The 4 types of artificial intelligence

How these AI are already changing the world of real estate

Intuition is the basis of most human-based decision-making. This poses a problem as 60% of predictive indicators come from non-traditional variables in the real estate industry. Artificial intelligence has the power to identify these variables, collect information and help stakeholders make better informed decisions based on data. A study from McKinsey shows how AI in real estate has helped stakeholders find properties that are lower risk and have higher potential for growth.

The world of real estate is going digital with buzzwords like “AI”, “Machine Learning” and “Big Data” thrown around in marketing campaigns, landing pages and TedTalks. Still, what are these technological advancements and what do they mean for the real estate industry?

To explain these terms, let’s take companies like PriceHubble in Zurich and HouseCanary in San Francisco who have already started to digitise real estate processes. These companies analyse “big data” or incredibly large datasets to predict the success of an investment.

Information is collected by data engineers who “scrape” the data from places like Google Maps where regular traffic information might help to determine how noisy a neighbourhood is. Data analysts then find trends in the data and team up with real estate investors to create maps, graphs and other organised information sets to help investors make decisions on which properties to buy.

Here, we explain the four most common types of AI and their potential functions within the real estate industry
to help investors better understand emerging digital innovations.

According to a PwC report, the four types of AI in real estate are:

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Automated Intelligence

Automate manual or cognitive and routine or non-routine tasks.

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Expanding Intelligence

Helping business make better and data-informed decisions.


Supporting Intelligence

Helping people to accomplish tasks faster and more efficiently.

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Autonomous Intelligence

Automation of decision-making processes without human intervention

1. Automated Intelligence

Automation is one of the most common ways in which AI is being used. We see intelligent automation everyday from assigning drivers on the Uber or Lyft apps, to predictive text that is personalised to our writing styles, to depositing checks and much more. According to a 2017 article from McKinsey many companies can use this AI to automate 50-70% of tasks reducing the need for redundant and time consuming tasks.

This automation can naturally be extended into the field of real estate. By collecting information on different features, automated information can be presented on traffic levels, the amount of sunlight a building receives, crime rates and neighbourhood cultural trends. Many tech-savy newcomers like COSI already use automated intelligence to make business operations run faster and more efficiently.

In the future, investors can also use automated intelligence to predict which asset class would grow the best in which environment by collecting information on the number and types of residential and commercial properties. This has been done to an extent by the company Spacemaker that helps urban planners and real estate investors decide on which types of properties to build where.

2. Supporting Intelligence

Before AI can automate tasks, it needs to collect millions of data points as well as integrate pre-established processes from engineers to make meaningful decisions. Collecting data points, placing values on them and giving these values meaning takes time, and it isn’t always perfect, which is why supporting intelligence can help.

In the real estate industry, supporting intelligence collects relevant data and presents it for analysis. This data might be as simple as the number of empty buildings in a neighbourhood and how long they stay empty until they are either sold or rented. This value can be presented to investors who are able to use their knowledge to judge whether to add certain properties to their portfolios.

Supporting intelligence could be used in the form of add-on extensions that sort and find commonalities. For hospitality owners, this might mean an add-on feature to booking websites like or AirBnb where supporting intelligence is able to sort through comments for repeated keywords and gives them to the account holder to identify potential problems.

3. Expanding Intelligence

Because AI still isn’t perfect, the data collected that trains it still need to be identified, collected, cleaned and analysed by data engineers and data analysts in order to work effectively. The cooperation of AI with human intelligence is expanding intelligence.

Humans are better at making creative decisions based on intuitive thinking and diverse experiences whereas AI can find patterns in information to help better direct intuitive thinking. Companies have been creating expanding intelligence programs that collect data on daily tasks and analyse it for patterns. Business leaders can take this analysed data to see if there are more efficient ways of completing routine tasks.

Companies like IPSOFT have created AIs like Amelia that track, collect and find patterns in data to create optimised and automated processes for clients. Amelia is capable of speaking different languages as well as adapting to emotional contexts depending on prior information. We can expect more AIs like Amelia in the future as other programs like Siri and Alexa beginto support humans and gather data on their interactions on a
regular basis.

4. Autonomous Intelligence

Autonomous decision-making without human interaction is much more difficult to establish than Automated Intelligence, Supporting Intelligence or Expanding Intelligence. True AI resembles something similar to the robots acting in the interest of goals and making decisions without human interference.

As of now, no business has an AI that will purchase a property without the involvement of an engineer or stakeholder. These AI are either expanding or supporting the decisions of real estate professionals by collecting, cleaning and presenting the data with the help of a team of engineers and data scientists.

What we are looking at now isn’t the era of robots rising past humans, but rather an era of collaboration with technology that can find patterns which support business operations and automate processes for more efficiency.

The next steps for AI development

The potential to create True AI is there, but it will take much more data than companies can currently gather on their own. Sharing information would speed up the process in which AI is developed and would benefit the entire industry through competition and higher standards.

Just sharing data with partners would allow digital ecosystems to grow as more insights are made. For real estate investors and urban planners, this might mean predicting the amount of demand a specific area has from data shared on local real estate listing platforms. This would mean new development where searches are the highest. Alternatively, data sharing could also be used to predict rising asset classes in the industry to help investors better understand market trends.

With AI, data can be presented in ways that could help the investor make better decisions based on numbers that support intuition. The future of real estate is a digitally enabled one where technology backs the decisions of investors by providing them with the information they need to make the best decisions.

James Gibson

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