Definition Argument Essay AssignmentGoal Write a 1,500.docx

Definition Argument Essay Assignment
Goal
Write a 1,500-1,750-word essay using five to seven academic
resources in which you argue that a contested “case” involving
the sale, trade, or donation of human organs fits (or does not fit)
within a given category. A case may include a specific news
article, story, or incident illustrating a dilemma or controversy
relating to the exchange of human organs. The case does not
need to be a court case.
Directions
Follow these steps when composing your essay:
1. Start by selecting a controversial case found in the media
involving the sale, trade, or donation of human organs. For
example, an appropriate case might include a story in the news
about an organ broker, and the term to define might be
“criminal.”
2. Decide what category you think your case belongs in, with
the understanding that others may disagree with you about the
definition of your category, and/or whether your chosen case
matches your category.
3. In the opening of your essay, introduce the case you will
examine and pose your definition question. Do not simply
summarize here. Instead, introduce the issue and offer context.
4. To support your argument, define the boundaries of your
category (criteria) by using a commonly used definition or by
developing your own extended definition. Defining your
boundaries simply means naming the criteria by which you will

discuss your chosen case involving the sale, trade, or donation
of human organs. If you determine, for example, that an organ
broker is a criminal, what criteria constitute this? A criminal
may intentionally harm others, which could be one of your
criteria.
5. In the second part of your argument (the match), show how
your case meets (or does not meet) your definition criteria.
Perhaps by comparing or sizing up your controversial case to
other cases can help you to develop your argument.
This essay is NOT simply a persuasive essay on the sale, trade,
or donation of human organs. It is an argumentative essay where
the writer explains what a term means and uses a specific case
to explore the meaning of that term in depth.
First Draft Grading
· You will receive completion points for the first draft based
upon the successful submission of a complete draft.
· Because your first draft is a completion grade, do not assume
that this grade reflects or predicts the final grade. If you do not
consider your instructor’s comments, you may be deducted
points on your final draft.
Final Draft Grading
The essay will be graded using a rubric. Please review the
rubric prior to beginning the assignment to become familiar
with the assignment criteria and expectations.
Sources
· Include in-text citations and a references page in GCU Style
for FIVE to SEVEN scholarly sources outside of class texts.
· These sources should be used to support any claims you make
and should be present in the text of the essay.
· Use the GCU Library to help you find sources.

· Include this research in the paper in a scholarly manner.
Format
Prepare this assignment according to the guidelines found in the
GCU Style Guide, located in the Student Success Center.
LopesWrite
You are required to submit this assignment to LopesWrite.
Refer to the LopesWrite Technical Support articles for
assistance.
© 2015. Grand Canyon University. All Rights Reserved.
•••••i
A National Profile of
the Real Estate Industry and
the Appraisal Profession
by J. Reid Cummings and Donald R. Epley, PhD, MAI, SRA
FEATURES
T
J- he
he real estate industry has been devastated on many fronts' in
the years
following the Great Recession, whieh began in 2007^ due to the
bursting of the
housing bubble and the subsequent finaneial crisis relating to

the mortgage
market meltdown.' The implosion of the mortgage markets
initially began when
two Bear Stearns mortgage-backed securities hedge funds,
holding nearly $10
billion in assets, disintegrated into nothing.* Panie quickly
spread to financial
institutions that could not hide the extent of their toxic,
subprime exposures, and
a massive, worldwide credit squeeze ensued; outright fear soon
replaced panic.
Subsequent eredit tightening and substantial illiquidity in the
financial markets
rapidly and severely affected the housing and construction
markets.' Throughout
the United States, properties of all kinds saw dramatic value
declines.
In thousands of cases, real estate foreclosures disrupted people's
lives,
forced businesses to close, eaused financial institutions to
falter, capsized wbole
market segments, devastated entire industries, and squeezed
municipal and state
government budgets dependent upon use and property tax
revenues.* While the
effeets of property value declines and the waves of foreclosures
in markets across
the country captured most of the headlines, one significant
impact of the upheaval
in US real estate markets has gone largely unreported: its
impact on employment
in the real estate industry, and specifically, the real estate
appraisal profession.
This article presents a

current employment
profile of the US real
estate industry, with
special attention given
to appraisal profes-
sionals. It serves as an
informative picture of
the appraisal profession
for use as a benchmark
for future assessment
of growth. As a
component of the real
estate industry, the
appraisal profession
ranks as the smallest
in employment, is
highly correlated to
movements in empioy-

ment of brokers and
agents, and relies on
commerciai banking,
credit, and real estate
lessors and managers
to deliver its products.
1. James R. DeLisle, "At the Crossroads of Expansion and
Recession," TheAppraisalJournal 75, no. 4 (Fall 2007):
314-322; James R. DeLisle, "The Perfect Storm Rippiing Over
to Reai Estate," The Appraisal Journal 76, no,
3 (Summer 2008): 200-210.
2. Randaii W. Eberts, "When Wiii US Empioyment Recover
from tiie Great Recession?" International Labor Brief
9, no. 2 (2011): 4-12 (W. E. Upjohn Institute for Employment
Research): Chad R. Wilkerson, "Recession and
Recovery Across the Nation: Lessons from History," Economic
Review 94, no. 2 (2009): 5-24.
3. Kataiina M. Bianco, The Subprime Lending Crisis: Causes
and Effects of the Mortgage Meltdown (New York:
CCH, inc., 2008): Lawrence H. White, "Fédérai Reserve Policy
and the Housing Bubbie," in Lessons From the
Financial Crisis: Causes, Consequences, and Our Economic
Future, ed. Robert W. Koib (Hoboken, NJ: John Wiley
& Sons, Inc., 2010), 453-460.
4. John Bellamy Foster, "The Financialization of Capital and the
Crisis," Monthiy Review 59, no. 11 (Aprii 2008):

1-19.
5. Major Coleman iV, Michael LaCour-Littie, and Kerry D.
Vandeii, "Subprime Lending and the Housing Bubbie: Taii
Wags Dog?" Journai of Housing Economics 17, no. 4 (2008):
272-290.
6. Dean Baker, "The Housing Bubbie and the Financiai Crisis,"
Rea/-Wor/d Economics Review no. 46 (2008): 7 3 - 8 1 .
ANationaLRrMlejlîheBeaLIstatdflctustry^ancIth&Apprms.aJ.Er
ofessLaa. _IJhe Appraisal Journal, Spring 2013
Hundreds of thousands of professionals
are involved in brokering, leasing, managing,
appraising, and developing all property types.
Service professionals include residential sales
agents, multifamily-property managers, commercial
investment advisors, industrial property brokers,
land developers, property appraisers, and many
others.^ Their professional education and training
includes academic work performed in colleges and
universities; industry-specific education and training
programs; advanced professional association
development and designation certifications; company
and franchise training; pre- and post-licensing
continuing education requirements; and many years
of on-the-job training and experience.
The disintegration of the housing and financial
markets has affected all professionals in the real
estate industry and its employment components.
This article shows professional real estate appraisers
have been particularly hard hit. Before the recession,

as property values and sales grew, and as demand for
loans increased, appraisers' workloads did as well.
When the bubble burst, appraisers felt its impact and
experienced significant declines in their businesses.
As a result, the real estate appraisal industry
experienced a significant loss in jobs. Recent growth
in employment within the appraisal profession has
neither mirrored other sectors in the real estate
industry, nor that of the US economy.
The purpose of this article is to provide a cross-
sectional view of the national real estate industry
with special attention given to employment in the
appraisal profession. Nothing in the professional
literature attempts to establish a data-driven profile
of the appraisal business, or compares and contrasts
it to other real estate-related professions. This article
is not a survey, but rather an effort to establish a basic
real estate appraisal employment baseline that will
serve as a benchmark for future trend comparisons.
This profile uses the latest data estimates from
private, state, and federal sources in support of
regional input-output tables used for the estimation
of economic impacts from events in a region.^
The results indicate that overall real estate industry
employment at the end of 2011 was higher than at
the beginning of 2001. However, the trend of annual
increases in the number employed evident in the
early years of the 2001—2011 study period reversed
itself during the recession. Declines in employment
appear to coincide with concurrent declines in the
economy during the latter years of the same period. The
results further show a significant correlation between
employment in the real estate appraisal profession and
production measures of the national economy, but not

with national employment This research is not only
very timely, it also is extremely important because
changes in the employment trends in the real estate
industry since the financial crisis began have been
substantial. The information and analysis presented
offer unique insights into understanding the current
state of the real estate industry, and in particular, the
real estate appraisal profession.
Employment Profile and Trends
This article examines national employment trends
in five real estate-related categories:
• Agents and Brokers
• Appraisers
• Lessors and Lessors' Agents
• Property Managers
• Other Services (i.e.. Escrow Agents, Consultants,
Fiduciaries, Asset Managers, and Listing Services)
It extracts the data according to the North American
Industry Classification System (NAICS) at the
six-digit code level across all real estate-related cat-
egories for the period 2001—2011.'' Each category
draws from information provided by the US Census
Bureau NAICS category definitions.
Agents and Brokers
The industry classification Offices of Real Estate
Agents and Brokers (NAICS Code 531210) includes
people primarily engaged in acting as agents and/or
brokers in one or more of the following: (1) selling
real estate for others, (2) buying real estate for others.
7. Association of Real Estate License Law Officiais, Digest of

