Will Artificial Intelligence eliminate Real Estate jobs? Part 1...


Vicky Pollard from the comedy classic Little Britain was famous for saying, very fast,“Yeah, but, no, but, yeah, but, no, but…”, and that is exactly the answer to the question of whether AI will eliminate real estate jobs. AI is unlikely to eliminate many entire jobs, but it is highly likely to eliminate a whole raft of ‘jobs to be done’ from the day to day lives of us humans.

In early 2017 McKinsey wrote this: “Overall, we estimate that 49 percent of the activities that people are paid to do in the global economy have the potential to be automated by adapting currently demonstrated technology.” And in July 17, in a report, published by the RICS no less, it was stated that ‘The Impact of Emerging Technologies on the Surveying Profession’, the RICS “Surveying appears to be an industry in which 88% of the core tasks are ripe for automation to a greater or lesser degree.”

What is going on?

Artificial Intelligence may have been first discussed way back in 1956 but it is only in the last few years that it has really morphed from the world of science fiction to become a mainstream technology. A confluence of improved algorithms, the recent availability of vast datasets and exponential increases in computing power have seen to that. Computer Vision (the ability of a camera to understand what it is looking at) and Voice Recognition have both gone from useless to utility in a matter of years. For example, the best voice recognition system had a ‘word/error’ rate of 23% in 2013; today that is less than 5%, which is superior to humans. In just three years, from 2013 - 2016, the speed neural networks could be trained (critical to AI) increased by 60 times.

In 2016 Google’s Deepmind program AlphaGo beat the world champion GO player Lee Sedol. In 2017 the next iteration AlphaGo Zero beat the original 100-0, after just 3 days practice. It then beat Stockfish, the No1 Chess playing software, after only 4 hours practice.

Be under no illusion, as Sundar Pichai CEO of Google has said, we have now moved to an AI first world. And this matters hugely to the everyone in Real Estate. In our industry what really matters, above all else, is what our customers want and need to do within the spaces and places we create. Everyone has a ‘job to be done’ and it is that that AI is fundamentally changing. The bottom line is that anything ‘Structured, Repeatable or Predictable’ will be automated. Not might be, will be. The incentive to do so is too great to be ignored: regardless of whether that suits us as individuals. This is the 49% of activities that McKinsey refer to, and the 88% from the RICS report. The only difference is that McKinsey are referring to today whereas the RICS is taking a 10 year view.

So, if anything ‘Structured, Repeatable or Predictable’ is going to be automated, what does that mean for us? What are we going to do? The answer lies in what I like to think of as ‘New Work’ and that is anything that involves design, imagination, inspiration, creation, empathising, intuition, innovation, collaboration and social intelligence. These are the foundational human skills that, luckily for us, are the antithesis of what computers are good out.

The key to avoid your job being eliminated by AI is how good you are at marrying your intrinsic human skills with the extraordinary processing power of the machines we now have at our disposal. Ex world champion chess player Gary Kasparov started an alternative chess format called Advanced Chess, where one or two humans, assisted by a chess computer, play a similar team. What he discovered running various tournaments vindicated his hypothesis that a human paired with a computer would beat a computer on its own. But what he also found was more telling: a “weak human + machine + better process was superior to a strong computer alone and, more remarkable, superior to a strong human + machine + inferior process.”. Ultimately it was the skill of the human who could best understand how to marry human + computer that came out top. It is the creativity in defining the ‘process of augmentation’ that really matters.

Steve Jobs was famously not a techie; he studied calligraphy at college. So it is perhaps not surprising that he understood this need for humans and computers to work alongside each other. At the launch of the iPad 2 in 2011 he said this: “It is in Apple's DNA that technology alone is not enough—it's technology married with liberal arts, married with the humanities, that yields us the results that make our heart sing.”

These parallel memes, that technology is developing at a phenomenal pace and will enable widespread automation, and that humans need to become skilled at augmenting themselves with complimentary technical skills, are the key to answering the question of whether AI will eliminate CRE jobs. How to do this will be the subject of part 2 of this post.


Space As A Service - but bigger than that


I first started talking and writing about #SpaceAsAService several years ago: Duke Long and I have ‘debates’ about who got there first! Either way it is gratifying that today the phrase is commonplace and barely a day goes past where it doesn’t crop up somewhere or other.

