Housing Market Recovery: Shadow Inventory?

We all want to know when the housing market will recover. A recent piece by David Leonhardt in the NY Times suggests that the housing market still has not reached bottom. Central to clearing the housing market is eliminating the oversupply of homes (see my earlier entry). There are several measures of oversupply: the National Association of Realtors provides a monthly measure of homes for sale; the Census Bureau provides a monthly measure of new homes for sale; and the Census Bureau also provides us with a quarterly measure of vacant homes for sale.

All of these measures rely upon the owner of the property putting the housing unit up for sale. As Calculated Risk has been saying (here, here, and here) there is most likely a large shadow inventory of sellers keeping their homes off the market. A recent piece in the San Franciso Chronicle (article here, Calc Risk Blog here) suggests that banks are also keeping foreclosed properties off the market.

There is no survey of banks and households that we can use to estimate the size of the possible shadow inventory. However, we can get a sense of it from the quarterly Housing Vacancies survey. This survey is the source for the data on owner-occupied and rental vacancy rates, and it provides a breakdown of vacant units beyond these headline vacancy rates. Table 1 provides the breakout of vacant housing units from Census. When census reports vacancy rates they exclude `Seasonal Vacant’ and `Held-off Market.’ The category Held-off market consists of three sub-categories: `Occasional Use’, `Usual Residence Elsewhere (URE)’ and `Other Reasons.’ These excluded categories are generally quite boring. They are generally increasing, following a trend, but display almost no cyclicality.


I say almost because the sub-category vacant held-off for other reasons has become quite interesting of late. Figure one plots the % of the housing stock that is vacant held-off the market for other reasons. My measure of the housing stock is the sum of total occupied, vacant for sale, vacant for rent, vacant sold or rented and vacant held-off market for other reasons. In the last two years this category of vacant units has surged from 2.3% to 2.9%. This suggests that there is a large shadow inventory equal to roughly 0.6% of the total housing stock, or 736,000 units.


Figure 2 plots the total residential vacancy rate with and without the shadow inventory (vacant units held off the market for other reasons). The total residential vacancy rate is the % of the housing stock that is vacant irrespective ownership status. The red line in figure 2 is the total vacancy rate solely from vacant for rent and vacant for sale, excluding the shadow inventory. The black line in figure 2 denotes the vacancy rate when the shadow inventory has been added to the vacant units for rent or sale. In the current crisis we see that the vacancy rate excluding the shadow inventory seems to be stabilizing while the vacancy rate including the shadow inventory seems to still be rising.


A rising vacancy rate is a potential bad omen for the recovery of the housing market. Ultimately, it is vacant units that are putting pressure on the cost of housing services, be it rents or house prices. Common sense suggests that stabilization of the vacancy rate is a necessary but not sufficient condition for stabilization of house prices. This suggests that inclusion of the shadow inventory implies that the housing market still has a long way to go before prices stop falling.

An interesting feature of the data is that the presence of a shadow inventory is not a new phenomenon. In figure 1 we see that during the crisis in 1974 there also was a surge in vacant held off market. In figure 2 we also see that the rise in the vacancy rate including the shadow inventory is more pronounced during the 1974 crisis. This suggests that movement in the shadow inventory is always part of the cyclical process of the housing market. The shadow inventory is just more visible during the severe downturns. Therefore, there may not be too large of an error in forecasts based upon historical estimates that ignore the shadow inventory.

The nature of the shadow inventory is not clear. The measure from the Census data is only vacant homes, so it does not consist of any owners who are waiting to put their house on the market. The vacant homes most likely consist of a large amount of foreclosed homes that are not listed as for sale. They could also be the former homes of the elderly who have moved to assisted living. When the housing market is in a downturn, the offspring of the elderly may take their time preparing a house before putting it on the market.

The vacant homes could also be abandoned homes. I was only just born during 1974 so I have no personal memory of that downturn, but it seems likely that it was during this period when there was an acceleration in the abandonment of vacant housing units in the cities that contributed to the inner city blight back then. Currently there is a same abandonment going on the midsize former industrial cities of the Midwest. For instance, the New York Times just had a recent piece about abandoned homes in Flint, see "An Effort to Save Flint, Mich, by Shriking It." Therefore, it is not clear whether the vacant units held off market for other reasons are a viable shadow inventory of future homes to be sold, or just an indication of a higher rate of depreciation via abandonment.

