Metro Summary-Eco & Market Analysis
aandc3
IUSA_DMSAF.pdf
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ANALYSIS
VITALITYRELATIVE COSTS LIVING BUSINESS RELATIVE OF LIFE
Best=1, Worst=378Best=1, Worst=403
STRENGTHS & WEAKNESSES
U.S.=100%
SHORT TERM
FORECAST RISKS
LONG TERM
RISK EXPOSURE 2019-2024
BUSINESS CYCLE STATUS
MOODY’S RATING
ECONOMIC DRIVERS
Highest=1 Lowest=403
EMPLOYMENT GROWTH RANK
Best=1, Worst=410
2018-2020 2018-2023 QUALITY
MOODY’S ANALYTICS / Précis® U.S. Metro / November 2019
HIGH TECH
FINANCIAL
CENTER
$ £ €
MEDICAL
CENTER
2013 2014 2015 2016 2017 2018 INDICATORS 2019 2020 2021 2022 2023 2024 163.1 173.5 186.6 198.9 214.0 230.8 Gross metro product (C12$ bil) 239.6 243.9 249.2 257.3 263.4 269.1 7.4 6.4 7.5 6.6 7.6 7.9 % change 3.8 1.8 2.2 3.3 2.3 2.2 953.6 995.6 1,044.2 1,086.2 1,111.1 1,144.0 Total employment (ths) 1,184.0 1,200.3 1,199.6 1,211.7 1,219.8 1,226.2 3.8 4.4 4.9 4.0 2.3 3.0 % change 3.5 1.4 -0.1 1.0 0.7 0.5 5.4 4.3 3.5 3.2 2.8 2.3 Unemployment rate (%) 2.2 2.4 3.4 4.0 4.2 4.3 1.7 10.3 10.1 6.1 6.8 5.1 Personal income growth (%) 3.8 6.2 4.3 6.2 5.7 5.5 84.8 90.8 98.3 105.2 112.8 117.6 Median household income ($ ths) 121.3 128.2 132.7 138.3 144.2 150.4 1,588.9 1,608.8 1,629.3 1,641.0 1,648.0 1,652.9 Population (ths) 1,660.0 1,670.0 1,681.2 1,693.2 1,705.7 1,718.3 1.3 1.3 1.3 0.7 0.4 0.3 % change 0.4 0.6 0.7 0.7 0.7 0.7 12.7 12.0 12.7 4.1 0.8 -0.8 Net migration (ths) 0.8 3.2 4.3 5.1 5.6 5.7 417 406 626 550 405 348 Single-family permits (#) 347 594 793 1,032 1,051 1,020 5,007 3,766 4,659 5,512 5,211 5,946 Multifamily permits (#) 3,769 3,322 3,801 4,031 3,982 3,929 281.6 321.9 362.2 395.9 424.1 472.3 FHFA house price (1995Q1=100) 474.3 499.4 544.2 595.6 649.5 708.9
Recent Performance. The economy of San Francisco-Redwood City-South San Francisco is strong with an unemployment rate hover- ing around historic lows even as the labor force expands. Although the Quarterly Census of Employment and Wages—which provides a comprehensive headcount compared with a survey on the monthly jobs data—indicates that the monthly survey is overestimating employment growth by about 0.5 percent- age point, job growth is still easily besting the state and national averages and is as strong as it has been since 2016. Residential permitting is little changed in the past few years, as build- ers face capacity constraints.
Feeding the beast. Technology firms will remain the driving force behind the SAF econ- omy, and the Bay Area in general will persist as the preeminent tech hub. Even as some firms, both large and small, opt to relocate to cheaper locales, workforce quality, abundant venture capital, and an entrepreneurial culture will have lasting appeal. Also, a new trend is emerging: startups that serve startups. Players in this arena include such companies as Brex, Carta and Guideline, which provide financial services to other startups.
Venture capital funding in 2019 was likely down slightly from last year, not only in SAF but nationwide. Nonetheless, Bay Area tech has still garnered more venture capital than any other tech hub this year. It is also important to note that 2018 set a record high by a large margin. Investors are likely becoming wary at this late stage of the business cycle. This year’s recession chatter and trade policy risks may have also dampened investor enthusiasm.
Departures. Escalating costs are a byprod- uct of the strong economy and will moderate but not derail growth for the remainder of the business cycle. Costs are undoubtedly a factor
in a number of high-profile company reloca- tions, including Charles Schwab’s recent de- cision to relocate its headquarters to Dallas. But other firms remain. For example, Stripe, a payment processing company, is merely relo- cating from downtown to Oyster Point—still in the metro area. Rather than an omen of doom, relocations will prove a useful check to prevent the economy from overheating even more.
