It is Not Entirely Out of the Realm of Possibility By Carter Pratt and Bryan Robinette The integration of digital automation in the real estate industry has made valuation, diligence and title services far more streamlined than they had been in the analog days. Today, with artificial intelligence-powered tools coming into the market, the dream of near frictionless real estate transactions seems like an increasingly likely reality. Real estate investing has always been a complicated business involving multiple stakeholders who participate in the transaction, from the buyers and sellers, lenders and lawyers, title companies and notary services all with their own stacks of paperwork to file, verify, sign, and register. In the not-so-distant past, the process of transferring property from one owner to another was an arduous, non-digital, labor-intensive operation of parties manually moving physical documents from one person to another with plenty of room for human errors, any one of which could have dire consequences for the entire transaction leaving all parties bewildered at the conclusion of the process. The Advent of Digitization The advent of digitization was a helpful step forward for both accuracy and speed. The ability to capture documents as digital images allowed paperwork to be filed, sorted, duplicated, and shared among all parties in a common format. For the title industry, uniform records helped eliminate some of the variability that had made searching for title in the past more of an art than a science. For other functions, the ability to see and compare documents helped investors make quick assessments of multiple properties. In a sense, digitization helped change real estate investing from a paper-bound business to a data processing one where information from multiple sources can be extracted, transformed, and loaded into a useable data repository before strategic analysis. Much of this transformation was jumpstarted because of the foreclosure meltdown and financial crisis that followed it in 2008. Deluged with available properties, investors struggled to evaluate opportunities in a heavily distressed market. The legwork required to execute an investment strategy was carried out with actual legs belonging to a national network of brokers, agents, and inspectors who could provide on-the-spot assessments of the condition of each property and an estimation of the cost of any repairs. Then the Covid-19 pandemic further complicated these already inefficient processes by making physical assessments nearly impossible just as the single-family rental (SFR) market was taking off. The industry has solved many of these challenges with technology. Today, there is a digital ecosystem that enables investors to assess dozens of properties, compare one to another, repair what is necessary, and then sell them without ever having seen the property in the real world. This ecosystem is powered by an automated array of technology features such as optical character recognition (OCR), robotic process automation (RPA), and extract, transform, and load processes (ETL). Working in concert, these technologies help review the content of hundreds, or even thousands, of documents and identify anomalies for additional review. A Decade of Progress It is amazing to look back and see how far we have come in a short amount of time. Just a decade ago, many of these technologies that the real estate investment industry now depends on did not exist. For example, the due diligence process was nearly entirely manual a decade ago. Due diligence required highly trained professionals to physically review mortgage documents, title policies, lease agreements, and other recorded documents and manually type inputs into a system. They would spend hours doing side-by-side comparisons of documents and data values to make sure they matched—a tedious process appropriately nicknamed stare-and-compare. As with any manual process, this introduced issues. For example, inconsistencies in how each reviewer entered information oftentimes resulted in repeating these input tasks in order to standardize everything. Today, by overlaying OCR and RPA technology, much of this work can be automated which can help to increase the ability to scale operations efficiently and accurately. This frees up the professionals to focus their attention on higher level decision-making rather than time-consuming and repeatable data entry. However, there are still limitations with the current generation of technology. While optical character recognition is able to extract data from a document, it lacks the ability to decipher nuance or context. But with the introduction of generative AI and large language models (LLM), technology is now better able to contextually understand the content of those documents through question prompts. For example, rather than sifting through a 150-page PDF document, a title insurer could ask, “Who is on title for this property?” and have that information retrieved in a consistent and integrated way. The potential of overlaying AI on top of automated processes could also help avoid some other common chores. AI can help us go beyond extracting data by interpreting the data based on nuances of the property location. For example, recording requirements vary from one of the 3,000+ counties in the United States to another. Generative AI trained on these requirements has the potential to help those without a career’s worth of experience instantly apply these requirements for recording a mortgage in Los Angeles or the requirements for clearing a lien in Westchester. Looking Ahead At the Radian family of companies, we are making huge strides towards judiciously exploring these capabilities. Over the past several years, in a controlled environment, we have invested heavily in building and training AI models to help improve, accelerate, and simplify transactions for clients. Pre-AI enhancements to homegenius Real Estate’s Pyramid Platform operating system and Single-Family Rental Due Diligence and Valuations Dashboards provided by Radian Real Estate Management have helped investors enhance their automation in nearly every step of the funding process based on their internal requirements and the bank’s requirements. These platforms can be accessed by the investor, the capital markets lending institution, and the diligence reviewer so that all parties can see what is occurring with their transactions in real time. A user can see at a glance the diligence requests for each property, who the