1
1
TOPIC 1
ECONOMIC ANALYSIS AND REAL ESTATE INVESTMENT
ECONOMIC ANALYSIS: The analysis of markets and empirical economic data for the purpose of evaluating and estimating future economic activity.
Gross Domestic Product (GDP), is the summary statistic for the general health of an economy. Often referred to as income, GDP is more generally defined as the value of all goods and services produced by labor and property located in an economy.
GDP = C + I + G + (X-M)
where,
C= consumption of goods and services
I = private domestic investment
G= government (consumption and investment)
X= exports
M= imports
Growth, changes in GDP and its components, is the focus of most economic analysis.
For the US national economy as of 1Q2017 (annualized trillion dollars, percent changes adjusted for inflation listed below, rounding errors adjusted to fit):
19.2= 13.3 + 3.2 + 3.3 + (2.3 – 2.9)
(1.8) (3.3) (3.6) (-0.3) (nm)
Annualized growth rates in real dollars: dollar values corrected for gains or losses of purchasing power attributable to inflation:
A closely related measure of income is Gross National Product. GNP is the value of goods and services produced by an economy’s citizenry, regardless of their location, i.e. GDP adjusted for income from international locations.
The US is a net importer and the trade deficit has generally increased over the last 30 years.
Economic indicators are categorized as:
· Leading: indicators that signal future changes in economic growth
· Coincident: indicators that reflect current changes in economic activity
· Lagging: indicators that reflect past changes in economic activity
Key Economic Indicators include:
Leading Coincident Lagging
Plant & equipment orders Prices Unemployment
NAPM Industrial production Inventory/Sales
DOW Personal Income Interest Rates
Consumer confidence Consumer confidence Consumer con.
Current Issues:
Monetary Normalization Infrastructure Investment
Global ZIRP Wage & Income Growth
WHAT IS MARKET ANALYSIS?
Real estate market analysis is the identification and study of demand and supply, usually for the purpose of real estate investment and development. Market analysis forms the basis for decisions regarding location and site, size, design and quality, features, and target use. Development projects should match locational demands of firms and households and satisfy those demands competitively. Market analysis continues through the development and eventual disposition of the project, particularly in unfamiliar or highly competitive markets. Real estate market analysis provides guidance for the many decision-makers, both private and public sector professionals involved in real estate development
A CRITICAL FLAW OF MANY MARKET ANALYSES IS PRESENTATION OF DATA WITHOUT ANALYSIS
· Qualitative analysis is becoming increasingly important, especially as markets become more segmented and specialized; traditional statistical models and formulas that rely on large data pools do not work in assessing realism of goals and objectives
· Large informal component made up of experience, observation, interdisciplinary analysis
· Must keep up with national and regional market trends: strong vs. weak market; monitor changes as they occur
WHAT IS URBAN ECONOMICS?
Urban economics explores the where of economic activity:
· the study of the location choices of firms and households
· spatial aspects of sub-national economies and public policy
Why urban economics?
· 75% of national employment resides in urban areas
· urban economics also examines location choices within cities
· the most important problems caused by location choices occur in urban areas
AGGLOMERATION: favorable externalities, external economies of scale, resulting from proximity to other businesses that ultimately reduce business costs and argue for spatial concentration, i.e. gains to firms from locating near similar firms, suppliers and customers.
· Localization economies: benefits that arise from proximity to firms in same industry
· Urbanization economies: benefits that arise from proximity to many different economic actors
Paul Krugman: neoclassical theory fails to explain rise of particular places central reason for agglomeration assumed away.
–By admitting external economies of scale, concentration in space is efficient.
CS3361 - Assignment 3.pdf
CS 3361 | Fall 2020 | Assignment #3 Lexical Analyzer
Assignment #3
Lexical Analyzer
Develop the C or C++ source code required to solve the following problem.
Problem Develop a lexical analyzer in C or C++ that can identify lexemes and tokens found in a source code file provided
by the user. Once the analyzer has identified the lexemes of the language and matched them to a token group,
the program should print each lexeme / token pair to the screen.
