Bim the Value Dimension 060715 Dwl Pc

Research

June 2015

Building Information Modelling and the Value Dimension

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2 RICS Research 2015


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RICS Research teamDr. Clare Eriksson FRICSDirector of Global Research & [email protected] Amanprit JohalGlobal Research and Policy Manager [email protected] Pratichi ChatterjeeGlobal Research & Policy Officer [email protected]

Published by the Royal Institution of Chartered Surveyors (RICS)RICS, Parliament Square, London SW1P 3AD

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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 system, without permission in writing from the publisher.

Copyright RICS 2015

Report for Royal Institution of Chartered Surveyors

Report written by:Associate Professor Sara J Wilkinson BSc MA MPhil PhD FRICS AAPI School of the Built Environment, University of Technology, Sydney, [email protected]

Associate Professor Julie Jupp BA BSc PhD School of the Built Environment, University of Technology, Sydney, Australia



ContentsGlossary of Terms ................................................................................................... 6

Executive Summary .............................................................................................. 7

1.0 Introduction and scope of research ..............................................10 1.1 Rationale for the research ..............................................................10 1.2 Research question, aims and objectives ......................................10 1.3 Limitations ..........................................................................................11 1.4 Structure of the report .....................................................................11

2.0 BIM and the Value Dimension ..............................................................12 2.1 Property Life Cycle ...........................................................................13 2.2 Data Types and Needs .......................................................................14 2.2.1 Property Information Requirements ............................................14 2.3 Education Issues ...............................................................................16 2.3.1 BIM within AEC Education (project-level lifecycle) ....................16 2.3.2 BIM within Property Education (property-level lifecycle) ........17 2.3.3 Developing New Knowledge Competencies in RICS ...................17

3.0 Research design and methodology .................................................19 3.1 Stage 1 Workshops ...........................................................................19 3.2 Stage 2 Online Questionnaire Survey ...........................................21

4.0 Workshop Analysis and Discussion ...............................................22 4.1 Workshop 1 Identifying Data Types and Needs ...........................22 4.2 Workshop 2 Identifying the Challenges .......................................25 4.2.1 Technology-based Challenges ........................................................27 4.2.2 Socio-technical Challenges .............................................................27 4.3 Workshop 2 and 3 Identifying Timelines & Mapping Data Needs Through Life .................................................................29

5.0 Survey Data Analysis and Discussion ............................................31 5.1 Part 1 Respondent Profiles, Current Awareness and

Usage of BIM........................................................................................31 5.2 Part 2 Experience Working with Information Technologies ..33 5.3 Part 3 Information Frequency and Need of Use .......................35 5.4 Part 4 Challenges & Benefits of BIM ...........................................40

6.0 Overall conclusions and further research .................................43 6.1 Data through-life ...............................................................................43 6.2 Challenges & Benefits of BIM ..........................................................43 6.3 Integration of BIM in Property Education .....................................44 6.4 Recommendations and further research .....................................44

7.0 References.....................................................................................................45

Appendices ...............................................................................................................47 Appendix 1 Property 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 property lifecycle (Workshop 2 output). ......................................................................................50

Special Thanks ........................................................................................................52

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List of Tables Table 1 Information Categories Developed for Workshops and Survey .....15Table 2 Descending relative importance of data types for

Stakeholder Groups (highest to lowest) .............................................23Table 3 Relative Importance of Five Main Information Types & Stakeholder Groups ..............................................................................23Table 4 Challenges to through-life information management and

corresponding RII .....................................................................................25Table 5 Comparison between Australian and UK participants perspectives

regarding the key drivers and challenges when sourcing, integrating and generating data through-life ...................................28

Table 6 Frequency of use of data types by area of practice / discipline.....36Table 7 Data need score by data type / area of practice ...............................37Table 8 Tests of Professional Differences in Information Importance.......39

List of FiguresFigure 1 Property Development and Management processes compared

with Single Facility Project Processes (Source: Authors) ..............13Figure 2 Selection of sort cards showing data types adapted from

Lutzendorf & Lorenz, 2011 ...................................................................20Figure 3 Importance of Main Information Types according to

Stakeholders and Activities across CPDM/ Project Lifecycle Phases .......................................................................................24

Figure 4 Relative Importance of Challenges to Through-life Information Management ......................................................................26

Figure 5 Data needs for a Buildings Surveyor Technical Due Diligence survey .......................................................................................29

Figure 6 Data needs for Portfolio Management Surveyors through the lifecycle ...............................................................................................30

