Five Year HP Forecasts for Large CBSAs: The Key Role of the Gap between the Level of Prices and the Level Predicted by the Fundamentals

Summary of the Five Year Forecasts

This two equation model is used to make five year projections of quarterly house prices for each of the 48 CBSAs. This requires a number of assumptions about the future values of the variables that drive the model. Our assumptions are these:

• The 30 year FRM, which affects the affordable price, increases from 4 percent in 2015 to 5 percent at 25 bps per year.

• Inflation is set equal to 4 percent per year.

• A vector autogression model (VAR) is used to project future values of the level of employment and household income, which is another factor that drives the affordable price. These VAR estimates are estimated separately for each CBSA and are intended offer future predictions that reflect long-term trends and relationships in these two variables.

• The residual gap declines by 5 percent per year during the forecast period beginning with its value in 2015:q1.

All of these assumptions can be adjusted in order to highlight the sensitivity of the predictions to these various assumptions.

Two exhibits are used to present predictions of house prices for these CBSAs through the end of 2019. The first is Figure 1 above, which plots the cumulative predicted change in house prices by CBSA. These are ranked from the lowest to the highest predictions. Note the three with the lowest predicted values, all of which are in the SF Bay Area. Our last article also highlighted these as areas with a potential threat of a bubble because the residual gaps for these CBSAs were relatively large and positive. This information leads to slower predicted growth in these CBSAs, all else equal. In theory, future projected growth in employment and household income might offset the large residual at the beginning of 2015 for these areas but the projections we offer for these variables do not do so. At the other end of the spectrum is an outlier – Warren MI. This CBSA has a large negative residual at the beginning of 2015; hence, higher growth rates are expected, all else equal. The average predicted growth rate in house prices for these CBSAs is about 20 percent or 4 percent per year, on average. As the chart shows, however, there is wide variation around the average.

A second exhibit looks at the quarterly predicted MSP for selected CBSAs (Figure 2 above). The predicted levels vary widely among these four CBSAs, as expected. However, they also show similar and modest upward trends. Indeed, the correlation among the predicted values for the five years is quite high. However, as captured in Figure 1, the cumulative changes do vary widely among the CBSAs.

What Are the Key Drivers of the Predictions?

A key question is what drives the cumulative predicted changes in house prices among these 48 large CBSAS. One approach to address this question is to estimate a simple model that explains the cumulative percentage change for each CBSA as a function of the key drivers — the predicted growth in employment and the affordable price and the size of the residual at the beginning of the forecast period (2015:q1). The results of this regression are in Table 2 below.

Note, first, that the explanatory power of the simple model is quite high; R2 is 99 percent. Second, predicted employment growth and house price growth grow one for one and the statistical significance of employment growth is quite large. Third, the growth in the affordable price is also significant but quantitatively less important than employment growth. Fourth and most importantly, the impact of the residual is quantitatively large, negative, and statistically significant. This is the key to 2 equation model; that is, CBSAs in which the residual gap is large and positive can expect, all else equal, lower growth rates than places with a small gap. This applies to the three SF Bay Area CBSAs in Figure 1. Likewise, those with relatively large negative gaps can expect relatively fast growth. This is the case with Warren, Michigan and some others with relatively large predicted growth rates.

One great challenge to this interpretation and approach is that the large gap may be representative of very high and realistic expectations of growth in the fundamentals such as employment and household income. That is, the gap may be a little misleading since it does not take into account future expected values of these fundamentals. However, our model tries to address this by basing predictions of employment and the affordable price using CBSA specific trends in these variables. Furthermore, we did some simple tests to see whether historical growth in employment and the affordable price showed a relationship with the residual. No evidence of this was found, hence, our judgment is that the adding the lagged residual to the HP model adds a legitimate and informative piece of information relevant to the predictions of house prices and confirms the thinking expressed in our previous article on bubbles.

The simple regression also provides an opportunity to adjust the predictions based upon alternative views about the growth in employment and the affordable price. For example, adjusting the employment growth rate for, say, San Francisco by 30 percent (6 percent per year) would increase the predicted cumulative price change from 24 to 27 percent. Other sensitivity analysis of this type could be performed for other places and with the affordable price.