arush@miami.edu **
http://www.icann.org/udrp/= Detail110.htm ). Decisions under the Uniform Domain-Name Dispute-Resolution Policy are subject to challenge by court act= ion. For information about a specific court action, contact the court handling t= he action or a legal search resource. The abbreviated names include: UDRP Proceedings--Arranged by Domain Name, Domain Name, Case Type, Date Commence= d, Date Decided, Proceeding Number, Status/Panel Decision, CPR=3DCPR Institute= for Dispute Resolution, DeC=3DDisputes.org/eResolution Consortium (used only for proceedings commenced before 16 October 2000, with numbers DeC AF-0060 to D= eC AF-0482), eRes=3DeResolution (used only for proceedings commenced on or aft= er 16 October 2000, beginning with proceeding number eRes AF-0483), NAF=3DNational Arbitration Forum, WIPO=3DWorld Intellectual Property Organization.
When we have to deal with a dispute resolution the cost benefit analysis behooves us to estimate the costs and the benefits. One of= the most important components of this is the appraised value of the domain name= . As we can see the CPR, NAF, and WIPO take some time to make their decisions, t= he resolutions vary from Split decision, Name transfer, Decision for responden= t, Pending, to Name transfer, etc. The decision for the domain 7cegetel.com, UDRP, started at 14-Oct-03, WIPO D2003-0799, and is still Pending, as of 6-15-08, how cost effective is this? Some of these domain na= me decisions are pending for years' such as 4.706607, all the way up to 6.0216= 75, and maybe forever. Is it wort= h it?
To help answer such questions to decide whether all pa= rties may be better off settling this question we apply an appraisal model. We can compare the value of the do= main name in an objective way include it in a cost benefit analysis and reach a = more cost effective decision than simply going to ICANN. = If a domain name is worth $300 it = may not be cost effective to spend $1,000,000&= nbsp; on lawyers, litigations, arbitration, and wait forever, in contrast,= if it is appraised at $2,000,000, such as computers.com, it maybe a different story altogether.
“Your domain name could be worth thousands of do= llars and may even be tax deductible,” especially if you donate it to qual= ified charity organization, but what is the deduction?&n= bsp; For this you may need to appraise the domain name. Likewise, if you submit the domain= to an auction, you may want to setup a minimum value acceptable for bids. If you appraised the domain at way= more than $1,000,000, like TigerDirect.com’s computers.com, you may want to set it up a minimum bid value, accepting no bids lower than such a threshol= d, eventually auctioned for over $2,000,000. &= nbsp; &nbs= p; &= nbsp;
http://www.domainnamenews.com/domain-aftermarket/mo=
niker-silent-auction-ends-tomorrow/127
reports in “Moniker Si=
lent
Auction Ends Tomorrow” by
Adam Strong in Categories: Domain
Aftermarket the following: “Did
you miss the live auction at TRAFFIC in NYC ? Well, the second best thing i=
s Monikers
silent auction. To some, it might be even better than the li=
ve
auction as they diligently scour the lists looking for the hidden gems, hop=
ing
that others won’t see them as well. I’ve been through the list
tonight, and I’ll probably be making a few bids myself. If you haven&=
#8217;t
been through the list, you better get on it. You don’t have much time
left. The auctions will be ending tomorrow. So far the bids seem low in vol=
ume,
but I’d guess around 11am EST, when the auctions begin to close, there
will be a frenzy of snipe bids being made.” Adam Strong fails to make a clear
distinction between sellers and buyers, as we find out. For seller the live auction is a g=
reat
advantage, since bidders may excite each other. They keep bidding and whip the aud=
ience
into a “buyers’ frenzy” over-bidding and raising the pric=
e of
the domain. In contrast, for
buyers, the absence of such “buyers’ frenzy” is an advant=
age.
