Yield Curve Watch

It looks like the market expectation is that this is the cyclical high point for the Fed Funds Rate.  It will be interesting to see if the FOMC insists on any more hikes.

In the meantime, the yield curve has become quite inverted.  Here is a chart of Eurodollar futures, which I like because it has a longer duration than Fed Funds futures.  The higher line is the yield curve on November 8, at the high point.  Even then, it was slightly inverted.  But, since then, even though near-term Fed Funds expectations have fallen, the yield curve in the 2-3 year range has fallen more.

Here, you can see how, at these low rates, there is a natural upward slope to the yield curve because the zero lower bound creates asymmetry in the expected yields on longer durations.  If you are using 10 year treasuries or some other longer term yield to estimate the yield curve, then you are getting a false signal.

I would say that, at this point, barring an unlikely additional bump in long term interest rates, the question is only how hard the landing will be, and that depends on how quickly the Fed reverses course.  It would be prudent if at the January meeting they pulled back 25 or 50 basis points, but that doesn't appear to even be in the set of potential options.  That would be the only chance at getting the "normalization" to 5%+ that I hear people talking about in long term interest rates.

It seems like the prudent position to take here is to maintain defensive positions until interest rates head back toward zero, and be ready to transition to equity at some point after the Fed starts to chase the natural rate down.  There could be a lot of noise between now and then, but it seems likely that in a year or so, equities will be available at prices near or below today's level and Treasury yields will be lower. (These are poorly informed opinions.  Do not use this blog for investment advice, etc. etc.)

Housing: Part 338 - Price/Rent ratios

One of the key ideas that fuels conventional wisdom about the financial crisis and the housing boom is that Price/Rent ratios (or, relatedly real home prices) shot way up outside the norm during the boom.  This seemed to be proof that credit markets were fueling an unsustainable price boom.

One of the key discoveries I made was that, oddly, even though rent is in the denominator of price/rent, it has such a strong effect on price that when rents rise, the price/rent ratio rises even more, and likewise, real home prices would rise even more.

idiosyncraticwhisk.com   2018   (income on log scale)

I think I have posted some version of this graph before.  But, before, I have just shown r-squared values.  This version of the graph shows 1991, 2007, and 2018.  And, in addition to the r-squared values, I looked at the p-values.  I was surprised at how small the p-values are.  And, these are not weighted by MSA size, which I suspect would lead to even higher r-squared and lower p-values, because very large MSAs populate the far end of the regression.

The p-values are:

1991: .162 (not significant)
2007: 5.5 x 10^-16(nearly zero)
2018: 2.5 x 10^-35 (nearly zero)

And confidence levels are pretty tight.  The coefficient, at the 95% confidence level is (these are on a natural log scale, so this is the expected change in Price/Rent for each doubling of rent):

1991: -0.7 to 2.0
2007: 6.5 to 10.4
2018:  5.6 to 7.3

Interestingly, if I regress Price/Rent against the median income of each MSA, or against the median price, the relationship is very strong for every year.  I have written previously about how, within MSAs, there is a strong systematic relationship between Price/Rent and all three measures (rent, price, and income).  Within MSAs, each doubling in price is associated with a Price/Rent increase of about 3.  Between MSAs, each doubling of price is associated with an increase of 4 1/2 to 6 1/2.  Possibly, similar influences are at work, and the steeper relationship between MSAs is created because between MSAs, there could be an added systematic factor - expected rent inflation.