Housing. House prices will rise at a much slower pace than earlier in the business cycle. The rapid, if short-lived, uptick in mortgage rates in late 2018 depressed housing demand to the point that house prices took a hit. Since then, prices have not regained momentum be- cause low affordability is also weighing on de- mand. SAF is one of the least affordable places in the country regardless of the business cy- cle. Low affordability is made worse by the tightness in the housing market. The current inventory-to-sales ratios for San Francisco and San Mateo counties are 1.7 and 1.8 months of sales, respectively, both close to a record low since data collection began in 2010. Thus, with both supply and demand suppressed, house prices will appreciate much more slowly in coming years. Mortgage rates are low for now, which will help demand, but low affordability will remain a burden.
San Francisco-Redwood City-South San Francisco’s job gains will eventually moder- ate, but in the near term income growth will remain strong in the extremely tight labor market. Builders will scramble to meet soar- ing demand for housing and office space, but supply constraints will keep rents elevated. SAF’s legacy of entrepreneurship and tech agglomeration ensures the area a bright fu- ture over the forecast horizon.
Laura Ratz November 2019
STRENGTHS » Well-educated and highly skilled workforce. » Very high incomes. » Expanding cluster of internet and other tech-
service companies. » Strong office market growth.
WEAKNESSES » High costs, congestion and business regulations
curb job creation. » Limited land, stringent regulations temper
residential and commercial construction.
UPSIDE » Demand for business infrastructure, a strong
global economy, and ripe financial conditions fuel faster tech expansion.
» Biotech companies expand more rapidly.
DOWNSIDE » High costs push more workers out of San
Francisco; net migration turns negative. » Potential trade barriers and limits to immigration
slow the pace of growth. » Overbuilding saturates the office market.
W X
CITY AS OF AUG 06, 2018Aaa
36 1st quintile
6149% Rank: 6
174%190%61 1st quintile
24 1st quintile
At Risk
Recovery
Mid Expansion
Late Expansion
In Recession
SAN FRANCISCO-REDWOOD CITY-SOUTH SAN FRANCISCO CA Data Buffet® MSA code: IUSA_DMSAF
MOODY’S ANALYTICS / Précis® U.S. Metro / November 2019
3-MO MA May 19 Jun 19 Jul 19 Aug 19 Sep 19 Oct 19 Employment, change, ths 3.4 3.6 3.7 3.2 2.3 1.9 Unemployment rate, % 2.3 2.3 2.3 2.2 2.1 2.1 Labor force participation rate, % 75.0 75.0 74.7 74.5 74.4 74.5 Average weekly hours, # 34.6 35.0 35.0 35.0 35.0 35.0 Industrial production, 2012=100 110.8 110.7 110.8 111.3 111.9 112.2 Residential permits, single-family, # 280 247 259 269 366 375 Residential permits, multifamily, # 3,645 4,247 3,414 3,729 2,754 4,293
Dec/Dec 2013 2014 2015 2016 2017 2018 Employment, change, ths 36.6 43.7 47.6 33.6 24.2 41.0
PRÉCIS® U.S. METRO • San Francisco-Redwood City-South San Francisco CA
SAF CA U.S. 09 10 11 12 13 14 15 16 17 18 19
90
100
110
120
130
140
14 15 16 17 18 19 0 1 2 3 4 5 6 7 8
SAF CA U.S. 14 15 16 17 18 19
50
55
60
65
70
75
SAF CA U.S. 09 10 11 12 13 14 15 16 17 18 19F 20F 21F 22F 23F
90
100
110
120
130
140
l ▼▲ l ▼▲ l ▼▲ l ▼▲
W l ▼▲
SAF CA U.S. 98 01 04 07 10 13 16 19
50 100 150 200 250 300 350 400 450
SAF CA U.S. 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6
SAF CA U.S. 0 1 2 3 4 5 6 7
SAF CA U.S. 96 98 00 02 04 06 08 10 12 14 16 18
0
50
100