The source code file provided by the user will be written in a new programming language called “DanC” and is
based upon the following grammar (in BNF):
P : : = S
S : : = V: =E | r ead ( V) | wr i te( V) | wh il e C d o S o d | S; S
C : : = E < E | E > E | E = E | E <> E | E <= E | E >= E
E : : = T | E + T | E - T
T : : = F | T * F | T / F
F : : = (E ) | N | V
V : : = a | b | … | y | z | aV | b V | … | y V | z V
N : : = 0 | 1 | … | 8 | 9 | 0 N | 1 N | … | 8 N | 9 N
Your analyzer should accept the source code file as a required command line argument and display an
appropriate error message if the argument is not provided or the file does not exist. The command to run your
application will look something like this:
Form: danc_analyzer <path_to_source_file>
Example: danc_analyzer test_file.danc
Lexeme formation is guided using the BNF rules / grammar above. Your application should output each lexeme
and its associated token. Invalid lexemes should output UNKNOWN as their token group. The following token
names should be used to identify each valid lexeme:
Lexeme Token Lexeme Token Lexeme Token
:= ASSIGN_OP + ADD_OP do KEY_DO
< LESSER_OP - SUB_OP od KEY_OD
> GREATER_OP * MULT_OP <variable name> IDENT
= EQUAL_OP / DIV_OP <integer> INT_LIT
<> NEQUAL_OP read KEY_READ ( LEFT_PAREN
<= LEQUAL_OP write KEY_WRITE ) RIGHT_PAREN
>= GEQUAL_OP while KEY_WHILE ; SEMICOLON
CS 3361 | Fall 2020 | Assignment #3 Lexical Analyzer
Additional Solution Rules Your solution must conform to the following rules:
1) Your solution should be able to use whitespace, tabs, and end of line characters as delimiters between
lexemes, however your solution should ignore these characters and not report them as lexemes nor
should it require these characters to delimit lexemes of different types.
a. Example: “while i<=n do”
i. This line will generate 5 lexemes “while”, “i”, “<=”, “n”, and “do”.
ii. This means the space between “while” and “i” separated the two lexemes but wasn’t a
lexeme itself.
iii. This also means that no space is required between the lexemes “i”, “<=”, and “n”.
2) Your solution should print out “DanC Analyzer :: R<#>” on the first line of output. The double colon “::”
is required for correct grading of your submission.
3) Your solution must be tested to ensure compatibility with the GNU C/C++ compiler version 5.4.0.
4) Lexemes that do not match to a known token should be reported as an “UNKNOWN” token. This should
not stop execution of your program or generate an error message.
Hints 1) Draw inspiration by looking at the lexical analyzer code discussed and distributed in class.
2) Start by focusing on writing the program in your usual C/C++ development environment.
3) Once your solution is correct, then work on testing it in Linux using the appropriate version of the GNU
compiler (gcc).
4) Linux/Makefile tutorials:
a. Linux Video walkthrough: http://www.depts.ttu.edu/hpcc/about/training.php#intro_linux
b. Linux Text walkthrough: http://www.ee.surrey.ac.uk/Teaching/Unix/
c. Makefile tutorial: https://www.tutorialspoint.com/makefile/index.htm
What to turn in to BlackBoard A zip archive (.zip) containing the following files:
• <FirstName>_<LastName>_<R#>_Assignment3.c / <FirstName>_<LastName>_<R#>_Assignment3.cpp
o C/C++ Source code file
o Example: Eric_Rees_R123456_Assignment3.c
• Makefile
o A makefile for compiling your C/C++ file.
o This makefile must work in the HPCC environment to compile your source code file and output
an executable named danc_analyzer.
