Monday, December 31, 2018

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.)

Friday, December 14, 2018

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.   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   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.   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.

Wednesday, December 12, 2018

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%.

Wednesday, December 5, 2018

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.

Tuesday, December 4, 2018

Discounted Pre-Orders for "Shut Out"

"Shut Out: How a Housing Shortage Caused the Great Recession and Crippled Our Economy" is now available for pre-order.  It will be ready to ship in January.

Great news: Enter this code on the Rowman & Littlefield site for a 30% discount: 4S18MERC30

If you know anyone who might be interested in the book, this is a good chance to get it at a better price: $28 instead of $40.

Monday, December 3, 2018

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.