Figure 7 RICS region respondent work in ...........................................................31Figure 8 Respondents area of current practice ..............................................32Figure 9 Land use types and sectors of property respondents work on .....32Figure 10 Use of Information Technologies in the workplace ..........................33Figure 11 Understanding of BIM .............................................................................34Figure 12 Experience of BIM ....................................................................................34Figure 13 Source of BIM training ............................................................................34Figure 14 Information Type Need versus Frequency .........................................38Figure 15 Key Challenges in information management through life ..............41Figure 16 Key benefits of digital information through life ...............................42




Glossary of Terms AEC Architecture, engineering, construction AECO Architecture, engineering, construction and operation BIM Building Information ModellingBMS Building Management SystemsPDM Property Development and ManagementO&FM Operations and Facilities Management PLM Product Lifecycle ManagementRICS Royal Institution of Chartered SurveyorsROI Return on investment VBM Virtual Building ModelTM Transaction Management 3D Third Dimension in BIM 3D geometry. 4D Fourth Dimension in BIM the time perspective 5D Fifth Dimension in BIM the cost perspective

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Building Information Modelling and the Value DimensionBuilding Information Modelling and the Value Dimension

Executive SummaryBuilding Information Modelling (BIM) offers rich opportunities for RICS property 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 opportunity 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 Sydney 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 aimsThe 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 extent 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 adopted a two-stage research design. The research had the characteristics of qualitative 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.



Key findingsThe 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 Situation

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

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 property lifecycle. Some professionals, such as Portfolio Management Surveyors (see figure 6) have repeated data needs 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 recommendationsThis research has shown that there is a place for BIM and the value dimension and that this will grow over time. There is great potential to expand the current use of BIM data for property professionals. There is also potential to expand the range of data linked to BIM for use by property professionals.

1. Map data needs and types across all RICS disciplinesOne 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, 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 The RICS APC group should develop appropriate property 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.

3. Develop a set of CPD events to raise awareness among property professionals of BIMAs a priority RICS should develop some online 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 BIMConcurrent 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 ForceWith 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.



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 produce, 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 characterised by three principal characteristics of property, 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 property and provides a description of its performance 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 BIMs 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.

1.0 Introduction and scope of researchToday, information technology is readily employed 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 (Becerik-Gerber et al, 2011). For those who are unfamiliar 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 the 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 LimitationsThe 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 BIMs ability to support client-side decision-making relative to risk, growth and depreciation variables as well as outlining areas for further research.



The lifecycles of complex, long-lived buildings mean that it is important for property professionals to have robust and reliable through-life information about a buildings 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 articles have investigated the impacts of BIM relative to project 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)

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 been 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 report 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 AEC companies and within larger construction projects, buildings and estates. Furthermore given that typically only 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 consider the impact on property value. However it is not known whether the information specified in AEC BIM models currently meets the needs of the property professionals.

2.0 BIM and the Value Dimension

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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;

1) Conception;

2) Planning and Feasibility;

3) Preparation;

4) Execution;

5) Operation and Maintenance (O&M) and

6) Recommissioning (see figure 1).

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 increase in digital information generated during AEC projects and throughout a propertys 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 property 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 property data.

Property Development and Management processes compared with Single Facility Project ProcessesFigure 1

Source: Authors

Conception(C)

(PD) (SD) (DD) (CD&CO) (OM)

Planning & Feasibility(SD)

Preparation(P)

Execution(E)

Operation Maintenance(OM)

Recommissioning(R)

Single Facility Project Lifecycle Phases

Commercial Property Development & Management Lifecycle Phases



2.2 Data Types & NeedsThe data sources that are required to provide a description and assessment of a propertys 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. Ltzendorf 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 Ltzendorf 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 information 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.

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 attributes 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 information 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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Information Categories Developed for Workshops and SurveyTable 1

Property descriptor types (Lutzendorf & Lorenz 2011)

Information types identified for workshops (Adapted from Lutzendorf & Lorenz 2011)

Categories of data defined for RICS survey (Based on Workshop Outcomes & Learning)

1. Location National Market Descriptors 1. Location Information Types, including: National Market Data Macro Location Data Micro Location Data

1. Market Data including; National Market Data State, Regional and Neighbourhood

Market Data Listings, Recent Sales, and Auctions

Data Property Transfers Data Property Marketing Statistics

2. Location Macro Location Descriptors 2. Property Location Data; Macro Location Data Micro Location Data

3. Location Micro Location Descriptors

4. Plot of land characteristics and configuration descriptors

2. Property Information Types, describing Plot of Land, including:

Characteristics and Configuration, Surrounding Contextual Data)

3 Property Site Data including; Property Lot Attributes Utilities Environmental Attributes Surrounding Building Context Property Development Details