For each doubling of MSA median income, the 95% confidence range of the coefficient for Price/Rent is:

1991: 5.4 to 8.8
2007: 9.3 to 14.4
2018:  6.9 to 9.8

idiosyncraticwhisk.com   2018   (income on log scale)

Those coefficients are huge.  The median US Price/Rent in those years was 10.7, 14.7, and 12.3.  So, doubling the median MSA income is associated with a change in Price/Rent that is nearly as high as the national median Price/Rent.  A log-linear relationship would mean that the median home price in a city with a median household income of about $20,000 would be $0.  Actually, look around some cities today, like Cleveland, and it isn't too far off that.

idiosyncraticwhisk.com   2018   (income on log scale)

So, there has always been a strong relationship between income and Price/Rent both within and between MSAs, probably for similar reasons, such as that higher priced homes make better tax shelters, are more likely to be owner-occupied, have less credit constrained buyers, etc.  Incidentally, this is one reason why it has been really bad to block households from mortgage access because of low incomes, etc.  Homes in low-income neighborhoods are cheap.  It's the rare asset class where investors of lesser means have a natural advantage for getting higher yields.

But, the most interesting thing about this is the difference between the income effect and the rent effect.  I have concentrated previously on how during the boom (and since) rent has become more and more a factor in home prices at the MSA level, not less important.  So that rising Price/Rent levels were not actually a good signal of a bubble.

But, here, we can see that the reason that rent did become a more important signal was because rent and expected increases in rent, started to correlate with income, because of the Closed Access problem.  So, yes, rent has become increasingly important, but here, we can confirm that rent has become increasingly important only as a side effect of MSA income becoming more important and becoming rationed through rent.

Housing: Part 337 - Shelter inflation

This isn't anything earth shattering, but as I was updating this month's CPI numbers, I realized that I had never attempted to quantify the portion of shelter inflation that has been directly attributable to the five "official" Closed Access cities.

The first graph here is just a comparison of various annual inflation rates:

• Grey line: Core CPI excluding Shelther
• Black line: Core CPI
• Green line: Non-Closed Access Shelter Inflation
• Blue line: US Shelter Inflation
• Red line: Closed Access Shelter Inflation

The main point to gather here is that, except for the foreclosure crisis, for the past 20 years or so, Closed Access rent inflation is pretty consistently in the 4% to 5% range.  During the housing boom, homes needed to be built in other locations, and the pressure pushing households into those homes from the Closed Access cities was continued demand for Closed Access homes.  That kept Closed Access rent inflation high, and the housing boom was facilitating the movement out of the Closed Access cities to further accommodate that demand.

As I have pointed out before, the top of the "bubble", in 2005, was the only point in the past 20 years where both shelter inflation and non-shelter inflation were both at approximately the 2% inflation target.  That was actually the closest we have been to a neutral monetary policy and residential investment level both at the same time.  As shown here, the decline in rent inflation at that point was entirely from non-Closed Access areas.  Then, the Fed raised rates to cut down residential investment, and non-Closed Access rent inflation moved back up.

During the recovery, the limit to building has been due to mortgage suppression, so it is nationwide, so rent inflation has been high everywhere - nearly as high in non-Closed Access areas as in Closed Access.

The next graph is a stacked graph.  Looking at the first graph, the gray and black lines are the same - core CPI without shelter and with shelter.  This shows how much of the gap is caused by non-Closed Access rent (gray to green) and how much is due to Closed Access rent (green to black).  The last graph is the three measures stacked again, but in reverse order.  First, the portion of US core CPI inflation that is due to Closed Access rent (red), then the portion due to non-Closed Access rent (red to green), then the portion caused by all other core inflation (green to black).

PS: One oddity is that, for non-shelter core inflation, the recession and immediate post-recession years are the only time that the measure was persistently near the target.

PPS: To clarify the stacked graphs, if non-shelter core inflation is 2% and shelter inflation is also 2%, then shelter inflation is shown as having no effect on core inflation.  The graph is showing how much of the gap between non-shelter core inflation and total core inflation is due to shelter inflation.

November 2018 CPI

Things continue to move sideways, not providing a strong new signal in either direction.  The next two months will be interesting, because core CPI excluding shelter last December and January totaled about 0.6% (not annualized).  Unless there is a similar statistical jump this year, by the end of January, core CPI will be back under 2% and core CPI excluding shelter will be back down close to 1.0%.  Potentially that could affect sentiment about future rate hikes.