What isn’t commonly understood is that this is the single biggest flashing red sign that #PropTech is at a watershed moment. #SpaceAsAService is only an everyday term because technology, in the widest sense, is fundamentally transforming the real estate industry. As Sundar Pichai, CEO of Google said recently, we are now in an ‘AI First’ world.

And this matters hugely to everyone in Real Estate.

In our industry what really matters, above all else, is what our customers want and need to do within the spaces and places we create. Everyone has a ‘job to be done’ and it is that that AI and other technologies is fundamentally changing. The bottom line is that anything ‘Structured, Repeatable or Predictable’ will be automated. Not might be, will be. The incentive to do so is too great to be ignored: regardless of whether that suits us as individuals. 

In early 2017 McKinsey wrote: “Overall, we estimate that 49 percent of the activities that people are paid to do in the global economy have the potential to be automated by adapting currently demonstrated technology.” And in July 17, in a report, published by the RICS no less, it was stated “Surveying appears to be an industry in which 88% of the core tasks are ripe for automation to a greater or lesser degree.” In this world the purpose, function and form of the spaces we create, as an industry, has to change. And that is why PropTech and #SpaceAsAService is at a watershed moment: the days of digitising the past are over. Either the industry embraces the ethos of #SpaceAsAService and leverages an emerging breed of #PropTech to catalyse this change, or we’ll all suffer the waves of destruction that follow losing ‘Product/Market’ fit.

#SpaceAsAService is a deceptive phrase; it is often used simply as a proxy for space that is available on demand, but it embraces far more than that. It is a actually a philosophy of space, a foundational way of thinking about the spaces and places we create, manage and occupy. It represents an attitude of mind, towards colleagues, clients and suppliers and between ‘landlord and tenant’. It is about thinking about Service not Product, long not short term. About networks, ecosystems and lifetime relationships with customers. In many ways it is the antithesis of the traditional real estate mindset.

Ant therein lies the problem, for the industry. To become a true #SpaceAsAService operator incumbents need to be transformed; organisationally, culturally and financially. A #SpaceAsAService real estate company needs to think like Apple does, who despite only having a 17% share of the smartphone market make 85% of all the profits. Why? Because they have embraced Steve Jobs’ mantra of marrying technology with the liberal arts, of controlling both hardware and software, and thus being able to create a uniquely desirable user experience, which their customers are prepared to pay uniquely high margins to acquire.

As with the iPhone I do not think #SpaceAsAService will be for everyone. Make no mistake about it, the iPhone is designed, branded and managed for a specific target audience. It is globally the smartphone for the upper end of the market, and everything Apple do is predicated on that market positioning. Within real estate I believe #SpaceAsAService will not be as ‘tribal’ as Apple is, but it will be for the most creative, innovative, progressive and knowledge based companies. 

It will be for companies that invest in their ‘people’, respect their skills and want to provide them with ‘Space’ that serves them up what they need, when and where they need it. Space that understands who they are, what they need and desire, and how to help them become as productive as they possibly can be. It will be for companies that are prepared to invest to make this possible, but who also understand that, like software, a workplace is always but a work in progress. It will be for landlords that can breach the tenant demise and instead of simply providing a functional shell for their occupiers to occupy as they wish, partner with them to provide a range of very domain specific skills that enable them together to create exceptional workplaces. 

Space needs to become #AsAService because in a world where the ‘structured, repeatable and predictable’ is taken care of by technology, creating a flexible, agile, activity based workplace is significantly more complicated than it used to be. The largest occupiers may have all the skills and resources to create these spaces but most companies do not, and cannot be expected to acquire them.

These companies are not after an office, they are after a productive workforce, and a true #SpaceAsAService operator (be it Landlord, 3rd party or partner) could be their route to achieving just that.

How to think about AI in Real Estate


AI, whether we realise it or not, is impacting on all our lives in significant ways, today. From monitoring our credit card usage for fraud, to filtering our email for spam, to recommending what to watch on Netflix, to recognising our friends in photographs on Facebook, to flying our planes for 95% of the time, to enabling self driving cars. For many day to day processes it is becoming pervasive, behind the scenes. 

AI will become pervasive within real estate as well. The extraordinary new capabilities of computers to understand the world around them, to see, hear and read as well as humans, are sure to provide the foundational tools that allow us to build a better built environment.