CS 3361 | Fall 2020 | Assignment #3 Lexical Analyzer
Example Execution The example execution below was run on Quanah, one of the HPCC clusters. It shows all the commands used to
compile and execute my analyzer. Bolded text is text from the Linux OS, text in red are the commands I typed
and executed, and the text in blue represents the output from each step.
quanah:/assignment_3$ make clean rm -f danc_analyzer quanah:/assignment_3$ make gcc -o danc_analyzer Eric_Rees_R123456_Assignment3.c quanah:/assignment_3$ ./danc_analyzer test.danc DanC Analyzer :: R123456 f IDENT := ASSIGN_OP 1 INT_LIT ; SEMICOLON i IDENT := ASSIGN_OP 1 INT_LIT ; SEMICOLON read KEY_READ ( LEFT_PAREN n IDENT ) RIGHT_PAREN ; SEMICOLON while KEY_WHILE i IDENT <= LEQUAL_OP n IDENT do KEY_DO f IDENT := ASSIGN_OP f IDENT * MULT_OP i IDENT ; SEMICOLON i IDENT := ASSIGN_OP i IDENT + ADD_OP 1 INT_LIT od KEY_OD ; SEMICOLON
CS3361 Programming Assignment Grading Rubric.pdf
CS3361 Programming Assignment Grading Rubric
The purpose of this document is to lay out the common criteria used to grade CS 3361 programming assignments. Each
criterion listed has four different levels of mastery, with a description of how a submission will attain each level and the
number of points awarded for achieving it.
Criteria
Program Specifications & Correctness This is the most important criterion as any submitted program must function correctly and meet the specifications given.
Submitted programs should always behave as desired and produce correct output and results for a variety of inputs.
This criterion also includes the need to meet all specifications laid out in the problem statement by writing a program in
a particular way, using a particular language feature, or not using a particular language feature.
If you believe a specification to be ambiguous or unclear, you should consult with the instructor instead of making any
assumptions.
Readability Code should be readable to both you as well as a knowledgeable third party. This involves:
• Using indentation consistently.
• Adding whitespace (blank lines or spaces) where appropriate to help distinguish parts of the program.
o Examples:
▪ Space after commas in a list
▪ Blank lines between functions or between blocks of related lines within function.
• Using meaningful variable names. Variables with names like A, B, C, foo, or bar give the reader no information
regarding the variable’s purpose or what information it may contain. Names like maximum, counter or
inputString are considerably more useful and make their purpose known. Loop variable names are an exception
to this rule and names like x, y, i, or j are allowed for loop variables.
• Properly organizing code to increase readability and reusability. Code should be organized into functions so that
blocks of code that can be re-used are contained within functions that enable that behavior.
Documentation Every file you submit that contains source code should start with the following header comments:
1) The name of the code’s author (you), R#, the date, and the assignment commented along the top.
2) A comment explaining the problem being solved.
Example:
## Eric Rees (R#123456) | Homework #1 | 09/01/2020
##
## This program accepts a temperature in Fahrenheit (floating point value) as input and outputs the
## integer form of that temperature in degrees Celsius.
##
All code in your program should also be well-commented. This requires that you strike a balance between:
1) Being overly verbose and commenting everything – which adds a great deal of unneeded noise to the code, and
2) Writing no comments in the code – which adds a great deal of unnecessary complexity to the code by not giving
any assistance to future readers
In general, you should aim to put a comment on any line of code that you might not understand yourself if you came
back to it in a month without having thought about it in the interim. Much like code organization and elegance,
appropriate commenting is a skill we will be learning as we write code this semester. As such, adherence to
documentation guidelines covered in class will be held to higher standards as the semester progresses.
Code Efficiency There are many ways to write the same functionality into your code, and many of them may be poor choices. They may
be poor choices because they require more lines of code than are necessary, they take considerably longer to execute
than necessary, or they consume considerably more system resources (such as RAM) during execution than necessary.
Whenever possible, code should be concise, stray from using overly complicated formulas, and use the most efficient
algorithms for solving the problem at hand.
Assignment Specifications Programming assignments in this course will often contain specifications or requirements beyond those required to
solve the problem itself. These include, but are not limited to, writing certain information as comments in your code or
the name of the file(s) you submit.
Grading Rubric Each criterion will make up an approximate percentage of the grade given to a programming assignment as indicated by the “%” column. Points will be assigned
for a criterion based on the guidelines listed below the “Excellent”, “Adequate”, “Poor”, and “Not Met” evaluations.
For example, an assignment that is marked as “Adequate” in the Programming Specifications & Correctness criterion, “Poor” for readability and “Excellent” for
all other criteria would receive a score of:
(0.8 * 0.6) + (0.5 * 0.15) + (1 * .1) + (1 * .05) + (1 * .1) = .805 = 80.5%
Criterion % Excellent (100%) Adequate (80%) Poor (50%) Not Met (0%)
Program Specifications &
Correctness*
60% No errors, program always works correctly and meets the specification(s).