IU Econonics Forum

We had an Economics Forum at IU on Friday afternoon. There were several excellent talks put on by very bright economists with ties to IU. Here is a link to all presentations. George von Furstenberg's talk goes over the legal grounds upon which the growth in power of the Treasury, the FDIC and the Fed rests. Unfortunately his comments and turns of phrase cannot be completely gleaned from his slides.

Myself, I tried to explain how standard open-market operations were used to influence the overnight market for liquidity. I attempted to construct an example of IU undergrads lending excess cash to each other to have while they go on dates. The collateral they put for the overnight trades is video games. High quality video-games can be thought of as short-term Treasuries. A guy called Ben uses video-games to control the supply of excess cash--too much cash and the dating market overheats--too little cash and too few people go on dates.

We can think of the onset of the crisis by imagining there are a set of students at IU who go on dates, but don't get to trade directly with this guy Ben. Instead, they create their own video games that work as collateral in the overnight market. Particularly, a couple of guys started to issue `sub-prime' video games. Initially people liked these games, but in the summer 2007 people playing the games realized they were junk. These games were now lemons, and the overnight lending market froze up. Things got so bad in September 2008 that Ben's trading in money and high-quality games become impotent. Essentially, people preferred to keep their money, rather than lend it out or go out on dates. Money and high quality video games were now perfect substitutes, now serving as a store of value. The students at IU were afraid that if they lent, they would become insolvent and they would never be able to go on dates again. Ben doesn't like this situation, so he has now started to trade other types of video-games for money. For instance, he trades lower quality games and games that last longer--such as fantasy or war games that take longer to finish. This creates more liquidity for video-games, but if it will convince the students to go out on dates, we do not yet know. It's not clear how this liquidity solves the insolvency and lemons problems. It does however stem the liquidity problems that have arisen from the insolvency and lemons problems.

Intermediate Macro Blog

I've started my Spring teaching. As a byproduct I'm using a blog for the Intermediate Macro class (undergrad). I start off most classes by going over data relevant to the current crisis. While the data is not directly housing related, I'll post links to them since I think most people will find them interesting.

So far I've posted two entries.

The first covers the fall and subsequent (and quite recent) rapid increase in savings. See "The Trainwreck".

The second post covers the changes in Households' wealth and debt from the Fed's Flow of Funds. See "Household Debt Leading up to the Crisis." The data shows quite clearly the folly in the logic a lot of people (myself included) in thinking that the high levels of mortgage debt to income were justified by the high house values--we had seen that story before and high mortgage debt to income leads to high debt to assets, just with a lag.

The Beveridge Curve in the Housing Market: The Rental Market is more out of Equilibrium than the Owner Market

The long-run equilibrium in the Housing Market is characterized by a Beveridge Curve. In Labour Economics, the Beveridge Curve denotes the negative relationship between the unemployment rate and the number of job vacancies (Rob Shimer maintains an updated curve from the labour market). In my research (slides), I have discovered a Beveridge Curve in the Housing Market given by a negative relationship between the growth rate of households and the vacancy rate. The relationship holds in the owner-occupied market, the rental market, and the total market independent of ownership status.

The relationship for all three markets is plotted in figure 1. On the x-axis is the vacancy rate and on the y-axis is the growth rate of households for each type of market. The data source is the Housing Vacancies Survey put out by the Census Bureau (data in excel). The data have been smoothed slightly due to noise (details in slides). There is a clear negative relationship between the vacancy rate and the growth rate of households in all three markets. Currently all three markets are at their all-time highs for vacancies. (I also have a Beveridge Curve by decades).

What is most striking about the current housing market is that the rental market is significantly more out of equilibrium than the owner occupied market. This suggests that in addition to falling house prices, there is significant pressure for rents to be falling as well. In fact, there is evidence of rents falling (see CalculatedRisk, NYTimes, Guardian).What we also see is that the disequilibrium started back in 2003; the writing was on the wall even back then.

We clearly have an over-accumulation of houses. The current market imbalance is not an own versus rent problem, but a house versus household problem. The price that needs adjusting is not just the rent-price ratio. What needs adjusting is the overall cost of housing services, regardless of ownership status.

We can use the Beveridge Curve to get a deeper understanding of the long-run equilibrium in the housing market. The curve can best be thought of as a supply condition. The demand for new houses comes from household formation. Let’s assume that the cost to produce homes is increasing in the growth rate of the housing stock (as is assumed by Glaeser, Gyourko and Saiz in their papers here and here). When household formation is low, the cost to produce the homes to satisfy the market is low.

