Founder, CEO of Blue Lake Capital LLC. Helps passive investors grow wealth through real estate. Podcast Host: REady2Scale.
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How do you determine whether one multifamily deal is better than another? Let me start by saying that a deal is only as good as the assumptions you’re making as an investor, and assumptions are not guaranteed to materialize. As a real estate investor, operator and syndicator through my company Blue Lake Capital, a significant part of our analysis when considering properties to invest in is looking at how the property performed in the past. That includes analyzing various factors that include vacancy rates, bad debt, concessions, income and expenses.
We not only look at how the property has performed over the past six or 12 months, but we also look for trends. This indicates the potential for how the property might perform in the future. The million-dollar question when analyzing potential properties is: “Are your assumptions based on a feeling and your knowledge of the market, or are they based on hard data?”
As a former lawyer, I live by the adage “Once a lawyer, always a lawyer,” so I’m always looking for the closest thing to “fact” as possible. Coupled with the influences of having attended MIT in graduate school, I learned long ago that hard data must support my assumptions. When considering investment opportunities, emotion must be put to the side and clear-cut logic should always prevail. That is why we use machine learning (ML) and artificial intelligence (AI) when underwriting a deal and evaluating properties; the technologies help us ensure that we make better investment decisions based on hard data.
Putting Technology To Work
Machine learning is a form of artificial intelligence that has the ability to learn from prior experience without doing any additional programming. AI is a form of computer science that uses computers to emulate intelligent human behavior at a far more rapid speed than what we as humans are capable of. You can use these technologies to analyze data in ways you simply wouldn’t be able to do on your own.
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For example, we use AI and ML to help us choose which markets to focus on by analyzing how properties will perform in the future. The assumptions are based on analysis of both a market’s and a property’s past performance. The technology also looks at predictions of how a market and submarket will grow on a quarterly and annual basis.
When we consider underwriting a property, we use AI software. In addition to standard metrics, you also can use AI to compare additional market data, including retention rates, lease terms and average vacant days, among other factors. AI also provides us with sales comps, lease transaction data and operational forecasts, which help us understand what the competition will look like in the future.
In addition, AI software is able to analyze trends and then make predictions based on the data that’s being analyzed. The predictions can be extensive. They even include neighborhood forecasting, tenant patterns and predictive maintenance, which is an area that AI excels at in other fields. All of this data can help you determine the quality of the potential investment’s future performance.
Using The Data
One of the key data points we use is in our analysis of potential rent growth. If AI is predicting a 5% rent growth, for example, we’ll still go with a lower number to be on the safe side when making projections. Because there are so many variables that come into play when predicting potential performance, we use AI and ML to create hard data that’s used in our projections, but we still mitigate risk by being conservative in our projections.
Determining which markets will perform and continue to be strong is complex, but the data provided by AI and ML technology makes the process significantly more reliable. That’s because the data includes analysis of performance not only at the property level, but at the market and submarket levels as well.
We’re also looking at incorporating new AI technology that will help predict which tenants will renew and which ones will leave the property when their lease expires. This technology looks at renewal rates for specific tenant groups, which helps to maintain a higher occupancy rate. This predictive data makes our underwriting assumptions more accurate.
Getting Started
When you’re evaluating your next investment, be sure to inquire about how and where the data is gathered for the underwriting and projections. If the assumptions being made are simply based on “industry standards,” that can prove to be a significant risk since there’s not a factual basis of data.
While many individual investors may not be interested in the expense of using AI for assessing their individual investment options, as the software can be quite costly, it’s perfectly appropriate to ask the sponsor to share the reports with you. Sources that are generally regarded as reputable include Axiometrics, Yardi Matrix and CBRE. When reviewing these reports, look carefully at the predictive analytics when it comes to rents, occupancy and concessions and see whether they reasonably align with the projections.
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