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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

McKinsey: SF Density & Cathedrals

Forensic Analysis of McKinsey Report
Part 2


This part of the analysis looks at the McKinsey Global Institute (MGI) report on "infill" housing that could be constructed in San Francisco and Los Angeles. We note a large number of glaring errors that anyone with even a casual knowledge of San Francisco could see are absurd.  In-fill housing to replace Grace Cathedral, St. Mary's Cathedral, and the Consulate of the People's Republic of China (among many others)??!
Grace Cathedral - McKinsey says it is underutilized housing

Link to this blog post for sharing:

The previous post on McKinsey's report is here:


McKinsey purports to have found space for 590,000 to 990,000 (average = 780,000) potential housing units in San Francisco by simply building-up the density of existing residential property spaces to their maximum capacity.  McKinsey pays particular attention to those severely underutilized marked in red (<25% utilization) and yellow (<50% utilization).  See map below:

Map is from report page 19, exhibit 13.
However, many of the spaces they marked as very under-built (i.e., less than 25% of zoned capacity) are actually landmark churches such as St Mary's Cathedral, Grace Cathedral, a hospital center, numerous historic landmark churches and various religious schools.  These represent the majority of severely under-utilized spaces yet are clearly not available.  Their estimate of 780,000 potential housing units in San Francisco is therefore invalid.

Some of the large spaces marked in red as "under-utilized housing" are such well known San Francisco landmarks that it is hard to believe that anyone (including the McKinsey authors) actually looked at the map McKinsey features so prominently.  See map below:

Annotated map,  More detail below.
There may or may not be opportunities for increasing density in San Francisco without changing zoning but McKinsey's "analysis" is so deeply and obviously flawed it is essentially "fake data".

The McKinsey report is available here:

Detailed Discussion:

Their key points are summarized on page 6 of the report.  Looking at McKinsey's item 2:

"Item 2.  Increase Density of Urban Areas"

MGI recommends increasing the housing supply by various means including building "nearly one million units on land zoned for multifamily development but underutilized" (page 6, upper right column).

On page 26, the MGI authors write:

"To determine the size of the opportunity, we mapped every land parcel in two counties: San Francisco and Los Angeles. We examined existing density on every residential parcel and identified parcels zoned for multifamily use that contain multifamily buildings that are not fully utilizing zoned capacity."

Whew!  Sounds like a lot of work examining "existing density on every residential parcel...that contain multifamily buildings that are not fully utilizing zoned capacity.".  

Here is the map of San Francisco they came up with to show underutilized parcels.  I added some numbered rectangles for reference to look at some of the blocks "that contain multifamily buildings that are not fully utilizing zoned capacity. The legend in the corner shows that dark red means less than 25% of the zoned capacity is used.  We need to remedy this!  Let's see who these culprits are!  

We check out some of the under-utilized lots in the numbered rectangles below.  Map is from report page 19, exhibit 13.

For those of you on the go, here is the short version.  The same map as above but with some (not all - there were too many to fit) of the more historic and "unlikely to be housing" places McKinsey marked as "underutilized" for housing.  Click on image to enlarge.

I had to leave off the map some of the historic landmark churches and other buildings in the above map but they are included in the full investigation below.  These are some fascinating stories that anyone who loves San Francisco (as I do) would find it worthwhile to learn about.  You may live there (as I did for 5 years) and walked right by these gems without realizing their history.  All the history that McKinsey Global Institute (MGI) urges be torn down and made into high rise apartments.

But the point remains.  Their entire story of underutilized housing has no credibility at all since they clearly did not look at the properties they designated as "under-utilized" - unless they are seriously recommending tearing down Grace Cathedral and many, many more landmark churches and schools for housing.

Block 1 - Purple: Holy Trinity Cathedral, St. Brigid's School

As you can see, at location A at the top is the clearly marked "Holy Trinity Cathedral" and at location B at the very bottom is "St. Brigid School" with a church right next to it.  These are marked by MGI in bright red as two of the "nearly one million units on land zoned for multifamily development but underutilized".  Just three small parcels but perhaps MGI was being a tad careless in checking their map for underutilized land?

