Transcript Case 1 Finalx

September 26, 2013
Real Estate
Economic Analysis
Zach Leighton | 516.318.0173 | [email protected] | HA 4200
Table of Contents
 DiPasquale-Weaton Four Quadrant Model: Page 3
 Shift Share Technique: Page 8
 Location Quotients: Page 11
 Economic Base Multiplier: Page 14
 Conditions in the Capital Market: Page 18
 Interest Rates: Page 21
 Drivers of Commercial Real Estate: Page 24
 Primary Drivers of Each Type of Commercial Property: Page 29
 The Housing Market: Page 34
2
DiPasquale-Wheaton
Four Quadrant Model
Illustrates how consumer confidence, retail sales, real estate production, and gross leasable area interact.
3
Quoted Rates vs. Total RBA
$14.00
Quoted Rates (rents per sqft)
$12.00
$10.00
NE Quadrant: The
Market for Space
$8.00
Demand is shifting out and to the right,
which indicates increased demand from
3Q2011 and 4Q2012.
$6.00
Quoted rents and existing industrial
space have an inverse relationship since
decreasing rents coincides with
increasing RBA. Firm profits increase in
the short run, but demand growth in the
space market eventually creates longrun equilibrium.
$4.00
$2.00
$0.00
0
500,000,000
1,000,000,000
1,500,000,000
Total RBA
3Q2011
4Q2012
Linear (3Q2011)
Linear (4Q2012)
4
Sold Price vs. Quoted Rate
$160.00
$140.00
Sold Price (per sqft)
$120.00
NW Quadrant:
Valuation Function
$100.00
As rent per square foot increases,
transaction price per square foot
increases. These variables have a
direct relationship, which is expected
since increasing rates coincides with
increasing sales prices.
$80.00
$60.00
$40.00
$20.00
$0.00
$0.00
$2.00
$4.00
$6.00
$8.00
$10.00
Quoted Rates (rents per sqft)
$12.00
$14.00
5
Change in SqFt Under Construction vs. Change in Sold Price
1,000,000
500,000
Change in SqFt Under Construction
0
-$50.00
-$40.00
-$30.00
-$20.00
-$10.00
$0.00
-500,000
-1,000,000
$10.00
$20.00
$30.00
SW Quadrant:
Construction Sector
As price of industrial properties
increases, change in the volume of
new construction increases. These
variables have a direct relationship.
However, this relationship is relatively
weak.
-1,500,000
-2,000,000
-2,500,000
Change in Sold Price (per sqft)
6
SqFt Under Construction vs. Existing RBA
1,400,000,000
1,200,000,000
SE Quadrant: Supply
Adjustment
Existing RBA
1,000,000,000
800,000,000
As industrial square feet under
construction increases, existing RBA
increases. These variables have a
direct relationship.
600,000,000
The trend line is not at a 45 degree
angle because construction does not
always create additional rentable
space. For example, projects can be
put on hold and permits can be used
to preserve a space.
400,000,000
200,000,000
0
0
1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 7,000,000
SqFt Under Construction
7
Shift Share Technique
Illustrates how cities’ and regions’ employment is affected by national trends, industrial, and compositional trends.
8
Correlation Coefficient Matrix
Industrial
Employment National
Mix
MSA FHFA HPI
Change
Effect
Effect Effect Appreciation
Phoenix, AZ
Employment Change
Shift Share National Effect
Components Industrial Mix Effect
MSA Effect
FHFA HPI Appreciation
1.00
0.93
0.85
0.94
0.59
1.00
0.79
0.76
0.44
Seattle, WA
Employment Change
Shift Share National Effect
Components Industrial Mix Effect
MSA Effect
FHFA HPI Appreciation
1.00
0.84
0.75
0.72
0.48
1.00
0.85
0.24
0.58
Raleigh, NC
Employment Change
Shift Share National Effect
Components Industrial Mix Effect
MSA Effect
FHFA HPI Appreciation
1.00
0.84
0.25
0.82
0.71
1.00
0.52
0.39
0.67
1.00
0.77
0.79
1.00
0.21
0.49
1.00
-0.19
0.42
1.00
0.63
1.00
Correlation Matrix of
Shift Share
Components
In Phoenix, the Industrial Mix Effect is most
highly positively correlated with FHFA HPI
Appreciation, and has the highest correlation
of .79. In Seattle and Raleigh, the National
Effect is most highly positively correlated with
FHFA HPI Appreciation.
