There are countless tedious tasks associated with owning or managing a property. Whether you’re a landlord, a homeowner selling a home, or a tenant seeking a home, you likely have to deal with contracts, open communication and property viewings.
However, the rise of machine learning — a subset of artificial intelligence which, put simply, allows computers to learn from data without being trained to do a specific task — has made automating these tasks easier. According to a PwC Canada survey, artificial intelligence is the second of the top five real estate disruptors.
If you’ve ever wondered about what the actual use cases are for automation, we’ve rounded up a list of how this technology is already having a major impact on this sector.
Speed up customer service
Chatbots are one of the most obvious ways that automation is impacting real estate, by allowing companies to communicate with their customers in an efficient and personalized way. Toronto-based Ask Avenue, for example, allow users to get chatbot-assisted lead qualification by neighbourhood, and allows them to identify the customers they’re talking to with activity tracking and auto-lead verification.
A solution like this is also helpful for evaluating how to better communicate with customers; the platform allows users to track how they manage chats, as well as response time and connection rates.
According to Mike McGown, vice-president of sales at real estate selling platform Chime, consumers are looking for quick answers to their questions in the digital age — meaning that having a chatbot can be the difference between losing out to a more available competitor. In July 2019, the company added an AI assistant feature to its platform, promising a 30-second instant response and intelligent interaction that mirrors chatting with a real person. “Brokers already know their web site can be a powerful lead generation tool, but by leveraging our AI-powered chatbot, they will soon have even more opportunities to make a connection – even if they aren’t in the office,” said McGowan.
Make processes more efficient
Machine learning allows both tenants and property owners to ensure that they are on top of tasks related to buying and selling. Properly, a startup in Toronto, uses machine learning to help homeowners quickly determine how much they could sell their home for, or sell their home directly to the startup itself with a guarantee that homeowners will receive at least 50 percent of the price if their home sells for more on Properly.
In some sense, a platform like this allows homeowners to have more control and understanding of how to sell their home. “The average home sale in Calgary takes about 60 days on market,” Anshull Ruparell, CEO of Properly, told tech publication BetaKit. “And that doesn’t include the time, cost, and inconvenience of keeping the house clean [and] managing showings, or the time it takes to close the ultimate buyer.”
Making complex documents accessible
Tools that automate complex legal and financial processes can help real estate operators better evaluate deals. Many contract analysis startups are launching that can quickly scan through a document, and pinpoint the most important information for a human to interpret.
Toronto-based Kira Systems, for example, uses machine learning to analyze the text in contracts and extract the most important provisions. The startup notes that some of its biggest use cases are in lease abstraction, compliance and due diligence. These AI-powered systems can help remove human bias from background checks, meaning that more people prohibited by traditional practices now have access to properties.
Toronto-based Naborly allows property owners to automate tenant screening using its platform, so fees associated with credit and background checks can be avoided. Naborly can also provide a credit risk profile of tenants based on rent history, financial history and employment history, so property owners can have peace of mind that the system is taking into account all factors. It also offers NaborlyShield, a product that covers rent in the event of late rent or early vacancy.
With data being such an integral part of evaluating properties and potential tenants, machine learning will only continue to be an important part of a real estate professional’s arsenal.
Are there other ways in which automation will impact the industry? Let us know in the comments.
Jessica Galang is a tech journalist who has been tracking the Canadian tech ecosystem for the last several years. In the past, she was news editor at BetaKit and a reporter at The Logic, interviewing hundreds of entrepreneurs in emerging industries.