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The rollout of artificial intelligence is a game-changer for industries across the board, and real estate is no exception. Emerging AI systems take advantage of the large amounts of proprietary or third-party data that real estate agents have already amassed about markets, neighborhoods, and individual properties.
Generative AI automates listings, leases, and leads
Generative AI, of which ChatGPT is only one example, can produce new creative content such as text, images, audio, and video. The iterative nature of generative AI means that its responses can improve in quality and refinement over a series of prompts.
As is the case for headhunters, recruiters, and anyone whose work relies on processing information from a variety of sources, increasing the efficiency of routine tasks in this way saves real estate professionals time and money.
Using generative AI, a real estate company can automate the creation of descriptions for property listings. Chatbots powered by the technology can answer questions from prospective buyers or renters immediately, 24/7, keeping them engaged. For property managers, chatbots can even respond to routine maintenance requests by identifying and contacting the appropriate maintenance staff or contractor. More complex requests from tenants can be routed to the property manager. In addition, agents can use AI to draft lease agreements, generate accurate appraisal estimates, and point out new investment opportunities and challenges. AI can produce marketing content quickly and efficiently and conduct fast, accurate database searches.
Generative AI can create virtual tours of multiple properties, making it easier for potential buyers to select the one that will be best for them—and thus making it easier for agents to close on listings sooner.
As a McKinsey report noted in 2023, generative AI holds the potential to produce anywhere from $110 to $180 billion or more for the real estate industry.
Computer vision sharpens the view for agents, buyers, and sellers
Computer vision technology is a market projected to potentially quadruple in size from approximately $9 billion in 2020 to more than $40 billion by 2030. Computer vision systems use machine learning and other AI capabilities to extract data from images quickly and at scale. This is the same type of technology that enables autonomous vehicles to see and understand road markings for safe navigation.
Using AI-assisted computer vision technology, a real estate team can also better examine the details of a property from photos, noting added-value items such as landscaping and in-ground pools.
Analyzing still images and video, an AI program can sharpen the accuracy of property descriptions to produce listings with greater accuracy and greater virtual curb appeal. The capability to create fast, accurate descriptions, and to provide real-time data on the condition of a property, also helps clear appraisal backlogs when there is a shortage of qualified appraisers.
Teamed up with automated valuation models—data-driven estimates of real property values—computer vision technologies have enabled real estate agents to boost the accuracy of their on-market valuation predictions by 7 percent or more.
Predictive analytics delineates the future of real estate
Using predictive analytics as a source of actionable intelligence has helped real estate agents to up their professional game, minimizing risk and optimizing their reach. Predictive analytics is a powerful tool that draws on historical data combined with algorithms to produce new insights into future market trends. Typically drawing on Big Data and Geographic Information Systems (GIS) that identify and analyze geographic data, it can also help determine property values and pinpoint potential new avenues for real estate investors.
Today’s AI-infused predictive analytics can produce a 360-degree, three-dimensional view of a property and its environment, drawing instantly on wide array of official documents like government agency environmental reports, traffic pattern data, flood plain and other geographic survey maps, and regional consumer data.
Predictive analytics also makes it much easier to ascertain the way specific groups of buyers and sellers will behave over the near and the long term. Using these algorithms, a real estate professional can target prospects likely to move over the course of the next year—possibly even before the individuals themselves are aware they’ll be considering relocating.
This marks a huge advance over the ways that real estate agents used information less than a generation ago. Until the advent of AI-aided predictive analytics, an agent might mostly use data to help produce a property valuation once a transaction was underway. This involved, for example, scanning through rent rolls and revenue sheets as part of due diligence to ensure that a commercial property would be a good investment.
In one recent instance, during the volatile market year of 2021, a real estate predictive analytics program was able to identify more than 2,000 properties with a high probability of coming onto the market the following year. The algorithm showed a success rate of about 23 percent, as more than 460 of those properties were listed in the specified time frame.
How did the programmers achieve such an impressive result?
They parsed through the ZIP codes assigned to their target market, keeping the size of their data set within manageable limits. Understanding the kinds of life events that drive relocation decisions, they programmed their algorithm to look for hundreds of data “triggers” evidenced by changes in household income and size, as well as in neighborhood trends.
Powerful tools to accelerate the industry
AI is a powerful tool that can turbocharge entire global real estate industry. From customer engagement to marketing content creation to predictive analytics, AI powered-technologies can bring the industry to a more efficient and profitable future.
