or 5D Modelling Systems Building Simulation and Analysis Other
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Figure 11
Figure 12
Understanding of BIM
What is your level of understanding of Building Information Modelling (BIM)?
Experience of BIM
Do you have any practical hands-on experience with BIM?
No understanding
12.1%
Less than 1 year
Excellent understanding
9%
12.1% 1–3 years
Good understanding
9%
No experience
Limited understanding
67%
48.3%
5+ years
12.1%
12%
4–5 years
3%
Of the 19 respondents that have had hands-on experience, Figure 13 shows where they received their training. Most received training on the job, followed by industry training courses, in-house training programmes and finally tertiary education. Clearly where training is delivered on the job, in house and via training courses individuals are exposed to a limited range of systems and technologies already selected or adopted by their employers.
Figure 13
This approach is u nderstandable where there is a need to up-skill existing members of a workforce. However, there is a greater potential in the education system for people, future RICS members, to be exposed to the theories underlying the technologies and to be exposed to a greater range of systems. On this basis we strongly encourage RICS to promote the adoption of BIM education into its accredited global property education provision.
Source of BIM training
Where did you receive your training in BIM? (select all that apply) Percentage (%) 0
Tertiary institution
10
20
30
40
On the job
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60
3 8
Industry training courses In-house training programs
50
7 11
70
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5.3 Part 3 – Information Frequency and Need of Use Part 3 of the survey, entitled “Understanding Information Value”, asked respondents to rank dif ferent types of information on the frequency of use and why the information is needed in the context of their current work activities. The answers were scored, with higher scores given to higher frequency of use and to more urgent need of use. The options and scoring for freq uency of use were: • Frequently – used in daily business, SCORE = 3 • Cyclically – used at regular intervals, SCORE = 2 • Infrequently – one or only in certain scenarios, SCORE = 1 • Never SCORE = 0 The options and scoring for nee d of use were: • Required by law SCORE = 3 • Necessary to carry out my business processes SCORE = 2 • Not needed by me SCORE = 1
Frequency of Information Use For each information typ e, an average score was calculated for the respondents. Given the low participation rate, we will not look at individual information types, but rather the categories of information and their relative scores. In Table 6, the items within Real Estate Data are very highly rated, indicating survey respondents use this category of information frequently. Conversely the 3D model objects and properties rated lowest in the frequency of use of data types. Documentation (specifications) and images (drawings) also rated highly and are used frequently by respondents. Other data types used most frequently were Spatial (area data) and Project data (construction and planning/feasibility attributes). The most frequently used Market Data is state, regional and neighbourhood market data. This is closely followed by the most frequently used Property Location Data which is micro-location information such as transport connections or reputation/image of the area, quality of local facilities/amenities such as shops, schools and so on.
Information Need When looking at information or data need a different range of attributes score highly. The highest need for data falls in the area of maintenance where information needs are space management, asset monitoring and tracking and information about alterations and repairs to buildings. This data is of use to Facilities Management, Proper ty Management and Building Surveyors. The next highest ranked need is for project data regarding feasibility and planning attributes, which has a high frequency use in Table 6. Similarly needs with regards to Documentation and Images (specifications and 2D drawings) ranked highly. This is a long served trad itional method of representing data in specifications and 2D drawings in the property and construction industry and this confirms the limited take-up and usage of BIM amongst many RICS members to date.
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Table 6
Frequency of use of data types Information Category
Survey item Property Insurance Claims Variables Affecting Property Insurance Rates Property Imagery Property Activity Property Value Attributes Specifications 2D Documentation (plans, elevations, sections, etc.) Area Construction Attributes Planning & Feasibility Attributes State, Regional and Neighbourhood Market Micro-location Property development Design management attributes Certifications (Permits, Ratings, etc.) 2D geometry National Market Maintenance, Alteration and Repair Macro-location Property Lot Attributes Tenant and Occupier Situation Listings, Recent Sales and Auction Vacancy and Letting Situation Environmental Attributes Surrounding Building Context Marketing Statistics Property transfers 3D rendered perspectives Utilities Asset Monitoring & Tracking Volume Orientation Operations and Maintenance Manuals Architectural Components 3D geometry Space management Structural components Heating, ventilation & air conditioning components Electrical and lighting components Mechanical & plant components Internal fittings, furnishings and fixtures External fittings, furnishings & fixtures Hard & soft landscaping components Payments Out Payments In
