Housing: Part 326 - Another example of how much priors determine conclusions

Adam Ozimek has a recent post up at Moody's about the causes of the slow housing recovery.  Adam generally does great work, and the funny thing about this post is that he approaches the topic just as I would like to think I would.  He tries to find neutral references to measure new data against.  He uses reasonable logic to take inferences from those measures.  He looks at individual metro areas instead of the national numbers.  But, his conclusions are upside-down wrong.

This is because, as is common on this topic, he (reasonably) builds on the conventional presumptions about the bubble and bust: A credit bubble led to a housing price shock, which led to overbuilding, which led to an inevitable bust.

But, I have found that, to the contrary, there was a housing supply shortage in the dynamic coastal urban centers which led to a housing price shock in those cities (accelerated by more flexible lending to young households with high incomes) , which led to a migration event as renters were forced away from and existing owners tactically sold out of those cities.  That migration event stressed the housing supply of the main destinations of those migrants which led to a secondary housing price shock in those cities (Phoenix, etc. - "Contagion cities"), which led to a moral panic about lending and building, which caused the migration event to suddenly stop, which left those cities with large inventories of unsold homes.  And, when the entire episode was blamed on excess lending, post-crisis lending policy was tightened to the extreme, creating a post-crisis housing bust from 2009-2012 in credit constrained areas while wealthier areas stabilized.

Put more simply, prices can rise because of high demand or low supply, and where one cause is presumed vs. another, it will tend to lead to diametrically opposite conclusions.  One clue is quantity.  If quantity is rising, that suggests a demand cause.  If quantity is declining, that suggests a supply cause.  This is especially tricky with an inferior good.  Rice prices and quantities could both rise if rice is an inferior good to meats and other foods in the midst of a famine.  Effectively, there was a supply-caused price spike in the Closed Access cities from the late 1990s to 2005, and eventually there was a price spike for houses in less expensive "Contagion" cities where houses were purchased as inferior substitutions for Closed Access homes.  The supply-constrained market eventually led to a bubble market in an inferior good.

In the national data, both types of cities are mashed together, and rising quantities in the cities serving as inferior substitutes combined with the rising prices of the Closed Access cities made it look like a national housing bubble.  The financial crisis developed because these extreme supply-constraint problems were treated as if they had been caused by excess demand, so a credit bust was engineered in an attempt to solve a supply bust.  Rather than a bubble-bust, we had a bust-bust.

All of that is a prologue to reviewing Ozimek's post.

First, he compares the current rate of permitting in each MSA to the rate of permitting before the crisis.  He finds that the cities where supply has recovered the most are the cities where price growth is the highest now and has been strongest since the bubble peak.  He concludes, reasonably, that supply constraints aren't the primary driver of rising prices, because building appears to be strongest where prices are rising the most.

He does several regressions to test the effect of home prices on the recovery in permits, and finds in several models that rising prices either since the bubble peak, since the bust trough, or just in the last year, all correlate with more supply recovery.  And deep price drops from the bubble peak to the trough correlate with lower supply recovery.  This also convincingly suggests that supply constraints aren't the reason for current price appreciation.  And, cities where permits haven't recovered seem to have overbuilt in the bubble (which is why their prices had declined so much in the bust).

He does one final regression where he adds job growth to the mix, and when he does that, price increases since the trough become less significant, and the recovery of permits is explained by job growth and by the depth of the previous price declines.  In other words, even in that regression, we might conclude that permits are strongest where job growth is strong and weakest where cities had overbuilt during the boom.

All of these conclusions are reasonable - obvious, even - if we operate from the conventional presumptions about the boom and bust.  But, the presumptions are doing all the work here.

Ozimek even takes a reasonable precaution when measuring the recovery of permits.  He is trying to measure the recovery to a reasonable level, not to bubble levels.  So, he doesn't compare current permit rates to the peak rates of 2005.  He compares them to the average from 2000 to 2004.  But, this only seems like a reasonable precaution because of the conventional presumptions.

This wasn't a building bubble.  This was a migration event.  The places that were building more homes weren't places that suddenly had spontaneous speculative bubbles.  The peak building years of 2004-2005 mostly reflected an acceleration of long-standing migration patterns.  So, cities that built more in 2004-2005 were generally cities that had always built more.  The credit bust after 2007 didn't undo a bubble.  It undermined longstanding migration patterns.

I have reviewed construction employment numbers at the state level.  After controlling for existing rates of construction, states that built more in 2004-2005 have done well.  They still have construction employment that is above average.  States that had low levels of construction employment before 2004 still have about the same level of construction employment today that they did then.  States that had high levels of construction employment before 2004 have suffered deep cuts in construction employment, and most of that drop in construction employment happened after the credit bust was imposed.

So, one reason that job growth and the depth of the bust are the most significant variables in Ozimek's last regression is that, after 2007, the credit bust killed off job growth and mortgage lending in the cities where growth had previously been the highest before the bubble.  The drop in home prices wasn't due to overbuilding.  It was due to the sharp collapse in mortgage lending.  That is why home price appreciation in most cities before the bust was uniform across the market, but home price collapses after 2007 were most severe at the low end in every city.

We can see this by looking at housing permits as a proportion of population.  I also have included scatterplots showing MSA incomes, home prices, and permits/capita.  And, a couple of graphs comparing home prices in Seattle and Atlanta.