For now, core CPI is 2.2%, Shelter CPI is 3.2%, and core CPI excluding Shelter is 1.5%.

Housing: Part 336 - Incomes and the Housing Market

Long-time readers have probably seen some version of this a number of times, but I have been poking around in the awesome Zillow data, and I don't think I have quite done this before.  I have posted individual cities before, but here, I have run regressions of MSA income against rent, prices, and various combinations of these measures.  I am trying to get a systematic time series representation of the importance of income on the housing market.  Here I have used the largest 64 MSAs.

In cross-sectional regressions against MSA median household income, from the 1990s to 2005, income became a much stronger predictor of both MSA median rents and MSA median Price/Rent.  It remains as strong a predictor today as it was in 2005.

Part of what has happened is that income has become a more important factor in MSA housing markets, and part of what has happened is that variance in incomes among MSAs has increased over time.

In the following graphs, the blue line is the US median.  The red line is the expected level for a city with median household income 1 standard deviation above the US median.  The green line is the expected level for a city with median household income 1 standard deviation below the US median.

There is a graph showing rents over time, price/income over time, and mortgage affordability over time.  This isn't news to any readers here, but:

1) The bubble wasn't driven by low-income markets.  Mortgage affordability was steady in low-income cities from 1995 to 2005 while it shot up nearly 50% in high income cities.

2) Whatever is causing housing starts to top out now, it sure as heck isn't high mortgage rates.  Mortgage affordability in low-income cities is well below any pre-crisis level.



The thing about low mortgage rates is that a low interest environment actually has some redistributive qualities.  Think of the housing market.  Home prices are somewhat sensitive to long term real interest rates.  So, when rates are low, people with wealth must pony up larger sums to purchase a home.  But borrowers shouldn't really care so much about the price.  If they can borrow cheaply, their liabilities and assets get matched up, and they can take out a mortgage with low payments and start to accumulate equity.  (Obviously, buyers must be careful about purchasing homes in low rate environments if they may need to sell the home soon when rates are higher, etc.)  But, this redistribution can't really happen if mortgage rates are low because there are obstacles to lending that correlate with socioeconomic status.

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Yield Curve Update

I have written previously about the yield curve.  It appears to me that as interest rates get lower, there is an option value embedded in long term rates because of the zero lower bound.  That means that it is harder for the curve to invert at lower rates.

I suspect this comes from my "Upside down CAPM" way of thinking.  There is a relatively stable expected return on at-risk assets like corporate equity, and fixed income is a way to trade off some of those expected returns in exchange for cash flow certainty.  So, a real 10 year yield of 1% is really a payment of about 6% subtracted from the expected real yield on corporate equities of 7%.  Low real rates are a sign of risk aversion.  They are not stimulative.  It seems that others view them as stimulative.  They are wrong.  And, this gives them a false signal about the yield curve.  It makes it look like an inverted yield curve is less dangerous at lower interest rates, because the low rates are seen as stimulative.  But, an inverted curve at low rates is actually more dangerous, not less dangerous.

Here is a graph of the yield curve slope, my adjusted slope, and forward changes in the unemployment rate.

We have been treading right along the edge of "adjusted" inversion since 2016.  It seems to me that at this point in the recovery, the long term interest rate is a simple and important signal.  If the Fed can keep the yield curve spread between 0% and 1% (or, if my claim that an adjustment is necessary is accurate, then the spread now should be between about 0.75% and 1.75%), then that seems like a great first step in thinking about monetary policy through an interest rate lens.

My main concern is that if my adjustment is accurate, a positive yield curve of 0.5% or so is actually equivalent to an inversion, and even people on the lookout for an inversion won't notice it until it is too late.  The expected December rate hike puts us into inversion territory, in that case.  I have been early to this worry, and was surprised by rising long term interest rates, so you may want to take this with a grain of salt.  But, it seems like something worth watching.  If the unadjusted yield curve inverts, it seems unlikely that the Fed will accommodate nearly quickly or strongly enough.