Minor details of the program specification are violated, program functions incorrectly for some inputs.
Significant details of the specification are violated, program often exhibits incorrect behavior.
Program only functions correctly in very limited cases or not at all.
Readability 15% No errors, code is clean, understandable, and well-organized.
Minor issues with consistent indentation, use of whitespace, variable naming, or general organization.
At least one major issue with indentation, whitespace, variable names, or organization.
Major problems with at three or four of the readability subcategories.
Documentation 10% No errors, code is well- commented.
One or two places that could benefit from comments are missing them or the code is overly commented.
File header missing, complicated lines or sections of code uncommented or lacking meaningful comments.
No file header or comments present.
Code Efficiency 5% No errors, code uses the best approach in every case.
N/A Code uses poorly-chosen approaches in at least one place.
Many things in the code could have been accomplished in an easier, faster, or otherwise better fashion.
Assignment Specifications
10% No errors. N/A Minor details of the assignment specification are violated, such as files named incorrectly or extra instructions slightly misunderstood.
Significant details of the specification are violated, such as extra instructions ignored or entirely misunderstood.
* As a special case, if a program does not meet the specifications at all or is entirely incorrect, no credit will be received for the other criteria either.
Adapted from Mark Liffiton’s Programming Rubric
makefile (1)
#I would strongly recommend NOT changing any lines below except the CC and MYFILE lines. #Before running this file, run the command: module load gnu EXECS=danc_analyzer #Replace the g++ with gcc if using C CC=g++ #Replace with the name of your C or C++ source code file. MYFILE=Eric_Rees_R123456_Assignment3.cpp all: ${EXECS} ${EXECS}: ${MYFILE} ${CC} -o ${EXECS} ${MYFILE} clean: rm -f ${EXECS}
test.danc
f:=1; i:=1; read(n); while i <= n do f:=f*i; i:=i+1 od;
1-866-275-3266 [email protected]
ANALYSIS
VITALITYRELATIVE COSTS LIVING BUSINESS RELATIVE OF LIFE
Best=1, Worst=378Best=1, Worst=403
STRENGTHS & WEAKNESSES
U.S.=100%
SHORT TERM
FORECAST RISKS
LONG TERM
RISK EXPOSURE 2019-2024
BUSINESS CYCLE STATUS
MOODY’S RATING
ECONOMIC DRIVERS
Highest=1 Lowest=403
EMPLOYMENT GROWTH RANK
Best=1, Worst=410
2018-2020 2018-2023 QUALITY
MOODY’S ANALYTICS / Précis® U.S. Metro / November 2019
MANUFAC
TURING
HIGH TECH
LOGISTICS
2013 2014 2015 2016 2017 2018 INDICATORS 2019 2020 2021 2022 2023 2024 232.6 243.9 252.6 261.8 277.9 298.3 Gross metro product (C12$ bil) 312.9 321.2 329.6 342.2 352.1 362.6 3.6 4.9 3.5 3.7 6.1 7.4 % change 4.9 2.6 2.6 3.8 2.9 3.0 1,502.3 1,543.6 1,592.8 1,644.2 1,685.5 1,725.1 Total employment (ths) 1,774.9 1,810.7 1,817.2 1,841.5 1,859.7 1,875.4 2.9 2.7 3.2 3.2 2.5 2.3 % change 2.9 2.0 0.4 1.3 1.0 0.8 5.3 4.6 4.1 3.7 3.6 3.4 Unemployment rate (%) 3.2 2.6 3.0 3.0 2.9 2.9 3.0 10.0 6.5 6.5 7.2 9.2 Personal income growth (%) 7.2 5.7 4.9 6.4 5.9 5.6 71.3 75.4 79.6 83.8 87.2 88.9 Median household income ($ ths) 90.7 94.2 96.9 100.4 104.1 107.7 2,791.8 2,843.1 2,896.6 2,954.3 3,007.3 3,048.1 Population (ths) 3,089.9 3,134.4 3,177.9 3,219.2 3,261.2 3,304.2 1.8 1.8 1.9 2.0 1.8 1.4 % change 1.4 1.4 1.4 1.3 1.3 1.3 31.0 34.0 36.5 40.2 35.2 24.1 Net migration (ths) 25.1 27.5 26.5 24.5 25.5 26.6 6,404 6,294 6,393 6,956 6,983 6,643 Single-family permits (#) 6,201 7,255 8,600 11,116 11,734 11,321 10,221 11,882 15,718 14,668 15,383 16,094 Multifamily permits (#) 15,545 14,234 14,402 17,801 18,699 18,772 212.0 231.9 253.5 284.5 323.5 359.0 FHFA house price (1995Q1=100) 366.9 375.4 388.8 405.4 427.8 457.7
