Tag Archives: affordable housing

US Home Price Gains Slump For 12th Straight Month, Weakest In 7 Years

Case-Shiller’s March home price index showed yet another deceleration in growth – the 12 months in a row of slowing equals the 2014 growth scare’s length but is the weakest growth since July 2012.

After February’s 20-City Composite 3.00% YoY print, expectations were for 2.55% growth in March and it surprised very modestly with a 2.68% YoY print (still the lowest in 7 years)…

https://www.zerohedge.com/s3/files/inline-images/bfm62C.jpg?itok=sZSBjokA

Nationally, home-price gains slowed to a 3.7% pace.

“Given the broader economic picture, housing should be doing better,” David Blitzer, chairman of the S&P index committee, said in a statement.

“Measures of household debt service do not reveal any problems and consumer sentiment surveys are upbeat. The difficulty facing housing may be too-high price increases,” which continue to outpace inflation, he said.

While all 20 cities in the index showed year-over-year gains, five were below 2%: Chicago, Los Angeles, San Diego, San Francisco and Seattle, which a year ago posted a 13% increase. Las Vegas led the nation in March with an 8.2% gain, followed by Phoenix.

Source: ZeroHedge

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Attention Millennials: You Can Now Buy Tiny Homes On Amazon

One of the main goals of the Federal Reserve’s monetary policies of the past decade was to generate the “wealth effect”: by pushing the valuations of homes higher, would make American households feel wealthier. But it didn’t. Most Americans can’t afford the traditional home with a white picket fence around a private yard (otherwise known as the American dream), and as a result, has led to the popularity of tiny homes among heavily indebted millennials.

Tiny homes are popping up across West Coast cities as a solution to out of control rents and bubbly home prices, also known as the housing affordability crisis.

https://www.zerohedge.com/s3/files/inline-images/tiny%20homes%20san%20fran.jpg?itok=fZ8EmDnq

Amazon has recognized the hot market for tiny homes among millennials and has recently started selling DIY kits and complete tiny homes.

One of the first tiny homes we spotted on Amazon is a $7,250 kit for a tiny home that can be assembled in about eight hours.

https://www.zerohedge.com/s3/files/inline-images/2019-05-16_14-30-04.png?itok=ssdiD-Ct

A more luxurious tiny home on the e-commerce website is selling for $49,995 +$1,745.49 for shipping. This one is certified by the RV Industry Association’s standards inspection program, which means millennials can travel from Seattle to San Diego in a nomadic fashion searching for gig-economy jobs.

https://www.zerohedge.com/s3/files/inline-images/2019-05-16_14-30-46.png?itok=v2XAJ5QF

Those who want a 20 ft/40 ft expandable container house with solar energy, well, Amazon has that too. This tiny home has it all: a post-industrial feel using an old shipping container, virtue signaling with solar panels, full bathroom, and a kitchen to make avocado and toast.

https://www.zerohedge.com/s3/files/inline-images/2019-05-16_14-31-41.png?itok=N9ksRa5o

With almost two-thirds of Millennials living paycheck to paycheck and less than half of them have $500 in savings, we’re sure this lost generation could afford one of these trailers tiny homes with their Amazon credit card. Nevertheless, the tiny home craze among millennials is more evidence that living standards are collapsing.

Source: ZeroHedge

Where Home Prices Are Rising the Fastest (Slowest) In America

Since the end of the great recession, home prices in America have rebounded substantially. Since the dark days of 2009, prices have steadily climbed and are up over 50% on average from the lowest point.

This is great news for homeowners whose homes may be worth more than their pre-recession values, but less great news for homebuyers who can afford less house for the dollar. What’s more is that in some places, home prices have spiked much faster than average, while in other places, home prices have remained depressed.

So where in America are home prices increasing the fastest and the slowest? In light of fluctuating mortgage interest rates, tax reform that’s limited many homeowner deductions, and an affordability crisis in many urban areas, along with Priceonomics customer RefiGuide.org thought we’d dive deeper into the home price data published, aggregated and made available by Zillow.

