The home-buying process can feel like an obstacle course — finding the perfect place, putting together an offer and, the biggest hurdle of all, securing a mortgage.
San Francisco-based real-estate technology company Doma is helping prospective homeowners clear that hurdle more quickly with the support of AI. Its machine learning models accelerate properties through the title search, underwriting and closing processes, helping complete home transactions up to 15 percent faster.
“There’s a lot of paperwork involved in this process,” said Brian Holligan, director of data science at Doma. “The better we are at using machine learning to identify different document types and extract relevant information, the faster and more seamless the process can be.”
Doma uses machine learning to identify different types of real estate documents and extract insights from those files. It’s also developing natural language understanding models to help everyone involved in a real estate transaction — from loan officers to real estate agents to homebuyers — rapidly interpret the numerous requests and inquiries that typically occur during the process.
Since its beginnings in 2016, Doma has accelerated over 100,000 real estate transactions with machine learning.
The company uses machine learning models — both transformer-based NLP tools and convolutional neural networks for computer vision — that rely on NVIDIA V100 Tensor Core GPUs through Microsoft Azure for model training.
“Working with a remote team, it’s nice to have the flexibility of GPUs in the cloud,” said Keesha Erickson, a data scientist at Doma. “We can spin up the right-sized machines based on the project or task at hand. If there’s a larger-scale model with a longer run time, we can grab the GPUs that are appropriate for the time constraints we’re under.”
Doma Machine Intelligence Delves Into Real Estate Docs
Once a seller and buyer have agreed on a purchase price for a home, they enter into a contract. But typically, a few weeks pass before keys actually change hands — a period known as escrow. During this process, the buyer’s mortgage loan is finalized and a title company investigates the home’s ownership, balance fees and tax history.
Doma’s GPU-accelerated machine learning models speed the title examination process by analyzing property records and mortgage files to help identify any risks that could disrupt the transaction.
Like other fields such as drug discovery or architecture, the real estate industry has jargon that a general NLP model may not be able to interpret. To tailor its AI to this lingo, Doma fine-tunes a suite of models, including BERT-based models, using a corpus of proprietary real estate data.
Doma’s technology also uses computer vision models to analyze older real estate documents. Many records come from county courthouses and clerk’s offices — and depending on the age of the home, records can be incredibly low-quality scans of paper documents that are decades old.
Doma machine learning engineer Juhi Chandalia, who works on machine learning models for this kind of document processing, found that using NVIDIA GPUs for inference cut the team’s time to insights by 4x, to under a minute.
“I’ve started training models on CPU instead of GPU before, and realized it would take weeks to complete,” Chandalia said. “My team relies on NVIDIA GPUs because otherwise, by the time we finished training and testing our machine learning models, they’d be out of date.”
Doma offers application programming interfaces, predefined integrations and custom versions of its platform to its lender partners. The company has teamed up with major mortgage lenders around the U.S., including Chase, Homepoint Financial, PennyMac and Sierra Pacific Mortgage, to accelerate the mortgage transaction and refinance process.
The company is also bringing some of its machine learning tools to individuals — real estate agents, buyers and sellers — to further streamline the complex process for all parties in a real estate transaction.
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