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Frequency of Use Average Score 3.61 3.52 2.74 2.67 2.55 1.90 1.90 1.88 1.87 1.75 1.73 1.63 1.59 1.59 1.55 1.54 1.53 1.51 1.46 1.46 1.45 1.45 1.44 1.42 1.40 1.38 1.36 1.36 1.36 1.34 1.32 1.27 1.23 1.23 1.21 1.21 1.20 1.03 1.00 0.98 0.95 0.95 0.92 0.88 0.87
a t a D t e k r a M
n o i t a c o L y t r e a p t o r a P D
a t a D e t a t s E l a e R
a t a D e t i S y t r e p o r P
a t a D l a i c n a n i F
a t a D e & c n n o a i t n e t a r i e n p a O M
s e t u b i r t t A l a i t a p S
s & t n c o e j s i b e t a O i t l t n e r e s d e m e o p o g M r u c a P o m D 3 & D I
a t a D t c e j o r P
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Table 7
Data need score by data type / area of practice Information Category
Survey item Space Management Asset Monitoring & Tracking Maintenance, alteration & repair Planning & feasibility attributes Specifications 2D Documentation (plans, elevations, sections, etc.) Environmental Attributes National Market Area Design management attributes Utilities Certifications (Permits, Ratings, etc.) Property Lot Attributes Property development Micro-location State, regional and neighbourhood market Surrounding Building Context 2D geometry Architectural Components Operation and Maintenance Manuals Macro-Location Property Value Attributes 3D Rendered Perspectives Orientation Tenant and Occupier Situation Volume Structural Components Property Activity Listings, Recent Sales and Auction Vacancy and Letting Situation Property Transfers Property Imagery Marketing Statistics 3D geometry Mechanical & Plant Components External Fittings, Furnishings & FIxtures Hard & Soft Landscaping Components Internal Fittings, Furnishings & Fixtures Electrical and lighting components Heating, ventilation & air conditioning components Payments In Payments Out Variables Affecting Property Insurance Rates Property Insurance Claims Construction Attributes
Need Average Score
a t a D t e k r a M
n o i t a c o L y t r e a p t o r a P D
a t a D e t a t s E l a e R
a t a D e t i S y t r e p o r P
a t a D l a i c n a n i F
a t a D e & c n n o a i t n e t a r i e n p a O M
s e t u b i r t t A l a i t a p S
s & t n c o e j s i b e t a O i t l t n e r e s d e m e o p o g M r u c a P o m D 3 & D I
a t a D t c e j o r P
2.38 2.32 2.26 1.97 1.90 1.85 1.84 1.83 1.83 1.83 1.78 1.78 1.76 1.76 1.76 1.74 1.73 1.70 1.70 1.69 1.69 1.69 1.68 1.66 1.64 1.63 1.63 1.60 1.60 1.58 1.57 1.57 1.56 1.55 1.55 1.55 1.55 1.54 1.54 1.53 1.43 1.40 1.22 1.22 1.93
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• Specifications – Documentation & Images
Figure 14 shows information types plotted by their ranking on need and frequency of use. Of particular note, is the disparity between the frequency and need rankings of the real estate data. Although the real estate information types were amongst the highest in ranking for frequency, they have relatively low rankings for need. This suggests that these are used with great frequency by a small proportion of the respondents for non-legal reasons, but that a large proportion of the respondents do not use them. The specific information types that rank as highest in terms of frequency and need (top right quadrant) are:
• Property Marketing Statistics – Market Data • Maintenance, Alteration & Repair – Operations & Maintenance Data • Construction Attributes – Project Data • Design Management Attributes – Project Data • Planning & Feasibility Attributes – Project Data • Micro-Location – Property location Data • Property Development – Property Site Data
• 2D Documentation (plans, elevations, sections, etc.) – Documentation & Images
• Property Lot Attributes – Property Site Data • 2D geometry – Spatial Attributes
• Certifications (Permits, Ratings, etc.) – Documentation & Images
Figure 14
• Area – Spatial Attributes.
Information Type Need Ranking versus Frequency Ranking
g n i k n a R d e e N n o i t a m r o f n I
Information Frequency Ranking 3D Model Objects & Properties Property Location Data
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Documentation & Images
Property Site Data
Market Data
Real Estate Data
Operations & Maintenance Data
Spatial Attributes
Financial Data
Project Data
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Although the respons e rate to the survey was not high enough to make a lot of cross-tabs or subgroup comparisons, the responses of the property professionals were compared to those of the construction professionals. A number of statistically signifi cant difference s were found between the two groups when it came to ranking information types. Table 8 shows the information types for which there was a statistically significant difference in the median scores between the two professional groups. These survey results are consistent wi th the differences found in the workshops. The workshop “Location” category was found to be less important to AEC stakeholders (refer to Table 3), which is equivalent to the above result that “Market” and “Property Location” categories are less important to construction
Table 8
professionals than to property professionals. Building descriptors were found to be more important to AEC stakeholders (construction and design professionals) in both the workshops and survey. Financial data is of more importance to property professionals. Several of the information types that were identified as being more important to property professionals also ranked in the highest quadrant of information types (Figure 14), including micro-location, property development, property lot attributes and property marketing statistics. These items might be indi cative of a gap for property professionals where these items have been relatively less important to AEC stakeholders but are of high frequency and need for work activities.