But how do you need to think about AI in Real Estate? How do you approach a topic which can be (because it is) somewhat overwhelming?

You need to think about AI in Real Estate via a process;

  • First you need to understand what AI is good for, and just as importantly what it is not.
  • Secondly you need to analyse your workflows and business processes to see where you can leverage what AI is good at.
  • Thirdly, you need to consider which of these use cases is commonplace and repeatable within your own business and/or applicable to many of your customers.
  • Fourthly, you need to look for use cases where you have or can obtain large quantities of data, and where there are clear metrics by which you could judge success.
  • Fifthly and lastly, if you have all of the above, is that something that will create significant value?

Let us take these in turn, starting with what is AI good for?

Over the last five years AI has developed very rapidly in three key areas: Computer Vision, Voice recognition and Natural Language Processing. Taking Computer Vision as an example, in 2013 the ImageNet Competition Winner (ImageNet runs annually and quantifies the capabilities of different systems) had an error rate of 11.3%. In 2017 that had come down to 2.2%. For reference Humans rate at 5%. Similar improvements apply to NLP and Voice recognition. In practical terms these three skills have gone from ‘Useless to Utility’ and this is why we have seen the rapid rise of driverless cars (or semi autonomous ones like Teslas) and the explosive rise of Amazon’s Alexa.

A combination of improved algorithms, vast new data sets to ‘train’ on, and massive computational power has meant that much that was foretold back in the early days of AI (the term was first coined at a conference at Dartmouth College in 1956) is now possible. And critically, because of the way AI works (essentially recursively - the more you know, the more you can know and the faster you go the faster you’ll be able to go) over the last five years in particular the pace of development has actually got faster. 

Which means that, today, AI is very good at:

  • Understanding what is happening in pictures and videos
  • Understanding people using language
  • Creating content (auto generated commentary, extracting data from reports, news etc)
  • Automating processes
  • Optimising complex systems
  • Making predictions

Essentially, anything that is ‘Structured, Repeatable or Predictable’ can (and most probably will) be automated.

Think of all of this as pattern recognition and you’ll get to grips with what is possible. 

In terms of what AI cannot do, the key point to understand is that computers operate within very specific boundaries. Google’s Alpha Go, that beat grandmaster Lee Seedol at the famously complex and complicated Chinese board game Go, would be completely useless if you used it to play chess.

It is true that Machine and Deep Learning, both of which are subsets of AI, and are differentiated by being systems that are not explicitly programmed to do A then B then C or D if X = Y, can to an extent ‘think for themselves’, but they can only do this within very narrow boundaries.

Computers are great at optimising complex systems because that plays entirely to their strengths but ask a computer to create something completely new that turns out to be complex and it will fail.

Even in an ‘AI first’ world, as Google boss Sundar Pichai said we were entering last year, a human ‘augmented’ by a computer will trump any computer on its own. By the same token a human/computer pairing will also trump a human on their own.

To make the most of AI you need to think in terms of what computers are good at and what human skills can be used to leverage those capabilities. 

We need each other, to be the best we can be, at what we are good at.

Once your thinking is clear about what AI is good at you need to look for use cases. What tasks do you or your company perform that are ‘Structured, Repeatable or Predictable’? Last year the RICS released a report saying that 88% of the tasks surveyors perform are vulnerable to being automated within 10 years. In 2016 McKinsey said that 49% of ALL tasks people are paid to perform globally are capable of being automated by ‘currently demonstrable technology’. So the answer is probably ‘quite a few’.

So list them out; What do you do that follows a pattern in some way? Either because they absolutely do involve linear processes, or because with these inputs you know you can produce those outputs. You’ll find workflows or processes from across the entire lifecycle of real estate, from planning, to design, construction, leasing, occupation and management and on to portfolio analysis and investment sales or acquisition.

What imagery or video content do you deal with, or what movements around space would be useful to track? What customers do you interact with and how much do you know about them? What reports do you have to generate and how are they put together; could these be automated? How do you handle questions from customers; do these follow a pattern? Etc, etc….

