AI in Real Estate: How AI Adds $180B to US Real Estate Annually

What is the forecast for AI in real estate? McKinsey figures for the US real estate market indicate that AI has a staggering total value of over $119 trillion USD. Yet, McKinsey figures identify that AI in real estate could add over $180 billion to the field every year. 

The revenue potential of AI in real estate stems from its ability to simplify or directly solve core issues that plague the sector. Inaccurate housing valuations, costly energy consumption in commercial real estate spaces, and low agent contact rates in house selling reduce industry profits and operational efficiency.

Artificial intelligence tools developed by real estate leaders are actively solving these issues, creating new revenue-generating opportunities, and enhancing processes to improve the bottom line. 

As artificial intelligence tools continue to innovate in this sector, they are one of the most significant additions to the modern shape of the real estate industry.

AI In Real Estate: How AI Improves Operations Across Commercial and Private Sales

The real estate sector is heavily data-dependent. Practical data analysis determines everything from historical housing prices to property valuations, market trends, and the likelihood of a sale.

Ineffective management or ignorance of its importance radically impacts profit margins in the real estate industry and can contribute to higher costs for inefficient operations. No matter the country market a real estate vendor operates in, they will typically encounter a central triad of problems due to an inability to manage data effectively:

• Inaccurate House Valuations: The Journal of Real Estate Finance and Economics outlines the term “Information Frictions” to refer to the different prices different parties can put on houses. Banks, agents, local councils, and homeowners may give distinct house valuations, reducing the speed with which a seller will put their home on the market. Artificial intelligence data analysis tools can compute hundreds of data points influencing house valuations and deliver a highly accurate figure, expediting the selling process.

• High Commercial Property Operating Costs: According to the National Renewable Energy Laboratory, 40% of total energy use in the United States goes to buildings. Suboptimal energy usage patterns in a business could mean that companies pay for far more energy than they need. Especially in offices or other commercial spaces, this can reduce a business's overall profits and eat into revenues. By incorporating AI tools into energy monitoring and management, businesses can better understand their energy consumption and develop strategies to reduce expenditures. 

• Low Real Estate Agent Contact Rates: Even when the housing market is booming, agents receive fewer commissions than ever, and customers rarely reach out to them. This problem has even led a leading professor to state that real estate agents are going extinct. Yet, using AI tools, real estate agents can use personalization to put suitable properties in front of the right customers, enhancing engagement and increasing commissions. 

Let’s explore how AI in real estate is actively solving each of these issues and contributing to this sector's booming profits. 

Opendoor Uses AI in Real Estate To Accurately Value Houses

The digital real estate platform Opendoor buys houses from sellers and then aims to flip them. It uses deep learning and artificial intelligence to value house prices more accurately. House prices are notoriously variable, with values that buyers get from banks compared to real estate agents sometimes being tens of thousands of dollars apart. Here are the ways they leverage AI in real estate:

• Accurate Price Modeling—Opendoor incorporates computer vision and AI-based modeling in its pricing models to account for home conditions, allowing it to provide more accurate and competitive preliminary offers. This improves the seller experience by offering more targeted initial prices and helps the company make better-informed purchasing decisions.

• Enhanced Customer Support—The company has implemented an AI-enabled text experience to help sellers meet their needs. Buyers and sellers can text their real estate questions and receive trusted information quickly, increasing engagement and making the process more convenient for customers.

• Expanded Market Reach—Opendoor utilizes AI and computer vision in its forecasting techniques to reduce bias in its pricing systems and better account for home conditions. This has allowed it to expand its "buy box" criteria, increasing its addressable market from $160 billion to over $600 billion annually and serving a wider range of homeowners.

By using its deep learning algorithm, Opendoor completely solves the issue of house pricing accuracy. Moreover, its ability to buy houses at a fair price at scale has allowed it to expand rapidly. The houses the company bought using its AI algorithm in 2021 rose in price by around 35% over one quarter. 

Despite the slight housing crash throughout 2022, Opendoor’s AI-first real estate purchasing strategy has still emerged as highly profitable. 

Commenting on OpenDoors’ innovative AI real estate buying strategy, CEO Carrie Wheeler stated: “With an improved cost structure, strong balance sheet, and scaled customer acquisition channels, we believe we have laid the foundation to emerge from this cycle more resilient and well-position for continued share gains and long-term profitability.”

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With this innovative use of AI in real estate, Opendoor allows sellers to get a fair price for their property in the quickest time possible, empowering users to sell their house in days rather than months. Gone are the days of going back and forth to collect different valuations before putting a house on the market. With Opendoor, the whole process of pricing to selling can take a few days. 

Verdigris Reduces Commerical Property Operating Costs with AI

Businesses in the commercial real estate space have to constantly battle against the high cost of running these spaces. Across energy usage in HVAC, lighting, and other systems, commercial building rental can eat into the profit of a business and reduce its overall value. High bills between tenancies can further cut the bottom line for real estate businesses owning these spaces.

Verdigris uses artificial intelligence to overcome these real estate issues directly. Their AI model collects electricity use data, energy pattern information, and HVAC and lighting operational times. By compounding this information, the Verdigris AI system learns the building’s optimal energy use strategy.

The AI model can then automatically adjust HVAC units and lighting intensity in areas of the building to result in an incredibly 30% reduction in energy usage. Moreover, these savings are concentrated on adjusting areas with little or no human traffic, meaning the people inside the building won’t notice any difference. 

While these AI systems can instantly reduce energy consumption, this is far from the only operating cost AI in real estate can achieve. When Verdigris AI in real estate solutions is fully integrated into business systems, it can make smarter energy pattern decisions, saving up to 50%. Companies worldwide, like T-Mobile, Nvidia, and Verizon, have already begun using Verdigris to enhance energy saving in commercial spaces.

Verdigris's use of AI in real estate radically reduces energy costs, saving businesses money and creating a more effective way of running commercial properties. 

Zillow Uses AI Personalization To Boost Agent Contact Rates

Property browsing websites typically have an incredibly vast user base but don’t see a high conversion percentage from viewers into customers. These platforms primarily help people find the right home, creating an accessible method of contacting the house’s agent to start deal conversations.

Zillow, for example, had an incredible 226 million active users on its website and application in 2023. Yet only a tiny fraction of these users ever contact an agent through the platform, which reduces Zillow’s profits as agents pay for every interaction they receive on the app. The real estate giant developed an AI personalization model to solve this issue.

By drawing data on over 50 aspects of users and how users engaged with the application, we can better understand the type of properties that a customer may want to engage with. The model then uses AI personalization to present the best possible properties to a user.

On average, this personalized system of recommending houses has increased engagement by 33%. This figure represents an incredible profit gain for the housing giant, using artificial intelligence to enhance its platform and deliver hyper-personalized listings. 

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Zillow’s AI model can also identify users who have a high likelihood of making a purchase through the platform. By pinpointing these users, it can use more ad spend to advertise properties the user is likely to enjoy, further increasing engagement opportunities and resulting in a higher percentage of users contacting agents. 

Zillow’s AI in real estate innovation actively solves the low communication rates between buyers and agents, creating a streamlined system that uses personalization to enhance the likelihood that buyers reach out and eventually buy a property.

Final Thoughts on AI in Real Estate

From streamlining house sales and agent communication to optimizing commercial real estate energy usage, AI in real estate proves to be a profit force. Artificial intelligence tools integrate into the real estate experience, helping to enhance operations for businesses in this sector and customers alike.

As more real estate giants, corporations, and commercial space owners look toward AI tools, this trillion-dollar industry will enhance profits, reduce friction, and create an accessible buying experience. 

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