Tests of Professional Differences in Information Importance
Independent-Samples Median Test Category of Data
Market Data
Property Location Data Financial Data
Property Site Data
Real Estate Data
Building Data – Spatial Attributes
Building Data – 3D Model Objects & Properties
Building Data – Documentation & Images
Information Type
Frequency Statistical Property Construction or Need Significance Professionals Professionals
State, Regional and Neighbourhood Market
Frequency
0.002
Listings, Recent Sales and Auction
Frequency
0.002
Property Transfers
Frequency
0.003
Property Marketing Statistics
Frequency
0.004
Micro-Location
Frequency
0.011
Tenant and Occupier Situation
Frequency
0.001
Vacancy and Letting Situation
Frequency
0.001
Property Lot Attributes
Frequency
0.001
Utilities
Frequency
0.004
Environmental Attributes
Frequency
0.001
Surrounding Building Context
Frequency
0.004
Property Development
Frequency
0.004
Property Value Attributes
Frequency
0.000
Property Imagery
Frequency
0.002
Property Activity
Frequency
0.000
3D geometry
Frequency
0.046
Elec tr ic al & Ligh ting Component s
F requenc y
0. 01 1
Heating, Ventilation & Air Conditioning Components
Frequency
0.027
Mechanical & Plant Components
Frequency
0.011
Heating, Ventilation & Air Conditioning Components
Need
0.043
Operations and Maintenance Manuals Frequency
More Important
Less Important
Less Important
More Important
0.029
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5.4 Part 4 – Challenges & Benefits of BIM In this part of the survey, respondents were asked to rank how significant they saw challenges and benefits of an integrated approach to information management throughout the life of the property. Options were: • Not significant • Slightly significant • Moderately significant • Significant • Very significant Figure 15 shows the percentage of respondents that ranked each challenge as “Very significant”. The top three challenges, indicated by dark bars, are (1) Data accuracy, consistency and reliability issues (36%) (2) Lack of protocols to verify and validate data (33%) and (3) Secure data authorship and storage (32%). These responses echo the co ncerns of the works hop participants, particularly with respect to data accuracy, consistency and reliability, which was the top challenge in both the workshop and the survey. Of note is that the top two challenges fall within the category of Data Quality and Protocols. Human Factors seem to be less of a concern, with several items of the lowest concern falling in this category, including lack of training at an organisational level and communication difficulties. Finally the significance of the perceived key benefits of an integrated approach to information management through the life of property are considered. Of highest significance
40
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are the industry benefits of potential for ‘performance improvements and increased transparency and open data sharing across sectors’. Of equal highest significance is the benefit of ‘having data that can be re-used and re-purposed’, which again can save time and costs and enable good design and construction to be replicated. Other notable significant benefits are ‘improvements to the assessment of building per formance’ which is potentially very significant in terms of buildings rated under sustainability rating tools, which aim to measure in-use performance. Respondents also ranked ‘new abilities to provide value added services’ which reflects members’ desires to maintain the highest standards possible in highly competitive markets. However when we examine the lowest ranked benefits, there appear to be contradictions evident as ‘improvements to information availability and completeness’ ranked the lowest of all whereas earlier in the survey respondents had said data accuracy and reliability was a concern. There seems to be little point in having an increased availability of data available to industry, which may be incomplete and thus unreliable and out of date. Second lowest ranked benefit is the ‘potential for greater levels of innovation in industry practice’ and third lowest ranked item was ‘improvements to the assessment of property value’. It appears that members currently do not perceive a great level of benefit to valuers and the valuation process from data contained in BIM. More benefit may lie in the benefits to portfolio managers and investment management surveyors to assess the ongoing value of properties within their portfolios based on building performance and property maintenance costs over time. Building Surveyors and Facilities Managers will benefit from access to data related to building performance in delivering some of their professional servic es.
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Figure 15
Key challenges in information management through life Percentage (%) 0.0
10.0
20.0
30.0
40.0
Lack of protocols to verify and validate data
Data Quality & Protocols
Trade offs between data quality and data quantity Data granularity Data accuracy, consistency and reliability issues Scale and complexity issues surrounding large datasets
Technical
Lack of industry standards to control consistent data reuse Interoperability issues in structuring disparate data sources Security of property and building metadata Secure data authorship and storage
Security & Privacy Concerns
Need for privacy preserving analytics and granular access control Increased IT infrastructure security across distributed networks and data stores Conflicts in interest relative to data transparency & business interests Cost surrounding new information management infrastructures Justification of business case for sourcing, organising and maintaining data Increased and continued reporting
Process & Workflow
Uncertainty surrounding value of data and its ongoing relevance Compressed timeframes for sourcing, organising and reusing data Differences in level of availability of data to all users Disjointed nature of information flow between organisations / sectors Human error Communication difficulties and differences in domain specific “language” Need for cultural change amidst feelings of fear and “loss of control”
Human Factors
Ineffective implementation due high staff turnover Lack of education and training at organisational level Lack of education and training at institutional level Lack of interdisciplinary knowledge Lack of digital skill sets
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Figure 16
Key benefits of digital information through life Percentage (%) 0.0 Improvements to industry performance
Industry Benefits
Increased transparency and open data sharing across industry sectors Potential for greater levels of innovation in industry practice Improvements to data quality and accuracy Potential for greater levels of innovation Faster assessment and reporting processes
Organisational Benefits
Reduction in data sourcing and co-ordination efforts New abilities to provide value added services Provision of a centralised point of control Improvements to organisational performance and operational efficiency Improvements to personal productivity Improvements to levels of acceptable risk Improvements to information availability & completeness
PracticeBased Benefits
Increased levels of transparency Increased decision support Greater accuracy and efficiency in property evaluations and assessments More effective control of resources and costs Improvements to the assessment of property value Improvements to the assessment of building performance
Information Quality
Information can be checked and validated Information, once captured, can be reused and repurposed
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5 .0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
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6.0 Overall conclusions The research question pos ed was: what is the role of the value dimension in BIM? Through a comprehensive review and analysis of data types and needs in respect of professional property tasks and services, this research finds that there is potentially a significant role for the adoption of property data into BIM and, also other digitised building systems not initially considered within the remit of this research, such as BMS. This potential role can be classified into those areas where data relevant to property professionals exists and can be used, providing access to data is provided. Other areas are identified where data, is not within the BIM and further consideration of whether to incorporate such data is required, as well as the mechanisms for incorporating such data. Who would, and should, provide that data; as well as the steps to ensure such data is accurate and updated as necessary, used in context, tackles issues of security and privacy also require addressing.
