Eurodollar Futures

I thought it was time to put my rate hike estimating tool to bed, but it looks like I'm back in business.

Here is what the curvature of the Eurodollars futures market is saying about future rate movements.

The next expected rate hike has dropped back to late 2017, but it is moving around a lot.  There is a lot of uncertainty.  The previous rate hike certainly did help meet the Fed's goal of keeping risk premiums high.  Macroprudence!

Coming movements in the yield curve should be telling.  Right now, I would say that the yield curve is close to being inverted, if we adjust for distortions from the zero lower bound.  But, it's not too late.  A reversal in the rate hike would be the least I would hope to expect in a sane world.  But, maybe we can move forward in any case, depending on how things develop.

In this second graph, we can see that the length of time between now and the expected next rate hike has spiked, but it isn't as long as the length of time that was expected before and during QE3.  If the yield curve flattens more, I don't expect things to turn out well.  But, maybe there is a chance that positive real economic growth can manage to salvage normalcy.  Of course, as always, mortgage expansion would solve all of this much more effectively than marginal rate changes.

(The gaps in the graphs reflect the period where the model was dormant because the curvature of the yield curve at the short end had disappeared.)

Odds & Ends

1: Amid all the bad news, the Atlanta Fed GDP forecast has become surprisingly bullish:

On Feb. 12, the #GDPNow model forecast for real GDP growth in Q12016 is 2.7% https://t.co/PkWNFJQxO0 pic.twitter.com/DUWSOb5Yg0

— Atlanta Fed (@AtlantaFed) February 12, 2016

Source

2: The Michigan Inflation Survey is falling to new lows.  In the year 2045, this graph will be referenced in a cutting edge article by the yet-to-be-named new Fed chair, explaining that, in hindsight, the Fed had erred by being to hawkish during and after the Great Recession.  I hope this is not an early signal of falling rents, which would be very bearish considering the severe lack of housing supply.



3: Mortgage debt remains flat.  I have bitten on a couple of false starts regarding housing recovery.  But, at this point, I am resigned.  I'm mostly on the sidelines until this madness sorts itself out.  In a sane world, the homebuilders would be leading a sit-in at the steps of the U.S. Congress and at least one candidate would make housing expansion - including mortgage expansion - a centerpiece of their platform.


4: Here is an interesting graph that ties in to my recent post on the age-related effects of the boom and bust.  Here, we can see how the 65+ group, which is very heavy in equity and is less vulnerable to labor markets, has sailed through the period as if nothing happened.  Meanwhile, the under-45 groups experienced newfound access to mortgage credit markets until the crisis, and have been especially stressed since then because they tend to have higher leverage.

Cheap debt: The avg 65-year-old borrower has 47% more mortgage debt than a 65 y/o in 2003 https://t.co/WJfmcJLNfy pic.twitter.com/y8euqq5swZ

— Nick Timiraos (@NickTimiraos) February 12, 2016

Housing Part 116 - The 2006-2007 Mystery and Synthetic CDOs

As the bubble story goes, synthetic CDOs were an insidious new creation that helped create the subprime bubble and allowed mortgage originators to keep originating mortgages with marginal borrowers.  You might have already guessed that I will disagree.  But, this one is so complicated it almost broke my brain, so I have put it below the fold.

Housing Part 115 - Value of New vs. Existing Homes

One of the oddities of the housing boom was the fact that median prices for new home did not rise as quickly as the median price of existing homes.  This may not seem so odd if one is able to explain it by claiming that many new mortgages were going to low income households, so that marginal new homes tended to be at lower price points.  But, this explanation doesn't work for me, since I have found that the average incomes of new home owners did not actually decline during the period.  My explanation has been that this was a product of location.  There are clear migration patterns away from high cost cities.  Housing starts in Open Access cities were much higher than housing starts in Closed Access cities, so it doesn't seem crazy to expect that the declining relative price of new homes would correlate with rising building rates in Open Access cities.  It seems obvious enough to me that I have been, more or less, stating it as fact.  But in this case, the data is not quite as clear as I would have thought.


The first graph compares the US Median home price, according to Zillow, to the median new home price according to the Census Bureau.  It is a little difficult to see the relative changes on this graph, so on the next graph, I have reproduced this as a ratio.  And, I have graphed it alongside the ratio of permits for new housing units in the Closed Access cities / Open Access cities.

