Eco & Market analysis question in real estate

Answer 15 question of Eco and Market analysis question in real estate field.

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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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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

2019-202

4

BUSINESS CYCLE STATUS

MOODY’S RATING

ECONOMIC DRIVERS

Highest=1
Lowest=403

EMPLOYMENT GROWTH RANK

Best=1, Worst=41

0

2018-2020 2018-20

23

QUALITY

MOODY’S ANALYTICS / Précis® U.S. Metro / November 20

19

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 6,4

20

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
1

10

115
120
125
1

30

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
1

40

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 $119,4

17

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 2,3

12

San Diego CA 2,194
Chicago IL 2,1

14

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 1

28

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

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.