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Friday, April 17, 2020

Corona Virus and Density in NYC


We look at New York City (NYC) and New York State to see what the connection is between population density and susceptibility to infection by Covid-19.  Lots of people living very close to each other would intuitively seem to explain why NYC has such a large number of cases of a communicable disease.

Link to this post for sharing:

NYC is crowded.  Seems like diseases should spread more easily.  Do they?

This intuitive idea fails.  There is no obvious correlation between population density and known infection or recorded death rates.  There does seem to be a correlation between "high rent" vs. "low rent" neighborhoods.

This seems to be true in the UK as well.  

“People living in more deprived areas have experienced COVID-19 mortality rates more than double those living in less deprived areas. General mortality rates are normally higher in more deprived areas, but so far COVID-19 appears to be taking them higher still.”

Nick Stripe, Head of Health Analysis, Office for National Statistics.  From:
For some unknown reason Covid-19 infection and death rates of some NYC suburban counties turn out to be higher than some NYC boroughs, including Manhattan.  One might hypothesize that suburban commuters could contract it riding on trains into NYC,  but then why would that produce a higher rate of infection than those who ride subways.  I leave that as a possibility, but it needs an explanation as to why that would produce a higher rate of infection than that of those who already live in the dense environment and ride subways.

There is a definite and strong connection between Covid-19 and the NYC Metro area but it does not appear related to population density since some of the suburban counties with high infection rates actually appear to be rural.  The main carriers of infection seem to have come from Europe to the North-Eastern US.

Other metropolitan areas in NY State have markedly lower infection and death rates than any of the urban or suburban counties around NYC.  Whatever the reason, population density does not appear to be the cause.


Below is a map of Covid-19 cases in NYC by zip code - darker color = more cases.  You don't need to be an expert on NYC geography to know that lower Manhattan is one of the densest parts of the city (and the world) yet it has relatively few cases of Covid-19.

Lighter areas have fewer infections.  Darker areas, more infections.
Data as of April 16, 2020.  Map from under title
"Percent of Patients Testing Positive by ZIP Code in NYC" (bottom of the page)
For those unfamiliar with NYC, the following map of the 5 boroughs will be helpful:

When we look at a 3-D density map of NYC we see that Manhattan is very densely populated as is the Bronx, (the borough to the north-east of Manhattan).  Yet Manhattan had a very low infection rate while the Bronx had a very high incidence of covid-19 infections. (click on image to enlarge).
Numerically we see that the highest number of cases per person was actually in Staten Island (8,112 persons/sq. mile), the least densely populated borough of NYC.  The lowest number of cases per person was in the most densely populated borough  - Manhattan (72,033 persons/sq. mile).
Since density doesn't match with Covid-19 infection rates, let us try matching the Covid-19 map with other maps of NYC to see if we can find some correlation.  There are number of interesting "heat maps" based on various criteria in a Huffington Post article.  The best match is a rent "heat map" of NYC seen below:

The darker the color, the higher the rent.
From Huffington Post:
To show how they match we put the two maps side-by-side below with arrows showing some visual correlations of low infection rates with higher rent areas:

Left map is Covid-19 infection rates.  Right map is rent map.
Arrows show 4 of the areas where low infection rates correspond to high rent areas.
In general, lighter areas on left (fewer cases) correspond with darker areas on right (higher rent).
This seems to be a clear correlation.  The higher the rent, the lower the infection rate.  Why would that be?

The education level map from the same Huff-Post article shows similar correlations but not as strong - see below (click on map to enlarge):

"Degree" map of NYC.  Highest educational levels are darkest orange, lowest are darkest blue.
Some similarities with infection rate, but not as significant as rent map.
In general, the higher the educational level, the higher the income so, based on the previous observations, you might expect higher educational levels to correspond to lower infection rates.  But, there is a lot of variation within each profession so the correlation of (education vs. income) is not as high as that of (rent vs. income).  See graph below:

The average Economics Major makes more than the median Humanities major, but the top 10% of Humanities majors make more than the average Economics major.  Chart from:
So why would paying higher rent mean you are less likely to get infected by Covid-19?