The ratio of permits was much higher in the late 1980s, mostly attributable to California.  But, it dropped sharply in the early 1990s, and remained low after that.

In this second graph, we can see that there was an earlier period with relatively low new home prices, in the late 1970s.  Home Price/Rent was high then, as it was in the 2000s.  And, in the 1970s, high inflation made mortgage payments high, too.  Nevertheless, homeownership was rising at that time - again, much like the 2000s.  It appears to me that the reasons for the lower new home prices then were somewhat similar to the reasons for it in the 2000s.  Households were trying to hedge against rising rents, and since home prices were generally efficient, marginal new homeowners were downsizing in order to accommodate the cash flows of ownership.  I think this explains why, counterintuitively, in both periods, even with rising relative home prices and rising ownership, growth of real housing expenditures shifted down (shown in the next graph).  Relatively lower value new homes is a sign of the answer to this conundrum.

But, what I thought we would find is that as new home prices declined, relative to existing homes, we would see a parallel decline in permits issued in the Closed Access cities.  It seems obvious that this should have happened, simply as a product of population growth.  In 1988, the three main Closed Access cities had 254% of the population of the four main Open Access cities.  By 2007, that was down to 174%.  Just by virtue of being larger, we should have expected the Open Access cities to be issuing more permits over time.

But, instead, the ratio remained pretty level from 1992 to 2006.  Now, I think partly what is happening is that the way we normally look at housing starts, in raw units, without adjusting for population, the level of recent periods gets overstated a little bit.  I have rendered permits as a proportion of population in these next graphs, which I do in order to compare rates of new building among cities.  When we do that, the rate of housing starts (or permits, which follow each other closely) are much more level in the 2000s than they appear to be in unadjusted long term graphs.

Even though the rates of new permit issuance in the Closed Access cities is so low that it is hard to tell, there was an upward trend of permit issuance in those cities during the boom.

I still think location played a large role in the relative decline of new homes, but some of it was playing out within metro areas, with existing core city properties rising sharply in price and some level of building in the suburbs serving as an outlet for households priced out of the core areas.

This trend reversed after the crisis.  There has been a small rise in the relative level of new building in the Closed Access cities, mostly because the mortgage crisis has hampered building in the Open Access cities.  But, the change isn't large enough to explain the extreme jump in relative new home prices.  I don't think location has much to do with the recent trend.  It has more to do with the fact that middle income families don't have reliable access to mortgage financing, so new building is skewed to the small portion of households at the top of the income distribution who can qualify for mortgages.

Here is one last graph, which I think helps us to think about the similarities and differences between the 1970s and the 2000s.  In the 1970s (and early 1980s), high inflation meant that mortgage payments were very high across the country.  This graph shows the median home in Houston, San Francisco, the US, and the median new home, all relative to median incomes in the respective areas.  The lower prices of new homes in the 1970s were helping to make mortgages affordable for marginal new households, across the country.

Mortgage rates had declined by the 1990s, and during that period, mortgage affordability for new homes moved higher above the median existing home, as affordability was less of a constraint.

By the 2000s, affordability was tied to location.  Homes in Houston were as affordable as ever, but San Francisco had nearly as much of an affordability issue as it had in the early 1980s.  Here, we can see the mortgage affordability of new homes moving back down closer to the affordability of existing homes.  And, if affordability was largely influenced by location at that time, we would expect this accommodation to happen through location.

Here is a graph of lot and home square footage.  In the late 1970s and in the 2000s, real long term interest rates were one cause of rising Price/Rent levels.  Since real long term interest rates don't particularly effect the cost of building, we would expect low real long term interest rates to lead to a substitution of more structures and less lots.  Since high inflation rates in the 1970s and early 1980s made affordability difficult regardless of location while low real long term interest rates were pushing up prices, we see a reaction to these factors in a very sharp decline in lot sizes, and a small decline in structure sizes.  There was a general downsizing and a substitution of structure for lot.

In the late 1990s and early 2000s, affordability problems were a product of location.  Households had to solve the affordability problem by building away from the cities.  So, during that period, falling long term real interest rates were creating a substitution of structure for lot, but also the "downsizing" by moving to less valuable locations meant that households were substituting both larger structure and lot sizes for less valuable locations.  So, the decline in lot size is less severe than in the early 1980s and home sizes were actually increasing during the 2000s, even though the BEA shows a decline in the growth trend of real housing expenditures, and new home prices were declining relative to existing homes.  The drop in real housing expenditures was a drop in the utilization of location value.  Households were building larger homes that were, nonetheless, less valuable.

