More on Beveridge Curve: Disequilibrium and Oversupply in the Housing Market

Earlier I posted on some research that I was working on about a Beveridge curve in the housing market (the work that motivates the name of this blog). The working paper version of the paper is now finished and available for downloading.

The main results of the paper:

• The Beveridge Curve represents a long-run supply condition
• Short run deviations represent periods of disequilibrium, either over or under supply
• Using a years of oversupply metric, the observation of 2007-2008 was an all-time high of 0.995 years of oversupply, more than three times the previous peak of 0.285 in 1973-1974.

Results regarding the differences between the owner-occupied and rental markets:

• Generally, oversupply is a phenomena in the rental market
• Oversupply in the rental market is twice as volatile as in the owner-occupied market
• Oversupply first shows up in the rental market

Last, I find that

• Oversupply in the owner-occupied market is related to house prices, reinforcing the idea that short run deviations in house prices from fundamentals (such as bubbles) can lead to periods of oversupply

Below, I give a summary of these results, except for the last bullet on house prices which I leave to another post.

The primary change from the earlier results is that instead of doing the fairly ad-hoc HP filter, I have gone ahead and estimated the model using biannual data. In my opinion this gives a better estimation. In addition to the new estimation I have also found some interesting results relating oversupply of houses to house prices. I will go over those results in my next entry, for now I give an overview of my previous results under the new estimation.

The main idea of the Beveridge curve is that it represents the long-run equilibrium in the housing market. The Beveridge curve has its origins in labour economics, where Lord Beveridge found a negative relationship between the unemployment rate and the amount of job vacancies. For an example, see Rob Shimer's website. In the housing market I have found a negative relationship between the rate of household formation and the residential vacancy rate. I deem this negative relationship the Beveridge curve in the housing market. The Beveridge curve relationship exists in the owner-occupied market, the rental market, and the overall market irrsepective of ownership. Figure 4 from the paper, shown here below, shows the relationship for the overall house market. There is a clear and statistically significant negative relationship and the R-squared from a linear regression is 0.626.

As I stated, the curve represents a long-run relationship, so that short-run deviations (two to four years) represent periods of disequilibrium in the housing market, these are periods of under or over supply, see figure 7 below.
The next figure shows the estimated time-series of oversupply for the total housing market irrespective of home-ownership. The metric of oversupply is in a % of the total housing stock. Here we clearly see three periods of oversupply since the start of the data in 1968: (1) the 1974 crisis; (2) the mid to late 1980s housing boom; and (3) the current crisis. The oversupply in the current crisis is similar in magnitude to the 1974 crisis, with both having an oversupply of just under 1% of the total housing stock.
However, the rate of household formation is much lower now, so that it may take much longer to work off the oversupply. The next figure plots the estimate of oversupply in years of oversupply. This is the oversupply in years of household formation. For instance, if the rate of household formation was 1% and the oversupply of the housing stock was 1%, then it would take one year of no housing production for the oversupply to disappear. Using this metric, the amount of oversupply is staggering, being about one year of supply in 2007-2008 compared to under 0.3 years in the 1974 crisis.

One point that I cannot stress enough is that what we are currently facing is a huge oversupply of housing, irrespective of whether it is rental or owner-occupied housing. As I stated in the earlier post there is actually more oversupply in the rental market. To see this, below is figure 12 from the paper. The metric is years of oversupply in terms of the total rate of household formation, so that we are comparing apples to apples. There are several striking features in this figure:

• Generally, oversupply is a phenomena in the rental market
• Oversupply in the rental market is twice as volatile as in the owner-occupied market
• Oversupply first shows up in the rental market

These results suggest that the rental market is sponge the soaks up oversupply in periods of overall oversupply, and then lets it out once housing becomes relatively scarce. This suggests that research on the housing market, especially empirical, needs to include data from the rental market when doing structural work.

Distressed Sales and House Prices

In my last entry I argued that a shock was causing existing home sales to rise relative to new home sales, and that this same shock was causing house prices to fall more than would be suggested by the level of existing home sales. This `shock' is more than likely a supply side shock: quantity up, prices down, Econ 101 at work. A question is how much of this is being driven by distressed sales where the house is being sold because the current owner has stopped paying the mortgage.

Andrew Leventis, a researcher at the Federal Housing Finance Agency has attemped to answer this question is a recent working paper: "The Impact of Distressed Sales on Repeat-Transactions House Price Indexes".

In the paper, Leventis uses transactions data from California and breaks down transactions into distressed and non-distressed. A transaction is distressed if a Notice of Default was filed on the property up to a year before the transaction occurred and no other transactions occurred for the same property between the transaction and the Notice of Default. He performs the analysis for two different groupings of data. The first grouping he calls `Enterprise' data, and this consists of the transactions that would be included for calculating the FHFA House Price Index (HPI). The second grouping is `Recorder' data, which consists of the data that would be used to calculate the Case-Shiller index. His figure one shows that the share of distressed sales has increased from less than 5% before the fourth quarter of 2006, and has been rising steadily ever since, now over 45% for the first quarter of 2009. The rise in distressed sales is very similar to the gap that has appeared between new and existing home sales.

The main question of the paper is "How much are these distressed sales driving down house prices?" To paraphrase Leventis, we can breakdown the effect of distressed sales on house prices into two categories:

1. Direct Effect: distressed houses sell at a discount, therefore, as the share of distressed sales in the sample increases, then the reported house price index will fall.
2. Indirect Effects: the more distressed sales there are, the harder it is for a non-distressed seller to sell a house, lowering the prices for all houses.

