Real Estate as a Service: Are flexible short term leases the new future? 16 Provocations!

Last week I took part in a round table panel discussing whether short term leases were the new future for real estate. My role was to provoke debate. So below you will find my quick overview of the market and then 16 ‘Provocations’.

One panel member disagreed with ‘at least 10’ of these. IMHO because they were on the wrong side of the trend….

What do you think?

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There appears to be a ‘perfect storm’ building for the traditional real estate industry. 

A world of solid assets, underpinned by long term leases from tenants with strong covenants and long histories, was the norm. Sure there were property cycles, but so long as you did not get your timing terribly wrong, your assets would increase in value, the leaseholder was on the line to fully repair and insure your buildings and in practice you did not even have to engage much with your customers. Once that lease was signed, they were stuck with you. Largely, one could sleep soundly, in the knowledge that tomorrow was going to be much like today.

That world is blowing up, and blowing up fast.

The average age of an S&P 500 company is under 20 years, down from 60 years in the 1950s, 

Today, 40% of the top 20 global companies are tech companies. 100% of the top five.

Ten years ago three of the top five were oil companies with just one tech company, Microsoft

And they do not employ many people - Google, Apple, Facebook only employ 225,000 people. Amazon employs 500,000 but most of those are low paid warehouse jobs.

Between them they are worth nearly $3 Trillion dollars

In January last year McKinsey stated that “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.”

In a similar vein last year the RICS published a report saying that “Surveying appears to be an industry in which 88% of the core tasks are ripe for automation to a greater or lesser degree.”

Various reports have estimated the % of jobs set to be lost to automation over the next two decades, ranging from 14-47%. Whatever way you cut it a lot of work is going to be automated away.

But that is not really the important bit. Regardless of how many jobs, in their entirely, are lost, the nature of the work we do is set to change fundamentally. Anything ‘structured, repeatable or predictable’ is going to be automated. AI is advancing at such a rapid clip that this is likely to be sooner rather than later.

Seth Godin wrote a blog post entitled ‘Goodbye to the Office’ in 2010 in which he suggested that ‘work was leaving the building’ in that everything we used to need an office to provide us with, in order to be able to do our work, was becoming redundant. We have laptops, the internet, the cloud, connectivity everywhere, supercomputers in our pockets. The only thing left he wrote was ‘I need someplace to go.’

And that is why the genie is out of the bottle:

• We do not need as much space as we used to
• We do not need an office to actually do our work
• The work we do is changing entirely anyway
• The type of space that we need therefore is entirely different
• A bland shell is no longer acceptable 

Add on top of this that the majority of occupied units are small (in the City of London 70% are less than 10,000 sq ft and 50% are less than 5,000 sq ft) and you start to see that the Product/Market fit of many of these spaces is breaking down.

In a world where the ‘structured, repeatable and predictable’ work is being done by machines then the work we all need to do, when together, is going to be intensely human, probably involving design, imagination, empathy, social intelligence and the like, and in that case the types of spaces we need are much more varied, softer, more engaging, more collaborative, and more specifically tailored to the exact type of task we need to do. i.e much more the Activity Based Working type of arrangements that we are all becoming familiar with.

The problem therefore, for perhaps at least half the occupiers of office space, is that they:

 A) do not have the skills, the time or frankly the inclination to get to grips with this much more complicated type of workplace and 

B) …even if they did, with half the occupied units less than 5,000 sq ft, they don’t have the space to economically create such a place.

So, the status quo is to remain where they are, taking on a lot of space they either do not need, or that does not help them to be productive. They have expensive employees, needing to do tasks that involve high order human skills and they’ve got them tied to fixed desks, with fixed phones and probably even fixed computers. Is it any wonder that in the Leesman Index surveys only 50% of respondents say their workplace helps them to be productive. Or that, for on average 50% of the time desks are unoccupied.

I call it the 50/50 double fail.

And into this double fail we have seen the rise of the co-working spaces, with the $20 billion monster that is WeWork leading the pack (and today representing London’s largest tenant). But it is of course much more than WeWork, with operators as varied as Fora, Huckletree and The Office Group appealing to different audiences. 

Flexible space may only account for 4% of the total space in central London but it is growing fast, and with roughly 18% of total take up last year being flexible, it does seem like this is an idea whose time has really come.

Perhaps the biggest driver of adoption today is pure familiarity; as more and more people experience office space that is not as dull, dreary and downright enervating as so many of us have endured for decades they realise that ‘it does not have to be this way’. And that feeds back into demand. Want the best staff? Give them the best space.

Critically it is not money that is driving this change, or at least not in a conventional sense. You pay more, sometimes much more, per sq ft for flexible space but the flip side is you are actually buying true #SpaceAsAService - space that provides you with the service you need, as and when you need it. That trade off seems to work for very many people. 

After all: ‘Companies do not want an office, they want a productive workforce.

