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How Real Estate Technology Helps Pre-Qualify Buyers

How Real Estate Technology Helps Pre-Qualify Buyers

It’s an understatement to say that real estate technology (colloquially termed “proptech”) has changed the industry. Proptech innovations like real estate digital marketplaces, investment blockchain apps, VR property touring platforms, etc., Create your private search have revolutionized the way practitioners work. And they have made buying and selling properties more convenient for consumers.

Looking at the plethora of innovations emerging each year, you get the sense that proptech is just heating up – that the disruptors, mavericks and outside-the-box thinkers are just getting started. Case in point, the latest buzzy concept in the proptech realm: pre-qualification tech.

Superb Crew asked Regan McGee – a real estate tech thought-leader who pioneered the end-to-end digital marketplace Nobul  – how he thought real estate transactions would change in the coming decade. The Nobul CEO predicted that “We will see further use of technology like… AI-supported bots that will help pre-qualify buyers.”

It’s a confident projection from one of the industry’s top innovators, but what does it mean? What exactly does tech-enabled buyer pre-qualification look like? This article takes a closer at the conditions necessary for proptech pre-qualification, and what kinks still need to be worked out in the process.

The First Condition: Data Availability

Important lending decisions like pre-qualification and pre-approval require a lot of data – or at least they should. Traditionally, it was incumbent on financial institutions to comb through basic financial data like an applicant’s income, monthly payments, savings, etc. From there, some might incorporate a hard or soft credit inquiry, itself an analysis of basic data. Together, these data sets sufficed for institutions to make decisions. The decisions weren’t always perfect, but lenders worked with what they had.

Nowadays, lenders and financial institutions have access to much more data, which means more accurate approvals. Moreover, they have the tools to analyze that fresh influx of data. Which leads to the next condition…

Greater Access to Necessary Computing Capacity

Voluminous and diverse data sets don’t mean much if a lender has no way of storing the data. For a time, the industry was stymied by a lack of computing capacity. The necessary computing capacity did exist, but deploying it was often prohibitively expensive.

In recent years, high computing capacity has become a) more affordable and b) more available, meaning more lenders are able to capitalize on concepts like big data.

Machine Learning and Artificial Intelligence

Storage is one issue; the other is analysis. It would be beyond any human’s ability – including the most capable lending professionals – to analyze the heaps of data necessary for accurate pre-approvals. That’s where the “AI-supported bots” that McGee mentioned are instrumental.

According to Fitch Ratings, one of the “big three” American credit rating agencies, lenders can use artificial intelligence and machine learning to “improve predictive models in operational risk management (and) credit assessment applications” for faster, more accurate pre-qualifications. The emerging technologies comb through data, analyze it, and “learn” patterns for accurately determining outcomes.

The Blind spots: Black-Box Decisions and Biases

There’s still a sizeable road ahead for proptech pre-qualification. Namely, some industry pundits worry that AI/ML make “blackbox decisions” – their predictions may be accurate, but the explanations for those predictions remain unknown and unknowable. Some experts worry that the unknowable nature of AI decision-making makes it vulnerable to biases and error.

In summary, real estate technology is capitalizing on favorable conditions to innovate the pre-qualification process: more data, better computing capacity, and emerging technology like AI and ML. While the technology still has issues, it’s a promising step forward for lender accuracy.


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