Could Commercial Real Estate Stakeholders Get More Out of Dynamic Pricing?
According to the Forbes technology council, “Leading companies know that in today’s digital marketplace, dynamic pricing is a key driver for growth and success.” But how does dynamic pricing work in practice for commercial real estate, and what data should companies be relying on?
The theory behind dynamic pricing is simple. A real estate company can improve both occupancy and revenues by optimizing their pricing based on supply and demand. It’s a tale as old as time. A cup of coffee might cost cents to make, but throw in a working space with a good internet connection, or a place to get out of the rain – and you can charge $5 without anyone thinking twice about reaching for their wallet. The airline costs for a single flight can range from $20-$50 per passenger, but travelers expect to pay at least 10x those costs if they want to fly during peak season.
Increasingly, stakeholders in commercial real estate are beginning to take advantage of the same game theory. Companies such as Airbnb use dynamic pricing to help their hosts make the best return on investment for their short-term rental properties, many of which are occupied by guests for as little as a day at a time. With longer-term occupancies, residential properties that hold tenants for a year or more, or commercial units that sometimes have contracts of 10 or 15 years, dynamic pricing is even more important.
What Data Do I Already Have?
The first step for building a dynamic pricing model for your company is your own internal data. To some extent, even if you don’t know it, your business is probably already using dynamic pricing. Let’s say you have a building with 20 units, and 10 are currently vacant. You’re likely to drop your rental pricing to help with improving occupancy rates. In contrast, if 19 of the units are occupied, you might try listing the unit at a higher price, aware that filling the unit doesn’t need to be your priority.
Artificial Intelligence and Machine Learning help you to take this idea further than you can do with gut instinct alone. Using data on occupancy, pricing, renovations and more, AI simply sees things that a human cannot. Historical data is a good example of this. Let’s say you have five tenants leaving your residential building at the end of the month, a fact that is driving you to consider dropping your rental prices. Historical data can show you that this time of year is a traditional time for tenants to leave, and that it is unlikely that you will have trouble filling the units at a good market rate. On the other hand, while two tenants leaving may not be instinctively a cause for concern, the data can predict that you’re entering a period where more tenants are likely to leave , and in that case lowering your rent may well be the right decision.
Including External Data to Optimize Dynamic Pricing
As dynamic pricing models get more sophisticated, external data is enhancing AI’s ability to make smart predictions. While a lot of companies are beginning to use dynamic pricing models, too many focus on just internal data to build their solution. Their reasoning and predictions will be inherently limited. A unit was previously listed at a certain price, and has recently had renovations done to transform it into a luxury unit. The price point now falls between X and Y. A great start, but the best results will come from utilizing dynamic pricing that takes external data into consideration, too.
With external data, the sky is the limit. Market rates are an obvious metric that can be taken into consideration, and can show the way an area is changing over time, whether that’s lessening in popularity for families, or evolving into a business district for office buildings. New build apartments could show a demand for more properties in the area, or it might dilute the need for your units, but census and third-party real estate data can accurately predict which is the case. In many cases, businesses have created headquarters or R&D centers in outlying areas, which have then gone on to revitalize a community and raise property prices exponentially. A new school could increase the need for family-friendly units that have the right amenities.
For residential units that are leased on shorter contracts, upcoming events can be used to build dynamic pricing models. You may know the market rate for an apartment building in Tokyo for example, but in Summer 2020, the demand will rise as visitors flood the city for the Olympics.
Okapi Dynamic Pricing
Putting all of this information together into actionable notifications on price point, Okapi takes your internal data, and enriches it with the relevant external information for your properties. From week to week, we can provide an accurate range of how you could be pricing your units to maximize occupancy while improving your bottom line.
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