Over the last year, the median home prices increased the fastest at the state level in Idaho, where prices increased by a staggering 17.2%. In just two states did home prices actually fall last year (Alaska and Delaware). The large cities with the fastest home appreciation were Newark, Dallas, and Buffalo where prices increased more than 15% in each place. The large city where prices decreased the fastest was Seattle, where home prices actually fell 2.4%.

Lastly, we looked at the expensive markets (where homes cost more than a million dollars) that had the highest price appreciation. St. Helena, CA, Quogue, NY and Stinson Beach, CA all had prices increase over 20% last year.

***

For this analysis, we looked at data from the beginning of March 2019 compared to prices one year earlier. We looked at Zillow’s seasonally adjusted median price estimate as published by Zillow Research Data.

Nationally, home prices increased 7.2% last year or about $15,000 more than the year before. However, in some states prices spiked much more than that.

https://www.zerohedge.com/s3/files/inline-images/state1.jpg?itok=FY7fZuHw

Idaho leads the country with home prices increasing by 17.2% last year, driven by strong demand in the Boise market. In Utah the impact of a thriving economy and growing population is that prices increased 14% in just one year. Nevada, likewise is seeing strong home price growth as people migrate from California and the state’s low taxes are more favorable under the most recent tax reform. Alaska and Delaware have the distinction of being the only states where home prices fell over the last year.

Next, we looked at home prices in the top one hundred largest housing markets, as measured by population. Which cities were experiencing rapid home equity appreciation and which ones are not? 

https://www.zerohedge.com/s3/files/inline-images/states2.jpg?itok=6VBwCrMd

At the city level, home prices have increased the fastest in Newark, NJ where prices have increased more than 17% as buyers who are priced out of New York City have purchased in this area. Dallas, a city with a strong economy and low taxes has seen home prices increase nearly 17% as well.

Notably, some of the most expensive and desirable cities like Seattle, Oakland and Portland have seen their prices decrease in the last year. Each of these locations has experienced price appreciation during this decade, however.

Were there any smaller cities and towns that experienced home prices rising faster than the big cities? Below shows the fifty places in the United States where home prices increased the most this last year:

https://www.zerohedge.com/s3/files/inline-images/states3.jpg?itok=m0ox7MnB

Across the Midwest and South, numerous smaller cities experienced price appreciation much greater than 25% last year. In Nettleton, MS prices increased 49% in just one year! Notably, almost none of these high-price growth cities are located on the coasts.

Lastly, what are expensive places to buy a home in America that are just getting more expensive? To conclude we looked at locations where the median home price was over one million dollars and the prices keep rising:

https://www.zerohedge.com/s3/files/inline-images/state4.jpg?itok=mLMpJ2ww

In this rarefied group, prices increased the most in Saint Helena, CA. In this tony town in Napa Valley, prices increased over 25% last year. In second place was Quogue, NY a town in the Hamptons. In fact, 9 out of the top 10 expensive cities with high price appreciation are in California or New York. More specifically, many of these locations are in the vicinity of San Francisco and New York City, the two very large economic engines that are driving home prices.

***

After nearly a decade of vibrant stock market and real estate returns, this year home prices have continued to climb at a steady clip. In only two states in America did prices actually fall, and in five states prices grew more than 10% in a year. As the economy has continued roaring, places that were once known for being affordable like Idaho, Utah, and Nevada have seen home prices spike. While expensive cities like Seattle, Portland and Oakland have seen prices level off in the last year, and places like Newark, Dallas and Buffalo have become less affordable. In this stage of American economic expansion, the once affordable places are seeing their prices escalate.

Source: ZeroHedge | by Priceonomics

Mapped: The Salary Needed To Buy A Home In 50 U.S. Metro Areas

Over the last year, home prices have risen in 49 of the biggest 50 metro areas in the United States.