From 2006 to 2009, home size remained level during the deep part of the bust, and now median new home size is rising along with the relative price of new homes because they now tend to be purchased by households with higher incomes.

Another Brief Note on the Yield Curve

Saw this on twitter today:

Striking Capitulation in the Fed Funds Futures Market: Probability of Fed Rate Hikes Now vs. Jan. pic.twitter.com/nEhE74ECjV

— Michael McDonough (@M_McDonough) February 8, 2016

Here is the CME Group interactive Fed Watch tool.  These models show that yesterday, the odds of a rate hike at all in 2016 were about 35%.  That has fallen to about 25% today.  My model, which is based on the curvature of the yield curve showed a mean expected rate hike date in August, yesterday.  But today, that has moved all the way back to May 2017, with a slope after that which is still about 8 or 9 basis points per quarter.

I use Excel Solver to do the estimate, and, interestingly, Solver seems to have more than one optimal answer on the yield curve data from yesterday and today.  One answer is the August and May inflection points.  The other answer is basically that the next rate hike is highly uncertain, and far in the future.  Since expectations of remaining at zero do not affect the inflection point, because there is no inflection point in that case, I think my model is giving a different answer than the models that are based on rate levels.  I think the correct way to read all of this is that about 1/3 of the market thinks rates will rise, with a mean date of next May, and it will rise by about 25 basis points per quarter.  The other 2/3 of the market thinks rates will not rise at all.

The biggest red flag here is that, as far as I can tell, there is apparently zero expectation of a possible retrenchment.  The clear response right now would be to take a do-over and move rates back to near zero.  And there is no expectation of that happening at all, that I can tell.

The populists and Austrians think this is just proof that we have a bubble economy that can't grow without monetary stimulus.  They seem to like it when this happens, as if the S&P 500 at 2100 was fake and it is only real at 1850, or maybe 1600, or maybe less.  We've got a case of national Munchausen Syndrome.

Don't even worry about suggesting some optimal monetary policy.  Just give me a policy that isn't vulnerable to explicit communal self-immolation.

The Yield Curve Curves Again

Last summer, the then-future rate hike was moving ahead in time, always 6 months in the future.  Until we revive the mortgage credit market and the single family homebuilder market, that may be the best we can hope for.  Where is capital supposed to go if we effectively obstruct trillions of dollars of potential investment?

As we moved through 2015, the Fed decided to impose a rate hike, and so, finally, the expected date of the rate hike became anchored in time, and we finally caught up to it in December.  Since then, the yield curve has flattened sharply.  In fact, we may be near the equivalent of an inverted yield curve now, with forward rates boosted by distortions of the zero lower bound.

In the most recent rate hike periods, rates have risen by about 50 to 70 basis points per quarter. During QE3, the slope implied future rising rates of only about 15 to 35 basis points per quarter.  I suspect this partly reflected expectations of slower rises and partly reflected expectations that we would not leave the zero lower bound.

Now that the Fed hiked rates, the slope has declined all the way to about 8 or 9 basis points per quarter.  There is also a lot of uncertainty about the date of future rate hikes.  This is so low that I think it reflects a very strong expectation that rate hikes will never come.  Long term rates in Japan were in the 2% range until recently during a long period of low short term rates.  This is basically a flat yield curve.

The fact that there isn't a unanimous call for the immediate reversal of the rate hike is pretty much the picture of our dysfunctional era.  We know what we want, "and deserve to get it good and hard."

In the meantime, the short end of the yield curve now curves up yet again.  And, we are back to a context where the next future rate hike is expected to be about 6 months in the future.  Probably the best we can hope for is that we are back in the context of the ever-moving rate hike, always 6 months in the future.  If we ever start building homes again, maybe it can rise naturally.  Until then, if the Fed decides to continue to pretend it controls short term rates in some sort of Phillips Curve fantasy, then they will push us over the edge again.  This time, though, it won't be preceded by a housing collapse or a tepid, but manageable NGDP growth path, because there is little housing market to ruin and NGDP growth is already tepid.  We basically are already back to 2007, where wage growth is strong, but is all being eaten up by rising rents.  I don't see any reason to think we can't walk right back into a 2008 situation, given public and FOMC viewpoints.