6.1 Data through-life
The experience of property professionals using BIM was found in the workshops and the survey to be minimal. Furthermore understanding of BIM within the property group is also limited in breadth and depth. We should bear in mind that currently BIM is largely restricted to larger newer buildings and that the majority of RICS members will be performing professional services on existing stock where information is not available in BIM format. T his is likely to change over time, however currently there is a need to raise awareness, increase the knowledge base and to develop skills within the profession.
6.2 Challenges & Benefits of BIM As with all data needs, it is critica l that the data is reliable and accurate and as up to date as possible. The workshops revealed 23 challenges, which were largely endorsed in the survey. Technology based challenges were ensuring data can be; 1. Compatible and interoperable over long timescales.
Property professionals were found to have a very broad range of data needs and used 24 different types of data in their professional services. These needs were identified in the literature, ratified in the workshops and confirmed in the survey. Currently sources are often separate and distinct and are at times unchecked with issues around accuracy of some data. The five main categories of property information were;
2. Sustained and updated over long timescales, and:
1. Market and location data.
1. Data Quality & Fidelity.
2. Property data (describing the plot of land)
2. Context-based Issues.
3. Property data (describing economic information)
3. Security & Privacy.
4. Building information; and,
4. Digital Skills & Knowledge Competencies.
5. Process qualities (planning information, construction information and FM information).
Largely, the survey responses echoed concerns raised by the workshop participants. There is a danger that in some cases there will be Building Information Models which are not well maintained and have inaccurate data entry that will, if relied upon by those unable to interrogate and understand the data, lead to poor decision making and professional judgements. This is a major challenge and the property profession need reassurance that the data they do access and use to base their professional judgements on is sound and reliable. Protocols needs to established as the range of professionals accessing BIM data widens, as the key benefit perceived by survey respondents of ‘improved performance’ may not be realised in practice. Furthermore the opportunity to provide clients with ‘value added services’ may not be realised if data is not perceived to be reliable, up to date and sound. Overall the survey respondents felt there would b e little benefit at this point in time to valuers in using BIM data, however it is considered that more benefit lies in the area of property por tfolio managers who will seek to rationalise properties within the portfolio based on performance amongst other variables.
Data needs were also found to vary from relatively simple at a single point in time, for example the Building Surveyors Technical Due Diligence report (figure 5) to very complex needs of Portfolio Management Surveyors over a whole of life timeframe (figure 6). The workshops and survey revealed good potential to use some of the data already in BIM for property professional practices for example, FM and Property Management tasks, Building Surveyors Technical Due Diligence reporting, and property por tfolio management. The opportunities lie largely in respect of the data on building performance in use. However, such data is typically found in the BMS as much as the BIM. Therefore RICS should investigate the opportunities within BMS technology to inform some property tasks, as many buildings may not have BIM but may have a BMS.
3. Organised such that it can be discovered and used. Not surprisingly the socio-technical challenges identified often reflect those of professions whether using technology systems or not and include aspects such as reliability, fidelity and quality. Socio-technical challenges summarised in Table 4, were grouped as;
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6.3 BIM in Property Education Given the low levels of understanding and practical experience of using BIM, there is considerable scope for incorporating some understanding of BIM technology into RICS accredited property courses at undergraduate and post graduate level. Clearly the obvious place to introduce student to the concept would in construction technology subjects, however it should also be referenced in property management, property investment, valuation, building surveying and facility management subjects as a potential source of information. In this way property students will start to see the potential for the use of BIM data across a range of their professional tasks. Macdonald (2012) has proposed a framework to assist AEC academics in implementing collaborative education programs with the aid of BIM tools and processes, and this could be adapted to incorporate property education. Clearly property education is not restricted to the tertiary sector and this research concludes that a broad program across all RICS disciplines at all levels of membership is desirable. Such a program should encompass provision of CPD for existing members, training short courses and provision of Information Papers and Best Practice Guidance Notes. A comprehensive strategy should be established to deliver a roll out of resources to members, under the leadership of an Education Task Force. This could build on the work already carried out to develop the RICS BIM Manager certification. There are various initiatives in this area being undertaken by professional bodies and other groups, and a unified, industry-wide approach may be worth considering, rather than separate task forces being set up that essentially have the same aims.
6.4 Recommendations and further research From this research, it is apparent that great potential exists to enhance the quality and accuracy of many aspects of property professional practice with the adoption and use of BIM in some tasks. There are five key recommendations that arise out of this research.