A good guess is that people who work in jobs such as retail clerk, or fast food worker are more likely to live in lower rent housing.  Their jobs put them in more frequent contact with more people and therefore they are more likely to be exposed to the virus.

On the other hand, a professional can work from home and interact with people over the internet with little chance of getting infected.  There are undoubtedly professionals in low rent areas and fast food workers in high rent areas, but that is not as common as the reverse.

To make it more confusing, it turns out that data from a different source ( ) shows several very suburban counties in the NYC metro area have higher rates of Covid-19 infection and deaths than Manhattan.  See following table and map looking at positives and deaths per 10,000 persons (click on image to enlarge)
The 5 suburban counties of Nassau, Suffolk, Westchester, Rockland, and Orange all have higher infection rates (boxed in green) than most of the boroughs of NYC.  Three have higher death rates (boxed in blue) than Manhattan.
The suburban counties mentioned above are shown below for those unfamiliar with the area.

Map of NYC and it's suburban counties.  Orange and Rockland Counties are more rural than suburban.
Orange County, NY has a population density of 471 persons/sq. mile, less than 0.7% that of Manhattan's, yet its "positives" rate is over 60% higher and the death rate is only 20% lower.  Below is an aerial view of Warwick, the largest city in Orange County, NY.

Warwick, NY - largest city in Orange County, NY
Google Maps satellite view
It is notable that other counties in NY State have very much lower infection and death rates than any of these NYC or suburban counties.  See table below, continued from previous table (click image to enlarge):
Counties in other parts of NY State have far, far lower rates of "positives" or deaths per 10,000 persons than NYC and its suburbs
Why would NYC's suburban areas have higher infection and death rates than much of the city itself?  It may be commuters to NYC catch the virus on a train to the city, or on subway once they reach NYC.   Or perhaps the quiet, suburban neighborhood they live in lulls them into a false sense of security.

Those are guesses but there is clearly some connection with NYC.  For example, the metro area of Albany-Schenectady-Troy has 1.1 million people but the counties in that metro area (listed above) have death rates less than 10% that of NYC and it's suburbs.

Might it be that because there are a number of international airports in NYC there are more travelers from abroad going there serving as carriers of the virus?  The article below from"Live Science" indicates that as a factor.

Many more infected people arrived at NYC than at LA or SF, and one person living in New Rochelle (a city in the northern suburb of Westchester County) was a "superspreader" infecting many more people.
New Rochelle, Westchester County, NY
In any event, it is not population density per se that results in higher infections.

Why Does this Matter?

The reason I looked into this was because I know NYC well enough to know that it is not all skyscrapers.  Staten Island would fit anyone's definition of suburban single-family-home neighborhoods even though it is just as much a part of NYC as Wall St. and Park Ave.

271 Isablella St., Staten Island - New York City
I was looking to see what the correlation is between density and infection rates.  To my surprise, there isn't any.  At least not an obvious one.

So who cares?

There is currently a rather fractious debate going on about the virtues of population density in urban areas vs. the less dense suburban areas.  Those living in suburban areas have been getting a little tired of being called names and have seized on the Covid-19 infection rates of NYC as a point against density.  However, the idea that density per se is conducive to the spread of viral infections does not appear to be borne out by the data.

Those arguing for or against density on the basis of infection rates will need to look elsewhere.

For this look at data, we have reached...

Sunday, April 5, 2020

Crash 2020 - Where's the Bottom?

Stock Market Collapse
An Avalanche Waiting to Happen

As the market crashes, a big question is where does it stop falling?

Using standard methods to value the stock market we find that in the year leading up to the current downturn the stock market was extremely overvalued - "an avalanche waiting to happen".

We find the "Dow Jones Industrial Avg." could easily fall over 50% from its top of roughly 30,000 to as low as 15,000 - possibly lower.

