Michael is Chief Executive Officer of Kiavi.
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For generations, real estate has proven to be a successful way to build wealth in America. People buy a home, often build equity over time, then sell their home.
CNBC reported in December that close to 95,000 homes were flipped in the third quarter of 2021, an increase for the second quarter in a row. In the past, buying single-family homes, fixing them up and selling them at a profit has largely been the purview of those with access to capital and privy to hard-to-obtain information, such as accurate data on home valuations and the true costs of conducting repairs. These acted as barriers to entry.
My company uses the power of data and technology to bring lending for real estate investors into the digital age, and I’ve observed technology has ushered dramatic changes into the market in recent years. If the real estate industry is to continue to grow and welcome groups of investors who have traditionally been walled out, I believe key stakeholders must continue to rid the home-buying process of high fees, needless complexity and inefficiencies, as well as expand access to capital.
Artificial intelligence is already creating change among lenders.
Buying a home obviously requires money, and that typically means acquiring a loan. To do that, an investor usually needs a good credit score. FICO is one of many ways lenders assess someone’s creditworthiness. Most measure factors such as someone’s level of debt, credit history, the type of credit used and new credit accounts. For years, critics have questioned whether FICO is an accurate way to predict someone’s ability to pay back a loan.
In recent years, more and more lenders have turned to alternative means to measure creditworthiness, my company included. The rise of artificial intelligence has begun to create massive change. The ability to find alternative ways to determine credit risk could open more doors to groups who have not always received a fair credit evaluation.
That said, much has been written about the problem of introducing bias into these AI algorithms. While I believe AI is still a good option, it is still important to consider some challenges associated with using AI in the lending process.
For example, AI-based engines exhibit many of the same biases as humans because they were trained on biased credit decisions and historical inequities in housing and lending markets data. In order to address these inequities, AI-based engines should be designed to encourage greater equity, rather than try to align with previous credit decisions. Lenders can achieve this by removing bias from data before a model is built, which includes eliminating model variables that directly or indirectly create fair lending disparities.
Moreover, it’s important to add more constraints to the model so that it can encourage equity. For example, these constraints can reduce the difference in outcomes for people in different zip codes who have the same risk profile. If AI-based engines are left unchecked, they can reinforce the inequities that lenders want them to eliminate.
There’s still more to be done.
Buying a home is a stressful process; identifying the right market, finding a home that fits the investor’s criteria, getting financing and closing on time can be challenging. An investor needs to study the market by researching statistics in the area, including housing prices, housing inventory, listing prices and days on the market. In addition, one must get prices for renovation materials and identify the right contractors. As such, investors need adequate tools to analyze different markets and deals.
Years ago, determining a home’s value required a real estate agent. Along with large institutional investors, agents were primarily the only ones with access to this information on a large scale. Now, technology has leveled the playing field, and a real estate investor can log on to Zillow, Redfin or similar sites and learn about price, value and trends regarding nearly any property in the country. This has simplified the buying process, but more needs to be done. Here are a few areas the real estate industry could work to address:
• Developing a better experience for virtual walkthroughs: Today, there are solutions that allow for virtual inspections to avoid the hassle of scheduling an in-person visit, which can be challenging, particularly if the property is out of state. But there is an opportunity to further streamline the process by leveraging technology. Virtual reality headsets showed early promise but haven’t taken off as expected, and there’s a significant need to improve the way to get an on-the-scene feeling for a property without spending the time and money to visit in person.
• Providing more digital tools and products: Tackling the different steps and paperwork involved with buying requires a degree of know-how. For real estate investors, speed is crucial, as an investor might be in the process of acquiring multiple properties at the same time while competing with other investors. It can be cumbersome and tedious to manage the paperwork for multiple properties at the same time. For this reason, companies in the real estate space can also aim to create technology that further streamlines the process, provides transparency every step of the way and helps scale.
The area is ripe for disruption. The goal for the players in the real estate industry should be to make the process of buying and selling a home much more akin to buying and selling a car. If we do that, we can truly transform the real estate industry.
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