1.00
0.09
1.00
0.49
1.00
1.00
Individual city trends make local city
economies different than the national
economy, thus preventing the correlation from
being even higher despite the large impact
that the national economy has on local
economy employment and eventual HPI.
The fact that both Seattle and Raleigh are
costal cities suggests that similar national
trends might be affecting their local
economies. Both Seattle and Raleigh have an
Industry Mix with a modest correlation with
house price appreciation.
9
Shift Share Analysis
Phoenix
Seattle
Raleigh
10
Location Quotients
Illustrates how concentrated industries are in varying regions and can be used in conjunction with the shift-share analysis.
11
NAICS Code Location Quotients
Base Industry: Total, all industries
Sector Level
NAICS 11
Agriculture, forestry, fishing and hunting
NAICS 21
Mining, quarrying, and oil and gas extraction
NAICS 22
Utilities
NAICS 23
Construction
NAICS 31-33 Manufacturing
NAICS 42
Wholesale trade
NAICS 44-45 Retail trade
NAICS 48-49 Transportation and warehousing
NAICS 51
Information
NAICS 61
Educational services
NAICS 62
Health care and social assistance
NAICS 71
Arts, entertainment, and recreation
NAICS 52
Finance and insurance
NAICS 53
Real estate and rental and leasing
NAICS 54
Professional and technical services
NAICS 55
Management of companies and enterprises
NAICS 56
Administrative and waste services
NAICS 72
Accommodation and food services
NAICS 81
Other services, except public administration
NAICS 99
Unclassified
Number of LQ>1 (Location Quotients > 1)
2011
Phoenix
2011
Seattle
2011
Raleigh
1
1
1
0.49
0.33
1.10
1.12
0.71
1.03
1.05
0.97
0.76
1.22
0.91
0.94
1.40
1.36
0.88
0.83
1.53
1.00
0.81
0.14
0.29
0.07
0.28
1.06
1.15
1.07
0.89
1.05
2.56
0.71
0.85
1.18
0.79
1.18
1.15
1.03
0.87
0.87
1.36
NC
ND
ND
0.73
1.35
0.62
1.02
1.05
0.60
1.71
0.82
0.83
1.29
0.84
1.12
1.41
1.45
1.36
1.02
0.88
0.01
8
10
10
25
25
25
LQ Level of
Aggregation
Location quotients help users to determine if
industries are concentrated compared to the
national average. A location quotient of 1 means
the region neither imports nor exports. A location
quotient greater than one indicates a region
exports, and below 1 indicates it imports. Focusing
on the sector level can give the user a broader
overlook of what general regions are focused on.
After identifying overarching trends through the
sector level, subsector location quotients can help
to explicitly identify the businesses and activities
that are driving each region’s economy.
For example, Seattle has a location quotient of .29
in agriculture, forestry, fishing, and hunting. On the
sub sector level, a location quotient of 9.70
indicates the predominance of the fishing, hunting,
and trapping industry in Seattle.
Sub-Sector Level
Number of LQ>1 (Location Quotients > 1)
12
Location Quotients (Continued)
Economic Base
LQ and Shift Share
▪ Key industries, defined by LQ>2, are different in
Phoenix, Seattle, and Raleigh.
▪ In Phoenix, the largest subsectors are computer and
electronic product manufacturing as well as lessors
of nonfinancial tangible assets.
▪ In Seattle, key industries include fishing, hunting,
and trapping as well as transportation equipment
manufacturing, nonstore retailers, publishing, and
water transportation.
▪ In Raleigh, the largest sector is real estate and rental
and leasing, but none of the subsectors have LQ>2.
▪ It is appropriate to think of the location quotient and
shift share as each city’s portfolio of industries,
similar to mutual funds. The MSA effect and effect on
employment change specifically indicates how
quickly those industries are growing in each region.