Recent Performance. There is little that can shake Seattle-Bellevue-Everett’s economy. Despite troubles at top employer Boeing, the metro division enters the 10th year of its expan- sion with plenty of wind in its sails. Job and labor force gains have accelerated over the past year, and residential and commercial construction is firming after a hiccup late last year. Steadfast tech is the source of the economy’s vigor, and robust hiring by mature firms and smaller com- panies alike sustains a growing labor force. Hous- ing affordability is a rare blemish on an otherwise sterling economy; though house price apprecia- tion is no longer outpacing incomes, two years of double-digit gains have eroded potential buyers’ purchasing power.
Flight risk. Tough times at Boeing dampen the near-term outlook for aerospace manufac- turing, but the factory sector will find its foot- ing next year as the firm resolves concerns over the flightworthiness of its flagship 737 MAX. The latest iteration of Boeing’s best-selling jet was grounded in March following two fa- tal crashes; subsequent tests by the Federal Aviation Administration revealed additional safety risks that prolonged its review. With in- ventories of the popular plane stacking up, the aerospace giant dialed back production at its Renton plant. Local suppliers have responded in kind. According to the ISM-Western Wash- ington index, a proxy for manufacturing con- ditions in the Puget Sound, factory output has risen less quickly in the six months since the jet’s grounding.
Although the MAX is unlikely to return to the skies before the middle of next year, rapid growth in regional air travel in the United States and globally will sustain demand for new aircraft. Automation will limit the need for labor, but ris- ing production will place a floor under the firm’s 70,000-strong local workforce.
The trade war is a larger concern. Boeing will cut production of the twin-aisle 787 in response to softer growth along international routes, and further escalation would impair the outlook for manufacturing output and employment in SEA.
Tech titans. Despite headline-grabbing ex- pansions outside of Washington state, top tech employers Amazon and Microsoft will double down on their presence in the Puget Sound, fur- thering the virtuous cycle of jobs and investment that has made the Emerald City a global bas- tion of high tech. Amazon’s SEA workforce has grown by almost 25% in the two years since the completion of its new South Lake Union campus; Google’s new offices across the street will inten- sify competition for top tech talent. While SEA will continue to play home base to mature tech firms, rising venture capital inflows will support new firms; new establishments in software de- velopment and information technology employ a small but growing share of the tech workforce.
Affordability. Declining affordability and slower growth in mid-paying jobs pose a risk to the outlook for homebuilding. Although hous- ing affordability is about even with the national average, broad measures of affordability belie a skewed income distribution that has grown more so with the shift in SEA’s industrial base from manufacturing to information technology. While house price growth has slowed, earlier gains have dented affordability and threaten the outlook for home sales and new construction.
Propelled by tech, Seattle-Bellevue-Ever- ett will grow faster for longer, with job and income growth topping that in most other large metro areas well into next year. How- ever, troubles at Boeing, declining affordabili- ty, and the prospect of an extended trade war pose risks longer term.
Jesse Rogers November 2019
STRENGTHS » Global center for cloud-computing and software
development. » Highly trained, well-educated labor force. » Large port with connections to emerging Asian
markets. » Relatively high per capita income.
WEAKNESSES » Tech exposed to discretionary spending. » High business costs compared with emerging tech
hubs.
UPSIDE » Greater demand for cloud-computing and IT
services boosts hiring of tech workers. » Venture capital draws more startups. » Population gains further stimulate housing
demand.