Housing: Part 335 - Homebuyers are hedgers, not speculators

I did get a chance to look at the paper I wrote about in yesterday's post.  They do present reasons for why credit conditions were looser in 2005 than the raw SLOOS survey numbers would suggest, and they have other measures of credit markets that suggest a more symmetrical measure of credit conditions before and after the bubble and bust.  They do not show any regressions that I see that only include the boom time, which is the source of my dis-satisfaction.  But, there are probably some correlations in the paper that would still be statistically significant in the pre-2006 data.

So, I stand by my initial reaction, though I suspect the authors would have some responses that would require more detailed critiques than I offered in the post.

In any case, upon looking at the paper, I realized that there was another chart that offers some food for thought.  This is from the University of Michigan's Survey of Consumers.

One of my reactions to papers like this is that there is an extreme case of publication bias on these issues.  At some point, if there are 1,000 papers published on the question of whether credit was an important causal factor in changing home prices and 5 papers on whether supply constraints were, then the consensus is destined to settle on a conclusion that credit was the important causal element.  It's sort of a meta-level exercise in p-hacking.

Another area where the rhetorical presumptions lead to the conclusions is the choice of questions to ask in consumer surveys.  You can choose to survey home buyers or home sellers.  And you can choose to ask them whether they think rents are going up or whether they think prices are going up.  Without changing the actual beliefs of the respondents, the choice of questions and the set of responders can create a deterministic conclusion.

For instance, home buyers may bid prices up because they are seeking a rent hedge, but if surveyors only ask them if they think prices are going up, not if rents are going up, then those buyers will appear to be speculators rather than hedgers.

And, that is what is interesting about this U of M data.  It includes a question about expected home prices, and expected rising prices are never an important factor for potential buyers (the red line).  Furthermore, there is no relationship between whether home prices are seen as low (green line) and whether prices are expected to rise.  If anything, when potential buyers think it is a buyer's market because prices are low, they tend to expect prices to remain low.

In other words, potential buyers are clearly hedgers, not speculators.  They don't see low prices as an opportunity to capture capital gains.  They see low prices as an alternative to renting.  So many analyses of the housing market ignore rental value and treat the market as a purely cyclical and speculative activity.  Highly respected analysts and economists sometimes talk about housing as if the value of the investment is entirely a product of capital gains rather than rental income value.  In reality, in most locations, in real terms, rental income value is the overwhelming source of value for homeowners.  Actual households seem to understand that, even if only subconsciously.

Housing: Part 334 - Credit supply and the housing bubble.

Tyler Cowen links to a new paper today, with this note: "Credit conditions really did matter for the housing bubble." (HT: Tyler)

I haven't looked at the paper yet, but I have looked at a set of slides, here.

My basic point of view here is:

1) Of course credit conditions matter.  This is standard finance.  Credit provides liquidity, and less liquid securities sell at a discount.  But, this is an asymmetric relationship in standard finance.  Liquidity doesn't lead to over-priced assets.  It just leads to asset prices that reflect the market rate of return with a lower liquidity discount.  One reason that homes are a good investment for many households is that liquidity is very constrained.  Transactions costs are high and they must be purchased as a whole, not piecemeal.  Returns on homeownership are highly correlated with the length of tenure, where these costs can be amortized over longer periods.  Developments that reduce the costs associated with those problems should increase home prices.

2) The outcome of the housing bubble and bust matches standard financial expectations.  Prices during the boom were as sensitive to long term real interest rates as we should expect them to be, highly sensitive to local rent inflation trends that were the result of a supply shortage, and sensitive to credit supply where the supply shortage had pushed prices high enough to create obstacles to conventional funding.  Credit supply is an ingredient here, but it is secondary to supply constraints.