At the same time, mortgage rates have hit seven-year highs, making things more expensive for any prospective home buyer.

With this context in mind, today’s map comes from HowMuch.net, and it shows the salary needed to buy a home in the 50 largest U.S. metro areas.

https://www.zerohedge.com/s3/files/inline-images/salary-needed-house-u-s-metro-areas_edit2.jpg?itok=7NPM9G4n

The Least and Most Expensive Metro Areas

As a reference point, Visual Capitalist’s Jeff Desjardins points out that the median home in the United States costs about $257,600, according to the National Association of Realtors.

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With a 20% down payment and a 4.90% mortgage rate, and taking into account what’s needed to pay principal, interest, taxes, and insurance (PITI) on the home, it would mean a prospective buyer would need to have $61,453.51 in salary to afford such a purchase.

However, based on your frame of reference, this national estimate may seem extremely low or quite high. That’s because the salary required to buy in different major cities in the U.S. can fall anywhere between $37,659 to $254,835.

The 10 Lowest Cost Metro Areas

Here are the lowest cost metro areas in the U.S., based on data and calculations from HSH.com:

https://www.zerohedge.com/s3/files/inline-images/2019-04-29_19-08-55.jpg?itok=qiNBNZFB

After the dust settles, Pittsburgh ranks as the cheapest metro area in the U.S. to buy a home. According to these calculations, buying a median home in Pittsburgh – which includes the surrounding metro area – requires an annual income of less than $40,000 to buy.

Just missing the list was Detroit, where a salary of $48,002.89 is needed.

The 10 Most Expensive Metro Areas

Now, here are the priciest markets in the country, also based on data from HSH.com:

https://www.zerohedge.com/s3/files/inline-images/2019-04-29_19-09-45.jpg?itok=izlHuYly

Topping the list of the most expensive metro areas are San Jose and San Francisco, which are both cities fueled by the economic boom in Silicon Valley. Meanwhile, two other major metro areas in California, Los Angeles and San Diego, are not far behind.

New York City only ranks in sixth here, though it is worth noting that the NYC metro area extends well beyond the five boroughs. It includes Newark, Jersey City, and many nearby counties as well.

As a final point, it’s worth mentioning that all cities here (with the exception of Denver) are in coastal states.

Notes on Calculations

Data on median home prices comes from the National Association of Realtors and is based on 2018 Q4 information, while national mortgage rate data is derived from weekly surveys by Freddie Mac and the Mortgage Bankers Association of America for 30-year fixed rate mortgages.

Calculations include tax and homeowners insurance costs to determine the annual salary it takes to afford the base cost of owning a home (principal, interest, property tax and homeowner’s insurance, or PITI) in the nation’s 50 largest metropolitan areas.

Standard 28% “front-end” debt ratios and a 20% down payments subtracted from the median-home-price data are used to arrive at these figures.

Source: ZeroHedge

Americans Can’t Afford To Buy A Home In 70% Of The Country

Even at a time of low interest rates and rising wages, Americans simply can’t afford a home in more than 70% of the country, according to CBS. Out of 473 US counties that were analyzed in a recent report, 335 listed median home prices were more than what average wage earners could afford. According to the report from ATTOM Data Solutions, these counties included Los Angeles and San Diego in California, as well as places like Maricopa County in Arizona.

New York City claimed the largest share of a person’s income to purchase a home. While on average, earners nationwide needed to spend only about 33% of their income on a home, residents in Brooklyn and Manhattan need to shell out more than 115% of their income. In San Francisco this number is about 103%. Homes were found to be affordable in places like Chicago, Houston and Philadelphia.

This news is stunning because homes are considerably more affordable today than they were a year ago. Although prices are rising in many areas, they are also falling in places like Manhattan. Unaffordability in the market has been the result of slower home building and owners staying in their homes longer. Both have reduced the supply of homes in the market.