5. Plot of Land Surrounding Context Descriptors

6. Mechanisms / Instruments 3. Property Information Types, describing Economic and Financial Data, including::

Payments In, Payments Out, Vacancy/Letting and Tenancy/Occupier Information

4. Financial Data including; Payments In, Payments Out, Vacancy / Letting and Tenancy Occupier Data

7. Economic Quality Payments In Descriptors

8. Economic Quality Payments Out Descriptors

9. Economic Quality Vacancy / Letting Descriptors

10. Economic Quality / Cash Flow Tenancy/Occupier Descriptors

11. Building Basic Building Quality Descriptors

4. Building Information Types, including: Building design information Technical and building systems

information Functional information, Environmental design information, Design/ Aesthetics information Contribution to urban quality User comfort & Post-occupancy

evaluation information Cultural value information Image and reputation value

information

5. Building Data, including: Spatial attributes 3D model objects (elements) and

properties (parameters) Building Documentation and Images

12. Building Technical Quality Descriptors

13. Building Functional Quality Descriptors

14. Building Environmental Quality Descriptors

15. Building Design / Aesthetics Quality Descriptors

6. Real Estate Data (Added to incorporate data typically collected that describes intangible value descriptors), including:

Property Value Attributes Property Imagery Property Activity Property Insurance Attributes Property Insurance Rate Variables

16. Building Urban Quality Descriptors

17. Building User Health / Comfort Quality Descriptors

18. Building Cultural Value Descriptors

19. Building Brand Value Descriptors

20. Process Quality Planning Descriptors

5. Process Information Types, including: Planning process information Design process information Construction process information Operations and Facilities

Management information

7. Project Data, including: Planning and Feasibility Data, Design Management Data Construction Process and Management Data

21. Process Quality Construction Descriptors

22. Process Quality Management Descriptors

8. Operations and Maintenance Data, including;

Maintenance, Alteration and Repair, Asset Monitoring and Tracking, Space Management



2.3 Education IssuesThis section examines some key issues around 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 property 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 (project-level 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).

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 particular import to property 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 models can be understood through accurate visualisation, students are able to gain a clients 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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The experiences of the BIM and Product 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 RICSHewitts 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 property 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, cross-functional professionals who are up-to-date with emerging technologies; able to perform new professional services associated with through-life requirements and activities.

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 experiencing 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.



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 Manufacturing 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 Universitys initiative to develop a PLM-literate workforce (Fillman et al, 2010). RICS could constitute an Education 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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3.1 Stage 1 WorkshopsTo 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 participants 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 participants 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 presented on 24 cards and participants asked to sort them on the basis of the information types 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 identify correlations between stakeholder data requirements.

3.0 Research Design and methodology



Selection of sort cards showing data types adapted from Lutzendorf & Lorenz, 2011Figure 2

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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 participated 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 downstream 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 designed adopting best practice 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.



4.1 Workshop 1 Identifying Data Types and NeedsParticipants 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 important (irrelevant) and 3 equals most important (essential) and were analysed by calculating the Relative Importance Index:

RII = W

A N

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).

4.0 Workshop Data Analysis and Discussion 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 being of most and equal importance. A summary of the variation in importance between stakeholder groups is shown in Table 3.

The most consistent information 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 BIMs application within the property profession.

The mapping in Figure 3 reveals those significant 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 groups activities occurred within the CPDM and Project lifecycles as shown in Figure 3.

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Relative Importance of Five Main Information Types & Stakeholder GroupsTable 3

Descending relative importance of data types for Stakeholder Groups (highest to lowest)Table 2

Information Types

All ResponsesAEC Professionals

Development & Asset Managers

Value & Cost 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

RII According to Stakeholder Groups Location Plot of Land

Building Descriptors Process Quality Economic Quality

Development & Asset Managers

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

Transaction Managers High Significance Low Significance

Medium Significance

Medium Significance High Significance



Importance of Main Information Types according to Stakeholders and Activities across CPDM/Project Lifecycle PhasesFigure 3

Single Facility Project Lifecycle Phases

Commercial Property Development & Management Lifecycle Phases

Development & Asset Mgmt. Stakeholders

Location Descriptors

Economic Quality Descriptors

Low significance

Low-Medium significance

Medium significance

Medium-High significance

High significance

Scope of professional practices

Process Quality Descriptors

Building Descriptors

Plot of land Descriptors

Valuation & Cost Mgmt. Stakeholders

AEC Stakeholders

Transaction Mgmt. Stakeholders

Conception(C)

Planning & Feasibility(SD)

Preparation(P)

Execution(E)

Operation Maintenance(OM)

Recommissioning(R)

(PD) (SD) (CD&CO) (OM)(DD)

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4.2 Workshop 2 Identifying the ChallengesParticipants 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,

3) Context,

4) Security and privacy, and;

5) Digital skills and knowledge competencies.

