In 2023, the use of artificial intelligence skyrocketed. The global AI market was valued at $196.63 billion in 2023* and is expected to increase at a compound annual growth rate (CAGR) of 37.3% from 2023 until 2030. From ChatGPT to self-driving cars, AI is entering all aspects of people's lives.

But where is AI in multifamily? The majority of residential owners and operators have yet to embrace the power of data and AI and its potential to reduce business risk and uncover opportunities, but at TheGuarantors, we’re leveraging AI to better serve owners, operators, and renters.

Elsa Liao, Vice President of Risk Management at TheGuarantors, sheds light on the workings of its AI-powered risk model and the potential impact of AI on the multifamily industry. As a leader in multifamily risk management, the company’s products enable renters to more easily secure housing and provide operators with a broader applicant pool while minimizing the risk of rental income loss.

How does TheGuarantors use AI?

We use several AI models for different use cases such as underwriting and pricing. Our underwriting model is what we use to predict the risk of every single renter application and decide if we approve them for an insurance policy.

This model uses two buckets of information created from more than 2,000 data points. On the renter side, we look at factors such as income, liquid assets, credit score, the details in their credit report, how expensive their potential rent is and whether they have roommates. It goes to a very granular level.

The second bucket looks at everything that describes the building’s physical environment: how old it is, the size, the distance to the nearest college and hospital, median house price, and other macroeconomic and multifamily housing trends.

There is a vast amount of information. For example, we’ve seen an inverse correlation between renter default rates and renter satisfaction with their overall living experience, so we factor a number of variables that contribute to renter satisfaction into our underwriting.

How does this compare to the information that most operators traditionally have to vet a potential renter?

Operators often rely on a narrower range of information, primarily emphasizing credit scores, income, and sometimes eviction history. While these elements are integral to our model, they don't capture the more nuanced details.

A credit report alone contains over 200 data points, including the length of credit history and potential debt-to-income ratios. We leverage every predictive data point available.

How can using this data to create an AI model help owners and operators?

Owners and operators are at a disadvantage relying only on surface-level information. Employing AI can significantly enhance their rental screening process, leading to greater accuracy.

Traditional screening does not do a good job of predicting who will default and who won’t. There are a lot of renters who look bad on paper for many reasons, such as not paying off a medical debt or having insufficient credit information because they are from another country. In an operator’s eyes, they appear risky, but neither of these issues mean they will necessarily default. 

With only this information, operators are missing out on many good renters and may therefore limit potential rental growth. At the same time, for renters, it is becoming increasingly difficult to rent a home because the system has too narrow a view. 

AI can do a much better job giving operators a fuller picture of renters than traditional underwriting. We did a comparison of how effective a FICO credit score is in evaluating renter default risk, compared to using AI. FICO gave a 55% accuracy rate, whereas our model achieves 89% accuracy.

In addition to default risk prediction, what other benefits does this model have for owners and operators?

We provide two areas of value to renters and operators. First, we offer denied and conditionally approved applicants a second opportunity to secure an apartment, expanding the pool of potential tenants for operators.

Second, we give owners and operators more protection for all their renters. We eliminate bad debt by covering the renter if they are unable to pay.

How else is TheGuarantors looking to use AI?

In addition to underwriting, we apply AI to enhance our customer service efficiency and strategically allocate our staffing resources.

There are so many applications for AI in real estate. Soon, I expect someone will develop an algorithm that can recommend apartments or houses to people using more than location, layout and budget just as how Spotify recommends music based on what you and millions of others listen to. The underlying machine learning logic is the same. 


*Grandview Research