There are two adjustments to clear the market when the rate of household formation falls. The first is the standard adjustment that house prices fall. The second adjustment relates to the vacancy rate. When the cost to produce a home is low, a builder may want to produce more homes, potentially adding variety. This lowers the probability of an individual house selling, but it raises the probability that an individual builder may sell one house. In essence, builders respond to the lower costs by raising supply. The higher supply shows up in a higher vacancy rate. At some point in time the vacancy rate rises enough to stem more production and the market returns to equilibrium. Therefore, we get the following result: A lower growth rate of households lowers the costs of production to meet demand, leading to lower prices and higher vacancy rates. Thus, the Beveridge Curve.

Figure 2, illustrates how we should think of the long-run relationship in the Beveridge Curve. When the current market condition is to the northeast, there is an over-supply of homes—the vacancy rate is too high relative to the rate of household formation. High prices could drive the market into this area. When the current market condition is to the southwest there is an undersupply of housing units—the vacancy rate is too low relative to the rate of household formation.

The estimated long-run Beveridge Curve, rendered by the solid lines in figure 1, gives us a value for the current over/under supply of housing units. The best way to measure the amount of over/under supply is to find the current deviation of the vacancy rate from its long run value implied by the current growth rate of households. The deviation provides a measure of over-supply in terms of a percentage of the total housing stock. For the total market, the current over-supply is 1.04% of the total housing stock, or 1.22 million units.

A better way to measure the over-supply is to normalize it by the long-run growth rate of households. We then get a measure of over-supply in terms of years of household formation. That is, the measure is the numbers of years to erase the over-supply if household formation remains at its same rate and no more houses are constructed. This is a ‘years of supply’ measure equivalent to the ‘months of supply’ concept used in New and Existing home sales.

Figure 3 graphs the oversupply for all three markets. We clearly see a large increase in over-supply starting in 2003. Almost all of the over-supply originates from the rental market. Only recently has there been an over-supply in the owner-occupied market. Currently the total over-supply is at 1.03 years of household growth; by far the largest over-supply ever. Note that for this metric, the high rates of household formation in the 1970s kept the over-supply around 1974 low, at 0.29 years. Also, for the entire sample, the rental market is where most of the adjustment takes place—most of the over/under supply is there.

Finally, let me address foreclosures. A foreclosure does not affect the over-supply in the total market. When a household loses its house through foreclosure, the result is a vacant house and a renting household. Provided that the household remains an independent household and does not become homeless or move in with relatives, the household rents out a previously vacant house. The result is no change in the equation between houses and households. This is not to say that foreclosures are not a problem. Foreclosures are affecting the financial system, they create psychological problems for households being foreclosed upon, and a foreclosure can hurt a neighborhood just like any vacant house can hurt a neighborhood. However, foreclosures do not affect the imbalance in the overall housing market. As Glaeser and Gyourko state "the tyranny of housing supply suggests that no credit market intervention of any sort is likely to be able to stop housing price declines."

In summary, there is a Beveridge Curve in the Housing Market that defines a long-run equilibrium. Currently there is an over-supply of 1.22 million units, roughly one year of supply. What is striking is that most of the over-supply is in the rental market, not the owner-occupied market. This suggests that not only does the price-rent ratio need to adjust, but rents also need to adjust.

In a few days I will be posting about what we can expect for household formation. The data used for the Beveridge Curve suggest that this is also bleak.

Fooled by Search: The Great Housing Bubble

In the housing market, people .... just do not know how to judge the overall level of prices. Much more salient in their minds is the rate of increase of prices.

--Robert J. Shiller, Irrational Exuberance, 2nd Ed., 2005. p. 208


In the United States housing market, data suggest that temporary movements in demand are related to permanent movements in house prices, whereas theory predicts that temporary movements in demand should be related to temporary movements in prices. Alternatively, the data suggest that a permanent change in demand leads to a permanent change in the growth rate of prices, whereas theory would predict only a change in the price level of houses. The data and theory are in disagreement. The disagreement is especially striking if we think that market tightness, or the number of buyers relative to sellers, impacts prices via the bargaining process between buyers and sellers. The divergence of data from theory explains almost three-fourths of the rise of detrended housing prices in the bubble that starts in 1998.

