Mike S. Shapiro, Chairman HOM Real Estate Group, Inc., Co-Founder and Managing Director Plunk, Forbes Author, Speaker, Coach and Investor.
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If you’re a real estate investor or homeowner, are you curious about how the ups and downs of the stock market’s major indices are reflected in residential real estate markets? After spending more than 30 years, combined, in luxury real estate and trading, I’ve long been fascinated by the potential correlations – the direct and inverse relationships – between the major indices and residential real estate markets. It took some time, though, before I realized how those correlations manifested.
Once I had a deeper understanding of the data, I was able to read the information more clearly, finding correlations that held over time. I could then use this insight to help predict buyer and seller behaviors – and help myself stay ahead of the game.
If you want to better understand the information to more effectively apply it to your unique situation, here is my process and the considerations I found to be key.
Relationships Between The Major Indices And Housing Data
When looking at correlations – or relational changes – between commodities, typically commodities that are more closely related will have more influence on each other’s performance, and less closely related commodities will have less influence. For example, silver and gold would typically be expected to show a closer correlation than corn and oil, and both precious metals would typically be expected to rise or decrease in value essentially in conjunction with each other.
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What can we learn about residential real estate when we study the major indices? Here are a few key considerations:
1. The major indices – the Dow, the S&P 500 and the Russell 5000 – can each be correlated to major segments of the residential real estate industry. In my research, the Dow tracks most closely to luxury real estate markets such as Newport Beach, California; Aspen, Colorado; and Naples, Florida. The S&P 500 tracks most closely to high-growth markets such as Nashville and Austin, Texas, and the Russell 5000 most closely correlates to the overall top-100 markets in the U.S. Obviously Covid-19 has changed the dynamics of these correlations, but whether they last in this new realm is to be determined.
2. When the major indices behave predictably, they’re proxies of investor sentiment: When the indices rise, it’s because investors are optimistic about industries, economies, politics and other major factors. As an example of major factors, we have the recent pandemic and continued mass inoculation
It’s fair to say that in typical times, the indices indicate investor sentiment for the next three to six months. With residential real estate, however, if you look at transactional data on any given day, you’re looking at data that’s already 30-90 days behind. Additionally, every housing transaction is valued based on the sale price on the day of the transaction; in the ensuing 30-90 days, a plethora of things can happen that impact value. Given this, it’s impossible to develop direct correlations between index performance and the housing markets if you compare daily information.
3. To effectively correlate residential real estate with other commodities, you have to factor in housing-data latency. Otherwise, the information you’re looking at has limited correlative value.
Understanding Housing-Data Latency
To more accurately correlate residential real estate markets to commodities markets, you have to factor in the housing industry’s data latency. What happens today in the major indices will be reflected in housing data that become available 30-90 days from now.
For example, the markets experienced some of their best months ever in late 2020 and early 2021. Given this, we can reasonably predict that housing data from November, which wasn’t available until early 2021, should likewise reflect the overall strength of the markets. Conversely, if we were to see a significant market depreciation, we would reasonably expect housing data to soften 30-90 days later.
Another inherent challenge is that the further out the data extends between the correlated commodities, the less accurate the correlation becomes. Further complicating things, market anomalies disrupt the correlations – and as we’ve experienced the last several years, the markets have been very volatile at times.
Among recent challenges is the global pandemic. If you take a look at the markets at the start of the widespread business closures in March and April, there was immediate economic fallout. Still, although the economy was decimated, the stock markets recovered and then some after an initial drop. Likewise, real estate experienced a quick drop, followed by surging prices as buyers jumped to get limited inventory in the suburbs and rural areas. In the months since then, the market indices and housing data have continued to correlate.
Using The Information
Overall, it’s an inefficient model, but if you understand its quirks, you’ll have a better foundation from which to find opportunities. It is also one of many ways to inform your decisions in real estate investing.
My recommendation? Spend some time studying the indices and spotting correlations in housing. Then, try to identify the situations and resulting human behaviors that influenced the correlations. From there, you can figure out how to leverage the opportunities you find, such as buying or selling in particular geographic areas. Understanding this data is one way to help you predict buyer and seller behaviors while giving yourself a chance to stay ahead of the game.
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