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<\/a>Photo: James Bombales<\/em><\/p>\nWith construction costs on the rise amidst red-hot market activity, every dollar counts when it comes to launching and selling new development projects these days. SquareFeet.ai<\/a>, a Montreal-based proptech startup, helps developers avoid leaving any cash on the table with its data-driven artificial intelligence tools.<\/p>\n SquareFeet.ai uses big market data and machine learning to provide market insights, price predictions and sales velocity optimization for multi-unit for-sale or rental developments. The company\u2019s price optimization platform<\/a> analyzes over 200 unique attributes of a new project, information that is then fed into algorithms to generate an initial unit price list before or during sales. Similar to airplane price models, the system automatically monitors the project’s sales or leasing performance to provide optimal pricing based on real-time factors like supply and demand, allowing developers to achieve maximum profit.<\/p>\n\n Co-founder and CEO Jordan Owen and his brother, Mark Owen, had been dreaming up the idea of SquareFeet.ai for years with hopes of selling the product to developers across the country. After successfully launching a reusable mask company at the start of the pandemic with fellow co-founder Sean Tass\u00e9, the brothers joined up with their fourth partner, Benoit Thibault, and used the profits from their mask business to create SquareFeet.ai.<\/p>\n As the company marks its one-year anniversary, SquareFeet.ai has priced approximately 5,000 units located across Montreal, Toronto and Nova Scotia, with a goal to get to 10,000 suites within the next few months and expand to more Canadian cities.<\/p>\n Livabl<\/em> spoke with Jordan to find out more about how the company is helping developers achieve their highest potential revenue by using their tech.<\/p>\n Parts of this interview have been edited or removed for clarity and brevity.<\/em><\/p>\n Livabl: You grew up alongside your family\u2019s real estate development firm, Mondev. You also worked as a broker at Colliers. How did these early experiences in new construction and sales prepare you for the eventual creation of SquareFeet.ai and understanding what the development industry needs?<\/strong><\/p>\n Jordan Owen: Well, my brother and I were definitely fortunate to have grown up in a real estate family. My mother is also in real estate, she is just on the academic side. She\u2019s a [professor] in sustainable development, and my father is the founder of Mondev, the real estate development company in Montreal. Growing up in that kind of environment, you\u2019re basically talking about family stuff and real estate. You\u2019re exposed early on and you learn to talk the talk, and you understand all of the issues that are around real estate.<\/p>\n On top of that, working with the family business was the next level of experience because you\u2019re given tons of exposure and tons of responsibilities. Having that freedom really helped conceptualize all of the ideas that came out of it.<\/p>\n I worked as a broker for a short period of time. I had an objective in mind. I really wanted to learn how to cold call and how to sell. I always wanted to learn how to structure deals, understand leases. It was my first experience out of university. I believe it was extremely valuable to [learn] the sales tools through cold calling and on-the-spot salesmanship is invaluable in my opinion. It was really a stepping stone for me in the brokerage to get back to the real estate development business with our family.<\/p>\n But then once I got back to the family business, I started to see how technology could impact real estate. From someone who doesn\u2019t even have a technical background, my brother and I were capable of building some technical systems that really added tremendous value to the business. And then I realized that I needed to learn more about technology and what we could do with technology. I decided to pursue further studies at MIT in real estate development and city planning, doing a double masters degree focusing on technology.<\/p>\n L: Tell us a little about how you all came together and started this company. What was the initial idea or conversation that led you to starting SquareFeet.ai?<\/strong><\/p>\n JO: In reality, COVID was a very strange year. My brother and I have had this idea, SquareFeet, for five years now. We basically came up with this price optimization, big data-driven pricing algorithm back when we started at our family business and we had developed a preliminary model that was functional and added tremendous value to our business.<\/p>\n We always wanted to develop it for outside. We wanted to build the platform so we could sell it to developers across Canada. It was just very hard to find the time. I was in school in 2019 and then COVID happened, and everyone was sent home. So during that time in March 2020, my brother, myself and Sean started a reusable face mask business [Bien Aller<\/a>]. The online school thing wasn\u2019t for me and I really wanted to focus on something that I would have had fun doing.<\/p>\n So we started a reusable mask business and it was extremely successful. We donated $150,000 to charity, we sold 350,000 masks across Canada and the US. That was a huge hit, and we used the profits that we made from that venture to use as seed funding for SquareFeet. And now our database is extremely powerful, we have clients that are ranging across Canada and we\u2019re extremely excited about what\u2019s coming next.<\/p>\n At this point, we\u2019re very excited to know that our platform is ready. We\u2019re able to onboard clients and the platform is effective across Canada. That\u2019s what we\u2019re excited about today. We were focusing really heavily on the product for the past year. The next year is going to be more sales and pushing the product and getting feedback and growing it from there.