The real estate sector is seeing notable advancements because to artificial intelligence, machine learning, and computer vision. With the help of these technologies, there are new ways to solve issues like automated data collection and fraud detection and boost productivity. Without going too technical, I’d want to offer some recommendations in this essay for getting started with AI in real estate.
Automating Workflows And Intelligent Document Handling
Handling real estate paperwork can take a lot of time. Data extraction and organizing are challenging due to disparate formats and asymmetric templates. By automatically extracting important data from complicated papers like leases, appraisals, and loan paperwork, artificial intelligence can help.
Recent developments in optical character recognition (OCR) technology allow for the accurate recognition of even poorly scanned or handwritten texts. This implies that crucial data, including loan terms and property values, can be quickly and easily recognized by AI.
With a little engineering work, this data can be incorporated into current methodologies. For example, commercial mortgage lenders usually aim to use the values and cap rates in their assessments for loan sizing and underwriting. You could automatically extract data from appraisals given to Box, SharePoint, or any other similar platform and push it to Salesforce if you use AI to extract this data and Zapier to move it around.
Creating Descriptions For Listings
Brokers find that writing listing descriptions is a time-consuming task that AI can automate. Even though there are paid options, you can start using ChatGPT and other free tools right away. To quickly create a draft listing description, use templates in ChatGPT containing property details and prompts.
However, as AI occasionally makes mistakes or invents facts, it is crucial to verify the generated descriptions for accuracy. Inaccurate information in multiple listing service (MLS) postings can lead to liability issues, even if it was developed by artificial intelligence (AI).
Observance And Fraud Investigation
AI-generated images and listing descriptions provide new challenges for MLS vendors. False descriptions and photographs with manipulation are far easier to make and can remain hidden for a long time. Because inaccurate listing data carries several fines and penalties, this is a serious problem for both MLS administrators and brokers.
AI is another tool that can be used to solve these problems. Computer vision and artificial intelligence systems can identify listing problems by comparing the attributes indicated in listings with those in property photographs. They can also spot indications of photo tampering, rival brokerage logos or signage in a property, and even assist in raising any Fair Housing Act compliance concerns.
MLS administrators can benefit from all of these elements in keeping correct and compliant listings.
Automation Of Due Diligence Tasks
Artificial Intelligence (AI) can help with data cross-checking and verification, which is crucial during the loan application process. Algorithms can quickly compare data points from several documents in order to find irregularities or inaccurate information. Because so much data is entered by humans in the real estate industry, errors are quite likely to occur.
Anomalies, such as values that don’t match or addresses that are specified differently on separate documents, can be found by comparing key values gathered from records like lease agreements and property condition assessments. By identifying inconsistencies and directing further research, fraud risk can be reduced or the loan approval process might be delayed. Both commercial mortgage lenders and loan purchasing companies like Fannie Mae and Freddie Mac may benefit from these applications.
Think About These Challenges
There are various restrictions on the application of AI in the real estate industry, despite its great potential. Since AI systems usually need access to financial and personal data, concerns regarding data security and ethical use are raised, and data privacy is one of the main issues. It’s understandable that a lot of lenders, brokers, and appraisers I’ve met with are worried that their work product would be used to train generative algorithms that will compete with them.
Algorithm prejudice and the potential for violations of fair housing laws pose additional risks. Due to AI’s skill in spotting patterns, it can provide suggestions based on user preferences at first and then learn from those choices to continuously refine results, such as when searching for a house. Depending on the criteria used and how narrow the search results are, this could effectively point users toward particular properties and communities. Steering is still steering, even if it was done by an AI.
Finally, the unintended expansion of the digital divide caused by the usage of artificial intelligence technology may make it more challenging for entry-level analysts to negotiate real estate agreements. I’ve heard time and time again how important it is for analysts and underwriters to “get in the weeds” and learn the process by doing the work; artificial intelligence could make this harder.
All things considered, artificial intelligence (AI) is revolutionizing the real estate industry by boosting productivity, reducing errors, and automating tedious tasks. Even if these examples are just the tip of the iceberg, they show how much AI can do in the real estate industry. In the future, we can expect even more disruptive changes as long as we keep using these technologies.