1. Mapping of data needs and types across all RICS disciplines One of the key priorities is to undertake a comprehensive mapping of data needs and types across all RICS disciplines to identify (a) what is currently within BIM that could be used by property professionals, (b) data needs and types currently in a digital format but found in databases outside of BIM that could be easily made compatible to BIM. At this point an assessment of the demand for the data would determine whether it is desirable to implement such a change. Thirdly this review
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would identify those data needs and types that are outside of BIM that could be digitised and incorporated due to the extent of potential usage within the property profession. In all cases issues identified in section 5.4 data quality and fidelity, context, security and privacy should be considered. In particular details on data format and source are needed. The full list should be categorised and prioritised, and where necessary negotiations with third parties should be initiated.
2. Introduce BIM professional competency in RICS APC for property professionals The RICS APC group sho uld develop appropriate prope rty discipline BIM competencies with the APC structure so that property professionals can obtain recognition for knowledge, skill and competency with the application of this knowledge in their professional practice. Given the innovation in the RICS BIM Certified Manager qualification, there may be some aspects which are transferable to the property disciplines.
3. Develop a set of CPD events to raise awareness among property professionals of BIM As a priority RIC S should develop some onlin e education resources for members to raise awareness and knowledge in respect of BIM and how property professionals could use data within the models.
4. Develop RICS training courses for existing members of the property disciplines in BIM Concurrent with the roll out of CPD courses for members and the development of online education resources, RICS should develop a series of training courses for existing members globally to realise the potential of BIM data in their professional practices.
5. RICS BIM & Property Education Task Force With regards to the integration of BIM into property education, RICS could consider updating accreditation criteria for universities to include requirement for collaborative working with other disciplines/using BIM data effectively. Furthermore RICS could form an Education Task Force to champion the roll out of BIM across property courses globally to ensure new members have the requisite awareness, knowledge and skills with respect to BIM and property; or ‘the value dimension’.
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7.0 References Australian Constr uction Industr y Forum (ACIF), 2015. A Framework for the Adoption of Project Team Integration and Buildi ng information Model ling. Retrieved on 31st May 2015 from; http://www.apcc.gov.au/ALLAPCC/ Framework_WEB.pdf Australian Insti tute of Architects. 2015. BIM & IPD working Groups. Retrieved on 31sy May 2015 from http://wp.architecture.com.au/bim/ groups / Azhar, S. (2011). Building information modeling (B IM): Trends, benefits, risks, and challenges for the AEC industry. Leadership and Management in Engineeri ng., 11(3), 241-252. BAF 2013. Embedding Building Infomration Modelling (BIM) within the taught curriculum. The Higher Education Academy. Retrieved on 31st May 2015 from http://codebim.com/wp-content/uploads/2013/06/Embed_ BIM_in_curriculum_UK.pdf Ball A, Darlington M, Howard T, McMahon C, & Culley S. (2012). Visualizing research data records for their better management. Journal of Digital Information, 13. Becerik-Gerber, B. and Kensek K. 2010. “Building Information Modeling in Architecture, Engineering, and Construction: Emerging Research Directions and Trends”, Journal of Professio nal Issues in Enginee ring Education and Practice 136( 3): 139 -147. Becerik-Gerb er, B., Jazizadeh, F., Li, N., & Calis, G. (2011), Application areas and data requirements for BIM-enabled facilities management. Journal of constructi on engineeri ng and management , 138(3), 431-442. Bryde, D., Broquetas, M., & Volm, J. M. (2013), The project benefits of building information modelling (BIM). Intl. Journal of Project Mgmt., 31(7), 971-980. BSI (2010), Constructing the business case for BIM , BSi, London, 2010. BSI (2014), BS 1192-4:2014, the Collaborative production of information Part 4: Fulfilling employers information exchange requirements using COBie, URL: http://shop.bsigroup.com/forms/BS-1192-4/ (accessed 14 Jan. 2015). BSI (2015), PAS 1192-5: Specification for security-minded building information model ling, digital bui lt environments and smar t asset management : Retrieved on 31st May 2015 from http://shop.bsigroup.com/ upload/271469/PAS1192-5-BSI.pdf BuildingSMA RT, (2012). MVD Proce ss. Retrieved on 14th January 2015 from: http://buildingsmart.com / standards/mvd/mvd-proce ss . Cambridge Semantics. 2015. Introduction to the Semantic Web. Retrieved on May 24th 2015 from http://www.cambridgesemantics.com/semanticuniversity/introduction-semantic-web Carroll, S. (2009). A well-kept secret: BIM in preconstruction wins clients. Constructor the magazine of the Association of General Contractors of America. Retrieved on 14th January 2015 from http://constructor.agc.org Corry, E., O’Donnell, J., Cur ry, E., Coakley, D., Pauwels, P., & Keane, M. (2014). Using semantic web technologies to access soft AEC data. Advanced Enginee ring Informatics , 28 (4), 370-380. Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management Science, 9(3), 458-467. de Souza, L.L.A., de Amorim, S.R.L., & de Magalhães Lyrio Filho, A (2009). Impact from the use of BIM in Architectural De sign Offic es: Real Estate Market Opportunities, Gestão & Tecnologia de Projetos , Vol. 4, Nº 2, Nov, pp 26-53. East, W.E (2007), Construction Operation Building Information Exchange . USACE ERDC. Eastman, C., Teicholz, P., Sacks, R & Liston, K, (2008). BIM Handbook: A Guide to Building Informati on Modeling for Owner s, Managers, Designers, Engineers and Contractors. Wiley & Sons Inc, New Jersey.