Several notable items are clear here:

• The reason some cities have high home prices is because they have high incomes and low rates of homebuilding.
• That relationship has been strengthening over time.
• One reason cities with expensive homes have shown the most recovery to pre-crisis permit rates is because they never had high permit rates.  Their permit rates during expansions are highly politically constrained.  Ironically, it is cities like LA and San Francisco (the red lines at the bottom) that Ozimek would identify as having recovered the most, even though they still have much lower building rates than the other cities.  And, in fact, the reason their job growth is strong is because families there have gone back to stuffing ever more uncomfortably into a stagnant housing stock in those cities because the credit bust has obstructed the avenue for building homes in the less expensive cities.
• Another reason cities with expensive homes have recovered the most is that the credit bust was highly correlated with incomes.  Cities with higher incomes are less credit constrained.  This is clear in the graphs comparing Seattle and Atlanta.  (One at market prices, and one with prices indexed to 2000 to compare relative changes.)  This is what most cities look like.  Top and bottom moved together during the boom, then after the credit bust, the bottom in every city dropped significantly compared to high tier markets.  So, the low end in Seattle has performed similarly to the high end in Atlanta.  So, the credit bust has caused low priced cities to decline more than high priced cities and the low tier within each city to decline more than the high tier.  That is because it was a credit bust that caused the collapse.
• In the graph of housing permits, notice that there aren't typically surges in permits.  Permits rise up to a typical expansion level for each city and then remain fairly level for the remainder of the expansion.  The difference between cities is much larger than fluctuations over the course of a building expansion than changes within a city.  That is because permitting rates are largely a product of migration patterns.  The reason rates of building were high in Atlanta and Phoenix was because a lot of people move there.  There were a few cities that had building spikes.  Phoenix did have one from 2001 to 2005, but that spike in building was matched household by household by a spike in in-migration because of the Closed Access migration event.  Builders in Phoenix weren't building tens of thousands of spec homes in 2005.  In fact, they were holding lotteries among buyers because they couldn't get lots permitted fast enough.
• If you think about the dominant effect of migration on these building patterns, it would be very difficult for the cities that build the most to overbuild, because for every home's worth of natural local growth, there are one or two households moving into the city.  If there isn't a negative shock to migration, then inventories in the cities that build the most will naturally be worked off the quickest.  The reason these cities might end up with excess inventory is because of local income shocks or migration shocks.  Here, there was a migration shock followed by an income shock.
• Oddly, permits declined in every city at roughly the same time even though clearly there wasn't oversupply in LA or San Francisco.  In fact, the end of the migration event meant that population started to rise again in LA and San Francisco just as housing starts collapsed.  The reason that cities that saw the largest drop in home prices and the weakest labor growth have had the weakest housing permit recovery is that the cities that had the largest collapses were cities that welcomed in-migrants.  The credit bust killed off longstanding migration patterns, which created a housing collapse in those cities and limited job growth because job growth had previously been correlated with rising population.

In a later post, Ozimek noted that the job recovery has been uneven and that places with lower home prices have seen lower job growth since the crisis.  This also can be explained by the credit bust and the decline of migration.  Before the crisis, households were moving away from places where incomes are higher.  This is a perverse pattern, but it did lead to lower job creation in prosperous places and more job creation in less prosperous places.  Instead of fixing that perversion by building more homes in prosperous places, we added a new perversion to it.  We made it more difficult for households to build homes in low cost places.  This has tilted job growth more toward prosperous places, but instead of creating more prosperity, it just intensifies the cost pressure.

This is the problem with having a canonized set of presumptions that are all wrong.  Ozimek did everything right in his analysis.  The presumptions are the issue.  Until the public comes around to the correct presumptions, good analysts will be drawn to all the wrong conclusions about what has happened and what we should do about it.  For starters, for those households who haven't been drummed out of low tier homeownership by foreclosure, we would do wonders for working class balance sheets if we opened up the mortgage window to FICO scores under 760 again so that prices in those markets could recover by 20% or more to where they should be.  And, the borrowing and building that would be triggered by that shift would create several positive developments.  It would be disinflationary, because rent inflation would finally be tamed.  It would also likely boost interest rates.  It's quite amazing how easy it is to get a headache to go away when you stop hitting your head with a mallet.  But it is the presumptions about the cause of this whole mess that will have to change for us to do that.

Housing: Part 325 - Lending and the Housing Market

I don't know if I have shared this graph before.  I think there are some interesting things to see here regarding lending and housing.

Surprisingly, the number of mortgage accounts outstanding continues to decline.  In early 2008, there were 98 million mortgages outstanding.  That dropped to about 81 million in 2013.  Today there are just under 80 million.  Since 2015, the number of owner-occupied homes has increased by about 3 million and the homeownership rate has finally leveled off.  This has happened in spite of lending markets.  That net gain in homeowners consists roughly of 4 million additional households with no mortgage and a decline of 1 million households with mortgages.



Sources: New York Fed Quarterly Report on Household Debt and Credit,
Census (HVS), Fed Financial Accounts of the United States

The average mortgage size has been growing, but home prices were rising more quickly, which has helped home equity levels recover to pre-crisis levels.  Now, home prices and mortgage sizes are rising at about the same rate.  But, since there continues to be a shift to owners with no mortgage at all, average home equity levels continue to rise.

It certainly could be the case that there is a baby boomer effect here, and that there is some growth in new mortgaged ownership, but that it is matched by baby boomers who are making the last payment on old mortgages and moving from mortgaged ownership to unmortgaged ownership.

But, I'm going to step out on a limb here and suggest that this doesn't look like a lending market in a healthy recovery, let alone a lending market that is in need of a macroprudential clamp down.


September 2018 CPI

Still limping along.  Non-shelter core inflation continues to be pacing along at around 1%.  Possibly shelter inflation might be starting to wane.  This continues to suggest that further rate hikes are unnecessary and potentially disruptive, but rising market rates since the Trump election continue to buffer rising policy rates.

Housing: Part 324 - Commercial Real Estate, Dean Baker, etc.

Arnold Kling points to this post by Dean Baker.  The last paragraph of the Baker post gives Baker's basic conclusion:

The basic story is that demand plummeted first and foremost because of the collapse of the housing bubble, along with the collapse of the bubble in non-residential construction that arose as the housing bubble began to deflate. The financial crisis undoubtedly hastened these collapses, but a steep drop in demand was made inevitable by these unsustainable bubbles that had been driving the recovery from the 2001 recession.