Link to this post for sharing:


The most respected valuation methods available suggest the current market downturn could very likely end with the S&P-500 in the range of 1500 to 2000 vs. it's current value around 2500.  That would represent an additional 20% to 40% decline in the the S&P-500.  The equivalent Dow values would be around 14,000 to 18,000.

However, that range would be the long term "median" or "average" value.  Looking at previous downturns such as the 2007-2009 crash, we see the S&P-500 could overshoot down as low as 1130 (or Dow at 12,000) to reach a bottom before rising again.

The economy will be in a worse recession than the 2007-2012 "Great Recession" with unemployment rising to 15% and taking a very long time to recover.

Nothing in here is intended to be investment advice or a recommendation on whether to buy or sell securities of any form.

Stock Market - Crashes and "Rallies"

Market downturns can take over 3 years to go from top to bottom with lots of 'rallies' in between.  The "Housing Bubble" crash of 2007-2009 took a relatively short 17 months with at least 6 rallies along the way - 3 rallies of more than a month - click on chart below to enlarge:

2007-2009 Market crash.  Each bar is a week.
Note 6 rallies with 3 of those rallies lasting 1 or 2 months.
We are currently in a "rally" retracing 50% of the previous drop.  That is a standard type of rally in a bear market - 50% "retracement" is the norm.  While nothing is certain, it is hard to believe it will persist with the widespread economic devastation that is already starting to manifest itself.  More "normal" is a "testing of the previous lows" as the standard market jargon has it.

Each bar is one trading day.  Note that the current rally has only lasted 13 days - less than 3 weeks.
As we saw in a previous chart, rallies can last 5-8 weeks before the downward trend resumes.
According to the President of the Wells-Fargo Investment Institute, since 1929, there have been 13 "waterfall" market crashes like the one in March. Each one was followed by a 50% "rally" (i.e., a retracement of lost value) like the one we see now in the above chart.  Every single one of those rallies failed and the downward trend resumed.  In 9 failures, the market reverse broke through new lows, in 3 the market hit the previous low and bounced off of that, and in 1 rally failure, the market stopped just short of hitting the previous low.  More here:

With unemployment predicted to be 18.8% in California by May, with auto sales projected to be under 12 million in 2020 vs 17 million in 2019, it is hard to see how this gets better in less than 2 years.

Economists' Views:

Dr. Nouriel Roubini of NYU accurately predicted the 2007 crash several months before it actually started.  His view is that the coming downturn will be worse than the "Great Recession" of 2009 when the market fell 58%.  Dr. Roubini interviewed here

"'It's way worse than the Great Depression,' says KPMG economist"

We expect this to be on par with the "Great Recession....We expect a U-shaped recovery but it could be far worse." - S&P Global U.S. Chief Economist Beth Ann Bovino:

'More than 45 million Americans,' could lose their jobs in a possible 'worst case scenario' (= 30% unemployment rate).  Quoting a study coming from the St. Louis Federal Reserve.  "...this resulted in a total number of unemployed persons of 52.81 million. Given the assumption of a constant labor force, this resulted in an unemployment rate of 32.1%."  From:

Stifel's Chief Economist Lindsey Piegza presents more "optimistic" view that unemployment could peak at 15% with a slow recovery.  Economy was already in downturn before the CoVid-19 shock:


On April 3rd, 2020, the US stock market (the S&P-500) was heading down after peaking in February 2020 at 3386.  The regression trend line from the February peak would have hit 1500 around May 10, 2020 at that time.  See graph below.

The S&P-500 at 1500 would represent a 56% drop from the high of 3386 reached on February 19, 2020.  Is that reasonable?  Yes - we had a 58% drop in 2007-2009.  We look at several ways of estimating what "fair value" of the S&P-500 would be and find that the S&P-500 "bottoming" around 1500 is not unreasonable.

