For the uninitiated, Building Information Modelling, commonly referred to as BIM, is a technology platform predicted to revolutionise how we visualise and use data within the built environment. Consisting of broadly two elements, BIM can be summarised as:
On 4 April 2016 housing providers were mandated to utilise BIM technologies for their new-build developments where the government provided funding for those schemes. And fast approaching the first-year anniversary of the introduction of BIM level 2, what has been its impact on the sector? From an asset management perspective – where, arguably, BIM delivers the most benefit – very little.
With new-build completions accounting for less than 1% of social homes, what is the driver to embed this technology and the visual enrichment that it promises in the operational life cycle of our assets? The usefulness of BIM in managing assets is unambiguous. From providing a deluge of data at handover of new developments to the facility to allow tenants to interact with a model of their home to self-serve repair reports – the benefits are tangible. But with so little coverage of housing stock currently integrated, BIM’s value will not be fully realised in the delivery of operational efficiency and data accuracy. So, what opportunity exists for landlords to bridge this data-gap and to baseline requirements? My answer, and the central theme of this article, is the stock condition survey.
Stock condition surveying is a mature practice in collecting information to assess the performance of buildings and assets. They are carried out by all social housing landlords and are, therefore, the perfect intervention to collect the relevant asset data to extend the practice and coverage of BIM. However, aside from increased surveying costs – which I feel are offset by the resultant enhanced data and 3D representation of the building – the biggest barriers to align current stock condition practices with those required to support BIM are information depth and reach.
In my consultancy experience, building elements are captured at a generic level, revealing a disconnect between characteristics held in a database and the asset physicality. For instance, providers are likely to record “windows” collectively for the dwelling as a single asset, along with the installed date, quantity, condition, unit cost and life expectancy. This is, perhaps, a legacy from Decent Homes and other national standards which focused on monitoring these elements at this high level. Still, this recording method prevents us from correlating repairs, servicing and replacement events to the actual assets and 3D model.
Against the backdrop of a real-term cut of 15% from rental income, housing boards are stress testing their business plans with a greater emphasis on exactitude so that their asset and development investment plans are ‘sustainable’. To achieve this, organisations use Net Present Value (NPV), or variants, when appraising the sustainability of their existing and future stock. NPV, alone, is a blunt tool for making this assessment as it does not control for social factors – after all, social homes are not simply financial assets. But it also hides the relative paucity of data used for asset spend over the business plan life as it may not contain sufficient information to factor in repairs, refurbishment and servicing expenses into its calculation. The data provided by BIM could heighten the precision so that bad decisions are avoided when considering the divestment of social housing stock.
As part of the process to broaden the reach of BIM the same techniques organisations use to extrapolate their sample survey data can be engaged. Using archetypes, the housing organisation can extend this more detailed data to those like-typed buildings and blend this information into the existing data for those dwellings not surveyed. Though not to the depth of a 100% BIM survey, this approach would enhance the prevailing data to form a more semantic picture of the asset performance and condition, whilst keeping costs down. Likewise, models and floorplans constructed as part of the survey can be mapped using the same techniques.
By its nature, BIM, along with its near-horizon siblings the Internet of Things and Big Data, requires granularity. Granularity provides nuance, semantics and accuracy. I argue that surveys should be collecting the level of data commissioned at development handover. This provides synergies with these other technologies to unlock data possibilities to better inform day-to-day decision-making. With manufacturers getting on board with BIM representations of their products, the management of this wealth of data becomes more automated as the supply chain collaborates. ‘Right first time’ repairs are delivered more frequently as the contractor is armed with the parts information ahead of their visit to the tenants’ home. Our offices may never hear the phrase “do you know where your stop tap is?” again.
Not so long ago, we planned road journeys with the ubiquitous ‘map of Britain’, supported by the city A-Zs that clogged up our car boots to navigate us through to our destination. Nowadays we think nothing of using Google Maps on our smartphones and tablets to direct us from A to B, pinching to zoom to focus on that fine detail provided by Street View. I’m hopeful that this analogy extends to how we communicate with our tenants, contractors and other stakeholders in the coming years when reporting reactive maintenance and planning major repairs to our housing stock, using BIM as the platform for that collaboration. If we recast how we think about commissioning stock condition surveys, we might just get there sooner.
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