The Dissertation Title Appears in Title Case and is CenteredComme.docx

The Dissertation Title Appears in Title Case and is Centered
Comment by GCU: American Psychological Association
(APA) Style is most commonly used to cite sources within the
social sciences. This resource, revised according to the 6th
edition, second printing of the Publication Manual of the
American Psychological Association, offers examples for the
general format of APA research papers, in-text citations,
footnotes, and the reference page. For specifics, consult the
Publication Manual of the American Psychological Association,
6th edition, second printing. For additional information on APA
Style, consult the APA website:
http://apastyle.org/learn/index.aspxNOTE: All notes and
comments are keyed to the Publication Manual of the American
Psychological Association, 6th edition, second
printing.GENERAL FORMAT RULES:Dissertations must be 12
–point Times New Roman typeface, double-spaced on quality
standard-sized paper (8.5" x 11") with 1-in. margins on the top,
bottom, and right side. For binding purposes, the left margin is
1.5 in. [8.03]. To set this in Word, go to:Page Layout > Page
Setup>Margins > Custom Margins> Top: 1” Bottom: 1” Left:
1.5” Right: 1” Click “Okay”Page
Layout>Orientation>Portrait>NOTE: All text lines are double-
spaced. This includes the title, headings, formal block quotes,
references, footnotes, and figure captions. Single-spacing is
only used within tables and figures [8.03]. The first line of each
paragraph is indented 0.5 in. Use the tab key which should be
set at five to seven spaces [8.03]. If a white tab appears in the
comment box, click on the tab to read additional information
included in the comment box. Comment by GCU: Formatting
note: The effect of the page being centered with a 1.5" left
margin is accomplished by the use of the first line indent here.
However, it would be correct to not use the first line indent, and
set the actual indent for these title pages at 1.5." Comment by
GCU: If the title is longer than one line, double-space it. As a

rule, the title should be approximately 12 words. Titles should
be descriptive and concise with no abbreviations, jargon, or
obscure technical terms. The title should be typed in uppercase
and lowercase letters [2.01].
Submitted by
Insert Your Full Legal Name (No Titles, Degrees, or Academic
Credentials) Comment by GCU: For example: Jane Elizabeth
Smith
Equal Spacing
~2.0” – 2.5”
A Dissertation Presented in Partial Fulfillment
of the Requirements for the Degree
Doctorate of Education
(or) Doctorate of Philosophy
(or) Doctorate of Business Administration
Equal Spacing~2.0” – 2.5” Comment by GCU: Delete yellow
highlighted “Helps” as your research project develops.
Grand Canyon University
Phoenix, Arizona Comment by GCU: HINT: There are several
“styles” that have been set up in this GCU Template. When you
work on your proposal or dissertation, “save as” this template in
order to preserve and make use of the preset styles. This will
save you hours of work!

[Insert Current Date Until Date of Dean’s Signature]
GCU Proposal Template V8.3 01.18.18
GCU Proposal Template V8.3 01.18.18
© by Your Full Legal Name (No Titles, Degrees, or Academic
Credentials), 2018 Comment by GCU: NOTE: This is an
optional page. If copyright is not desired, delete this page. The
copyright page is included in the final dissertation and not part
of the proposal. Comment by GCU: For example: © by Jane
Elizabeth Smith, 2012This page is centered. This page is
counted, not numbered, and should not appear in the Table of
Contents.
All rights reserved.
GRAND CANYON UNIVERSITY Comment by GCU: The
Signature Page is only included in the final dissertation and not
part of the proposal.
Comment by GCU: If the title is longer than one line,
double-space it. The title should be typed in upper and
lowercase letters.
by
Insert Your Full Legal Name (No Titles, Degrees, or Academic
Credentials) Comment by GCU: For example: Jane Elizabeth
Smith
Approved
[Insert Current Date Until Date of Dean’s Signature]

DISSERTATION COMMITTEE:
Full Legal Name, Ed.D., DBA, or Ph.D., Dissertation Chair
Full Legal Name, Ed.D., DBA, or Ph.D., Committee Member
Full Legal Name, Ed.D., DBA, or Ph.D., Committee Member
ACCEPTED AND SIGNED:
________________________________________
Michael R. Berger, Ed.D.
Dean, College of Doctoral Studies
_________________________________________
Date
GRAND CANYON UNIVERSITY Comment by GCU: This
page is only included in the final dissertation and not part of the
proposal. However, the learner is responsible for ensuring the
proposal and dissertation are original research, that all scholarly
sources are accurately reported, cited, and referenced, and the
study protocol was executed and complies with the IRB
approval granted by GCU.
I verify that my dissertation represents original research, is not
falsified or plagiarized, and that I accurately reported, cited,
and referenced all sources within this manuscript in strict
compliance with APA and Grand Canyon University (GCU)
guidelines. I also verify my dissertation complies with the
approval(s) granted for this research investigation by GCU
Institutional Review Board (IRB).

_____________________________________________
______________________
[Type Doctoral Learner Name Beneath Signature] Date
Comment by GCU: This page requires a “wet signature.”
Remove the brackets and type in the learner’s name. The learner
needs to sign and date this page and insert a copy into the
dissertation manuscript as an image (JPEG) or PDF text box.
This page must be signed and dated prior to final AQR Level 5
review.
Abstract Comment by GCU: On the first line of the page,
center the word “Abstract” (boldface) Style with “TOC
Heading”Beginning with the next line, write the abstract.
Abstract text is one paragraph with no indentation and is
double-spaced. This page is counted, not numbered, and does
not appear in the Table of Contents. Abstracts do not include
references or citations.The abstract should be between 150-250
words, most importantly the abstract must fit on one page.The
abstract is only included in the final dissertation and not part of
the proposal.
The abstract is required for the dissertation manuscript only. It
is not a required page for the proposal. The abstract, typically
read first by other researchers, is intended as an accurate,
nonevaluative, concise summary, or synopsis of the research
study. It is usually the last item completed when writing the
dissertation. The purpose of the abstract is to assist future
researchers in accessing the research material and other vital
information contained in the dissertation. Although few people
typically read the full dissertation after publication, the abstract
will be read by many scholars and researchers. Consequently,
great care must be taken in writing this page of the dissertation.
The content of the abstract covers the purpose of the study,
problem statement, theoretical foundation, research questions
stated in narrative format, sample, location, methodology,
design, data sources, data analysis, results, and a valid

conclusion of the research. The most important finding(s)
should be stated with actual data/numbers (quantitative) or
themes (qualitative) to support the conclusion(s). The abstract
does not appear in the table of contents and has no page
number. The abstract is double-spaced, fully justified with no
indentations or citations, and no longer than one page. Refer to
the APA Publication Manual, 6th Edition, for additional
guidelines for the development of the dissertation abstract.
Make sure to add the keywords at the bottom of the abstract to
assist future researchers. Comment by GCU: Please note this is
crucial and must be included in the abstract at the final
dissertation stage. This is required for dean’s signature.
Keywords: Abstract, assist future researchers, 150 to 250 words,
vital information Comment by GCU: Librarians and
researchers use the abstract to catalogue and locate vital
research material.
Criterion
*(Score = 0, 1, 2, or 3)
Learner Score
Chair Score
Methodologist Score
Content Expert Score
ABSTRACT
(Dissertation Only—Not Required for the Proposal)
The abstract is typically read first by other researchers and is an
accurate, non-evaluative, concise summary or synopsis of the
research study. The abstract provides a succinct summary of the
study and MUST include the purpose of the study, theoretical
foundation, research questions (stated in narrative format),
sample, location, methodology, design, data analysis, and
results, as well as, a valid conclusion of the research. Abstracts
must be double-spaced, fully justified with no indentions. (one
page)
The abstract provides a succinct summary of the study and
MUST include: the purpose of the study, theoretical foundation,

research questions stated in narrative format, sample, location,
methodology, design, data sources, data analysis, results, and a
valid conclusion of the research. Note: The most important
finding(s) should be stated with actual data/numbers
(quantitative) ~or~ themes (qualitative) to support the
conclusion(s).
The abstract is written in APA format, one paragraph fully
justified with no indentations, double-spaced with no citations,
and includes key search words. Keywords are on a new line and
indented.
The abstract is written in a way that is well structured, has a
logical flow, uses correct paragraph structure, uses correct
sentence structure, uses correct punctuation, and uses correct
APA format.
*Score each requirement listed in the criteria table using the
following scale:
0 = Item Not Present or Unacceptable. Substantial Revisions are
Required.
1 = Item is Present. Does Not Meet Expectations. Revisions are
Required.
2 = Item is Acceptable. Meets Expectations. Some Revisions
May be Suggested or Required.
3 = Item Exceeds Expectations. No Revisions are Required.
Reviewer Comments:

Dedication Comment by GCU: The Dedication page is the
first page in the dissertation with a Roman Numeral. In the final
dissertation this is usually page vi, so we have set it as vi.The
dedication is only included in the final dissertation, not the
proposal.
An optional dedication may be included here. While a
dissertation is an objective, scientific document, this is the
place to use the first person and to be subjective. The dedication
page is numbered with a Roman numeral, but the page number
does not appear in the Table of Contents. It is only included in
the final dissertation and is not part of the proposal. If this page
is not to be included, delete the heading, the body text, and the
page break below. Comment by GCU: If you cannot see the
page break, click on the top toolbar in Word (Home). Click on
the paragraph icon. ¶Show/Hide button (go to the Home tab and
then to the Paragraph toolbar).
Acknowledgments Comment by GCU: See formatting note for
DedicationThe Acknowledgements section is included only in
the final dissertation, not the proposal.
An optional acknowledgements page can be included here. This
is another place to use the first person. If applicable,
acknowledge and identify grants and other means of financial
support. Also acknowledge supportive colleagues who rendered
assistance. The acknowledgments page is numbered with a
Roman numeral, but the page number does not appear in the
table of contents. This page provides a formal opportunity to
thank family, friends, and faculty members who have been
helpful and supportive. The acknowledgements page is only
included in the final dissertation and is not part of the proposal.
If this page is not to be included, delete the heading, the body
text, and the page break below. Comment by GCU: If you

cannot see the page break, click on the top toolbar in Word
(Home). Click on the paragraph icon. ¶Show/Hide button (go to
the Home tab and then to the Paragraph toolbar).Do not use
section breaks!
Table of Contents
List of Tables xi
List of Figures xii
Chapter 1: Introduction to the Study 1
Introduction 1
Background of the Study 6
Problem Statement 7
Purpose of the Study 10
Research Questions and/or Hypotheses 11
Advancing Scientific Knowledge and Significance of the Study
14
Rationale for Methodology 16
Nature of the Research Design for the Study 17
Definition of Terms 19
Assumptions, Limitations, Delimitations 21
Assumptions. 22
Limitations and delimitations. 22
Summary and Organization of the Remainder of the Study 24
Chapter 2: Literature Review 26
Introduction to the Chapter and Background to the Problem 26
Identification of the Gap 28
Theoretical Foundations and/or Conceptual Framework 30
Review of the Literature 32
Methodology and instrumentation/data sources/research
materials 36
Summary 39
Chapter 3: Methodology 42
Introduction 42
Statement of the Problem 43
Research Questions and/or Hypotheses 44
Research Methodology 45

Research Design 47
Population and Sample Selection 48
Quantitative sample size 48
Qualitative sample size 50
Research Materials, Instrumentation OR Sources of Data54
Trustworthiness (for Qualitative Studies) 58
Credibility. 59
Transferability 59
Dependability. 60
Confirmability.61
Validity (for Quantitative Studies) 63
Reliability (for Quantitative Studies) 65
Data Collection and Management 66
Data Analysis Procedures 68
Ethical Considerations 72
Limitations and Delimitations 75
Summary 76
References 78
Appendix A. Site Authorization Letter(s)83
Appendix B. IRB Approval Letter 84
Appendix C. Informed Consent 85
Appendix D. Copy of Instruments and Permissions Letters to
Use the Instruments 86
Appendix E. Power Analyses for Sample Size Calculation
(Quantitative Only) 87
Appendix F. Additional Appendices 88
List of Tables Comment by GCU: This List of Tables has been
set up to update automatically (when you click to do so). The
List of Figures “reads” the style “Table Title,” which should be
used in the text for the table title and subtitle of each table.
Check “Help” in Word on how to update the TOC.The List of
Tables follows the Table of Contents. The List of Tables is
included in the Table of Contents and shows a Roman numeral
page number at the top right. The page number is right justified
with a 1 in. margin on each page. Dot leaders must be used. The

title is bolded.On the List of Tables, each table title and subtitle
will appear on the same line are are single spaced if more than
one line, and double-spaced between entries. See 5.01-5.19 for
details and specifics on Tables and Data Display. The
preferences for the Table of Figures (style for the List of
Tables) have been set up in this template.The automatic List of
Tables (set up here) uses the style “Table of Figures, which has
been formatted to achieve correct single space/double space
formatting.All tables are numbered with Arabic numerals in the
order in which they are first mentioned. [5.05]
Table 1. Correct Formatting for a Multiple Line Table Title is
Single Spacing and
Should Look Like this Example 36
Table 2. Equality of Emotional Intelligence Mean Scores by
Gender 66
Note: Single space multiple-line table titles; double space
between entries per example above. The List of Tables and List
of Figures (styled as Table of Figures) have been formatted as
such in this template. Update the List of Tables in the following
manner: [Right click Update Field Update Entire Table], and
the table title and subtitle will show up with the in-text
formatting. After you update your List of Tables, you will need
to manually remove the italics from each of your table titles per
the example above.
List of Figures Comment by GCU: This is an example of a List
of Figures “boiler plate.” Freely edit and adapt this to fit the
particular dissertation. In Word, “overtype” edits and
adaptations.The List of Figures follows the List of Tables. The
title “List of Figures” is styled as Heading 1.The List of Figures
is included in the Table of Contents (which will show up
automatically since it is styled as Heading 1). and shows a
Roman numeral page number at the top right. The list of figures
has been set up with the style “Table of Figures,” for which all
preferences have been set in this template (hanging indent tab
stop 5.99” right justified with dot leader). Figures, in the text of

the manuscript, include graphs, charts, maps, drawings,
cartoons, and photographs [5.21]. In the List of Figures, single-
space figure titles and double-space between entries. This has
been set up in the “Table of Figures” style in this template. See
5.20-5.30 for details and specifics on Figures and Data
Display.All figures are numbered with Arabic numerals in the
order in which they are first mentioned. [5.05] The figure title
included in the Table of Contents should match the title found
in the text. Note: Captions are written in sentence case unless
there is a proper noun, which is capitalized.
Figure 1. Correlation for SAT composite score and time spent
on Facebook. 69
Figure 2. IRB alert. 73
Note: single-space multiple line figure titles; double-space
between entries per example in List of Tables on previous page.
Use sentence case for figure titles. After you update your List
of Figures, you will need to manually remove the italics per the
example above.
87
Chapter 1: Introduction to the Study Comment by GCU:
This heading is styled according to APA Level 1 heading (style:
“Heading 1”) [3.03]. Do not modify or delete as it will impact
your automated table of contentsIntroduction Comment by
GCU: This heading is styled according to APA Level 2 heading
(style: “Heading 2”) [3.03]. Do not modify or delete as it will
impact your automated table of contents
Monetary policies promote price stability and economic growth
in Nigeria. Ajayi and Aluko (2017) stated monetary policy is
primarily concerned with the management of interest rates and
the regulation of money supply in the economy. Imoisi (2019)
claimed most nations use interest rates to achieve price
stability, and Nigeria’s goal is to achieve sustainable economic

growth. Okwori and Abu (2017) added economic growth causes
variations in interest rates. Ayodeji and Oluwole (2018)
revealed interest rates had a positive but slightly insignificant
effect on economic growth in Nigeria. Furthermore, Ufoeze,
Odimgbe, Ezeabalisi and Alajekwu’s (2018) research clearly
showed interest rates effects 98% of the variations in economic
growth in Nigeria. Interest rates have shown to significantly
effect Nigeria’s economy; however, other monetary policies
may be a predictor of Nigeria’s economic growth.
There is a gap in the literature relative to other monetary
policies that may be a predictor of Nigeria’s growth. The aim of
this study is to examine to what extent monetary policy rate,
cash reserve ratio, liquidity ratio, and money supply predict
consumer price index. Imoisi (2018) claimed there is an existing
gap in the literature relative to the effectiveness of monetary
policies. Inam and Ime (2017) recommended further research to
understand if the predicative relationship between the actual
level of money supply and price stability. Lawal, Somoye,
Babajide, and Nwanji, (2018) further specified a detailed study
should be conducted showing the variations and interactions
between monetary and fiscal policies and how they predict price
stability in Nigeria. This study seeks to examine if and to what
extent economic indicators other than interest rates, specifically
monetary policy rate, cash reserve ratio, liquidity ratio, and
money supply, predict consumer price index in Nigeria.
Criterion
*(Score = 0, 1, 2, or 3)
Learner Score
Chair Score
Methodologist Score
Introduction
This section provides a brief overview of the research focus or
problem, explains why this study is worth conducting, and
discusses how this study will be completed. (Minimum three to
four paragraphs or approximately one page)