The timing is likely to be considerably longer than the few months the regression curve indicates.  Significant downturns have always had intermediate rallies which can last for months.  The "dot-com bubble" in 2000 took 3 years to go from peak to trough.  The "housing bubble" of 2007-2009 took 1.5 years to do the same.  The "Crash of 1929" took nearly 4 years to reach bottom.  See chart below.

1.  Previous Boom-to-Bust Ratios

We had stock highs followed by crashes in 2000 ("dot-com" bubble) and 2007 ("housing bubble").

The S&P-500 peaked at 1552 in 2000, and 1576 in 2007.  Then it dropped.  We see how far it dropped in the chart below from "Yahoo Finance".

Each bar in the chart is a month.  Note the "rallies" interspersed along the downward trend.
Looking at the above chart, we see a 43% drop in the 3-year long 2000-2003 crash after the "dot-com" bubble.  A 43% drop now would mean the S&P-500 would bottom out at 1930 (or maybe "1929").

We saw a 58% drop in the 17-month crash of 2007-2009 after the "housing bubble". A similar sized drop now would give us 1422 on the S&P-500.

Based on previous boom-bust periods, a drop of the S&P-500 to the range [1422 - 1930] is not unreasonable - so 1500 is distinctly possible.    The Dow equivalent range would be 13,000 to 18,000.

2.  Shiller CAPE Ratio:

Our second way to find a bottom is to look at "market value" and see what a fair value would be.  One estimate of market value is the Shiller CAPE Index.  Dr. Robert Shiller of Yale University won the 2013 Nobel Prize in Economics for his work in 'Asset Valuation'.

Dr. Robert Shiller in Stockholm 2013
The stock market is one asset his valuations are most famous for (housing is another).  The Shiller Cyclically Adjusted Price Earnings (CAPE) Ratio simply takes the price of an equity or index and divides it by the 10-year average annual earnings of the stock or index.

CAPE Ratio = Price/(10-Year Avg. Earnings)

The 10-year span of earnings smooths out the almost random year-to-year cyclical fluctuations.  Annual earnings may significantly decline in the next year but the 10-year average will not move that much so this is a reasonable measuring stick.  The next chart shows the CAPE Ratio going back to the 1870s.  Note that it indicates extraordinary overvaluation in 2018, higher than "Black Tuesday" in 1929:

Shiller PE at 23.48 on April 3, 2020
The Shiller CAPE ratio is currently at 23.5 but the median over the last 150 years is 15.8.  For the ratio to get to its' historic median it would need to drop about 33% from current levels.  Further, the CAPE ratio overshoots both ways.  In 2000 it overshot to the high side to hit 44, and in 1982 it overshot to the low at around 7 - see above chart.

With the S&P 500 currently at 2485, to achieve the median Shiller P/E value, the market would need to drop to [ (15.77/23.48) x 2485 ] = 1669 to reach the median value of 15.8.  Nothing prevents it from going below that median value (for years) as it did in the 1970's, 1940's, and early 1900's - see chart above.  If it went as low as 10 where it was from 1975 to 1985, the S&P-500 would bottom at 1058.

While higher than 1500, S&P-500 = 1669 is close enough that 1500 isn't unreasonable, especially when we consider the various times when it has gone below the median value and stayed there for extended periods.

3.  Buffett Ratio:

If you add up the value of all the shares in the stock market you get what is called the "Total Market Capitalization" (TMC).  Warren Buffett popularized a way of determining if the broader stock market is over- or under-valued using the TMC of all stocks divided by the GDP (Gross Domestic Product = all goods and services in the US economy).

Buffett Indicator = TMC/GDP

The chart below shows GDP in green, Total Market Cap in blue.  When the TMC is above the GDP the stock market is expensive by Buffet's criterion.
The next graph shows the TMC/GDP Ratio, which is what we are really interested in.  In the graph below, "Fair Value" is marked between the red 90% line and the blue 75% line.  Note that since the early 1970's we've had a "fairly valued" market only in passing.
The market has been mostly undervalued or overvalued according to Buffett's indicator.  Please note that by this criterion, the market was extraordinarily overvalued in January 2020 at 150%, even more overvalued than the 140% reached in the Dot-Com Bubble of 2000.  We saw similar overvaluation in the previous Shiller chart as well.  This is why we say the current market drop was an avalanche waiting to happen.

