Real estate is not a new industry. It has been formed over the centuries to help make the risky and important process of buying, selling, and leasing properties safer and easier. Many technologies have been adopted to help property professionals make their jobs more efficient. Websites, email, spreadsheets, CRMs, valuation calculators, search engines, all of these have helped change the way real estate is transacted. But it seems like we are approaching the limit on how technology can help humans do their job more efficiently. For the real estate industry to level up it will have to learn how to teach software how to do some of the tasks altogether. Be it speeding up the sales process, using real-time fee calculators, spotting errors in documents, or sorting through piles of data to help make complicated decisions, smart software is learning how to do these—better than a human ever could.
How is this done? Artificial intelligence. A growing list of technology companies are finding ways to use AI to help make real estate more efficient and, of course, make the people in real estate a lot of money by doing so.
Here are some of the leading AI firms in real estate pushing the industry forward.
With 95 markets across the US and Canada, the company’s mission is to “redefine real estate in the customer’s favor.” Redfin got its start in 2006 with their inventing map-based search, but then became a trailblazer with their approach to speed up the process of home sales, saving customers over $1b in fees over the past decade. “Redfin operates the most popular nationwide brokerage search site, so we have an efficient channel for our local agents to connect with buyers and sellers,” said a spokesperson at Redfin. “This means that Redfin agents can spend their time working with customers rather than trying to drum up business, which is one reason Redfin agents are three times as productive as the average agent.”
They use AI to recommend homes that buyers will like, and when a home is likely to sell quickly. They claim to be able to sell homes faster for more money and charge a lower fee, due to the fact that their data helps them market their listings to only serious buyers online. They use AI to give homeowners an accurate estimate of their home’s value and this same machine learning algorithm helps to power the cash offers they make to buy homes directly through their iBuying business, RedfinNow.
This Seattle-based company uses machine learning for a faster, user-friendly and transparent real estate experience by offering the buyer, the seller and the agent a simple fee calculation tool. It isn’t overloaded with a ton of fields to fill out, it sticks to essentials while integrating local data. Using real-time examples of buildings and homes, the site’s fee estimator allows you to look up buildings (residential or otherwise) and see its price, refinance, and loan information.
They aim for “drama-free closing” when it comes to real estate sales. Their goal is to not make you feel like you’re talking to a robot, as they’ve assembled a team with a common mission: “leveraging systems, processes, and technology to create a better, more human real estate title and settlement experience,” according to David Wolf, the CTO of JetClosing. “There are a lot of moving parts to closing a real estate transaction, with many different involved, hard deadlines, and large sums of money moving around,” he said. “It’s easy for people to get anxious and escalate tensions when they’re not confident everything is going smoothly. We try to eliminate that drama by being as proactive as possible every step of the way, providing great communication and full transparency through our technology platform.”
“Closing on a home should be a celebration, not exhausting. A big part of how AI technology helps with this is in the analysis of documents and communication like emails. AI is used in converting a digital version of a paper document, like a PDF, to a quality digital representation with accurate semantic analysis,” said Wolf. “For example, there may be a dozen important fields in a mortgage payoff document that we need to capture as part of a closing. That can be done manually, but AI-based document analysis can dramatically help find those pieces of information in a payoff, which doesn’t have any industry standard format.”
Wolf notes that there’s a misconception that AI and ML are used to replace manual processes, but that isn’t always true. “There is enormous opportunity to take the drudgery out of manual processes, giving the human experts more time to apply their valuable knowledge and judgement,” he said. “We apply these principles in tools we’ve built internally to analyze and handle email communication. The human element is necessary, but 90 percent of the busy work has been automated away with technology, a win for customers and employees.”
This AI-assisted underwriting platform for multifamily buildings allows buyers and renters to look at millions of properties and survey expenses, rent, fees, and opportunities in comparison to the competition using machine learning. As Marc Rutzen, co-founder and CEO of Enodo says, automation and predictive analysis can help real estate analysts underwrite faster and more effectively: “Machine learning algorithms will not skew results based on human bias, as there is no incentive for an algorithm to overstate or understate the expected performance of a deal,” writes Rutzen. “Algorithms follow a simple and consistent set of rules and work the same way no matter what the situation. For this reason, they can often be used as an objective, third-party source to confirm an analysis is unbiased.”