He is arguing against Bernanke's recent posts where Bernanke claims the recession was deepened more by the financial panics than by the housing bust.  (I basically agree with Bernanke, and I would say that the panics were largely caused by Fed policy choices in 2006-2008, and the losses were made permanent/justified by the extremely tight lending standards imposed by the post-conservatorship GSEs and CFPB.)

Bernanke points to the post-crisis drop in non-residential investment as evidence of the importance of the financial crisis in creating the deep recession.  Baker counters that the drop in non-residential investment was mostly a drop in non-residential construction, and was simply a part of the same bubble that had infected residential building.

I'm not sure if I have that much new to add here.  The entire thing pivots basically on this comment by Baker: "Again, the collapse of Lehman hastened this decline, but the end of this bubble was inevitable."  Whether the bust was truly inevitable or not is beside the point.  The bust was inevitable because the zeitgeist had deemed it inevitable.  The conclusions are a product of the presumptions.

And, looking at the CEPR paper that forms the basis of Baker's post, we can see the source of the false presumptions.  In the bullet points that summarize the paper, he notes, among other things:

The decline in residential construction during the downturn was mostly just a return to trend levels of construction, along with a predictable reduction due to the overbuilding of the bubble years. Any impact of the financial crisis was very much secondary.

 ….

The bubble and the risks it posed should have been evident to any careful observer. We saw an unprecedented run-up in house prices with no plausible explanation in the fundamentals of the housing market. Rents largely rose in step with inflation, which was inconsistent with house prices being driven by a shortage of housing.

Unfortunately, these assertions are broadly accepted as canon.  Obviously, taking opposition to the overbuilding issue is central to my work.  In the paper, Baker includes figures for residential construction as a % of GDP, which begins at 1980, and for non-residential construction as a % of GDP, which begins at 2002.  Here is a graph of those two measures, dating to 1960.

I agree with Kling that Baker seems to be an independent thinker. But his choice of start dates seem especially useful for magnifying the level of these measures during the "bubble" years.  I don't think he is trying to be misleading.  The bubble is canonized and setting the timeframe to maximize the apparent excess is part of the public hypnosis in support of the false canon.

In addition to the long-term view, there are a couple of points that might be made about these measures, which it is possible that Baker missed.  Within the non-residential category, "mining exploration, shafts, and wells" increased from 0.3% to 0.9% of GDP from 2003 to the end 2008.

Also the residential category includes brokers commissions on real estate transactions.  From 2000 to 2005, that increased from 0.9% to 1.4% of GDP.  If you subtract that from the residential investment measure, the peak level is at about the same % of GDP as the peaks in the 1970s.  Brokers commissions have nothing to do with building.  In fact, they were bloated specifically because of under-building.  They were bloated because of existing homes in coastal California selling for a million dollars.

But, nonetheless, building was strong at the same time prices were rising, which brings us to the second canonized false presumption that Baker references above: the idea that rising prices were unrelated to rising rent.  This is, again, a product of the public hypnosis on this issue.  Even looking nationally, rent inflation had been above non-rent core CPI inflation for the entire period from 1995 to 2008 - far above non-rent inflation for much of that time.  From the end of 1994 to Sept. 2008, non-shelter core CPI averaged 1.8% and shelter CPI averaged 3%.  But, the problem is even worse when you look at the MSA level instead of the national level.  Generally where prices increased, rents had increased.  So, it's more like there were many places with moderate prices and normal rent inflation and places with high prices and rent inflation persistently well above general inflation.  And, those were places that definitely were not over-investing in construction.  At the MSA level rent explains almost everything.  And, on this point, the public hypnosis is striking.  Open coastal urban newspapers or twitter and the topic is high rents in the coastal metropolises.  It isn't as if this is a secret.  But, hypnosis is strong enough to create mental silos on this issue.

This is part of the story on rising prices.  Before the mid-1990s, if rent affordability got worse in a city, it tended to revert to the mean.  But, beginning in the 1990s, the economy became characterized by this new regime, where urbanization has new value, the urban centers that would create that value do not grow, and workers must segregate by skill and income into and out of those cities.  So, now migration patterns exacerbate the rent inflation problem rather than causing rents and incomes to revert to the national mean.  Prices in 2005 reflected this regime shift.

But, the bubble was canonized before this realization was made.  One might argue that rents should still revert to the mean and that bubble prices still reflected over-optimism, even if rents had been rising for a decade.  (That would be wrong, as rent inflation has resumed after the crisis, but at least it would be an argument that addressed the facts.)  Instead, a false reality is invoked, rent inflation is ignored, and discussions of housing market sentiment revolve largely around price expectations, which, by presumption, leads to behavioral explanations.  Again, I don't say this to be harsh regarding Baker.  To treat rent correctly would be a radical, contrarian position.  Until this correction gets made in the zeitgeist, you might as well complain that he references gravity without engaging in an experimental proof.  It's canon, and the canon is wrong.

Regarding prices, here is a graph of various property types.

The thick orange line is non-residential commercial real estate.  The thick blue line is residential commercial real estate (multi-family buildings).  These are from CoStar.

Figure 5 in Baker's CEPR paper, published in September 2018, shows commercial real estate prices from 2002 to 2010 in order to show how strong the bubble was in commercial building.  He writes:

The plunge following the collapse of Lehman is not a surprise. Non-residential construction is largely dependent on bank credit, and when this dried up with the financial crisis, it was inevitable that it would take a serious hit. But the financial crisis was only the proximate cause of the drop in non-residential construction. The bursting of the bubble was inevitable in any case, the only question was the timing and specific events that set it in motion.

I do not disagree about the importance of credit.  This is all about presumptions.  This entire discussion hinges on one word: "inevitable".