Real Estate License Laws and Current Issues (Chicago:
Association of Reai Estate License
Law Officiais, 2011).
8. Proprietary data obtained by paid license from Economic
Modeiing Speciaiists. Intl. For information on purchasing
licenses enabling information access,
see http://www.economicmodelihg.com.
9. NAICS codes adopted by several government agencies such
as the US Bureau of Ecohomic Analysis and the US Bureau of
Labor Statistics for the
standardization and reporting of data such as employmeht ahd
income. Further expianation of the accounts used ahd specialties
covered is shown in
the Appendix at the end of this articie.
appraisai Journal, Spring 2 0 1 3 , ^ -EcoJile Qflhe
and (3) renting real estate for others. Figure 1 shows
that at the end of 2001,1,061,482 people in the United
States worked in Offices of Real Estate Agents and
Brokers. At the end of 2011,1,717,627 people worked
in this classification, or 61.8% more than in 2001. The
annual employment number increased each year in
2001-2007, peaking in 2007 at 1,857,576. However,
coinciding with the beginning of the recession, the
number of people in this classification began to
decline, and the annual decreases continued until a
slight increase occurred in 2011 over 2010.
Two caveats are noteworthy. First, substantial
increases in employment during the early years of the
period may be due to entry of new licensees hoping

to capitalize on the potential income opportunities
provided by Üie booming, pre-financial crisis real estate
markets. Therefore, tbe sharp growth trend may have
been an unsustainable anomaly. Second, the data does
not differentiate between those licensed professionals
who work full-time versus those who only work part-
time. Therefore, some portions of categorical declines
in the post-flnancial crisis economy may be due to
part-üme licensees choosing not to renew their licenses
during the economic downturn.
Appraisers
The industry classification Ofiices of Real Estate
Appraisers (NAICS Code 531320) includes people
primarily engaged in estimating the fair market
value of real estate. Figure 2 shows that at the end
of 2001,80,724 people in the United States worked in
this classification. At year-end 2011,111,253 people
worked in this classificaüon, or 37.8% more than in
2001. The annual employment number increased
each year in 2001-2007, peaking in 2007 at 118,657.
In addition, again coinciding with the beginning of
the recession, the number of people in this classifica-
tion began to decline, and the decreases confinued
through 2011.
Although the percentages of growth in this
category are different from those of the category
Offices of Real Estate Agents and Brokers, it is
possible the explanafions are similar. The booming
real estate markets prior to the financial crisis
increased demand for appraisals, and therefore,
more people entered the profession. Likewise,
as the markets slowed after the crisis began and
appraisal demand declined, so did the demand

for appraisers. Due to the reduced demand, some
licensed appraisers may have sought other types
of employment, or suspended or terminated their
licenses. Further, some lenders, especially those
focusing on the residential mortgage sector,
increased use of alternafive valuation products or
turned to using broker price opinions (BPOs).'"
Figure 1 US Offices of Real Estate Agents and Brokers (NAICS
Code 531210)
a.
S0.
'S
be
r
1
2,000,000 -|
1,800,000 -
1,600,000 -
1,400,000 -
1,200,000 -
1,000,000 -
800,000 -
600,000 -

400,000 -
200,000 -
0 -
o
48
2
,6
7( ,3
7
W H ^ ,
1 1 1
2001 2002 2003 2004
M
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(0
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2005
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1 i
2006 2007
Year

o"
00
2008
t
m
H
2009
,6
9:
71
4
2010
M
71
7
H
2011
10. So many real estate brokers began performing BPOs after
the financial crisis that in IVlay 2011, the National Association
of Realtors (NAR) introduced a
new BPO training and certification program. Information
obtained from the Nationai Association of Reaitors available at
http://www.realtororg/rmodaiiy.

nsf/pages/News2011051306.
aJMonal2rMkoíJlifiJüaL£síalfiJndiJ.stryjDd the AppraisalJr l i i
e Appraisal Journal, Spring20;
Figure 2 US Offices of Real Estate Appraisers (NAICS Code 5 3
1 3 2 0 )
'S.
o
'S
140,000
120,000
100,000
80,000
60,000
40,000 -h-
20,000 -— ,̂.,-
2001 2002 2003 2004 2005 2006 2007
Year
2008 2009 2010 2011
Lessors and Lessors' Agents
The industry classification Lessors of Residential
Buildings and Dwellings (NAICS Code 531110)

includes people primarily engaged in acting as les-
sors of buildings used as residences or dwellings,
such as single-family homes, apartment buildings,
and townhomes. Included in this classification are
owner-lessors of residential buildings and dwellings
or people employed by them.
Figure 3 shows that at the end of 2001, 683,905
people in the United States worked as Lessors of
Residential Buildings and Dwellings. At year-end
2011,1,057,764 people worked in this classification.
or 54.7% more than in 2001. The annual employment
number increased each year in 2001—2007, peaking
in 2007 at 1,083,847. However, coinciding with the
beginning of the recession, the number of people
employed in this classification began to decline,
dipping slighüy in 2008 and 2009. The trend reversed
in 2010 and 2011.
The industry classification Lessors of Non-
Residential Buildings (NAICS Code 531120) includes
people primarily engaged in acting as lessors
of huildings (except mini-warehouses and self-
storage units) that are not residences or dwellings.
Included in this industry sector are owner-lessors
Figure 3 Offices of US Lessors of Residentiai Buildings and
Dwellings (NAICS Code 5 3 1 1 1 0 )
4)
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o
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be
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1,200,000 -1
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800,000 -
600,000 -
400,000 -
200,000 -
0 -
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9(
00
CO
2001
S
2002

S
76
4
2003
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2004
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2005
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2006
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2007
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2008
54
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2009
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2010
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05
7
11
2011
! Appraisal Journal, Spring 2O13L lPröfile of
th&RftaiXslatalcuksítyjnd the AppraisalÄ
of non-residential buildings and people employed
by tbem.
people in the United States worked in the Lessors
of Non-Residential Buildings classification. At year-
end 2011, 493,600 people worked in this industry
classification, or 33.7% more than in 2001. The annual
number of people increased each year in 2001—2005,
decreased slightly in 2006, and increased in 2007

and 2008, when it peaked at 510,576. Thereafter,
the annual number of people employed in this
classification decreased each year in 2009—2011.
The industry classification Lessors of Mini-
Warehouses and Self-Storage Units (NAICS Code
531130) includes people primarily engaged in
renting or leasing self-storage space (e.g., rooms,
compartments, lockers, containers, or outdoor space)
where clients can store and retrieve their goods.
Mini-Warehouses and Self-Storage Units. At the
end of 2011, 280,702, or 112.6% more than in 2001,
worked in this classification.
The annual number of people in this classification
increased each year in the study period except for
2009, when it decreased shghtiy by -2,393, or -0.86%
less than 2008. A possible explanation for the strong
growth performance could be a combination of
Americans continuing to accumulate more material
possessions and the downsizing of residences,
increasing the need for storage of their possessions.
Another explanation might be that foreclosures
forced people to place their possessions in storage
as they transitioned to other residences.
The industry classification Lessors of Other
Real Estate Property (NAICS Code 531190) includes
people primarily engaged in acting as lessors of real
estate (except buildings), such as manufactured-
home sites, vacant lots, and grazing land. Figure 6
shows that at the end of 2001,125,915 people in the
United States worked as Lessors of Other Real Estate

Property. At the end of 2011,146,858 people, or 16.6%
Figure 4 Offices of US Lessors of Non-Residential Buildings
(NAICS Code 5 3 1 1 2 0 )
of
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p
b
er
z
600,000 -
500,000 -
400,000 -
300,000 -
200,000 -
100,000 -
0 -
CO
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CO
2001
Figure 5 Offices of US
of

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eo
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um
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300,000 -1
250,000 -
200,000 -
150,000 -
100,000 -
50,000 -
0 -
rt
2001
,^
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CO
CO
I
2002
Lessors

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2002
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2003
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2004
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2005
of Mini-Warehouse and
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2003

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2006
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2007
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2008
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2009
Self-Storage Units (NAICS Code
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2006
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2011

531130)
72
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2010
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2011
JThe Appraisal Journal, Spring 20:
Figure 6 Offices of US Lessors of Other Real Property (NAICS
Code 531190)
pi
e
Pe
o
ro
f
N

u
m
b
e
1 ön onn -
160,000 -
140,000 -
120,000 -
100,000 -
80,000 ^
60,000 -
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r
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Figure 7 Offices
io
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t

3
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250,000 -
200,000 -
150,000 -
100,000 -
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2001
of US
00
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2005
Residentiai Property Managers
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2004
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2006
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(NAICS
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2006
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2007
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2008
Code 531311)
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2007
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2008
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2009
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2011
more than in 2001, worked in this classification. The
increases and decreases in the number of people in
this classification are inconsistent, showing increases
in 2001-2005,2007, and 2010, but decreases in 2006,
2008-2009, and 2011.
Property Managers
The industry classification Residential Property
Managers includes people primarily engaged in
managing residential real estate for others. Figure 7
shows that at the end of 2001, 178,244 people in the
United States worked in this industry classification,
and atthe end of 2011,289,706 people, or 62.5% more
than in 2001, worked in this classification.
During 2001—2011, the number of people in tbis
classification increased each year, with the highest
annual increase (10.7%) occurring in 2007, which

coincided with the beginning of the recession. The
10.7% increase in 2007 was the only double-digit
increase during the study period. One possible
explanation for this is that 2007 was the first year people
began losing their homes to foreclosure hecause of the
recession. As the demand for rental units increased due
to increased home foreclosures, there may have been
a eommensurate inerease in tbe need for residential
managers. Anotber explanation could be that more
apartment eomplexes came on line in 2007 due to the
rapid expansion of eonstrucüon of multifamily units in
the middle part of the decade, resulting in employment
of more residential property managers.
The industry classification Non-Residential
Property Managers (NAICS Code 531312) includes
people primarily engaged in managing non-
residential real estate for others. Figure 8 shows at the
end of 2001, 83,213 people in the United States were
employed as Non-Residenüal Property Managers. At
the end of 2011,130,346 people, or 56.6% more than
in 2001 worked in this classification.
ppraisal Journal, Spring 2013. A National Profile of tlie^R&aJ
£state.iDáiistry,.aad the Appraisal Profession
Figure 8 Offices of US Non-Residential Property IVIanagers
(NAICS Code 5 3 1 3 1 2 )
P
e
o
p