Dissertation topic is introduced and value of conducting the
study is discussed.
Note:The College of Doctoral Studies recognizes the diversity
of learners in our programs and the varied interests in research
topics for their dissertations in the Social Sciences.
Dissertation topics must, at a minimum, be aligned to General
Psychology in the Ph.D. program, Leadership in the Ed.D.
Organizational Leadership program, Adult Instruction in the
Ed.D. Teaching and Learning program, Management in the DBA
program, and Counseling Practice, Counselor Education,
Clinical Supervision or Advocacy/Leadership within the
Counseling field in the Counselor Education Ph.D. program.
If there are questions regarding appropriate alignment of a
dissertation topic to the program, the respective program chair
will be the final authority for approval decisions.
Specifically, although the College prefers a learner’s topic align
with the program emphasis, this alignment is not “required.”
The College will remain flexible on the learner’s dissertation
topic if it aligns with the degree program in which the learner is
enrolled. The Ph.D. program in General Psychology does not
support clinically based research.
Discussion provides an overview of what is contained in the
chapter.
Section is written in a way that is well structured, has a logical
flow, uses correct paragraph structure, uses correct sentence
structure, uses correct punctuation, and uses correct APA
format.

following scale:
Required.
Required.
Reviewer Comments:
Background of the Study Comment by GCU: This heading uses
the style “Heading 2” [3.03].
Price instability is a problem for developing countries. Manu
(2018) stated price instability is the main problem for Africa
and Nigeria during the past thirty years. Studies conducted by
Gertler and Gilchrist (1991), Batini (2004), Folawewo and
Osinubi (2006), Onyemu (2012), and Fasanya et al. (2013)
noted irrespective of efforts aimed at achieving
macroeconomics objectives by means of monetary policy, there
has been an unacceptable rate of inflation, especially in less
developed economies. Nigeria is not an exception to this rule.
Nigeria is an oil rich nation plagued with price instability.
Ayodeji and Oluwole (2018) stated monetary policy is the tool
used in achieving monetary and price stability. Itodo, Akadiri
and Ekundayo (2017) stated price instability tops the list of
economic challenges negatively affecting the Nigerian economic
environment. Imoisi (2019) added price instability causes the
problem of unmanageable economic growth and development in
Nigeria. Ayodeji and Oluwole (2018) stated that the Nigerian
economy has also witnessed periods of growth and shrinkage
with an unmanageable growth pattern. Imoisi (2018) stated
monetary policy if targeted directly towards inflation stimulates

growth directly. Nevertheless, the issue of whether monetary
policy effectively curtails price instability is still unsolved.
consumer price index. Imoisi (2018) analyzed how monetary
policies promoted economic growth in Nigeria from 1980-2017.
The result showed approximately 62% of gross domestic
product (GDP) is explained by variables monetary policy rate,
cash reserve ratio, liquidity ratio, and money supply. Imoisi
concluded monetary policies did not have a significant impact
on Nigeria’s economic growth in the short run but significantly
affected the country’s growth in the long run. Imoisi (2018)
claimed there is an existing gap in the literature relative to the
effectiveness of monetary policies. Ubi-Abai and Ekere (2018)
analyzed the effects of fiscal and monetary policies on
economic growth in a panel of 47 sub-Saharan African
economies from 1996 to 2016. The findings showed that fiscal
and monetary policies affected economic growth positively in
the sub-region. Ubi-Abail and Ekere stated it is not clear how
other monetary policies strategies effectively curtails price
instability in the sub-Saharan region and therefore
recommended future research examine this problem. Lawal,
Somoye, Babajide, and Nwanji, (2018) examined the impact of
the interactions between fiscal and monetary policies on stock
market behavior (ASI) and the impact of the volatility of these
interactions on the Nigerian stock market. The study analyzed
monthly data using the ARDL and EGARCH models. The results
show the interaction between monetary and fiscal policies
influence on stock market returns in Nigeria. The ARDL results
show evidence of long run relationship between stock market
behavior (ASI) and Monetary-fiscal policies. The results from
the volatility estimates showed the stock market behavior (ASI)
volatility is largely sensitive to volatility in the interactions
between the two policy instruments. Future research was

recommended to examine the relationship between monetary
policies and price variations in the Nigerian economy. This
study seeks to examine if and to what extent economic
indicators other than interest rates, specifically monetary policy
rate, cash reserve ratio, liquidity ratio, and money supply,
predict consumer price index in Nigeria. Comment by Roselyn
Polk: both are really good because it ssupports smaller sample
size.
Criterion
*(Score = 0, 1, 2, or 3)
Learner Score
Chair Score
Methodologist Score
Background of the Study
Minimum two to three paragraphs or approximately one page
The background section of Chapter 1 provides a brief history of
the problem.
Provides a summary of results from the prior empirical research
on the topic.
Using results, societal needs, recommendations for further
study, or needs identified in three to five research studies
(primarily from the last three years), the learner identifies the
stated need, called a gap.
Builds a justification for the current study, using a logical set of
arguments supported by citations.
The problem is discussed as applicable beyond the local setting
and contributes to societal and/or professional needs.

format.
following scale:
Required.
Required.
Reviewer Comments:
Problem Statement Comment by GCU: Levels of headings must
accurately reflect the organization of the paper [3.02–3.03].For
example, this is a level 2 heading, and has been “styled” as
Heading 2.
It is not known if and to what extent economic indicators other
than interest rates, specifically monetary policy rate, cash
reserve ratio, liquidity ratio, and money supply predict
consumer price index in Nigeria. The population affected is the
Nigerian economy. The unit of analysis is annual time series
data measuring the Nigerian economy. This study would
contribute to existing knowledge on monetary policies and how
these policies predicts price stability in Nigeria. Ayodeji and
Oluwole (2018) stated the Nigerian economy has experienced
economic expansions and depressions with an inconsistent
growth. Nigeria suffers from poor monetary policies that
continuously keeps Nigerian citizenry underprivileged. This
study will be of great importance to scholars, policy makers,
economists, governmental agencies seeking to understand and

examine economic policies in developing countries that
experience inconsistent growth due to economic expansions and
depressions.
Criterion
*(Score = 0, 1, 2, or 3)
Learner Score
Chair Score
Methodologist Score
Problem Statement
Minimum three or four paragraphs or approximately one page
States the specific problem proposed for research with a clear
declarative statement.
Discusses the problem statement in relation to the gap or need
in the world, considering such issues as: real issues affecting
society, students, or organizations; the frequency that the
problem occurs; the extent of human suffering the problem
produces, the perceived lack of attention in the past; the
discussion of the problem in the literature and research about
what should be addressed vis à vis the problem; the negative
outcomes …
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
results may be greatly affected by the insufficient sample data.
These mentioned real estate appraisal methods are useful but
have a common limitation.
They obtain estimates by using mathematical modelling
combined with sample data. However,
the information of the sample data cannot fully reflect the
reality [31,32]. For example, in a thin real
estate market, such as a rural area, the correlated sample data
for estimated objects is lacking, which
limits the estimates of these methods. Those people who are
connected with the estimated object
know the real estate market well, such as the householders
living in the same community or real estate
agents working in the same area. The real estate market price
depends not only on the value, supply,
and demand, but also on the subjective feeling of the

householder, such as ventilation, daylighting,
dust fall, etc. The professional real estate appraisers can
provide professional evaluation for real estate
based on its value, supply, and demand, but not on the actual
subjective feeling about the house.
For example, suppose the house is poorly ventilated, which
would cause a decrease in price. In this
scenario, if the professional appraisers do not know about it, it
is hard for them to evaluate the market
price. However, these people who are related to the selling
house, such as some householders in
the same community or real estate agents in the same area,
know this well. Although they are not
professional, they have a basic understanding of the real estate
around them because of their life or
work. In addition, it is easy for them to know these actual
situations about the object. So we think
these people have enough market insights for the estimated
objects.
This paper presents an optimal rubrics-based approach in real
estate appraisal, which brings in
crowdsourcing. The proposed method should be a complement
to regular valuations. It is not only
based on the information of the existing sample data (just like
the traditional methods), but also on
the extra real-time market information from online
crowdsourcing users’ feedback, which makes the
estimated result close to that of the market. The valuation
estimate can serve as a market indication for
the potential real estate buyers or sellers. It is an attempt to use

the human-computer interaction in the
real estate appraisal field.
2. Methodology
The optimal rubrics-based approach in real estate appraisal
consists of five steps. The first is
the construction of the rubrics model according to the sample
data. In the sample data, each record
is regarded as a rubric. The discrete sample data will build the
rubrics model. In the second step,
similarity is defined by relevance and diversity. The linear
combination of relevance and diversity
can describe the level of similarity between the estimated point
and each rubric. The third is the
measurement of the similarity through the cosine similarity
function. In the fourth, the optimal rubrics
are selected based on the similarity. In the final step, the
attribute information of the selected optimal
rubric and estimated point is posted on the crowdsourcing
platform. Those users interested in the
estimated object can then complete the appraisal by comparing
the information of the estimated
point with rubrics on the platform with their knowledge of the
real estate market. The average of the
crowdsourcing result is finally used as the appraisal result.
2.1. Construction of the Rubrics Model
The rating model indicates a standard of performance for a
particular group, which has been a
common research topic in social science [33–35]. In this study,
a rating model has been used for real
estate appraisal through a crowdsourcing platform. However,
crowdsourcing rating models are often
constrained by a lack of reference standards, here named

rubrics, which generally play an important
role in appraisals or forecasts. The design of good rubrics for
real estate appraisal has never been
studied, so we focus on the research of rubrics for real estate
appraisal.
2.1.1. Crowdsourcing
The recent development of crowdsourcing presents a new
opportunity to engage users in the
process of answering queries [36–40]. Crowdsourcing provides
a new problem-solving paradigm
and has become a part of several research fields [41–46]. In
crowdsourcing, the responses to a task
or questionnaire are collected from a large number of
individuals, and especially from an online
community. This is a powerful tool for collecting human-
enabled ideas in various research fields or
different situations [47–49]. Researchers are interested in the
crowdsourcing platform because of the
relative ease of soliciting responses that has the ability to
achieve large-scale information collection
from a more diverse group of participants. Therefore, compared
with traditional appraisal methods,
appraisal through crowdsourcing can be more objective and
practical [50].
2.1.2. Rating Model
Crowdsourcing has been widely used to collect ratings for a
wide range of items and issues [51–53].
It is necessary to construct a rating model when collecting the
information through a crowdsourcing
platform. Massive open online courses (MOOC), for example,
often require participants to rate others’
homework. As shown in Figure 1, crowdsourcing workers are

required to rate, from a grade of A to D,
an art assignment of a landscape painting submitted by a student
from an online class. In this paper,
a rating model is used in real estate appraisal.
rate, from a grade of A to D, an art assignment of a landscape
painting submitted by a student from an
online class. In this paper, a rating model is used in real estate
appraisal.
Figure 1. A rating model used for an art assignment of
landscape painting.
However, a crowdsourcing rating model is often constrained by
a lack of reference standards
because it is very difficult for crowdsourcing workers to make
an accurate appraisal without a
reference. Crowdsourcing workers are interested in the rated
item, and often have no expertise.
When rating an item, since there is neither a clear boundary
between adjacent rating grades, nor
enough expertise background, workers are usually stuck in a
dilemma. In other words, the rating
results will be inconsistent across workers in this situation. For
the rating task in Figure 1, the art
assignment was rated by 30 crowdsourcing workers. The number
of workers rating grades of A, B,
C, and D were 8, 12, 4, and 6, respectively. The crowdsourcing
results are highly inconsistent, and it

is difficult to make an accurate appraisal.
2.1.3. Rubrics
To address the difficulties of the above problem, a sample data
driven rubric is proposed.
Before rating the estimated items, it is necessary to prepare a
list of sample data related to the
estimated items; these sample data could be historical data,
recent transaction data, statistical
yearbook data, or government reports. Each record in the
sample data is regarded as a rubric, and
the most similar rubrics are used for the appraisal. These rubrics
can train the workers so that they
can have a better understanding of the rating criteria, and it
improves the consistency of the
appraisal result [33]. For the MOOC rating, if a number of
sample homework grades were prepared,
one could more quickly and more easily rate a new assignment.
In the example shown in Figure 2, a well-designed sample data
driven rubric with four graded
assignments is provided from the MOOC database. Even without
expertise, it can be clearly seen
that assignments a, c, and d demonstrate outstanding
performance compared to the assignment in
Figure 1, whereas assignment b is analogous. Note that there is
no definite correct rating, but the
sample data driven rubric is much more likely to result in a
reasonable rating; i.e., grade D in this
example. In the experiment, the task in Figure 1 was assigned
together with the rubrics in Figure 2.
Thirty crowdsourcing workers rated the grades A, B, C, and D,
and the resulting number of votes were
0, 0, 5, and 25, respectively. The consistency and accuracy are
thus significantly improved with the

help of the sample data driven rubrics.
Figure 1. A rating model used for an art assignment of
landscape painting.
However, a crowdsourcing rating model is often constrained by
a lack of reference standards
because it is very difficult for crowdsourcing workers to make
an accurate appraisal without a reference.
Crowdsourcing workers are interested in the rated item, and
often have no expertise. When rating
an item, since there is neither a clear boundary between
adjacent rating grades, nor enough expertise
background, workers are usually stuck in a dilemma. In other
words, the rating results will be
inconsistent across workers in this situation. For the rating task
in Figure 1, the art assignment was
rated by 30 crowdsourcing workers. The number of workers
rating grades of A, B, C, and D were 8, 12,
4, and 6, respectively. The crowdsourcing results are highly
inconsistent, and it is difficult to make an
accurate appraisal.
2.1.3. Rubrics
To address the difficulties of the above problem, a sample data
driven rubric is proposed. Before
rating the estimated items, it is necessary to prepare a list of
sample data related to the estimated
items; these sample data could be historical data, recent
transaction data, statistical yearbook data,
or government reports. Each record in the sample data is
regarded as a rubric, and the most similar
rubrics are used for the appraisal. These rubrics can train the
workers so that they can have a better
understanding of the rating criteria, and it improves the

consistency of the appraisal result [33]. For the
MOOC rating, if a number of sample homework grades were
prepared, one could more quickly and
more easily rate a new assignment.
In the example shown in Figure 2, a well-designed sample data
driven rubric with four graded
assignments is provided from the MOOC database. Even without
expertise, it can be clearly seen
that assignments a, c, and d demonstrate outstanding
performance compared to the assignment in
Figure 1, whereas assignment b is analogous. Note that there is
no definite correct rating, but the
sample data driven rubric is much more likely to result in a
reasonable rating; i.e., grade D in this
example. In the experiment, the task in Figure 1 was assigned
together with the rubrics in Figure 2.
Thirty crowdsourcing workers rated the grades A, B, C, and D,
and the resulting number of votes were
0, 0, 5, and 25, respectively. The consistency and accuracy are
thus significantly improved with the
help of the sample data driven rubrics.
Figure 2. A well-designed sample data driven rubric. (a) The
painting assignment rated B; (b) The
painting assignment rated D; (c) The painting assignment rated
C; (d) The painting assignment
rated A.
2.2. Definition of Similarity

However, not just any collection of rubrics would improve the
final performance of a
crowdsourcing rating. In extreme cases, a biased rubric may
even worsen the performance. There are
two major factors that affect the utility of rubrics for a given
task: relevance and diversity. These two
factors can express the similarity between the estimated item
and rubrics. They will be discussed
below.
2.2.1. Relevance
Relevance indicates how closely the rubrics are related to the
rating task. A good rubric tends to
be connected to the rating task in a way that makes it useful for
a rater. For example, when rating a
math homework assignment, another math assignment from a
class lectured by the same professor,
as a rubric, would be more helpful than a chemistry homework
assignment. Referring back to Figure
1 again, if the rubrics in Figure 3 are used, the number of
crowdsourcing votes for A, B, C, and D
become 5, 8, 14, and 3, respectively. Compared with Figure 2,
the performance of the rubrics in
Figure 3 are not good. This is because the art assignments in
Figure 3 are cartoons, not landscape
paintings, with different rating scales, so their relevance is low.
Figure 3. Effect of relevance. (a) The painting assignment rated
C; (b) The painting assignment rated
D; (c) The painting assignment rated A; (d) The painting
assignment rated B.

C; (d) The painting assignment rated A.
and rubrics. They will be discussed below.
2.2.1. Relevance
assignment. Referring back to Figure 1
again, if the rubrics in Figure 3 are used, the number of
crowdsourcing votes for A, B, C, and D become
5, 8, 14, and 3, respectively. Compared with Figure 2, the
performance of the rubrics in Figure 3 are
not good. This is because the art assignments in Figure 3 are
cartoons, not landscape paintings, with
different rating scales, so their relevance is low.

C; (d) The painting assignment
rated A.
and rubrics. They will be discussed
below.
2.2.1. Relevance
assignment. Referring back to Figure
1 again, if the rubrics in Figure 3 are used, the number of
crowdsourcing votes for A, B, C, and D
become 5, 8, 14, and 3, respectively. Compared with Figure 2,
the performance of the rubrics in
Figure 3 are not good. This is because the art assignments in
Figure 3 are cartoons, not landscape
paintings, with different rating scales, so their relevance is low.