3) The problem with analysis of the housing bubble and the financial crisis is that the notion that there was an unsustainable bubble that was destined to collapse was canonized before it was established empirically.  So, evidence that explains the bust is taken as evidence that explains the boom, and vice versa.  But, if the bust was not inevitable, then correlations during the bust don't tell us anything about the boom.  This goes back to points 1 and 2.  The bust is certainly explained largely by a negative credit shock, but this is an asymmetric relationship.  From that, it doesn't necessarily follow that a boom had been created by a positive credit shock.

If I get a chance to see the full paper, I will be happy to retract my comments here.  But, these slides associated with the paper do not appear to avoid these issues.

Here is a graph of credit standards from the slides.

Not only is the relationship between liquidity and yields or prices asymmetrical, but in this particular case, the scale of the negative shock was far greater than the scale of any other shift in lending standards.  The relationship between credit standards and home prices from 2006-2010 will dominate any statistical analysis here.

So, given my priors, what I would like to see from an analysis like this is the relationship for the period up to 2005 or 2006 and the relationship for the period after 2006 or 2007.

Here is a table of results from the slides.  They run regressions from 1991-2017, 2005-2013, and 2007-2017.  Elsewhere, they use 2000-2010.  This is unsatisfying.  There is a clear trend break to a negative shock that starts in 2006.  There is no analysis of the relationship during the boom that doesn't include that period.  For someone who looks at this with the standard presumption that the boom and bust are necessarily related, this might seem like more evidence that a bubble was largely due to loose credit.  I would like to see the regression from 1991-2005.

Here is a chart comparing the one year change in real home prices to the trend in credit standards.  The asymmetrical relationship is clear here.  I have not precisely replicated the regressions shown in the slide.  I have simply done regressions of the two measures shown in my chart.  For the periods analyzed in the slide, I find similar, strong correlations as the authors do over the periods they use.  For the period from 1991-2005, I find no correlation.

When I see the paper, I will update regarding whether this is addressed there.  In the meantime, this seems like another paper that found that collapsing credit markets were highly correlated with the housing bust and concluded that loose credit caused the boom...which is a shame, because the conclusion that does clearly follow from this data - that a negative credit shock led to a housing bust and a financial crisis - is the conclusion that should be motivating current public policy and retrospectives about the crisis.

Housing: Part 333 - David Beckworth interviews Robert Kaplan

David Beckworth recently interviewed Robert Kaplan from the Dallas Federal Reserve Bank (transcript).  They discussed many interesting things regarding monetary policy.  There were a couple of items that I thought might be interesting to get into here.

Here is one spot:

Robert Kaplan: ...The nominal GDP targeting has a lot of appeal in that it takes into account inflation. It takes into account growth. The other thing is we are a very highly leveraged country. It's nominal GDP that services our debt.
David Beckworth: That's right.
Robert Kaplan: In other words, you need to generate nominal GDP to service the debt. There are some challenges though with this approach and others, which I actually would like to see us debate.
What's an example? How to explain nominal GDP targeting, in that there's a catch‑up mechanism in nominal GDP targeting and a lot of other aspects that I think are not going to be easy to communicate. The good news about the current framework is it's relatively straightforward to communicate.

This seems true, on the surface, but I think the more important point is that, in a way, NGDP targeting really wouldn't require communication.  How can I say that?  Well, what I'm thinking of is the countless conversations today about whether the Phillips Curve is useful, whether inflation trends will reverse or accelerate, whether expanding credit is feeding "overheating", etc.  Think of the millions of hours of debate and analysis that go into developing or forecasting Federal Reserve policy choices and their consequences.  The problem with the current dual mandate is that there is too much communication, and all the communication we could muster will never lead to consensus or certainty about near term economic activity.

With a functional nominal GDP targeting regime, there would be little to communicate.  And, what a relief that would be!

The following excerpt is more to the point of the focus of this blog - credit markets and the financial crisis.  As David points out, even this conversation would be less salient in an NGDP targeting world.  Management and regulation of credit markets wouldn't be so important if it wasn't an important ingredient in sudden negative NGDP shocks.  Kaplan's response to that notion is a window into the problem of seeing the housing bubble as a result of excess credit rather than a shortage of housing supply.