Box 1, Location A:  Holy Trinity Cathedral - Built 1909

The oldest Orthodox parish in the lower 48.
Built 1909 after the 1906 Earthquake destroyed earlier one.
The oldest Orthodox parish in the lower 48.  Holy Trinity Cathedral Parish traces its history to December 2, 1857.  Bright red on MGI's map.

The bells in the cathedral were made in 1888 in Moscow.  "And I love the bells calling people to the church.  The ringing is not sing-songy as is common in America." Review at: 

Box 1, Location B:  Saint Brigid Church - Built 1902

Saint Brigid Church - Built 1902
California Landmark
Saint Brigid Church on 2151 Van Ness Avenue at Broadway.  And right behind it on Broadway is St. Brigid's school.  Both are marked on the map in red by McKinsey Global Institute as less than 25% of potential housing capacity.

St. Brigid's School.  125 years of education.
McKinley Global Institute lists it as severely "underutilized" for housing.

Block 2 - Light Blue: Grace Cathedral, Numerous Hotels, &c.

Continuing on looking at some of the "nearly one million units on land zoned for multifamily development but underutilized"  Several very large red blocs all together certainly stand out in block 2 in the right of the map.  Out of curiosity I looked more closely.  Fortunately, key streets like California and Van Ness are clearly marked on MGI's map so it is easy to locate on a Google map where the corresponding red blocks indicating "<25% utilization" are on MGI's map.

It turns out none of the areas marked in red are "underutilized for housing".  MGI gets a zero for this one.  Click image to enlarge.

For those unfamiliar with San Francisco's landmarks in this area, I have assembled the following information on these "underutilized housing units".

Grace Episcopal Cathedral
(Upper Left Corner of Boxed Area)
Cornerstone laid in 1910
Included on MGI's map of areas of San Francisco underutilized for housing.
Historic Landmark and fully functioning Episcopal cathedral - including a school.  Wikipedia entry:  

For good measure, MGI has also included as underutilized "multi-family housing" the large Masonic Lodge on the other side of California street in the lower left corner.  I have omitted a picture.  It isn't that interesting.  It also isn't housing.

James C. Flood Mansion - 1886
Pacific Union Club

Historic Landmark - currently occupied by the Pacific Union Club.  It is the only one of the Nob Hill Mansions to survive the fires following the 1906 earthquake.  The other mansions were made of wood painted to look like stone but the Flood Mansion was made of real stone imported from Vermont.  Wikipedia entry: 

Landmark Hotels
Fairmont, Mark Hopkins, Huntington, Stanford Court, 
All have Historical Landmark Status
None of them are currently "underutilized housing".

Fairmont Hotel - built 1906

Mark Hopkins Hotel - built in 1926

Stanford Court Hotel - 1911
Originally Luxury Apartments
Huntington Hotel - 1922
Originally Apartments - converted to hotel in 1924
None of the areas marked in bright red as "underutilized" for housing are underutilized housing areas.  Not one.  One might give McKinsey a pass on the earlier churches and school since they were small parcels, but not noticing some of the most famous hotels in the country, not to mention the highly visible Grace Cathedral, has to bring into question the validity of their research on this.  Even if they were made housing this is one of the priciest areas in San Francisco - any housing there is going to be very, very expensive.  Let us go on.  It gets worse.

Block 3 - Purple: St. Mary's Cathedral, Historic Churches, Chinese Consulate, Schools

St. Mary's Cathedral in the center.
Click map above to enlarge for readability
Anyone who has lived in San Francisco has to know Geary Blvd.  And one of the most notable landmarks on Geary is St. Mary's Roman Catholic Cathedral.  This is also marked in bright red by MGI (center upper right of box) as one of  "nearly one million units on land zoned for multifamily development but underutilized"  Click image to enlarge.

Cathedral of Saint Mary of the Assumption
Commonly called St. Mary's Cathedral
MGI marks it as underutilized housing.
(click image to enlarge)
The cornerstone was laid in 1967.  In 2017, Architecture Digest named it one of the 10 most beautiful churches in the United States.  It is not hard to miss - it is impossible to miss!  

It seems MGI didn't even look at their own map.