A diversified basket of multi-family real estate bought at the peak of the "bubble" at the end of 2006 would have returned 44% of capital gains in addition to rental income over the following 12 years.  In the 9 years since the nadir at the end of 2009, it would have returned 124%.

In the graph above, I have also included the national Case-Shiller home price index (black), price levels from Zillow for the top and bottom of the Atlanta market (gray) and the LA market (red/orange).  Maybe I am confusing matters by including LA.  Many will see the clear signs of a credit-fueled bubble in the low tier prices in LA, but the truth of the matter is too complicated to go into here.  But, these measures tell a more complete story about what happened.

Once we recognize that rising rents are the main difference between LA and Atlanta and that credit at the extensive margin was not an important factor in the boom (which is clear in Atlanta and most other cities where price appreciation was not very different between top and bottom tier homes during the boom), we can see a different story.

Remove "inevitable" from your presumptions.  The consensus around "inevitable" led to acceptance of, even demands for, a negative credit shock in owner-occupier housing markets that continues to this day.  Nothing was inevitable.  Residential housing markets look like they track along with commercial markets for the entire period.  But, they are a chimera.  They contain open markets and closed markets.  Before 2007, prices in most markets, like Atlanta, were benign, and prices were very high in housing constrained markets.  Sentiment and credit access began to turn in a series of trend shifts and events from the end of 2005 to 2008.  Housing starts started to collapse in 2006 and prices eventually fell sharply after mid-2007.  This happened in Atlanta and LA and everywhere in between.  Since intrinsic value remained strong, commercial building, even in residential, remained strong until 2008, and rent inflation across the country spiked in 2006 and 2007 as new single family supply dried up.

Then, we imposed the "inevitable" bust on the owner-occupier housing market.  Instead of looking for ways to stabilize mortgage markets, lending was largely cut off to the bottom half of the market from 2008 on, and we can see the devastating effect if we look within cities, most of which look like Atlanta, where low tier prices took a post-crisis hit to valuations, frequently of 30% or more.  This has caused the market price of low tier homes to drop below the cost of construction, causing new building to dry up in low tier housing markets.  The lack of supply in those markets has been a boon to commercial residential builders, who have access to equity and borrowed capital.  Ample building is happening there, but it can't make up for the tremendous hit that owner-occupied single family homes have taken, and it can't create ample coastal urban supply.  So, the boon to multi-family builders continues for the same reason prices were high in 2005: there aren't enough units, especially where demand is greatest.

The national multi-family market reflects a price level that is not credit constrained, but is supply constrained.  The national home price reflects a price level that is credit constrained, which is a mixture of cities like LA, which is supply constrained, and Atlanta, which is especially credit constrained, and is only supply constrained now because it is credit constrained.

Housing: Part 323: Construction Employment during the crisis

I have dug into employment numbers a bit, and I think there are some interesting things here.

What happened?  A reasonable person might say this: There was overbuilding by the end of 2005.  This required a shift out of construction employment as the housing bubble wound down.  Federal officials underestimated how much the housing correction would bleed into the rest of the economy.  Eventually the collapse of the construction market in the bubble cities metastasized and caused employment and consumption to contract more broadly.

As I have so often found, the truth may be closer to the opposite of that.

Here, I am using state data, and I am using two independent variables to describe each state.  The first variable is the level of construction employment in December 2003 as a percentage of total state employment.



Construction as % of Total Employment: higher vs lower construction states

Here is a graph of construction employment as a percentage of total, over time, for states that had construction employment one standard deviation above or below normal in 2003.  There are several interesting points here.

(1)  States with high construction employment in 2003 are states that typically had high construction employment in the past(in other words, building lots of homes and growing).  And, notice that from the end of 2003 to early 2006, states with less construction employment continued to have flat construction employment but construction employment in the high construction states went higher. This supports the idea that the housing boom was just an acceleration of longstanding patterns of migration.

(2) When the CDO panic hit in the summer of 2007 and the recession officially started in December 2007, construction employment was still near the peak of the boom.  There was no re-sorting out of construction into other forms of employment before the recession.  Then, during the first year of the recession, construction employment did start to drop somewhat in the growing states.  But, it was only after the financial crisis that construction employment saw its steepest decline.  The recession caused the contraction in construction employment.  Construction continues to run below the pre-boom levels in the states that had previously been high construction/high growth states.

1 Year Change in Total Employment, by State

But, interestingly, there was a contraction that preceded the recession in total employment growth in high growth states.  It's just that that contraction was not focused on construction employment.  It was a general contraction.  And it was sharp.  Employment growth in states that had low construction employment continued along at its (low) flat rate of just under 1% annually.

So, there was an employment slowdown in the states that had been building a lot of houses, but it wasn't a slowdown in construction employment - even though housing starts were dropping sharply.

Also, this graph shows that there was an especially wide gap in employment growth in 2004-2006 between high construction and low construction states, but, compared to the 1990s, the gap wasn't wider because the high growth states were growing more.  It was wider because the low growth states were growing less.



Construction employment as % of total: bubble and non-bubble states

The second independent variable I used was the change in construction employment from December 2003 to March 2006 after accounting for the correlation between the pre-existing level of construction employment and rising construction employment that is visible in the first graph.  In other words, the first independent variable is a measure of persistent migration patterns and the second independent variable is a measure of "bubble" activity from 2003-2006.

The pattern is similar during the boom and early crisis.  Construction employment peaked in 2006 and remained relatively high until the recession began, then started to decline, and especially declined after the financial crisis.

But, notice the difference after the crisis.  The "bubble" states - states that had unusual growth of construction employment during the boom - never saw construction employment contract below the level of construction employment in non-bubble states, and then recovered more strongly after the recession, so that now, they are back near the construction levels of 1996-2003.