And the market may continue to create better conditions for buyers. Affordability could improve because of the fact that homes are out of reach for so many seekers, according to Todd Teta, chief product officer at ATTOM Data Solutions. Today’s market is also more affordable than it was a decade ago, before the crisis. Home prices were about the same prior to the crisis, even though income adjusted for inflation was lower.

“What kept the market going was looser lending standards, so that was compensating for affordability issues,” Teta said. Since then, standards have toughened (for now, at least).

We recently wrote about residents of New York City who simply claimed they couldn’t afford to live there.

More than a third of New York residents complained that they “can’t afford to live there” anymore (and yet they do). On top of that, many believe that economic hardships are going to force them to leave the city in five years or less, according to a Quinnipiac poll published a couple weeks ago. The poll surveyed 1,216 voters between March 13 and 18.

In total, 41% of New York residents said they couldn’t cope with the city’s high cost of living. They believe they will be forced to go somewhere where the “economic climate is more welcoming”, according to the report.

Ari Buitron, a 49-year-old paralegal from Queens said: “They are making this city a city for the wealthy, and they are really choking out the middle class. A lot of my friends have had to move to Florida, Texas, Oregon. You go to your local shop, and it’s $5 for a gallon of milk and $13 for shampoo. Do you know how much a one-bedroom, one-bathroom apartment is? $1700! What’s wrong with this picture?”

Source: ZeroHedge

The Cycle That Has Been Saving Home Buyers $3,000 Per Year Just Ran Out Of Fuel

Summary

  • After five years of supporting rising home prices, the latest phase of a long-term financial cycle is nearing its end.
  • While little followed in the real estate market, this cycle of yield curve spread compression has been one of the largest determinants of home affordability and housing prices.
  • Using a detailed analysis of national statistics, it is demonstrated that average home buyers in 2018 have been saving about $250 per month, or $3,000 per year.
  • The reasons why the cycle is ending are mathematically and visually demonstrated.

(Daniel Amerian) Home buyers in every city and state have been benefiting from a powerful financial cycle for almost five years. Most people are not aware of this cycle, but it has lowered the average monthly mortgage payment for home buyers on a national basis by about $250 per month since the end of 2013.

The interest rate cycle in question is one of “yield curve spread” expansion and compression, with yield curve spreads being the difference between long-term and short-term interest rates. This interest rate spread has been going through a compression phase in its ongoing cycle, meaning that the gap between long-term interest rates and short-term interest rates fell sharply in recent years.

https://static.seekingalpha.com/uploads/2018/11/2/566013-15411612062108982.jpg

The green bars in the graph above show national average mortgage payments (principal and interest only), and they fell from $861 a month in 2013 to $809 a month in 2016 and have now risen to $894 per month. However, without the narrowing of the spread between short-term rates and long-term rates, mortgage payments would have been entirely different (and likely home prices as well).

Without the cycle of yield curve spread compression then, as shown with the blue bars, average mortgage payments would have been above $900 per month even in 2014, and they would have risen every year since without exception. If it had not been for compression, national average mortgage payments would have reached $978 per month in 2016 (instead of $809) and then $1,138 per month in 2018 (instead of $894).

The yellow bars show the average monthly savings for everyone buying a home during the years from 2014 to 2018. The monthly reduction in mortgage payments has risen from $57 per month in 2014 to $169 per month in 2016, to $244 per month by 2018 (through the week of October 11th).

In other words, the average home buyer in the U.S. in 2018 is saving almost $3,000 per year in mortgage payments because of this little-known cycle, even if they’ve never heard of the term “yield curve.” Indeed, while the particulars vary by location, home affordability, home prices and disposable household income have been powerfully impacted in each of the years shown by this interest rate cycle, in every city and neighborhood across the nation.

While knowledge of this cyclical cash flow engine has not been necessary for home buyers (and sellers) to enjoy these benefits in previous years, an issue has developed over the course of 2018 – the “fuel” available to power the engine has almost run out. That means that mortgage payments, home affordability and housing prices could be traveling a quite different path in the months and years ahead.