C; (b) The painting assignment rated
D; (c) The painting assignment rated A; (d) The painting
assignment rated B.
C; (b) The painting assignment rated D;
(c) The painting assignment rated A; (d) The painting
assignment rated B.
2.2.2. Diversity
Diversity, on the other hand, requires that the selected rubrics
be distinct from each other, so that
the rater can obtain more information from the rubrics and have
a more comprehensive understanding
of the estimated items; it is good for appraisal. For example, a
rater would prefer to rate some
homework assignments submitted from students with different
levels of math skill rather than rate
the same number of homework assignments from one level. If
the rubrics in Figure 4 are used to rate
Figure 1, the grades of A, B, C, or D are given 0, 2, 14, and 14
times, respectively. Note that all the
assignments in Figure 4 are rated B, so a worker can easily
determine that the rating task (i.e., Figure 1)
is below the level of B, but it is not clear if it should be graded
C or D. Comparatively speaking,
the performance of the rubrics in Figure 2 is better.

2.2.2. Diversity
Diversity, on the other hand, requires that the selected rubrics
be distinct from each other, so
that the rater can obtain more information from the rubrics and
have a more comprehensive
understanding of the estimated items; it is good for appraisal.
For example, a rater would prefer to
rate some homework assignments submitted from students with
different levels of math skill rather
than rate the same number of homework assignments from one
level. If the rubrics in Figure 4 are
used to rate Figure 1, the grades of A, B, C, or D are given 0, 2,
14, and 14 times, respectively. Note
that all the assignments in Figure 4 are rated B, so a worker can
easily determine that the rating task
(i.e., Figure 1) is below the level of B, but it is not clear if it
should be graded C or D. Comparatively
speaking, the performance of the rubrics in Figure 2 is better.
Figure 4. Effect of diversity. (a) The painting assignment rated
B; (b) The painting assignment rated
B; (c) The painting assignment rated B; (d) The painting
assignment rated B.
2.3. Similarity Measurement
As mentioned, the linear combination of relevance and diversity
can represent the similarity
between the estimated item and rubrics. Before the measurement
of these two factors, the estimated
item and the sample data (namely rubrics) should be
numerically graded. For example, in the
student assignment grading sample data, grades over 80 are

rated an A, grades between 70 and 79
are rated a B, grades between 60 and 69 are rated a C and
grades below 59 are rated a D. Then, the
grading index should be numeric; if A is digitized to 1, B
should be digitized to 2, and so on. In
Figure 4, the grading result G (B, B, B, B) of the assignment
rubrics can be expressed by G (2, 2, 2, 2).
So the estimated item and rubrics can be transformed into the
numeric attribute vector.
The sample data, such as the real estate trading data, may
contain value attributes and
non-numeric attributes. It is convenient to convert the sample
data into attribute vector data, which
meet the characteristics of the cosine similarity function.
Compared with Euclidean distance, it is
more appropriate to use the cosine similarity function to
calculate the similarity. In this paper, the
cosine similarity function is used to calculate the cosine of the
angle between the estimated item
attribute vector and rubrics attribute vector for the measurement
of relevance and diversity. The
linear combination of relevance and diversity denotes the
similarity.
2.3.1. Cosine Similarity Function
A cosine similarity function is a measurement between two
vectors in an inner product space
that measures the cosine of the angle between them. The cosine
value of 0° is 1, and it is less than 1
for any other angle. It is thus a judgment of orientation and not
magnitude: two vectors with the
same orientation have a cosine value of 1, two vectors at 90°
have a cosine value of 0, and two vectors

Figure 4. Effect of diversity. (a) The painting assignment rated
B; (b) The painting assignment rated B;
(c) The painting assignment rated B; (d) The painting
assignment rated B.
2.3. Similarity Measurement
As mentioned, the linear combination of relevance and diversity
can represent the similarity
between the estimated item and rubrics. Before the measurement
of these two factors, the estimated
item and the sample data (namely rubrics) should be
numerically graded. For example, in the student
assignment grading sample data, grades over 80 are rated an A,
grades between 70 and 79 are rated a
B, grades between 60 and 69 are rated a C and grades below 59
are rated a D. Then, the grading index
should be numeric; if A is digitized to 1, B should be digitized
to 2, and so on. In Figure 4, the grading
result G (B, B, B, B) of the assignment rubrics can be expressed
by G (2, 2, 2, 2). So the estimated item
and rubrics can be transformed into the numeric attribute
vector.
The sample data, such as the real estate trading data, may
contain value attributes and
non-numeric attributes. It is convenient to convert the sample
data into attribute vector data, which
meet the characteristics of the cosine similarity function.
Compared with Euclidean distance, it is
more appropriate to use the cosine similarity function to
calculate the similarity. In this paper,
the cosine similarity function is used to calculate the cosine of
the angle between the estimated item
attribute vector and rubrics attribute vector for the measurement
of relevance and diversity. The linear

combination of relevance and diversity denotes the similarity.
2.3.1. Cosine Similarity Function
A cosine similarity function is a measurement between two
vectors in an inner product space
that measures the cosine of the angle between them. The cosine
value of 0◦ is 1, and it is less than
1 for any other angle. It is thus a judgment of orientation and
not magnitude: two vectors with the
same orientation have a cosine value of 1, two vectors at 90◦
have a cosine value of 0, and two vectors
diametrically opposed have a cosine value of −1, independent of
their magnitude [54]. It is appropriate
to use the cosine similarity function to measure the cosine
similarity value between an estimated item
attribute vector and a rubric attribute vector. For the sake of
generality, Sim(x, y) is considered to be
an abstract function that measures the cosine similarity value.
Sim (x, y) =
x·y
‖x‖‖y‖
, (1)
Given two attribute vectors x, y, each vector has p attributes, ‖
is the Euclidean paradigm
of x =
(

x1, x2, · · · , xp
)
, and ‖x‖ =
√
x12 + x22 + · · ·+ xp 2.
Example: Assume x = (1, 1, 2), y = (1, 3, 1). The cosine value
between vectors x and y will be
Sim(x, y) = x·y‖x‖‖y‖ =
(1,1,2)·(1,3,1)√
12+12+22·
√
12+32+12
≈ 0.74.
For demonstration purposes, in this part, a random …
2017 V45 2: pp. 259–300
DOI: 10.1111/1540-6229.12127
REAL ESTATE
ECONOMICS
U.S. House Prices over the Last 30 Years:
Bubbles, Regime Shifts and Market
(In)Efficiency
Rose Neng Lai* and Robert Van Order**
This paper studies U.S. house prices across 45 metropolitan

areas from 1980
to 2012. It applies a version of the Gordon dividend discount
model for long-run
“fundamentals” and uses Mean Group and Pooled Mean Group
estimation to
estimate long-run and short-run determinants of house prices.
We find great
similarity across cities in that the long-run house prices are
largely explained
by the same fundamentals; the long-run rent to price ratio is
approximately 5%
plus 0.75 times the real interest rate (which is on the order of
2%). However, ad-
justments to deviations from the fundamentals are slow, in the
long-run, closing
the gap at a rate of around 10% per year. We find sharp
differences in short-
run adjustments (momentum) away from the fundamentals
across cities, and
the differences are correlated with local supply elasticities
(more momentum
with lower elasticity). Analysis of residuals suggests strong
cyclical deviations,
which are mean-reverting.
Introduction
The U.S. real estate market underwent a boom (or “bubble”)
period after 2000
until about 2006, when property prices started to fall and
mortgage default
started to rise, leading to the collapse of the securitized
mortgage market and
then the collapse of many financial institutions. Since then
house prices have
largely recovered. The fluctuations varied widely across cities.

The purpose
of this paper is to use a simple asset pricing model as the basis
for explaining
house price fluctuations across cities and time in the United
States. The
model allows easy separation of adjustments into long-run
fundamentals and
short-run and long-run dynamics.
Studies of the housing bubble and house price dynamics have
become abun-
dant by now. Examples are Capozza, Hendershott and Mack
(2004), Chan, Lee
*Faculty of Business Administration, University of Macau,
Taipa, Macau SAR, China
or [email protected]
**George Washington University, Washington D.C. or
[email protected]
C© 2016 American Real Estate and Urban Economics
Association
260 Lai and Van Order
Figure 1 � Ratio of U.S. National Rent Index to National House
Price Index.
and Woo (2001), Chang, Cutts and Green (2005), Black, Fraser
and Hoesli
(2006), Coleman, LaCour-Little and Vandell (2008), Hwang,
Quigley and Son
(2006), Lai and Van Order (2010), Taipalus (2006), Wheaton
and Nechayef
(2008), and Nneji, Brooks and Ward (2013). Case, Cotter and

Gabriel (2011)
explain the speculative forces with housing asset pricing
models, while Ling,
Ooi and Le (2015) use nonfundamentals-based sentiments of
home buyers,
builders, and lenders in explaining how the feedback effects
result in housing
boom and bust periods.
We analyze price fluctuations over the past few decades by
exploiting U.S.
data on equivalent rents for owner-occupied housing; we use it
in the same
way as dividends in pricing shares. This allows us to cut out the
use of
variables like local income, employment, housing supply and
other factors
that represent local market conditions. We can then focus on an
asset pricing
approach. Many papers (see e.g., Clark 1995, Ayuso and Restoy
2006, Lai and
Van Order 2010, Sommer et al. 2011) have studied the
determinants of rent
to price ratios as a way of estimating determinants of house
prices. It is clear
from Figure 1, which depicts rent to price ratio in the aggregate,
over time,
that the ratio was relatively stable before the “bubble years,”
decreased sharply
and then increased thereafter, almost returning to prebust levels,
suggesting
momentum away from fundamentals but also mean reversion.
What follows

U.S. House Prices over the Last 30 Years 261
assesses the extent to which Figure 1 can be explained over time
and across
cities.
Our contribution is the use of more recent data and, more
importantly, in the
structure of our model, which provides a clean separation
among: long-run
effects, which are given by the fundamentals (through a
dividend discount
model); long-run adjustment to the fundamentals (given by a
speed of ad-
justment coefficient); and short-run momentum away from the
fundamentals
(given by the sums of coefficients of lagged rent to price
ratios). We use a
version of the Gordon dividend discount model to model long-
run fundamen-
tals, and analyze data on house prices across 45 metropolitan
areas (MSAs)
from 1980 to 2012. The Mean and Pooled Mean Group
estimations allow
us to constrain the long-run to look like the Gordon model, and
tease out
long-run adjustment speeds, while allowing a looser
specification of short-
run variations, including momentum. To the best of our
knowledge, this is the
first paper to exploit the data, the Gordon model and the
estimation technique
in this manner.
We find an intuitively plausible result, that in the long-run the
ratio of rents

to prices (aka cap rate) is around 5% plus 0.75 times the real
interest rate,
and that this result is very similar across specifications and
cities. However,
there is a long lag in adjustment—the gap between current and
long-run rent
to price ratios closes at a rate of about 10% per year, and is
similar across
cities. We find considerable momentum which, although not
explosive, varies
considerably across cities. The variation is related to a well-
known measure
(Saiz 2010) of housing supply elasticity. Hence, we find that
some rather
simple rules of thumb are broadly consistent with house price
behavior in the
United States over the past few decades.
However, while house prices are somewhat predictable, because
of the het-
erogeneity of momentum government policies applied
throughout the country
(e.g., monetary policy) in an attempt to curb bubbles might not
be effective.
We also find that while the boom from 2000 to 2006 was longer
than usual,
examination of the residuals reveals that the boom was on the
verge of cool-
ing off around 2002 or 2003, but started up again in a way that
is consistent
with stimulus from the newly emerging subprime securitization
business.
Fundamental Models and Models for Estimation
Modeling House Price Growth

The equilibrium condition for holding property is that the
current dividend,
or rent, from the property equal the appropriate (risk-adjusted)
interest rate
plus expected capital gains over the period. Then, given an
information set,
�t , the equilibrium condition for holding property at time t is
given by
1
Rt /Pt = it + α − E (( Pt +1/Pt ) − 1|�t ) ≡ it + α − πt , (1)
where Pt is the price of a constant quality house, Rt is
corresponding net
rental income, which in our case is the imputed net rent for
owner-occupied
housing, it is the risk-adjusted hurdle rate, α is (constant)
depreciation, and πt
is expected house price growth. Equation (1) applies to a
particular location.
We add location notation when we perform estimation for
individual MSAs
later.
Equation (1) can be used to determine house prices given
expected future
prices. Because future prices depend on future rents, current
price depends
on future rents via the expected present value relationship:

Pt =
∞∑
i =0
E ( Rt +i /It +i |�t ) + lim E (1/It +i |�t ) , (2)
where the discount factor is given by It = 1+ it, and therefore
It+i is the
discount rate for an i-period loan at time t. Assuming that the
second term
approaches zero, and dividing through by Rt, gives the usual
expected present
value formulation:
Pt /Rt =
∞∑
i =0
E
(
1/Dt +i |�t
)
, (2’)
where Dt +i = (1 + it +i )/(1 + πt +i ∗ ), and π t+i* is the
expected rate of growth
of rent from period t to period i. If D and the rate of growth of
rents are
constant in the long-run, then the reciprocal of (2’) will
converge to (1), which
gives the long-run fundamentals. We can interpret bubbles as
situations where
the second term in (2) does not converge.

We take the imputed rent from owner-occupied property to be
the market rent
of comparable properties, which works if Equation (2’) is
applied to an owner
who is indifferent between owning and renting. Rent is
observable at any
time t, and it is assumed to be forecastable thereafter. The
advantage of this
approach is that it does not require development of a model of
housing demand
and supply. For instance, Glaeser, Gyourko and Saks (2005)
emphasizes the
role of inelastic supply in house price growth, especially due to
local policy
variation. Our rent variable captures this effect without having
to estimate
supply (or demand) elasticities across cities and time. This,
together with
1See Lai and Van Order (2010).
allowing momentum to vary across cities, facilitates capturing
heterogeneity
across cities.
The model is not operational in its current form because it
requires a model
of how expectations are formed. More broadly, it needs to
acknowledge
transaction costs that can make adjustment to (2’) gradual. It
has been well
known at least since Case and Shiller (1989) that house prices

adjust slowly
to shocks, making them more predictable than is consistent with
standard
notions of market efficiency. We take Glaeser and Nathanson
(2015) as our
point of departure. They develop a pricing model for house
prices where
traders are “almost” rational. The “almost” is because rational
expectation
models are subject to big errors for small mistakes; as a result
their optimal
forecasting procedure uses past prices to forecast housing in a
way that
allows short-run momentum (positive feedback), long-run mean
reversion,
and excess volatility. Our estimation allows for all of these
properties. We
add the restriction that the long-run mean is given by the
Gordon dividend
model.
Long-Run Specification
Theory suggests that we should expect prices and rents to move
together in
the long-run, in a way that depends on real interest rates. In the
long-run, the
Gordon model implies
Rt
Pt
= it − πt + α ≡ rt + α, (1’)
where rt is the real rate. For housing, we should allow for
possible tax and

other effects. For instance, if the focus is on the tax break for
not paying tax
on imputed rent for owner-occupied housing and not taxing
capital gains on
housing, then
Rt
Pt
= (1 − θ )it − πt + α ≡ −θ i + r + α, (3)
where θ is marginal tax rate for the marginal homeowner
(marginal in the
sense of being indifferent between owning and renting).
However, it may be
the case that in an inflationary world high nominal interest rates
provide a
cash-flow problem for home buyers (even if real rates are
constant), who can-
not draw down savings or borrow against human capital. Then
the coefficient
of i is ambiguous.
We formulate the long-run as:
Rt
Pt
= ct , (4)
where ct = αi it − απ πt + α ≡ γi it + γr rt + α.

Then c is the “cap rate” for housing. Our tests are of whether
property values
converge to rent divided by cap rate, how fast they converge,
the nature of
short-run deviations, and whether coefficients make sense. We
use long-run
risk-free rates for i, so that estimates of α contain risk
adjustments as well as
depreciation and long-run expected future rent growth, that vary
across cities
but do not change over time. We also use a direct measure of
real risk-free
rates.
Instead of defining short-run fundamentals, we analyze how
short-run devi-
ations move over time. In general, we expect γr to be close to 1,
α to be
a number of around 4% or 5% and undetermined about γi which
would be
expected to be zero absent tax and liquidity effects. In our data
set we have
R and P only in the form of indices. Hence, testing for the
magnitude of
coefficients (e.g., whether γr is close to 1) requires calibration
assumptions,
which are made below.
Dynamic Heterogeneous Panel Estimation
Following a variation on Glaeser and Nathanson (2015), we
assume that R/P
depends on a lagged function of past levels of R, P and i. We
decompose the
relationship into long-run and short-run effects using the Pooled
Mean Group

(PMG) and Mean Group (MG) estimation models developed in
Pesaran, Shin
and Smith (1997, 1999).2 Our hypothesis is that the information
set �t in (1),
(2) and (2’) contains only past rents, prices and interest rates,
and that prices
ultimately adjust to fundamentals.
The MG and PMG models are restricted maximum likelihood
estimations,
based on an autoregressive distributed lag (ARDL) model (see
Pesaran and
Shin 1997). Traditionally economic analysis has focused on
long-run rela-
tionships among the dependent variables and the regressors.
PMG estimation
facilitates identifying common long-run relationships
(expression (1’)) and
individual short-run dynamics separately. The intercepts that
reflect the fixed
effect, the short-run coefficients and the error variances are
allowed to differ
across cities, but the long-run coefficients are constrained to be
identical. MG
2Ott (2014) uses PMG to study the house price dynamics in the
Euro area.
estimation is different in that the long-run coefficients are also
allowed to
vary across cities.