150
200
250
Overvalued Undervalued 98 01 04 07 10 13 16 19
-40
-30
-20
-10
0
10
20
30
% CHANGE YR AGO, 3-MO MA Oct 18 Apr 19 Oct 19 Total 3.4 3.7 3.5 Mining -0.0 0.0 0.2 Construction 8.5 10.6 7.9 Manufacturing -1.5 -0.6 -0.6 Trade 0.2 -0.6 -1.8 Trans/Utilities 7.0 5.3 4.0 Information 10.3 8.6 6.1 Financial Activities 3.3 3.8 5.8 Prof & Business Svcs. 4.7 4.7 4.2 Edu & Health Svcs. 3.1 3.8 4.4 Leisure & Hospitality 0.9 3.6 3.9 Other Services 2.8 3.9 -0.8 Government 0.9 0.7 2.5
FORECAST VS. 6 MO PRIOR
2-Yr 5-Yr
3-DIGIT NAICS LEVEL, 6-MO MA
Sources: BLS, Moody’s Analytics
ECONOMIC HEALTH CHECK BUSINESS CYCLE INDEX
RELATIVE EMPLOYMENT PERFORMANCE
CURRENT EMPLOYMENT TRENDS
Source: Moody’s Analytics
Sources: BLS, Moody’s AnalyticsSources: BLS, Moody’s Analytics
Sources: BLS, Moody’s Analytics
% CHANGE YR AGO
Government Goods producing Private services
HOUSE PRICE
Sources: FHFA, Moody’s Analytics
Better than prior 3-mo MA Unchanged from prior 3-mo MA Worse than prior 3-mo MA Sources: BLS, Census Bureau, Moody’s Analytics
VACANCY RATES HOMEOWNER, % HOUSES FOR SALE
DIFFUSION INDEX
HOUSE PRICE TRENDS
Sources: NAR, Moody’s Analytics
GREATER THAN 100=MORE AFFORDABLE
HOUSING AFFORDABILITY
Sources: FHFA, Moody’s Analytics
%
JAN 2009=100
JAN 2009=100 1998Q1=100, NSA
RENTAL, % INVENTORY FOR RENT
Sources: Census Bureau, ACS, Moody’s Analytics, 2018
LO W
H IG
H
Ths % of total
Ths % of total
MOODY’S ANALYTICS / Précis® U.S. Metro / November 2019
PRÉCIS® U.S. METRO • San Francisco-Redwood City-South San Francisco CA
Source: Moody’s Analytics, 2018
95%
0
20
40
60
80
100
151
100
SAF U.S.
0.42
0.00
0.20
0.40
0.60
0.80
1.00
Location Employees NAICS Industry Quotient (ths)
GVL Local Government 0.7 73.4 5415 Computer systems design & related srvcs. 4.7 66.3 GVS State Government 0.9 34.6 5511 Management of companies & enterprises 1.8 29.4 6241 Individual and family services 2.2 35.3 7211 Traveler accommodation 1.7 22.8 5613 Employment services 0.8 20.1 5617 Services to buildings and dwellings 1.3 19.0 7225 Restaurants and other eating places 1.1 80.6 4451 Grocery stores 0.8 15.2 7223 Special food services 2.1 9.9 4481 Clothing stores 1.2 8.5
Source: Moody’s Analytics, 2018
Federal 16,880 State 36,924 Local 77,423
2018
SAF 104.1 9.1
U.S. 14,296.2 9.6
SAF 205.5 18.0
U.S. 7,261.0 4.9
University of California, San Francisco 25,522 Genentech Inc. 8,800 Wells Fargo 7,838 Oracle Corp. 7,000 Salesforce.com Inc. 7,000 Sutter Health 6,447 California Pacific Medical Center 6,000 Dignity Health 5,393 Uber Technologies Inc. 5,000 Kaiser Permanente 4,517 PG&E Corp. 4,325 Gap Inc. 4,268 Williams-Sonoma Inc. 3,756 Visa USA 3,707 VMware Inc. 3,509 Va Palo Alto Health Care System 3,501 San Francisco State University 3,227 Lucile Packard Health Care System 3,023 Space Systems 2,980 Charles Schwab Corp. 2,695
Source: San Francisco Business Times, Book of Lists 2017
Product $ mil Food and kindred products ND Chemicals 2,764.7 Primary metal manufacturing ND Fabricated metal products ND Machinery, except electrical 4,372.3 Computer and electronic products 6,126.6 Transportation equipment 4,393.4 Miscellaneous manufacturing ND Other products 6,868.2 Total 27,417.0
Destination $ mil Africa 134.2 Asia 13,853.0 European Union 5,514.5 Canada & Mexico 3,879.4 South America 995.0 Rest of world 3,041.0 Total 27,417.0
% of GDP 5.0 Rank among all metro areas 149
Sources: BEA, International Trade Administration, Moody’s Analytics, 2018
141,195 110,574 92,001
SAF CA U.S.