So, the states that had accommodated persistent in-migration for decades have been permanently hobbled by the housing bust (They continue to have higher construction employment than other states, but not as high as they previously had.), while the states that actually had unusual construction employment growth during the boom continue to have unusual construction employment growth compared to other states.


As with other data, this suggests that we didn't bust a housing bubble.  Instead, there was an acceleration of long-standing migration patterns, and those migration patterns have been hampered by a crippled housing market.

Unemployment rate: high vs. low construction states

There is a similar pattern in the unemployment rate among states.  The unemployment rate was lower in both the long-term growth states and the bubble states until mid-2008, then the unemployment rate in all states rose together through 2009, regardless of their previous construction employment levels.  As shown above, it was then that construction employment really collapsed.  In the bubble states (the states with unusual construction employment growth from 2003-2006, not shown in this chart) the unemployment rate continued to move in line with other states.  But, in the states where there had been long-term high levels of construction employment, the time period where their unemployment rates were especially high was 2010 through 2012.

This last graph is of the unemployment rate for the states that had construction employment one standard deviation above and below average in 2003.  And, here I have added a hypothetical state with zero construction employment, which I think gives an interesting baseline for thinking about construction and non-construction employment before, during, and after the crisis.

The crackdown on lending in 2008 and after, and the consensus view that new construction was problematic were the primary causes of dislocation.  Imagine if construction employment in high growth states had managed to bottom out at even 5.5% of total employment in 2009 and recovered from there.  Or, if it had recovered more quickly, as it had in the 1990s (which wasn't exactly a building boom decade, itself).

I am hoping to get a chance to look more thoroughly at this, but in the meantime, this seemed worth sharing.

Housing: Part 322 - The strange American housing morality play

One of the overwhelming tendencies one finds in the popular literature about the housing market is the nearly universal cynicism about housing consumption and housing finance:

• Everyone buys too much house.
• The real estate lobby has Washington on a leash.
• The GSEs have spent decades lobbying for special treatment and excess lending.
• The Fed is pumping up bubbles.
• We lionize homeownership.
• In the aggregate, homebuying decisions are characterized by speculative thinking.

I could go on and on.  Every book that purports to explain the housing bubble becomes a litany of decades of activities, all meant to get too many homeowners to buy overpriced houses with too much debt.  The rabidity and ubiquity of this treatment of the real estate asset class defines the topic.

The United States does not, on net, subsidize housing.

As with so many issues on this topic, the distance between the consensus and reality is extreme.

Let's look at the subsidies to housing.  For a sense of scale, the BEA estimates total annual rental value of about $2.1 trillion, and net operating surplus (rent after depreciation, expenses, and taxes) of about $1.1 trillion:

There are two biggies.  (Well, one biggie in reality and one in rhetoric):
1) income tax benefits.  This includes untaxed rent, mortgage interest deduction, and untaxed capital gains.  The Treasury estimates that in 2018, these are worth about $230 billion.

2) the GSE subsidy (this is a little cloudier with conservatorship).  When the GSEs were semi-private, it seems that reasonable estimates of their effect on mortgage rates was about 0.25%.  In other words, the implied federal guarantee on their debt led to mortgage rates about 0.25% lower.  Before the bust, they guaranteed about $5 trillion in mortgages.  $5 trillion x 0.25% = $12.5 billion.

Number 1 is much more of a biggie than number 2.  This is why the focus that so many people have on the GSEs baffles me.  As a subsidy to housing, it's a pittance.  I would prefer to get rid of the income tax benefits.  They are regressive and destabilizing.  The GSEs, on the other hand, comported themselves quite well during the bubble, and were a stabilizing factor in the crisis, where they were allowed to be.  As a result of my research, I have become more supportive of the idea that the federal government should provide a credit guarantee on conventional mortgages.  That function is a public good which can only be provided by the government.  The federal agencies should be retained in some form, and the inflationary effect they have on home prices is small.

What about taxes on housing:

There is one biggie:
1) Property tax on residential housing, which is about $250 billion per year, according to the BEA.

On net, these primary factors put negative pressure on housing demand.  Income taxes represent a subsidy of more than 20% of the aggregate net income, property taxes represent a tax of more than 20% of the aggregate net income, and the GSEs amount to a percent or two.

What about other factors?

The realtor lobby wants a lot of housing demand, and they push for maintaining things like the mortgage interest deduction.  But, probably their primary input here is protecting the realtor cartel that charges 6% for realtor services.  High transaction costs clearly pull down the market prices of homes and the demand for housing.

And, what about the 30 year fixed rate mortgage that is supported by the GSE framework?  The 30 year mortgage with a prepayment option puts peculiar risks on lenders, and they require a premium for taking prepayment risk.  That makes mortgages more expensive, which pulls down the market prices of homes and the demand for housing.

There have been other programs related to encouraging home ownership in various ways, but the effect on aggregate demand for shelter or on home prices is marginal.  Certainly not close to the scale of the effects of property taxes and income tax benefits.  Programs meant to increase homeownership can't amount to much.  Consider a very aggressive program that would increase ownership by 5%.  Those households wouldn't necessarily increase their housing consumption by that much, and any pressure they might create in home prices would also be marginal.  So, that program would affect aggregate consumption or prices by 5% x some small percentage representing the marginal new capacity of those buyers to consume more housing.

Nothing else can really come close to the effect of income tax benefits on housing consumption and home prices, and income tax benefits are cancelled out by property taxes.  The only way to rectify this is to argue that property taxes should be ignored.  So, the entire case for claiming that there is some sort of American public mania for housing consumption comes from observer bias.  You have to ignore a very large tax that is imposed specifically on this asset class.  Don't get me wrong.  I think there are a lot of good reasons for having healthy property taxes.  I just don't think you can have them and also claim that real estate is being heavily subsidized relative to other forms of spending.