Robert Kaplan: If you look at the household sector in this country, the household sector was extremely leveraged. Meaning if you took household debt divided by gross domestic product for the households, there was a very high degree of leverage.
The reason we didn't notice it is if you looked at household debt relative to asset values, it actually didn't look excessive, back to home prices. What the housing crisis exposed is a lot of households were dramatically over‑leveraged, but they were comforted by the fact that there were easy mortgage conditions and home prices were very high.
Obviously, I don't need to remind people when the housing sector collapsed, all of a sudden, the household sector, it was clear, were very highly leveraged. They've spent the last eight or nine years deleveraging.
I think one of the lessons also, which relates to mortgage availability and so on, was we've got to watch the health of the household sector. Even with that, the aggressiveness on mortgage offerings were probably the tip of the iceberg.
It's all the securitizations upon securitizations upon securitizations of those mortgage obligations which magnified those excesses. If we didn't have all the securitizations on top of this aggressive mortgage lending, it still would have been painful, but it wouldn't have been anywhere near as painful as what ultimately happened.
David Beckworth: This goes back to the point you made earlier about nominal GDP targeting. Again, in a different world, a counterfactual world where we did have a nominal GDP level targeted, this would have made that crash a whole lot nicer or less severe.
Robert Kaplan: Truthfully, I wasn't at the Fed. I've been at the Fed only three years. I actually probably have a slightly different take. I think there's a number of things we do at the Fed. One of them is monetary policy, but another big one is macroprudential policy.
I think if you don't have good macroprudential policy, it's very difficult to run a sensible...It makes monetary policy harder. I think we need to do both. You could debate, and I've been part of those debates, to question monetary policy leading up to the crisis, approaches for monetary policy.
I think if you don't have good macroprudential policy for, again, stress testing, monitoring of the non‑bank financials, I think it makes it very hard to avoid instability.
David Beckworth: That's a fair point. If you did have those imbalances build up, let's say, for the sake of argument, you did have that leverage, I think the point you made earlier is that a nominal income target, a nominal GDP target that would make the unwinding of that leverage much more manageable. Is that fair?
Robert Kaplan: Listen, what I've learned is if the household sector gets over‑leveraged, you've got to accept it's going to take a number of years for households to deleverage. They're not like companies, who can sell assets, raise equity, restructure, restructure their debt. Households can't do that.
I think the trick is a little bit of prevention. I think we want to get into a situation where we monitor the household sector more carefully and try to take steps to maybe moderate excessive debt growth at the household sector relative to income. 

Kaplan's comments reflect what I think is considered an uncontroversial set of stipulations:

• Excessive credit led to home prices and household debt that were bloated.
• When home prices collapsed, households were left with the excessive debt.
• Deleveraging from that debt slowed down the recovery.

The solutions to these stipulated risks are:

• Prevent household debt from rising.
• Prevent excessive use of multi-level securitizations and financial derivatives.

First, I'll point out a bit of a contradiction here.  Multi-level securitizations and credit default swaps on those securitizations were developed in order to create securitizations that didn't require new mortgages.  High household debt and excessive complex securitizations and derivatives are substitutes, not complements.  They didn't additively lead to a more acute crisis.  In fact, the rise of complex securitizations and mortgage-based derivatives came from having more savers looking for safe assets than there were investors taking the primary risk positions on either securitizations or home equity, itself.  The reason complex securitizations were profitable for their underwriters was because investors were willing to pay a premium for securities with lower expected risk.

I have discussed this many times, so I won't go into it here again in more detail, but this is an important, if subtle, correction to the credit-fueled bubble narrative.  Synthetic CDOs, CDO-squareds, etc. were the first stage of the bust, and they came about because the core cause of the bubble was a lack of housing supply, but the bubble was addressed as if it was due to a lack of fear.  Investors in the CDO AAA-securities were risk-averse.