So, the provocations are:

1. Flexible, short term space, for much of the market, is the future. The standard, traditional office ‘Product’ is no longer fit for purpose. It no longer has ‘Product/Market’ fit.
2. The Real Estate business is no longer about Real Estate: the industry has to movefrom selling a Product to delivering a Service
3. No REIT has a ‘Brand’ designed for this future. they are all Brands aimed at investors, not customers. 
4. The Real Estate Customer is now every single person who enters into a property - Real Estate is moving from being B2B to B2C.
5. As with B2C markets, Landlords/Investors will need to be thinking ‘lifetime value of customer’ - how to build a Brand that can support a customer from 25 - 65.
6. Traditional Real Estate companies know almost nothing about their customers - in the future the best will know a very great deal, and use it to shape personalised #SpaceAsAService for each and every one.
7. Landlords HAVE to choose a role: A) Outsource ‘Service’ to an operator like WeWork, and leave a lot of money on the table. B) Turn themselves from Product to Service companies: with all the cultural, operational, structural and financial change that entails or C) Partner closely with an ecosystem of providers who can help them build a strong Brand and value proposition
8. In the future UX = Brand & Brand = Value
9. Whoever is the curator of the user experience reaps the highest reward
10. Landlords need to offer more than a ‘Workspace’, they need to offer high quality ‘Workplace’ solutions - ‘sell me a productive workforce, not an office’
11. Buildings need operators who understand engineering, technology and data and are equally skilled in hospitality and the curation of customer experience. These skills are at the top of the value tree. 
12. The best, most profitable Landlords of the future will embrace all these skills.
13. Valuations will be based on rolling income histories - the Operator makes or breaks an asset. 
14. Bond like valuations based on long leases and covenants will exist, but will bevery low yielding investments as the demand for ‘old school boring income’ will be intense.
15. The Real Estate market will become barbell shaped. You will either be a winner or a loser, not much will exist in the middle. Traditional landlords, only skilled in physical real estate will not be winners.
16. Real Estate companies need to become technology companies and treat their buildings like iPhones; marriages of hardware and software, and services.

So, there you go. 16 Provocations. I do hope they 'provoke'!

Antony

Will Artificial Intelligence eliminate Real Estate jobs? Part 2...

In Part 1 we discussed how 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. Now we will look at how to make sure it is not your job that AI eliminates.

There are four steps.

First, DO NOT stay in a job that involves tasks that are largely ‘structured, repeatable or predictable’. This extends beyond whether they are simply ‘1+1 = 2’ but includes anything where the inputs are the same each time, in general terms even if the specifics vary, and the outputs are also the same. If the task does not follow a pattern and/or requires judgement during the processing then you are probably ok, but if not…… watch out.

Over time AI will move ever higher up the ‘cognitive stack’. Machine Learning means just that; the machine learns through experience, so whilst a decade ago AI largely involved tasks that needed to be explicitly programmed, going forward this is becoming less and less true. Just think of a self driving car. There is no way every possible eventuality could be encoded into a system, so the AI must try and intuit ‘what would a human do’ in any circumstance. They are learning that by analysing millions of hours of driving by real people. 

Secondly you need to learn how to think about AI. In 6 steps.

1. Understand what AI is good for, and what it is not.
   Knowledge, reasoning, planning, perception and communication are the five key areas that AI researchers have been working on since 1956. Reduced down to an essence you could say AI is about reducing the cost of prediction. What is the probability of X happening?
2. Look for use cases’: 88% of the core tasks are ripe for automation’ said the AI report from the RICS last year. Over a ten year period admittedly but in some form or other those will be ‘structured, repeatable or predictable’ tasks. Look for the ones with the highest value first, or where your competitors are automating but you are not.
3. Are these commonplace and repeatable within your own business and/or applicable to many of your customers? This is a numbers game to an extent. Find the tasks you can automate that are most common, or take up the most time. Not automating tasks that can be automated is just a waste of time.
4. Do you have or can you obtain large quantities of data? AI does require data. Systems need to be trained and then tested, and that requires data to train on and data to test against. Humans learn by experience, so do machines. Data is what enables this.
5. Are there clear metrics by which you could judge success? Working with data often throws up correlations that are not causations. You need to be able to assign metrics to outputs. You cannot learn if you cannot measure.
6. If you have all of the above, is that something that will create significant value? Just because you can do something does not mean you should. Does the effort involved in creating this AI have significant value? If not, probably best to think of an easier solution. AI is not a trivial pursuit, so make sure it is worthwhile.

Thirdly in a world of exponential technology you need to become exponentially human. You need to develop all your human skills - of imagination, empathy, design, social intelligence, problem solving, judgement etc - because that is your value.

Do not try and beat the machines at what they are good at - don’t bring a knife to a gunfight. Instead work on how, with your understanding of AI, you can use it to augment your own human skills.

As prediction becomes cheaper, the value of judgment will increase. All this technology is an input: we are responsible for deciding the output we desire. Those with great judgment, who can weigh up upsides and downsides of predictions will see their value rise.

AI will give us more options: how we exploit them will be down to human judgement.

And think ‘education, education, education’ - in a fast changing world education is a marathon, an ongoing process. You will never run out of new skills to learn.

And then finally you need to deeply consider how AI/Data and all technological change is going to change the world.

There will be winners and there will be losers. Oftentimes each of us will be both. We will lose some things but gain others.

Mostly it is tasks that will be lost rather than entire jobs but there certainly will be many jobs that do become redundant.

There will also be a lot of real estate that becomes redundant: as the work we do changes the nature of the office is going to change fundamentally. Where and how we live as well. And as for retail, well there is much change coming there.

Pay attention to those four rules and your job will not be overtaken by an AI. In fact you will most likely thrive in an AI world. Either way though it is always wise to remember that it is not perfection you are seeking to attain: what you need to ensure is simply that you are better than your peers.

The AI juggernaut is coming, just make sure it is not you it flattens.

Antony

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.

Antony

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

Antony

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."