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The yield curve spread is shown in the blue area above, and it was quite wide at the beginning of this particular cycle, equaling 2.62% as of the beginning of 2014. It has been steadily used up since that time, however, with the compression of the spread being shown in red. As of the current time, the yield curve compression which has powered the reduction in mortgage payments has almost maxed out, the blue area is almost gone and the ability to further compress (absent an inversion) is almost over.

This analysis is part of a series of related analyses; an overview of the rest of the series is linked here.

(More information on the data sources and calculations supporting the summary numbers above can be found in the rest of series, as well as in the more detailed analysis below. A quick summary is that mortgage rates are from the Freddie Mac Primary Mortgage Market Survey, Treasury yields are from the Federal Reserve, the national median home sale price is from Zillow for the year 2017 and the assumed mortgage LTV is 80%.)

A Cyclical Home Buyer Savings Engine

A yield curve spread is the difference in yields between short-term and long-term investments, and the most common yield curve measure the markets looks to is the difference between the 2-year and 10-year U.S. Treasury yields.

An introduction to what yield curves are and why they matter can be found in the analysis “A Remarkably Accurate Warning Indicator For Economic And Market Perils.” As can be seen in the graph below and as is explored in more detail in some of the linked analyses, there is a very long history of yield curve spreads expanding and compressing as part of the overall business cycle of economic expansions and recessions, as well as the related Federal Reserve cycles of increasing and decreasing interest rates.

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Since the beginning of 2014, the rapid shrinkage of the blue area shows the current compression cycle, and a resemblance (in broad strokes) can be seen with the compression cycles of 1992-2000 and of 2003-2006.

What has seized the attention of the markets in recent months is what followed next in some previous cycles, which is that yield curve spreads went to zero and then became negative, creating “inversions” where short-term yields are higher than long-term yields (as shown in the golden areas). This is important because, while such inversions are quite uncommon, when they do occur they have had a perfect record in recent decades (over the last 35 years) of being followed by economic recessions within about 1-2 years.

However, yield curves don’t have to actually invert in order to turn the markets upside down, and as explored in the analysis linked here, when the Fed goes through cycles of increasing interest rates, we have a long-term history of yield curve spreads acting as a counter cyclical “shock absorber” and shielding long-term interest rates and bond prices from the Fed actions.

That only works until the “shock absorber” is used up, however, and as of the end of the third quarter of 2018, the yield curve “shock absorber” has been almost entirely used up. So, when the Fed increased short-term rates in late September of 2018, there was almost no buffer, and that increase passed straight through to 10-year Treasury yields. The results were painful for bond prices, stock prices and even the value of emerging market currencies.

The same lack of compression led to a sudden and sharp leap to the highest mortgage rates in seven years. Unfortunately, that jump may also potentially be just a taste of what could be on the way, with little further room for the yield curve to compress (without inverting).

Understanding The Relationships Between Mortgage Rates, Treasury Yields and Yield Curve Spreads

The graphic below shows weekly yields for Fed Funds, 2-year Treasuries, 10-year Treasuries and 30-year fixed-rate mortgages since the beginning of 2014.

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The first relationship is the visually obvious close correlation between the top purple line of mortgage rates and the green line of 10-year Treasury yields. Mortgage amortization and prepayments mean that most mortgage principal is returned to investors well before the 30-year term of the mortgage, and therefore, investors typically price those mortgage rates at a spread (the distance between the green and purple lines) above 10-year Treasury yields. It isn’t a perfect relationship – the 10-year Treasury tends to be a bit more volatile – but is a close one.

The bottom two lines are the short-term yields, with the yellow line being effective overnight Fed Funds rates, and the red line being 2-year Treasury yields. Because the yield curve has been positive over the entire time period shown (as it almost always is), long-term rates have consistently been higher than short-term rates, and 10-year Treasury yields have been higher than 2-year Treasury yields, which have been higher than Fed Funds rates.