Regarding the other points, what if high home prices are generally due to an urban supply shortage, and rising mortgage levels are a side-effect of that problem?  Then, what will happen as a result of the proposed solutions?

• Home prices will remain somewhat elevated because of high rents.
• Since credit is a side effect of high prices, there will be natural pressures pushing up demand for household debt.
• To reduce that demand for household debt, taxes or non-price constraints will need to be implemented to reduce the quantity of household debt.
• In order to keep household debt at a normal level as a percentage of income, debt will have to be held low as a percentage of home values and/or homeownership will have to be lowered.
• Regulatory obstacles to home ownership will raise the yield on home equity - to some extent through lower prices and to some extent through higher rents.

So, the policy that seems like the prudent policy for the Federal Reserve to follow is a policy that will create high yields for a set of households who meet regulatory approval and that will create high costs for households who do not meet regulatory approval.  Over the past several years, this has been the case.  Using BEA data on housing value added and Fed data on mortgage and real estate values, the past few years have been unique in providing real returns on home equity that are higher than nominal yields on mortgages outstanding.

And, it is highly likely that regulatory approval will fall sharply along socio-economic status lines.

I am not arguing here that high debt levels are not systemically destabilizing.  I am not arguing that we shouldn't be concerned about them.  I am simply pointing out that the only realistic way to enforce this macroprudential policy is to enforce higher-than-market returns for select Americans while limiting access to those returns.  To be honest about that means being clear-eyed about the cause of high levels of household debt.

Or, to put this another way, there are many sources of value in an economy.  A marketable college degree creates value, in the form of human capital, but it is difficult to have liquid markets in human capital.  So, there isn't a ZillowPeople.com where you can see the current market value of college graduates and their current market wage.

Yet, in a way, housing sort of serves as a substitute for the market in human capital.  If a banker feels confident enough in your earning ability, she will allow you to take out a mortgage to commit to transferring some of the high wages you can earn to future payments.  The potential to foreclose on the house serves as a financial tool that facilitates this trade in human capital.  The banker serves as an intermediary, using the liquidity of the mortgage market and the stability of the housing market to facilitate trading activity in the human capital market.

That is what was happening before the crisis.  In most places, the mortgage and housing markets have developed to the point that more than 80% of households can complete that trade at some point in their lives.  This is a testament to the development of human capital (broad access to above-subsistence wages) and of real estate and mortgage markets.  But, our economy was hamstrung by a political limit to urbanization, which created a dichotomy: places that were exclusive and places that weren't.

That exclusivity is rationed through housing, and by happenstance there is a liquid market that measures the value of that exclusion.  There is a Zillow.com for houses.  Before the crisis, this trade in human capital and housing was still functioning, but in the Closed Access cities, this meant that only those with a large excess of human capital could engage in that trade.  They had to transfer a large stake in their future earnings over to the existing real estate owners to claim their place in exclusive labor markets.  In order to fully accrue the full potential of their human capital, they had to pay the toll to access the markets where wages were highest.

Home prices reflected the value of that exclusion, and homes traded at a value at reflected their claim on that earning power.  Certainly, the existence of these credit markets facilitated the market that revealed those values.

By focusing on credit as the cause of high prices, these transactions between human capital and the housing stock have been hobbled.  The undiscounted total value of future rents on properties has not been reduced.  "Macroprudential" management on mortgage markets has just added a significant premium to the discount rate that is applied to those future rental incomes.  This has lowered home prices in Closed Access markets from where they would have been, and it certainly has reduced household debt from where it would be in this Closed Access context.  But, because this is a misdiagnosis of the problem, where its effect has been the worst has been to block access to low tier housing markets in cities across the country that were never out of whack.  (I touched on this in the previous post.)

Macroprudential management has effectively been a step backwards to a less sophisticated economy, where access to ownership of real property requires a pre-existing stockpile of wealth, and those who have wealth earn higher returns on it.