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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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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