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Analytics and AI in Residential Real Estate Pricing

This blog will focus on the how and the why of using analytics and AI to better price residential real estate units. Specifically, how to price rental properties.


Properly pricing your products is crucial in any industry – and this holds especially true in residential real estate. Whether you are a property manager or leasing agent for a large apartment building, trying to lease out a community of single-family homes, or renting for a student community – properly pricing your products (units) is essential. Really, your goal should be to maximize net operating income by optimizing your rent revenue and decreasing your vacancy cost (cost of units being empty with no one paying rent). The impact of pricing can be big; in fact, a 1% increase in rent can lead to a 5% increase in profits (assuming a 20% margin).


Yet, pricing real estate units is an extremely difficult and outdated task. Why?


For one, there are a multitude of factors that go into how a unit should be priced. For example, how do you determine what physical attributes such as a balcony, views, floor level, windows, amenities, etc. are worthy to a prospect. Beyond the physical attributes, you then must consider several dynamic attributes such as demand (website visits, inquiries, appointments, offers, etc.), your availability and vacancy, the overall market, your management goals, and a lot more! There’s no way the human brain can properly comprehend all these factors and spit out a price that is accurate.


Second, the real estate market moves fast. Just look at how the Toronto market price per square footage changes over a two-year period.

As you can see, keeping up with the rapidly changing market can be quite challenging. If your prices are lagging in a down-market, your products will be overpriced and you will see a massive increase in vacancy, and therefore vacancy cost. In an up-market, your products will be underpriced, and you will miss out on possible rent revenue you could have received.


Finally, many portfolios rely on competition pricing to price their units. Although this should be a factor in your decision, it shouldn’t be the guiding force as competition data is imperfect. For example, prices you see online may be outdated, you may not know what situation your competitor is in (massive vacancy and mortgage to pay back, so just need some type of rent), etc.


Clearly, there has got to be a better way to price real estate products. No longer can you rely on gutfeel or competition. That is where data, analytics and AI can come in.


AI-Based Dynamic Pricing

Through AI, you can create an algorithm that is constantly running thousands of pricing ‘experiments’ in order to spit out the optimal rental price for each unit to optimize net operating income in the long term. It automatically ingests a number of factors to dynamically change the price of a unit, with the goal of maximizing the bottom line of your portfolio.


At AAARL, we have been perfecting our very own revenue management system which utilizes AI-based dynamic pricing. This is the high-level overview of how our algorithm prices real estate products…


Step 1: Base Rent

First, the system deliver’s a base rent price. To do this, thorough analysis and research into the regional market, competition pricing, as well as management preferences and goals are used to determine a psf (per square foot) price. Once combined with the square footage of each property’s units, a base rent is determined.


Step 2: Physical Attributes

From there, the specific physical attributes I mentioned earlier (views, floor level, appliances, direction facing, etc.) are automatically added to each unit’s base rent. Some attributes will negatively affect the price (obstructed view), while some will positively affect the price of the unit (sunset view). How does the algorithm know what each of these features are worth? Well, beyond the years of research and analytics our team has done, the system continues to learn the true worth of each of these attributes as more data is collected. In fact, it even becomes aware that certain attributes mean more or less in units with different layouts or in different regions. Therefore, the system continuously adjusts to be able to scientifically determine the true worth of each of these features.


Step 3: Dynamic Attributes

Finally, once the physical attributes are added to the base rent, dynamic modules such as availability (meaning how many units in your building, region, portfolio, etc.), vacancy, demand, etc. are added to the price. Let’s use demand as an example. The system is automatically pulling your website visits, inquires, appointments, etc., and using that information to determine the demand trends. If demand is rising, your units will be priced up. Conversely, if demand is dropping, your units will be priced down. Therefore, you can stay ahead of the competition and optimize your results.


After the dynamic attributes are added, each unit is priced in order to find the optimal point between rent revenue and vacancy cost. Although technically the system is changing the price continuously, we usually recommend weekly price changes to your portfolio.


Benefits

Through this AI-based dynamic pricing system, you first will see an increase in your rent revenue and a decrease in vacancy rates and costs. In fact, we have seen a 2-5% increase in rent compared to the market, as well as 20%+ savings in vacancy costs from our system. Beyond these obvious financial benefits, huge benefits stem from the process improvements and the amount of time your team will save. As this system uses data you are most likely already collecting, the whole process can be automated. Finally, as your data is now in one organized place, you can create (or receive from our team), much more thorough reports and analysis of your operations to help make more informed business decisions.


Conclusion

It is time for the residential real-estate industry to upgrade how they currently price their products through the use of analytics and AI. Other industries like airlines, ride sharing, and hotels have already adopted these principles and seen the benefits firsthand. It can not only help improve your bottom line, but it really does make your life easier. Reach out to us today to learn more about our AI-based dynamic pricing system and see how we can help!

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