In his paper, Leventis can only get at the direct effect. His figure 4 shows the discount for distressed sales. We can see that before 2006, that distressed sales sold at about a 4% discount. At the peak of the bubble in 2006, the discount almost went completely away--it didn't matter if a seller was distressed, seemed that buyers were willing to buy everything and anything in sight. However, after 2006, the discount gets significant, approaching almost 20% for the `Enterprise' data, and 15% for the `Recorder' data (there is an interesting divergence between the two types of data in the last couple of quarters).
Therefore, by the end of 2008, distressed sales were selling at a 20% discount and made up roughly 45% of sales. Relative to the peak in the housing bubble in 2006 (when the discount was essentially zero), if this sales pattern would maintain itself for a year, it would imply that the house price index is being pushed down an extra 9% due to the direct effect from distressed sales. However, the total effect so far has been smaller. His figure 2 shows the effect of the distressed sales on year-over-year price growth for the `Enterprise' data. The figure contains two plots: one showing price growth for the entire sample, another with the distressed sales removed. The effects are fairly small. The cumulative effect of the distressed sales is estimated to drive down prices an extra 5.3% from the peak, for a fall of 41.3% relative to only 36.0% when the distressed sales are excluded. The effect on the `Recorder' data is smaller, implying an extra house price fall of 1.9% from 44.8% to 46.7% (see his figure 3, not shown here).

To summarize, the divergence between new and existing homes seems related to the surge in distressed sales. The work by Leventis suggests that the direct effect of the distressed sales on the reported house price indexes is most likely small relative to the total decline we've seen in house prices. What we do not know is whether the distressed sales are directly responsible for the fall in house prices and the increase in existing sales relative to new sales, or if the distressed sales are simply the result of the large supply of housing, which is then responsible for distressed sales, housing price falls, and an increase in existing relative to new sales. My viewpoint is that we are just seeing the effects of supply at work.

House Prices and New versus Existing Homes Sales

This week we have received the May data on existing home sales (from the National Association of Realtors) and new home sales (from Census). Existing home sales have been flat or rising a bit for 2009. Such stabilization in the market for existing homes has been a sign to many observers that we may be reaching the bottom in house prices. However, at the same time new home sales have continued to fall relative to their 2008 levels. As people have noted (see CalculatedRisk), there is now a gap between new home sales and existing home sales that did not exist before 2006. As I show here, whatever is driving a gap between new and existing homes sales is also driving a gap between the relationship of existing home sales and real house price growth. The evidence suggests that a supply side shock is driving up existing homes sales relative to new home sales and at the same time driving down prices, just as standard econ 101 predicts. Furthermore, price growth is related to new home sales, not existing home sales. The punchline: if we are seeking stability in house prices we should look at new home sales, not existing sales.




Figure 1 plots annual single family existing homes sales and single family new homes sales as a percentage of the total housing stock (the total housing stock is taken from Census). We see that new home sales average about 1% of the total housing stock, while existing home sales average roughly 4%. To get a feel for how the two series move together, figure 2 plots the percentage deviation for each series from its mean from 1975-2008. We see clearly that from 1975 to 2006 (the solid lines) that new home sales and existing homes sales move around together, with a correlation of 0.944 over the the time period up to 2006. However, as shown by the dashed lines, a gap has developed post 2006, resulting in the correlation for the sample from 1975-2008 falling to 0.876. There seems to be some type of a shock that is driving existing homes sale up relative to new homes sales.


Turning to prices, figure 3 graphs expected annual real house price growth next to existing homes sales. Expected real house price growth is the FHFA (formerly OFHEO) house price index, made real by the rate of expected inflation from the Philly Fed survey of forecasters. The existing sales series is the same as in figure 2. Once again, we can see clearly that from 1975 to 2006 both series move around together, with a correlation of 0.921. However, post 2006, a gap develops just like the gap between new home sales and existing home sales. Using the whole sample up to 2008 the correlation falls to 0.812.


Both of the gaps suggest a shock hitting the housing market. To put it more clearly, figure 4 plots both a `price shock' and and `existing sales shock'. The price shock is the shock to price growth that is not explained by existing sales in figure 3. (This is actually the error term from a linear regression of price growth on existing homes sales relative to the housing stock). The existing sales shock is the shock to existing homes relative to new home sales, the difference in figure 2. (the existing sales shock has been normalized to be on the same graph as the price shock). From 1975 to 2006 these two shocks are essentially unrelated. However, in 2007 and 2008 both shocks are sizeable, moving in opposite directions. What we have is a classic supply side shock: quantities rising and prices falling. The glut of vacant houses on the market are doing what they do: drive down prices, and drive up sales.


The implication is that the supply shock is breaking down the standard relationship between sales and price growth. Existing home sales is not the place to be looking for stability in house prices. Instead, figure 5 plots new home sales relative to house price growth. Here we can see that new home sales are related to house price growth, and this relationship has maintained itself through the crisis. The correlation from 1975-2006 is 0.881, while for the whole sample from 1975 to 2008 it is 0.899. Therefore, if we are looking for price stability, we should look to new home sales, not existing home sales. The supply side shock that is hurting prices and raising existing home sales causes new home sales to fall. To stress the point a bit more, the stability in the housing market that we want for economic recovery is stable prices and new home construction. Stable prices are associated with new home sales not existing home sales. The current stability in existing home sales is most likely just the effects of a supply side shock that is driving down both prices and new home sales. In my next entry I will use search theory to guide us in understanding this behavior in the housing market.