Even though the economy is cooling down, the US housing market is still exceptionally hot, and buyers are facing much higher prices than before the pandemic. Making things worse, mortgage rates have more than doubled since the start of the year, and the Federal Reserve promises more rate hikes before fall, assuring even more pain for buyers. The combination of rising prices and rising rates have made many Americans desperate, making it far more likely that they will fall victim to financial transaction abnormalities — questionable fees and charges that may significantly increase the costs of homeownership for decades to come. Technology can help.
Consider the onus placed on the borrower these days. When they sit down with their closing statement, they are expected to be able to immediately differentiate between “real” mortgage fees and questionable charges, some of which may have been added at the last moment. But few homebuyers will know standard industry practices or jargon — in other words, what represents an essential service. Legitimate fees, for example, include title insurance fees, appraisal fees, home inspection fees, application fees, loan origination fees, credit report fees, recording fees, and document preparation fees. Everything else is an abnormality, or what credit rating agency Experian calls a “junk fee.”
To be clear, abnormal charges are more than just nuisances. They disguise the mortgage’s (and the home’s) true cost, making it impossible for consumers to compare apples-to-apples when shopping for a loan. However, Artificial Intelligence (AI) and machine learning (ML) can be valuable tools in fighting back and (literally) cutting through the crap.
First, a lender’s Loan Estimate could be run through an AI application, which compares fees and charges against TILA-RESPA regulations, local market conditions, and standard industry practices. Over time, ML would enable the application to develop a model mortgage, making the app a local industry expert of sorts. The app could develop the intuition needed to match up fees and charges in the future, even if different lenders use different names for essentially the same thing.
This is important because most buyers won’t have the bandwidth to sort out lenders’ bids for their mortgages in the heat of a house bidding war. By the time the buyer has had time to digest their loan estimates and/or closing statements, they may be too late — the house bidding process may be over, or mortgage rates might have shot up again.
There is also a lot of historical data to work with, thanks to longstanding documentation and filing laws. Fintech companies are already harvesting synthetic data by anonymizing vast historical data. AI can then be applied to this data to predict the outcome of a new financial instrument. In this regard, home buyers need not guess what the ultimate ramifications of one mortgage versus another might be down the road; AI can tell them with stunning accuracy in seconds.
A home mortgage is typically the biggest liability any individual will acquire in their lifetime. Yet, the hot housing market, combined with rising rates, has pushed a lot of consumers into a rush to judgment that benefits the industry by allowing it to lop on abnormal charges and fees.
Artificial intelligence(AI) 101 for Entrepreneurs(Opens in a new browser tab)
We have seen so often that the problem is not the consumer’s lack of intelligence but rather the industry’s lack of transparency. We can’t do anything about the latter, but technology can make consumers more intelligent by leveling the playing field.
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