Our model can be represented by:
�
Rc,t
Pc,t
=
l∑
j =1
λc, j,�
Rc,t − j
Pc,t − j
+
q∑
j =0
n∑
k=1
δ
k
c, j x
k
c,t − j + δc + εc ,t , (5)
where Rc,t
Pc,t
is property rent to price ratio in city c, at time t

δc captures city specific fixed effects
xkc,t-j is the kth of n regressors for city c
δkc, j is the coefficient of the kth regressor for city c
λc,j are scalars
εc,t are the city specific errors
c represents panels or cities, i = 1,2, . . . ,N
t represents time in quarters, t = 1,2, . . . ,T
j is an indicator of lags
j = 0,1,2, . . . ,l for lagged dependent variable
j = 0,1,2, . . . ,q lags for regressors
Letting ρ = R
P
, (3) can be written as:
�ρct = λcρc,t −1 +
q∑
j =0
n∑
k=1
δ
k
c, j �x
k
c,t − j + δc + εc,t (6)
which, when written in error correction form, yields:
�ρct = ϕc
{

ρc,t −1 −
n∑
k=1
βc
k x kc,t
}
−
q∑
j =0
n∑
k=1
δ
k
c, j �x
k
c,t − j + δc + εc,t , (7)
where
ϕc = −(1 − λc ), βkc =
δk c,0
(1 − λc )
.
Expression (7) is used for the MG estimation model. It allows
us to restrict
some of the parameters inside the brackets to be zero so we can

get to a
long-run specification that looks like the Gordon model, as
given in (1’),
but with fewer restrictions on short-run adjustment parameters
across cities.
Among the items inside the bracket in (7) are long-run fixed
effects, αc,
and αc = δc/ϕc . The coefficients (one for each city) before the
brackets, ϕc ,
denote the speed of reversion to the long-run, after short-run
deviations. The
adjustment outside the brackets is momentum, which will
disappear if the
model is not explosive.
For PMG we assume homogeneous long-run relations; i.e., βc
k = βk for all
cities. Then:
�ρct = ϕc
{
ρc,t −1 −
n∑
k=1
β
k x kc,t

}
−
q∑
j =0
n∑
k=1
δ
k
c, j �x
k
c,t − j + δc + εct . (8)
The double summation term in (7) and (8) can include lagged
values of
changes in the dependent variable, which is our measure of
momentum.
We measure the level of momentum by the sum of these
coefficients. We
expect the error correction coefficients, ϕc , to be negative and
the sums of
the coefficients of lagged changes in R/P (momentum) to be
positive but less
than 1 (in order that the model converge). Hence, the model can
have the
properties of short-run momentum and long-run mean reversion
in Glaeser
and Nathanson (2015), to which we add the effect of forcing the
reversion to
look like the Gordon model and testing to see if all of it holds
together.
Note that the model requires rents and prices to grow at a

constant rate within
each city in the long-run,3 but the presence of δc allows the
growth rates to
vary across cities in the long-run, which in turn causes the long-
run level of
R/P to differ across cities. Long-run equilibrium is given by:
ρc =
n∑
k=1
β
k x kc − δc/ϕc. (9)
Recall that the last term in (9), which is the negative of the ratio
of the constant
term in (8) (short-run constant term) divided by the correction
speed (which
is negative), is the long-run constant term, αc. This allows for
differences in
risk premia and growth rates across cities that are not time-
varying.
Preliminary Tests
Before testing for the existence of a long-run relationship,
however, we check
if the series are stationary. If some or all the rental income
relative to house
prices and interest rates are nonstationary, and are integrated of
the same
order, we can check for their long-run relationship with
cointegration tests.
Hence, the first step is to test if these series are unit roots.

3We also tried to relax this condition by adding a linear time
trend, common to all
cities inside the brackets in (6). Results are similar, and
therefore are omitted here.
We perform cointegration analysis tests developed by
Westerlund (2007) to
confirm the existence of long-run relationships among the
series. Specifically,
Westerlund (2007) relies on the error correction based
cointegration. That is,
as in expression (7), when ϕi , the error correction parameter, is
significantly
different from zero, then there is a long-run relationship (i.e.,
cointegration).
Formally, H0: ϕi = 0 and H1: ϕi < 0. Westerlund (2007)
proposes four tests.
The first two are “group mean statistics” which state that
rejecting the null of
no cointegration means that at least one or more cities are
cointegrated. The
test statistics are
Gτ =
1
N
N∑
i =1
ϕ̂i

SE(ϕ̂i )
and Gα =
1
N
N∑
i =1
T ϕ̂i
ϕ̂i (1)
, (10)
where N is the number of cities, SE(ϕ̂i ) is the usual standard
error of ϕ̂i , and
ϕ̂i (1) is the kernel estimator of ϕi (1) = 1 −
∑l
j =1 ϕi j . The first expression is
the t-ratio while the latter is the coefficient statistics (analogous
to the rho-
statistics of Phillips and Perron (1988)).The other two are
“panel statistics,”
where a rejection of the null of no cointegration means rejection
for the panel
as a whole. Formally, they are
Pτ =
ϕ̂
SE(ϕ̂)
and Pα = T ϕ̂. (11)

Again, the first expression is the t-ratio while the latter is the
coefficient
statistic. Westerlund (2007) shows that these statistics are more
accurate than
the widely used cointegration test due to Pedroni (2004) when
the residuals
in expression (3), εi,t, are moving average series.
Given that long-run cointegration exists, we next find the long-
run and short-
run effects among variables using the MG and PMG models.
The Hausman
test can be used to check if a common long-run coefficient
exists. That is, not
rejecting the null hypothesis of common coefficients between
the MG and
PMG means common coefficients should be adopted.
Testing
Our measure of house price is the quarterly house price index
released by the
Federal Housing Finance Administration (FHFA), which
provides a repeat
sales house price index for over 100 individual Metropolitan
Statistical Areas
(MSAs) since 1980. This is primarily an index of sales price of
owner-
occupied houses. The rent series is the “owner’s equivalent rent
of primary
residence” obtained from the Bureau of Labor Statistics. It is an
estimate of
what owner-occupied units would rent for if rented in the
market.

We use 10-year Treasury bonds as a measure of nominal long
term discount
rate; we also use the 10-year Treasury Inflation-Protected
Securities (TIPS)
(bonds issued by the U.S. Treasury that are indexed to inflation)
as a direct
measure of real interest rates in some variations of the model.
This requires
assuming expected rent growth to be the same as expected CPI
growth; using
it allows elimination of expected inflation from our cap rate.
TIPS data are
available only after 1998. We interpolate the series back to
1979 Q4, as is
explained in Section 3, to obtain a TIPs series for the entire
period. Since it
is also possible that market risk could affect the cap rate, we
use the Merrill
Lynch 1-year high yield rates minus the 1-year Treasury to
generate a yield
spread to represent market-wide risk.
There is a total of 45 MSAs that have all data available for the
required
sample period. Since some cities that are more prone to boom
might behave
differently from those less prone to boom, for purposes of
comparison, we
follow Lai and Van Order (2010) in classifying the MSAs into
bubble MSAs
and nonbubble MSAs, based on house price growth rates in
previous periods

(see Appendix A for names of the cities). This classification is
for compar-
ison only; we do not have separate estimates for the two
categories, unless
specified. Our purpose is to examine whether the classifications
of bubble
and nonbubble cities in Lai and Van Order (2010) (for which
data stopped
before the recovery) continue to hold after the bust, in the sense
of whether
the differences in momentum found in this paper correspond to
the bubble
city classifications.
Panel Unit Root and Cointegration Tests
If property markets were efficient in the usual sense, house
prices relative to
rents would resemble random walk series, and therefore be
nonstationary. If
these series are not integrated of order 1 (i.e., I(1)),
cointegration tests fail,
and MG and PMG estimations cannot be applied. We perform
panel unit root
tests for the rent to price ratio for different sample periods.
Several panel unit
root tests are adopted here, such as Harris and Tzavalis (1999)
test, Breitung
test due to Breitung (2000) and Breitung and Das (2005), test
due to Hadri
(2000), and the IPS, and Fisher-type, due to Im, Pesaran and
Shin (2003),
and Choi (2001) respectively, which are suitable for unbalanced
data (not
all the MSAs time series have the same length). Except for the
test due to

Hadri (2000), all tests have the null hypotheses as existence of
unit root, and
alternative hypotheses as at least one panel stationary. The null
hypothesis of
Hadri (2000) is that all panels are stationary, while the
alternative is to have
some panels containing unit root.
Table 1 shows that the rent–price ratio is nonstationary in all
the tests, while all
the differenced series are stationary, whether de-meaned or not.
We also test
for stationarity for the interest rate series using the Phillips–
Perron unit root
test and the Augmented Dickey Fuller test, which also show that
interest rates
are in general nonstationary, or vaguely stationary, while their
differenced
series are stationary.
We then verify that there is a long-run relationship with the
Westerlund
(2007) panel cointegration test. We perform cointegration tests
separately with
nonbubble MSAs and bubble MSAs. Results are shown in Table
2. While the
results are not very strong throughout, there is pair-wise
cointegration between
the rent to price ratio and the various interest rates, particularly
with the
10-year Treasury rates, and the 10-year TIPs. Apparently the
bubble MSAs

have stronger cointegration with interest rates than the
nonbubble MSAs. For
instance, while the overall and the nonbubble MSAs rent to
price ratios do not
seem to be cointegrated with the high yield rate, there is strong
cointegration
in the bubble MSAs. This is a strong hint that bubble MSAs
might be riskier
than their counterparts, and they might be driven by different
forces. Taken as
a whole, the tests suggest that the study of long term
relationships is feasible.
Model Estimation and Tests on Regime Shift
An important feature of MG and PMG is that the models are
able to show
regime shifts across time because there are both long-run and
short-run com-
ponents, the latter of which would reflect intertemporal
differences, while
avoiding problems of insufficient data series length. Therefore,
testing for
subperiods to account for possible regime shifts is not
essential.4
We run MG and PMG estimation with variations on expressions
(5) and (6),
taking account of various lags of the short-term variables. We
tried 1-, 2- and
4-lag models and found that the 4-lag (four quarters) models in
general were
more stable across subperiods and tests than the other two. We
used variations
on lagged R/P and real and nominal interest rates as short-run
factors.

PMG is chosen over MG if a small Hausman test statistic is
coupled with a
correspondingly large p-value; that is, the null hypothesis that
there is a com-
mon long-run effect is not rejected. Otherwise, MG estimation
is better, and
4We have run the tests for various subperiods including 1980–
1990, 1991–1998,
1999–2006 and 2007–2013. Tests results are roughly similar
although not very stable,
probably because of loss of degrees of freedom for such short
sample periods. More
importantly, while MG estimations are valid, PMG estimations
fail in several cases.
Even for MG estimations, some variables do not have
reasonable coefficients.
T
ab
le
1
�
T
es
ts
fo

r
st
at
io
na
ri
ty
.
P
an
el
A
:
T
es
ts
fo
r
N
on
st
at
io
na

ri
ty
R
en
t
to
P
ri
ce
R
at
io
D
if
fe
re
nc
ed
R
en
t
to
P
ri

ce
R
at
io
T
es
ts
D
E
M
E
A
N
no
t-
D
E
M
E
A
N
D
E
M

E
A
N
no
t-
D
E
M
E
A
N
H
ar
ri
s
an
d
T
za
va
li
s
(1

99
9)
0.
97
88
0.
99
0
0.
17
52
**
*
0.
10
1*
**
B
re
it
un
g
(2
00

0)
;
B
re
it
un
g
an
d
D
as
(2
00
5)
3.
07
1
3.
02
5
–9
.6
50
8*

**
–7
.3
35
**
*
Im
–P
es
ar
an
–S
hi
n
(2
00
3)
5.
86
9
9.
58
3
–2

7.
73
29
**
*
–4
0.
24
2*
**
F
is
he
r-
ty
pe
(C
ho
i
20
01
)
–3
.8

15
–4
.9
46
69
.5
61
4*
**
10
9.
79
2*
**
H
ad
ri
(2
00
0)
39
.5
82
**

*
31
.6
12
**
*
9.
77
17
**
*
13
.6
60
**
*
P
an
el
B
:
U
ni
t

R
oo
t
T
es
ts
fo
r
V
ar
io
us
In
te
re
st
R
at
es O
n
V
ar
ia
bl

es
O
n
D
if
fe
re
nc
ed
V
ar
ia
bl
es
V
ar
ia
bl
es
A
D
F
te
st

P
hi
ll
ip
s–
P
er
ro
n
te
st
A
D
F
te
st
P
hi
ll
ip
s–
P
er
ro

n
te
st
10
-Y
ea
r
ra
te
s
–1
.3
69
–1
.3
37
–1
2.
93
1*
**
–1
2.

89
5*
**
10
-Y
ea
r
T
IP
ra
te
s
w
it
h
In
te
rp
ol
at
ed
19
80
–1

99
7
V
al
ue
s
–2
.6
97
*
–2
.6
39
*
–1
2.
54
2*
**
–1
2.
59
3*
**

H
ig
h
Y
ie
ld
ra
te
s
–1
.4
14
–1
.6
69
–1
0.
99
5*
**
–1
1.
05
1*

**
H
ig
h
Y
ie
ld
S
pr
ea
d
–2
.8
09
*
–2
.9
63
**
–1
3.
11
7*

**
–1
2.
97
0*
**
N
o
te
:
“*
**
”
de
no
te
si
gn
ifi
ca
nc
e
at
1%

le
ve
ls
.
E
xc
ep
t
fo
r
H
ad
ri
L
M
te
st
,
al
l
te
st
s
ha

ve
H
0
:
A
ll
pa
ne
ls
co
nt
ai
n
un
it
ro
ot
s.
F
or
H
ad
ri
L

M
te
st
,
H
0
:
A
ll
pa
ne
ls
ar
e
st
at
io
na
ry
.
H
a:
S
om
e

pa
ne
ls
co
nt
ai
n
un
it
ro
ot
s.
*
,
**
an
d
**
*
de
no
te
si
gn

ifi
ca
nc
e
at
th
e
10
%
,
5%
an
d
1%
le
ve
ls
,
re
sp
ec
ti
ve
ly

.
Table 2 � Cointegration tests of rent to price and various rates
based on Westerlund
(2007).
Gτ Gα Pτ Pα
Rent to Price Ratio and 10-Year Rate
All MSAs –1.413*** –1.650 –8.576*** –1.486
Nonbubble MSAs –1.036 –0.814 –4.539** –0.713
Bubble MSAs –2.110*** –3.193 –7.63*** –3.166***
Rent to Price Ratio and 10-Year TIPs
All MSAs –2.133*** –1.91 –13.643*** –2.112**
Nonbubble MSAs –1.958*** –1.561 –10.717*** –1.75
Bubble MSAs –2.456*** –2.555 –8.443*** –2.547**
Rent to Price Ratio and High Yield Rate
All MSAs –0.880 –1.709 –6.97*** –1.732*
Nonbubble MSAs –0.266 –0.400 –1.443 –0.329
Bubble MSAs –2.014*** –4.125*** –7.442*** –4.17***
Rent to Price Ratio and High Yield Spread
All MSAs –0.566 –0.575 –4.292 –0.674
Nonbubble MSAs –0.323 –0.253 –1.675 –0.259
Bubble MSAs –1.014 –1.17 –3.877** –1.272
Rent to Price Ratio and Interpolated 10-year TIPs

All MSAs –1.868*** –2.262 –11.531*** –2.255***
Nonbubble MSAs –1.575*** –1.433 –7.293*** –1.348
Bubble MSAs –2.41*** –3.791 –8.758*** –3.769***
Note: *, ** and *** denote significance at the 10%, 5% and 1%
levels, …
IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 19, NO. 12,
DECEMBER 2017 2751
Image-Based Appraisal of Real Estate Properties
Quanzeng You , Ran Pang, Liangliang Cao, and Jiebo Luo,
Fellow, IEEE
Abstract—Real estate appraisal, which is the process of
estimating the price for real estate properties, is crucial for both
buyers and sellers as the basis for negotiation and transaction.
Traditionally, the repeat sales model has been widely adopted
to estimate real estate prices. However, it depends on the design
and calculation of a complex economic-related index, which is
challenging to estimate accurately. Today, real estate brokers
provide easy access to detailed online information on real estate
properties to their clients. We are interested in estimating the
real estate price from these large amounts of easily accessed
data.
In particular, we analyze the prediction power of online house
pictures, which is one of the key factors for online users to
make
a potential visiting decision. The development of robust
computer
vision algorithms makes the analysis of visual content possible.
In this paper, we employ a recurrent neural network to predict
real estate prices using the state-of-the-art visual features. The

experimental results indicate that our model outperforms several
other state-of-the-art baseline algorithms in terms of both mean
absolute error and mean absolute percentage error.
Index Terms—Deep neural networks, real estate, visual content
analysis.
I. INTRODUCTION
R EAL estate appraisal, which is the process of estimatingthe
price for real estate properties, is crucial for both buys
and sellers as the basis for negotiation and transaction. Real
estate plays a vital role in all aspects of our contemporary
society. In a report published by the European Public Real
Estate Association (EPRA http://alturl.com/7snxx), it was
shown that real estate in all its forms accounts for nearly 20%
of the economic activity. Therefore, accurate prediction of real
estate prices or the trends of real estate prices help governments
and companies make informed decisions. On the other hand, for
most of the working class, housing has been one of the largest
expenses. A right decision on a house, which heavily depends
on
their judgement on the value of the property, can possibly help
them save money or even make profits from their investment in
Manuscript received March 28, 2016; revised February 26, 2017
and April
18, 2017; accepted May 15, 2017. Date of publication June 1,
2017; date
of current version November 15, 2017. The associate editor
coordinating the
review of this manuscript and approving it for publication was
Prof. Benoit
Huet. (Corresponding author: Quanzeng You.)
Q. You and J. Luo are with the Department of Computer