How do you feel about additional marginal consumption of, say, health care, or education?  Or, for that matter, bananas, or books, or boots?  Compare public expressions about these forms of marginal new consumption to public expressions about marginal new consumption of housing.  At the risk of being a bore, I must say that when I read any history of the housing bubble, it is this universal attitude that strikes me as the fundamental source of irrational public sentiment that caused the crisis.  Once you see it from a different perspective, so that you notice it oozing and dripping rhetorically over every description of the history of American housing, it becomes fairly oppressive.

It's sort of an interesting problem.  For writers, it is a posture that one must take to establish credibility with the audience.  But, starting from that prior, every marginal increase in housing consumption is automatically suspect.  That can only lead to one conclusion.  It should destroy the credibility of the writer, because the conclusion has been predetermined.  For some reason, though, that predetermined conclusion, fundamentally, is the product that the American public wants to consume.  And, the extreme bias this creates is clear.  The national conversation for 20 years has been about what to do about the overconsumption of housing, and real housing consumption has been declining relative to incomes for more than 30 years.

Housing: Part 321 - What about those naive bubble investors?

There is a story from "The Big Short" where a stripper in Las Vegas explains that she has several highly leveraged investment properties.  This is also a response I have heard and that I see frequently when people take umbrage with my assertions about the housing boom.  "Look, this is all very interesting, but I remember what it was like back then.  The janitor at my office had 7 properties.  It was nuts."  This is especially true of Phoenix and Las Vegas.

This a great example of how some basic factual truths can completely turn your conclusions upside down with just some subtle changes in interpretation.

These people existed - in some significant number.  But, let's think about this.  The housing stock is a big, slow-moving beast.  It doesn't change by more than a few percentage points a year, at most, in a fast growing city.  If you know, say, 4 people that have purchased 5 homes as speculators in the past couple of years, then you should also know about 20 people who have sold homes.  Every home has one owner and every transaction has a buyer and a seller.  So, if you say that you suddenly knew 4 people that each owned 5 speculative properties, then that is basically the same statement as saying you knew 20 people that had sold out of the real estate market.  Two sides of the same coin.

Outside of extreme circumstances, if you know four people that are deep into property speculation and you don't know 20 people who have sold out of properties, then you have stumbled into a deep case of observer's bias.

It happens that in 2006, there were extreme circumstances.  In 2005, annual population growth in Phoenix was over 3% and it was over 4% in Las Vegas.  Between 2005 and 2009, it fell to less than 1% in both cities - a rate of growth slower than either city had seen in decades.  This was a combination of more people moving away and fewer people moving in.  Builders were actually pretty sensitive to this shift, and permits for new construction fell sharply along with population growth.  But, in addition to those migration shifts, tens of thousands of potential new home buyers had entered into contracts to build new homes, and upon seeing the turn in the market, they reneged.  They let the builders keep their small escrow deposits, and they left those homes with the builders.  There was a massive shadow inventory of homes left to builders long before owners were defaulting and leaving homes with the banks.

So, if you knew 4 people who owned 5 homes, you came upon your observer's bias honestly.  Those 20 housing shorts weren't in your frame of vision.  They had either left town or had never moved to town. When you were sitting at the barber shop listening to the guy talking about the seven condos he was flipping, the seven housing shorts that were an integral part of that story were getting their hair cut in LA and Chicago.

This is one reason why migration is such an important corrective to our conception of what happened.

Note that the truth is even there in the conventional telling of the story.  In the scene in The Big Short, when the stripper tells Mark Baum that she has six leveraged properties, he warns her, "Well, prices have leveled off, though."  The trigger for expanding investor share was the negative change in sentiment among homeowners, and the leveling off of prices in the Closed Access cities which reduced the rate of tactical Closed Access sellers.  That scene immediately cuts from the strip club to him making a phone call and saying, "Hey, there's a bubble."  What he had actually just seen  was evidence of the bust, not a bubble.

Were many of those new speculators naïve?  Were they late to the party?  Could we bemoan their lack of judgment?  Sure.  Can we blame them for high prices?  No.  Investor buying was somewhat elevated in 2005.  Maybe it could have added a few percentage points to the average home price at the peak.  Investor buying share was highest in 2006-2007 when prices were stable - and investor buying was, by then, a stabilizing influence on prices.  Investor share declined in 2008 and 2009, and during that period, investor defaults were probably also destabilizing, because investors are quicker to default in declining markets than homeowners are.  Then, investor activity settled in at levels in 2010 that were still above 2004 levels, again providing support in markets where homeowners were now credit constrained.

It's possible to dissect the different types of investors and speculators and to point out where there were more reckless or even fraudulent speculators, which appears mostly to involve investors who claimed to be homeowners, which would cause lenders to underestimate their tendency to default on high LTV loans during a crash.  And it is possible to point to some brief points in the timeline where those investors may have been a bullish force in markets that were rising already.  But, their activity just can't be pushed back far enough in the timeline of events to attribute much of the aggregate national value of real estate to them.

Through the main characters in The Big Short, we can see how easily this can lead public sentiment astray.  There were many people who had been calling the market a bubble for years by 2006.  They identified themselves as people who new the value of things and who could be more wise than the average investor about avoiding poor investments.  That's a great identity to have, and for the main characters in The Big Short, it appears to be plausibly accurate.  But, it is just a short step from that to a posture of attribution error - I do things because of the constraints I face, but other people do things because they are greedy or reckless.  Multiply that by a few million people who sit down and watch The Big Short, and think, subconsciously, "I know value.  I identify with these characters.  We all recognize the greed and recklessness of all those background characters, which created the bubble."

That sentiment was part of a positive feedback loop that led to widespread blame on speculating and lending, and that blame only strengthened with each year of rising prices.  So, when migration stopped and these investors and speculators became a noticeable part of the market, it didn't look like a sentiment shift.  It looked like more of the same.  More of those other people acting on greed and recklessness.  And, the tricky part is, many of them were acting on greed and recklessness.  But, that doesn't change the fact that they didn't cause the bubble and that, in reality, they were a red flag signaling a coming crisis.