The European Group of Valuers As sociations ( TEGoVA). (2003), European Property and Market Rating. TEGoVA. Brussels. Fillman, S.A., Wilde, K. L., Kochert, J.F., Homan, S.R., & Tomovic, C.L. (2010). Entry-level engineering professionals and product lifecycle management: a competency model, Int. J. of Manufacturing Technology and Management, 13(3/4), 306-311. Ford, G, Bar tley, T, Igba, J, Turner, A, & McMahon, C (2013), Product Life Cycle Data Management: A Cross-Sectoral Review, in B. Alain, R. Louis, D. Debasish, (eds.) Product Lifecycle Management for Society, FIP Adv in Inf. and Comm. Tech ., Vol. 409, pp. 58-67. Fuerst, F. & McAllister, P. 2012. Green Noi se or Gree n Value? Measurin g the effects of environmental certification in commercial office values. Real Estate Economics. 39 (1) pp45-69. Green Property Alliance (2010). Establishing the Ground Rules for Property: Industry-wide Sustainability Metrics , GPA, London. Hewett, A, (2009) Product Lifecycle Management (PLM): Critical Issues and Challenges in Implementation, Inf. Tech. and Product De v., Annals of Inf. Sys., Vol. 5, No. 1, pp.81-105. Hutchins, G. (2004). SME Speaks: Manufacturing Engineers Must Reduce Competency Gaps. Manufacturing Engineering magazine 132(2). Retrieved (Feb. 2014) from www.sme.org/MEMagazine/ Articl e.aspx?id=31088&taxid=1427 Holness, G. V.R.: 2008. “BIM Gaining Momentum.” ASHRAE Journal, June 28-29 Jupp, J., & Awad, R. (2013). Developing digital literacy in construction management education: a design thinking led approach. Journal of pedagogic devel opment . Jupp, J. R. (2013), Incomplete BIM implementation: Challenges and role of product lifecycle management functions, in B. Alain, R. Louis, D. Debasish, (eds.) Product Lifecycle Management for Society, IFIP Advances in Informatio n and Communications Technologies . Vol. 409, pp. 630-640, Springer Berlin Heidelberg Jupp, J. R., & Nepal, M. (2014). BIM and PLM: Comparing and Learning from Changes to Professional Practice Across Sectors. In Product Lifecycle Management for a Global Market (pp. 41-50). Springer Berlin Heidelberg. Jupp, J. R. & Singh, V. 2014, Similar Concepts, Distinct Solutions, Common Problems: Learning from PLM and BIM, Intl. Conf. on Product Lifecycle Management , 9-11 July, Japan Jupp, J., & Wilkinson, S. 2015. Challenges of Through-Life Property Data Management. In the proceedings of the RICS COBRA Conference UTS Sydney July 8-10th 2015. ISBN 978-1-78321-071-8. To appear. Linstone, H. A., & Turoff, M. (Eds.). (1975). The Delphi method: Techniques and applicatio ns (Vol. 29). Reading, MA: Addison-Wesley. Lützkendorf, T., & Lorenz, D. (2011). Capturing sustainability-related information for property valuation. Building Research & Information, 39 (3), 256-273. Macdonald, J.A. and Mills, J.E. (2013) ‘An IPD approach to construction education’, Australasian Jour nal of Construction Economi cs and Building , 13 (2) 93-103 Macdonald, J.A. and Granroth, M. (2013), ‘Multidisciplinary AEC Education Utilising BIM / PLIM Tools and Processes’, in Product Lifecycle Management for Society IFIP Advances in Information and Communication Technology (Proc. of 10th IFIP WG 5.1 International Conference, PLM 2013, Nantes, Franc e, July 6-10, 2013), Vol. 409, 2013, pp 663-674. Macdonald, J.A. (2012), ‘A Framework for collaborative BIM education across the AEC disciplines’, in Proc. of the 37th Annual Conference of the Australasian Universities Building Educators Association (AUBEA), 4-6 July, Melbourne, pp223-230.
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Mason H. ISO 10303 – STEP, A key standard for the global market, ISO Bulletin 2002 4:9-13. McGraw Hill. (2008). “ Smart Market Report, Building information modeling: Transforming design and construction to achieve greater industry productivit y.” McGraw Hill, NY. McGraw Hill. (2009). “ Smart Market Report: The business value of building information model ing: Getting building inf ormation modeling to the bottom line”, McGraw Hill, NY. McGraw Hill. (2010). “ Smart Market Report, The Business Value of BIM in Europe.” McGraw Hill, NY. McGraw Hi ll. (2014). Smart Market Report: Business value of BIM for construction in major global markets: How contractors around the world are driving innovation wit h building informatio n modelling . McGraw Hill Construction. Millington, A. F., 2014. 5th ed. An Introduction to Property Valuation. Routledge, Oxon, UK. Newell, G., MacFarlane, J., & Kok, N. (2011) Buildings Better Returns. API, Australia. Retrieve d on May 23rd 2015 from http://www.api.org.au/ assets/media_library/000/000/219/original.pdf NIBS (2007), US National BIM Standard: Version 1 – Part 1: Overview, Principles and Methodologies, National Institute of Building Sciences, URL: www.wbdg.org/bim/nbims.php (accesse d 14 Jan. 2015). Redmond, A., Hore, A., Alshawi, M., & West, R. (2012). Exploring how information exchanges can be enhanced through Cloud BIM. Automation in Const . 24, 175-183. RICS, 2015. BIM Manager Certification. Retrieved on 31st May 2015 from http://www.rics.org/uk/join/member-accreditations-list/bimmanager-certification/ Royal Institution of Chartered Surveyors (2009) 6th Ed. RICS Valuation Standards. RICS, London. Rowlinson, S., Coll ins, R., Tuuli, M. M., & Jia, Y. 2010. Implementation of building information modeling (BIM) in construction: a comparative case study. AIP Conference Proceedings, 1233 (PART 1), pp. 572 – 577 Sebastian, R. & Berlo, L. van 2010. Tool for Benchmarking BIM Performance of Design, Engineering and Construction Firms in The Netherlands. Archi tectura l engi neeri ng and des ign manage ment 6: 254–263. Silverman, D. 2013. Doing Qualitative Research. A Practical Handbook. Sage Publications. London.
