AI, machine learning, and computer vision are enabling significant improvements in the real estate industry. These technologies provide new methods for increasing efficiency and addressing concerns such as automated data collecting and fraud detection. In this essay, I’d want to give some suggestions for getting started with AI in real estate (without becoming too technical).
Workflow Automation and Intelligent Document Processing
Real estate documentation can be time-consuming to handle. Different formats and uneven templates make data extraction and organization difficult. Artificial intelligence can assist by automatically extracting crucial information from complex documents such as leases, appraisals, and loan paperwork.
Even badly scanned or handwritten documents can now be recognised with great accuracy thanks to recent advances in optical character recognition (OCR) technology. This means that AI can easily identify important data such as property valuations, loan terms, and more.
This data can be integrated into existing workflows with some engineering. Commercial mortgage lenders, example, typically seek to capture the values and cap rates in assessments for underwriting and loan sizing. If you utilize AI to extract this data and Zapier to transfer it around, you could automatically extract data from appraisals submitted to Box, SharePoint, or anything similar and push it to Salesforce.
Producing Listing Descriptions
Writing listing descriptions is a time-consuming chore for brokers, but it is one that AI can automate. Although premium solutions are available, you can get started for free with tools like ChatGPT. Create templates with property facts and prompts and enter them into ChatGPT to generate a draft listing description in seconds.
However, it is critical to double-check the generated descriptions for correctness, as AI can occasionally make errors or invent information. Even if an AI created it, incorrect information in multiple listing service (MLS) listings can result in liability difficulties.
Compliance And Fraud Detection
With AI-generated photos and listing descriptions, MLS suppliers face new obstacles. Manipulated photos and false descriptions are much easier to create and can go undetected just as easily. This is a significant issue for both MLS administrators and brokers, as there are numerous fines and penalties for erroneous listing data.
AI can also be utilized to address these challenges. By comparing listed qualities in listings with those in property photos, computer vision and AI systems can detect listing errors. They can also detect competitor brokerage logos or signage in a listing, identify signs of picture tampering, and even help flag potential Fair Housing Act compliance issues.
All of these factors can assist MLS administrators in maintaining accurate and compliant listings.
Due Diligence Task Automation
Cross-checking and verifying data between documents, which is critical during the loan application process, can be aided by AI. Algorithms may swiftly compare data points from different documents to detect anomalies or inaccuracies. There is a lot of possibility for error in real estate due to all of the human data entry.
Key values collected from documents such as lease agreements and property condition evaluations can be compared to identify anomalies such as mismatched values or different addresses mentioned on each document. Inconsistencies can be identified for further investigation, lowering the risk of fraud or delaying the loan approval process. These applications could be beneficial to both commercial mortgage lenders and loan buying organizations such as Fannie Mae and Freddie Mac.
Consider These Obstacles
Despite the fact that the use of AI in the real estate business has enormous promise, there are certain limitations. Data privacy is a major worry, as AI systems frequently require access to personal and financial data, generating worries about data security and ethical use. Many lenders, brokers, and appraisers I’ve spoken with are concerned that their work product will be used to train generative algorithms that will compete with them—and this is not an unreasonable concern.
Another danger is algorithm bias and the possibility of fair housing breaches. Because AI is adept at detecting patterns, it can provide recommendations based on initial user preferences, then learn from their choices to continually narrow results in a house search, for example. This could efficiently direct users to certain properties and communities depending on the qualities utilized and how limited the search results become. Even if an AI did it, steering is still steering.
Finally, the use of artificial intelligence technology may unintentionally expand the digital gap, making it more difficult for entry-level analysts to negotiate real estate deals. I’ve heard several times how crucial it is for underwriters and analysts to “get in the weeds” and understand the process by doing the work—AI may make this more difficult.
Overall, AI is transforming the real estate sector by increasing operational efficiency, decreasing errors, and automating manual operations. While the examples presented here are only the tip of the iceberg, they demonstrate the enormous potential of AI in real estate. As we continue to adopt these technologies, we can anticipate even more disruptive changes in the future.