Instead of clamping down on credit and money growth, we should have been aiming for stability.  We should have been adding nominal support for these markets that were about to be hit with a migration whiplash.  What about moral hazard, you ask?  I suppose that if we had done that, some of this "dumb money" would have been somewhat better off (although, even a moderately accommodative credit market and monetary policy at the time would not have been likely to reignite the migration event.  Las Vegas and Phoenix would have likely still seen some price retraction.)  But, there is no benefit to punishing the "dumb money".  "Dumb money" didn't cause the bubble.  The bubble drew in "dumb money".  Hurting those late-cycle speculators did nothing to prevent a future bubble.

It seems to most people like it would, because those late speculators just seem like one more fish in a school that includes Alt-A homeowners in San Francisco in 2004, Fannie and Freddie borrowers in 2002, and new young first-time buyers in 1999.  Moral hazard is not why the median home in Los Angeles was selling for over $600,000 in 2006, though.  In fact, it is the opposite.  Prices in LA were that high because anyone who wants to build some housing units must first spend the better part of a decade addressing every single possible objection to building housing units.

The Wall Street Journal gives my work a shout out.

Holman Jenkins at the Wall Street Journal has noticed my work.    He does a good job in the first couple of paragraphs of laying out the basics of the work and tying it into Scott Sumner and the market monetarists' point of view on the Fed and the crisis.

Looks like word is starting to get out.

Housing: Part 320- Debt Growth and Home Price Appreciation

When I was thinking about the previous housing post, I decided to revisit some basic data on debt outstanding to get a sense of the relationship between debt and home prices.  It appears that, as with many measures pertaining to this subject, the story the numbers tell flips upside down, depending on if you look at it from a national level or from a more local level.

Here I am using the New York Fed Quarterly Report on Household Debt and Credit (Total Debt Balance Per Capita By State, Chart 20) for the debt measure, and the All Transactions House Price Index from the FHFA for the home price measure for various states.



www.idiosyncraticwhisk.blogspot,com   2018

In this graph of national measures, I have also added the national S&P/Case-Shiller home price index.  Since the housing boom was largely facilitating the movement of Americans away from expensive cities, the S&P/Case-Shiller index of all existing homes rose more than indexes based on sales of homes, especially during the boom.  The S&P/Case-Shiller measure moves more in line with rising per-capita debt levels until 2006.  It is probably the case that the all-transactions measure understates changing home values, because Federal Reserve Flow of Funds data during the boom don't point to rising leverage, suggesting that prices and debt were rising in parallel.



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In any case, using the FHFA average price measure, it appears that, at the national level, before 2004, debt was rising faster than the prices of homes for sale.  Then, after 2005, debt continued to rise, even though prices levelled off.  This appears to support the idea that rising debt fueled rising prices and also that rising price, then, led to more rising debt, in the classic positive feedback of a bubble.

The New York Fed provides debt data on several states, and comparing per capita debt among states seems to confirm this story.  States where per capita debt was the highest in 2008 were "bubble" states - California, Nevada, Arizona, New Jersey, Florida.

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But, what happens if we compare debt levels to home prices?

For the following graphs, I have indexed both home prices and debt levels to 1 in January 1999 to compare relative changes over time.  In this next graph, in each state, I index the ratio of per capita debt / average home price to 1 in January 1999.  On this measure, the relative order of the states is flipped upside down from the basic measure of debt-per-capita.  Here, which is a broad estimate of changing leverage, it is the non-bubble states where leverage increased during the boom - Illinois, Michigan, Ohio.  It's only after prices fall in the bubble states that debt/price levels rise.  In fact, in the bubble states, debt/price levels were slightly declining during the boom.

Research has shown that, in the aggregate, homeowners harvest about a quarter of new home equity gains.  Comparing the change in home prices over time to the change in debt, it appears that this data reflects that tendency.  The next graph is a scatterplot comparing the change in debt to the change in home prices in each of these states over various periods of time.  In each case, for each percentage point increase in home prices in a given state, debt rises by between 0.2% and 0.3%.



www.idiosyncraticwhisk.blogspot,com   2018

Over time, we should expect the relationship between debt and price to be close to 1:1.  This is not because of equity extraction, but simply that if leverage levels remain fairly stable over time, then if, over a long period of time, property values double, we should generally expect debt outstanding to double too.

What's interesting is that leverage over the 1999-2005 time period was relatively stable.  But, what we can see here is that, apparently, the stable level of national leverage was really a mixture of places where prices were relatively stable but leverage was rising; and places where prices were rising and leverage was declining.

A hypothetical state with no change in home prices would have expected debt per capita to rise by about 50% from 1999 to 2005 and about 20% from 2005 to 2011, for a total of about 70% over the total period.

(An aside: Remember back to the first graph.  It could be that the all-transactions measure of home prices was understated by about 20% in 2005, and reconverged with other price measures by 2011.  In that case, if we could use other price measures, the 1999-2005 plot (blue) would move right by about 20% and the 2005-2011 plot (red) would move left about 20%.  This would pull their y-axis intercepts closer together.)

So, according to this measure, if there was a debt bubble, it was concentrated in the places with the least price appreciation.  The subtle issue here is that there never would have been a moral panic in favor of watching home prices drop by 20% or 30% based on higher leverage in states with stable home prices.  The early rise in foreclosures in 2007 did emanate from Michigan and Ohio.  But, this was related to local economic problems, and, in fact, debt growth in those states from 2005-2007 was quite a bit lower than the US average.  It was speculation in places like Phoenix that led to complacency about "disciplining" the housing market.  In fact, if the focus had been more on working class households losing their homes in the rust belt in 2007, maybe public sentiment would have been more counter-cyclical.  Maybe that would have fed a more typical populist response in favor of inflation.

