Clark Twiddy is the President of Twiddy & Company, a hospitality and asset management firm along North Carolina’s Outer Banks.
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Most of us are at least somewhat aware of the idea of latency in financial markets. In short, latency is the measured delay between an input into a system and the recognition (or display) of that same input within the system. It’s been a big deal in financial markets for some time, and the capacity to have the smallest measured delay in terms of speed is considered a competitive advantage for groups that can move more quickly to capture marginal value as markets shift. The faster markets move, the more important latency becomes as a profitable advantage.
With the real estate market continuing to be scorching hot in many areas of the country, the rapid interaction of supply and demand in hyperlocal markets is drawing many comparisons to the speed of larger financial markets. And with those comparisons also comes similar — if new to the sector — terminology. Said another way, with so much rapid movement between determining supply and demand at any one moment, the latent ability to move the price of a real estate asset to reflect the market in real time is becoming a measurable competitive advantage for real estate firms in the same way latency has been an advantage in financial firms.
As an example of this new emergence, recently, a high-level professional conference focused on the short-term rental industry was held in Charleston, South Carolina. In a prime takeaway from the conference, it’s clear that there has never been so much interest and subsequent investment in so-called “dynamic pricing” — determining where something is valued right now relative to demand and being able to make changes as quickly and accurately as possible. Right behind this concept are large-scale, yet still fragmented investments in machine learning, artificial intelligence and smartphone user pattern analysis.
This newly evolving technological capability is good news for real estate firms, particularly those with a short-term rental focus. With the right kind of comparative data and the agility to move price quickly, the ability to get out ahead of any subtle market shift means the potential for higher margins on any upside movement and the simultaneous protection of potential losses on any downside demand movement.
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In recognizing this potential, the real question becomes what is “fast enough” compared to the market? While there remains some debate on that topic across regions with different booking patterns (think the fly-to Grand Canyon at one end of a scale and the drive-to Outer Banks at another), the future of short-term rentals is daily pricing of rental availability based on daily demand within defined patterns that become the borders of machine-driven pricing.
For consumers, this new capability has the potential to be both good and bad — firms are much more likely to be able to recognize and respond to micro-trends in the market, meaning that pricing might be high when specific travelers are most likely to travel and lowest when travel is at an ebb, even within tight demographics (think state school systems here, for example).
In addition, while discounting has long been a staple of future-minded travelers, that may not be something we see a great deal of in the near future as the capacity for smart pricing grows and latency with price adjustments protects against the risk of lower margins via discounting.
At the same time, consumer confidence in getting a fair deal should increase, knowing that there is a great deal of study going into pricing at any given moment and that you are most likely paying an accurate and fair price for an asset experience relative to demand. In other words, the risks of an unjustly inflated price should be rapidly decreasing, minimizing the fear of getting a bad deal.
In summary, the astounding value creation across real estate markets through much of the country has brought the industry as a whole closer to the financial exchange markets of the 21st century — heavily automated, capable of digesting massive amounts of timely data and with access to more fingerprint-level consumer data than thought possible a decade ago.
The art of pricing will slowly take a back seat to the science of pricing in many respects. The more these systems know us, the more they’ll be able to predict, with stunning accuracy, what we’re likely to pay at any one time for a real estate asset, particularly in high-volume sectors like short-term rentals. If nothing else, knowing we’re getting the right price is getting easier all the time.
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