This site has a search engine built for interior design nerds. It uses AI to help people find their dream homes based on design photos you’d typically see in Architectural Digest, for example. With different sections devoted to kitchens with marble island counters, sprawling bedrooms with sliding doors and huge windows, or spacious living rooms, buyers no longer have to sift through dozens of photos to find the home of their dreams, as they can search them by the room that’s most important to them. This is helpful in the case of co-buyers, who can collaborate to see if they can agree on an opinion of a home. It also allows buyers to search both for new homes or decor specifics and home improvements, and explore local trends, which are usually inspiring and innovative, as the site is laden with luxe photography.
This company uses a data-driven approach for real estate investing. By using actionable data, they allow buyers to make the smartest purchases possible with passive investments. They track up-and-coming neighborhoods with the kind of data you might overlook (avocado toast, trendy bookstores, and French bulldog sightings all contribute to wealthy residents moving in, for example). It uses AI to vet homes targeting 25 percent IRR and use deep learning and alternative data to identify the highest returning SFR’s.
In the coming year, the firm is building for the future. “We are focusing on raising our fund to invest in AI vetted properties located in neighborhoods primed for rapid appreciation,” said Max Ball, co-founder of Lofty AI. “These neighborhoods typically have trendy retail shops like breweries and coffee shops, art murals popping up, and increases in electric scooters among many other factors.” They also plan on improving their AI system “by adding more unconventional, alternative sources that have high correlations to property price growth,” adds Ball. “A few data points we’re looking into include dating profile data and sewage data.”
As one of the first real estate companies to bring algorithms into the rental market, Beyond Pricing took what hotels and airlines were doing in the travel industry with AI and machine learning and helped apply it to the short-term vacation rental market. They’ve managed over $4 billion in bookings across over 330,000 listings in more than 7,000 cities across the world with a real-time system showing automated pricing. It’s being called “a life changing pricing tool” for property managers, as its platform saves them time, hassle and energy. It also helps adjust prices at a granular level, which many would not be able to predict otherwise. “Beyond Pricing is an automated dynamic pricing solution,” explains Jeffrey Breece, the manager of revenue analytics at Beyond Pricing. “We bring the same sophistication utilized by the largest hotels and airlines in the world to the individual Airbnb and vacation rental hosts.”
This appraisal platform uses AI to filter properties and their real market value. Based in the Netherlands, the startup raised $33 million in financing, and is co-founded by two architects, Teun van den Dries and Sander Mulders, focusing on European and US markets. As part of their new approach to property valuation, they’ve gathered an enormous database of over 150m properties, along with sales data, transport information, crime rates, density, and satellite images to create machine learning algorithms to make links between demographics, location, and value (among them, they focus on 90,000 local areas in the US market). With their “Neighborhoods” tool, investors and lenders can quickly assess the attractiveness and risk across neighborhoods within a submarket. “By providing a quicker, more reliable and intelligent service, Geophy helps its customers make better decisions and promotes a better-functioning market,” said van den Dries.
This Montreal-based firm is fighting climate change with AI. At the forefront of a new era in building automation, they’re part of the new wave of leading the green building revolution. They work with a team of AI, data science and real estate experts to make energy-efficient buildings, while saving energy on older properties (20 percent of global greenhouse gas emissions originate from buildings and HVAC systems are responsible for over 50 percent of those emissions). Their goal is to decarbonize properties. “Our self-adapting artificial intelligence technology is fundamentally changing how Heating, Ventilation and Air Conditioning (HVAC) systems operate,” said Jamie Hahn at Brainbox. “Since HVAC accounts for approximately 45% of the total energy usage in commercial buildings and roughly 30% of that energy is wasted, there is a significant opportunity for improvement.”
They work with AI “to work better, smarter and greener,” adds Hahn. Their biggest priority for buildings right now is safely and securely re-occupying spaces without comprising their ESG and sustainability efforts. “Tenants are demanding both healthier and more sustainable built environments,” he said. “Regulators are enacting carbon neutrality legislation in more and more cities and investors have stated clear mandates to deploy capital into low or net neutral carbon real estate assets.”
This New York and Tel Aviv-based AI investment management analytic firm works in the commercial real estate realm with engineering, real estate, and data science teams that use data-generated insights with CRE to create investment vehicles powered by AI. Since 2017, they’ve been creating ever-evolving, next-generation investment approaches with computer science and engineering. For investment management, they use Utilizing over 200 traditional and non-traditional data sources, Skyline AI uses its artificial intelligence technology to process this data using machine learning models. AI investment management supports faster, more confident decisions which achieve better performance, one which goes beyond the industry benchmark to predict a quality surplus.