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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 mirrored that of the US as a whole.  Ownership 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 rate 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.  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.  This leaves a lot of couples in W. Virginia in homes by themselves, and means there are more young people in CA or TX just starting out in life and renting until they can afford a home.

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.  That may be, 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 Household

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
McKinsey Calls it "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 pseudo-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 - 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).  HCD knew there was more data and had to have had access to it or they couldn't have selected the data they published.  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 data comes out yearly so they had plenty of time to get the latest data for publication.

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 readily available real data in full context over a meaningful time period, 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 last 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):  

Home Ownership in CA by County
(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 1970'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...

Tuesday, August 13, 2019

GHG Emissions w.r.t. Climate Action

Climate Action - What is Attainable?


Sunnyvale has published their "Climate Action Playbook".  It appears to rely heavily on reduction in Vehicle Miles Traveled (VMT) though denser living.  We show that is a counterfactual concept using US Census data from 2002 to 2015.  This shows an 18% increase in Sunnyvale's population resulted in a 33% increase in VMT of those living in Sunnyvale.

Link to this post (for sharing):


When looking at Sunnyvale's "Climate Action Playbook" I was struck by the attempt to lessen Greenhouse Gas emissions (GHGe) by reducing the total Vehicle Miles Traveled (VMT).  The thought is to do this by building more "mixed-use" (retail + housing).  I guess the thought is that if there are stores and work places nearby people won't drive so much.

Using VMT as a metric makes little sense for several reasons.  One is the rather obvious reason that someone driving 100 miles in an electric car has different GHG emissions than someone driving a 15 mpg pickup.

Both have VMT = 12,000 miles per year.
Left Side: Electric = NO Tailpipe Emissions  -  Right Side: V8 = LOTS of GHG Emissions
Why are we concerned with VMT?
In Palo Alto CA, 30% of new cars were Electric Vehicles.

As the price of batteries continues to drop we will see that repeated around the world.  Price parity between Electric Vehicles and Internal Combustion Engines by 2025.  VW is converting 3 factories to 100% EV production by 2021 for 1 Million EVs per year.  See slide below for just 1 factory (Zwickau, Germany):
The future is coming faster than most realize.

But there are other consideration which make VMT even odder as a metric.

Let's look at the 2017 GHGe by source for California from the California Air Resources Board (CARB).

Figure 1 (click image to enlarge):

California Greenhouse Gas Emissions for 2000 to 2017

by California Air Resources Board
28% Due to Passenger Vehicles

We see above that transportation is 40.1% of California's GHG emissions but 12% is due to trucks, planes, heavy equipment, etc., leaving passenger vehicles at 28%.   We really can't do anything as a city about the 12% of non-passenger vehicle GHGe (including ships and planes) .  That leaves us with passenger cars. 

Between 25% and 30% of passenger car's VMT is for commuting.  That means that to achieve a 20% reduction in GHGe from vehicles virtually no commuting by any sort of vehicle would be possible.
See figure 2 below:

Figure 2 (click image to enlarge)
VMT By Purpose
Around 27% of VMT
US Dept. of Energy
This is, practically speaking, impossible.  About 85% of commute VMT is by the 50% of workers who commute more than 10 miles.  It is inconceivable that we can get 50% of families to abandon their houses and move closer to work.  For a lot of workers - like plumbers, electricians, construction workers - this isn't even possible because "work place" changes every hour.

VMT for commuting is covered in depth in  Buses go about 10 miles/hour so 10 miles by bus is the upper limit most people would allow for commuting.

There was very little change over the 40-year time period 1969 - 2009.  In 1969 the percentage of household vehicle miles commuting was 33.7% and by 2009 it was 26.7% shown in figure 3 below:

Figure 3 (click image to enlarge)

Percentage of VMT by Purpose
From Federal Highway Administration document
This reduction in percentage of VMT commuting was due to an increase in total VMT per person. The reduction in %-age simply meant that commuting distances increased less than other passenger vehicle uses.