Once prepared you can move to the third stage. Take your list and give each item two scores of 1 to 10. First how common is this task in your business, and secondly how common is it outside of your business, either amongst customers or competitors. You are looking for tasks that are repeated over and over again. What are the primary ‘Jobs to be Done’ in your area of real estate? This is a proxy for market size in terms of the potential AI service that addresses each task. We’re looking to use AI to help us do tasks much faster than now, or much cheaper, or more accurately so it is sensible to look for those tasks we, or our customers, have to do a lot of.

Now we need to look at data. How much of it about each task do we have? Here you should think of the four V’s: Volume, Variety, Velocity and Veracity. How much data do we have, from how many different sources, how fast is it being generated (as per seconds, hours, days, months, years) and how accurate is it? Regarding accuracy you must be honest; we all know a lot of real estate data is, being polite, somewhat lacking in rigour. If utilised by AI inaccurate training data will lead to inaccurate results, and it is frankly better not to waste your time with it.

It is not an absolute that more data makes for better AI but in many cases, a lot of solid, accurate data is a great benefit. Also, knowing what you have and what you lack is very useful. You might be able to acquire what you need from third parties but without a good data audit you won’t know where to start. Thinking about AI involves a lot of thinking about data.

You need to think about correlation not being causation and apply that to your data. You need to think in terms of ‘with these data points I could arrive at this conclusion’. For example, if I knew exactly how occupiers of my building moved around, I could optimise the AC and lighting. Or, if I knew these characteristics of an investment I could match them against these characteristics of an investor.

Think big here. Hypothesise about being able to do something that you cannot do today. What data would I need to make that happen? Work on the assumption that if you had said data we now have the tools to analyse it deeply. ‘How could I filter out from thousands or tens of thousands of companies which ones will lease my new office development?’ 

And moving to the fifth stage, ‘where’s the money’? From all that we have looked at which areas, with what solutions, could generate the most value? This might mean saving money, or time, or it might mean creating a much larger market or more valuable products or services. Value comes in many forms.

It will sometimes be the case that ‘digitising the past’, where you simply apply new technology to existing business processes, makes sense and is worthwhile, but the real value in this field, applying AI, will be in creating products or services that did not exist before. There is little competitive advantage in doing what everyone else can do, so your thinking needs to be focussed on the mountain top beyond the horizon.

By this stage you should be a long way towards that goal.

In conclusion then, thinking about AI in Real Estate involves understanding the new technologies, looking for use cases, determining what use cases are core and ongoing or transitory, getting to grips with data, what you need and whether you have it or can get it, and then homing in on where real value lies.

There is not a shortcut through this process, though it’ll probably turn out to be not as daunting as you might imagine. Once you have a structured way to think about this new world things do tend to fall into place quicker than you imagine. Utilising AI is as much a mindset as a technology.

At all times though what must remain uppermost in your thinking is that whoever really cracks using AI in real estate will be orders of magnitude more productive, and profitable, than the non AI embracing mainstream. AI is potentially truly disruptive.

Want to know more?

Get in touch with us at www.propai.co.uk


PS. PropAI has launched today:

"PropAI are delighted to be partnering with the Oxford Foundation for Theoretical Neuroscience and Artificial Intelligence (OFTNAI). 

PropAI believe that artificial intelligence has the potential to redefine the operations of the commercial real estate industry. From how buildings are designed, to how they are built, occupied, managed and traded, AI will become part of the everyday lives of all of us.

We are looking to understand how the very latest technologies, with hitherto unseen capabilities, can be utilised to augment human skills and enable the industry to be 10X more efficient, effective and impactful."

In Commercial Real Estate? Forget Blockchain and Bitcoin, AI is where you should be focussing

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The great Dotcom Bubble peaked in March 2000. Over the next 18 months trillions of dollars of investment capital evaporated. The Great Bitcoin/Blockchain bubble feels very much like it did in 1998-99. One cannot move without being accosted by someone screaming that you just have to get on board today. Of course such bubbles are tenacious things, and just as the smartest man ever, Sir Isaac Newton, eventually could not resist going all in with the South Sea Bubble (only to lose most of his money) today I am seeing very many smart people bowing down before the call of crypto.

If you are one of them you most likely will lose your money as well. Not because you are wrong per se, but just as in the Dotcom Bubble, you are probably a decade too early. Most of the ‘crazy’ ideas that raised hundreds of millions of dollars in the 90’s blew up not because they were bad ideas but because the technology did not exist to enable them to actually happen. There were very many 1990’s simulacrums of Netflix for example.