▪ Cities with higher correlations between MSA Effect
and FHFA Home Price Appreciation seem to rely
more heavily on fewer industries and vice versa. In
other words, these cities are more diversified in
terms of prominent sectors.
▪ For example, Seattle, which has a correlation
between MSA Effect and HPI has relatively greater
sub-sectors with LQ>2.
▪ See page 9 for the primary effect that drives house
price appreciation in each respective city.
13
Economic Base Multiplier
Providing analysis for the flow of money throughout the economy and real estate.
14
Growth Rate in Income in Goods Producing Sector vs. Growth Rate in
Total Income
20.0%
San Antonio
Austin
Houston
15.0%
Economic Base
Multiplier based on
Income
y = 0.3702x + 0.0854
R² = 0.6205
Pittsburgh
Honolulu
Baltimore
Growth Rate in Total Income
Raleigh-Cary
-60.0%
Nashville
-50.0%
-40.0%
Miami
-30.0%
Boston Salt Lake City
Dallas-Ft Worth-Arlington
Columbus
Denver
San Diego
San Jose
Indianapolis
Philadelphia
Minneapolis
Kansas City Milwaukee
Cincinnati-Middletown,
OH-KY-IN
5.0%
San Francisco
St. Louis
New York
Cleveland
Chicago
Charlotte
Sacramento
10.0%
Seattle
Memphis
Atlanta
Los Angeles-Long Beach
-20.0%
-10.0%
0.0%
0.0%
Phoenix-Mesa-Scottsdale
Riverside-San Bernardino-Ontario
10.0%
20.0%
Relatively speaking, Seattle and
Raleigh both have positive growth
rates when compared to Phoenix and
should be considered for investment
opportunities. These cities were fast
growing from 2007 through 2011 since
they are above the trendline.
Detroit
-5.0%
Las Vegas
-10.0%
The multiplier effect does hold true
since there is a positive correlation
between growth rate in income in the
goods sector and growth rate in total
income. For every 1% increase in
growth in income in basic industries,
total MSA increases by .37, on
average.
-15.0%
Growth Rate in Income in Goods Producing Sector
15
Growth Rate in Earnings in the Goods Producing Sector vs. House Price
Appreciation
10.0%
Pittsburgh
-60.0%
-50.0%
-40.0%
-30.0%
y = 0.9631x - 0.0801Houston
Austin
R² = 0.6044
Dallas-Ft
Worth-Arlington
San Antonio
0.0%
-10.0% Indianapolis
0.0%
10.0%
Denver
-20.0%
Raleigh-Cary
Columbus
Nashville
Cincinnati-Middletown,
Charlotte
OH-KY-IN
Kansas City Honolulu
St. Louis Philadelphia
Boston
Cleveland
Milwaukee
-10.0%
Memphis
New York
House Price Appreciation
Atlanta
Baltimore
San Francisco
Salt Lake City
Minneapolis
San Jose
20.0%
Impact of Growth in
Basic Industries
(Goods Producing
Sector) on House
Price Appreciation
-20.0%
Chicago
Seattle
San Diego
Detroit
Los Angeles-Long Beach
-30.0%
Sacramento
Riverside-San Bernardino-Ontario
Miami
-40.0%
Phoenix-Mesa-Scottsdale
-50.0%
Las Vegas
The house price appreciation has
been similar to its goods producing
sector earnings growth for Raleigh
and Phoenix, but not Seattle, which
has moved below the trendline.
For every 1% increase in growth rate
in earnings in the goods producing
sector, house price appreciation
increases by .963 – an even greater
effect than the 1% increase had on
total income.
-60.0%
-70.0%
Growth Rate in Earnings in the Goods Producing Sector
16
Putting it All Together
▪ Seattle warrants further investigation for real estate investment opportunities. For the
cities analyzed, Seattle is above the trend line. As basic industries grow, Seattle also
grows significantly. However, house prices in Seattle are not consistent with this
reasoning, which warrants further investigation.
▪ Narrowing in on the sub-sector level, Seattle has an extremely high quotient in
fishing, hunting, and trapping, which makes sense due to it’s coastal and northern
location. In Seattle, the National Effect is the most correlated to house price increases
suggesting impacts from the national economy that might relate to Seattle’s close
proximity to large cities on the West coast, specifically in California.