From the peak value of 3386, we would need to see S&P-500 fall to between [1693 to 2031] (15,000 to 20,000 Dow avg.) to get to the [0.75 - 0.90] TMC/GDP "fair value" range.  However, that assumes a constant GDP (the denominator).  If GDP shrinks, then the TMC (numerator) would need to decline as well to achieve these indicator levels.  This would cause the actual value of the S&P-500 to fall into a lower range than the above.

Note that the last two market crashes from the 2000 and 2007 high values broke through the lower 75% line - almost reaching 50% in 2009.  A drop to a 50% TMC/GDP Ratio would bring the S&P-500 to 1129! (Dow equivalent 10,000!)  This would be extreme but we saw a similar drop in 2009, with a very swift rebound into the "Fair Value" range.

Also worth noting is that the Buffett ratio stayed well below "Fair Value" from March, 1973 to September, 1995 - more than 22 years!  There is no guarantee of a swift rebound.

Again the numbers we are getting are diverse but indicate that the S&P-500 at 1500 is not unrealistic.

4.  Price to Sales Ratio (P/S)

This is a pretty easy-to-understand measure of stock market valuations.  Divide the price of the stock or index by the sales per share.  More on this method here:

As seen in the graph below, the price-to-sales ratio for the S&P-500 was extraordinarily high (2.32) at the peak in December, 2019.  The price-sales ratio was at the median value before the 2008 crash before sinking to a very low 0.80 in March 2009.  The median value is 1.48.
To get back to the median Price/Sales ratio of 1.48 the market would need to drop to 64% of it's maximum value of 3386.  At 64% of its peak value, this puts the S&P-500 at 2167If it overshoots down to 0.80 as it did after the housing bubble crash of 2008-2009 then we would see an S&P-500 at 1168!

Note that this overshoot down gets us extremely close to the Buffett indicator value of S&P-500 = 1129 we saw as a lower bound for the same 2008-2009 period.  Having two dissimilar indicators produce similar results for extreme conditions gives more confidence in the result.

This only looks at the "price" part of the ratio - the numerator.  It assumes the "sales" part - the divisor - stays constant.  It is far more likely that sales will decline as well, and the S&P value of 2167 value will be excessively optimistic.

In any event, S&P-500 at 1500 is distinctly possible.

5.  EV to EBITDA Ratio

This metric is a variation of the popular P/E (Price/Earnings) ratio.  Instead of price it uses Enterprise Value (EV).  Enterprise value is the market capitalization minus debt.  Instead of earnings it uses the Earnings, Before, Interest, Taxes, Depreciation, and Amortization (EBITDA - sometimes shortened to EBIT).  More at:

Reported earnings can be manipulated by companies to show greater or less earnings while EBITDA comes before any manipulation.  More on EBITDA here:

The next chart shows the EV/EBITDA ratio (in red) tracks very closely with the Price-Sales ratio (in blue) and by the end of 2019 was similarly out of whack.  Since EV/EBIT and Price/Sales are so similar we mention it as yet another indicator confirming our previous estimates of where a bottom to the current crash might be.
We will spare you (and ourselves) the pain of going through another set of calculations.  We just point out that here is yet another widely accepted indicator showing the S&P-500 was seriously overvalued in 2019 - a crash waiting to happen.

Yet again, we see that S&P-500 at 1500 (or the Dow around 14,000) would be perfectly in line with standard metrics and historical precedent.

Nothing is certain, especially with the CoVid-19 virus mutating, potential vaccines, and other unknowns affecting things.  It does seem that the market was unusually over-valued in 2018-2019 and was overdue for a "correction".  That "correction" seems to have a while to go both in time and distance.

