Housing Was Not Massively Overbuilt

It’s widely taken as a given that because we have too many empty housing units now and because prices have collapsed, that housing was overbuilt. For example, from Unnatural Rent:

In addition, the recession and rising unemployment have slowed down new household formation, encouraging people to live with roommates. In many markets, apartment rents are unlikely to post any growth during this year, and some may even see declines.
This drop in demand has been combined with a massive increase in the supply of housing (both single family and multifamily) over the past decade. While office and industrial did not experience a huge wave of overbuilding, that isn't quite the case for retail and multifamily.

This is true in a sense – if we had fewer housing units now the situation would be better.  However, throughout the bubble years supply and demand were balanced. My argument is premised on the idea that additions to housing supply should roughly correspond to additions to employment:

1 new job = 1 additional unit

Obviously, not every person who gets a job creates a new household, but households are also created without jobs, and in my experience nothing too bad happens to housing markets where job growth exceeds new housing additions. The data for job creation and residential permits issued since 2004 is summarized below:

Supply and demand were in synch until 2007. In 2008, demand went off a cliff, which goes to show that jobs can be lost faster than residential development can wind down.

I think this data also supports the notion that the bubble price escalation was driven by easy financing, and not fundamental demand.

Employment data is from this BLS website, permit data at this Census Department website.

Is Overbuilding Responsible for Excess Housing Inventory?

The President of the National Association of Home Builders says “The excess housing inventory in today’s market is the result of unprecedented foreclosures, not overbuilding.” Paul Jackson, Housing Wire, suggests this statement “borders on the certifiably insane ." I may be certifiably insane, but I think the NAHB position is closer to the truth.

Obviously, we have excess inventory. The amount is subject to debate, but arguments can be made for between 1.75 to 4 million excess units (see this Calculated Risk post, for example). Obviously, many of the excess homes are newly completed builder inventory. You can read the story behind the pictured subdivision here.  So, in a sense builders are responsible for at least a portion of the excess inventory. They built it, it’s empty, end of story.

But, of course, it’s not that simple. There are a lot of people who are living in substandard housing, in apartments, with their parents, with roommates, etc. who would be delighted to be living in these “excess” units. The problem is much of the excess is located in places people don’t want to live or can’t find jobs (read, for example, these depressing posts about Detroit in The Big Picture and The Weekly Standard). And, much of the excess is not affordable even at today’s depressed prices to the people who want the units.

I think Miami is a good example of what actually occurred. Here is a chart of residential permits issued in Miami between 1999 and November, 2008:

(click on images to open larger versions in a new window)

On it’s own, this is about as clear a case as you can get of overbuilding – permits obviously spiked between 2004 and 2006, which nicely dovetails with the peak of the subprime craziness. But, consider employment growth in Miami during the same period:

At the same time permits were peaking at around 45K per year, Miami was adding jobs at 100K a year. Can you really say builders were overbuilding when there are twice as many people with new jobs as units being added to supply? If anything, the numbers imply a housing shortage in the peak period. Here is a chart showing the ratio between new jobs and residential permits:

From mid-2002 through 2007 Miami was adding more jobs than housing units, and for most of this period it was adding around two jobs for every new housing unit. This was not an overbuilt market during that period.

In contrast, here is an equivalent chart for Houston:

Housing prices have held up relatively well in Houston, and most people do not consider it to have been one of the bubble markets. But, note Miami had substantially more jobs added per new unit than Houston did during this period. The data suggest Houston was relatively overbuilt compared to Miami.

In fact, the data suggest that maybe part of the problem in the bubble markets was builders didn’t build fast enough to keep pace with the demand created by new jobs (you can see similar charts for Los Angeles, San Diego, Las Vegas, and many more markets here). I’m not ready to go so far as to suggest they should have done so – had lenders stuck to reasonable underwriting standards more of that demand would have shifted to the rental market and we would have seen higher rents and less vacancy in that segment, which I think we all agree in hindsight would have been better than putting people in houses they couldn’t afford.

Cliff Diving: Los Angeles Multifamily

For many years I have tracked the relationship between a market’s employment change over the previous 12 months and the number of residential permits issued over the same period. Employment growth soaks up supply additions, so when permits exceed the number of jobs created multifamily markets almost always weaken. Conversely, when more jobs are being created than housing units added, vacancy rates decrease and rents increase.

The November numbers for Los Angeles are very bad:

(Click on charts to open larger versions in a new window)

More than 170,000 jobs have been lost in Los Angeles over the last year, and at the same time more than 15,000 units were added, almost a –11 to 1 ratio. Look for significant softening in the multifamily market.

Which Markets have the Strongest Housing Fundamentals?

Although all markets are experiencing the effects of tighter mortgage underwriting, there are a number of markets which have very strong demand - supply fundamentals. The chart at left ranks the markets tracked by Residential Property Analytics. The numerical rating is the number of jobs created in the market over the last year divided by the number of residential permits issued. In other words, Denver added more than 2 jobs for each residential unit permitted, while Detroit lost more than 8 jobs for each unit permitted. In our experience, when the ratio falls below 1.0 markets start to soften. A full explanation of the data and how it is calculated can be found in the free sample market report which can be downloaded at our website. Obviously this ratio is not the only factor affecting markets - there are plenty of foreclosures attributable to the subprime mess which are acting as a drag on markets everywhere. Still, the markets with good underlying fundamentals should recover first, while the markets with poor employment growth are going to suffer longer.

Home Values, Employment, and Permit Data Issues and Limitations

A lot of the analysis I do is based on home value data derived from either the S&P/Case-Shiller House Price Indexes or OFHEO's Housing Price Indexes. I also use the Bureau of Labor Statistics Local Area Employment data and the Commerce Department's C-40 Residential Permit Reports for supply and demand trends. These data sets are useful because they drill down to metropolitan areas (there is wide variation in performance between markets). Also, they are published frequently (monthly for S&P Case-Shiller, BLS, and Commerce Department data, quarterly for OFHEO). However, like all data there are issues with each data set.

Although the S&P/Case-Shiller and OFHEO indexes use a similar approach to estimating value changes, there are important differences which are explained Andrew Leventis (an OFHEO economist) here. The S&P/Case-Shiller data has less complete geographic coverage, so data may not be available for some markets, and the aggregate index may be skewed by the smaller set of markets covered. The OFHEO index relies on data from Fannie Mae and Freddie Mac loans which means homes using jumbo loan financing are not represented. If, for example, prices on high priced homes are falling faster than more affordable homes and the OFHEO sample is overweighted with affordable homes the OFHEO index will not fall as fast as an index including the higher priced homes. When possible I try to use both indexes and keep in mind their potential biases.

The primary issue with the BLS employment data is explained in this post from The Big Picture. The BLS attempts to estimate employment changes from new businesses just starting up and from businesses which have terminated using a birth/death model. The consensus is the model tends to understate job losses during a slowdown and understate job gains at the start of a recovery. Again, the best approach seems to be an awareness of the potential bias.

Finally, the permit data issues relate to potential reporting and sampling errors. The Commerce Department summarizes the issues here.

None of these data sets seem fatally flawed, especially if the data is looked at over a period of time and averaged over a number of periods to miminize the impact of errors or distortions in any one period.