ie
N
um
be
r
140,000
120,000
100,000
t 80,000 —
60,000
40,000
20,000
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en
tn
o
H
2001 2002 2003 2004 2005 2006
Year
i i i i
2007 2008 2009 2010 2011
With the exception of 2009-2010, when growth
was relatively flat, the number of people working in
the Non-Residential Property Managers classification

increased during the study period, with the highest
annual increase (9.4%) occurring in 2008. A possible
explanation for the significantly higher increase in
2008 is that demand for asset managers increased
due to the increased foreclosures of non-residential
properties. Another possible explanation is that
demand for commercial real estate was increasing
in the years prior to the financial crisis—peaking in
2008—and thus, more real estate firms employed
more non-residential property managers to service
the industry. It is important to note that because this
NAICS industry classification includes only those
managing non-residential real estate for others,
property management services for owner-occupied
properties are not included.
Other Real Estate Activities
The industry classification Other Activities Related
to Real Estate (NAICS Code 531390) includes people
primarily engaged in performing real estate-related
services (except lessors of real estate, olfices of real
estate agents and brokers, real estate property man-
agers, and offices of real estate appraisers). Figure 9
United States worked in Other Activities Related to
Real Estate. At the end of 2011, 852,824 people, or
44% more than in 2001, worked in this classification.
The e m p l o y m e n t growth t r e n d of this
classification is similar to the growth trend in the
classification Offices of Real Estate Appraisers. The
annual number increased each year in 2001—2005,
and peaked in 2007 at 890,100. Coinciding with the
employed in this classification then began to decline

and the decreases continued through 2011.
Correlations and Summary
The analysis in this article compares employment
categories of the appraisal profession to other seg-
ments of the real estate industry and various national
economic indicators. The statistical test used is a
simple correlation analysis utilizing the Pearson"
method to produce correlation eoefiicients between
the appraisal profession and other segments of the
real estate industry. The purpose of performing
this statistical test was to uneover strong and weak
relationships with other parts of the eeonomy that
could serve as future indieators of the welfare of the
Correlation analysis examines the degree
to which relationships exist between variables.
Correlations, labeled as eoefiicients, are numbers
between -1 and +1. A coefficient between 0 and +1
suggests a positive relationship between the variables,
whereas a coefficient between -1 and 0 suggests a
negafive one. Correlation analysis helps reduce the
range of uncertainty about the relaüonships between
the variables. Hence, correlation analysis produces
greater variance of the predieted outcomes—how
much movement of one variable is related to
movement of another variable—that are eloser to
1 1 . Joseph F. Hair Jr., Mary Wolfinbarger Ceisi, Arthur
Money, Phillip Samouel, and Michael J. Page, Essentials of
Business Research Methods, 2nd ed.
(Armonk, New York: M. E. Sharpe, inc., 2011).
^ ^ The Appraisai Journai, Spring 20:

Figure 9 US Offices of Other Activities Related to Real Estate
(NAICS Code 531390)
P
e
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p
le
N
u
m
b
e
1,000,000
800,000
600,000
400,000
200,000
0
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reality. A simple correlation is between two variables.
Perfect correlation exists between two variables
when the correlation coefficient is either +1 or - 1 .
Table 1 shows the correlation analysis results
for the study.'̂ They reveal a posifive relationship
between the appraisal profession and the other
sectors. The highest correlafion of+0.998 was with
the classification Offices of Real Estate Agents and
Brokers, which was statistically significant at the
0.01 level (this means that 99.8 times out of 100, this

relationship will exist and will be highly, posifively
correlated). Also, a strong, positive relationship
of +0.997 was revealed with the classification
Residential Property Managers, which was highly
significant at the 0.01 level. The interpretation is that
as employment in the sectors identified goes up or
down, employment in the appraisal profession will
do likewise.
The analysis leads to the following conclusions
related to the Real Estate Appraisers classificafion:
1. The industry classification Real Estate Appraisers
is the smallest among all real estate sectors
examined, with 111,233 johs in 2011.
2. Employment increased annually from 80,724
in 2001 to a high of 118,657 in 2007, for a total
increase of 37,933, or 46.99%.
3. Employment decreased annually from 118,657
in 2007 to a low of 111,233 in 2011, for a total
decrease of-7,424, or -6.3%.
4. During the study period, the largest annual
decrease was from 118,657 in 2007 to 114,397
in 2008, a decrease of-4,260 or -3.6%.
5. The smallest decrease, between 2009 and 2010,
was -271 or -0.24%.
6. The most recent decrease, between 2010 and
2011, was-1,705 or-1.51%.
Total Requirements Needed to Operate

The Bureau of Economic Analysis prepares and
publishes a variety of economic statistics on indus-
tries. Its data on total requirements represent the
total demand for goods or services that an industry
needs to produce its particular goods or services.'^
While other industries or resources operafing or
existing within the region saüsfy some of the demand,
in many instances not all of the requirements
are satisfied from within the same region. This
unsatisfied or leftover demand is satisfied through
imports into the region. Thus, the total requirements
equal the amount safisfied within the region plus the
amount of imports into the region.
Figure 10 displays the US 2010 total requirements
for real estate-related industries. Because this data
is for the entire United States, the region is the
entire country as well. The 2010 total requirements
for all real estate-related sectors totaled over $1.09
12. The correlations shown in Table 1 are between people
working in the appraisai profession and other real estate-reiated
sectors.
13. The totai requirements (TR) technique does not derive
estimates based on empioyment but instead focuses on the totai
demand for goods or services
that an industry needs in order to produce its particular goods or
services. In the United States, the Department of Commerce's
Bureau of Economic
Anaiysis (BEA) produces two types of TR tables, in coefficient
form, using benchmark input-output information drawn from
make and use tables. The tables
present input values of goods or services purchased directiy in
order to produce one dollar of output. The coefficients of the

TR tables provide the totai
sum of direct and indirect inputs necessary to produce output.
For example, the direct purchases (inputs) necessary to produce
an airplane wouid inciude
the steel and aiuminum used in the construction of the aircraft
fuselage, and the indirect purchases wouid include the energy
resources necessary to
produce the steel and the aluminum. The different types of
direct and totai requirements information produced by the BEA
depend on whether the defined
goods and services are industries or commodities. For a
comprehensive explanation of the BEA's methodology and data-
derivation techniques, refer to the
BEA's Methodology Paper Series and other methodoiogies oh
the nationai, industry, international, and regional accounts
avaiiable at http://www.bea.gov/
methodoiogies/index.htm and articies pubiished in the Survey of
Current Business avaiiable at
http://www.bea.gov/scb/index.htm.
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•••••i
A National Profile of
the Real Estate Industry and
the Appraisal Profession
by J. Reid Cummings and Donald R. Epley, PhD, MAI, SRA
FEATURES
T
J- he
he real estate industry has been devastated on many fronts' in
the years
following the Great Recession, whieh began in 2007^ due to the
bursting of the
housing bubble and the subsequent finaneial crisis relating to
the mortgage
market meltdown.' The implosion of the mortgage markets
initially began when
two Bear Stearns mortgage-backed securities hedge funds,
holding nearly $10
billion in assets, disintegrated into nothing.* Panie quickly

spread to financial
institutions that could not hide the extent of their toxic,
subprime exposures, and
a massive, worldwide credit squeeze ensued; outright fear soon
replaced panic.
Subsequent eredit tightening and substantial illiquidity in the
financial markets
rapidly and severely affected the housing and construction
markets.' Throughout
the United States, properties of all kinds saw dramatic value
declines.
In thousands of cases, real estate foreclosures disrupted people's
lives,
forced businesses to close, eaused financial institutions to
falter, capsized wbole
market segments, devastated entire industries, and squeezed
municipal and state
government budgets dependent upon use and property tax
revenues.* While the
effeets of property value declines and the waves of foreclosures
in markets across
the country captured most of the headlines, one significant
impact of the upheaval
in US real estate markets has gone largely unreported: its
impact on employment
in the real estate industry, and specifically, the real estate
This article presents a
current employment
profile of the US real
estate industry, with

special attention given
to appraisal profes-
sionals. It serves as an
informative picture of
the appraisal profession
for use as a benchmark
for future assessment
of growth. As a
component of the real
estate industry, the
appraisal profession
ranks as the smallest
in employment, is
highly correlated to
movements in empioy-
ment of brokers and
agents, and relies on
commerciai banking,

credit, and real estate
lessors and managers
to deliver its products.
1. James R. DeLisle, "At the Crossroads of Expansion and
Recession," TheAppraisalJournal 75, no. 4 (Fall 2007):
314-322; James R. DeLisle, "The Perfect Storm Rippiing Over
to Reai Estate," The Appraisal Journal 76, no,
3 (Summer 2008): 200-210.
2. Randaii W. Eberts, "When Wiii US Empioyment Recover
from tiie Great Recession?" International Labor Brief
9, no. 2 (2011): 4-12 (W. E. Upjohn Institute for Employment
Research): Chad R. Wilkerson, "Recession and
Recovery Across the Nation: Lessons from History," Economic
Review 94, no. 2 (2009): 5-24.
3. Kataiina M. Bianco, The Subprime Lending Crisis: Causes
and Effects of the Mortgage Meltdown (New York:
CCH, inc., 2008): Lawrence H. White, "Fédérai Reserve Policy
and the Housing Bubbie," in Lessons From the
Financial Crisis: Causes, Consequences, and Our Economic
Future, ed. Robert W. Koib (Hoboken, NJ: John Wiley
& Sons, Inc., 2010), 453-460.
4. John Bellamy Foster, "The Financialization of Capital and the
Crisis," Monthiy Review 59, no. 11 (Aprii 2008):
1-19.
5. Major Coleman iV, Michael LaCour-Littie, and Kerry D.
Vandeii, "Subprime Lending and the Housing Bubbie: Taii
Wags Dog?" Journai of Housing Economics 17, no. 4 (2008):
272-290.