0 50 100 150 200 250 300
COMPARATIVE EMPLOYMENT AND INCOME % OF TOTAL EMPLOYMENT AVERAGE ANNUAL EARNINGS Sector SAF CA U.S. SAF CA U.S. Mining 0.0 0.1 0.5 nd $66,127 $103,785 Construction 3.7 5.0 4.9 $113,211 $78,900 $68,455 Manufacturing 3.4 7.7 8.5 $157,982 $108,759 $83,365 Durable 47.4 64.0 62.6 nd $124,251 $86,331 Nondurable 52.6 36.0 37.4 nd $83,145 $78,483 Transportation/Utilities 4.1 3.9 4.0 $98,516 $57,283 $60,890 Wholesale Trade 2.4 4.1 3.9 $143,796 $91,917 $88,316 Retail Trade 7.1 9.8 10.6 $67,859 $42,493 $35,245 Information 7.5 3.2 1.9 nd $184,240 $119,417 Financial Activities 7.1 4.9 5.7 $149,079 $68,248 $59,540 Prof. and Bus. Services 25.1 15.5 14.1 $147,305 $82,844 $71,767 Educ. and Health Services 12.2 15.9 15.9 $66,192 $57,703 $56,400 Leisure and Hosp. Services 12.4 11.6 11.0 $48,135 $36,319 $29,108 Other Services 3.6 3.3 3.9 $59,298 $42,403 $38,639 Government 11.5 15.1 15.1 $126,886 $98,813 $78,273
Sources: Percent of total employment — BLS, Moody’s Analytics, 2018, Average annual earnings — BEA, Moody’s Analytics, 2017
SAF CA U.S. 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6
EMPLOYMENT AND INDUSTRY ENTREPRENEURSHIP
Due to U.S. fl uctuations Relative to U.S.
TOP EMPLOYERS
PUBLIC
INDUSTRIAL DIVERSITY
EMPLOYMENT VOLATILITY
Due to U.S.
Most Diverse (U.S.)
Least Diverse
Not due to U.S. M
ID
LEADING INDUSTRIES BY WAGE TIER HIGH-TECH
EMPLOYMENT
HOUSING-RELATED EMPLOYMENT
BUSINESS COSTS
Source: Moody’s Analytics
U.S.=100
EXPORTS
PRODUCTIVITY
Total
Unit labor
Energy
State and local taxes
Offi ce rent
REAL OUTPUT PER WORKER, $
EMPLOYMENT IN NEW COMPANIES, % OF TOTAL
Sources: BEA, Moody’s Analytics, 2017
2012 2017
Sources: Census Bureau, Moody’s Analytics, avg 2012-2016
NET MIGRATION, #
MOODY’S ANALYTICS / Précis® U.S. Metro / November 2019
SAF U.S.
Sources: Census Bureau, ACS, Moody’s Analytics, 2018
MSA CA U.S.
08 09 10 11 12 13 14 15 16 17 18 20
40
60
80
100
120
140
INTO SAN FRANCISCO CA Number of
Migrants San Jose CA 11,280 Oakland CA 11,047 New York NY 4,232 Los Angeles CA 3,809 Sacramento CA 1,810 San Diego CA 1,720 Chicago IL 1,573 Seattle WA 1,449 San Rafael CA 1,384 Washington DC 1,248 Total in-migration 66,201
FROM SAN FRANCISCO CA Oakland CA 23,240 San Jose CA 9,081 Los Angeles CA 4,006 Sacramento CA 3,594 San Rafael CA 3,449 New York NY 2,569 Seattle WA 1,945 Portland OR 1,844 San Diego CA 1,667 Vallejo CA 1,631 Total out-migration 82,879
Net migration -16,678
Index 2018 Rank* Gini coefficient 0.51 18 Palma ratio 4.6 2 Poverty rate 8.5% 370
*Most unequal=1; Most equal=403
16 21
29
21 13
13 20
34
23
0 2 4 6 8 10 12
Net Migration, SAF
15 16 17 18 -2,000
0 2,000 4,000 6,000 8,000
10,000 12,000 14,000
0 5 10 15 20 25 30 35
0 5 10 15 20 25 30
SAF U.S.
0 5 10 15 20 25 30
2015 2016 2017 2018 Domestic -900 -8,120 -10,305 -11,841 Foreign 13,622 12,219 11,102 11,005 Total 12,722 4,099 797 -836
Sources: IRS (top), 2016, Census Bureau, Moody’s Analytics
SAF CA U.S.