<\/p>\n L: You\u2019ve mentioned in previous interviews that developers are selling themselves short by mispricing new units for sale or rent. How much of a misprice are we talking about?<\/strong><\/p>\n JO: The fact is that the data isn\u2019t available across Canada. We wouldn\u2019t be able to claim \u201cX percent is mispriced\u201d but [for] the clients that we onboard, what we\u2019ve done is we\u2019ve studied all of our clients\u2019 past projects and looked at their pricing and we\u2019ve looked at their sales velocity and we\u2019ve optimized our algorithm by studying their historical data. And what we\u2019re able to show is there\u2019s three to four percent mispricing. It means that if they had implemented our algorithm, the sales velocity optimization would have added three to four percent.<\/p>\n Just to contextualize that and to put more human numbers and words to that, the pricing scheme that we noticed is that the price per foot for the corner unit with the most beautiful view with a huge terrace was priced at the same rate as a studio tucked in the corner without a view. What we saw is that there wasn\u2019t any quality pricing in the industry. It\u2019s very limited. There are some who do it, but it\u2019s not close to optimal.<\/p>\n Let\u2019s say you have a hundred units. A hundred units is not an unusual-sized project. It\u2019s standard to the skyscrapers in Toronto, or 200-plus or 300-plus units. We\u2019ll make it standard [at] 100 units [with] an average price of $400,000 per unit. Total revenue for that project would be $40 million. Let\u2019s say there\u2019s one percent mispricing. That\u2019s $400,000 left on the table, multiplied by four percent. It\u2019s about $2 million on a one hundred-unit project that is being left on the table. It\u2019s significant.<\/p>\n That\u2019s just bottom-line profit that\u2019s being staved, and it\u2019s just one aspect of the supply chain that needs a one percent tweak, a two percent tweak to add that much value.<\/p>\n L: Let\u2019s talk about SquareFeet.ai\u2019s price optimization system. In simple terms, you feed the SquareFeet.ai system floorplan and unit information, then it spits out an initial price list based on a number of data factors. That price list is then monitored and adjusted based on supply and demand in the project. Can you contextualize how this system works?<\/strong><\/p>\n JO: Let\u2019s say your studio units are renting or selling faster. What would happen is the algorithm is going to see that and re-price it on a daily basis. So the studios that are selling faster are going to be recommended to increase in price.<\/p>\n We do the same thing with market data. If the market as a whole is shifting \u2014 let\u2019s say COVID happens, let\u2019s say certain changes happen \u2014 real estate projects [tend to] last 12 to 18 months, so over that 12-18 month period, we\u2019re monitoring the market and repricing it as the market is shifting upwards, ideally.<\/p>\n Other things that we [look at], we study the velocity of sales of the project. If the project is selling faster than the market is absorbing, it means that it\u2019s underpriced compared to the market. There\u2019s several of these algorithms that we\u2019ve incorporated, I\u2019ve just named really three high-level ones. If the project is selling faster than the market is absorbing on average, the project is underpriced, we\u2019ll increase the price there as well.<\/p>\n L: You\u2019ve mentioned that SquareFeet.ai uses multiple sources of market data, including census data. Can you expand on where you\u2019re gathering information from and how you\u2019re using it?<\/strong><\/p>\n JO: There\u2019s a few layers of data. The census data, what we use that for is to find similar neighbourhoods. In census data, there\u2019s population density, there\u2019s income distribution, age distribution. Are they renters, buyers? All that kind of data. A practice that has been implemented in the [United] States is called clustering and finding similar neighbourhoods. We\u2019ve run a clustering algorithm to find the census tract, which are the neighbourhoods that are the most similar.<\/p>\n Let\u2019s say you\u2019re deep in the suburbs and you\u2019re developing a new project, but there\u2019s no comparables directly around you. A traditional market report wouldn\u2019t be able to expand their market analysis past the radius. You\u2019d have to do it manually and cherry-pick projects. Our algorithm automatically clusters the neighbourhood and expands the market data. You could be looking at markets 10 kilometres away that have the exact same demographic profile. The results are very stunning, and they work very well.<\/p>\n That\u2019s the census data. Then there\u2019s all kinds of real estate data that we\u2019ve incorporated. Transit times, so how long does it take to transport yourself via public transport to the centre of the city? And then all of the listing platforms. We have about 20 listing platforms that are in the database that are being compiled everyday.<\/p>\n L: At what sales stage would SquareFeet.ai generally get involved in a project?<\/strong><\/p>\n JO: We do conceptualization. The earlier the better, because we have data that adjusts how to design units. So depending on where you\u2019re located, there\u2019s different unit sizes that are optimal. So, if you\u2019re right in the heart of the city, you might need smaller units. We could tell you exactly what size unit you\u2019ll need to maximize your price per foot.<\/p>\n If you\u2019re in the suburbs, you might need larger units. We\u2019ll tell you exactly the size unit you\u2019ll need to maximize your price per [square] foot. At the beginning, it\u2019s very good to onboard us. We even recommend how many bathrooms should be designed into the unit and all that.<\/p>\n<\/a>Company Co-founders (From Left to Right): Benoit Thibault (CTO), Mark Owen (CDO), Jordan Owen (CEO) and Sean Tass\u00e9 (COO). Photo Courtesy SquareFeet.ai.\u00a0<\/em><\/p>\n<\/a>Photo: James Bombales<\/em><\/p>\n<\/a>Photo: James Bombales<\/em><\/p>\n