6. Dean Baker, "The Housing Bubbie and the Financiai Crisis,"
Rea/-Wor/d Economics Review no. 46 (2008): 7 3 - 8 1 .
ANationaLRrMlejlîheBeaLIstatdflctustry^ancIth&Apprms.aJ.Er
ofessLaa. _IJhe Appraisal Journal, Spring 2013
Hundreds of thousands of professionals
are involved in brokering, leasing, managing,
appraising, and developing all property types.
Service professionals include residential sales
agents, multifamily-property managers, commercial
investment advisors, industrial property brokers,
land developers, property appraisers, and many
others.^ Their professional education and training
includes academic work performed in colleges and
universities; industry-specific education and training
programs; advanced professional association
development and designation certifications; company
and franchise training; pre- and post-licensing
continuing education requirements; and many years
of on-the-job training and experience.
The disintegration of the housing and financial
markets has affected all professionals in the real
estate industry and its employment components.
This article shows professional real estate appraisers
have been particularly hard hit. Before the recession,
as property values and sales grew, and as demand for
loans increased, appraisers' workloads did as well.
When the bubble burst, appraisers felt its impact and
experienced significant declines in their businesses.
As a result, the real estate appraisal industry
experienced a significant loss in jobs. Recent growth

in employment within the appraisal profession has
neither mirrored other sectors in the real estate
industry, nor that of the US economy.
The purpose of this article is to provide a cross-
sectional view of the national real estate industry
with special attention given to employment in the
appraisal profession. Nothing in the professional
literature attempts to establish a data-driven profile
of the appraisal business, or compares and contrasts
it to other real estate-related professions. This article
is not a survey, but rather an effort to establish a basic
real estate appraisal employment baseline that will
serve as a benchmark for future trend comparisons.
This profile uses the latest data estimates from
private, state, and federal sources in support of
regional input-output tables used for the estimation
of economic impacts from events in a region.^
The results indicate that overall real estate industry
employment at the end of 2011 was higher than at
the beginning of 2001. However, the trend of annual
increases in the number employed evident in the
early years of the 2001—2011 study period reversed
itself during the recession. Declines in employment
appear to coincide with concurrent declines in the
economy during the latter years of the same period. The
results further show a significant correlation between
employment in the real estate appraisal profession and
production measures of the national economy, but not
with national employment This research is not only
very timely, it also is extremely important because
changes in the employment trends in the real estate
industry since the financial crisis began have been
substantial. The information and analysis presented
offer unique insights into understanding the current

state of the real estate industry, and in particular, the
real estate appraisal profession.
Employment Profile and Trends
This article examines national employment trends
in five real estate-related categories:
• Agents and Brokers
• Appraisers
• Lessors and Lessors' Agents
• Property Managers
• Other Services (i.e.. Escrow Agents, Consultants,
Fiduciaries, Asset Managers, and Listing Services)
It extracts the data according to the North American
Industry Classification System (NAICS) at the
six-digit code level across all real estate-related cat-
egories for the period 2001—2011.'' Each category
draws from information provided by the US Census
Bureau NAICS category definitions.
Agents and Brokers
The industry classification Offices of Real Estate
Agents and Brokers (NAICS Code 531210) includes
people primarily engaged in acting as agents and/or
brokers in one or more of the following: (1) selling
real estate for others, (2) buying real estate for others.
7. Association of Real Estate License Law Officiais, Digest of
Real Estate License Laws and Current Issues (Chicago:
Association of Reai Estate License
Law Officiais, 2011).
8. Proprietary data obtained by paid license from Economic
Modeiing Speciaiists. Intl. For information on purchasing

licenses enabling information access,
see http://www.economicmodelihg.com.
9. NAICS codes adopted by several government agencies such
as the US Bureau of Ecohomic Analysis and the US Bureau of
Labor Statistics for the
standardization and reporting of data such as employmeht ahd
income. Further expianation of the accounts used ahd specialties
covered is shown in
the Appendix at the end of this articie.
appraisai Journal, Spring 2 0 1 3 , ^ -EcoJile Qflhe
and (3) renting real estate for others. Figure 1 shows
that at the end of 2001,1,061,482 people in the United
States worked in Offices of Real Estate Agents and
Brokers. At the end of 2011,1,717,627 people worked
in this classification, or 61.8% more than in 2001. The
annual employment number increased each year in
2001-2007, peaking in 2007 at 1,857,576. However,
coinciding with the beginning of the recession, the
number of people in this classification began to
decline, and the annual decreases continued until a
slight increase occurred in 2011 over 2010.
Two caveats are noteworthy. First, substantial
increases in employment during the early years of the
period may be due to entry of new licensees hoping
to capitalize on the potential income opportunities
provided by Üie booming, pre-financial crisis real estate
markets. Therefore, tbe sharp growth trend may have
been an unsustainable anomaly. Second, the data does
not differentiate between those licensed professionals
who work full-time versus those who only work part-

time. Therefore, some portions of categorical declines
in the post-flnancial crisis economy may be due to
part-üme licensees choosing not to renew their licenses
during the economic downturn.
Appraisers
The industry classification Ofiices of Real Estate
Appraisers (NAICS Code 531320) includes people
primarily engaged in estimating the fair market
value of real estate. Figure 2 shows that at the end
of 2001,80,724 people in the United States worked in
this classification. At year-end 2011,111,253 people
worked in this classificaüon, or 37.8% more than in
2001. The annual employment number increased
each year in 2001-2007, peaking in 2007 at 118,657.
In addition, again coinciding with the beginning of
the recession, the number of people in this classifica-
tion began to decline, and the decreases confinued
through 2011.
Although the percentages of growth in this
category are different from those of the category
Offices of Real Estate Agents and Brokers, it is
possible the explanafions are similar. The booming
real estate markets prior to the financial crisis
increased demand for appraisals, and therefore,
more people entered the profession. Likewise,
as the markets slowed after the crisis began and
appraisal demand declined, so did the demand
for appraisers. Due to the reduced demand, some
licensed appraisers may have sought other types
of employment, or suspended or terminated their
licenses. Further, some lenders, especially those
focusing on the residential mortgage sector,
increased use of alternafive valuation products or

turned to using broker price opinions (BPOs).'"
Figure 1 US Offices of Real Estate Agents and Brokers (NAICS
Code 531210)
a.
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2,000,000 -|
1,800,000 -
1,600,000 -
1,400,000 -
1,200,000 -
1,000,000 -
800,000 -
600,000 -
400,000 -
200,000 -
0 -

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10. So many real estate brokers began performing BPOs after
the financial crisis that in IVlay 2011, the National Association
of Realtors (NAR) introduced a
new BPO training and certification program. Information
obtained from the Nationai Association of Reaitors available at
http://www.realtororg/rmodaiiy.
nsf/pages/News2011051306.
aJMonal2rMkoíJlifiJüaL£síalfiJndiJ.stryjDd the AppraisalJr l i i
e Appraisal Journal, Spring20;

Figure 2 US Offices of Real Estate Appraisers (NAICS Code 5 3
1 3 2 0 )
'S.
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140,000
120,000
100,000
80,000
60,000
40,000 -h-
20,000 -— ,̂.,-
2001 2002 2003 2004 2005 2006 2007
Year
2008 2009 2010 2011
Lessors and Lessors' Agents
The industry classification Lessors of Residential
Buildings and Dwellings (NAICS Code 531110)
includes people primarily engaged in acting as les-
sors of buildings used as residences or dwellings,
such as single-family homes, apartment buildings,
and townhomes. Included in this classification are
owner-lessors of residential buildings and dwellings
or people employed by them.

Residential Buildings and Dwellings. At year-end
2011,1,057,764 people worked in this classification.
or 54.7% more than in 2001. The annual employment
number increased each year in 2001—2007, peaking
in 2007 at 1,083,847. However, coinciding with the
employed in this classification began to decline,
dipping slighüy in 2008 and 2009. The trend reversed
in 2010 and 2011.
The industry classification Lessors of Non-
Residential Buildings (NAICS Code 531120) includes
people primarily engaged in acting as lessors
of huildings (except mini-warehouses and self-
storage units) that are not residences or dwellings.
Included in this industry sector are owner-lessors
Figure 3 Offices of US Lessors of Residentiai Buildings and
Dwellings (NAICS Code 5 3 1 1 1 0 )
4)
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2001
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2011
! Appraisal Journal, Spring 2O13L lPröfile of
th&RftaiXslatalcuksítyjnd the AppraisalÄ
of non-residential buildings and people employed
by tbem.
people in the United States worked in the Lessors
of Non-Residential Buildings classification. At year-
end 2011, 493,600 people worked in this industry
classification, or 33.7% more than in 2001. The annual
number of people increased each year in 2001—2005,
decreased slightly in 2006, and increased in 2007
and 2008, when it peaked at 510,576. Thereafter,
the annual number of people employed in this
classification decreased each year in 2009—2011.
The industry classification Lessors of Mini-
Warehouses and Self-Storage Units (NAICS Code

531130) includes people primarily engaged in
renting or leasing self-storage space (e.g., rooms,
compartments, lockers, containers, or outdoor space)
where clients can store and retrieve their goods.
Mini-Warehouses and Self-Storage Units. At the
end of 2011, 280,702, or 112.6% more than in 2001,
worked in this classification.
The annual number of people in this classification
increased each year in the study period except for
2009, when it decreased shghtiy by -2,393, or -0.86%
less than 2008. A possible explanation for the strong
growth performance could be a combination of
Americans continuing to accumulate more material
possessions and the downsizing of residences,
increasing the need for storage of their possessions.
Another explanation might be that foreclosures
forced people to place their possessions in storage
as they transitioned to other residences.
The industry classification Lessors of Other
Real Estate Property (NAICS Code 531190) includes
people primarily engaged in acting as lessors of real
estate (except buildings), such as manufactured-
home sites, vacant lots, and grazing land. Figure 6
shows that at the end of 2001,125,915 people in the
United States worked as Lessors of Other Real Estate
Property. At the end of 2011,146,858 people, or 16.6%
Figure 4 Offices of US Lessors of Non-Residential Buildings
(NAICS Code 5 3 1 1 2 0 )
of