68.9%
Top Five Outside Sources of Workers San Francisco CA Share Oakland CA 17.8 San Jose CA 5.6 San Rafael CA 2.8 Vallejo CA 1.1 Santa Rosa CA 0.8
83.6%
Top Five Outside Sources of Jobs San Francisco CA Share San Jose CA 9.3 Oakland CA 5.0 San Rafael CA 1.0 Santa Rosa CA 0.1 Sacramento CA 0.1
Sources: Census Bureau, Moody’s Analytics, avg 2009-2013
RESIDENTS WHO WORK IN SAF WORKERS WHO LIVE IN SAF
PRÉCIS® U.S. METRO • San Francisco-Redwood City-South San Francisco CA
SAF
SAF
COMMUTER FLOWS
ECONOMIC DISENFRANCHISEMENT
Undereducated Balanced Overeducated
MIGRATION FLOWS
GENERATIONAL BREAKDOWN
SKILLS MISMATCH
EDUCATIONAL ATTAINMENT
% OF ADULTS 25 AND OLDER
< High school High school Some college College Graduate school
100
80
60
40
20
0
POPULATION BY AGE, %
U.S.
≥75 70-74 65-69 60-64 55-59 50-54 45-49 40-44 35-39 30-34 25-29 20-24 15-19 10-14
5-9 0-4
PER CAPITA INCOME
Sources: BEA, Moody’s Analytics
$ THS
POPULATION BY GENERATION, %
% OF TOTAL
Less than HS
High School
Some College
Associate’s
Bachelor’s
Graduate
Occupations Population
HOUSEHOLDS BY INCOME, %
Gen Z
Millennial
Gen X
Baby Boom
Silent & Greatest
0-19,999 20,000-39,999 40,000-59,999 60,000-74,999 75,000-99,999
100,000-124,999 125,000-149,999 150,000-199,999
200,000+
U.S.
NET MIGRATION, #
12
27
29
20 13
2018 SAF $123,807 CA $63,557 U.S. $54,446
Sources: Census Bureau, ACS, Moody’s Analytics, 2018
Sources: Census Bureau, Moody’s Analytics, 2018 Sources: Census Bureau, ACS, Moody’s Analytics, 2018 Sources: Census Bureau, Moody’s Analytics, 2018
MOODY’S ANALYTICS / Précis® U.S. Metro / November 2019
GEOGRAPHIC PROFILE
Sources: ACS, Moody’s Analytics
POPULATION DENSITY MEDIAN HOUSEHOLD INCOME AVERAGE COMMUTE TIME
PRÉCIS® U.S. METRO • San Francisco-Redwood City-South San Francisco CA
POPULATION & HOUSING CHARACTERISTICS
Units Value Rank*
Total area sq mi 972.8 308
Total water area sq mi 477.5 47
Total land area sq mi 495.3 377
Land area - developable sq mi 356.4 365
Land area - undevelopable sq mi 138.9 280
Population density pop. to developable land 4,560.0 4
Total population ths 1,652.9 46
U.S. citizen at birth % of population 63.7 399
Naturalized U.S. citizen % of population 20.8 3
Not a U.S. citizen % of population 13.5 18
Median age 38.9 175
Total housing units ths 680.9 49
Owner occupied % of total 43.1 392
Renter occupied % of total 48.3 3
Vacant % of total 8.6 279
1-unit; detached % of total 34.7 401
1-unit; attached % of total 9.9 37
Multifamily % of total 54.7 2
Median year built 1956
* Areas & pop. density, out of 410 metro areas/divisions, including metros in Puerto Rico; all others, out of 403 metros.
Sources: Census Bureau, Moody’s Analytics, 2018 except land area 2010
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Additional terms for Japan only: Moody’s Japan K.K. (“MJKK”) is a wholly-owned credit rating agency subsidiary of Moody’s Group Japan G.K., which is wholly-owned by Moody’s Overseas Holdings Inc., a wholly-owned subsidiary of MCO. Moody’s SF Japan K.K. (“MSFJ”) is a wholly-owned credit rating agency subsidiary of MJKK. MSFJ is not a Nationally Recognized Statistical Rating Organization (“NRSRO”). Therefore, credit ratings assigned by MSFJ are Non-NRSRO Credit Ratings. Non-NRSRO Credit Ratings are assigned by an entity that is not a NRSRO and, consequently, the rated obligation will not qualify for certain types of treatment under U.S. laws. MJKK and MSFJ are credit rating agencies registered with the Japan Financial Services Agency and their registration numbers are FSA Commissioner (Ratings) No. 2 and 3 respectively.
MJKK or MSFJ (as applicable) hereby disclose that most issuers of debt securities (including corporate and municipal bonds, debentures, notes and commercial paper) and preferred stock rated by MJKK or MSFJ (as applicable) have, prior to assignment of any rating, agreed to pay to MJKK or MSFJ (as applicable) for appraisal and rating services rendered by it fees ranging from JPY200,000 to approximately JPY350,000,000.
MJKK and MSFJ also maintain policies and procedures to address Japanese regulatory requirements.