Housing Part 114 - More on Homeownership Rates

In the recent post on homeownership rates by age, Ironman helpfully pointed me to some archived Census information before 1994.  I haven't been able to find age-specific homeownership rates in the old pdf files, but I did realize that there is digital data going back to 1982.

This confirms that there was a similar rise in homeownership among the younger age groups in the late 1970s and early 1980s.  I did find one other source with some decadal age data, and it suggests that homeownership rates in 1970 were slightly lower than 1980 for both young and old households.

It looks like homeownership has been pushed up slightly because of a permanent increase in ownership rates for households over 65 years old from 1982 to around 2000, where it leveled off at a rate similar to 55 to 64 year olds.

From 1982 to 2005, homeownership rates for 45 to 64 year olds were fairly stable at high levels.  Note that there was very little change in ownership among these groups during the boom, but ownership has fallen by about 5% for both of those groups since then.  This is a story of a bust, not a bubble, folks.

In 1982, homeownership for 35-44 year olds was higher than it was at its peak in 2005.  The rate for households younger than 35 was also near the 2004 peak in 1982, and the decadal source suggests that it also peaked at a level at least as high as 2004.  Of course, 1982 was a time of crazy speculation, when households took on unsustainable mortgages because they were convinced that home prices would never stop rising because of loose monetary policy.

But seriously, as I have mentioned before, this seems like strong evidence that credit fueled demand and easy credit terms aren't as strong a factor as people seem to think.  Before 1982, real long term interest rates were very low and nominal rates were very high.  That made mortgage term onerous.  But, the low real rates meant that homes had high intrinsic values because future rents were worth more in present value.  Also, the high inflation meant that imputed rental income and accumulating capital gains on homes provided a significant tax advantage.  Whichever of these factors dominated, they clearly overpowered the negative influence of those payments on 12% mortgage rates.

If intrinsic value is what dominates, then these changing homeownership rates simply reflect marginal reactions to real long term interest rates.  If the tax benefits of inflationary gains are what dominate, then that same effect would not have been as important in the 2000s, because inflation was lower.  So, the rising homeownership rates in the 2000s could reflect some ease of ownership resulting from low interest rates and aggressive lending.  And, there might have been a secondary effect of the tax benefits on the high home price appreciation that was happening at the time, even though that wasn't a reflection of broader inflation.

The 1970s and the 1995-2005 periods also were periods with rent inflation, so rising homeownership rates in both periods could have been a sort of hedging reaction, where younger households felt more incentive to avoid the uncertainty of future rising rents.  (By the way, put another knot in the rope for the theory that urban housing supply constrictions are actually a cause of the declining real interest rates, because they remove some uses of capital while producing capital gains for existing capital.  It so happens that we have two periods where rent inflation was high, real long term interest rates were low, and home prices were high.  I don't have detailed data on metropolitan specific housing measures for the 1970s, but there seems to be evidence that urban housing constraints were ratcheted up during that period.)

In any case, what is clear is that for households over 45 years old, homeownership was never elevated, and has dropped precipitously since the bust.  And for households under 45 years old, there do seem to be systematic fluctuations in homeownership over time, and homeownership rates in 2005 were within the range of ownership levels we had seen before.  In fact, they were at a level we had seen when mortgage rates were over 10%, so there simply is no reason to think that marginal homeowners in the 2000s needed to be any different or less suited than households who might have owned homes at other times in the HUD era.


PS: I also found this graph on page 131 of this report, which gives us some age-specific information going back to 1960.  This also shows 1980 homeownership rates for working-age households at the same level as in 2000.  And households skewed younger in 1980 than in 2000, so within each age group, ownership would have been higher in 1980 to make up for the demographic shift.

Housing Part 113 - Fixed Investment vs. Location

Since supply is such a large factor in the shape of our housing markets, we have this strange circumstance where we are building houses in precisely the places where they are the least valuable.  Isn't that a strange economic turn of events?  And, since that is the case, it is kind of odd to me that supply isn't the central topic in all of the public discussions about housing and home prices.

A hunch I have had about this curiosity is that, since the building mostly happens in places with moderate prices, this is inflating private fixed investment.  The space over San Francisco and Manhattan is filled with hundreds of thousands - millions - of extremely valuable little cubes hanging in the air.  For a half million dollars' worth of steel, gypsum board, and concrete, a little million dollar condo is just waiting their for us to claim.  There is a tremendous amount of location value sitting there like gold buried in the ground.  And, frustratingly, it is only there because the density enabled by centuries of wise and prescient planning made it so.