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500,000 -
400,000 -
300,000 -
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Figure 5 Offices of US
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200,000 -
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JThe Appraisal Journal, Spring 20:
Figure 6 Offices of US Lessors of Other Real Property (NAICS
Code 531190)
pi
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more than in 2001, worked in this classification. The
increases and decreases in the number of people in
this classification are inconsistent, showing increases
in 2001-2005,2007, and 2010, but decreases in 2006,
2008-2009, and 2011.
Property Managers
The industry classification Residential Property
Managers includes people primarily engaged in
managing residential real estate for others. Figure 7
United States worked in this industry classification,
and atthe end of 2011,289,706 people, or 62.5% more
than in 2001, worked in this classification.
During 2001—2011, the number of people in tbis
classification increased each year, with the highest
annual increase (10.7%) occurring in 2007, which
coincided with the beginning of the recession. The
10.7% increase in 2007 was the only double-digit
increase during the study period. One possible
explanation for this is that 2007 was the first year people
began losing their homes to foreclosure hecause of the

recession. As the demand for rental units increased due
to increased home foreclosures, there may have been
a eommensurate inerease in tbe need for residential
managers. Anotber explanation could be that more
apartment eomplexes came on line in 2007 due to the
rapid expansion of eonstrucüon of multifamily units in
the middle part of the decade, resulting in employment
of more residential property managers.
The industry classification Non-Residential
Property Managers (NAICS Code 531312) includes
people primarily engaged in managing non-
residential real estate for others. Figure 8 shows at the
end of 2001, 83,213 people in the United States were
employed as Non-Residenüal Property Managers. At
the end of 2011,130,346 people, or 56.6% more than
in 2001 worked in this classification.
ppraisal Journal, Spring 2013. A National Profile of tlie^R&aJ
£state.iDáiistry,.aad the Appraisal Profession
Figure 8 Offices of US Non-Residential Property IVIanagers
(NAICS Code 5 3 1 3 1 2 )
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140,000
120,000
100,000
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2001 2002 2003 2004 2005 2006
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2007 2008 2009 2010 2011
With the exception of 2009-2010, when growth
was relatively flat, the number of people working in
the Non-Residential Property Managers classification
increased during the study period, with the highest
annual increase (9.4%) occurring in 2008. A possible
explanation for the significantly higher increase in
2008 is that demand for asset managers increased
due to the increased foreclosures of non-residential
properties. Another possible explanation is that

demand for commercial real estate was increasing
in the years prior to the financial crisis—peaking in
2008—and thus, more real estate firms employed
more non-residential property managers to service
the industry. It is important to note that because this
NAICS industry classification includes only those
managing non-residential real estate for others,
property management services for owner-occupied
properties are not included.
Other Real Estate Activities
The industry classification Other Activities Related
to Real Estate (NAICS Code 531390) includes people
primarily engaged in performing real estate-related
services (except lessors of real estate, olfices of real
estate agents and brokers, real estate property man-
agers, and offices of real estate appraisers). Figure 9
United States worked in Other Activities Related to
Real Estate. At the end of 2011, 852,824 people, or
44% more than in 2001, worked in this classification.
The e m p l o y m e n t growth t r e n d of this
classification is similar to the growth trend in the
classification Offices of Real Estate Appraisers. The
annual number increased each year in 2001—2005,
and peaked in 2007 at 890,100. Coinciding with the
employed in this classification then began to decline
and the decreases continued through 2011.
Correlations and Summary
The analysis in this article compares employment
categories of the appraisal profession to other seg-
ments of the real estate industry and various national

economic indicators. The statistical test used is a
simple correlation analysis utilizing the Pearson"
method to produce correlation eoefiicients between
the appraisal profession and other segments of the
real estate industry. The purpose of performing
this statistical test was to uneover strong and weak
relationships with other parts of the eeonomy that
could serve as future indieators of the welfare of the
Correlation analysis examines the degree
to which relationships exist between variables.
Correlations, labeled as eoefiicients, are numbers
between -1 and +1. A coefficient between 0 and +1
suggests a positive relationship between the variables,
whereas a coefficient between -1 and 0 suggests a
negafive one. Correlation analysis helps reduce the
range of uncertainty about the relaüonships between
the variables. Hence, correlation analysis produces
greater variance of the predieted outcomes—how
much movement of one variable is related to
movement of another variable—that are eloser to
1 1 . Joseph F. Hair Jr., Mary Wolfinbarger Ceisi, Arthur
Money, Phillip Samouel, and Michael J. Page, Essentials of
Business Research Methods, 2nd ed.
(Armonk, New York: M. E. Sharpe, inc., 2011).
^ ^ The Appraisai Journai, Spring 20:
Figure 9 US Offices of Other Activities Related to Real Estate
(NAICS Code 531390)
P

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1,000,000
800,000
600,000
400,000
200,000
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2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
Year
reality. A simple correlation is between two variables.
Perfect correlation exists between two variables
when the correlation coefficient is either +1 or - 1 .
Table 1 shows the correlation analysis results
for the study.'̂ They reveal a posifive relationship
between the appraisal profession and the other
sectors. The highest correlafion of+0.998 was with
the classification Offices of Real Estate Agents and
Brokers, which was statistically significant at the
0.01 level (this means that 99.8 times out of 100, this
relationship will exist and will be highly, posifively
correlated). Also, a strong, positive relationship
of +0.997 was revealed with the classification
Residential Property Managers, which was highly
significant at the 0.01 level. The interpretation is that
as employment in the sectors identified goes up or

down, employment in the appraisal profession will
do likewise.
The analysis leads to the following conclusions
related to the Real Estate Appraisers classificafion:
1. The industry classification Real Estate Appraisers
is the smallest among all real estate sectors
examined, with 111,233 johs in 2011.
2. Employment increased annually from 80,724
in 2001 to a high of 118,657 in 2007, for a total
increase of 37,933, or 46.99%.
3. Employment decreased annually from 118,657
in 2007 to a low of 111,233 in 2011, for a total
decrease of-7,424, or -6.3%.
4. During the study period, the largest annual
decrease was from 118,657 in 2007 to 114,397
in 2008, a decrease of-4,260 or -3.6%.
5. The smallest decrease, between 2009 and 2010,
was -271 or -0.24%.
6. The most recent decrease, between 2010 and
2011, was-1,705 or-1.51%.
Total Requirements Needed to Operate
The Bureau of Economic Analysis prepares and
publishes a variety of economic statistics on indus-
tries. Its data on total requirements represent the
total demand for goods or services that an industry
needs to produce its particular goods or services.'^

While other industries or resources operafing or
existing within the region saüsfy some of the demand,
in many instances not all of the requirements
are satisfied from within the same region. This
unsatisfied or leftover demand is satisfied through
imports into the region. Thus, the total requirements
equal the amount safisfied within the region plus the
amount of imports into the region.
Figure 10 displays the US 2010 total requirements
for real estate-related industries. Because this data
is for the entire United States, the region is the
entire country as well. The 2010 total requirements
for all real estate-related sectors totaled over $1.09
12. The correlations shown in Table 1 are between people
working in the appraisai profession and other real estate-reiated
sectors.
13. The totai requirements (TR) technique does not derive
estimates based on empioyment but instead focuses on the totai
demand for goods or services
that an industry needs in order to produce its particular goods or
services. In the United States, the Department of Commerce's
Bureau of Economic
Anaiysis (BEA) produces two types of TR tables, in coefficient
form, using benchmark input-output information drawn from
make and use tables. The tables
present input values of goods or services purchased directiy in
order to produce one dollar of output. The coefficients of the
TR tables provide the totai
sum of direct and indirect inputs necessary to produce output.
For example, the direct purchases (inputs) necessary to produce
an airplane wouid inciude
the steel and aiuminum used in the construction of the aircraft
fuselage, and the indirect purchases wouid include the energy

resources necessary to
produce the steel and the aluminum. The different types of
direct and totai requirements information produced by the BEA
depend on whether the defined
goods and services are industries or commodities. For a
comprehensive explanation of the BEA's methodology and data-
derivation techniques, refer to the
BEA's Methodology Paper Series and other methodoiogies oh
the nationai, industry, international, and regional accounts
avaiiable at http://www.bea.gov/
methodoiogies/index.htm and articies pubiished in the Survey of
Current Business avaiiable at
http://www.bea.gov/scb/index.htm.
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…
2016 V44 3: pp. 658–690
DOI: 10.1111/1540-6229.12120
REAL ESTATE
ECONOMICS
The Impact of the Home Valuation Code of
Conduct on Appraisal and Mortgage
Outcomes
Lei Ding* and Leonard Nakamura**
The accuracy of appraisals came into scrutiny during the
housing crisis, and a
set of policies and regulations was adopted to address the
conflict-of-interest
issues in the appraisal practices. In response to an investigation
by the New
York State Attorney General’s office, the Home Valuation Code
of Conduct
(HVCC) was agreed to by Fannie Mae, Freddie Mac and the
Federal Housing
Finance Agency. Using unique data sets that contain both
approved and non-
approved mortgage applications, this study provides an
empirical examination
of the impact of the HVCC on appraisal and mortgage
outcomes. The results
suggest that the HVCC has led to a reduction in the probability

of inflated
valuations, although valuations remained on average inflated,
and induced a
significant increase in the incidence of low appraisals. The
well-intentioned
HVCC rule made it more difficult to obtain mortgages to
purchase homes dur-
ing the housing price crash, possibly exacerbating the fall in
prices.
Introduction
The fallout from the housing bubble raised questions about the
accuracy of
appraisals before the housing crisis, and, as a response, a set of
policies
and regulations was adopted to address the conflict-of-interest
issues in the
appraisal practices.1 With significantly tightened regulations
and the decline
in housing prices in many areas, there were concerns that more
home val-
uations were underestimated and new mortgages became harder
to obtain
*Federal Reserve Bank of Philadelphia or [email protected]
**Federal Reserve Bank of Philadelphia or [email protected]
1Important regulations and rules related to appraisal include at
least the Home Valu-
ation Code of Conduct (HVCC); the Dodd-Frank Wall Street
Reform and Consumer
Protection Act; revised Interagency Appraisal and Evaluation
Guidelines from the
federal banking regulators issued in December 2010; and the
government-sponsored
enterprises’ new appraiser independence requirements that

