AI in Real Estate
How AI is Enhancing the Back Office
Artificial intelligence (AI) is fast evolving from an experimental, emerging technology to a critical tool for real estate firms.
In 2023, a survey of 750 real estate CFOs reported:
97% of real estate firms were actively interested in AI | 14% were already using AI | 28% were in early-stage adoption
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30% were piloting the technology
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It’s no secret why there’s industry buzz surrounding AI: The technology offers significant benefits for real estate firms, from cost savings to burnout reduction, especially when applied to back-office functions.
At the same time, seeing the benefits of AI isn’t as easy as pushing a button and getting results. Many real estate leaders struggle to understand how to use the technology, what it can and cannot do, and how to mitigate its associated risks.
Interested in learning how AI can support the real estate back office? Read on to discover top AI use cases, common roadblocks, and implementation tactics.
AI for the Real Estate Back Office
Back-office functions can be time consuming and tedious, particularly when handled manually, resulting in high rates of burnout and increased labor costs. Fortunately, these functions are often standardized and supported by clear and simple processes, which makes them ideal use cases for AI in real estate. Let’s explore some of the most common use cases for AI in the back office of a real estate firm:
Use Case | How it Works | Key Benefits |
Streamlining communications | Firms can build chatbots to automatically answer simple renter questions like when rent is due and how to pay it. Chatbots can route more complex questions to appropriate parties when necessary. Because these chatbots are relatively easy and inexpensive to create, they represent a great first use case for AI. |
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Automating administrative tasks | AI can automate many routine tasks, including scheduling property viewings, sending rent payment reminders, managing maintenance requests, renewing leases, drafting client communications, submitting insurance claims, and posting financials. |
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Managing documentation | AI can help manage the contract-creation process, including preparing, organizing, and validating contracts to ensure there are no missing fields or invalid information. Firms can also use AI to analyze contracts and identify trends related to financial performance and risk management. |
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Analyzing data | AI can extract data from disparate sources such as contract databases. It can also clean, standardize, and store data and apply the data to financial modeling and predictive analytics. AI can also analyze data written in non-standardized formats, such as plain text language. |
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Improving Investor Relations | AI can help real estate firms and PE firms who invest in real estate identify potential investors and tailor messaging to specific investors. AI can also provide troves of data and streamline data analysis so real estate firms can offer more high-quality information to potential investors. |
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Analyzing properties | AI can generate property value estimates based on information about the property (e.g., location, proximity to a school, risk of flooding) and relevant market trends. It’s important to note that a certified appraiser should always review the AI outputs for accuracy. |
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Maintaining compliance | AI can review real estate listings to verify accuracy and compliance with the Fair Housing Act. |
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Understanding AI Risks
AI offers many potential benefits to real estate firms, especially when applied to back-office functions. But like any technology, it can open a firm to serious risk without proper oversight. That’s why always keeping a human in the loop to review AI outputs and having strong governance are crucial to ensuring AI is working correctly and securely.
Here are some of the top risks real estate leaders must proactively address if they plan to use AI:
- Inaccurate outputs. AI can generate incorrect outputs — otherwise known as hallucinations — especially when the underlying data is incomplete, low quality, or poisoned. Even when data is sound and the tool is used properly, AI can still produce hallucinations. Having a dedicated professional review AI outputs is crucial to identifying inaccuracies and addressing the underlying issues that created them.
- AI can fall victim to the same biases as its users, as those human biases may be embedded in training data. These biases can result in violations of the Fair Housing Act, for example, by generating different rental prices for people of different demographics. Real estate professionals should regularly review training data and AI outputs for possible instances of bias.
- Security issues. AI can expose real estate firms to data breaches and hacks, especially if they use AI trained on public or open-source data. If that data source is breached, it provides a gateway into the company. Bad actors may also poison the data, impacting the accuracy of the firm’s AI tools. AI also increases connectivity between systems, for example, by connecting disparate databases to feed data from across the company into one AI platform. More points of connectivity can make it easier for bad actors to gain access to — and infect — more systems across the organization. Fortunately, proper data governance can help secure systems and mitigate risk.
Overcoming AI Roadblocks
Once real estate leaders understand the benefits and risks of AI, the next step is to select an AI use case, explore potential tools and platforms, and plan for the implementation process. Before moving forward with implementation, leaders should understand and proactively address the most common roadblocks to AI success:
Roadblock | Description | Strategies |
Lack of buy-in | Oftentimes, leaders aren’t bought into AI initiatives, which means these projects don’t receive the support, resources, or level of priority needed to be successful. | Clearly explain the benefits of the new technology, along with supporting evidence and examples, to the leadership team. Look for ways to quantify the benefits of AI (e.g. estimated cost savings). |
Unclear vision | Leaders don’t always have a clear understanding of their goals in deploying AI, including the right use cases to introduce AI into the business. | Align on a detailed AI strategy. Your strategy should include an AI use policy and governance, implementation plan, project timeline, change management plan, and a definition of success based on clear and measurable KPIs. |
Low-quality data | Poor data governance leads to low-quality data, which can result in poor AI performance and hallucinations. | Implement strong data governance practices like assigning a data steward to oversee data quality and integrity, creating a data classification system, enforcing strict access controls, and defining policies for data retention and disposal. |
Written by Brent Horak, Kristi Gibson and Kirstie Tiernan. Copyright © 2024 BDO USA, P.C. All rights reserved. www.bdo.com