Why the "Erdmann Housing Tracker"?
I have developed a novel and consequential framework for understanding the importance of housing (specifically, the lack of it) in the 21st century American economy. I will be publishing analysis using that framework through this newsletter. I hope that both visitors and subscribers will find value in that analysis, and that some of you will find it worthy of your support.
But, let me cut to the chase. The reason I am using the Substack platform is that I specifically have a model that can provide users with real-time quantitative information about the risks for housing valuations, updated monthly. This is a model based on macro-level supply and demand factors, but those factors affect every neighborhood differently, and so I can provide unique insight into some of the current risk of owning a $120,000 home in San Antonio vs. a $1,000,000 home in Los Angeles.
This can be valuable for real estate investors, new home buyers, hedge fund managers, family offices, private equity, portfolio managers considering positions in any number of sectors such as homebuilders or REITs, etc.
My model can’t tell you if a neighborhood has a good future, if an apartment building is well managed, or if some economic development will boost local incomes where you are investing. What it can do is give you insight into the changing macro-level factors determining the prices of homes within every major metropolitan area. If that information is useful to you, you might say that my data is a necessary, if not sufficient, source of information for your decision. And, it is unique. Few analysts understand the most important aspects of the 21st century housing market that have made it such a volatile component of the economy. None can provide you with this particular addition to your decision-making toolkit.
Figure 1 is a time series from 2002 to 2021, tracking the 3 factors influencing home prices, nationally. This is data that monthly subscribers will receive. The three factors are:
Cyclical: If this is positive, it is a sign of unsustainably strong demand. It reached 20% nationally in 2006 and has recently risen back to positive territory, nationally, although that number varies quite a bit by metro area. This is the component that could lead to short-term cyclical price fluctuations.
Supply: If this is positive, it is a sign that a lack of adequate construction activity has artificially increased housing costs. Construction doesn’t happen in a moment, so this is a slow-moving factor that is unlikely to change much cyclically. Generally, where this factor is above zero, rents are also high.
Credit: This is a reflection of the one-time extreme tightening of credit access that happened during the Great Recession, beyond a reversal of the credit access that became common from 2003 to 2007. This also will likely not matter much in any upcoming cyclical price fluctuations. Mortgage access is unlikely to be tightened further, and if it was loosened back to previous norms, price gains from improved credit access would mostly be countered by declines in the supply factor.
Over the coming weeks and days, I will expand on all of this so that you can see how it all helps to understand the housing market more clearly. For now, suffice it to say that the US housing market has recently been pretty hot. There is some potential for reversals in real home prices. I will use this framework to help you and I know, with a little more specificity, how much risk there is, as the months march on.
Here is an example of the sort of output that founding subscribers will receive. This is a report from June 2021, just after the post-Covid price boom started.
As Figure 2 shows, entering the Covid recession, homes in San Antonio at these income levels were valued 10-20% higher than what you might call a neutral price. Little of that was sensitive to cyclical reversal. San Diego had a 40%-60% price premium (all values are based on a continually compounded scale, so that approx. 70% is a doubling), and a few percentage points of that were cyclically sensitive. You can see here that San Diego had some cyclical overheating before 2006 and San Antonio did not and that San Diego has a much larger supply problem. I will look much more thoroughly into many details such as those in future posts.
I can estimate the relative importance of the three factors for any income level or home price point in any major metropolitan area. Since the supply factor is inflated by a lack of building, initial effects of a contraction in construction may even push prices higher through this factor. (Notice that is what happened in 2006 and 2007 - the initial cooldown in cyclical prices was countered by rising costs from supply effects as housing starts declined.) Eventually, a contraction deep enough to include a foreclosure crisis, etc. could cause a bit of a reversal in the supply effect (as it did after 2007), but as you can see in Figure 1, it is a slow-moving factor. So, unless things get really bad, the supply factor and the credit factor are more important for policy considerations than they are for cyclical price fluctuations. (Some of the numbers may seem surprising. I look forward to digging into all of this for you in upcoming posts.)
Cyclical overheating had just begun by June 2021. Most areas have continued to move higher in the year since then. In addition to tracking cyclical risks, I will track home sales, starts, signals of migration patterns, etc. to get as much foresight into when and how strong cyclical reversals might arrive. As of June 2021, and still as of June 2022, neither San Antonio or San Diego show signals of cyclical reversals. By this, I don’t mean that the cyclical overheating will continue to increase, but they do not show signs of reversals strong enough to lead to substantial nominal losses for the typical home, yet.
Subscriptions at this time are:
Monthly subscribers ($12/month or $100/year) will receive a monthly estimate of national home price trends and sensitivities, which will be posted here on substack.
Founding Members ($300/year) will receive the monthly national report and the report on every major national metropolitan area.
I will be following up with some more posts explaining this way of thinking about the housing market over the next few days. I’m open to suggestions if there is a tier of access that some of you would like, which is not described here.
I look forward to digging into the nuts and bolts of the model, and the insights it creates. I would love to hear any suggestions you might have about the best way to distribute this work. And, please, especially as I add new posts that might help new readers understand how this framework is useful: share, share, share, with anyone you know that makes investment decisions for whom this can be useful.