replaced the HVCC in
October 2010 (U.S. Government Accountability Office, or GAO
2012).
C© 2015 American Real Estate and Urban Economics
Association
The Impact of the Home Valuation Code of Conduct 659
during the crisis,2 though the upward bias in appraisals that had
prevailed
during the subprime boom has been reduced somewhat in many
markets.
Despite the controversial role of appraisers before and during
the most re-
cent housing crisis, there is a lack of empirical research about
the pattern
of appraisal outcomes and the effects of the interventions
adopted since the
crisis on appraisals and the housing market overall. This study
provides the
first empirical examination of the impact of a major appraisal
rule, the now-
superseded Home Valuation Code of Conduct (HVCC), which
was adopted
in the middle of the housing crisis, on low appraisals and
mortgage outcomes.
Appraisal ratio is defined as appraised value less the contract
price as a per-
cent of the contract price in this study, while low appraisal is
defined as one
in which appraised value falls below the contract price.3
The HVCC was enacted on May 1, 2009, as the result of a joint

agreement
between Fannie Mae and Freddie Mac (government-sponsored
enterprises, or
GSEs), the Federal Housing Finance Agency (FHFA),4 and the
New York
State Attorney General.5 The HVCC was set to expire in August
2010. The
Dodd-Frank Wall Street Reform and Consumer Protection Act
(Dodd-Frank
Act), enacted on July 21, 2010, declared that the HVCC was no
longer in
effect, but it actually codified several of the HVCC’s
provisions. The HVCC
has several unique features. First, as a private agreement
between the GSEs
and the New York State Attorney General, the HVCC is an
industry stan-
dard instead of a federal regulation. In fact, the HVCC was
implemented
2See the Reuters article
http://www.reuters.com/article/2011/08/24/us-usa-
economy-appraisals-idUSTRE77N2PM20110824 and the New
York Times articles
http://www.nytimes.com/2012/10/13/business/scrutiny-for-
home-appraisers-as-the-
market-struggles.html and
http://www.nytimes.com/2013/09/15/realestate/when-
appraisals-come-in-low.html.
3Similarly, significantly low appraisal is defined as one in
which appraisal is at least
5% below the contract price. Share of low appraisals represents
the share of appraisals
with appraised values below the contract price. An appraisal is
only an opinion of a
property’s value so a deviation between appraised value and

contract price does not
necessarily mean the appraisal is wrong or biased. See similar
measures of appraisal
bias in Cho and Megbolugbe (1996), Chinloy, Cho and
Megbolugbe (1997) and
LaCour-Little and Green (1998).
4The agreement was initially known as the Home Value
Protection Program and Co-
operation Agreement. The Office of Federal Housing Enterprise
Oversight (OFHEO)
still existed as the independent regulatory agency of Fannie Mae
and Freddie Mac
when the HVCC was introduced in March 2008. In July 2008,
the FHFA was formed
by merging the OFHEO, the Federal Housing Finance Board,
and the U.S. Department
of Housing and Urban Development government-sponsored
enterprise function.
5The HVCC, which was introduced on March 3, 2008, was a
direct result of the
Washington Mutual legal case. In November 2007, the New
York Attorney General
filed suit against Washington Mutual. Because GSEs
purchased/securitized a large
portion of its mortgages from Washington Mutual, the legal case
pushed the GSEs to
issue the HVCC.
660 Ding and Nakamura
despite opposition from major federal bank regulators
(Abernethy and Hol-
lans 2010). Second, while the HVCC initially only covered GSE
loans, it

had marketwide effects as a result of the oligopoly power of the
GSEs and
the lack of a robust alternative secondary market for residential
mortgages.6
Third, the HVCC is believed to be a well-intentioned rule;
however, some
regulatory agencies and industry stakeholders have questioned it
for its poten-
tial jurisdictional problems and unintended consequences (U.S.
Government
Accountability Office, or GAO 2011). The rule introduced
tighter scrutiny for
appraisers, lenders, GSEs, and other stakeholders to ensure the
independence
of the appraisal process for GSE loans.7 However, as the
HVCC’s efforts to
address the conflict-of-interest issues in the middle of the crisis
induced radi-
cal changes of the entire appraisal industry, concerns arose
about the possible
decline in appraisal quality and increased difficulty in credit
access (GAO
2011, 2012). For example, one direct effect of the HVCC was
the greater use
of appraisal management companies (AMCs).8 AMCs, which act
as interme-
diaries between lenders, only accounted for a small market
share before the
crisis and received little oversight by regulators during the
crisis.9 So on the
one hand, with less influence from lenders, brokers, and other
stakeholders,
appraisers are expected to achieve more objective appraisals
and reduce the
incidence of the previously widespread inflated appraisals. On

the other hand,
because of the greater use of AMCs partly induced by the
HVCC and the
potential overreaction by lenders and appraisers, the quality of
appraisals may
6FHA adopted the HVCC on January 1, 2010, eight months later
than the GSEs. GSEs
accounted for about 69.4% of all mortgage originations in 2009;
the GSEs and FHA
together accounted for about 90% (Inside Mortgage Finance
2013).
7The HVCC was designed to enhance the independence and
accuracy of the
appraisal process primarily by the following: (1) prohibiting
lenders and third parties
with an interest in the mortgage transaction from influencing
the development,
reporting, result, or review of an appraisal report, (2) requiring
only the lender
or any third party specifically authorized by the lender to select,
retain, and
provide for payment of all compensation to the appraiser, (3)
requiring absolute
independence between the loan production function and the
appraisal function
within a lender’s organization, (4) limiting communications
between loan pro-
duction staff and appraisers, and (5) requiring lenders to ensure
that borrowers
receive a copy of the appraisal report within a certain period
before closing. See
http://www.fhfa.gov/Media/PublicAffairs/Documents/HVCCFin
alCODE122308_N
508.pdf for more details about the HVCC.
8GAO (2012) suggests that some practitioners reported that the

HVCC led some
lenders to outsource appraisal functions to AMCs because they
thought using AMCs
would allow them to demonstrate compliance with these
requirements easily.
9One major concern is that more appraisals are being done by
AMC appraisers, who
likely had received more business because of the HVCC. These
appraisers may lack
the knowledge of the local market because AMCs operate
nationally but do not have
appraisers in all local areas. In addition, AMC appraisers are
usually paid less, which
may induce them to invest less time and introduce more bias
(GAO, 2012).
deteriorate and the share of low appraisals could become
artificially high after
the HVCC. This, in turn, could cause real estate deals to fall
apart.
Using a unique transaction-level appraisal data set that contains
both approved
and nonapproved mortgage applications, this study examined
the effect of the
HVCC using a difference-in-differences approach. Because not
all mortgage
applications were subject to the HVCC, we can use statistical
models to iso-
late the effects of the HVCC by comparing changes in appraisal
and mortgage
outcomes pre- and post-HVCC for the HVCC-covered loans,

relative to those
of transactions that were not subject to the HVCC. We found
that the HVCC
has led to a significantly increased incidence of low appraisals
and a reduc-
tion in the probability of inflated valuations: The probability of
low appraisals
among HVCC-covered transactions was at least 2.1 percentage
points higher
than those transactions not covered by the HVCC while the
share of signif-
icantly high appraisals (5% or higher than contract prices) also
decreased.
The results are robust when different evaluation periods or
different control
groups are used.
A higher incidence of low appraisals also induces higher rates
of mortgage
denials. The overall denial rates in the purchase market started
to decline
after 2009; however, the decrease in denial rates, especially in
the collateral
denial rates, was significantly lower for the HVCC-covered
applications. The
probability of denials due to insufficient collateral increase by
1.2 percentage
points post-HVCC, relative to the control group; the control
group has a 5.6%
probability of denial due to insufficient collateral.
The empirical results suggest that the HVCC has done some of
what it
was supposed to do by partially reducing inflated valuations
that were more
prevalent during the subprime boom. However, this well-

intentioned rule also
increases the likelihood of low appraisals and made the
origination of purchase
mortgages more difficult. Because access to mortgage credit has
been tight
since the housing crisis, more limited credit availability may
have more severe
consequences in the long term for certain populations and
neighborhoods.
Background: Home Mortgage Appraisal and the HVCC
Appraisal and Appraisal Bias
Lending institutions compare the loan amount with the market
value of the
home in making loan decisions. Such a comparison is important
because
lenders need to know the property’s market value in order to
provide informa-
tion for assessing the risk of the mortgage and their potential
loss exposure if
the borrower defaults. Appraisals, which provide an estimate of
market value
based on market research and analysis as of a specific date,
have been the
most commonly used valuation method for residential mortgage
originations
(GAO 2012).10 The appraised value and the difference between
the appraisal
and the contract price influence both the likelihood that the

mortgage will
default and the options that the mortgage lender has if the
borrower defaults
on the mortgage.11
In theory, an appraisal should provide an objective valuation of
the true
market value of a property; however, appraisals are often biased
and can be
significantly different from a home’s true market value. Recent
studies of the
accuracy of home mortgage appraisals in the United States
started with an
article by Cho and Megbolugbe (1996), who compared purchase
prices with
appraised values to determine whether there were systematic
differences based
on the 1993 Fannie Mae loan acquisition file. They found that
appraisals may
be biased since too many mortgage appraisals were exactly the
same as the
transaction price, and the distribution was highly asymmetric.
More than 65%
of appraised values were above the purchase prices; about 30%
had appraisals
that were exactly the same as transaction prices; and only 5%
had appraisals
that were lower than the transaction prices. Appraisers only
assign different
value estimates when differences between perceived values and
transaction
prices are substantial. In more than 80% of the cases, the
appraisal was
between 0% and 5% above the transaction purchase price.
Chinloy, Cho and
Megbolugbe (1997) expanded on the earlier research and

