More Detail on February Erdmann Housing Tracker Trends

For subscribers

February was a potentially interesting month, so I think the tracker deserves a bit more detail this month. This could be a case where my simple model provides some useful insights about the market.

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For readers new to EHT, here are a couple of posts about the basics of the peculiarities of today’s housing market and how the model captures it:

“Our Cantilever Housing Market”

“Why the Erdmann Housing Tracker?”

Home Price Trends in One Dimension

Figure 1 shows the trend in the Zillow estimate of the typical US home price since January 2020. It peaked in August, and has declined by about a quarter percent per month since then, slowing down a bit in February, but still declining.

Figure 1

This is the US housing market in a single dimension. However, by using systematic trends within each city, we can add extra dimensions to this, and extract more information about the direction of the American housing market.

Figure 2 shows the price/income ratio of homes in ZIP codes across Miami and Austin, arranged by income. Cyclical price changes tend to change the entire city uniformly. Local supply constraints tend to push prices up systematically the greatest for residents with lower incomes, so supply conditions steepen the slope of the lines in Figure 2.

You can see that after March 2020, prices in Miami increased for both cyclical and supply reasons, but have been relatively stable since then, relative to local incomes. In Austin, prices increased for both cyclical and supply reasons after March 2020. Since June 2022, prices have cyclically contracted a bit in Austin.

Figure 2

In recent months, I have been using the average ZIP code in each city (shown in Figure 2 with the vertical black line) to consider trends in these different factors, by considering how much of the price change for the average ZIP code was due to cyclical factors (eg. a rising line) and how much was due to supply constraints (eg. a steepening line). Below, I will look at price trends for “High tier” homes in each city, which by construction of the model are entirely defined by cyclical changes, and “Low tier” homes, which can be affected by both cyclical and supply factors.

This is just a different method for making similar inferences. Maybe some of you will find the high vs low tier approach more intuitive. Roughly speaking, low tier price changes are dominated by supply effects and high tier prices are dominated by cyclical changes.

Below the fold, let’s add some dimensions to the February home price trend.