"You needn't be so haughty.  I lost just as much in the stock market as you did!"
from 1930 "New Yorker"
For now, this is...

Sunday, September 22, 2019

The "Myth" of a Housing Crisis

A Housing Crisis?

Crisis:  "A crucial or decisive point or situation, especially a difficult or unstable situation involving an impending change".  Does that describe housing in California?

California's legislative season is over and with it the death of some very bad legislation like SB-50.  This was legislation that would have ruined local communities, driving more Californians out of the state in search of a livable place to make their home.

Link to this post:

More on SB-50 (which might be up for consideration in 2020):

With these nightmare laws dead (for now), we can take a look and see that in fact things are as they have been for the last 50 years.  California's home ownership rate peaked during the housing bubble and then collapsed afterward.  It is now back to where it has been historically and is rising (see graph).  It remains consistently higher than NY State's, which is still declining.

Home Ownership Rate
(click image to enlarge)
US rae is similar to that of CA.
CA (Blue): 53% in 1984, 60% in 2006, declined to 53% after the bubble burst.
NY's rate (light green) rose similarly but is even now declining

Home ownership rate rises with average age so, for example, Maine and West Virginia have 73% home ownership rates because there are few jobs there so young people migrate to growing economies like California.

In San Francisco, ground zero for the so-called "crisis", the percent of rent/mortgage "burdened" households has dropped significantly.  That fraction is below that of not only Boston, but also Brooklyn, NY, and even relatively cheap areas like Portland, Oregon.

Rent/Mortgage Burdened Households - 1
(click image to enlarge)
Kings County = Brooklyn Borough
Suffolk County, MA = Boston
Multnomah County = Portland, OR
What the heck, throw in Las Vegas, Chicago, and Orlando, FL for good measure (following chart).

Rent/Mortgage Burdened Households - 2
(click image to enlarge)
Orange County, FL = Orlando
Clark County, NV = Las Vegas
Cook County, IL = Chicago
One can reasonably argue that San Francisco is too expensive for lower income people so they migrate to other communities.  Okay, but even the other communities with lower housing costs have lots of people for whom "the rent is too damn high".  This suggests it is really an income problem not a housing problem.  Let's raise people's incomes with better education and training - that might actually work better than building luxury apts/condos with inadequate parking.

The progress graphed above was slow but steady improvement.  It came without the help of state intervention.  Just the normal ebb and flow of economics with local control keeping development at a human scale, in tune with local communities.

So where did the idea of a housing crisis originate?  Whence Governor Newsom's "3.5 million homes by 2025" urgency?

McKinsey's Contribution to the "Myth"

That "3.5 million homes" comes straight from the McKinsey Global Institute's 2016 Report "A Tool Kit to Close California's Housing Gap: 3.5 Million Homes by 2025".  Central to that was a chart of housing units per person.  See chart below:
McKinsey - Housing Units per Capita
"Exhibit 3" in McKinsey Report, Page 3
More details at
McKinsey claimed that California being 49th in the above chart of "housing units per capita" was the cause of high housing prices in California - so their "solution" was to build more housing to fill this supposed "gap".

Except the bar chart makes no sense.  If being 49th caused California housing to be expensive then the 50th should be even more expensive. The 50th state is Utah.  And the 47th should be almost as expensive - the 47th is Texas.  Neither one is particularly expensive.  Might as well say it plainly - the graph is absurd.

More details here:

It gets worse.  McKinsey then went on to hold up NY state as a model for California to emulate.  But we've already seen that in terms of "housing cost-burdened population" and "home-ownership rate" both NY state and city are worse than California and San Francisco, respectively.

It gets worse.  Switch from McKinsey's "housing units per capita" to a more reasonable "housing units per household" and the difference mostly disappears.  All the states then are seen to have a surplus of housing, including California.  See chart below:

Housing Units per Capita

California has 1,100 housing units for every 1,000 households - a 10% surplus.
Adding 3.5 Million more housing units would result in 3.5 million empty housing units.
Original data for above comes from:,NY,FL,TX,CA,US/HSG010218#HSG010217
The data and McKinsey's misuse of it is discussed more fully at:

The McKinsey report was probably the most widely cited and least read of all the promoters of the myth of a "housing crisis".  If anyone had read past the executive summary, they would have seen the above oddities.