DOWNSIDE » Trade tensions hurt sales of Boeing jets. » Housing affordability erodes faster. » Less freight traffic hurts trade, transport.
X X
COUNTY AS OF SEP 19, 2017Aaa
27 1st quintile
21142% Rank: 7
107%146%36 1st quintile
23 1st quintile
At Risk
Recovery
Mid Expansion
Late Expansion
In Recession
SEATTLE-BELLEVUE-EVERETT WA Data Buffet® MSA code: IUSA_DMSEA
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 6.7 7.8 8.0 5.6 4.4 3.5 Unemployment rate, % 3.5 3.4 3.3 3.2 3.1 3.0 Labor force participation rate, % 68.9 68.9 68.8 68.8 68.9 69.0 Average weekly hours, # 35.2 35.3 35.3 35.2 35.1 35.2 Industrial production, 2012=100 107.3 106.7 106.6 106.9 107.3 107.5 Residential permits, single-family, # 5,995 5,772 6,183 6,243 6,641 6,420 Residential permits, multifamily, # 20,127 15,882 14,934 13,576 16,851 13,784
Dec/Dec 2013 2014 2015 2016 2017 2018 Employment, change, ths 42.7 44.6 49.3 47.9 40.1 43.6
PRÉCIS® U.S. METRO • Seattle-Bellevue-Everett WA
SEA WA U.S. 09 10 11 12 13 14 15 16 17 18 19
90 95
100 105 110 115 120 125 130
14 15 16 17 18 19 -4
-2
0
2
4
6
SEA WA U.S. 14 15 16 17 18 19
58 60 62 64 66 68 70 72
SEA WA U.S. 09 10 11 12 13 14 15 16 17 18 19F 20F 21F 22F 23F
90 95
100 105 110 115 120 125 130
W l ▼▲ W l ▼▲
W l ▼▲
SEA WA U.S. 98 01 04 07 10 13 16 19
50 100 150 200 250 300 350
SEA WA U.S. 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6
SEA WA U.S. 0 1 2 3 4 5 6 7
SEA WA U.S. 96 98 00 02 04 06 08 10 12 14 16 18
60 80
100 120 140 160 180 200 220
Overvalued Undervalued 98 01 04 07 10 13 16 19
-30
-20
-10
0
10
20
30
% CHANGE YR AGO, 3-MO MA Oct 18 Apr 19 Oct 19 Total 2.3 2.3 3.3 Mining -0.1 -0.1 0.0 Construction 6.2 4.1 3.7 Manufacturing 1.3 3.9 4.8 Trade 1.1 0.2 0.0 Trans/Utilities 2.6 5.9 5.1 Information 7.8 6.0 6.2 Financial Activities 2.5 3.0 6.7 Prof & Business Svcs. 2.4 1.8 4.0 Edu & Health Svcs. 3.2 4.0 5.1 Leisure & Hospitality 3.6 4.0 3.0 Other Services 2.1 4.1 2.9 Government -1.9 -3.0 0.2
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 • Seattle-Bellevue-Everett WA
Source: Moody’s Analytics, 2018
98%
0
20
40
60
80
100
134 100
SEA U.S.