Since we won't unlock that value, we instead build houses in Dallas and Atlanta, where a half million dollars' worth of lumber and gypsum board creates a home worth a little more than half a million dollars.  This isn't to cast aspersions at those Open Access cities.  There isn't location value there because they are doing things right, because they haven't created economic rents through limited access to capital investment.  Right now, most of that location value in Manhattan and San Francisco is due to those rents.  So, the true measure of value we would get from building in those cities would eventually come from pulling their market values back down to their intrinsic, "Open Access" values.  That is probably still somewhat higher than Dallas and Atlanta, but not nearly as high as their current market values.  The main benefits from building in the Closed Access cities would be through the decline in all of the costs related to those cities and the goods and services they produce.

But, back to my hunch.  I think part of the reason that the boom looked especially bubbly was because we measure all that lumber and gypsum board in private fixed investment, but we don't measure intrinsic location value in private fixed investment.  So, the suboptimal pattern of not building in valuable locations, ironically, makes it look like we are investing more.

Source

Well, I finally got around to checking the data on this.  The first graph here is single unit building permits in the Dallas, Atlanta, and Phoenix metro areas, versus San Francisco and New York City.  We can see how building in the Open Access cities out-paced building in the Closed Access cities, in the late 1990s and early 2000s, until the bust temporarily pulled them down.

A more complete analysis regarding the dense city cores would obviously consider multi-unit values, too, but I'm not sure if there is a measure of the market value of new multi-unit structures that I can use for the comparison.  To give an idea of the scale, single unit structures usually account for 2% to 3% of GDP (although it has averaged around 1% since the crisis).  Multi unit private fixed investment in structures used to climb above 1% of GDP during expansions, but hasn't reached 0.4% since the 1980s.  Building in our prosperous cities of an additional 0.5% of GDP would release a large amount of location value.  It would be like investing in a 401k when your employer has a matching program.

Source

The second graph compares private fixed investment in single unit structures to the estimated total value of new single unit homes sold.  In other words, for each $1 of new houses we built, how much lumber and gypsum board did we need to use.  And, we can see that it fits the pattern of my hunch.  Homes in the 1990s and early 2000s required more inputs.  They were composed of less location value and more materials value.  When building in the Open Cities collapsed in the crisis, relative to the Closed Cities, the few homes we were building had more location value, so the proportion temporarily fell.  Even though homebuilding is still highly constrained, it has recovered somewhat in the moderately priced cities, so the proportion has risen again.

Also, here, we can see something that I hadn't fully appreciated before.  Total building in the Closed Access cities has been strong (relative to the depression levels of the rest of the country).  But, that has been almost entirely because of multi-unit building.  This isn't because those cities have suddenly seen the light.  It's just because the constraints created by our hindered mortgage market aren't a constraint for large corporate developers, so their building rates are still determined by the same bureaucratic obstacles they always are.  Urban multi-unit building is still much lower than it needs to be, but it isn't particularly constrained by our self-imposed credit bust.  Despite the high location value, single unit homebuilding in the Closed Access cities remains very low.  I had thought they would be higher.

Source

The last graph here compares the normal measure of private fixed investment in single unit structures, as a proportion of GDP, to a measure of the market value of those structures, including both location value and input values.  They have been set to 100 in 1990 for the sake of comparison.  We can see here the extent to which private fixed investment has been inflated because we have been substituting materials and work for intrinsic location value.  Starting at 100 in 1990, the red line is the relative level of the cost of those inputs. The blue line is the relative level in the market value of the new homes (which includes materials and location value).

Maybe it's a small thing.  There was certainly a healthy amount of building going on in the 2000s, in either case.  But, another brick in the wall, as they say.


PS: I'm not sure I'm happy with the indexed graph above.  Here is the same graph, shown as a % of GDP.  Here, the difference between the blue line and the red line is a broad estimate of location value.

The complication here is that location value is mostly economic rents.  So, the end result of either having the problem (little building in valuable locations) or solving the problem (extensive building in valuable locations) would be to have lower location value.