Now, the long-term rates have been moving together, and while the relationship is not quite as close, the short-term rates have also been generally moving together, with the 2-year Treasury yield more or less moving up with the Fed’s cycle of increasing interest rates (each “step” in the yellow staircase is another 0.25% increase in interest rates by the Federal Reserve).

However, the long-term rates have not been moving with the short-term rates. As can be seen with point “D,” 10-year Treasury yields were 3.01% at the beginning of 2014, 2-year Treasury yields were a mere 0.39% and the yield curve spread – the difference between the yields – was a very wide 2.62%.

About a year later, by late January of 2015 (point “E”), 10-year Treasury yields had fallen to 1.77%, while 2-year Treasury yields had climbed to 0.51%. The yield curve spread – the distance between the green and red lines – had narrowed to only 1.26%, or a little less than half of the previous 2.62% spread.

It can be a little hard to accurately track the relative distance between two lines that are each continually changing, so the graphic below shows just that distance. The top of the blue area is the yield curve spread; it begins at 2.62% at point “D” and falls to 1.26% by point “E.” The great reduction between points “D” and “E” is now visually obvious.

https://static.seekingalpha.com/uploads/2018/11/2/566013-15411616451828508.jpg

So, if there had been no change in yield curve spreads, and the 2-year Treasury had risen to 0.51% while the spread remained constant at 2.62%, then the 10-year Treasury yields would have had to have moved to 3.13%.

But they didn’t – the yield curve compressed by 1.36% (2.62% – 1.26%) between points “D” and “E,” and the compression can be seen in the growing size of the red area labeled “Cumulative Yield Curve Compression.” If we start with a 2.62% interest rate spread, and that spread falls to 1.26% (the blue area), then we have used up 1.36% (the red area) of the starting spread and it is no longer available for us.

The critical importance of this yield curve compression for homeowners and housing investors, as well as some REIT investors, can be seen in the graphic below:

https://static.seekingalpha.com/uploads/2018/11/2/566013-15411616777403066.jpg

The top of the green area is the national average 30-year mortgage rate as reported weekly by Freddie Mac. That rate fell from 4.53% in the beginning of 2014 (point “D”) to 3.66% in late January of 2015.

But remember the tight relationship between the green and purple lines in the graph of all four yields / rates. Mortgage investors demand a spread above the 10-year Treasury, mortgage lenders will only lend at rates that will enable them to meet that spread requirement (and sell the mortgages), and therefore, it was the reduction in 10-year Treasury yields that drove the reduction in mortgage rates. And if the yield curve compression had not occurred, then neither would have the major reduction in mortgage rates.

As we saw in the “Running Out Of Room” graphic, the red area of yield curve compression increased by 1.36% between points “D” and “E.” If we simply take the red area of yield curve compression from that graph and we add it to the green area of actual mortgage rates, then we get what mortgage rates would have been with no yield curve compression (all else being equal).

With no yield curve compression, mortgage rates of 3.66% at point “E” would have been 5.02% instead (3.66% + 1.36% – 5.02%).

With a $176,766 mortgage in late January of 2015, a monthly P&I payment at a 3.66% rate is $810. (This is based on a national median home sale price for 2017 of $220,958 (per Zillow) and an assumed 80% mortgage LTV.)

At a 5.02% mortgage rate – which is what it would have been with no yield curve compression – the payment would have been $951. This meant that for any given size mortgage, monthly payments were reduced by 15% over the time period as a result of yield curve spread compression ($810 / $951 = 85%).

Now, at that time, housing prices were still in a somewhat fragile position. The largest decrease in home prices in modern history had just taken place between the peak year of 2006 and the floor years of 2011-2012. Nationally, average home prices had recovered by 9.5% in 2013, and then another 6.4% in 2014.