continued to argue
that appraisal bias was present. They estimated an upward bias
of 2% and
found that appraisals exceeded purchase prices in approximately
60% of the
cases.
Agarwal, Ben-David and Yao (forthcoming) documented the
appraisal bias
for residential refinance transactions. They used the difference
in the ini-
tial appraisal of the refinance transaction and the subsequent
purchase price
compared with changes in the prices of pairs of consecutive
purchase trans-
actions, as a proxy for valuation bias. They found that the
appraisal bias
10Methods other than appraisals, such as broker price opinions,
automated valuation
models, or other mixed methods usually take less time and are
less expensive but
are often less reliable. When performing appraisals, appraisers
can use one or several
approaches to determine value, including sales comparison,
cost, and income. Of these,
the sales comparison approach is most widely used, which
compares and contrasts the
property under appraisal with recent offerings and sales of
similar properties.
11The precise value of the home on the market provides crucial
information to the mort-
gage lender because the equity stake of a mortgage at
origination, usually measured
by the loan-to-value (LTV) ratio, reflects the credit risk of a
mortgage application. In

practice, lenders usually use the lesser of sales price and
appraisal as the value of the
property in calculating LTV ratios (Nakamura 2010).
for residential refinance transactions was above 5% for a
national sample of
conforming loans. The bias was found to be larger for highly
leveraged trans-
actions (high loan-to-value or LTV), around critical leverage
thresholds, and
for transactions through a broker. However, in a study focusing
on the behav-
ior of appraisal professionals, Tzioumis (forthcoming) found
only a minority
of residential real estate appraisers systematically inflated
appraisal values for
home purchase loan applications.
When appraisals are biased upward, they provide documentation
for loans
larger than what the collateral’s market value justified. This
makes mortgages
riskier, and the risk of mortgage default increases.
Unfortunately, there has
been very little academic work examining the impact of biased
appraisals
despite the importance of the subject. LaCour-Little and
Malpezzi (2003) used
a small data set from Alaska in the 1980s to illustrate that for a
single thrift
institution in that state, appraisal bias was positively associated
with more

frequent defaults. Agarwal, Ben-David and Yao (forthcoming)
also found
that refinance mortgages with inflated appraisals default more
often; however,
lenders account for the appraisal bias through pricing by
charging higher rates
for mortgages that have higher appraisal bias.
Determining Factors of Appraisal Bias and the HVCC
The conflict-of-interest issues related to appraisals have been
cited as a po-
tential explanation for the upward bias in several empirical
studies (e.g., Cho
and Megbolugbe 1996; Chinloy, Cho and Megbolugbe 1997).
Appraisers
face asymmetric costs from overstating versus understating:
While an above-
contract price appraisal will have no direct impact, deals could
be threatened
by appraisals that fall below the prices that buyers and sellers
had agreed to
previously. Buyers, sellers and real estate agents, as well as
lenders who do
not bear the risk of originated loans, all have a vested interest
in getting an ap-
praisal that is not less than the contract price and completing
the sale. The way
to ensure the deal is for the appraisers to assess slightly higher
than (or equal
to) contract prices. Much anecdotal evidence suggests that such
bias exists,
such as the well-known legal case involving Washington Mutual
in which the
lender was found to put pressure on eAppraiseIT (an AMC) to
generate sys-

tematically high appraisals between July 2006 and April
2007.12 The HVCC,
together with a set of other regulations and policies, was
developed to govern
12According to Agarwal, Ben-David and Yao (forthcoming),
Washington Mutual
threatened to discontinue its contract with eAppraiseIT and
actually did so in a number
of cases. With the pressure from Washington Mutual,
eAppraiseIT produced a list of
“proven accepted” (by Washington Mutual) appraisers. In
November 2007, the New
York Attorney General filed a lawsuit against Washington
Mutual, resulting in the
HVCC.
the selection, communication, and possible coercion of
appraisers in an effort
to address the conflict-of-interest issues related to appraisal
practices.
Cho and Megbolugbe (1996) found that appraisal outcomes are
different for
loans with different characteristics: Approved loans by Fannie
Mae with low
LTV ratios and/or high house prices are more likely to have
negative appraisal
gaps (low appraisals). They suspect that these loans are more
likely to be
approved despite negative appraisal gaps. LaCour-Little and
Green (1998)

conducted the only known empirical study that examines the
role of appraisals
in the residential mortgage lending process, though the study
sample is quite
small (fewer than 3,000 observations). They found that low
appraised value
is related to proxies for neighborhood quality instead of census
tract racial
composition. Properties securing adjustable rate mortgages,
condominiums,
and properties purchased by African American buyers are also
found to have
an increased probability of low appraisals.
Based on a theoretical model and empirical evidence, Calem,
Lambie-Hanson
and Nakamura (2014) demonstrated that the mortgage practice
that requires
the use of the lesser of the transaction price and the appraised
value in the
calculation of LTV ratios results in upward bias of appraisals,
especially
the extremely high incidence of appraisals which are exactly the
same as or
slightly higher than contract prices. They consider the
proportion of appraisals
that are set at the accepted offer price or very slightly above as
“informa-
tion loss,” since no precise information is conveyed by these
appraisals.
Except for this study, the only rigorous empirical study on the
impact of
HVCC was Agarwal, Ambrose and Yao (2014), which found the
magnitude
of the observed appraisal bias in the refinance market was
reduced after

HVCC.
It needs to be noted that the housing market was experiencing
significant
changes when the HVCC was first introduced. The lack of
market sales,
especially mortgage-financed sales, may lead to high degrees of
uncertainty
in appraisals (Lang and Nakamura 1993) and could lead to more
mortgage
denials (e.g., Blackburn and Vermilyea 2007). The sharp
increase in distressed
property sales, which could be recorded and used as
comparables in the
appraisals of nondistressed properties,13 may cause a downward
drag on
house value estimates.
13According to the Appraisal Institute (2008), an appraiser
should not ignore foreclo-
sure sales if the consideration of such sales is necessary to
develop a credible value
opinion. Only sales that might have involved atypical seller
motivations (e.g., a highly
motivated seller), such as a short sale, could be ignored.
Appraisal and Mortgage Lending Decisions
Appraised values and the difference between appraisals and
contract prices
have a direct effect on mortgage outcomes. A low appraisal may
force a seller

to sell the property at a price lower than the agreed-upon
amount. If a seller is
not willing to take a loss, the sale could be canceled. Second,
low appraisals
may cause lenders to seek larger down payments. Low appraised
value may
simply push the loan applicant to get a higher LTV loan. When
the borrower
is capital constrained, however, this may cause the lender to
reject the loan
application. So while an above-contract price appraisal will
have no direct
impact, a low appraisal may require buyers to come up with an
extra down
payment or pay a higher price (a higher interest rate or
mortgage insurance
that otherwise may not be needed), or it may result in a buyer
withdrawing or
a lender rejecting the application. LaCour-Little and Green
(1998) confirmed
that a low appraised value significantly increases the
probability of mortgage
loan application rejection. A low appraisal raises the likelihood
of denial by
1.8 percentage points, while an appraisal that is the same as the
offer price
also raises the probability of denial by 0.6 percentage point.
This study is related to studies on lending disparities in the
mortgage market,
which tested the associations between neighborhood income,
racial compo-
nent, or center city location and mortgage lending (see review
in Ladd 1998).
Other studies, which are more relevant to this analysis, have
investigated

the impact of various government regulations on mortgage
lending decisions.
Such examples include studies on the impact of state
antipredatory lending
laws on mortgage lending (e.g., Harvey and Nigro 2004; Bostic
et al. 2008)
or on the impact of state foreclosure laws on mortgage lending
(Pence 2006).
This study contributes to the literature by providing new
evidence of the im-
pact of the HVCC, a major appraisal rule enacted in the housing
crisis, on
appraisal and mortgage outcomes.
Data
This analysis used two primary data sets. The first one is the
FNC, Inc.’s
collateral database (FNC data), which provides a national
sample of appraisal
records, regardless of whether they end up with mortgage
originations. The
FNC data have been built from the data aggregated from major
mortgage
lenders that agreed to share their nonconfidential appraisal data
with FNC.
The FNC data have information on property type, contract date,
appraisal
date, rounded sales price (rounded up to the next $50,000),
appraisal-price
percent difference, zip code and county code of the property.
The second
data set is the expanded Home Mortgage Disclosure Act
(HMDA) data with
information on mortgage application action dates (approval
dates, denial dates

or other action dates) compiled by the staff of the Board of
Governors of the
Federal Reserve System. Compared with the publicly available
HMDA data,
this data set allows us to identify the timing of mortgage
applications much
more precisely.
The FNC data have some unique features compared with the
data sets used in
prior studies and can provide insights about the appraisal
practices during the
housing crisis. Prior studies using approved loans only suffer
from a selec-
tion bias: Appraisals in the approved samples are a conditional
distribution-
conditional on the loan being made. Since applications with
appraised values
lower than contract prices are more likely to be denied, the
focus on the
approved loans induces an underestimate in the incidence of low
appraisals.
Second, data sets with approved mortgages usually allow only
for a compari-
son of appraised values with transaction prices, instead of the
initial contract
prices, which are not always the same as the final transaction
prices. If the
seller has been forced to renegotiate the asking price when the
appraised
value of the property is below the contract price, the observed