Science, Univer-
sity of Rochester, Rochester, NY 14623 USA (e-mail:
[email protected];
[email protected]).
R. Pang is with PayPaL, San Jose, CA 95131 USA (e-mail:
[email protected]
gmail.com).
L. Cao is with the Electrical Engineering and Computer
Sciences Department,
Columbia University, New York, NY 10013 USA, and also with
customerser-
viceAI, New York, NY 10013 USA (e-mail: [email protected]).
Color versions of one or more of the figures in this paper are
available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/TMM.2017.2710804
Fig. 1. Example of homes for sale from Realtor.
their homes. From this perspective, real estate appraisal is also
closely related to people’s lives.
Current research from both estate industry and academia has
reached the conclusion that real estate value is closely related
to property infrastructure [1], traffic [2], online user reviews [3]
and so on. Generally speaking, there are several different types
of appraisal values. In particular, we are interested in the
market
value, which refers to the trade price in a competitive Walrasian
auction setting [4]. Today, people are likely to trade through
real estate brokers, who provide easy access online websites for
browsing real estate property in an interactive and convenient

way. Fig. 1 shows an example of house listing from Realtor
(http://www.realtor.com/), which is the largest real estate
broker
in North America. From the figure, we see that a typical piece
of
listing on a real estate property will introduce the infrastructure
data in text for the house along with some pictures of the house.
Typically, a buyer will look at those pictures to obtain a general
idea of the overall property in a selected area before making his
next move.
Traditionally, both real estate industry professionals and
researchers have relied on a number of factors, such as eco-
nomic index, house age, history trade and neighborhood en-
vironment [5] and so on to estimate the price. Indeed, these
factors have been proved to be related to the house price, which
is
quite difficult to estimate and sensitive to many different human
activities. Therefore, researchers have devoted much effort in
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2752 IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 19,
NO. 12, DECEMBER 2017
building a robust house price index [6]–[9]. In addition, quan-

titative features including Area, Year, Storeys, Rooms and Cen-
tre [10], [11] are also employed to build neural network models
for estimating house prices. However, pictures, which is proba-
bly the most important factor on a buyer’s initial decision
making
process [12], have been ignored in this process. This is partially
due to the fact that visual content is very difficult to interpret or
quantify by computers compared with human beings.
A picture is worth a thousand words. One advantage with im-
ages and videos is that they act like universal languages. People
with different backgrounds can easily understand the main con-
tent of an image or video. In the real estate industry, pictures
can
easily tell people exactly how the house looks like, which is im-
possible to be described in many ways using language. For the
given house pictures, people can easily have an overall feeling
of the house, e.g. what is the overall construction style, how the
neighboring environment looks like. These high-level attributes
are difficult to be quantitatively described. On the other hand,
today’s computational infrastructure is also much cheaper and
more powerful to make the analysis of computationally inten-
sive visual content analysis feasible. Indeed, there are existing
works on focusing the analysis of visual content for tasks such
as prediction [13], [14], and online user profiling [15]. Due to
the recently developed deep learning, computers have become
smart enough to interpret visual content in a way similar to
human beings.
Recently, deep learning has enabled robust and accurate
feature learning, which in turn produces the state-of-the-art per-
formance on many computer vision related tasks, e.g., digit
recognition [16], [17], image classification [18], [19], aesthet-
ics estimation [20] and scene recognition [21]. These systems
suggest that deep learning is very effective in learning robust
features in a supervised or unsupervised fashion. Even though

deep neural networks may be trapped in local optima [22], [23],
using different optimization techniques, one can achieve the
state-of-the-art performance on many challenging tasks men-
tioned above.
Inspired by the recent successes of deep learning, in this
work we are interested in solving the challenging real estate ap-
praisal problem using deep visual features. In particular, for
images related tasks, Convolutional Neural Network (CNN)
are widely used due to the usage of convolutional layers. It
takes into consideration the locations and neighbors of image
pixels, which are important to capture useful features for vi-
sual tasks. Convolutional Neural Networks [18], [19], [24] have
been proved very powerful in solving computer vision related
tasks.
We intend to employ the pictures for the task of real es-
tate price estimation. We want to know whether visual features,
which is a reflection of a real estate property, can help estimate
the real estate price. Intuitively, if visual features can charac-
terize a property in a way similar to human beings, we should
be able to quantify the house features using those visual re-
sponses. Meanwhile, real estate properties are closely related to
the neighborhood. In this work, we develop algorithms which
only rely on: 1) the neighbor information and 2) the attributes
from pictures to estimate real estate property price.
To preserve the local relation among properties we employ
a novel approach, which employs random walks to generate
house sequences. In building the random walk graph, only the
locations of houses are utilized. In this way, the problem of real
estate appraisal has been transformed into a sequence learn-
ing problem. Recurrent Neural Network (RNN) is particularly
designed to solve sequence related problems. Recently, RNNs
have been successfully applied to challenging tasks including
machine translation [25], image captioning [26], and speech

recognition [27]. Inspired by the success of RNN, we deploy
RNN to learn regression models on the transformed problem.
The main contributions of our work are as follows.
1) To the best of our knowledge, we are the first to quan-
tify the impact of visual content on real estate price es-
timation. We attribute the possibility of our work to the
newly designed computer vision algorithms, in particular
Convolutional Neural Networks (CNNs).
2) We employ random walks to generate house sequences
according to the locations of each house. In this way, we
are able to transform the problem into a novel sequence
prediction problem, which is able to preserve the relation
among houses.
3) We employ the novel Recurrent Neural Networks (RNNs)
to predict real estate properties and achieve accurate
results.
II. RELATED WORK
Real estate appraisal has been studied by both real estate in-
dustrial professionals and academia researchers. Earlier work
focused on building price indexes for real properties. The semi-
nal work in [6] built price index according to the repeat prices
of
the same property at different times. They employed regression
analysis to build the price index, which shows good perfor-
mances. Another widely used regression model, Hedonic re-
gression, is developed on the assumption that the characteristics
of a house can predict its price [7], [8]. However, it is argued
that the Hedonic regression model requires more assumptions
in terms of explaining its target [28]. They also mentioned that
for repeat sales model, the main problem is lack of data, which

may lead to failure of the model. Recent work in [9] employed
locations and sale price series to build an autoregressive com-
ponent. Their model is able to use both single sale homes and
repeat sales homes, which can offer a more robust sale price
index.
More studies are conducted on employing feed forward neu-
ral networks for real estate appraisal [29]–[32]. However, their
results suggest that neural network models are unstable even us-
ing the same package with different run times [29]. The perfor-
mance of neural networks are closely related to the features and
data size [32]. Recently, Kontrimas and Verikas [33]
empirically
studied several different models on selected 12 dimensional fea-
tures, e.g., type of the house, size, and construction year. Their
results show that linear regression outperforms neural network
on their selected 100 houses.
More recent studies in [1] propose a ranking objective, which
takes geographical individual, peer and zone dependencies into
YOU et al.: IMAGE-BASED APPRAISAL OF REAL ESTATE
PROPERTIES 2753
consideration. Their method is able to use various estate related
data, which helps improve their ranking results based on prop-
erties’ investment values. Furthermore, the work in [3] studied
online user’s reviews and mobile users’ moving behaviors on
the problem of real estate ranking. Their proposed sparsity
regu-

larized learning model demonstrated competitive performance.
In contrast, we are trying to solve this problem using the
attributes reflected in the visual appearances of houses. In
particular, our model does not use the meta data of a house
(e.g., size, number of rooms, and construction year). We intend
to utilize the location information in a novel way such that our
model is able to use the state-of-the-art deep learning for
feature
extraction (Convolutional Neural Network) and model learning
(Recurrent Neural Network).
III. RECURRENT NEURAL NETWORK FOR REAL ESTATE
PRICE ESTIMATION
In this section, we present the main components of our frame-
work. We describe how to transform the problem into a prob-
lem that can be solved by the Recurrent Neural Network. The
architecture of our model is also presented.
A. Random Walks
One main feature of real estate properties is its location. In
particular, for houses in the same neighborhood, they tend to
have similar extrinsic features including traffic, schools and so
on. We build an undirected graph G for all the houses collected,
where each node vi represent the i-th house in our data set. The
similarity sij between house hi and house hj is defined using
the Gaussian kernel function, which is a widely used similarity
measure1
sij = exp
(
dist(hi,hj )
2σ2

)
(1)
where dist(hi,hj ) is the geodesic distance between house hi
and hj . σ is the hyper-parameter, which controls the similarity
decaying velocity with the increase of distance. In all of our
experiments, we set σ to 0.5 miles so that houses within the
1.5 (within 3σ) miles will have a relatively larger similarity.
The �-neighborhood graph [34] is employed to build G in our
implementation. We assign the weight of each edge eij as the
similarity sij between house hi and the house hj .
Given this graph G, we can then employ random walks to gen-
erate sequences. In particular, every time, we randomly choose
one node vi as the root node, then we proportionally jump to
its neighboring nodes vj according to the weights between vi
and its neighbors. The probability of jumping to node vj is
defined as
pj =
eji∑
k∈ N (i) eki
(2)
where N(i) is the set of neighbor nodes of vi. We continue to
employ this process until we generate the desired length of se-
quence. The employment of random walks is mainly motivated
1[Online]. Available:
http://en.wikipedia.org/wiki/Radial_basis_function_
kernel
Algorithm 1: Random Walks
Require: H = {h1,h2, . . . ,hn} geo-coordinates of n

houses σ hyper-parameter for Gaussian Kernel t
threshold for distance M total number of desired
sequences
1: Calculate the Vincenty distance between any pair of
houses
2: Calculate the similarity between houses according to
the Gaussian kernel function (see (1)).
3: repeat
4: Initialize sc = {}
5: Randomly pick one node hi and add hi to sc
6: set hc = hi
7: while size(sc) < L do
8: Pick hc’s neighbor node hj with probability pj
defined in (2)
9: add hj to sc
10: set hc = hj
11: end whileadd sc to S
12: until size (S) = M
13: return The set of sequence S
by the recent proposed DeepWalk [35] to learn feature represen-
tations for graph nodes. It has been shown that random walks
can capture the local structure of the graphs. In this way, we
can
keep the local location structure of houses and build sequences
for houses in the graph. Algorithm 1 summarizes the detailed
steps for generating sequences from a similarity graph.
We have generated sequences by employing random walks.
In each sequence, we have a number of houses, which is related

in terms of their locations. Since we build the graph on top of
house locations, the houses within the same sequence are highly
possible to be close to each other. In other words, the prices of
houses in the same sequence are related to each other. We can
employ this context for estimating real estate property price,
which can be solved by recurrent neural network discussed in
following sections.
B. Recurrent Neural Network
With a Recurrent Neural Network (RNN), we are trying to
predict the output sequence {y1,y2, . . . ,yT } given the input
sequence {x1,x2, . . . ,xT }. Between the input layer and the
output layer, there is a hidden layer, which is usually estimated
as in
ht = Δ(W
i
hht−1 + Wxxt + bh). (3)
Δ represents some selected activation function or other com-
plex architecture employed to process the input xt and ht. One
of the most widely deployed architectures is Long Short-Term
Memory (LSTM) cell [36], which can overcome the vanishing
and exploding gradient problem [37] when training RNN with
gradient descent. Fig. 2 shows the details of a single Long
Short-
Term Memory (LSTM) block [38]. Each LSTM cell contains
an input gate, an output gate and an forget gate, which is also
called a memory cell in that it is able to remember the error in

Fig. 2. Illustration of a single long short-term memory (LSTM)
cell.
the error propagation stage [39]. In this way, LSTM is capable
of modeling long-range dependencies than conventional RNNs.
For completeness, we give the detailed calculation of ht
given input xt and ht−1 in the following equations. Let Wi. ,
Wf. , W
o
. represent the parameters related to input, forget and
output gate respectively. � denotes the element-wise
multiplica-
tion between two vectors. φ and ψ are some selected activation
functions and σ is the fixed logistic sigmoid function. Follow-
ing [27], [38], [40], we employ tanh for both φ in (6) and ψ
in (8):
it = σ(W
i
xxt + W
i
hht−1 + W
i
c ct−1 + bi) (4)
ft = σ(W
f

x xt + W
f
h ht−1 + W
f
c ct−1 + bf ) (5)
ct = ft � ct−1 + it � φ(Wcxxt + Wchht−1 + bc) (6)
ot = σ(W
o
x xt + W
o
h ht−1 + W
o
c ct + bo) (7)
ht = ot � ψ(ct). (8)
C. Multilayer Bidirectional LSTM
In previous sections, we have discussed the generation of
sequences as well as Recurrent Neural Network. Recall that
we have built an undirected graph in generating the sequences,
which indicates that the price of one house is related to all the
houses in the same sequence including those in the later part.
Bidirectional Recurrent Neural Network (BRNN) [41] has been
proposed to enable the usage of both earlier and future contexts.
In bidirectional recurrent neural network, there is an additional
backward hidden layer iterating from the last of the sequence
to the first. The output layer is calculated by employing both
forward and backward hidden layer.

Bidirectional-LSTM (B-LSTM) is a particular type of BRNN,
where each hidden node is calculated by the long short-term
memory as shown in Fig. 2. Graves et al. [40] have employed
Bidirectional-LSTM for speech recognition. Fig. 3 shows the
architecture of the bidirectional recurrent neural network. We
have two Bidirectional-LSTM layers. During the forward pass
of the network, we calculate the response of both the forward
and the backward hidden layers in the 1st-LSTM and 2nd-LSTM
Algorithm 2: Training Multi-Layer B-LSTM
Require: H = {h1,h2, . . . ,hn} geo-coordinates of n
houses X = {x1,x2, . . . ,xn} features of the n
house Y ={y1,y2, . . . ,yn} prices of the n houses
1: S = RandomWalks (see Algorithm 1)
2: Split S into mini-batches
3: repeat
4: Calculate the gradient of L in (9) and update the
parameters using RMSProp.
5: until Convergence
6: return The learned model M
layer respectively. Next, the output (in our problem, the output
is the price of each house) of each house is calculated using the
output of the 2nd-LSTM layer as input to the output layer.
The objective function for training the Multi-Layer Bidirec-
tional LSTM is defined as follows:
L =
1
N
N∑

n= 1
∑
j
‖ ŷij − yij ‖2 (9)
where W is the the set of all the weights between different
layers. yij is the actual trade price for the j-th house in the
generated i-th sequence and ŷij is the corresponding estimated
price for this house.
When training our Multi-Layer B-LSTM model, we employ
the RMSProp [42] optimizer, which is an adaptive method for
automatically adjust the learning rates. In particular, it normal-
izes the gradients by the average of its recent magnitude.
We conduct the back propagation in a mini-batch approach.
Algorithm 2 summarizes the main steps for our proposed
algorithm.
D. Prediction
In the prediction stage, the first step is also generating se-
quence. For each testing house, we add it as a new node into our
previously build similarity graph on the training data. Each test-
ing house is a new node in the graph. Next, we add edges to the
testing nodes and the training nodes. We use the same settings
when adding edges to the new �-neighborhood graph. Given
the new graph G′, we randomly generate sequences and keep
those sequences that contain one and only one testing node. In
this way, for each house, we are able to generate many different
sequences that contain this house. Fig. 4 shows the idea. Each
testing sequence only has one testing house. The remaining
nodes in the sequence are the known training houses.

a) Average: The above strategy implies that we are able to
build many different sequences for each testing house. To
obtain
the final prediction price for each testing house, one simple
strat-
egy is to average the prediction results from different sequences
and report the average price as the final prediction price.
IV. EXPERIMENTAL RESULTS
In this section, we discuss how to collect data and evalu-
ate the proposed framework as well as several state-of-the-art
PROPERTIES 2755
Fig. 3. Multilayer BRNN architecture for real estate price
estimation. There are two bidirectional recurrent layers in this
architecture. For real estate price
estimation, the price of each house is related to all houses in the
same sequence, which is the main motivation to employ
bidirectional recurrent layers.
Fig. 4. Testing sequence h1 → h2 → · · · → hT . In each testing
sequence,
there is one and only one testing node in that sequence. The
remaining nodes
are all come from training data.
approaches. In this work, all the data are collected from Real-

tor (http://www.realtor.com/), which is the largest realtor asso-
ciation in North America. We collect data from San Jose, CA,
one of the most active cities in U.S., and Rochester, NY, one of
the least active cities in U.S., over a period of one year. In the
next section, we will discuss the details on how to preprocess
the data for further experiments.
A. Data Preparation
The data collected from Realtor contains description, school
information and possible pictures about each real property as
shown in Fig. 1 show. We are particularly interested in employ-
ing the pictures of each house to conduct the price estimation.
We filter out those houses without image in our data set. Since
houses located in the same neighborhood seem to have similar
price, the location is another important features in our data set.
However, after an inspection of the data, we notice that some of
the house price are abnormal. Thus, we preprocess the data by
filtering out houses with extremely high or low price compared
with their neighborhood.
Table I shows the overall statistics of our dataset after filter-
ing. Overall, the city of San Jose has more houses than
Rochester
on the market (as expected for one of the hottest market in the
TABLE I
AVERAGE PRICE PER SQUARE FOOT AND THE
STANDARD DEVIATION
(STD) OF THE PRICE OF THE TWO STUDIED CITIES
City # of Houses Avg Price std of Price
San Jose 3064 454.2 132.1
Rochester 1500 76.4 21.2

country). The house prices in the two cities also have
significant
differences. Fig. 5 shows some of the example house pictures
from the two cities, respectively. From these pictures, we ob-
serve that houses whose prices are above average typically have
larger yards and better curb appeal, and vice versa. The same
can be observed among house interior pictures (examples not
shown due to space).
Realtor does not provide the exact geo-location for each
house. However, geo-location is important for us to build the
�-neighborhood graph for random walks. We employ Microsoft
Bing Map API (https://msdn.microsoft.com/en-us/library/
ff701715.aspx) to obtain the latitude and longitude for each
house given its collected address. Fig. 6 shows some of the
houses in our collected data from San Jose and Rochester using
the returned geo-locations from Bing Map API.
According to these coordinates, we are able to calcu-
late the distance between any pair of houses. In particular,
we employ Vincenty distance (https://en.wikipedia.org/wiki/
Vincenty’s_formulae) to calculate the geodesic distances ac-
cording to the coordinates. Fig. 7 shows distribution of the dis-
tance between any pair of houses in our data set. The distance
is less than 4 miles for most randomly picked pair of houses. In
building our �-neighborhood graph, we assign an edge between
any pair of houses, which has a distance smaller than 5 miles
(� = 5 miles).

