I asked seven AI engines for a mortgage recommendation. The results looked surprisingly old-school.

The experiment you conducted highlights an interesting phenomenon in the mortgage industry, particularly when it comes to the use of artificial intelligence in finding lenders. By querying seven AI engines for a mortgage recommendation with a specific focus on Knoxville and Debt Service Coverage Ratio (DSCR), you found that the results often pointed to lenders with city-specific pages and strong Search Engine Optimization (SEO) signals. However, these lenders frequently lacked strong local ties, which could be a critical factor for borrowers seeking personalized service and local market expertise.

This outcome suggests that while AI can efficiently sift through vast amounts of data to provide mortgage recommendations, it may still rely heavily on surface-level indicators such as SEO optimization rather than deeper, more meaningful criteria like local market presence or community reputation. For borrowers, especially those in niche markets or seeking specific types of loans like DSCR mortgages, this could mean missing out on lenders that, although less digitally prominent, offer better terms, service, or understanding of local market conditions.

As the mortgage industry continues to evolve with technology, it will be important to watch how AI platforms refine their algorithms to prioritize more nuanced and relevant factors in lender recommendations. For now, borrowers would do well to not only consider AI-generated suggestions but also to conduct their own research into local lenders, perhaps through community referrals or direct outreach, to ensure they find the best fit for their mortgage needs. Keeping an eye on how these platforms adapt and improve will be crucial for both consumers and industry professionals.

Originally reported by housingwire.com. PaintNews adds analysis for real estate & property readers.