Introducing the VTS Demand Model
What a transformation three and a half years makes.?
You likely saw that we just announced VTS 4, which will give our customers the means to predict tenant demand for office space through enhanced data and AI-generated insights.?
The “AI-generated insights” part is most exciting to me. The topic is now ubiquitous, but VTS has not been using AI for AI’s sake, nor data science for data science’s sake. What we have been doing – for years – is intentionally building a model that takes the scale of data VTS has about tenants, spaces, buildings, deals, and markets to give our customers what they really want from AI - which are answers. Answers to what tenant demand & pricing trends look like, and what they’re going to look like in the future.?
The VTS Demand Model is the means to these answers, and we’re proud to publicly announce its development alongside VTS 4.
First, a little background, and why this announcement matters.
The CRE industry is built off of backwards-looking demand data. Trends develop up to a year and a half before stats are “recorded”. Due to quarterly reporting lags, it can be another 1-3 months before those stats are “reported” for consumption, analysis, and incorporated into an investment strategy.?
This is a broken information model. We accepted it as the way things were because it worked in a period where the entire industry benefited from decades of cap rate compression, but that period could be over for good.?
Three and a half years ago, I was approached by VTS with an opportunity to co-lead the creation of a predictive insights solution alongside a newly hired team of data scientists and researchers.?
While I had known about VTS throughout my stints at CBRE, American Realty Advisors, and The Irvine Company, I was blown away by the promise of what “could be”. The ability to harness hundreds of millions of real-time data points across demand, pricing, and supply had the potential to be groundbreaking.?
When learning about this opportunity, I thought to myself, “This is what everyone has been waiting for”. In my time as a researcher and economist, it always struck me that the datasets our industry works with to make leasing projections either account for factors external to leasing (think employment rate, population growth, etc.), or are reflective of the trends that happened months ago, if not over a year ago.?
In other words, the data is incomplete and doesn’t capture the full universe of what’s happening on the ground, in granular detail, in real-time. Not only that, but you have the highest chance of making a forecasting error at turning points in the market – exactly when the need for accuracy and conviction is the greatest.
It’s not incidental that my time at VTS has run parallel with the re-establishment of the office market. To say the last three and a half years have been a rollercoaster for the office market is an understatement. Along with my team, I have been deeply committed to helping our customers navigate this period of time.
But we’re now at an inflection point. Office demand has been up for eight straight months, and the world’s savviest investors are once again engaging with their long-term office strategies. While there’s still risk, the next 18-24 months may prove to be one of the greatest periods in years to deploy capital into office.?
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To capitalize on this moment in time, we want our customers to have every informational and competitive advantage possible. The VTS Demand Model powers VTS 4, which unlocks the informational and competitive advantage our customers deserve.
What is the VTS Demand Model??
I’ve grappled a lot with how to describe the VTS Demand Model in simple terms. It can be heady stuff, and ultimately the outcomes the model drives are what’s most important. So, instead of providing one blanket way to unpack this, I’ll explain what the model is as if I’m talking to three different people: A (curious) third grader; a first-year broker; and a CRE executive.
Hopefully that sums it up nicely (a few times). You might be wondering about the AI part, though, and how it applies to the Demand Model. Let’s get into it.
How is AI applied in the VTS Demand Model?
There are three categories of AI use cases within the VTS Demand Model. These categories are refinement, personalization, and forecasting.?
Our use of AI to personalize insights is even more powerful when it comes to tenant requirements not yet known to the market. Over the last few years we’ve seen that, on average, prospective tenants look at space online 90-120 days before they begin physically touring space and their requirement is known. Not only can we capture the companies looking at your spaces online, but with AI we’re once again able to surface whether you have an existing deal or relationship with that tenant.?
This is only the beginning.
Today we’re making use of our global supply, demand, pricing, and marketing analytics data. Soon we’ll be able to innovate with data in an exponential manner, bringing into our model all of the tenant experience and property operations data created through VTS Activate.?
We’re beyond excited for the promise of the VTS Demand Model. What a transformation three and a half years makes.?