The failure of theory can be explained by a behavioral inefficiency, where buyers and sellers who are currently in the market (irrationally) interpret prices of past transactions as the permanent value of a house. If buyers and sellers behave as such, then high demand for houses causes prices to be bid up relative to past prices. Future buyers and sellers then interpret the resulting price change as a change to the permanent value of a house. In this way temporary demand movements cause permanent movements in prices. If demand remains high, then prices continue to be bid up, so that there is an increase in the growth rate of prices. In this way, a permanent increase in demand results in a permanent increase in the growth rate of prices.

As I argue below, interpreting past prices as the permanent value of a house is irrational if the housing market suffers from `search frictions', which are the frictions in the decentralized trade of houses that cause market tightness to impact prices via the bargaining process. When these frictions are present, an increase in demand, even if there is no change to the fundamental value of house, will increase the price of a house by the interactions between sellers and buyers bidding up the price of a house since sellers know that they can easily find another buyer. However, when future market participants interpret that price change as a change in the fundamental value of a house, they are fooled. In essence they are fooled by `search frictions' into thinking that there has been an increase in the fundamental value of a house. As Shiller's quote states, households have a hard time understanding price levels, instead, they understand price increases.

In an estimated model, the irrational interpretation of past prices as the fundamental value of a house is responsible for over half of the rise in house prices from 1998 to 2006. To see this, figure Figure One plots the real price growth from the data along with an estimated counterfactual where the irrational assumption has been removed, marked `Rational Counterfactual.' The counterfactual is the model's prediction for the increase in prices due to high demand for homes over this period, if households had rationally interpreted that part of the past price increases were due to search frictions. Figure One also plots trend price growth. Removing the trend growth of prices, almost three-fourths of the rise in prices can be explained by households' ignorance of search frictions on past prices.

To see the relationship between prices and demand in the data, examine Figure Two. The most striking feature is the line marked `price level,' which is the real price of houses as reported by the Office of Federal Housing Enterprise Oversight (OFHEO), with inflation and the trend growth in prices removed (trend growth is assumed to be the average growth from 1976 to 2003). Clearly, the level of housing prices relative to trend is significantly greater than any that have been observed since the OFHEO series started. Also on the figure are turnover and price growth. Price growth is the percentage increase in prices from year to year, while turnover is the total sales of houses (new and existing, single family) over the total owner stock of homes (consists of owner-occupied houses plus vacant houses for sale as reported by the Census Bureau). Besides the bubble, we easily see in figure two that turnover moves with price growth, not the price level. Note that this is not an implication from the relative magnitudes of the curves, rather, it is the difference in the timing of the peaks and valleys of the curves: the price level lags turnover, while price growth and turnover move together. The correlation between price growth and turnover is 0.89 and turnover explains 79\% of the changes in price growth.

Turnover and price growth moving together suggests that movements in demand drive the housing market. When demand rises, more homes are sold and prices rise. Note that movements in supply would cause prices and turnover to move in opposite directions. The high correlation between turnover and price growth means that temporary movements in demand are related with a permanent change in prices. Alternatively, a permanent increase in demand is related with a permanent increase in the growth rate of prices. To see this in the data, in figure two, examine the period from 1998 to 2002. We see that turnover rose above its long-run level and stayed there for three years. In those same years we see an approximate permanent change in the growth of detrended prices from zero, being on trend, to something much larger.

Example of Turkish Bazaar

Turning to the theory, let's consider a Turkish bazaar where rugs are sold. There are a fixed amount of sellers who know that they can buy rugs for a certain price in the wholesale market. Each day buyers arrive and search for a rug to buy from a seller. Each rug is slightly differentiated and buyers have particular tastes in their rug preferences. Buyers keep searching from seller to seller looking for a rug to buy. Once a buyer finds a rug she likes, she bargains with the seller over a price. The price is influenced by how easily the buyer can find another seller from which to buy a rug. The sellers and buyers may not know all of the other buyers and sellers, but they can see the level of market tightness, given by the number of buyers to sellers, by observing how many buyers are stopping by each store. The tighter is the market, meaning the more buyers there are, the easier it is for a seller to sell the rug to someone else. In bargaining, both the buyer and seller know this, so increased market tightness leads to higher prices.

Now let's suppose that the market is in a steady-state where market tightness and thus the price is the same from day to day. Now suppose one week that there is an increase in the number of buyers. This would result in more sales, raising turnover. Prices would also increase from the increased market tightness. Now suppose the following week the number of buyers falls back down to the usual, or steady-state level. We would expect that the price of rugs would fall back down to the usual price. This analysis suggests that temporary movements in demand should only cause temporary movements in prices.