The "Climate Action Playbook" (CAP) looks for a 20% reduction in VMT per person by 2030 and 25% reduction by 2050.  This is an enormous (i.e., improbable) undertaking as we can see in figure 4 below:

Figure 4 (click image to enlarge)

US VMT per Person 1970-2018
20% reduction = 1987 Levels
25% reduction = 1985 Levels
Graph from "Federal Reserve Economic Data" (FRED) charting tool.
What makes this drastic reduction even more improbable is that US Census data shows as SF Bay Area population density increases the average VMT increases as well.

For example, in Sunnyvale over the period from 2002 to 2015, there was an 18% increase in resident workers yet the VMT of those commuting out of Sunnyvale increased in all categories with a total VMT increase of 33%.  See figure 5 below:

Figure 5 (Click image to enlarge)

Sunnyvale Residents:
Population Increases 18%
VMT Increases 33%
2002 - 2015

In tabular form it looks like this:

Table 1:  Sunnyvale Resident Worker Commuting OUT of Sunnyvale

Data is from OnTheMap as seen in a sample in figure 6 below:

Figure 6:  OnTheMap results for 2015.  Sunnyvale selected as "Home" in "settings".

Tool address:
Based on this historical data, increasing density increases VMT.  We can see exactly the same thing happening when looking at Palo Alto which has a LOT of jobs.  Nonetheless, as more residents moved to Palo Alto, both the number and

Young People Not Driving?

There is an idea that the younger generation is less inclined to use cars for transit.  There is some truth to this, but it is not a huge effect and is dwarfed by the increase in the number of young people.  Overall the effect is invisible in re VMT reduction.  See figure 7 below:

Figure 7 (click image to enlarge)

20-24 Y.O.'s with Driver's License:
Drop of 4.3% of 20-24 Y.O. with License, but..
15% Increase in Total Number of 20-24 Y.O. Drivers

Data from:
Chart from:

Electrification of Vehicles:

So what is the answer to GHGe from vehicles?  Electrification of transport is proceeding very rapidly.  The decline in price of batteries and therefore of electric vehicles is following a reliable path so that by 2025 the purchase price of a new electric vehicle should be the same as that of new internal combustion engine.
Prices of EVs (Electric Vehicles) will decline from that point on and it will become increasingly uneconomical to buy a petroleum-burning vehicle.  See figures below:

Figure 8:
Battery Price Decline
Figure 9:

Electric Vehicle (EV) Price Decline
Price Parity with Petroleum Cars by 2025
Cheaper after 2025!
A medium sized car (e.g., Camry, Malibu) will be cheaper as an Electric Vehicle than a gas guzzler.

Figure 10 (click to enlarge):


Any program that relies on changes in human behavior is highly unlikely to be successful.  That would rule out significant reductions in VMT through denser housing arrangements.  Much better in terms of attaining goals should look at what can be realistically achieved without postulating changes in human nature.

In the case of Sunnyvale's "Climate Action Playbook" that more realistic action would be inducing companies and households in Sunnyvale to go to net zero buildings with minimal to zero affect on their living situation.

Tuesday, June 18, 2019

McKinsey: Housing Gap? Part 1-A

McKinsey Global Institute
California's "Housing Shortage(?)"
(AKA 'Housing is expensive in Utah and Texas.')

The oft-repeated claim of a 3.5 million homes "shortage" in California is partly based on the McKinsey Global Institute's 2016 Report "A Tool Kit to Close California's Housing Gap: 3.5 Million Homes by 2025".  The McKinsey report takes data so far out of context as to be counterfactual.

Link to this post for sharing:

McKinsey Global Institute Report
The McKinsey Global Institute's (MGI) 2016 Report on their perception of California housing issues is available at:


We look at McKinsey's presentation of their main thesis that housing is expensive in California because there is a shortage of it.  This is based on comparing states using "housing units per capita" as the metric.  Using instead housing units per household we find the disparity between California and other states disappears.  Moreover, California has a surplus of housing units per household, as do all other states.

This is not to say that housing is not expensive in parts of California, but rather that a shortage of housing is not the cause.  The causes will be discussed in a later post.