Housing, A Series: Part 112 - Defaults in the Two Americas

I have mis-spoken.  A few posts back, I made the following observation:

(I)f we look at the housing market, as we should, as two markets - the supply constrained market and the open market - even this idea loses credibility.  Why?  Because home price increases were concentrated in a few cities.  For home buyers in most of the country, there wasn't an unusual level of home equity to draw on.  So, if the crisis was precipitated by the unsustainability of subprime loans, in 75% of the country, those borrowers should have been defaulting well before 2007.

It happens that defaults were higher in some Open Access areas, but in interesting ways.  Zillow has foreclosure information on selected cities.  I want to start with the Open Access cities.  Zillow doesn't have foreclosure data on Houston or Atlanta, so here I will use Dallas, along with Columbus, Charlotte, and Nashville.  The other large Texas metro areas also appear to have followed this pattern.  These are all cities with Open Access characteristics - high population growth and low home prices.

All of these cities had high default rates as early as 2004.  But, it is interesting what we don't see here.  We don't see collapsing home prices.  These defaults didn't lead to systemic losses in subprime mortgage securities in 2004 and 2005.  Housing starts didn't collapse in 2004 and 2005.

And, generally, when the national collapse occurred in 2007, home prices in these cities fell somewhat, but defaults remained relatively level (and somewhat elevated).

This is what a credit boom looks like in an Open Access economy.  In an Open Access economy, it is very hard to build too many houses because of credit expansion.  There have been plenty of households to buy up the housing stock in these cities.  In fact, rents are high, now that we have imposed the bust on them.  In an Open Access economy, credit doesn't lead to a doubling or tripling or quadrupling of home prices, and defaults don't lead to a collapse in home prices.  If the entire country had an Open Access housing market, we could have had a subprime bubble, and default bubble, a building boom, but there would have been nothing that anyone would have thought to call a housing bubble.

Now, it seems that foreclosures remain high even though the subprime mortgage market has been out of commission for a decade.  And, while price changes are imperceptible compared to the Closed Access cities, they were rising slightly during the subprime boom and are now rising slightly with no subprime market.

What do we see if we look at the Closed Access cities?  These are cities where housing prices have never been lower than the highest prices we see in the Open Access cities.  Here, defaults were low from the late 1990s until late 2006, when they rocketed to well above the US average at the same time that home prices were collapsing.  (Be careful of the time-scales in the graphs.) Foreclosures were never high in New York City, although since 2009, this may be because foreclosures are more difficult.

In these cities, foreclosure rates have risen and fallen sharply along with prices.  And, they have had rising prices along with very low foreclosure rates both during the subprime boom and now after it has been collapsed.

So, we have three periods, the credit boom period, the crisis period, and the credit bust period.  In cities that are pure examples of Closed Access or Open Access housing policies, each has characteristics that don't appreciably change between the credit boom and credit bust periods.  And home prices declined in both during the crisis period, but much more in the high priced Closed Access cities.

I had previously included Riverside with "Closing Access" cities and Phoenix with "Open Access" cities.  But, I think it may be more useful to categorize them as "Contagion Cities".  These cities are cities that generally are willing to approve large numbers of new homes, and that have a history of relatively low home prices.

The period from 2004 to 2005 is a deviation for these cities.  Prices rose sharply, not to levels seen in the coastal California cities, but to levels roughly double the typical range for these cities.  These are the cities that saw prices which seem to have been unsustainable, considering their longstanding pro-housing policies.  Certainly, these prices were facilitated by a generous credit environment.  They were also boosted by the real estate capital gains that California households were re-investing, and by the increasing migration pressures from coastal California.  At least in Phoenix, during the bubble period, builders simply didn't have enough permitted lots to sell to all the available buyers.  At least temporarily, there was a wind shear of generous credit and constrained supply that met in these cities, creating a cyclone of housing activity.

Here we see the same crisis behavior that the Closed cities had - sharply falling prices and rising foreclosures, but even more extreme.  Notice that before 2004, prices in these cities were moderate and foreclosures were somewhat elevated - just like the pure Open Access cities.  Then, during the height of the boom and during the bust, these cities looked like Closed Access cities.  In effect, these cities became extreme exurban extensions of the coastal California cities.  Nearly half of Riverside workers commute to the coastal metro areas.  It is common to meet people in Phoenix who telecommute or commute in some way, at least part of the time, to coastal California.