Here is a question to consider: Would housing prices have risen by 6.4% in 2014 if mortgage rates had not reduced monthly mortgage payments by 15%?

The Next Yield Curve Spread Compression

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Our next key period to look at is between points “E” and “G,” late January of 2015 to late August of 2016. We are now beginning a rising interest rate cycle when it comes to short-term rates. The Fed had done its first slow and tentative 0.25% increase in Fed Funds rates, and 2-year Treasury yields were up to 0.80%, which was a 0.29% increase.

All else being equal, when we focus on the yellow and red lines of short-term interest rates, mortgage rates should have climbed as well. (Graphs are repeated for ease of scrolling.)

https://static.seekingalpha.com/uploads/2018/11/2/566013-15411617690603347.jpg

However, that isn’t what happened. After a brief jump upwards at point “F,” yield curve spreads had substantially fallen to 0.78% by point “G,” as can be seen in the reduction of the blue area above. For this to happen, the compression of yield curve spreads had to materially increase to 1.84%, as can be seen in the growth of the red area.

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In the early stages of a cycle of rising interest rates (as part of the larger cycle of exiting the containment of crisis), mortgage rates did not rise, but fell from the very low level of 3.66% at point “E” to an even lower level of 3.46% at point “G,” as can be seen in the reduction of the green area.

To get that reduction in the green area during a rising interest rate cycle required a major growth in the red area of yield curve compression. To see what mortgage rates would have been without yield curve compression (all else being equal), we add the red area of cumulative yield curve compression of 1.84% to the green area of actual mortgage rates of 3.46% and find that mortgage rates would have been 5.30%.

Returning to our $176,766 mortgage example, the monthly mortgage payment (P&I only) is $790 with a 3.46% mortgage rate, and is $982 with a 5.30% mortgage rate. Yield curve compression was responsible for a 20% reduction in mortgage payments for any given borrowing amount by late August of 2016.

However, a problem is that by late August of 2016, the 1.84% cumulative cyclical compression of the yield curve meant that only 0.78% of yield curve spreads remained. A full 70% of the initial yield curve spread had been used up.

(Please note that the mortgage payments in this section of the analysis are calculated based on historical mortgage rates for the particular weeks identified. The annual average payments presented in the beginning of this analysis are the average of all weekly payment calculations for a given year, and therefore, do not correspond to any given week.)

Using Up The Rest Of The Fuel (Yield Curve Spreads):

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After its slow and tentative start, the Federal Reserve returned to 0.25% Fed Funds rate increases in December of 2016, and has kept up a much steadier pace since that time. As of October of 2018, Fed Funds rates are now up a total of 2% from their floor. As can be seen in the line graph of the yield curve over time, 2-year Treasury yields have also been steadily climbing and were up to 2.85% by point “J,” the week ending October 11th.

However, 10-year Treasury yields are not up by nearly that amount. By late August of 2018, 10-year Treasury yields were only up to 2.87%, which was 1.29% above where they had been two years before.

https://static.seekingalpha.com/uploads/2018/11/2/566013-15411618892487876.jpg

The difference can be found by looking at the very small amount of blue area left by point “J” – yield curve spreads were down to a mere 0.22% by the week ending August 29th, or less than one 0.25% Fed Funds rate increase. This meant that the red area of total cumulative yield curve compression was up to 2.40%, which means that 92% of the “fuel” that had been driving the compression profit engine had been used up – before the Fed’s 0.25% Fed Funds rate increase of September 2018.

As explored in much more detail in the previous analysis linked here, when the Federal Reserve raised rates for the eighth time in September, the yield curve did not compress. Such a compression could have been problematic, as the yield curve would have been right on the very edge of inverting, and there is that troubling history when it comes to yield curve inversions being such an accurate warning signal of coming recessions.