▪ Houston, Pittsburgh, San Antonio, and Austin are other examples of MSAs that
warrant further investigation for real estate investment opportunities. These cities
appear to be even faster growing than Raleigh and Seattle when looking at the
economic base multiplier analysis.
17
Conditions in the Capital
Market
Reflected through the mortgage interest rate and risk premium.
18
Actual Cap Rate vs. Imputed Cap Rate
18.0
16.0
14.0
Synthetic (Built Up)
Cap Rate
12.0
10.0
The imputed cap rate does a good job
of predicting movements in the actual
cap rate. The imputed cap rate mirrors
the actual cap rate strongly as can be
seen not only graphically, but also by
the correlation coefficient of .834
8.0
6.0
4.0
2.0
Correlation Coefficient = .834.
0.0
1977Q41980Q41983Q41986Q41989Q41992Q41995Q41998Q42001Q42004Q42007Q42010Q4
Actual Cap Rate
Imputed Cap Rate
19
Chemical Activity Barometer and CRE Risk Premium
120.0
6.000
5.000
100.0
4.000
3.000
80.0
Risk Premium using
the Cap Rate
2.000
60.0
1.000
0.000
The Chemical Activity Barometer and
Commercial Real Estate Risk
Premium are strongly positively
correlated with a Correlation
Coefficient of .741
40.0
-1.000
-2.000
20.0
-3.000
Correlation Coefficient = .741.
0.0
-4.000
1
8
15 22 29 36 43 50 57 64 71 78 85 92 99 106 113 120 127 134 141
Chemical Activity Barometer
CRE Risk Premium
20
Interest Rates
Illustrates how bank profits are affected by the risk premium, mortgage rates, and credit spreads.
21
Mortgage Interest and Default Premium
16.00
14.00
Decomposing the
Mortgage Interest
Rate
12.00
10.00
The mortgage interest is positively
correlated with the default premium
and has a correlation coefficient of
.504. The mortgage interest has a
much greater magnitude than the
default premium. This relationship is
predictable since growing interest
rates make the likelihood of defaulting
increase.
8.00
6.00
4.00
2.00
1977Q4
1978Q4
1979Q4
1980Q4
1981Q4
1982Q4
1983Q4
1984Q4
1985Q4
1986Q4
1987Q4
1988Q4
1989Q4
1990Q4
1991Q4
1992Q4
1993Q4
1994Q4
1995Q4
1996Q4
1997Q4
1998Q4
1999Q4
2000Q4
2001Q4
2002Q4
2003Q4
2004Q4
2005Q4
2006Q4
2007Q4
2008Q4
2009Q4
2010Q4
2011Q4
2012Q4
0.00
Constant Maturary Tbond 30Yr
Risk Premium
22
Mortgage Risk Premium vs. Credit Spread
4.50
4.00
Mortgage Risk
Premium vs. Credit
Spread
3.50
3.00
2.50
The mortgage risk premium and the
credit spread are positively correlated
with correlation coefficient of .562. The
default premium that corresponds to
mortgages is similar to default
premiums associated with general
corporate debt. However, the default
premium is higher on mortgages
relative to corporate bonds due the
increased risk of mortgages and the
likelihood of default. A mortgage is
given to one individual, while
corporate bonds have diversified
investors.
2.00
1.50
1.00
0.50
0.00
1
7
13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139
Risk Premium
Credit Spread
23
Drivers of Commercial
Real Estate
Reflecting upon how the real estate market can effect the economy in its entirety.
24
Commercial Property Returns and Credit Rationing
8
0.45
6
0.4
4
0.35
2
0.3
0
1977Q4 1981Q1 1984Q2 1987Q3 1990Q4 1994Q1 1997Q2 2000Q3 2003Q4 2007Q1 2010Q2
0.25
-2
0.2
-4
0.15
-6
0.1
-8
0.05
-10
Commercial Property
Returns and Credit
Rationing
The relationship between returns on
CRE and the tightening or loosening
of credit is negatively correlated with
correlation coefficient of -.038. As
property returns increase the
investment to loans ratio decreases
since the denominator (loans)
increases with an increase in property
returns.