Fig. 5. Examples of house pictures of the two cities,
respectively. Top row: houses whose prices (per square foot)
are above the average of their neighborhood.
Bottom row: houses whose prices (per square foot) are below
the average of their neighborhood. (a) Rochester. (b) San Jose.
Fig. 6. Distribution of the houses in our collected data for both
San Jose
and Rochester according to their geo-locations. (a) San Jose,
CA, USA (b)
Rochester, NY, USA.
Fig. 7. Distribution of distances between different pairs of
houses.
B. Feature Extraction and Baseline Algorithms
In our implementation, we experimented with GoogleNet
model [43], which is one of the state-of-the-art deep neural
architectures. In particular, we use the response from the last
avg − pooling layer as the visual features for each image. In
this way, we obtain a 1,024 dimensional feature vector for each
image. Each house may have several different pictures on dif-
ferent angles of the same property. We average features of all
the images of the same house (also known as average-pooling)2
to obtain the feature representation of the house.
We compare the proposed framework with the following
algorithms.
1) Regression Model (LASSO): Regression model has been
employed to analyze real estate price index [6]. Recently, the

results in Fu et al. [3] show that sparse regularization can obtain
better performance in real estate ranking. Thus, we choose to
use
LASSO (http://statweb.stanford.edu/˜tibs/lasso.html), which is
a l1-constrained regression model, as one of our baseline
algorithms.
2) DeepWalk: Deepwalk [35] is another way of employing
random walks for unsupervised feature learning of graphs. The
main approach is inspired by distributed word representation
learning. In using DeepWalk, we also use �-neighborhood
graph
with the same settings with the graph we built for generating
sequences for B-LSTM. The learned features are also fed into
a LASSO model for learning the regression weights. Indeed,
deepwalk can be thought as a simpler version of our algorithm,
where only the graph structure are employed to learn features.
Our framework can employ both the graph structure and other
features, i.e. visual attributes, for building regression model.
C. Training a Multilayer B-LSTM Model
With the above mentioned similarity graph, we are able to
generate sequences using random walks following the steps
described in Algorithm 1. For each city, we randomly split the
houses into training (80%) and testing set (20%). Next, we
generate sequences using random walks on the training houses
only to build our training sequences for Multi-layer B-LSTM.
2We also tried max-pooling. However, the results are not as
good as average-
pooling. In the following experiments, we report the results
using average-
pooling.

PROPERTIES 2757
TABLE II
PREDICTION DEVIATION OF DIFFERENT MODELS FROM
THE ACTUAL SALE PRICES
City LASSO DeepWalk RNN-best RNN-avg
MAE MAPE MAE MAPE MAE MAPE MAE MAPE
San Jose 70.79 16.92% 68.05 16.12% 17.98 4.58% 66.3 16.11%
Rochester 14.19 24.83% 13.68 23.28% 5.21 9.94% 13.32
22.69%
Note that RNN-best is the upper-bound performance of the RNN
based model proposed in this work.
For both cities, we build 200,000 …
REAL ESTATE ISSUES Volume 39, Number 1, 2014
FEATURE
Accuracy of Zillow’s Home
Value Estimates
BY CHARLES CORCORAN, PH.D., CFA, AND FEI LIU
INTRODUCTION

Zillow is a real estate website that enjoys
tremendous name recognition. Buyers use it to search for
homes; sellers type in their addresses and get what they
believe to be a value of their homes. But is the site accurate
and should consumers rely upon it?
LITERATURE REVIEW
In recent years, home value estimates have been subject
to heightened scrutiny, with a housing price bubble
followed by a sharp downturn. Interested parties such as
appraisers, tax assessors, buyers and sellers seek reliable
data from which they can derive an unbiased estimate of
value. Th e real estate industry is based on “information
asymmetry,” which means that one party (typically the
seller) knows more about a product than the other (the
buyer). It’s an opaque market that encourages obfuscation
and leads to fl awed pricing. A motivation behind the
founding of Zillow.com in 2006 was to make real estate
more like a stock exchange, a transparent market where all
information about every property is readily available and,
as a result, pricing is less imperfect.1
Zillow provides an estimate of market value for more
than 100 million homes based on a proprietary formula.
In general, it off ers free value estimates, or “Zestimates,”
using data from appraisal districts and from multiple
listing services (MLSs), depending on availability. Zillow
uses a “static” formula employing tax information, and
applies it uniformly across the country. Th eir stated
mission is “to empower consumers with information and
tools to make smart decisions about homes, real estate and
mortgages.”2 Zillow is a home and real estate marketplace
created to help homeowners, homebuyers, sellers, renters,
real estate agents, mortgage professionals, landlords and

property managers fi nd and share vital information about
homes, real estate, mortgages and home improvement.
Th ey assert to be “transforming the way consumers make
home-related decisions and connect with professionals.”
Zillow partnered with Yahoo! in 2011 to provide the vast
majority of Yahoo’s real estate listings online, cementing
their place as the largest real estate network on the Web
according to several online measurement agencies.3
Th e focus of this article is to determine whether Zillow’s
Zestimates refl ect actual sale prices. Realtors generally
have been critical of the values produced by Zillow,
claiming the data are secondhand, not locally sourced and
out of date. Realtors with specifi c market knowledge are
more likely to know specifi c factors aff ecting the sale of
a home such as the overall condition of the home, room
fl ow, landscaping, views, traffi c noise and privacy. Th ese
factors have been called unzillowable.4
Hagerty5 studied the accuracy of Zillow’s estimates and
found that they “oft en are very good, frequently within
a few percentage points of the actual price paid. But
Charles P. Corcoran, Ph.D., CFA,
is a professor and chair of the Accounting
and Finance Department at the University of
Wisconsin/River Falls. His recent publications
have appeared in Asset International’s CIO,
Global Journal of Business Research,
Journal of International Business and
Economics, The Journal of Accounting
and Finance Research, the Journal of
Instructional Pedagogy, among others. Corcoran teaches Real
Estate Finance. He received his Ph.D. from the University of

Minnesota.
Fei Liu is a visiting scholar at the
University of Wisconsin/River Falls.
Fei is pursuing a Ph.D. in Trade and
Finance from Central China Agricultural
University, Wuhon, China.
About the Authors
45
FEATURE
Accuracy of Zillow’s Home Value Estimates
when Zillow is bad, it can be terrible.” O’Brien6 asserts
that “Zillow has Zestimated the value of 57 percent of
U.S. housing stock, but only 65 percent of that could be
considered ‘accurate’—by its defi nition, within 10 percent
of the actual selling price. And even that accuracy isn’t
equally distributed.” Th e article cites the state of Louisiana
as an example, where “the site is just about worthless.”
Th e National Community Reinvestment Coalition fi led
a complaint with the Federal Trade Commission stating
that Zillow was “intentionally misleading consumers
and real-estate professionals to rely upon the accuracy
of its valuation services, despite the full knowledge of
the company offi cials that their valuation Automated
Valuation Model (AVM) mechanism is highly inaccurate
and misleading.”7

Zillow oft en overestimates home values, much as
homeowners themselves do. Goodman and Ittner8
compare owners’ estimates of value with subsequent sale
prices; their results indicate that homeowners overestimate
value by approximately six percent. Riel and Zabel9 fi nd
an 8.4 percent overestimate compared to sale prices.
Th ese fi ndings suggest that Zillow estimates are not as
accurate as homeowners’ estimates. Hollas, Rutherford
and Th omson10 fi nd that Zillow estimates overvalue
homes by 10 percent compared to the sale price. Zillow
also overestimates values for approximately 80 percent of
the houses in their sample by at least one percent. Th ey
conclude that homeowners’ estimates of value may be
more accurate than Zillow’s estimates. Th e coeffi cients
on a Zillow model compared to the coeffi cients on a sale
price model indicate that Zillow prices some housing
characteristics diff erently than the market. Specifi cally,
vacant properties are overvalued. It appears that Zillow
does not track the occupancy of a property, yet vacancy
is known to aff ect value. Moreover, Doshan11 asserts that
Zestimates are “gamed.” Zillow uses the Zestimate “on
or before the sales date.” In other words, they use the
Zestimate aft er the listing price becomes public. Th at
makes their Zestimate look more accurate than it really
is since the Zestimate can be drastically aff ected by the
listing price.
In response to homeowners’ complaints about the quality
of the data Zillow extracts from public archives across
the United States, in 2011 Zillow added tools that enable
homeowners to edit facts and add information about
their properties. Zillow also off ers listing services for
homeowners and real estate agents, which enable these
users to edit and add information, both manually and
through automated data feeds. Th ese tools are becoming

increasingly popular. At present, nearly 20 percent of
archived properties have been edited through such tools.
By default, Zillow shows the facts that are supplied by
the owner or agent, and these facts are supplemented by
public data. Zillow also uses the user-contributed facts
when computing Zestimates. Zillow’s website declares:
“we’ve made it easier for our users to help us improve
accuracy by incorporating edited home facts into our
Zestimate calculations.”12 Zillow asserts that the improved
algorithm models have improved the Zestimate median
margin of error to 8.5 percent from 12.7 percent. However,
Gelman and Wu13 fi nd that edited facts improve the
completeness of the information that Zillow has in store,
but the “accuracy of Zillow’s edited facts is not high.”
An inherent shortcoming in Zillow’s AVM formulation
is its reliance on assessed valuation. If a property
happens to be in a Proposition Th irteen (California)
type of jurisdiction, with limited periodic assessment
increases, over time its assessed valuation could be well
below market value. Recent sales and reassessments of
valuation impact the Zestimate. So Zestimate values can
be “off ” signifi cantly for a property with no sales history,
in a jurisdiction where assessed value is not signifi cantly
increased until a sale occurs.
Zillow’s no-cost, no-hassle model seems to stand apart
from most competitors. Redfi n14 off ers a free, no-strings-
attached service but its model is rudimentary, considering
only comparables in deriving value. Trulia.com and
HomeValues.com require a return contact from a realtor;
RealEstate.com requires registration, including disclosure
of phone number and email address; RealEstateABC.
com relies on Zillow’s Zestimates. FreddieMac off ers
its Home Value Explorer. Th is AVM tool generates an
estimate of property value quickly, relying on a proprietary

algorithm that blends model estimates, a repeat sales
model and a hedonic model. Th is product is licensed and
serviced through a distributor network. Each distributor
adds services and charges fees.15 LexisNexis provides a
seemingly sophisticated AVM model incorporating price
indexing, tax assessment values, and a hedonic model that
utilizes comparables sold in the previous year. Th ere is a
fee for this service.16
METHODOLOGY
Th e objective of this research is to compare diff erences
between Zillow’s Zestimates and actual sale prices in
diff erent markets and at diff erent price ranges for single-
46
FEATURE
family homes. For 2,005 transactions, the following model
was developed for measuring mean error:
(Zestimate value – sale price) / sale price.
To measure for signifi cant diff erences between the two
markets, and within fi ve price ranges in each market,
a one-way analysis of variance (ANOVA) was used.
Th e ANOVA is used to determine whether there are
signifi cant diff erences among the means of three or
more independent groups. In this study there are ten

groups altogether, fi ve price ranges within two markets—
suburban St. Louis, Missouri, and St. Paul, Minnesota.
ANOVA compares the variance (or variation) between any
two markets’ data sets to variation within each particular
market sample. If the between variation is much larger
than the within variation, as measured by the F-ratio17,
the means of diff erent samples will not be equal. If the
between and within variations are approximately the same
size, then there will be no signifi cant diff erence between
means. Tukey’s test is a post-hoc test, meaning that it is
performed aft er an ANOVA test. Th e purpose of Tukey’s
test is to determine which groups in the sample diff er. Th e
ANOVA measures only whether groups in the sample
diff er; it does not measure which groups diff er.
Th is study seeks to measure Zestimate accuracy along two
dimensions. First, measuring accuracy between markets.
Is the Zestimate value more accurate in markets with
better data inputs? And second, between price ranges.
Is Zestimate accuracy between the markets aff ected by
property price?
For comparison purposes, a Zillow one-star market
(suburban St. Louis) and a Zillow four-star market
(suburban St. Paul), segregated into fi ve price ranges, are
analyzed. Th ese are both large suburban markets in the
Midwest, for which the quality of valuation information
diff ers considerably, according to Zillow’s four-star
rating scheme. Four-star markets supposedly provide
the most accurate, “best” Zestimates, followed by three-
star markets, noted as “good,” “fair” two-star markets
and, fi nally, one-star markets where estimates cannot be
computed accurately or are simply the tax assessor’s value.
Zestimate accuracy is computed by comparing a property’s
fi nal sale price to the Zestimate on or before the sale date.
Ratings are based on accumulated data over the previous

three months. Zillow promotes the star-rating scheme
from an implied presumption that a four-star rating must
be good, as it exceeds the other three-star categories and
is termed “best.” A Tukey post-hoc test was conducted on
multiple price range comparisons between the
two markets.
Of the 2,005 properties analyzed, 849 were in the St.
Paul market and 1,156 were in the St. Louis market.
Five price ranges were employed: (1) < $103,000; (2)
$103,000–$203,000; (3) >$203,000–$253,000; (4)
>$253,000–$353,000; and (5) > $353,000. Th e $203,000
price benchmark was based on the median existing single-
family home price for the second quarter of 2013.18
FINDINGS
In aggregate, for both markets and for all prices ranges,
the mean error is 24.8 percent. Mean error rates in the
four-star (St. Paul) market compared with the one-star
(St. Louis) market are signifi cantly diff erent, with a mean
error rate of 17.15 percent in the four-star market and
30.48 percent in the one-star market. Th e signifi cance level
is 0.000 (p = .000), which is below 0.05. Note the large
F-ratio. See Figure 1 and bottom of Figure 2.
Even though Zestimate values are signifi cantly closer
to sale prices in the four-star market compared with
the one-star market, the diff erences are most prevalent
among properties with sale prices under $203,000, the
benchmark price level used in this study. For homes under
$103,000, four-star market data may not have signifi cantly
better information value than the one-star market, given
mean error rates of 52.43 percent and 64.23 percent,
respectively. Further, overestimates are far more common

on the lower-priced homes. Zestimates exceed actual
market values in 63.44 percent of all transactions, but for
properties with sale prices under $103,000, 93.08 percent
(121/130) of properties in the four-star market and 95.14
percent (333/350) of properties in the one-star market are
associated with overestimated Zillow values.
Figure 1
One-Way ANOVA
Diff erence Sum of
Squares
df Mean
Square
F Sig.
Between
Groups
Within
Groups
Total
85.976
137.958
233.934
9
1995

2004
9.553
.069
138.143 .000
Signifi cance at .05 level
Source: SPSS statistical package
47
FEATURE
For homes priced between $103,000 and $203,000, the
four-star market does provide an outcome signifi cantly
diff erent from the one-star market, with mean error rates
of 10.77 percent and 19.68 percent, respectively.
Within higher price ranges, above $203,000, diff erences
between the two markets are not signifi cant, with mean
error rates ranging from 9.53 percent to 14.63 percent.
See Figure 2.
CONCLUSION
Th e four-star market had a signifi cantly lower mean error
rate than the one-star market, 17.15 percent versus 30.48
percent. High mean error rates are concentrated among

lower-priced homes. At prices above the median home
price of $203,000, diff erences between the four-star and
one-star markets are not signifi cant.
While diff erences between the two markets are signifi cant
for homes selling for less than $103,000, the mean error
rates are so great that they are of little value in either the
four-star or one-star markets. A four-star’s mean error of
52.43 percent indicates little more credibility than a one-
star’s 64.23 percent. While diff erences at all price levels in
both markets are usually overestimates, at this lowest price
level they are almost always overestimates.
Diff erences between the two markets are also signifi cant in
the $103,000–$203,000 price range. But with a mean error
in the four-star market of 10.77 percent, this is close to the
10 percent error level noted by O’Brien as an acceptable
threshold. So for properties in this price range, a four-star
rating may be meaningful.
For the three price ranges beginning with the national
median of $203,000 and above, diff erences between the
four-star and one-star markets are not signifi cant. With
the exception of the $203,000–$253,000 price range, this
does not imply improved outcomes in the four-star market
for the top two price ranges. Diff erences in both markets,
while not statistically signifi cant, are quite large, with
mean error rates ranging from 11.54 percent to
14.63 percent.
Within the middle price range, $203,000–$253,000, the
smallest diff erences are found within both markets. In the
four-star market, the mean error rate is 9.53 percent, while
in the one-star market it is 12.38 percent. Th is diff erence
is, again, statistically insignifi cant.