One set of issues is their analysis of available sites for more housing construction in San Francisco.  The map is problematic enough to deserve it's own separate post.  That post is here:
(Spoiler - McKinsey lists Grace Cathedral - among other landmarks - as underutilized residential housing).
McKinsey's version of "underutilized residential housing"

Forensic Analysis - Part 1-A:

McKinsey's Thesis: that there is a "housing gap" in California.  It is expressed in the statement that:

"..the combination of higher demand for housing and insufficient supply has inevitably pushed up California’s real estate prices" 

(second paragraph on page 4 of MGI report - PDF page 12)

Crucial to McKinsey's thesis is their "Exhibit 3" found on page 3 - the bar graph reproduced below. This purports to show that housing in California is expensive because California is 49th out of 50 states in "housing units per capita" - a metric which McKinsey derived from other census statistics.

Graph 1: (Click image to enlarge)

Housing Units Per Capita 
for 13 States
In "Housing units per capita"
Utah is 50th (fewest) California is 49th, Texas is 47th.
("Exhibit 3" in McKinsey Report, Page 3)
McKinsey uses this chart to argue that since being 49th makes California expensive then for California to become more affordable, it needs to 'catch up' with New York and New Jersey in housing units per capita.  To do this California needs to build about 2 million more housing units.

Here's what's wrong with McKinsey's argument:

The chart shows Utah is 50th - meaning that Utah has the fewest housing units per capita - and Texas is 47th.  If California housing is so expensive because it is 49th on the chart then, by that reasoning, housing in Utah should be even more expensive and housing in Texas should be only slightly less expensive.

Utah's median house value is $238,300 - little more than half California's median house value of $443,400 (US Census - 2013-2017).  An example of Utah's "median house value" is below:

Median Utah House - 5 BR, 2 BA, 1,802 sq. ft. 
(Click on image to enlarge)
Utah - Lowest on McKinsey's "Housing Units per Capita"
By McKinsey's reasoning, Utah should be the most expensive in the US
Utah's median house price is $238,300
Showing house values for those same states in the same order as McKinsey's graph gives us graph 2 below (US average added - red bar):

Graph 2:  (Click on chart below to expand)

Median House Values - 2018
McKinsey's 13 States 
Same Order as "Exhibit 3"
Data From US Census Bureau's "QuickFacts"
Add or remove states from "QuickFacts" as needed
Texas - 47th in McKinsey's list - is far below the US average in housing costs at $151,500 - roughly one-third of California's $443,400.

Texas 3 BR 1 BA, 1,641 Sq. Ft. Typical Home
(click on image to enlarge)
Texas - 47th out of 50 states in "Housing Units per Capita"
Lowest Price of McKinsey's selection of 13 states
House above approx. = Texas' median house price of $151,500
Clearly, there is no relationship between housing values and "housing units per capita".  McKinsey's chart and therefore its conclusions as to cause and solution to housing costs is invalid.

The error in McKinsey's argument is that households live in housing.  The household may be a single individual or a multi-generational family but one needs to look at housing units per household. not per capita.

Households sizes vary widely among states.  We can see this in the following chart of the household sizes of the 13 states McKinsey chose - with the US added as a red bar:

Graph 3:  (Click on chart below to expand)
Household Size by State
Same Order as McKinsey's "Exhibit 3"
Data From US Census Bureau's "QuickFacts",NY,FL,TX,CA,US/HSG010218#HSG010217
Add or remove states from "QuickFacts" as needed
Redoing McKinsey's "Exhibit 3" per household instead of per capita we see the disparity among states disappears - see graph 4 below.

Graph 4:  (Click on chart below to expand)
Housing Units Per Household 2018
Same States in Same Order as McKinsey's Exhibit 3
Data From US Census Bureau's "QuickFacts",NY,FL,TX,CA,US/HSG010218#HSG010217
Add or remove states from "QuickFacts" as needed
Corrected for "household" instead of "per capita" we see a surplus of housing in every state.  California has 1,100 housing units (apartments, condos, or single family houses) for every 1,000 households.

In switching from McKinsey's metric of "per capita" to "per household",
  1. Utah goes from being the lowest in housing units per capita to being dead average in housing units per household for the US.  
  2. Wisconsin goes from being well above the US average per capita to exactly the US average per household.  
  3. Massachusetts goes from being exactly the US average per capita to the second lowest per household of the states McKinsey chose.
  4. Texas goes from well below average per capita to above average per household.
  5. California goes from well below average to a little below average but still with 10% more housing units than households.
By using the metric housing per capita (unique to McKinsey) instead of housing per household, McKinsey creates a housing disparity that does not exist.  Utah's household size of 3.14 is 34% bigger than Maine's 2.34.  That is why Utah has fewer housing units per person - because each household has more persons.