0
All Ppty Return (NCREIF)
Investment to Loans
25
REIT Flows and Commercial Property Returns
400000
8
6
300000
4
200000
2
0
100000
-2
0
-4
1992Q1 1994Q1 1996Q1 1998Q1 2000Q1 2002Q1 2004Q1 2006Q1 2008Q1 2010Q1 2012Q1
-6
-100000
-8
Correlation Coefficient = .335.
-200000
Commercial Property
Returns (NCREIF) and
Flow of Funds into
Commercial Real
Estate (CRE)
The investor funds correlate positively
with commercial property returns with a
correlation coefficient of .335. Investors
flow funds into commercial real estate
when returns are high. Investors might
also invest in CRE to help boost demand
for the property with the goal of
ultimately increasing returns.
It is important to note that investors did
not begin to heavily invest in REITS until
1993. Real estate is a function of supply
and demand: as demand increases,
evidenced through flows of funds into
REITS, prices and returns rise. This is
intuitive: as demand and money is put
into the real estate system, real estate
asset values rise and property returns
increase, and vice versa.
-10
REIT Flows
All Ppty Return (NCREIF)
26
Change in Expected S&P500 EPS and Commercial
Property Returns
8
100.00%
6
80.00%
4
60.00%
2
Expected Corporate
Profits (EPS) and
Commercial Property
Returns based on
Appraised Values
40.00%
0
1978Q1 1981Q2 1984Q3 1987Q4 1991Q1 1994Q2 1997Q3 2000Q4 2004Q1 2007Q2 2010Q3
20.00%
-2
0.00%
-4
The change in expected S&P500 EPS
has a very slight correlation with
commercial property returns with a
correlation coefficient of .027. Real
estate does not dominate many firms’
ability to generate sales revenue. As a
result, corporate profits have little to
no affect on property returns.
-20.00%
-6
-40.00%
-8
-10
-60.00%
All Ppty Return (NCREIF)
Change in Expected S&P500 EPS
27
Architecture Billing Index & Commercial Property
Returns
70.00
8
6
60.00
4
50.00
2
40.00
0
-2
30.00
-4
20.00
-6
Expected CRE Market
Conditions and
Realized Commercial
Property Returns
Architecture Billing Index scores are
positively correlated with commercial
property returns with a correlation
coefficient of .743. This suggests a
strong positive relationship between
CRE market conditions and CRE
returns. Growing Architecture Billing
Index scores suggest growth in design
activity and, ultimately, CRE returns.
10.00
-8
0.00
-10
1 3 5 7 9 111315171921232527293133353739414345474951535557596163656769
Architecture Billing Index
All Ppty Return (NCREIF)
28
Primary Drivers of Each
Type of Commercial
Property
Understanding the different drivers of retail, office, hotel, apartment, and industrial real estate.
29
Manager Expectations and Industrial Property Returns
80.00
8
6
70.00
4
60.00
2
50.00
0
40.00
-2
30.00
-4
20.00
-6
10.00
-8
0.00
Industrial Properties:
Manufacturers’
Expectations about
CapEx
The relationship between purchasing
managers’ expectations if their capital
expenditures near term and the returns
on industrial properties is positively
correlated. Generally, the ISM
Purchasing Managers’ Index leads
Industrial Returns.
This is sensible because the Purchasing
Managers’ Index reflects the acquisition
of goods by purchasing mangers. Thus,
as the economy improves and
purchasing managers are reporting
better overall conditions, it is indicative of
an improving economic environment
which in turn fuels industrial returns.
-10
1
7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 121 127 133 139
ISM Purchasing Managers' Index
Industrial Returns (NCREIF)
30
Growth Rate in Office Employment & Office Property
Returns
15
3.00%
10
2.00%
5
1.00%
0
1977Q4 1981Q1 1984Q2 1987Q3 1990Q4 1994Q1 1997Q2 2000Q3 2003Q4 2007Q1 2010Q2
0.00%
-5
-1.00%
-10
-2.00%
-15
-3.00%
Office Returns (NCREIF)
Quarterly Growth Rate in Office Employment
Office Properties:
Growth in Office
Employment
The quarterly growth rate in office
employment is positively correlated with
the quarterly returns on office properties
and has a Correlation Coefficient of .510.