Had they gotten even a little further into the report they would have seen McKinsey's map of San Francisco's "underutilized residential" blocks.  The map of "underutilized residential" includes Grace Cathedral, St. Mary's Cathedral, the Chinese Consulate, a hospital, and almost every landmark house of worship that survived the 1906 earthquake.

SF's Grace Cathedral
"Underutilized" Housing?
McKinsey mapped this as "underutilized" residential potential.  
"Underutilized" Housing Map
Red blocks are the most "underutilized"
(Click map to enlarge)

More details on McKinsey's "underutilized residential" map at:

So okay, McKinsey's report is nonsense - but they couldn't create the "housing crisis" myth all on their own.  There were plenty of others with similarly bizarre ideas of economics.

The LAO's Contribution to the "Myth"

California's own LAO (Legislative Analyst Organization) came up with an analysis of housing prices.  They concluded that if an additional 100,000 units annually had been built over the last 35 years, housing costs would have been lower with 100,000 x 35 = 3.5 million more housing units.  That is where the 3.5 million number originally came from.

This "build more to make the price go down" sounds reasonable at first.   But look around you now (September, 2019) and you see builders avoiding the SF Bay Area because rents and prices are declining a little.  C.f.,

With no idea of how low rents and prices will go, banks won't loan money for construction.  No one wants to get stuck with buildings that sell/rent for less than the cost to build them.  We have seen this before - 100% up followed by 10% down, then 100% up, and again 11% down.  When housing costs go down, builders look elsewhere until rents rise again.  The following chart shows these cycles going back to 1985.

So the "build more" idea doesn't work - as soon as the price drops even a little, the building stops and then at the next boom, the prices rise even more.  The LAO is hypothesizing that builders will build even when it makes no economic sense.  The LAO's hypothesis is clearly false as reality keeps repeating.

HCD's Contribution to the "Myth"

HCD = "Housing and Community Development"

Home Ownership:

California State's Department of Housing and Community Development (HCD) has made their own contribution to the "housing crisis" myth.  In a 2018 publication they showed home ownership in California as the lowest in 40 years.  HCD's graph is shown below:

Home Ownership Levels - US and CA
(click image to enlarge)
From: "California's Housing Future: Challenges and Opportunities Final Statewide Housing Assessment 2025"
We saw earlier that California's home-ownership rate went up with the housing bubble and declined when the bubble burst - similarly to the rest of the US.  Yet for the graph above, HCD selected a small subset of available data to show only the decline.  This can be seen in the Federal Reserve Economic Data ("FRED") chart below:

CA Home Ownership Levels
HCD Data Selection
(click image to enlarge)
Data for 1984 - 2018; Updated April 4, 2019
US Census data on home ownership goes way back to the early 1900's which HCD acknowledges when they write "...reaching the lowest rate since the 1940s" (op. cit., page 18).  Yet HCD decided to show only a 10-year period of declining ownership.  HCD's report was published in 2018 yet they stopped their data selection at 2015.  The HCD truncated data selection was cited early in California State Senator Scott Wiener's SB-50 (2019) as justification of the extreme measures in his bill.  Without the context of available data this serves to promote the myth of a "housing crisis".

Putting a Builder in Charge of HCD

It is hardly a surprise that California State's Department of Housing and Community Development added to the myth when for years the person in charge of it was a developer himself.  Naturally he will have his staff cherry-pick the data so he can argue against single family housing and for "by right development" - i.e., fewer home ownership opportunities, more rental apartments, and no restrictions by pesky local residents and their elected representatives.  For his arguments in full see:


HCD also is in charge of California's "Regional Housing Needs Allocation" (RHNA) requirements.  These requirements have been widely misconstrued.  People think RHNA numbers are state requirements that cities must cause to be built a certain amount of housing.  That isn't what RHNA numbers are for.  RHNA numbers are a planning tool.  RHNA requires cities and counties to zone for housing.  Cities and counties have no way to build housing - that's up to builders and the market.