Instead, the short-term Fed Funds rate increase went straight through to the long-term 10-year Treasury yields, full force, with no buffering or mitigation of the rate increase by yield curve compression. The resulting shock as the 10-year Treasury yield leaped to 3.22% led to sharp losses in bonds, stocks and even emerging market currencies.

The same shock also passed through in mostly un-buffered form to the mortgage market via the demand for mortgage investors to be able to buy mortgages at a spread above the 10-year Treasury bond. Thirty-year mortgage rates leaped from 4.71% to 4.90%, an increase of 0.19%, and the highest rate seen in more than seven years.

(I’ve concentrated on the 2- to 10-year yield curve spread in this analysis to keep things simple, to correspond to the market norm for the most commonly tracked yield curve spread and because it has a strong explanatory power for the big picture over time. If one wants to get more precise (and therefore, quite a bit messier), there are also the generally much smaller spread fluctuations between 1) Fed Funds rates and 2-year Treasury yields; and 2) 10-year Treasury yields and mortgage rates.)

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When we look at the period between points “G” and “J,” it looks quite different than either of the previous periods we looked at. Mortgage rates have been rising, with the largest spike occurring at the time that the Federal Reserve proved it was serious about actually materially increasing interest rates with the Fed Funds rate increase of December 2016 (point “H”).

However, this does not mean that the money saving power of yield curve compression had lost its potency. Between points “D” and “J,” early January of 2014 and early October of 2018, average annual mortgage rates rose from 4.53% to 4.90%, as can be seen in the green area – which is an increase of only 0.37%. Meanwhile, the yield curve spread between the 2- and 10-year Treasuries was compressing from 2.62% to 0.29%, which was a yield curve compression of 2.33%. Adding the red area of cumulative yield curve compression to the green area of actual mortgage rates shows that current mortgage rates would be 7.23% if there had been no yield curve compression (all else being equal).

Mortgage principal and interest payments on a 30-year $176,766 mortgage with 4.90% interest rate are $938 per month, and they are $1,203 per month with a 7.23% mortgage rate. This means that yield curve compression has reduced the national average mortgage payment by about 22%.

Turning The Impossible Into The Possible:

This particular analysis is a specialized “outtake” from the much more comprehensive foundation built in the Five Graphs series linked here, which explores the cycles that have created a very different real estate market over the past twenty or so years.

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As developed in that series, as part of the #1 cycle of the containment of crisis, the attempts to cure the financial and economic damage resulting from the collapse of the tech stock bubble and the resulting recession, the Federal Reserve pushed Fed Funds rates down into an outlier range (shown in gold), the lowest rates seen in almost 50 years.

As part of the #3 cycle of the containment of crisis, in the attempt to overcome the financial and economic damage from the Financial Crisis of 2008 and the resulting Great Recession, the Federal Reserve pushed interest rates even further into the golden outlier range, with near-zero percent Fed Funds rates that were the lowest in history.

By the time we reach early January of 2014 to late January of 2015, points “D” to “E,” Fed Funds rates were still where they had been the previous five to six years – near zero. Mathematically, there was no room to reduce interest rates, without the U.S. going to negative nominal interest rates.

But yet, mortgage rates fell sharply, from an already low 4.53% to an extraordinarily low 3.66%. This sharp reduction in rates transformed the housing markets and would steer extraordinary profits to homeowners and investors over the years that followed. However, none of it would have been possible without the compression of yield curve spreads.

Once the past has already happened, it is easy to not only take it for granted, but to internalize it and to make it the pattern that we believe is right and natural. Once this happens, the next natural step is to then either explicitly or implicitly project this assumed reality forward, as that trend line then becomes the basis for our financial and investment decisions.

However, where this natural process can run into difficulties is when what made the past possible becomes impossible. Yield curve spread compression took what would have been impossible – a plunge in mortgage rates even as short-term rates remained near a floor – and made it possible. But that pattern can’t repeat (at least not in that manner) when there is no longer the spread to compress.

Source: by Daniel Amerian | Seeking Alpha