During the credit bust, Riverside continues to look like a light-version of the Closed Access cities.  Phoenix and Las Vegas look more like Open Access cities.  It will be interesting to see if the contagion pushes out to these cities again as recovery ages.

Florida has been a bit of a mystery to me, but I think I was misled by cursory geography.  Realistically, despite the distance, Florida really does serve the same function for the dysfunctional Northeast Atlantic cities that Nevada and Arizona serve for California.  In the Boston Fed report that I recently looked at, there is a table of migration levels into and out of New England.  From 2000 to 2007, net migration from New England to Florida accounted for 62% of all net migration out of New England!

The remaining large metropolitan areas have various patterns that mostly seem to reflect local conditions.  A few cities, like Seattle, are beginning to show signs of housing related costs.  Foreclosures in these cities tended to rise during the bust period, but at the MSA level, there don't seem to be systematic patterns.  Minneapolis had especially high foreclosures, but prices were quite moderate there.  In fact, I had previously included Minneapolis in the list of "Closing Access" cities, but it probably has more in common with other Midwestern cities with moderate prices, or even the pure Open Access cities.

Foreclosures tended to be low before the bust in these cities, because housing starts tended to be lower in these cities than in the high-growth sunbelt cities.  The foreclosures in these cities tended to be more strongly related to falling home prices.

Local issues are important to what was happening in these cities during the boom, and since I believe I have found much evidence on the national level that the collapse in prices was unnecessary, I will probably not be able to give these cities the attention I would like to, since they tend to be more dominated by local issues.

Housing Part 111 - More data on mortgages to low income households

Previously, I have looked at homeownership rates through the Survey of Consumer Finances.  And, there I find no evidence of a rise in homeownership among low income households.  This is incredible, given the vats of ink that have been spilled discussing that very topic.  It happens that the Census Bureau has some detailed data on homeownership, going back to 1994, which covers just enough time for us to analyze the boom.  This data also shows absolutely no rise in the relative share of low income homeownership.

Here is the graph of Census data.  Homeownership rates rose for both households above and below the median income.  The black line is the proportion of owner-occupied homes owned by the top half of the income distribution.  This line is straight as an arrow, just above 60%.  As I pointed out in the earlier post on the subject, in a period with rising homeownership rates, we should expect to see this decline.  For instance, if the homeownership rate was 100%, then 50% of homes would be owned by the top half of the income distribution.  So, in order for this measure to remain flat, new homeowners among the pool of potential buyers had to be slightly biased to higher incomes.

The Census Bureau also tracks ownership rates by age group.  Somewhere back in a previous post, I have taken a stab at demographically adjusted homeownership rates before.  But, this data is more complete than what I used before.

Here are several graphs to help think through the effects.

First is simply a graph of homeownership rates, by age.  Then, below that, I have included a graph of these age-specific homeownership rates, relative to the levels as of 1Q 2004, when homeownership had generally peaked.

Notice that homeownership among older households was fairly flat.  Households over 65 years old generally have very high equity positions in their homes.  (They also tend to have lower incomes than they did when they were younger and working.  This tends to create confusion regarding statistics in the lower income quintile.)  Their large equity positions tended to protect them from the collapse.

As we move down the age scale, homeownership tends to have risen more steeply and then fallen more steeply.  I think this may not be very widely appreciated.  But, when we look at homeownership by age, homeownership rates for all age groups under 65 are well below the rates that applied back in 1994.

But, the aggregate homeownership rate is at about the same level as in 1994.  How can this be?  As with so many things, homeownership rates are being skewed upward as baby boomers move into age ranges that tend to have high ownership.  So, homeownership rates have collapsed much more sharply than it first appears.

The next graph shows the actual homeownership rate, and an estimate of what the homeownership rate would be if age demographics were still what they were in 1994.  I had thought that the peak homeownership rates might have overstated the rise in homeownership because of these demographic issues.  And, it did, somewhat.  But, much more than that, the demographic effects have masked the devastating fall that has come with the collapse.

If we adjust for demographics, the current homeownership rate has fallen to below any level we have seen since the Census Bureau began tracking it on an annual basis, back in the mid 1960s.  And, to think that many observers are warning about a new phase in irresponsible lending.