Florida is another example of the error of using housing units per capita, Florida has a very large housing surplus - 26% more housing units than households.  I.e., Florida has 5 housing units for every 4 households.  This is due to the many second homes and vacation condos for "snowbird" tourists from the northern US, Canada, and other countries.  

Florida's Excess Housing Units per Capita
Looks nice!  For rent by the week or day. 
Part of why Florida has so many housing units.
Maine has an incredible 34% more housing units than households.  That's over 4 housing units for every 3 households!  This is because the Maine coast is a vacation escape from the sweltering Summers of the northeast urban areas.  For a sparsely populated state like Maine, it doesn't take many vacation homes to make a big difference.

The two examples of Florida and Maine show how much information is hidden by using housing units per capita.

California's housing surplus of 10% (using housing units per household) is less than that of the other 12 states that McKinsey selected for comparison but not by a huge amount.  An additional 2% to 2.5% more housing units would make California comparable in housing units per household to Massachusetts, Washington, or Oregon.  That is only an additional 322,500 housing units.  Not a big deal and certainly nothing like the 2 million housing shortfall McKinsey claims.  See graph 5 below:

Graph 5:  (Click on chart below to expand)

Here are the calculations:
  1. California has 14,176,670 housing units for it's 12,888,128 households.
  2. To get to Oregon's 1,125 per 1,000 households it would need 1.125 * 12,888,128 (households) = 14,499,144 housing units.
  3. That is (goal - current) or (14,499,144 - 14,176,670) = 322,474 more housing units than it currently has.
To further illustrate the errors of McKinsey's "Exhibit 3" we look at graph 6 (below) which includes all 50 states ordered from left to right by McKinsey's "housing units per capita".  (Click on graph to enlarge).  It includes for every state average home ownership (bars) and "housing units per household" (purple line). 

We see very, very little variation in housing units per household (purple line on top).  Note that New York (mentioned by McKinsey as a "reference case") is the only state with lower home ownership (54% - green bar in graph 6) than the 55% of Nevada (yellow bar) and California (gold bar).  Yet McKinsey selects New York as a model for California to emulate.

Graph 6:  (Click on chart below to expand)
Housing Units Per 1,000 Households
2018 - With Home Ownership Rates
States in Order of "per capita" as in McKinsey's Exhibit 3
From US Census "QuickFacts",ny,fl,tx,ca,US/HSG010217#HSG010217
Add and delete states as needed.
As seen above, West Virginia has the highest home ownership rate (73%).  This is because it is in economic decline.  Young people leaving school find no jobs near home so they leave the state.  Their families remain in the old family home that was paid off years ago and for which there are no buyers.  Most of the other states with home ownership above 70% have stable populations with lots of flat open space to build on.

"Housing units per household" actually varies a lot not just from state to state but quite a bit year-to-year even for the same state and the US as a whole.  Graph 7 below shows that the metric "Housing Units Per 1,000 Households" for the US varies from 1,080 to 1,120 over the 19 years 2000-2018.  That means the surplus of housing can run from 8% to 12% over 19 years.  The largest number of housing units per household was at the peak of the housing bubble in 2010.

Graph 7:  (Click on chart below to expand)
US Housing Units Per 1,000 Households
High 1,120 - Low 1,080
The number of Housing Units always exceeds the number of households.
"Housing Units to Households" varies with recessions and growth.
Data from
use "Edit Graph" to add "ETOTALUSQ176N"
(The ratio differs a little from "QuickFacts" numbers because "QuickFacts" uses a 5-year average which tends to overstate the number of current housing units.)

McKinsey clearly knows about the US Census' count of "household".  The McKinsey report states (footnote 2, PDF page 10 = report page 2):

"By focusing on units per person instead of units per household, we control for variations in household size that may be caused by differences in housing prices."

