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A Commercial Real Estate Executive’s Guide to Artificial Intelligence

The commercial real estate orbit is swarming lately with terms like “AI,” “big data,” “machine learning” and “predictive analytics,” as yet another cluster of tech buzzwords takes center stage. Along with the newfound interest, big investment dollars are flowing into technology companies at every niche of the industry.

With all this hype looming, sensible CRE executives can’t just stay indifferent to the promises of AI tech. Many will tell you they are planning to incorporate AI into their processes or that it’s “in the roadmap.” But what does this mean? What are the objectives of AI implementation in real estate, to what particular field can it be applied, and which KPIs will measure its success?

Beyond focusing on applications, executives should also be concerned with the AI-authenticity of tech products being offered them by vendors. In fact, a recent study revealed that almost half of all companies classified as ‘AI startups’ actually have very little to do with artificial intelligence. How can we know that offerings claiming to be AI are in fact using next-level artificial intelligence technology to bring real innovation and value to the table for investors and clients, and not just riding the publicity wave to hype their own product?

Now would be a good time to clearly identify what AI is – and isn’t – and why it matters for CRE executives.

The Buzzword Dictionary

Artificial Intelligence (AI): AI has been around for decades, and refers to technology programmed by humans that uses data and algorithms to automate a certain capacity of work that otherwise could only be done by humans, such as play chess or detect anomalies in an MRI exam.

Machine Learning (ML): Algorithmic machine learning is a subset of AI. In ML, humans don’t write all the code lines; rather, they teach the machine to train itself by running data through the model, resulting in its ability to learn new things and change its own rules as it goes along. Some of the machine learning algorithms in popular use today are modeled after the human brain, in what’s known as artificial neural networks. Technologies built on such topologies are what we consider to be AI/ML.

Deep Learning (DL): A subset of machine learning, deep learning is considered the “holy grail” of AI’s progressive evolution. Simply put, the great step forward of DL is its ability to automatically learn from all kinds of raw data types without human training – that is, autonomously teaching itself.

In its various forms, AI today allows for much higher processing and automation than ever before – and is currently the best available way to harness big data. It will eventually carry out high-level predictive analytics with high accuracy – making sense of enormous amounts of relevant data and even improving our ability to predict the future.

Differentiating AI from A-Try

The current CRE techscape can be confusing, as there are a good deal of tech-based services that may seem to be AI, but are actually not. For instance, many companies now offer dashboard services which aggregate lots of data from various providers, such as transactional data, rent data, etc. These companies often market themselves as AI. Though they may provide helpful investor advice and value, these aggregation products are rarely AI-based. As another example, investment procedural solutions are rising in popularity. These services streamline the various steps involved along the legal investment process. Here again, the promise of AI may or may not be entirely accurate.

It is often possible to determine the authenticity of a core AI offering by looking at its output. Where real AI is at work, you can expect the product to support output decision-making insights, not just aggregated market information or recommendations. Ask about the actionability of the product’s final output as an important barometer of authentic AI and to gauge what parts of the product offering are utilizing AI.

Another way to detect a CRE tech company’s degree of intention—and capability—is by looking into the organization’s team. Is it comprised of real estate veterans and an IT guy? Or expert data scientists and engineers with solid backgrounds in deep tech? If the latter, what’s the ratio between engineers and AI/data scientists (ideally about 1:1)? The answers to these questions will be very telling about the seriousness of the company’s AI hype.

Obtaining Impactful AI Applications for CRE Execs

So how should a real estate executive find the right AI-based solution to take his or her investment potential to the next level? Here are some crucial points to keep in mind:

  • AI is not a goal in and of itself. Define your objectives and KPIs and determine that AI is the best technology for achieving them.
  • When it comes to CRE, there is a huge difference between AI applications being used peripherally, such as SaaS portfolio managers or cloud analytics platforms, and platforms using AI for core business objectives. It’s the difference between an IoT house with automated lights and remote heating capabilities, and AI-powered autonomously driving vehicles. Different organizations have different AI needs – but it’s crucial to identify which solutions align with which organizational necessities.
  • There’s no such thing as one-size-fits-all when it comes to deep data solutions for your business. A fully bespoke AI integration must take into account the strategies and corresponding data groups of your particular business model. Potential partners should display a deep understanding of your business goals and methods.
  • Core AI system building requires highly specific specialties. This will usually necessitate partnerships with technology professionals deeply knowledgeable of AI’s building blocks and capabilities, particularly as they relate to real estate investment. This is not likely to be an in-house effort.
  • Define the areas most crucial to your bottom line strategies and see who can make the biggest impact there. The additions should be implemented seamlessly into your normal operations, yet still bring quantifiable improvement to your business goals.
  • Consider how AI can be directed to create alpha for your organization. Effective AI-based CRE insight should fuse the expertise of your company’s real estate professionals with machine learning models to identify value creation opportunities.
  • When efficiency or automation is the objective, incorporating AI shouldn’t just improve things incrementally – it needs to give you a serious exponential jump. As such, your KPIs for relevant values should be through the roof. Don’t be misled into thinking AI implementation should be measured in any other way but its effect on your bottom line.

Over the last few years, we’ve seen AI disrupt a number of traditional industries – and the commercial real estate market should be no different. By understanding vast amounts of data that affect the entire real estate investment lifecycle, AI technology has the potential to unlock billions of dollars in hidden value. Now’s the time for real estate leaders to develop in-depth knowledge of the new tools available, and how to apply them to their business objectives. By sourcing authentic, best-in-class AI technology, real estate professionals will develop a far deeper understanding of the real estate market than ever possible.

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