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 addressing 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.

Challenges to through-life information management and corresponding RIITable 4

Type Sub Type Challenges Identified

Technology based Challenges

Inter-operability & Data Standards

1. Ensuring data to be compatible and interoperable over long timescales (RII 0.90)

2. Ensuring data can be sustained and updated over long timescales (RII 0.85)

3. Ensuring data can be organised such that it can be discovered and exploited (RII 0.92)

Socio-Technical Challenges

Data Quality & Fidelity

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)

Context-based Issues

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)

Security & Privacy

13. Conflict in interests relative to data transparency and business interests (RII 0.74)

14. Confidence in IT infrastructure security in distributed networks & data stores (RII 0.81)

15. Privacy preserving 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)

Digital Skills & Knowledge Competencies

19. Lack of digital skill sets and domain knowledge (RII 0.85)

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)



Relative Importance of Challenges to Through-life Information ManagementFigure 4

Interoperability and Data Standards

Ensuring data to be compatible and interoperable over long timescales

Ensuring data can be sustained and updated over long timescales

Ensuring data can be organised such that it can be discovered and exploited

Data quality and fidelity

Human error, information overload and cognitive limitations

Data consistency, accuracy and reliability

Data granularity and its consistent specification

Data verification and validation (GIGO Garbage in, Garbage out)

Impact of context

Degree of interpretation and human manipulation

Communication differences and difficulties between domain specific languages

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

Privacy and Security

Conflict in interests relative to data transparency and business interests

Confidence in IT infrastructure security in distributed networks and data stores

Privacy preserving analytics and granular access control

Secure data storage and data provenance

Intellectual property and information ownership

End-point validation and filtering

Digital Skills and Knowledge Competencies

Lack of domain knowledge and digital skill sets, lack of education and training programs

Complexity of incorporating operational simulations

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

1.00.80.40.2 0.60.0

Importance

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4.2.1 Technology-based ChallengesWorkshop attendees identified three key technology-based challenges. These are:

1. Information generated over a propertys 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 ChallengesParticipants 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 fidelityParticipants 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 specification 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 information systems can overcome these issues to some extent, but it is more difficult to address the deliberate falsification of data/records.

Context based issuesKey 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.

Security and privacySix 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 property (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.



Need for New Digital Skill Sets and Knowledge CompetenciesThere 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

Comparison between Australian and UK participants perspectives regarding the key drivers and challenges when sourcing, integrating and generating data through-lifeTable 5

Data Quality and Fidelity Au

s

UK Process and

Workflow Aus

UK

Human Error Aus

UK

Security and Privacy Aus

UK

Data consistency, accuracy & reliability across all lifecycle phases

Disjointed nature of information flow

Lack of combined domain-specific knowledge & digital skill sets

Conflicts in interest relative to data transparency & business interest

Data format and interoperability

Differences in level of availability of data to all users through-life

Lack of education and training- both institutional & organisational

IT infrastructure security in distributed networks & data stores

Data granularity & level of details (LoD)

Lack of automation & integration between information systems

Black box systems & loss of corporate knowledge due to dynamic workforce

Privacy preserving analytic & granular access control

Data quantity Vs quality

Compressed timeframes for data generation & analysis

Need for cultural change admit feelings for fear & loss of control

Secure data storage & data provenance

Objective Vs. subjective data, information &knowledge

Uncertainty surrounding value of data & its ongoing use through-life

Communication difficulties & differences in domain specific language

End-point validation and filtering

Data verification and validation: GIGO (Garbage in Garbage out)

Lack of standards & protocols for data use, entry, verification and validation

Human error, cognitive limitations & information overload

Security of property and building metadata tags through-life

Complexity of incorporating operational simulation

Continual reporting/justification of business case for data collection and upgrading

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.

A similar process was undertaken 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 practitioners 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.

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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 mapping 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 property 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.

Data needs for a Building Surveyor Technical Due Diligence surveyFigure 5

MINOR COSTSMAJOR COSTS

Redevelopment/ strategic optioneering

Use, maintenance and repairs

Redevelopment, sale, demolition

Health and User Comfort

Basic Building

description

Access to Maintenance Data

Functional quality

Land features

Discussion with Semas /Structural Engineers

Operational Quality

Environmental Context

Technical Quality

Visual Inspections

Environmental Quality

Site features

Discussion with F.M.

Report to Client



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