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Proptech startup prices condo, rental projects

Montreal proptech startup says its use of artificial intelligence (AI) is helping d...

IMAGE: The senior leadership team of, from left, Benoit Thibault, Mark Owen, Jordan Owen and Sean Tassé. (Courtesy

The senior leadership team of, from left, Benoit Thibault, Mark Owen, Jordan Owen and Sean Tassé. (Courtesy

Montreal proptech startup says its use of artificial intelligence (AI) is helping developers more accurately price their new condo or rental projects and maximize revenues.

CEO Jordan Owen said developers are selling themselves short by mispricing new units for sale or rent.

“We didn’t know the extent of how inefficient (pricing) really was until we started speaking with clients,” he said.

The company’s residential real estate pricing solution provides three services – automated market research, initial price lists and real-time revenue optimization.

Owen said some developers try to create their own algorithms to price units in their properties, but they’re far from optimal given that real estate people are not experts in data science. “Having a product like ours that does it all automatically is a huge value-add.”

Founders have real estate backgrounds

Owen and his brother, co-founder Mark Owen, have “been talking about real estate forever,” given that they grew up in the family real estate development firm Mondev.

Jordan Owen also previously worked as a commercial and industrial broker at Colliers in Montreal: “I was doing mostly cold-calling, so I learned how to sell.”

He then went on to work for the family business Mondev, where he worked on deals from start to finish. Brother Mark Owen also worked at Mondev and in investment banking.

Jordan Owen’s experience taught him the way real estate prices are established often misses the boat.

“I wanted to find ways to increase the efficiency and overall pricing that a project could get, so I started to dive into data analytics and understanding how to price effectively,” he said. “I really fell in love with the ability to systematize pricing using big data and quantifying qualitative attributes.”

From Bien Aller to

When the pandemic was declared last March, Owen was doing a dual degree in real estate development and city planning at Massachusetts Institute of Technology (MIT). He returned to Montreal after in-person classes were cancelled.

Shortly after, he and his brother along with Sean Tassé, a real estate lawyer who worked in construction management, started a reusable face mask business. They called it Bien Aller, after the phrase “Ça va bien aller” (things will be OK), which became popular in Quebec in the early days of the pandemic.

In its first eight months, the company sold 350,000 masks and donated more than $100,000 to charity.

In June, the three Bien Aller co-founders took some proceeds from mask sales to launch and added Benoit Thibault, who has a computer science background, as a co-founder.

“We decided to focus our energies on what we’re all really experts at, which is real estate and tech.”

They’ve since received help from government grants, incubators and university competitions to propel forward.

Meanwhile, given that face masks are now “worth very little,” Bien Aller is being repositioned to sell T-shirts, hats, socks and more “so that in a year, when we try to sell it, it’s actually worth something,” he said.

The early growth of has 10 to 15 clients, mostly in Montreal, including developers Kodem, RoseFellow, Armco and Mondev. The company also has clients in Toronto and recently picked up its first Nova Scotia client.

Owen said developers and brokers who establish the pricing for new condos and rental often make the mistake of setting the same square foot price per unit, no matter its quality. As a result, “you end up renting or selling all of your most beautiful units first and then you’re left with the ones that were mispriced because they were too expensive.”

The initial price list provides to clients is based on market comparables and includes data such as population density, income distribution, age distribution and public listings. The location and design attributes of each unit are also quantified.

Owen said’s pricing solution works for projects in pre-development by using data to suggest optimal unit mixes and sizes.

For example, SquareFeet recently completed reports for a client for a development in Griffintown, near downtown Montreal, and another in Lachenaie, off the eastern tip of the island of Montreal.

In Griffintown, the product found the optimal size for a one-bedroom was between 500 and 600 square feet while in Lachenaie, it was 800 square feet. “It’s data-driven decisions that we’re giving our clients.”

Dynamic unit pricing model’s model creates a form of dynamic pricing for real estate akin to the system used by airlines. When there is increased demand for a specific type of unit – such as studios selling faster than one-bedrooms – pricing is increased to take into account that increased demand.

As part of its sales efforts, has been asking potential clients to provide sale dates and prices of previous projects. It then provides them reports to show how would have priced their units at launch and during the last year of sales.

Owen said one client had a project with waterfront view units that were essentially priced the same as those without the views. The units with waterfront views sold rapidly while those without were still available.

“In our calculation, there’s about $1 million left on the table from this mispricing. Having this sort of tool would save a lot of energy for the developer.” is now developing a product for existing homes Owen believes could tap a larger market than its existing product for new condos and rentals. It could be launched by the end of summer.

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