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Singh, V., Gu, N., & Wang, X. (2011). A theoret ical fra mework of a BI Mbased multi-disciplinary collaboration platform. Automation in Const., 20(2), 134-144. Stark, J. (2011). Product lifecycle management: 21st century paradigm for product realisation, 2nd ed., Decision engineering, Springer. Succar, B., Sher, W., & Williams, A. (2013). An integrated approach to BIM competency assessment, acquisition and application. Automation in Construction , 35, 174-189. Teicholz, P. (Ed.). (2013). BIM for facility managers. John Wiley & Sons. Törmä, S. & Granholm, L., 2011. Managing building informatio n as a set of interrelated partial models, Espoo: Working paper United Nations Environment Programme (2009) UNEPFI/SBCI’S Financial & Sustainability Metrics Report, UNEP. Retrieved on 15th January 2-15 from: www.unepfi.org/fileadmin/documents/metrics_ report_01.pdf Vanlande, R., Nicolle, C., & Cruz, C. (2008), IFC and building lifecycle management. Automation in Construction, 18(1), 70–78. Wilkinson, S.J., 2015. Building approval data and the quantification of the uptake of sustainability measures: A case study of Australia and England. Structural Survey . Vol. 33, issue 2. ISBN 0263-080X. Wilkinson, S., & Jupp, J. 2015. Managing property data through life: BIM and the value dimension. In proceedings of RICS COBRA Conference UTS Sydney July 8-10th 2015. ISBN 978-1-78321-071-8. (to appear). Young, N., Jones, S., Bernstein, H. M., & Gudgel, J. E.: 2009. The Business Value of BIM: Getting Building Information Modeling to the Bottom Line, Smart Market Report: McGraw Hill Construction Yu, K., Froese, T., & Grobler, F. (2000). A development framework for dat a models for computer-integrated facilities management. Automation in construction, 9(2), 145-167.
Useful web sites HM Government BIM Task Group main website http://www.bimtaskgroup.org / AEC UK CAD and BIM Stand ards Site http://aecuk.wordpress.com / Construction Industry Council website http://cic.org.uk /
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8.0 Appendices Appendix 1
Propert y professionals data types and needs ............................48
Appendix 2
Key to symbols used in figures 5 and 6 and Appendix 3 ............49
Appendix 3
Managing data through the propert y lifecycle (Workshop 2 output) .........................................................................50
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Appendix 1 – Property professionals data types and needs Market Data National Market
E.g. Overall national economic situation, political, legal and administrative conditions
State, Regional and Neighbourhood Market
E.g. Economic situation, political, legal and administrative conditions, inv estment data (annual growth, median price, median rent , rental yield and rent demand)
Listings, Recent Sales and Auction
E.g. Property listings, sale transactions and records, national auction results and clearance rates, rental listings and applications
Property Transfers
E.g. Property sales & transfer s from Valuer General, real estate industr y data on annual transfers
Property Marketing Statistics
E.g. Online, print and phone marketing data
Property Location Data Macro-Location
E.g. Regional transportation infrastructure & transport connections, socio-demographic development, population structure & development, regional image, economic structure and situation, purchasing power
Micro-Location
E.g. Local-context data, suitability of location for propert y type, image of district, local transport connect ions, quality of public spaces and facilities (shopping, services, s ocial & medical facilities), distance to amenities.