Instead we could examine the situation where the increase in buyers is permanent. In this situation, we would expect the price increase to also be permanent. This suggests that a permanent rise in demand should permanently raise the price of rugs, but it should not lead to an increase in the growth rate of the price of rugs.

To better understand the implications of the data from the housing market, place the results from figure two in the context of the rug market in the Turkish Bazaar. Consider first the temporary movement in demand. In this case, after the temporary increase in demand, if the price of rugs behaved like U.S. house prices, the price of rugs would remain high the week after the increase in demand, even though demand had fallen back to normal. In this sense, if you were shopping the week after the high demand you would end up paying too much for the rug. In fact, prices would remain high until there was a fall in demand below its usual level, driving prices back down to their steady-state level.

Next consider the permanent increase in demand. In the Turkish Bazaar this should only cause a permanent increase in the price level of rugs. However, the data on house prices would imply a permanent increase in the growth rate of prices. To put this context, if Turkish Rugs behaved like U.S. Houses, a permanent increase in the demand for Turkish Rugs would result in prices growing at a higher rate forever, or at least as long as demand remained high.

What is the difference between the Turkish Bazaar and the United States Housing Market? One explanation could be that the sellers in the bazaar do understand the price level. They know the cost to them to buy another rug in the wholesale market. Prices cannot deviate too much from the level, since some sellers would start to advertise cheaper rugs. The United States House Market does not behave this way. The equivalent of the wholesale market is new construction. But new construction is only a part of the housing market, and for certain types of houses, new construction is a very loose substitute. This would seem to be especially true in the coastal areas where bubbles have historically been more prevalent.

Instead of a wholesale market, there are new sellers (and buyers) entering the housing market each month. They probably have very little idea as to what the fundamental value of house is that would affect the price level. Efficient markets theory (Fama 1970 Journal of Finance) tells them that past prices should reflect all the relevant information, including fundamentals, for prices. But if households ignore that search frictions are part of those fundamentals they are making a mistake. This is especially true if they are then affected by search frictions in their own pricing of houses. In this sense, they recognize that search frictions affect pricing, but ignore that search frictions may have affected past prices, instead interpreting past prices as reflecting the fundamental value of a house in a frictionless world.

Another way to understand the difference, It's as if the Turkish Bazaar had new buyer and sellers each week and that the sellers had already made their own rugs. In addition, at the entrance of the market is a database with all of the transactions from the previous week. The buyers and sellers search the database looking for the prices of comparables to a rug that they are thinking about buying or selling. However, when doing this, the buyers and sellers ignore that the level of activity the previous week, which they can see, may have affected the price level of all of the rugs. When the buyers and sellers trade in the market, the current level of market tightness does affect their bargaining over prices. They then use the past prices as an anchor and bargain relative to them, not relative to a steady-state price level. Therefore, high demand causes increases in prices relative to the previous week. Each week the process repeats itself so that temporary changes in demand lead to permanent price changes, and permanent changes in demand lead to permanent changes in price growth.

Summary

To summarize, the data on the U.S. House Market is consistent with temporary movements in demand causing permanent movements in house prices, equivalently, permanent movements in demand cause permanent changes in the growth rate of prices. This contradicts rational theory that predicts that temporary movements in demand should only cause temporary movements in prices and permanent movements in demand should only reflect an increase in the price level, not price growth. This is especially true if there are search frictions in the housing market where market tightness affects the bargaining between buyers and sellers. Theory can be reconciled with the data by assuming that buyers and sellers interpret past prices as an indicator of the fundamental value of a house in a frictionless world, households are `Fooled by Search.' The past prices then serve as anchor for bargaining. The confusion between price levels and price growth is consistent with Shiller's hypothesis that households have Irrational Exuberance and interpret temporary movements in levels as permanent changes in growth rates.

In order for people to not be fooled by search, a separate housing price index could be started that adjusts the market price of houses to take into account the over and undervaluation of houses due to search frictions. This index would be a type of aggregate index that could be thought of as existing alongside a standard price index such as the OFHEO index or the Case-Shiller index. This index would give a better idea of the fundamental value of housing. Such an index could be useful for mortgage originators to use to think about valuing housing collateral. In addition, the index could help educate the public, to try and forestall the formation of Irrational Exuberance.