It is a leading indicator since labor is a
factor of production and positively
impacts returns.
Office leases are generally signed for a
year or more at a time. Therefore, as
office employment growth and leases
expired and companies changed or no
longer needed certain office space, office
returns declined as the leases expired.
31
Retail Properties: Consumer expectations and
Rooftops
Consumer Expectations & Retail Property Returns
Retail Returns & Home Price Appreciation
160.00
10
10
5.00
140.00
8
8
4.00
6
6
3.00
4
4
2.00
2
2
1.00
0
0
0.00
-2
-2
-1.00
-4
-4
-2.00
20.00
-6
-6
-3.00
0.00
-8
-8
-4.00
120.00
100.00
80.00
60.00
40.00
Consumer Confidence Index
Retail Returns (NCREIF)
Retail property returns are positively correlated with the
consumer expectation index with a correlation coefficient of
.376. Consumer confidence appears to lag retail returns.
Expectedly, as retail returns increase, the economy is
generally improving, which gives consumers more
disposable income to spend more.
Retail Returns (NCREIF)
SF Home Price Appreciation (FHFA)
Retail returns are positively correlated with home price
appreciation with a correlation coefficient of .370.
Graphically, these variables show that “retailing follows
rooftops” as retail returns coincide with home price
appreciation. As home prices improve, which increases
equity in consumers’ housing, people will spend more.
Generally, retail returns lag home price appreciation.
32
Hotel Properties: The Role of Consumer and
Business Expectations about the Economy
Consumer Expectations and
Hotel Returns
70.00
15.00
10.00
60.00
10.00
120.00
5.00
0.00
-5.00
40.00
Consumer Confidence Index
-10.00
-15.00
0.00
-15.00
Hotel Returns (NCREIF)
Consumer expectations relate positively with hotel
property returns with a correlation coefficient of .472.
Consumer expectations is a leading factor to hotel
property returns. Predictably, increasing consumer
confidence will result in greater consumer spending in
travel and leisure, which will lead to increased returns
for hotel properties.
ISM Purchasing Managers' Index
2009Q2
10.00
2006Q2
-10.00
2009Q2
2006Q2
2003Q2
2000Q2
1997Q2
1994Q2
1991Q2
1988Q2
0.00
-5.00
20.00
2003Q2
20.00
0.00
30.00
2000Q2
60.00
5.00
40.00
1997Q2
80.00
50.00
1994Q2
100.00
1991Q2
140.00
15.00
1988Q2
160.00
Business Confidence and Hotel
Returns
Hotel Returns (NCREIF)
Business confidence (as measured by the ISM
Purchasing Managers’ Index), is positively correlated
with hotel property returns. When business is doing
well, executives are more willing to spend on overnight
stays. When the PMI is above 50 and manufacturing
and purchasing is expanding, hotel returns are
generally improving, but not necessarily positive.
33
The Housing Market
An analysis of equity extraction by home owners.
34
Equity Extraction & Home Price Appreciation
5.00
0.06
4.00
0.04
3.00
0.02
2.00
0.00
1.00
-0.02
0.00
1977Q4 1981Q1 1984Q2 1987Q3 1990Q4 1994Q1 1997Q2 2000Q3 2003Q4 2007Q1 2010Q2
-0.04
-1.00
-0.06
-2.00
-0.08
-3.00
-4.00
Are Houses ATM
Machines?
Positive home equity withdrawals tend to
occur when home price appreciation
increases above 0, or in other words,
when home prices are high. Currently,
since home price appreciation is near 0,
homeowners are generally not using
their home equity to increase their
disposable income.
As home prices appreciate, homeowners
tend to use more of the home equity to
increase their disposable income. As
home prices decline, consumers are less
likely to spend more and are more
concerned about paying down their
mortgage, which results in negative
equity extraction.
-0.10
SF Home Price Appreciation (FHFA)
Equity Extraction
35