This lines up with a separate analysis I have done regarding price and rent.  Across metro areas, changing prices from the 1990s to 2005 correlate pretty strongly with changing rents.  And, if you assume that home prices have a moderate sensitivity to real long term interest rates, then interest rates basically explain the change in home prices across the country and rent inflation explains price changes in local hot spots.  Using such a model, prices in 2005 are not out of line.  They reflect interest rates and rent inflation.  In order to make prices in 2005 look high, in the aggregate, you must assume that home prices are not sensitive to real long term interest rates.  The relationship between rent inflation and price remains, regardless of the sensitivity to interest rates.  And, since rent inflation has little effect on home prices in Texas and Ohio, then, interest rate sensitivity is more important to prices in low priced places than it is in high priced places.

In other words, given the undeniable relationship between rent inflation and price inflation, in order to believe prices were too high in 2005, you must believe that it was places like Texas and Ohio where prices were especially too high, not California and Massachusetts.  And, similarly, as shown above, leverage rose more where prices were more moderate.  It appears that if one is to believe that debt was the driving force in the housing market, that would need to be squared with the fact that prices didn't seem to be highly sensitive to changing debt levels during the boom, and, as researchers like Mian and Sufi have pointed out, some of the effect is from causation in the other direction, where rising prices lead to cash out refinancing.

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Here, it may be worthwhile to look at the 2005-2011 period more closely.  There was still an expansion of debt from 2005-2007.  Here, we can see that there was a large difference between states during that period that was unrelated to the concurrent change in home prices (the blue dots in this graph).  But, in a plot of the 2005-2007 change in debt against the 1999-2005 change in price (red dots), the correlation is quite strong, and similar to other periods.

During that time, there was a combination of two factors - the harvesting of equity, as noted by Mian and Sufi; and the slow tendency toward an equilibrium leverage level that I mentioned above.  Surely, over long periods of time, we should expect debt levels to rise at a similar rate as price levels as new mortgaged buyers replace older owners.  In other words, even after prices stopped rising, we should expect debt to continue to rise in the places where prices are the highest as new leveraged owners enter the market and as existing owners harvest home equity.  In any natural long term scenario, this should continue over time until the regression line approaches a slope of 1.

Then, from 2007-2011, prices and debt both sharply moved into negative territory and the relationship steepened.  This was the period dominated by foreclosures, short sales, etc.

Looking back at the previous graph, for the entire period from 1999-2017, the relationship remains fairly stable.  If prices have gone up an additional percentage point over that long period of time in a given state, per capita debt has only risen by about 0.2%.  This shouldn't be the case.  That should tend toward 1:1.

The lack of a strong relationship is clear if we look at each individual state, over time.  In each of the following graphs, the black line is the US aggregate number, and the colored lines are individual states.  I have categorized them, roughly, by the type of market, although the "Closed Access" states are really a mixture of Closed Access metros, Contagion, and Rust Belt areas.  In each graph, I have clearly marked the 4th Quarter of 2003 and 2005, to get a sense of where each state was at those points in time.



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Remember back to the first graph, also.  These graphs are based on the all-transactions home price measure, which may be somewhat understated from 2003-2007, so the aggregate US measure is probably the useful comparison to use as an estimate of how leveraged households were becoming in each state, and with a more comprehensive measure, it may have moved more in line with the 45 degree line until 2006 (stable leverage).

By 2003, leverage (relative to 1999) was high in Michigan and Ohio (where home prices were especially low) and was low in Texas (where prices were near the national average).  During that period, it appears that price and debt were inversely related, if anything, and where debt was high, it was likely the result of households in economically challenged locations using home equity as a financial safety net.  Note also that Nevada and Arizona had roughly moved with the national average in terms of both debt and price over that time.



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From 2003 to 2005, prices accelerated in all states.  In Ohio and Michigan, debt growth since 1999 retracted back toward the national average and home prices didn't rise as sharply as in other places.  In Texas, both prices and debt levels remained relatively low.

During this period, debt levels did rise more quickly than average in Nevada and California.  In other states, debt rose at a rate similar to the national average.  There is no systematic difference here, regarding debt, between the Closed Access and Contagion states and the other states.  In general, the "bubble" states didn't move up during this period, or even move diagonally along the 45 degree line.  They moved horizontally to the right.  Valuations changed in those states, but there is little sign of systematic differences in debt levels.

After that, leveling off and declining prices dominate the behavior, so where prices had previously risen more, debt continued to rise more as prices stabilized, then where prices declined more, debt declined more along with them.  This creates a pattern of concentric circles around the national average, especially in the Contagion states.  It is the lagging nature of debt growth that creates that counterclockwise shape.  And, after all of that, generally, states that had higher appreciation from 1999 to 2005 have had prices rebound so that they have more price appreciation today than the US average (with the exception of Nevada) and they have less debt than the US average (with the exception of New York).

The only other states with per capita debt growth higher than the national average from 1999-2017, besides New York, are Texas and Pennsylvania.  Those are also the two states who had the smallest price shocks after 2005.

There is little evidence here that debt was an important causal factor in systematic differences between states.  There is some evidence of price as a causal factor in the differences in debt growth between states.  I would suggest one other factor that seems important here, and that is property taxes.  There seems to be a relationship between higher property taxes and less volatile housing markets, mostly because higher property taxes moderate prices when they are rising.

August 2018 CPI

CPI of all items less food, energy, and shelter for the previous 6 months (annualized) is about 0.6%.  I suspect that the year-over-year measure will fall back to below 1% over the next 6 months, but rent inflation will keep core CPI near the 2% target.

This increases my confidence that the remaining rate hikes in 2018 will be contractionary.  Forward rate markets seem little affected, though.