0.42
0.00
0.20
0.40
0.60
0.80
1.00
Location Employees NAICS Industry Quotient (ths)
3364 Aerospace product & parts manuf. 16.3 88.4 5112 Software publishers 14.4 54.7 5415 Computer systems design & related srvcs. 1.9 40.5 6221 General medical and surgical hospitals 0.7 32.8 GVL Local Government 0.8 127.0 GVS State Government 1.2 65.6 5613 Employment services 0.8 32.3 2382 Building equipment contractors 1.1 23.2 7225 Restaurants and other eating places 0.9 101.0 6241 Individual and family services 1.3 32.2 4451 Grocery stores 0.9 26.5 7139 Other amusement and recreation industries 1.1 15.3
Source: Moody’s Analytics, 2018
Federal 21,571 State 61,102 Local 135,672
2018
SEA 176.4 10.2
U.S. 14,296.2 9.6
SEA 191.3 11.1
U.S. 7,261.0 4.9
Boeing Co. 64,300 Amazon 45,000 Microsoft Corp. 43,031 University of Washington 30,200 Providence Health & Services 17,553 Walmart Inc. 16,000 Fred Meyer Stores 15,500 Starbucks Corp. 11,239 Swedish Health Services 10,758 Costco Wholesale Corp. 9,264 Nordstrom Inc. 8,982 Alaska Air Group Inc. 7,403 Group Health Cooperative 7,271 Naval Station Everett 5,950 Virginia Mason Medical Center 5,611 Madigan Army Medical Center 5,554 T-Mobile 5,500 Washington State University 5,437 Quality Food Centers 5,400 Target 5,200
Source: Puget Sound Business Journal, 2018
Product $ mil Food and kindred products ND Chemicals ND Primary metal manufacturing ND Fabricated metal products ND Machinery, except electrical 1,304.5 Computer and electronic products 3,961.2 Transportation equipment 42,936.1 Miscellaneous manufacturing ND Other products 6,314.1 Total 59,742.9
Destination $ mil Africa ND Asia 32,101.8 European Union 11,290.8 Canada & Mexico 7,465.5 South America 985.8 Rest of world ND Total 59,742.9
% of GDP 15.2 Rank among all metro areas 30
Sources: BEA, International Trade Administration, Moody’s Analytics, 2018
129,246 108,910 92,001
SEA WA U.S.
0 20 40 60 80 100 120 140
COMPARATIVE EMPLOYMENT AND INCOME % OF TOTAL EMPLOYMENT AVERAGE ANNUAL EARNINGS Sector SEA WA U.S. SEA WA U.S. Mining 0.0 0.1 0.5 $62,026 $55,817 $103,785 Construction 5.9 6.3 4.9 $93,873 $80,182 $68,455 Manufacturing 9.4 8.4 8.5 $107,598 $91,769 $83,365 Durable 81.9 70.2 62.6 nd $102,472 $86,331 Nondurable 18.1 29.8 37.4 nd $66,733 $78,483 Transportation/Utilities 3.4 3.4 4.0 $75,613 $59,106 $60,890 Wholesale Trade 4.3 4.0 3.9 $102,091 $89,164 $88,316 Retail Trade 11.1 11.3 10.6 $77,531 $54,880 $35,245 Information 6.7 3.9 1.9 $204,667 $179,197 $119,417 Financial Activities 5.0 4.6 5.7 $60,009 $49,639 $59,540 Prof. and Bus. Services 15.2 12.5 14.1 $89,200 $74,854 $71,767 Educ. and Health Services 12.8 14.4 15.9 $64,970 $60,170 $56,400 Leisure and Hosp. Services 10.0 10.1 11.0 $39,334 $31,563 $29,108 Other Services 3.5 3.6 3.9 $48,457 $42,833 $38,639 Government 12.7 17.2 15.1 $89,717 $82,930 $78,273
Sources: Percent of total employment — BLS, Moody’s Analytics, 2018, Average annual earnings — BEA, Moody’s Analytics, 2017
SEA WA U.S. 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
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
SEA U.S.
Sources: Census Bureau, ACS, Moody’s Analytics, 2018
MSE WA U.S.
08 09 10 11 12 13 14 15 16 17 18 30
40
50
60
70
80
90
INTO SEATTLE WA Number of
Migrants Tacoma WA 11,182 Los Angeles CA 3,888 Portland OR 3,686 New York NY 2,648 Phoenix AZ 2,312 San Diego CA 2,194 Chicago IL 2,114 San Jose CA 2,007 San Francisco CA 1,945 Oakland CA 1,904 Total in-migration 108,247
FROM SEATTLE WA Tacoma WA 18,442 Portland OR 3,552 Phoenix AZ 2,671 Bremerton WA 2,577 Los Angeles CA 2,358 Spokane WA 2,041 Bellingham WA 2,014 Olympia WA 1,989 Mount Vernon WA 1,916 San Jose CA 1,462 Total out-migration 98,824
Net migration 9,423
Index 2018 Rank* Gini coefficient 0.46 217 Palma ratio 3.3 128 Poverty rate 8.8% 363
*Most unequal=1; Most equal=403
8 22
33
23
14
17
28
29
19
0 2 4 6 8 10
Net Migration, SEA
15 16 17 18 0
10,000
20,000
30,000
40,000
50,000
0 5 10 15 20 25 30
0 5 10 15 20
SEA U.S.