McKinsey is arguing that if people were to live together in households to save on housing costs then the number of people in a household would be artificially high.  This would skew the data on a "per household" basis.  I.e., they hypothesize that higher housing costs induce greater household size.

We can test this hypothesis to see if it is true.  By McKinsey's logic, if higher cost housing induces larger household sizes, then lower cost states should have smaller household size.  To see that is not the case look at the 13 states in graph 2 (repeated below) that McKinsey chose in their "Exhibit 3".  We saw before that Texas and Utah have large household sizes yet we see here (again) they have lower housing costs.

Continuing with graph 2 below, we see that Massachusetts has also very high housing costs.  If high housing costs induce larger households, as McKinsey hypothesizes, then Massachusetts should have a large household size.  However, Massachusetts' household size is below the US average (graph 3 earlier).  Yet again, the McKinsey argument sounds plausible until you look at the data - then it falls apart.

Graph 2 (repeated)

McKinsey's claim of a housing shortage includes the idea that new housing unit construction is inadequate.  This is based on the years 2009-2014.  Graph 8, below, shows new housing unit construction with red bars for the years that McKinsey selected for data analysis.  As can be seen, the 5-6 years McKinsey focused on were the absolute worst for California's housing construction in the last 40 years.  Did McKinsey cherry-pick" the time frame?

Of equal significance is that by 2018 the market was responding to the situation with near record construction rates.  Even by McKinsey's metric of "housing units per capita" the situation is being remedied without extreme measures needing to be taken.

Graph 8:  (Click on chart below to expand)
California 1989-2018 (40 Years)
New Housing Units per 1,000 new Residents
Add "CAPOP" via "Edit Graph" function
A housing surplus of 8% is normal.  Additional homes are vacation homes, vacant apartments waiting for renters, housing in transition from one owner to another, etc.  The US Dept. of Housing and Urban Development document below discusses the number of second homes and the difficulty in counting them here:  

A US Census document describes the recent situation (see graph below).  In Q1 of 2011 there was a US vacancy rate of 9.7% in rental units and 2.1% vacancy in own-able units.  This was due to a surplus left over from the speculative over-building that took place during the housing bubble that peaked in 2007.  Eight years later (Q1 2019) this had shrunk to 7.0% vacancy for rental units and 1.4% for own-able units.

Graph 9:  (Click on chart below to expand)

The home vacancy rate for California during the 2009-2014 period that McKinsey uses - which they term a period of "robust growth" - included the highest vacancy rates in the 33 years for which data was collected (1986-2018).

Graph 10:  (Click on chart below to expand)
In the next graph we see that New York state (McKinsey's "reference case") saw a much smaller variation in vacancies so there was relatively little decrease in home construction.  McKinsey's using New York state as a reference case again seems like "cherry-picking" data.  As the vacancy rates in California fell, home building there resumed.  In the last few years we see vacancy rates rising back to normal.  See graph 11 below:

Graph 11:  (Click on chart below to expand)
Vacancy Rate 1986-2018
NY and CA - Rental & Own-able
Use edit graph to add other data sets
Of course, new home construction dropped precipitously during the post "housing bubble" period as seen in graph 12 below.  This was due to the record number of housing units sitting vacant (graphs 9 & 10, above).  As seen in graph 12 below, housing starts were over 20,000/month in 2005.  A year and a quarter (16 months) at that rate and the housing numbers could be the same as Oregon's or Washington's.  Even at the recent 2019 pace of about 10,000/month, that would take only about 3 years.  However since we saw earlier that housing quantity does not seem to correlate with price, even that is irrelevant.

Graph 12
Monthly Building Permits
NY and CA
1988 - 2019


We covered the mistakes McKinsey made to reach a number of a "gap" of 2 million housing units and found that, in fact, California has a surplus of housing.  Even if it is not enough of a surplus, it would only need at most about 2.3% more housing units to reach Oregon's and Washington's level of "Housing Units Per Household".  That number can be easily built at current rates.

That still leaves unexplained where the additional 1.5 million housing units McKinsey claims are needed by 2025.  That number is derived from expectations of future growth.  There are similar problems with McKinsey's analysis there but we will save that for a future post.

There is much more to write about MGI's report but this is getting too long so we will cut it short for now and return in Part 1-B