Rental Vacancy and Household Debt: A Data-Driven Perspective

“Survive to ‘25 is not even enough,” reflected Ira Perlmuter, Chief Investment Officer at IJP Family Partners, a family office based private equity firm. “You are going to see 36 months of really tight problems,” he added.

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- Ira Perlmuter, Chief Investment Officer at IJP Family Partners

Hi Nuvo Community,

_{The first point of interest is the raw data from the Federal Reserve of St Louis, particularly the correlation between Rental Vacancy Rates and Household Debt Service Payments. We're not talking about a casual relationship here, one does not necessarily cause the other to move. With a correlation coefficient of 0.843 at a 10-quarter lag, the two are closely linked. What's more, the strongest correlation in Household Debt Service Payments tends to lead Rental Vacancy Rates by about 2.5 years. This suggests that shifts in how many households are saving or spending can be a leading indicator for changes in rental vacancies. Just looking at the chart below, you can see the two tend to move in the same direction most of the time.}

_{Charts can be deceiving, so we’ve included a graphic below illustrating the exact correlation between the two. No matter how many quarters we lag Household Debt Service, it still strongly correlates with Rental Vacancy. The lowest correlation is still over .65. Meaning the correlation is strong in real-time, and it continues to get stronger as a leading indicator up to 10 quarters in the future. In the following sections, we’ll take it a step further.}

_{The Significance of Year-over-Year Changes}

_{While raw data provides valuable insights, it often includes seasonal fluctuations and short-term noise that can cloud the underlying trends. That's why we turn to year-over-year changes. This metric focuses on meaningful, long-term changes and is a more targeted way to assess how variables move in tandem over time.}

_{Here, the correlation between the year-over-year changes in Rental Vacancy and Household Debt is 0.508, albeit at a shorter lag of 5 quarters. Looking at the chart below, you can see it appears “messier” than the raw data chart. There’s not a clear trend or correlation. This almost always happens when you transform raw data into a year-over-year (YoY) percentage change. You might even wonder if this is a strong enough relationship to act upon…}

_{The Power of a 0.50+ Correlation}

_{In the realm of data science, a correlation above 0.50 is noteworthy, especially in the context of economic data that is influenced by a myriad of variables. It's like finding a pattern in a sea of noise. It tells us that even moderate shifts in Household Debt Payments can have a tangible impact on Rental Vacancy Rates over a little more than a year. The chart below illustrates the correlation between YoY change in the two variables. It looks very different from the raw data. While the strongest correlation is .51, it happens in quarter 5 instead of quarter 10. This makes Household Debt Service much more useful as a leading indicator, as it shortens the time frame needed to become useful. There is also a clear trend of the correlation getting stronger from time zero (real-time) to time 5 (5 quarters in the future).}

_{Why Year-over-Year Metrics Offer More}

_{Year-over-year metrics offer several advantages that make them a compelling choice for in-depth analysis. First, they normalize the data, allowing for consistent comparisons over time. Second, they remove the influence of seasonality and other cyclical factors that can distort the raw numbers. Lastly, these metrics are actionable; they provide a clearer line of sight to emerging trends, making them particularly useful for tactical decision-making.}

_{Real-world Implications for Multifamily Real Estate}

_{For those deeply vested in multifamily real estate, these insights are not just numbers but tools for strategic foresight. The strong correlation found in the raw data is valuable for long-term planning, but the YoY change is even more compelling in our eyes. Whether you're contemplating new acquisitions, planning rent adjustments, or evaluating the market for potential exits, these numbers serve as a guide.}

Implications

For real estate investors-know the market you are investing in, or at least make sure your operating partner does. Does the submarket already have a high percentage of burdened households? A high debt service ratio? These are just 2 of the more than 100 data points Nuvo Capital Partners collects and analyzes before investing in a market.

Happy Real Estate!

All the best,

Yuri - Your Real Estate Investigator

Credit: Brian Underdahl, Chief Analytics Officer of Nuvo Capital Partners

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