0 5 10 15 20 25 30
2015 2016 2017 2018 Domestic 11,892 18,339 10,475 -464 Foreign 24,554 21,835 24,747 24,558 Total 36,446 40,174 35,222 24,094
Sources: IRS (top), 2016, Census Bureau, Moody’s Analytics
SEA WA U.S.
90.4%
Top Five Outside Sources of Workers Seattle WA Share Tacoma WA 6.1 Bremerton WA 0.9 Mount Vernon WA 0.4 Olympia WA 0.3 Bellingham WA 0.2
96.2%
Top Five Outside Sources of Jobs Seattle WA Share Tacoma WA 2.1 Mount Vernon WA 0.3 Bremerton WA 0.2 Olympia WA 0.2 Bellingham WA 0.1
Sources: Census Bureau, Moody’s Analytics, avg 2009-2013
RESIDENTS WHO WORK IN SEA WORKERS WHO LIVE IN SEA
PRÉCIS® U.S. METRO • Seattle-Bellevue-Everett WA
SEA
SEA
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 SEA $81,471 WA $62,026 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
POPULATION & HOUSING CHARACTERISTICS
Units Value Rank*
Total area sq mi 4,503.2 59
Total water area sq mi 300.4 76
Total land area sq mi 4,202.8 60
Land area - developable sq mi 1,662.6 68
Land area - undevelopable sq mi 2,540.2 53
Population density pop. to developable land 1,728.7 18
Total population ths 3,048.1 14
U.S. citizen at birth % of population 76.7 372
Naturalized U.S. citizen % of population 10.6 32
Not a U.S. citizen % of population 11.0 32
Median age 37.1 263
Total housing units ths 1,266.4 12
Owner occupied % of total 55.7 254
Renter occupied % of total 38.8 38
Vacant % of total 5.4 380
1-unit; detached % of total 55.9 357
1-unit; attached % of total 4.8 151
Multifamily % of total 36.6 22
Median year built 1983
* 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
PRÉCIS® U.S. METRO • Seattle-Bellevue-Everett WA
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Submit in MSWord putting answers below each Sub/Question
1) REAL ESTATE MARKETS
(a) Define and identify the economic purpose of: 1) the Property Market and 2) the Asset Market. Identify one exogenous force acting upon each market.
(b) Identify the primary determinants on capitalization rates and their expected directional impacts (positively or negatively correlated) on the cap rate.
(c) How would the asset/property framework explain why a reduction in long-term mortgage rates has the opposite impact on residential market values than a reduction in short-term rates.
(d) Many expect long term interest rates to rise after Covid subsides. Explain the impact on market values, rents, and construction. Include the role of linkages across markets in determining the combined impact on the sector as a whole. A diagram is not required, but you may find using one helpful.
2) URBAN ECONOMICS AND ECONOMIC BASE THEORY
a. Define Agglomeration and explain why it is often characterized as the source of urban efficiency.
b. Describe the multiplier process and its significance to growth of an urban economy.
c. Using the Seattle Outlook Precis posted on NYU Classes, profile the economic bases of Seattle and briefly describe the city’s prospects for future growth using the most relevant economic terms learned.
d. Explain the statement, “An urban economy dependent on any particular set of export industries is likely to face an episodic economic future.”
e. Explain why globalization leads to further urbanization.
3) LAND USE AND THE IMPACT OF LOCAL GOVERNMENTS
(a) Define externality. Provide one example in relation to real estate markets.
(b) Define (1) fiscal zoning and (2) fiscal capitalization.
(c) Explain why the property tax is often described as the most important own-source revenue of local governments.
(d) What is the long-run economic incidence of an increase of the property tax? Explain.
4) ECONOMICS OF HIGHEST AND BEST USE
(a) Define Highest and Best Use.
(b) Explain the statement: “Cities are Value Propositions, not Cost Propositions.”

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