Real Estate Data Property Value Attributes
E.g. Property attributes such as property type, land use, zoning, lot/plan number, existing owner, number of bedrooms, bathrooms, car spaces, previous sales information
Property Imagery
E.g. Aerial, internal and external proper ty images, mapping images
Property Activity
E.g. Activi ty / interest in a property, evaluation of a property
Property Insurance Claims data
E.g. Insurance claims data such as residential property claims
Variables affecting Property Insurance Rate
E.g. Value and risk data surrounding absolute property value, location, zoning, securit y & crime rates, mean area property price, environment al conditions
Property Site Data Property Lot Attributes
E.g. Orientation, layout, size/area, inclination, topography, soil characteristics, rainwater drainage, easements, groundwater, degree of hard surface sealing
Utilities
E.g. Energy supplies, water supplies, waste water supplies, communications services
Environmental Attributes
E.g. Environmental situat ion, green areas & plantation, contribution to maintaining biodiversit y, greenfield & brownfield c onditions, climate & geo data, air, noise & soil pollution
Surrounding Building Context
E.g. Distance to surrounding buildings, views & visual context, sunlight & shading levels, street layout, design & usage of open spaces, internal/external accessibility, neighbourhood safety, traffic conditions
Property Development
E.g. Data surrounding development applications, site selection/acquisition, details of the development, development cer tificates, building permission and planning regulations
Financial Data
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Tenant and Occupier Situation
E.g. Number of tenants, tenants’ image and solvency, duration and structure of rental contract s
Vacancy and Letting Situation
E.g. Vacancy rate, tenant retention, tenant fluctuation, duration of letting process, general letting prospects, investment volume, expected rates of return
Payments-In
E.g. Rental payments, advance payments for utilities, rental growth potential, and inflation expectations, other payments-in (e.g. facade advertising, energy-feed-in)
Payments Out
E.g. Payments for construction, acquisition, disposal, payments for operating costs, payments attributable/non-attributable to tenants,marketing/letting (e.g. estate agent’s fee), payments for modernisation, payments for operations
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Appendix 2 – Key to symbols used in figures 5 and 6 and Appendix 3 Node types
Dependency Deliverables Represent packages of information or materials that are considered, created or modified by tasks.
Simple tasks Represent tasks which take account of inputs to create outputs. All the outputs of a simple task are created (or updated) at the same time, when the task is complete.
Compound tasks Similar to a simple task, but can have one or more output ‘scenarios’. Each scenario can represent a different ‘forward branch’ and contain one or more deliverables.
Iteration constructs
The four types of nod e can be connected using two types of dependency (line with arrows
Flow dependencies The dependency contributes to the timing of the downstream task (eg. the upstream deliverable must be available to start the task)
Data dependencies The dependency indicates that the upstream information is used while executing the downstream task, but doesn’t determine when the task can be attempted.
Milestone Decision or stage gate, typically occurring between main project phases.
Similar to a compound task, but represent the possibility of generating a ‘backward branch’ (iteration).
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Appendix 3 – Managing data through the property lifecycle (Workshop 2 output) Appendix 3A
Example of a Transactions Managers participant’s data needs at various stages of the property lifecycle t n e r r r m r e e p e t e s g g o g c g t l e a e a d a e a j n s n n n v n o e a r a s a u a D m P m A m F m
e e t t r e i m p a P m o C
S T S O C R O N I M
U B & H / g n i n o i t i s o p e R / g n i n n a l p t e s s A
t n e m t s e v n I
t e s k r e a t a M R
h t s w e t o r a G R
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s t u p t u O d n a s t u p n I n o i t a e f r i m o L f n h I g h t u i o r w s h k T s a T a l t a a n o D i s y s t e r f e o r p P y o r t r P e g p o r n P i g g n a i n p a p a M M
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S T S O C R O J A M
/ t t e e u c p s t d e a . n e r c c h g o e n r c l i o o e i p C f T d e r
s t n e n i m y a P
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s i s y s l e a l a n S A
s t t n n e e m m e t l s t u t j e d S A
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s t n t e m u y o a P
s n o i t c e p s n I
s g n i o g t u O
g n i r e e n o i t p o c i g e t a r t s / t n e m p o l e v e d e R
n o i t i l o m e d , e l a s , t n e m p o l e v e d e R
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s r n e i p v o o t u d a r t n d e r a n p a t H a o s
t n e t m e e s g s A a n a m
n o s i t t c c e u f r t e s d n d o n C a n o i t t c n e u r t m s h n e i s t l o i c s b - a d e t r n s P a e
n g i s e D
n g i s e D
S T S O C R O N I M
l a u t p n e g c i n s o e C D
l a t e n k o r i t a a M N
g n i n n a l P
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s t u p n i a r t x E
g n i m m a r g o r P
n o o r i c t i a M c o l
t r o p e R O E G
d r n n e o a i i t p t n u a u a c t n c i e O T S
g g n y n n i o i t c i t t t e e n a k L a u r c t a i a d V S M n a
s t n t e u m y O a P
s t n e n I m y a P
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Special Thanks Special thanks to the following people: Andrew Hannel Opus, Sydney, Australia Andrew Partridge Eureka Funds Management, Sydney Ben Elder RICS, London, UK Christopher Stokes ESurv, Mid Anglia, UK Clinton Ostwald Urbis, Sydney, Australia David Wagstaff Pembroke, London, UK Doug Rayment AECOM, Sydney, Australia Hernan Jerrez Guerrero Ridley and Co Sydney Australia Jack Moseley Civic Valuations, Sydney Jennifer Macdonald University of Technology, Sydney, Australia John Kavanagh RICS, London, UK Kath Fontana BAM FM, Hemel Hempstead, UK Leon Carroll AMP, Sydney, Australia Paul Zahara Cranleigh, Sydney, Australia Phil Boyne Lend Lease, London, UK Richard Quartermaine Hammerson Plc, London, UK Richard Stacey Calibre Capital, Sydney, Australia Sarah Sayce University of Kingston, London, UK
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