Zillow’s value as a pricing tool is questionable. With the
possible exception of the $203,000–$253,000 price range,
the four-star designation is of little value. Even the best
results in the four-star market produce mean error rates
approaching 10 percent. In both markets and for all
other price levels, mean error rates are above the
10 percent level. Accuracy of 10 percent still implies an
error of more than $20,000 for an average price property.
While Zillow may be a useful tool, providing an ever-
changing snapshot of home prices, don’t bet the ranch
on it. ■
ENDNOTES
1. For details about Zillow’s estimation methods and models,
see
http://www.zillow.com/zestimate/#what.
2. http://www.zillow.com/corp/About.htm.
3. http://websearch.about.com/od/Alternative-Search-Engines/p/
Zillow-Com-Real-Estate-Search-Made-Simple.htm.
4.
http://forsalebylocals.wordpress.com/2006/08/18/unzillowable-
the-
perfect-term/
5. Hagerty, James R., “How Good Are Zillow’s Estimates?”
Th e Wall Street Journal, Feb. 14, 2007, sec. D.
6. O’Brien, Jeff rey, “What’s Your House Really Worth?”,
Fortune,
Feb. 15, 2007.
Figure 2

Tukey Post-Hoc Test for Multiple Comparisons
Price (x1000)
<103
103-203
<203-253
<253-353
<353
All
Diff erences
between
markets
(mean values)
SP - SL
-.11793235*
-.08910191*
.02845627
.008355306
.02245725
Signifi cance
.001

.000
.997
1.000
1.000
SP
0.52434 (130)
0.10771 (434)
0.09531 (133)
0.11541 (99)
0.12386 (53)
0.17147 (849)
SL
0.64227 (350)
0.19682 (344)
0.12376 (138)
0.12376 (208)
0.14632 (116)
0.30475 (1,156)

*denotes signifi cance at the .05 level.
SP=St. Paul, SL= St. Louis
Source: SPSS statistical package
Mean percent difference
within markets, (sample size)
(Zest.-sale price)/sale price
48
FEATURE
7. http://www.housing-information.org/articles/ft
c_complaint_against_
zillow_online_appraisal_site.
8. Goodman, John L., Jr., and John B. Ittner, “Th e Accuracy of
Home
Owners’ Estimates of House Value,” Journal of Housing
Economics,
Vol. 2, Issue 4, December 1992, pp. 339–357.
9. Kiel, Katherine A. and Jeff rey E. Zabel, “Th e Accuracy of
Owner-
Provided House Values: Th e 1978-1991 American Housing
Survey,”
Real Estate Economics, Vol. 27, Issue 2, 1999, pp. 263–298.

10. Hollas, Daniel, Ronald Rutherford and Th omas Th omson,
Appraisal Journal, Winter 2010, Vol. 78, Issue 1, pp. 26–32.
11. Doshan, Brett, http://www.HomeVisor.com, Oct. 19, 2012.
12. http://www.zillow.com/zestimate/#update, April 4, 2014.
13. Gelman, Irit and Ningning Wu, Proceedings of the 44th
Hawaii
International Conference on System Sciences, p. 9, Jan. 5,
2011.
14. https://www.redfi n.com/what-is-my-home-
worth?estPropertyId=
51230374&src=landing-page, April 5, 2014.
15. http://www.freddiemac.com/hve/distributors.html, April 5,
2014.
16. http://www.lexisnexis.com/legalnewsroom/lexis-
hub/b/legaltoolbox/
archive/2011/09/23/automated-valuation-models-from-
lexisnexis.aspx.
17. Th e F ratio is the ratio of the variance between groups to
the variance
within groups, i.e., the ratio of the explained variance to the
unexplained variance.
18. Op. cit. at 12.
49

4
Editor’s Note
Mary C. Bujold, CRE
5
Contributors
F E AT U R E S A N D P E R S P E C T I V E S
9
The Boom and Bust of the Greek Housing Market
Nicholas Chatzitsolis, CRE, FRICS, and Prodromos Vlamis,
Ph.D.
Th e Greek housing market may be characterized as imperfect
and opaque. Th e aim of this article is to present a review of the
recent developments in the Greek residential market and
identify
the possible links with all of its “peculiarities.” Considerations
under assessment include socioeconomic factors such as the ill-
based concept that every family must own at least one
residential
unit for “security” purposes; the extensive land fragmentation
in Greece; the trend to concentrate residential development
in virtually two cities (Athens and Th essaloniki); and the
“unique”—by global standards—development process known as
“counter performance.” Th e authors expect their analysis of the
Greek residential market to be useful for industry professionals,
policymakers and real estate investors alike.
18
Watch Your Real Estate Language!
Jack P. Friedman Ph.D., CRE, FRICS, MAI; Barry A. Diskin,

Ph.D.,
CRE; and Jack C. Harris, Ph.D.
Th e same word, spelling and all, can take on diff erent
meanings.
In this article, the authors hope to illustrate that when using
a real estate term that has a diff erent meaning in another
profession, oft en as used in accounting, it may be necessary to
explain the defi nition used in order to avoid misunderstanding.
21
Landfi lls: Operations and Opportunities
Joe W. Parker, CRE, MAI, FRICS, and Curtis A. Gentry IV,
MAI
Landfi lls are unique properties that present both questions and
opportunities for real estate professionals. In this article, the
authors emphasize that the better that real estate professionals
understand what landfi lls are and how they work, the better
they can help their clients who either have or anticipate
business
issues related to landfi lls.
29
Form-Based Zoning from Theory to Practice
David Walters and Dustin C. Read, Ph.D., J.D.
In this article, the authors explore the potential advantages and
disadvantages of form-based zoning to understand how it can be
used eff ectively to support development that is fi nancially
viable
and socially benefi cial.

Instead of focusing mainly on “use” as the controlling factor
in regulating development, form-based zoning is primarily
intended to enhance the “public good” derived from private
sector development by defi ning the “urban character” of
neighborhoods and districts. Th is involves managing the siting,
massing and frontage design of buildings in ways that create
safe,
attractive and effi cient public spaces for movement and public
activities.
By emphasizing urban design features, as opposed to use
restrictions, and by the inclusion of key “by-right” provisions in
the code, form-based zoning can provide real estate developers
with greater fl exibility to respond to market forces. If properly
administered, form-based zoning can also reduce the amount
of uncertainty faced by developers in the entitlement process.
However, both these advantages can be compromised through
the structure and (mis)application of local regulations.
37
Historic Tax Credit Transactions in the Wake of Revenue
Procedure 2014-12
Doug Banghart, J.D., LL.M., and Jeff Gaulin, J.D.
Th e historic rehabilitation tax credit (HTC) market was all but
frozen by the highly controversial Historic Boardwalk Hall,
LLC, v. Commissioner (HBH) court decision of August 2012.
Th en last December, the HTC market was given new life by
the Internal Revenue Service’s highly anticipated issuance of
Revenue Procedure 2014-12. Th is article summarizes the HTC,
describes typical investment structures before HBH, recounts
the court case and its impact on those structures, and analyzes
the practical implications of the Revenue Procedure. While the
HTC industry is still adjusting to the new HTC landscape, the
authors suggest that investors and principals should be able to

craft arrangements that, though not free from risk for
developers
or investors, have far more tax certainty for both sides than was
the case immediately aft er HBH. For that reason they anticipate
the Revenue Procedure will bring old as well as new investors
into the HTC market.
45
Accuracy of Zillow Home Estimates
Charles Corcoran, Ph.D., CFA, and Fei Liu
Th is article compares Zillow.com’s home estimate values
(Zestimates) with actual sale prices of 2,005 single-family
residential properties in two markets in November 2013. A
Zillow “four-star” market in suburban St. Paul, Minnesota, and
a Zillow “one-star” market in suburban St. Louis, Missouri, are
analyzed in terms of Zestimate accuracy between these two
markets, as well as within specifi c price ranges. In aggregate,
for
both markets and within all prices ranges, the mean diff erence
between Zestimates and sale prices is 24.8 percent. Comparing
the two markets, Zestimate accuracy is signifi cantly better in
CONTENTS
2
REAL ESTATE ISSUES®
Published by THE COUNSELORS OF REAL ESTATE®

the four-star market as compared with the one-star market,
with a mean diff erence between Zestimates and sales prices of
17.15 percent and 30.48 percent, respectively. However, with
the
possible exception of the middle market price range, $203,000–
$253,000, diff erences between Zestimates and sale prices are
so great as to render doubt about the usefulness of Zestimates,
regardless of the market’s star rating. Diff erences usually are
overestimates, with subsequent sale prices below Zestimate
values.
50
Renewables, Tax Credits and Ad Valorem Taxes: Are
Policies Aligned?
P. Barton DeLacy, CRE, FRICS, MAI
As the renewable energy industry matures, growing controversy
swirls around its funding and, ironically, its sustainability.
Left unchecked, local assessors can undermine the operating
effi ciencies of wind and solar farms with assessments based on
replacement cost rather than market value.
In this article, the author explores the implications of how wind
and solar farms are project fi nanced and poses two questions
that
bear directly on their ad valorem assessment:
1. Given that, but for production or investment tax
credits, most projects would not be built—do these
credits accrue to market value, or are they a form of
inverse economic obsolescence?
2. Th e relative productivity of a wind or solar farm is
a function of its nameplate capacity. A “Net Capacity
Factor” measures its effi ciency. Might the latter serve

as a measure of functional obsolescence?
Th ese issues now are being raised in Lost Creek Wind LLC v.
DeKalb County Assessor before the State Tax Commission and
Circuit Court of Missouri.
V I E W P O I N T
26
The Death of Corporate Reputation
Bowen H. McCoy, CRE
For more than a century law fi rms, investment banks,
accounting
fi rms, credit rating agencies and companies seeking regular
access to U. S. capital markets made large investments in their
reputations. Th ey generally treated their customers well and
occasionally even endured losses to maintain their reputations
as faithful brokers, dealers, issuers and gatekeepers. Many
would
conclude that this has changed. In this “Viewpoint,” the fi rst
of more to come, the author expresses his concern that today’s
leading capital market participants no longer treat customers as
valued counterparties whose trust must be earned and nurtured,
but as distant counterparties to whom no duties are required.
Th e rough and tumble norms of the marketplace have replaced
the long standing fi duciary model in U. S. fi nance. Th e result
has
been unrelenting fi nancial scandal.
R E S O U R C E R E V I E W S
59
The Metropolitan Revolution: How Cities and Metros are

Fixing our Broken Politics and Fragile Economy
Reviewed by Owen M. Beitsch, Ph.D., CRE
In Th e Metropolitan Revolution: How Cities and Metros are
Fixing
our Broken Politics and Fragile Economy, Bruce Katz and
Jennifer
Bradley, both of the Brookings Institution, off er a blueprint
for action which can rebuild economies and is determinedly
self-reliant. Th ey speak of a revolution in thought and actions
stemming from “cities and metropolitan areas [as] the engines
of economic prosperity and social transformation in the United
States.” If they are correct in their outlook, they are capturing
the essence of a sustainable movement because cities matter,
and the strategic solutions breed largely from locally renewable
resources.
Covering a range of community-building activities, Katz and
Bradley make the case that local developers and their local
governments can achieve an extraordinary range of major
improvements by linking with grass root activists, civic
institutions, local foundations, and local banks historically
bypassed in favor of federal resources. Reviewer Owen Beitsch,
CRE, gives the book a “thumbs up” saying “the kernels in this
book…shine.”
62
The End of the Suburbs: Where the American Dream Is
Moving
Reviewed By Roy J. Schneiderman, CRE, FRICS
Not oft en does a book reviewed in Real Estate Issues get a
“thumbs down,” but reviewer Roy J. Schneiderman, CRE,
FRICS, recommends “giving a pass” to this one. “Th …

14
Dissertation Prospectus
The impact Impact of Monetary Policies on Price Stability in
Nigeria 1963-2015.
Submitted by
Michael K Saale
05/31/2019
Dr. Derrick Tennial
7/3/19: RKP: Hi Michael. Very good work on the revisions. I
have determined you understand the research process and have
sufficient knowledge base and alignment to move forward to the
proposal. Make sure any comments provided here are also
addressed in the proposal. Basically, there is still a continued
need to understand the analysis but that isn’t something learned
overnight. Too, the analysis doesn’t appear to match how the
RQs are framed. So continue to work on these. Nice discussion
on study’s and their years ranges as that works toward
justification of your study number of years. Overall, you have
made significant progress. Congratulations on reaching this
important milestone.
6/5/19: RKP: Hi Michael. Nice to meet you. Interesting study. It
appears I will learn a lot about economic indicators during our
journey together. While a very nice start, there is need to
clarify, define and expand some sections. My reaching out for

some clarity on sample size since you have a finite sample lead
to a minor revision of the problem statement (apply to purpose
and RQs as needed) and questions about the unit of observation.
Give you only have 52 years and 4 variables (normal sample
size would be +114), how are you going to address this when it
comes to test assumptions? Will you have enough data to get a
decent effect size and possible significance? what are the
implications of a small sample size in QT regression analysis?
Those are all concerns you must not only take into account but
must be prepared to defend.
· questions that arose when reached out to other methodologists
for clarity: For example, is the analysis about YEARS? Are
YEARS the unit of observation? For example, take the variable
LIQUIDITY RATIO. Are the raw data the value for this
variable for each of the years in the study? If so, then n = 52,
the number of data years, right?
Writing: there appears to be good sentence and paragraph
structure. As you continue and especially when moving to the
prospectus ensure you have synthesis evident, remembering
synthesis is more than just multiple authors in the same
paragraph. Also begin to introduce some critical thought.
Formatting of looks good but some errors in formatting appear
in references.
Throughout the document you will see comments, questions and
resources. Please review carefully. Also provided below is an
alignment document so you can see the alignment (or lack
thereof) visually. Also ensure you follow the required revision
protocol listed below. Reach out to me via course email or
schedule a zoom if you have questions or need comment
clarification.
Scores on the rubric saying the section meets expectations does
not mean the section is complete and all elements of the section
are addressed. There may be need for further revision based on
comments. Ensure you review comments for missing criteria
and for improving the narrative to meet the criteria.
Alignment Checklist:

Gap: There is a gap in the literature relative to other monetary
consumer price index. Imoisi, (2018) claimed there is an
existing gap in the literature relative to the effectiveness of
monetary policies. Inam and Ime (2017) recommended further
research to understand if the predicative relationship between
the actual level of money supply and price stability. Lawal,
Somoye, Babajide, and Nwanji, (2018) further specified a
detailed study should be conducted showing the variations and
interactions between monetary and fiscal policies and how they
predict price stability in Nigeria.
Theory/conceptual framework: monetary theory of inflation
Problem Statement: It is not known if and to what extent
economic indicators other than interest rates, specifically
money supply, predict consumer price index in Nigeria
Purpose Statement: The purpose of this quantitative
correlational study is to examine if and to what extent economic
indicators other than interest rates, specificlaly monetary policy
predict consumer price index in Nigeria from 1963 to 2015
RQs:
RQ1: To what extent does monetary policy rate (MPR) predicts
consumer price index (CPI) in Nigeria.
RQ 2: To what extent does cash reserve ratio (CRR) predict
consumer price index (CPI) in Nigeria?
RQ 3: To what extent does liquidity ratio (LQR) predict
RQ 4: To what extent does money supply (MS) predict
Methodology/design: QT correlational
Instruments/data sources: Knoema Integrated Global Data
Analysis plan: linear regression