Amador City, CA  Population 186...
... and declining
Amador's RHNA numbers were for 2 housing units.  No one built them so..
Amador didn't "make their RHNA numbers"...
...and for that are subject to penalties under SB-35
There are over a dozen counties in California that have actually lost population in the last decade.  There is no reason for anyone to build there.  So, those counties didn't "make their RHNA numbers" - i.e., no one built the housing to fill the zoned areas.  Because they "didn't make their RHNA numbers" they are subject to penalties under SB-35.  Those who don't understand RHNA numbers think it is cities and counties standing in the way of housing, so the myth gains traction.

Homelessness - is that the Housing Crisis?

Stories of the homeless and the displaced are always in the news.  But these problems are worldwide.  A report from Yale shows the US with about 0.17% of the population being homeless.  This is about average among the OECD countries - between Austria and the Netherlands - and well below the rates in Canada and Germany.  See bar graph below:

Homeless %-age of Population
(click to enlarge)

"Trends in homelessness among OECD countries with available data are mixed. In recent years rates of homelessness are reported to have increased in Denmark, England, France, Ireland, Italy, the Netherlands and New Zealand, while decreasing in Finland and the United States."

More efforts and money should be put into housing the homeless - especially veterans and families with children -  but homelessness may never go away until humans find a cure for bad luck, addiction, mental illness, etc.

Causes of Homelessness
(click to enlarge)
Job loss + substance abuse + jail = 56% of causes
More housing and treatment centers would have been a worthy use for the $21 billion budget surplus California had this year.

California's 2018 $21 Billion Surplus
(click to enlarge)
The 2019 California state budget includes "$1 billion for homelessness—to support local governments in developing an integrated approach to tackle their homelessness issues." out of a 2019-2020 revenue of $144 billion.  From page 71 of CA State budget:

California Renters and Owners

One measure of housing affordability is home ownership.  In the US, about 64% of adults own their own home - a number that has been pretty constant over the last 5 decades.  In states with older populations it tends to be higher and in states with younger populations (like California) it tends to be lower but there are exceptions.  New York State, for example, has the lowest rate of home ownership.

By that standard, California as a whole is affordable - i.e., most people own their own home.  In all but two of the 58 counties in California the majority own the home they live in.  The only exceptions are the counties of San Francisco and Los Angeles.  Those two counties also happen to be where the major California media outlets are. 

For example, in Alameda County, across the Bay from San Francisco, owner occupancy is 53%.  In Santa Clara County, it is 57%.  In San Diego County it is 53%.  In California as a whole, it is 55%. 

Looking at the following graph it is hard to determine any pattern.  Rural inexpensive Lassen County has about the same ownership rate as expensive suburban Contra Costa County, rural Colusa County about the same as very expensive Marin County.  See bar graph below (not all 58 counties are included for space reasons):  (click on graph to enlarge)

Some smaller counties have been omitted for space considerations. 
Data from US Census available here: add or subtract counties as desired
This data is available at:,alamedacountycalifornia,losangelescountycalifornia,sandiegocountycalifornia,sanfranciscocountycalifornia,CA/HSG010218
Counties may be added or subtracted using Census search bar in upper right of link above

The myth gains even more traction as young reporters find that their salary doesn't go as far as they had hoped.


So what will happen with housing in California?  The same thing that has been happening since the 1980's when it started getting expensive.  Housing costs will decline a little more, maybe go flat for a while, and then go back up as more startups grow, bringing in well-paid talent that can afford the housing.  Some people will leave for less expensive places, others will come for the high-tech job opportunities. People will complain - they always do.  For most people it will all turn out right.

For now, this is...