St. Mary's interior
St. Mary's is one of the "multifamily buildings that are not fully utilizing zoned capacity."  I guess MGI figures a nice 25 story apartment building should go there.  "Really Bishop, it will be very nice, all glass and steel, you can use the basement for bingo night, lots of underground parking."

Also in the map above are some much smaller historical landmark churches still in active use (marked by McKinsey in red, of course).  In the upper right corner of box 3 in the aerial view is: 

First Unitarian Church - built 1889
"Apartments go here!" according to MGI
Church Website:
Located at 1187 Franklin Street at Geary on Cathedral Hill, built 1889 (survived 1906 earthquake), it is still in active use.  It is "Historic San Francisco Landmark" #40 

Right next to it (in red) is a Montessori School -
MGI marks this as severely "underutilized" housing.
McKinsey Global Institute doesn't like playtime.  

MGI goes in the corner until they learn to play well with others.
In the same red area is the Sarcophagus of Thomas Starr King honoring a Unitarian minister from the Civil War era.  He helped put California on the Union side in the American Civil War and worked to form a precursor to the US Red Cross.  Rev. King has a mountain named after him in Yosemite, another in New Hampshire, and a statue in Golden Gate Park.  The phrase "A man to match our mountains" refers to him.  The sarcophagus is Historical Landmark #691. 

Just below the First Unitarian Church in the far right of box 3 is St. Mark's Lutheran Church, also marked in red.  Dedicated in 1895, it survived the 1906 earthquake and fires and is in active use.
St. Mark's Lutheran Church - 1895
San Francisco Historic Landmark #41
Some of McKinsey's "underutilized housing"

St. Mark's interior.  Photo from:
Looking down the aerial photograph you see part of Sacred Heart Cathedral Preparatory high school associated with St. Mary's Cathedral.  It was founded in 1852 though obviously the building is newer.  This was also marked by MGI in bright red as underutilized housing.
Exterior View of Sacred Heart Cathedral Prep.
School website:  Wikipedia entry:

Also in box 3 just to the left of St. Mary's Cathedral (marked in red as severely "underutilized" housing) is the "Consulate of the People's Republic of China".  It is clearly identified on any map of San Francisco.  The building itself is of no particular importance (as far as I know) except as an indicator of the quality of MGI's attempt to identify "underutilized" housing.

Box 3 was a "target-rich environment" in terms of finding McKinsey's mistakes so I need to repeat the map to point out even more "red block" errors.  This time I have put in letter keys to point out the buildings.

A:  Most of red block to the right of "A" in the lower right is Chinese American International School (CAIS) Middle School.  More here:  The rightmost corner of the red block is a gas station - not "underutilized housing".  The little red rectangle to left above "A" is a soccer field - also not underutilized housing.

B: Little red block surrounded by orange is the Buchanan YMCA 

C: Central Gardens Convalescent Center and Phoebe Hearst Preschool in red rectangle to left of "C"

D: Glad Tidings Church to the north and FGSF English Ministry (a church) to the south of "D"

For box 3 (area around Geary Blvd.) of all the red boxes McKinsey claimed indicated underutilized housing - not one was right.  Not. A. Single. One.

Block 4 - Green: Two Landmark Churches and a Jewish HS

Letters A and B on the map refer to the: Macang Monastery and Art Museum, former St. Patrick's Church and Holy Cross Church.  It seems MGI listed the museum as only mildly underutilized housing (yellow) while the Monastery itself is highly underutilized (bright red).

Former St. Patrick's (1854) in foreground
Former Holy Cross Church (1899) in back
1822 Eddy St, San Francisco, CA
The little white frame building in the foreground in the picture above is the old St. Patrick's church.  It was moved from downtown many years ago.  It dates from 1854.  It is now the Macang Monastery's museum.  It is the oldest frame structure in San Francisco.  

The bigger stone building is the former Holy Cross Church finished in 1899.  Both buildings survived the 1906 San Francisco earthquake. They are "Historic San Francisco Landmarks".  Apparently McKinsey thinks they should be torn down and replaced with housing.  Either that or they are simply incompetent.

C: The big building on the right marked "C" in the map - where MGI put a big red box indicating severely underutilized housing - is (mostly) the Jewish Community High School. (There is a small parking garage on the right edge.)
McKinsey thinks this should be high density housing.

