It is a natural human condition to iterate. Early in our lives we iterate to learn — repeating our ten times tables over and over at school is one simple reminder of this approach. For many of us, the iteration used at schools extends into adult life — for example, through the daily ritual of dropping off our children at school. However tiredness, boredom, anxiety, hunger, or just a loss of interest get in the way of optimising these iterations. Belabouring the school drop off metaphor: sometimes we might try to optimise by changing our route to avoid heavy traffic — often just finding that our new route has even heavier traffic than the original path. We quickly turn to technology to assist — pulling out our mobile phones and traffic apps to show us a faster way.
When Clayton Christensen famously opined that the future of work started with identifying the ‘jobs to be done’, he was acknowledging the fact that much of our life revolves around iterative actions, and that disruptive innovation and optimisation of these jobs was required for companies to survive and thrive. His 1995 article on Disruptive Technologies: Catching the Wave — co-authored with Joseph L Bower — paved the way for a swathe of disruptive innovation which presents itself today by way of digital transformation.
At the top of the list of disruptive technologies that are changing our world is artificial intelligence. With apologies to the purists amongst us, permit me to extend that oft used term to cover the full gamut of technological developments which assist humans in optimising repeatable, iterative tasks. If I may, there exists a continuum which starts with the humble spreadsheet macro, extends through robotic process (or intelligent) automation, and ends with singularity. As Wikipedia states, artificial intelligence is “any system that perceives its environment and takes actions that maximise its chance of achieving its goals”. The value of such a system lies in the ability to undertake these tasks faster and more accurately than a human — in part because the system doesn’t get tired, bored, anxious, hungry, or lose interest. Even more importantly, the big data that can be supplied to the system enables optimisation on a grand scale, factoring in many more iterations than any human could undertake in a very short period.
A computer doesn’t care if it does the task five times or five million times.
So what are the ‘jobs to be done’ in property which are highly iterative, but are not yet fully optimised? There are many! To name a few: identifying suitable sites for development, deciding upon the optimal building layout for a site, optimising the construction process, and streamlining building operations and maintenance. In all these areas, where common tasks are repeatedly undertaken within a defined set of parameters, artificial intelligence is already assisting in optimising iterative outcomes.
If you want to determine an optimal approach to selecting the best place to develop a new building or precinct, you may see value in the Masters Level Real Estate Data Science course offered by PropertyQuants in Singapore. They offer one of the world’s first data science, machine learning, and GIS courses dedicated to analysing, investing in and forecasting property globally. Through a series of interactive online lectures, hands-on learning and the completion of key capstone projects, they provide knowledge and expertise to construct indexes, automate valuations, analyse clusters and forecast time series. Their course utilises large datasets to determine fair transaction prices, forecast future returns and analyse optimal locations with geographic information systems (GIS).
There are several emerging products that leverage artificial intelligence to assist with finding and assessing the best sites, and then designing the most optimal and feasible buildings for those sites. Lendlease Podium Envision is one such example. Podium Envision is a generative design tool which streamlines and improves decision making along the property development process, from feasibility through to design and construction. It can generate building designs with real time financial analysis and detailed bill of materials in minutes.
In similar fashion, there are now a wide range of products and tools that optimise the construction process by utilising the power of artificial intelligence. In recent times, the power of a simulation digital twin is reshaping the speed, cost, sustainability, and safety outcomes of construction. The digital twin can optimise the approach undertaken to: identify appropriate construction materials; deliver them to site just in time for construction; determine the optimal construction staging process; and report on the level of completion of construction. The myriad of ways to optimise iterative tasks during construction using artificial intelligence are too many to mention, but they extend from simple tasks like determining the best time of day for a specific concrete pour, to complex tasks like reducing the number of crane lifts for an overall build program.
Perhaps the most beneficial time to apply artificial intelligence in property relates to the ongoing performance of the building or precinct. Buildings are becoming smarter by the day, with all manner of technology being introduced to record, monitor and optimise their operations and maintenance. Artificial intelligence is being utilised to predict the behaviour of such buildings — simulating and optimising key performance attributes such as energy consumption, occupancy levels, thermal conditions, and air quality to ensure the best possible tenant experience. Products are now emerging that will lead to buildings being autonomous — making automated decisions about how the building perceives its environment and taking actions that maximise its chance of achieving the goals of various stakeholders — the building owner, the tenants, regulators, and the community.
We are just starting to fully appreciate the value AI can bring to optimising iterative actions within property. The next few years will see massive leaps forward in this space.
Colin is the head of Services for Lendlease Podium which includes the data science capabilities for Podium: making use of AI, machine learning, natural language processing and advanced analytics to develop the autonomous buildings of the future.