transaction
price could actually be lower than the contract price. Of course,
this data set
has limitations, such as the sparse information on the borrower
and mortgage
characteristics and the underrepresentation in certain markets.14
Figure 1 based on the FNC data shows the change in the share
of low
appraisals over time. In 2006 and 2007, the share of low
appraisals was
between 4% and 6% nationally. After increasing slightly in
2008, the share
of low appraisals started to increase sharply after the enactment
of HVCC
(from 8.3% in the fourth quarter of 2008 to 14% and 15.2% in
the second and
third quarters of 2009, respectively), with a peak in the third
quarter of 2009.
Of course, the decline in housing prices and increase in
mortgage defaults
during this period may also help explain the dynamics of low
appraisal rates:
Housing prices bottomed out in the first quarter of 2009 while
the mortgage
serious delinquency rate peaked in the fourth quarter of 2009
(Figure 1).
Figure 2 further compares the distribution of the appraisal ratio
pre- and post-
HVCC. The share of low appraisals increased from 9.1% in the
six months
before the HVCC to 15.0% in the six months after the HVCC.
The share of
appraisals slightly higher than (or equal to) contract prices (0%
to 1%) also
increased slightly, while the share of significantly high

appraisals decreased
significantly, from 22.3% pre-HVCC to 14.6% post-HVCC.
Overall, at the
14Due to privacy considerations, geographical information in
FNC data is only avail-
able at the zip code level. Information on individual borrowers,
mortgage applications,
property condition, and property address is generally
unavailable. Some sand states
including Arizona, California, Florida and Nevada are
overrepresented, while the
Midwest areas are slightly underrepresented (see Table 9).
Figure 1 � Share of low appraisals for the United States first
quarter 2006 to third
quarter 2012.
Note: Share of low appraisals represents the share of appraisals
with appraised values below the
contract price.
Source: FNC data, LPS data, and CoreLogic HPI.
aggregate level, the distribution curve became more leptokurtic
and shifted to
the left after the HVCC: More appraisals came in below, equal
to, or slightly
higher than the contract prices, while there were fewer
appraisals that were
significantly higher than contract prices.
Descriptive Analysis: A Difference-in-Differences Approach

The changes in the appraisal ratio after the HVCC at the
aggregate level do
not necessarily reflect the independent effect of the HVCC on
appraisal out-
comes. As a source of plausibly exogenous variation, we exploit
the fact that
by regulation, only mortgages below the Conforming Loan
Limits (CLL)15
15The national conforming loan limit for mortgages that finance
single-family one-
unit properties was $417,000 for 2006–2008, with higher limits
for certain statu-
torily designated high-cost areas and mortgages secured by
multifamily dwellings.
The Economic Stimulus Act (ESA) of 2008 temporarily raised
the CLLs in des-
ignated high-cost areas in the contiguous United States to up to
$729,750. These
higher temporary CLLs were then extended several times,
finally expiring on
September 30, 2011. Data for the CLLs at the county level are
available at
http://www.fhfa.gov/DataTools/Downloads/Pages/Conforming-
Loan-Limits.aspx.
Figure 2 � Distribution of appraisal ratios pre- and post-HVCC.
Note: Appraisal ratio is defined as appraised value less contract
price as a percent of contract
price. Pre- and post-HVCC periods are defined here as the six

months before and after the HVCC
(October 1, 2008, to March 31, 2009, versus June 1, 2009, to
November 30, 2009). All appraisals
are included.
Source: FNC data.
are eligible for GSE purchase and thus subject to the HVCC. By
regulation,
the CLL is a key determinant of whether a loan application is
eligible to be
purchased/securitized by GSEs and subject to the HVCC:
Mortgages below
the CLL are eligible to be purchased by the two GSEs, which
either hold the
mortgages or package them into securities and sell the securities
to investors,
while applications for mortgages above the CLL (jumbo loans)
are ineligi-
ble to be purchased by GSEs, and thus are not subject to the
HVCC.16 So
appraisals for loans under the CLLs can be roughly treated as
the treatment
group, while appraisals for loans above the CLLs can be
considered as the
control group.17 By comparing the changes in the appraisal and
loan applica-
tion outcomes pre- and post-HVCC between the treatment and
control groups
16Jumbo mortgages are only a subset of the nonconforming
market because loan
characteristics other than size can also make a loan
nonconforming. But these other
underwriting criteria are not as clearly defined as the size limit.
17When the CLL changed during the study period in a number
of areas, the highest

level CLL was used to determine the control group, while the
lowest level CLL was
used to identify the …
sustainability
Article
An Optimal Rubrics-Based Approach to Real
Estate Appraisal
Zhangcheng Chen 1,2,3,4, Yueming Hu 1,2,3,4,5,*, Chen Jason
Zhang 6 and Yilun Liu 1,2,3,4,*
1 College of Natural Resources and Environment, South China
Agricultural University, Guangzhou 510642,
China; [email protected]
2 Key Laboratory of the Ministry of Land and Resources for
Construction Land Transformation,
South China Agricultural University, Guangzhou 510642, China
3 Guangdong Provincial Key Laboratory of Land Use and
Consolidation,
4 Guangdong Province Land Information Engineering
Technology Research Center,
5 College of Agriculture and Animal Husbandry, Qinghai
University, Xining 810016, China
6 Department of Computer Science and Engineering, Hong
Kong University of Science and Technology,

Hong Kong, China; [email protected]
* Correspondence: [email protected] (Y.H.); [email protected]
(Y.L.)
Academic Editors: Laurence T. Yang, Qingchen Zhang, M.
Jamal Deen and Steve Yau
Received: 27 February 2017; Accepted: 26 May 2017;
Published: 29 May 2017
Abstract: Traditional real estate appraisal methods obtain
estimates of real estate by using
mathematical modeling to analyze the existing sample data.
However, the information of sample
data sometimes cannot fully reflect the real-time quotes. For
example, in a thin real estate market,
the correlated sample data for estimated object is lacking, which
limits the estimates of these
traditional methods. In this paper, an optimal rubrics-based
approach to real estate appraisal is
proposed, which brings in crowdsourcing. The valuation
estimate can serve as a market indication
for the potential real estate buyers or sellers. It is not only
based on the information of the existing
sample data (just like these traditional methods), but also on the
extra real-time market information
from online crowdsourcing feedback, which makes the
estimated result close to that of the market.
The proposed method constructs the rubrics model from sample
data. Based on this, the cosine
similarity function is used to calculate the similarity between
each rubric for selecting the optimal
rubrics. The selected optimal rubrics and the estimated point are
posted on a crowdsourcing platform.
After comparing the information of the estimated point with the
optimal rubrics on the crowdsourcing
platform, those users who are connected with the estimated

object complete the appraisal with their
knowledge of the real estate market. The experiment results
show that the average accuracy of the
proposed approach is over 70%; the maximum accuracy is 90%.
This supports that the proposed
method can easily provide a valuable market reference for the
potential real estate buyers or sellers,
and is an attempt to use the human-computer interaction in the
real estate appraisal field.
Keywords: real estate appraisal; optimal rubrics; similarity;
cosine similarity function; crowdsourcing
1. Introduction
Real estate prices are a major concern. They are associated with
economic development, which
in turn affects governmental decision making and general well-
being [1–5]. Developing an appraisal
method for real estate is thus important to academic research
and to government decision making and
could fill a real estate industry need [6–9]. It helps to promote
the sustainable development of the real
estate market.
Sustainability 2017, 9, 909; doi:10.3390/su9060909
www.mdpi.com/journal/sustainability
http://www.mdpi.com/journal/sustainability
http://www.mdpi.com
http://dx.doi.org/10.3390/su9060909
http://www.mdpi.com/journal/sustainability
Sustainability 2017, 9, 909 2 of 19

The real estate trade is a process of negotiation between buyers
and sellers. With the development
of economic society, there is an urgent need to develop an
effective and efficient approach for estimating
the market price of real estate, which can provide a market
indication for the potential real estate
buyers or sellers [10].
There are three common traditional real estate appraisal
approaches: the cost approach, the income
approach, and the market-comparison approach [11,12]. The
cost approach is based on the cost of real
estate during development and construction and uses the cost to
represent the real estate price [2].
The cost of real estate includes land cost, buildings cost,
supporting facilities cost, marketing cost, etc.
Although the cost approach is suitable for situations where the
real estate does not bring direct revenue
or has some particular purpose, such as schools, parks, and
public squares, it has limitations [13].
The main problem is that the real estate price is not only
decided by the cost but also by the revenue
the real estate will bring and by other factors [14]. For example,
in the real estate price of a shopping
mall and office building, the cost price is only a small part, and
the majority is the gross yield and tax.
The income approach is based on a utility theory of economics,
which evaluates the real estate
price by discounting its expected profitability [15,16]. With the
exception of the net cost of the real
estate in question, it will consistently gain in value over time.
Although the income approach can be
widely used for evaluating real estate prices in a recurring
income situation, like office buildings, hotels,
and apartments, it also has limitations; that is, not all real estate

has expected revenue. The income
approach is not appropriate for appraising non-revenue
producing real estate, such as schools, parks,
and churches [17].
The market comparison approach uses experts to evaluate real
estate prices, who optimize and
modify the coefficient according to the recent sale records of
similar transactions and finally confirm
the real estate price [10]. Although the method can best fit real
economic activities and is currently
the most popular approach for real estate appraisal, it is limited
by the recent similar transaction
information [18,19]. It does not work well when applied in a
thin market.
Some other emergent methodologies are growing in acceptance,
named automated valuation
models (AVMs) [20]. The International Association Assessing
Officers (IAAO), the International
Valuation Standards Council (IVSC), and the Royal Institution
of Chartered Surveyors (RICS) all have
formulated and promulgated the Standard on Automated
Valuation Models [21,22]. These standards
define the Automated Valuation Models (AVM) as mathematical
models based on computer programs,
which can evaluate real estate through analyzing the
characteristic information of the real estate in
the collected sample data. There are many varieties of these
mathematical models, such as hedonic
regression analysis, clustering regression, multiple regression
analysis, neural network, or geographic
information systems [23–30]. If AVMs are used in a very
homogeneous area, the estimates can be quite
accurate. However, when they are used in a heterogeneous area,
such as a rural area, the estimate

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