Block 5 - Black: Hospital, Library, & Jewish Synagogue

The large red box at the top of the box turns out to be "The California Pacific Medical Center" (CPMC - known locally as "Can't Park My Car") - a hospital.  It is a teaching hospital affiliated with Stanford, Dartmouth, and UC-San Francisco.
The California Pacific Medical Center
Who needs hospitals!  MGI says apartments could go there!
Continuing on in this strange world of "multifamily buildings that are not fully utilizing zoned capacity", AKA churches, schools, and hospitals I looked just a little to the south of the hospital at the smaller red blocks.  They are a Library and Synagogue - Historic Landmarks.

Health Sciences Library Building - 1912
The library was "Designed by Albert Pissis and built in 1912, CPMC’s 20,000-square-foot Health Sciences Library Building at 2395 Sacramento Street was designated a San Francisco Historic Landmark 37 years ago."

And in the back of the picture above is Sherith Israel Synagogue, built in 1905, also designed by Albert Pissis.  It survived the 1906 and 1986 earthquakes, has been seismically retrofitted and is in active use.  The synagogue's Wikipedia entry states:

"Congregation Sherith Israel is one of the oldest synagogues in the United States. It was established during California's Gold Rush period and reflects the ambitions of early Jewish settlers to San Francisco. ...Its historic sanctuary building is one of San Francisco's most prominent architectural landmarks and attracts visitors from all over the world"  

Sherith Israel Synagogue - 1905
In active use.  "Attracts visitors from around the world"
The "McKinsey Global Institute" paints this bright red - i.e., much better utilized as apartments.

There are a few more churches on Van Ness Ave. (Highway 101) that McKinsey showed in bright red as being underutilized housing opportunities.

One was Old First Presbyterian Church at 1751 Sacramento St.  The parish was formed May 20, 1849 which makes it California's oldest protestant congregation.  They actively worked against slavery and to put California on the Union side against slavery during the Civil War.

Church dedicated in 1911.  Still an active parish church.
"Old First" is home to California's oldest active Protestant congregation.
Another is St. Luke's Episcopal Church at 1755 Clay Street.  The parish was founded in 1868.  As such it is one of the oldest Episcopal parishes in California.

French Gothic sanctuary, built in 1910
Stained glass windows from 1911
1755 Clay Street (@ Van Ness)

There may be more San Francisco historic landmarks that "McKinsey Global Institute" has identified as "multifamily buildings that are not fully utilizing zoned capacity" (their words) but this isn't intended to be an exhaustive search.  All I did was scan a Google map for a few minutes to find these gems.  Anyone can do that.  McKinsey could have done that.  They obviously didn't check their map at all.  Neither did anyone else who goes around citing the McKinsey report

Los Angeles(?)

MGI also looked at Los Angeles County.  Their map of LA (below) is found on page 20 of their report:

The Los Angeles map is harder to read since LA is about 7 times larger in area than San Francisco. McKinsey did not include the La Brea Tar Pits as "multifamily buildings that are not fully utilizing zoned capacity" (I knew you'd wonder so I checked).  I decided not to go through their LA map.  What's the point?  We saw McKinley Global Institute's level of competency with regards to mapping housing opportunity in their San Francisco map.

Conclusion:  No one can doubt it is possible to increase the density of San Francisco and Los Angeles.  You can make any city look like Hong Kong or Lower Manhattan or any of a number of densely packed cities.

Chong Qing, China
Dense enough for you?
The purpose of the McKinsey mapping effort cannot be to indicate more housing could be built.  That is obvious to anyone.  Rather it supports the narrative that more housing can be built within the current zoning regimens of those cities.  This argues that housing would be more plentiful (and by implication cheaper) without any change in the appearance or loss of local control if cities would simply "allow" more housing to be built.

Looking at these historic landmarks - houses of worship, library, hospital, schools - one has to wonder why people couldn't just look at a Google or MapQuest map and see these things.  Not just the "McKinsey Global Institute" report writers but the many people that supposedly read the report and drew conclusions based on it.  This says something about the report but also about the people that supposedly read the report.

For now this is

of this part.  To be continued.