Revision Protocol:
Please use the following when revising your document. Doing
so will help reduce the amount of time for review and keep
moving you forward more quickly.
1. Please use track changes to address revisions but please i
delete formatting and extraneous tracking (bubbles due to you
highlighting, deleting, adding, etc.) so document is less messy.
DO NOT remove any of my comments-- bubble or in criterion
tables.
2. Comment directly in my bubbles when you have addressed
a comment, or you have a question, but do not add a new
bubble.
The Prospectus Overview and Instructions
The prospectus is brief document that serves as a road map for
the dissertation. It provides the essential framework to guide the
development of the dissertation proposal. The prospectus builds
on the 10 Strategic Points (shown in Appendix A) and should be
no longer than 6-10 pages, excluding the criteria tables and the
appendices. The prospectus will be expanded to become the
dissertation proposal (Chapters 1, 2 and 3 of the dissertation),
which will, in turn, be expanded to become the complete
dissertation (Chapters 1-5). In short, the prospectus is a plan for
the proposal. Prior to developing the prospectus, the 10
Strategic points should be reviewed with the chair and
committee to ensure the points are aligned and form a clear,
defined, and doable study. The10 Strategic Points should be
included in Appendix A of this prospectus document.
It is important to ensure the prospectus is well written from the
very first draft. The most important consideration when writing
the prospectus is using the required criteria specified in the
criterion table below each section and writing specifically to
each criterion! Also critical is for learners to follow standard
paragraph structure: (1) contains a topic sentence defining the

focus of the paragraph, (2) discusses only that single topic, (3)
contains three to five sentences, and (4) includes a transition
sentence to the next paragraph or section. The sentences should
also be structurally correct, short, and focused. Throughout the
dissertation process, learners are expected to always produce a
well-written document as committee members and peer
reviewers will not edit writing. If prospectus it is not well
written, reviewers may reject the document and require the
learner to address writing issues before they will review it
again. Remove this page and the sample criterion table below
upon submission for review.
Prospectus Instructions:
1. Read the entire Prospectus Template to understand the
requirements for writing your prospectus. Each section contains
a narrative overview of what should be included in the section
and a table with required criteria for each section. WRITE TO
THE CRITERIA, as they will be used to assess the prospectus
for overall quality and feasibility of your proposed research
study.
2. As you draft each section, delete the narrative instructions
and insert your work related to that section. Use the criterion
table for each section to ensure that you address the
requirements for that particular section. Do not delete/remove
the criterion table as this is used by you and your committee to
evaluate your prospectus.
3. Prior to submitting your prospectus for review by your chair
or methodologist, use the criteria table for each section to
complete a realistic self-evaluation, inserting what you believe
is your score for each listed criterion into the Learner Self-
Evaluation column. This is an exercise in self-evaluation and
critical reflection, and to ensure that you completed all sections,
addressing all required criteria for that section.
4. The scoring for the criteria ranges from a 0-3 as defined
below. Complete a realistic and thoughtful evaluation of your

work. Your chair and methodologist will also use the criterion
tables to evaluate your work.
5. Your Prospectus should be no longer than 6-10 pages when
the tables are deleted.
Score
Assessment
0
Item Not Present
1
Item is Present. Does Not Meet Expectations. Revisions are
Required: Not all components are present. Large gaps are
present in the components that leave the reader with significant
questions. All items scored at 1 must be addressed by learner
per reviewer comments.
2
Item is Acceptable. Meets Expectations.Some Revisions May Be
Required Now or in the Future. Component is present and
adequate. Small gaps are present that leave the reader with
questions. Any item scored at 2 must be addressed by the
learner per the reviewer comments.
3
Item Exceeds Expectations. No Revisions Required. Component
is addressed clearly and comprehensively. No gaps are present
that leave the reader with questions. No changes required.
Dissertation Prospectus
Introduction
Monetary policies promote price stability and economic growth
in Nigeria. Ajayi and Aluko (2017) stated monetary policy is
primarily concerned with the management of interest rates and
the regulation of money supply in the economy. Imoisi (2019)
claimed most nations use interest rates to achieve price
stability, and Nigeria’s goal is to achieve sustainable economic
growth. Okwori and Abu (2017) added economic growth causes

variations in interest rates. Ayodeji and Oluwole (2018)
revealed interest rates had a positive but slightly insignificant
effect on economic growth in Nigeria. Furthermore, Ufoeze,
Odimgbe, Ezeabalisi and Alajekwu’s (2018) research clearly
showed interest rates effects 98% of the variations in economic
growth in Nigeria. Interest rates have shown to significantly
effect Nigeria’s economy; however, other monetary policies
may be a predictor of Nigeria’s economic growth.
consumer price index. Imoisi (2018) claimed there is an existing
gap in the literature relative to the effectiveness of monetary
policies. Inam and Ime (2017) recommended further research to
understand if the predicative relationship between the actual
level of money supply and price stability. Lawal, Somoye,
Babajide, and Nwanji, (2018) further specified a detailed study
should be conducted showing the variations and interactions
between monetary and fiscal policies and how they predict price
stability in Nigeria. This study seeks to examine if and to what
extent economic indicators other than interest rates, specifically
money supply, predict consumer price index in Nigeria.
Criteria
Learner Self-Evaluation Score
(0-3)
Chair Evaluation Score
(0-3)
Reviewer Score
(0-3)
Introduction
This section briefly overviews the research focus or problem,
why this study is worth conducting, and how this study will be
completed.
The recommended length for this section is two to three

paragraphs.
1. Dissertation topic is introduced along with why the study is
needed.
2
2
2
2. Provides a summary of results from the prior empirical
research on the topic.
2
2
2
3. Using results, societal needs, recommendations for further
study, or needs identified in three to five research studies
(primarily from the last three years), the learner identifies the
stated need, called a gap.
2
2
2
4. Section is written in a way that is well structured, has a
APA format.
2
2
2
NOTE: This Introduction section elaborates on the Topic from
the 10 Strategic Points. This Introduction section provides the
foundation for the Introduction section in Chapter 1 of the
Proposal.
Reviewer Comments:
Background of the Problem
Price instability is a problem for developing countries. Manu
(2018) stated price instability is the main problem for Africa

and Nigeria during the past thirty years. Studies conducted by
Gertler and Gilchrist (1991), Batini (2004), Folawewo and
Osinubi (2006), Onyemu (2012), and Fasanya et al. (2013)
noted irrespective of efforts aimed at achieving
macroeconomics objectives by means of monetary policy, there
has been an unacceptable rate of inflation, especially in less
developed economies. Nigeria is not an exception to this rule.
Nigeria is an oil rich nation plagued with price instability.
Ayodeji and Oluwole (2018) stated monetary policy is the tool
used in achieving monetary and price stability. Itodo, Akadiri
and Ekundayo (2017) stated price instability tops the list of
economic challenges negatively affecting the Nigerian economic
environment. Imoisi (2019) added price instability causes the
problem of unmanageable economic growth and development in
Nigeria. Ayodeji and Oluwole (2018) stated that the Nigerian
economy has also witnessed periods of growth and shrinkage
with an unmanageable growth pattern. Imoisi (2018) stated
monetary policy if targeted directly towards inflation stimulates
growth directly. Nevertheless, the issue of whether monetary
policy effectively curtails price instability is still unsolved.
consumer price index. Imoisi (2018) analyzed how monetary
policies promoted economic growth in Nigeria from 1980-2017.
The result showed approximately 62% of gross domestic
product (GDP) is explained by variables monetary policy rate,
cash reserve ratio, liquidity ratio, and money supply. Imoisi
concluded monetary policies did not have a significant impact
on Nigeria’s economic growth in the short run but significantly
affected the country’s growth in the long run. Imoisi (2018)
claimed there is an existing gap in the literature relative to the
effectiveness of monetary policies. Ubi-Abai and Ekere (2018)
analyzed the effects of fiscal and monetary policies on
economic growth in a panel of 47 sub-Saharan African

economies from 1996 to 2016. The findings showed that fiscal
and monetary policies affected economic growth positively in
the sub-region. Ubi-Abail and Ekere stated it is not clear how
other monetary policies strategies effectively curtails price
instability in the sub-Saharan region and therefore
recommended future research examine this problem. Lawal,
Somoye, Babajide, and Nwanji, (2018) examined the impact of
the interactions between fiscal and monetary policies on stock
market behavior (ASI) and the impact of the volatility of these
interactions on the Nigerian stock market. The study analyzed
monthly data using the ARDL and EGARCH models. The results
show the interaction between monetary and fiscal policies
influence on stock market returns in Nigeria. The ARDL results
show evidence of long run relationship between stock market
behavior (ASI) and Monetary-fiscal policies. The results from
the volatility estimates showed the stock market behavior (ASI)
volatility is largely sensitive to volatility in the interactions
between the two policy instruments. Future research was
recommended to examine the relationship between monetary
policies and price variations in the Nigerian economy. This
study seeks to examine if and to what extent economic
indicators other than interest rates, specifically monetary policy
predict consumer price index in Nigeria. Comment by Roselyn
Polk: both are really good because it ssupports smaller sample
size.
Criteria
(0-3)
Chair or Score
(0-3)
Reviewer Score
(0-3)
Background of the Problem
This section uses the literature to provide the reader with a

definition and statement of the research gap and problem the
study will address. This section further presents a brief
historical perspective of when the problem started and how it
has evolved over time.
The recommended length for this section is two-three
paragraphs.
1. Includes a brief discussion demonstrating how literature has
established the gap and a clear statement informing the reader
of the gap.
2
2
2
2. Discusses how the “need” or “defined gap” has evolved
historically into the current problem or opportunity to be
addressed by the proposed study (citing seminal and/or current
research).
2
2
2
3. ALIGNMENT: The problem statement for the dissertation
will be developed from and justified by the “need” or “defined
gap” that is described in this section and supported by the
empirical research literature published within the past 3-5 years.
2
2
2
APA format.
2
2
2
NOTE: This Background of the Problem section uses
information from the Literature Review in the 10 Strategic

Points. This Background of the Problem section becomes the
Background of the Study in Note, this section develops the
foundation for Chapter 1 in the Proposal. It is then expanded to
develop the comprehensive Background to the Problem section
and Identification of the GAP sections in Chapter 2 (Literature
Review) in the Proposal.
Reviewer Comments:
Theoretical Foundations and Review of the Literature/Themes
The theoretical foundation for this research is on monetary
theory of inflation. This theory states change in money supply is
the major reason for changes in economic activities. When
monetary theory is put into practice, central banks, which
control monetary policy, can exercise a great deal of power over
economic growth rates. Monetarism refers to the followers of
M. Friedman who hold that “Only money matters” and as such,
monetary policy is a more potent instrument instruments than
fiscal policy in economic stabilization. Monetary theory states
that According to Friedman (1963), money supply is the key
factor affecting the wellbeing of the economy. According to
Ahuja (2011), monetarists argue that money has significant
effect on price level or inflation in the economy in the long run
and have real effects on output and employment in the short
run. According to Khabo, (2002), monetarists believe “money
matters” and therefore there is a direct link between monetary
sector and the real sector of the economy. Friedman (1963)
equally argued that changes in money supply will therefore have
both direct and indirect effect on spending and investment
respectively since money supply is substitutive not just for
bonds but also for many goods and services.
Review of the literature/themes.
Price stability. Imoisi (2018) mentioned Central banks must
focus on price stability as the primary objective of monetary
policy.

Monetary policy rate. Nwamuo (2018) stated monetary policy
rates moderate consumer prices, credit expansion,
exchange rates and other variables.
Liquidity Ratio. Itodo, Akadiri and Ekundayo (2017)
emphasized the importance of closely monitoring liquidity
because liquidity ratio significantly impacts the economy.
Cash Reserve Ratio. Udeh (2015) stated cash ratio is effective
for curtailing excess liquidity in the economy and can be easily
monitored daily since they are held by Central Bank of Nigeria.
Money Supply. Ibrahim (2019) mentioned targeting money
supply growth is considered an appropriate method of targeting
inflation in the Nigerian economy.
Criteria
(0-3)
Chair or Score
(0-3)
Reviewer Score
(0-3)
Theoretical Foundations and/or Conceptual Framework
This section identifies the theory(s) or model(s) that provide the
foundation for the research. This section should present the
theory(s) or models(s) and explain how the problem under
investigation relates to the theory or model. The theory(s) or
models(s) guide the research questions and justify what is being
measured (variables) as well as how those variables are related
(quantitative) or the phenomena being investigated (qualitative).
Review of the Literature/Themes
This section provides a broad, balanced overview of the existing
literature related to the proposed
research topic. It describes the literature in related topic areas
and its relevance to the proposed research topic findings,
providing a short 3-4 sentence description of each theme and
identifies its relevance to the research problem supporting it

with at least two citations from the empirical literature from the
past 3-5 years.
The recommended length for this section is 1 paragraph for
Theoretical Foundations and a bulleted list for Literature
Themes section.
1. Theoretical Foundationssection identifies the theory(s),
model(s) relevant to the variables (quantitative study) or
phenomenon (qualitative study). This section should explain
how the study topic or problem coming out of the “need” or
“defined gap” in the as described in the Background to the
Problem section relates to the theory(s) or model(s) presented in
this section. (One paragraph)
2
2
2
2. Review of the Literature Themes section: This section is a
bulleted list of the major themes or topics related to the
research topic. Each theme or topic should have a one-two
sentence summary.
2
2
2
3. ALIGNMENT: The Theoretical Foundations models and
theories need to be related to and support the problem statement
or study topic. The sections in the Review of the Literature are
topical areas needed to understand the various aspects of the
phenomenon (qualitative) or variables/groups (quantitative)
being studied; to select the design needed to address the
Problem Statement; to select surveys or instruments to collect
information on variables/groups; to define the population and
sample for the study; to describe components or factors that
comprise the phenomenon; to describe key topics related to the
study topic, etc.
2
2

2
APA format.
2
2
2
NOTE: The two parts of this section use information about the
Literature Review and Theoretical Foundations/Conceptual
Framework from the 10 Strategic Points.
This Theoretical Foundations section is expanded upon to
become the Theoretical Foundations section in Chapter 2
(Literature Review). The Theoretical Foundations and the
Literature Review sections are also used to help create the
Advancing Scientific Knowledge/Review of the Literature
section in Chapter 2 (Literature Review).
Reviewer Comments:
Problem Statement
It is not known if and to what extent economic indicators other
than interest rates, specifically monetary policy rate, cash
reserve ratio, liquidity ratio, and money supply predict
consumer price index in Nigeria. The population affected is the
Nigerian economy. The unit of analysis is annual time series
data measuring the Nigerian economy. This study would
contribute to existing knowledge on monetary policies and how
these policies predicts price stability in Nigeria. Ayodeji and
Oluwole (2018) stated the Nigerian economy has experienced
economic expansions and depressions with an inconsistent
growth. Nigeria suffers from poor monetary policies that
continuously keeps Nigerian citizenry underprivileged. This
study will be of great importance to scholars, policy makers,
economists, governmental agencies seeking to understand and
examine economic policies in developing countries that
experience inconsistent growth due to economic expansions and

depressions.
Criteria
(0-3)
Chair or Score
(0-3)
Reviewer Score
(0-3)
Problem Statement
This section includes the problem statement, the population
affected, and how the study will contribute to solving the
problem.
The recommended length for this section is one paragraph.
1. States the specific problem proposed for research with a clear
declarative statement.
2
2
2
Describes the population of interest affected by the problem.
The general population refers to all individuals that could be
affected by the study problem.
2
2
2
Describes the unit of analysis.
For qualitative studies this describes how the phenomenon will
be studied. This can be individuals, group, or organization
under study.
For quantitative studies, the unit of analysis needs to be defined
in terms of the variable structure (conceptual, operational, and
measurement).
2
2
2
Discusses the importance, scope, or opportunity for the problem

and the importance of addressing the problem.
2
2
12
The problem statement is developed based on the need or gap
defined in the Background to the Study section.
2
2
2
format.
2
2
2
NOTE: This section elaborates on the Problem Statement from
the 10 Strategic Points. This section becomes the foundation for
the Problem Statement section in Chapter 1 and other Chapters
where appropriate in the Proposal.
Reviewer Comments:
Purpose of the Study
The purpose of this quantitative correlational study is to
examine if and to what extent economic indicators other than
interest rates, specifically monetary policy rate, cash reserve
ratio, liquidity ratio, and money supply predict consumer price
index in Nigeria from 1963 to 2015. The predictor variables are
monetary policy rate, cash reserve ratio, liquidity ratio and
money supply. The criterion variable is consumer price index.
Criteria
(0-3)
Chair or Score
(0-3)
Reviewer Score

(0-3)
PURPOSE OF THE STUDY
This section reflects what the study is about, connecting the
problem statement, methodology & research design, target
population, variables/phenomena, and geographic location.
The recommended length for this section is one paragraph.
1. Begins with one sentence that identifies the research
methodology and design, target population, variables
(quantitative) or phenomena (qualitative) to be studied and
geographic location.
2
2
2
Quantitative Studies: Defines the variables and relationship of
variables.
Qualitative Studies: Describes the nature of the phenomena to
be explored.
2
2
2
format.
2
2
2
NOTE: This section elaborates on information in the Purpose
Statement from the 10 Strategic Points. This section becomes
the foundation for the Purpose of the Study section in Chapter 1
and other Chapters where appropriate in the Proposal.
Reviewer Comments:
Research Questions and/or Hypotheses Comment by Roselyn
Polk: Question: you have 4 RQs and they are set up for simple

tests, not regression. A regression would be one RQ wouldn’t
it? you don’t appear to be interested in the relationship among
the variables, rather the relationship between consumer price
index and each individual variable according to the RQs.two
things: in the proposal make sure you place null and hypothesis
under the related DQ, not separately, and 2) address the
elements below. 1) Present each variable in a list format and
providethe following information for each one:· Variable
measurement: ordinal, categorical, nominal or continuous·
Conceptual definition—cited with literature· Operational
definition—how measured 2) What is the unit of analysis and
unit of observation for the studyThis YouTube variable is very
informative (3.32
minutes)https://www.youtube.com/watch?v=fLhbRUOvrt0&t=28
s
RQ1: To what extent does monetary policy rate (MPR) predicts
consumer price index (CPI) in Nigeria.
RQ 2: To what extent does cash reserve ratio (CRR) predict
RQ 3: To what extent does liquidity ratio (LQR) predict
consumer price index (CPI) in …

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