Blockchains will have huge utility, as will crypto currencies (the two by the way HAVE to go together - a Blockchain without a cryptocurrency IS NOT a Blockchain) but not for a while. There are real technical hurdles to cross before this happens. So my advice is to put no more than 5% of your wealth into the crypto market and forget about it. If you are lucky and invest in the winner you will do extremely well. But there will be many losers and no, you are not bright enough to know which will be which. No one is.

Put your real effort, and money, into AI, Artificial Intelligence, because this is an area whose time has come. A term first mentioned in 1956 at a conference at Dartmouth College, AI has already gone through two prolonged ‘Winters’ but over the last few years has advanced in leaps and bounds as a combination of improved algorithms, massive computing power and unprecedented access to vast quantities of data has enabled theory to move into practice. 

Sundar Pichai, CEO of Google sums up the current situation perfectly: “We are now witnessing a new shift in computing: the move from a mobile-first to an AI-first world”

Artificial Intelligence has had a storming half decade. In just five years it has gone from the world of science fiction to mainstream discourse. Autonomous vehicles, robots that can perform backflips, computers that now rule the roost at Poker and GO as well as the long since vanquished Chess; all around us we read of one new breakthrough after another. According to Gartner’s famed Hype Cycle, Machine Learning (a subset of AI) is now at ‘peak hype’, meaning that we can expect widespread adoption to be just 2-5 years away.

In three key areas AI has enabled computers to rapidly go from ‘Useless to Utility’. Computer Vision (face and image recognition), Voice recognition and NLP (natural language processing) have all moved from being 20-30 percent error prone a few years ago to now being on a par, or even surpassing in certain circumstances, human ability.

In effect computers can now see, hear and read as well as we can.

And in the commercial real estate industry that really matters. The ability to understand the build environment by pointing a camera at it, to read the voluminous quantities of paperwork that weaves its way around the industry, and to be able to interact with our customers (everyone who enters into any of our spaces and places) just by listening to what they have to say, is truly a transformational power we now have at our disposal.

For example, what if we could enable customers to search for space visually, by choosing images that appeal to them, instead of making them fill in forms and describe what it is they are after? Or understand the ‘mood’ of visitors to our shopping centres, or find potential new development sites by analysing drone footage? Or understand exactly how people are using our offices, or shops, the better to configure them for reality as opposed to hunch?

What if we could analyse all correspondence that goes out of or into our businesses in real time, to look for patterns, insights and ‘unknown unknowns’. Or prioritise work orders, deal flows, job applications automatically and route them appropriately. Or have reports collated, processed and presented in realtime that we can then spend time analysing rather than labouring over.

What if we could support our customers better by asking them to just talk to us? 24 hours a day, 7 days a week, 365 days a year we should be able to answer the needs of our customers in a way that suits them. We should be able to talk to our buildings, and have them understand us; it’s too hot, or cold, or bright or dark, or stuffy. Where is X, or who is Y? Just walk around the City and see how many people are talking into the ether, plugged in as they are to the microphone and headphones; Voice is the new computing interface. 

In an AI powered world the commercial real estate industry will be able to do three things, of huge importance, that it cannot do today:

First, we will be able to understand exactly how our buildings are working, at a very granular level, and in so doing we will be able to run them far more efficiently and effectively.

Secondly we will be able to understand exactly how everyone who uses our buildings, spaces and places really does use them. Where do they go, what do they use, when do they use it; all this will enable us to define, refine and curate the UX, the user experience, of all our customers in a way we simply cannot do now. This will be a true #SpaceAsAService world where we will be able to provide exactly the spaces and services that people need, wherever and whenever they need them.

And thirdly, we will be able to understand exactly who our customers are, what they need, desire and are pleased by in a way we’ve not been able to to date. 

As we understand how our buildings operate, how they are used, and the needs and requirements of those who use them better, we will be able to build a better built environment for them. Today, research tells us that roughly 50% of office occupiers do not find their offices help them be productive, and roughly 50% of the time these spaces are not in use anyway. With wise use of Artificial intelligence, coupled with the very best human skills we should be able to do much better than this. 

So forget about Blockchain and Bitcoin for now; concentrate on learning how AI can, and will, radically and rapidly transform what it means to be in the real estate industry.