I would also like to point out how this relates to a topic I have been reviewing in a couple of recent posts.  Low real interest rates appear to have a much stronger affect on homeownership than credit terms.  Price/Rent ratios were high in the 1970s, even when mortgage payments were extremely high.  This should be somewhat shocking.  Even in that environment, where, surely, outrageously high mortgage payments would have served as a high obstacle to both ownership and to buyer willingness to pay, home prices appear to have approximated the higher intrinsic value created by low long term real interest rates.  But, it is even more shocking than that.  Not only were prices efficient, but, homeownership rates were high then.  And, adjusted for demographics, they were nearly as high as they were at the top of the boom in 2004.

Given low long term real interest rates, it appears that the facts that nominal rates were under 6% instead of being over 12%, and many new financial instruments were being used to help households take on mortgage debt, had very little effect on homeownership.


Taking all of this in, a broad theme starts to coalesce, I think.  There is a significant age story here.  There are many attempted explanations about why young families are less likely to buy homes than they used to.  But, since the story of what happened is so misunderstood, nobody is fingering the cause.  The unnecessary housing bust decimated the balance sheets of young households.  Look back at those age-group graphs.  The boom was mostly about increasing homeownership for households under 45 years of age.

The marginal new mortgage originations weren't facilitating new homeownership of poor households.  They were facilitating ownership for high income young households.

One of the themes that runs through Mian and Sufi's book, "House of Debt", is that the boom and bust especially hurt low-wealth households, who tend to hold a lot of debt, while it may have actually benefited high-wealth households, who have claims on that debt because they are savers.  This is all true enough, as far as it goes.  And, it must seem to fit into the standard narrative of the unsustainable bubble, built on the backs of low income households.

But, we have to be careful about who we imagine these households to be.  We tend to think of a category of households that are "poor" - both low wealth and low income.  But, this creates confusion.  In truth,  families we tend to think of as poor tend to have very little debt.  Low income households who own homes tend to have high equity levels, because the lowest income households don't tend to take out mortgages, regardless of the frightening anecdotes that have been traded around since the boom.  Debt is held, mostly, by high net worth, high income, and young households.  And, when it comes to debt and net worth, age is the most important factor.

So, really, what Mian and Sufi are describing is a loss of wealth for young households and an advantage to old households.  The households that really took a hit were young households who had tried to become new homeowners, who, it appears, tended to have high incomes.  In fact, since incomes also tend to rise with age, the relative tendency of new homeowners during the boom to have higher incomes is especially strong given that they also tended to skew younger.  But, since they were young, they had high debt levels and low net worth.  The younger the age group, the worse the collapse in homeownership rates has been.  This is because young families tend to be new homeowners with little equity.

While households over 65 have generally recovered - especially those with high net worths - as of 2011, the median household in the 45-54 age range had net worth 35% below the 2005 level, and the median household in the 35-44 age range had net worth less than half the 2005 level.

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In the reading I have done so far, I have not yet seen a single reference to this piece of evidence.  It's sort of fascinating for me, because I now have some support for publishing my findings in book form, and the task is daunting, because I have to go, piece by piece, through all the evidence that I can find and explain why my thesis stands.  If something like this census data was out there that contradicted my story, and I didn't have a good explanation for it, it would be a significant black mark against my argument.  Something this broad and clear would probably lead many readers to write off the story and go no further.  But, among all the writers who have filled library shelves with stories of a credit bubble, I haven't seen a single one, yet, that even noticed this data.  This isn't a state secret.  Numerous people are involved in creating this data and making it available.  I assume some number of people look at it on a regular basis.  I think it has just been edited out of notice.  I mean, if everyone believes a story very strongly, and they are all sharing what seems to be insurmountable evidence for it, if you see something that blatantly doesn't fit, it seems reasonable to simply ignore it.  Something must be wrong with it.  We all do this everyday.  Making these decisions is a necessary part of understanding our world.

So, on the one hand, the task before me is daunting, because I don't have that benefit.  I can't just ignore the data that doesn't fit my story.  On the other hand, while normally there aren't any $100 bills on the ground, because "someone would have picked them up already", on this topic, I am swimming in them.  Telling the story is as easy as reproducing basic charts from the Census Bureau.  The hardest part will be before readers even open the book.  The most important part of the publishing process, I think, will be getting a broad range of authorities to say, clearly, "This is a book you need to read to the end.  I was surprised by it, and it changed me."  Otherwise, at the slightest hint of a weak argument, readers will be tempted to put it down as a lark before they see all the intertwining facets.