Tuesday, September 25, 2018

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.

Monday, September 24, 2018

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.

Thursday, September 20, 2018

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 na├»ve?  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.

Tuesday, September 18, 2018

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.

Friday, September 14, 2018

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.


www.idiosyncraticwhisk.blogspot,com   2018
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.

www.idiosyncraticwhisk.blogspot,com   2018
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.
www.idiosyncraticwhisk.blogspot,com   2018

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.


www.idiosyncraticwhisk.blogspot,com   2018
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.


www.idiosyncraticwhisk.blogspot,com   2018
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.

Thursday, September 13, 2018

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.

Wednesday, September 12, 2018

Housing: Part 319 - "Talk of a Coming Decline"

Here is a Wall Street Journal article from February 2002, headlined, "Housing Market's Sustained Growth Prompts Talk of a Coming Decline" (HT: Nick Timiraos)

It's a good example of how strong the idea of a predetermined bubble/bust was.  Even by 2002, this was becoming the canon, presumptively.  By the time the bust came, there was a broad exasperation with the supposed bubble that had refused to bust, in spite of years of insistence that it must.

Here is a graph from OECD data of home price/income measures for several countries.  The difference between the US and the other countries is that when we became enamored with a communal case of attribution error, we were singularly capable of imposing a crisis on ourselves in order to play out the national narrative we had constructed.  And, as we imposed that narrative on our housing market, the attribution error that led us to create the crisis also led us to blame the scapegoats that attribution error had created.  So, to this day, practically any article from any point of view about what happened during the crisis will begin with the presumption that lenders and speculators did this to us by creating a bubble that inevitably would crash.

There are a handful of countries that have had stable home prices - Germany, Switzerland, Japan, etc.  But the countries most like the US, in a number of ways (higher income growth, trade deficits, lower manufacturing employment, etc.), are the countries shown above.