Monikers
silent auction,” will be incorrect, it will be false.<=
span
style=3D'mso-spacerun:yes'> Rather, it will be the 1st best
thing for buyers is Monikers silent auction, and the sec=
ond
best thing is Monikers live auction. In contrast, for sellers, it is co=
rrect
that “the second best thing is Monikers silent auction” while=
the 1st
best thing is Monikers live auction.
http:/=
/www.domaininformer.com/news/press/070216Moniker.html
in “About Moniker,”<=
/strong>
describes Moniker as the company that “pionee=
red
the industry's first domain appraisal formula. It is considered the industr=
y's
premier marketplace to buy and sell domain names.” To calculate the full value of the
domain name plus the lost advantage of missing the live auction, one should=
use
such a “domain appraisal formula.” For the present study such a formu=
la is
beyond the scope of the current issue, we may deal with it in a future stud=
y.
LIVE
VERSUS SILENT AUCTIONS
htt=
p://marketplacepro.moniker.com/auction/auction_faq.html#ql1
describes the relationship between live and silent auction on moniker.com
auctions.
“Live Domain Auc=
tion- What
is a Live Domain Auction? some
of the most premier domains are available for sale to the highest bidder.
Moniker partners … bring buyers and sellers together in the top
marketplace for premier domains. The Live Domain Auctions are standard open=
cry
auctions, managed by a professional auctioneer. Bidders are required to
register… in advance. This may be in addition to any event registrati=
on
required for admittance to the conference in which the auction is being hel=
d.
Please see registration requirements for each individual auction for comple=
te
details. Moniker also produces Silent Domain Auctions. These are often run =
in
conjunction with Live Domain Auctions, but may also stand on their
own…...
How will the domains g=
oing
into the live auction be determined?
In most
cases, Moniker will be operating a Silent Auction on conjunction with the L=
ive
Auction. Moniker selects the master list of domains to be auctioned in both
platforms based upon our evaluation of sales potential. This criteria may
include: Appraised value, Revenue generation and/or traffic history (Please
note, only DomainSponsor
monetization results will interface directly to the names in the Live Aucti=
on
and in Marketplace. Seller-submitted information may also be available in t=
he
comments section of the domain profile, but cannot be verified by Moniker. )
Marketability and appreciation potential, All brand-worthy names with no
minimum reserve price will also be considered.
Once the
master list for both Live and Silent Auctions has been selected, Moniker th=
en
applies more detailed analysis and statistics, and refines the list to iden=
tify
the best 200-300 domain values that will go into the live event. The total
number of domains offered in the live event is dependent on the time allott=
ed
and the type of audience attending.
In addi=
tion,
Moniker may include domains that receive greater interest as indicated by
potential bidders attending the event. Event attendees will be able to revi=
ew
the domains submitted in advance on the main Moniker Auction inventory syst=
em,
and may choose to indicate their interest using the designated links on a g=
iven
auction lot. Domains that receive the highest interest from bidders may be
included on the Live Auction. Moniker also reserves the right to add or rem=
ove
any domains from the final Live Auction list, based upon our evaluation of
sales potential. As the reader can see the auctioneer has a lot of discress=
ion
in deciding which domains will be included in which auction type, and that =
may
raise some disputes. Especial=
ly, if
this assumption affects the price on the domain and the ROI of the sellers =
and
buyer…
How do I know if my na=
mes
are good candidates for the auctions? What's the best way to attract potent=
ial
buyers in the auctions? best
position your names for selection and sale in Monikers Live and Silent Doma=
in
Auctions: Get your parked dom=
ains
running on the DomainSponsor
monetization service. Using DomainSponsor allows Moniker to verify any
revenue or traffic statistics. DomainSponsor statistics will be directly
interfaced to the auction names and will be published for potential buyers =
to
review. Have your domai=
ns
appraised using the Moniker
appraisal service. Moniker's service is currently the only system
recognized by the IRS and domain financing companies. Transfer registration of your doma=
ins to
Moniker. As the vendor of choice for the Live Auction, once a domain is sold
via auction, the closing and transfer of assets can be completed right away=
if
the domains are already at Moniker. “
DOMAIN
NAME LIVE AND SILENT AUCTION PRICE AND COST FORECASTING METHODOLOGY HYPOTHE=
SIS
TESTING
This
study deals with price and cost forecasting methodology. The sellers on live and silent dom=
ain
name auction want to maximize the price of the domain name that they are
selling. In contrast, the buy=
ers
want to minimize the same item, except that for them it constitutes the cost
rather than the price. We will
review the methodologies associated with such forecast and estimates. Managerial auditors frequently rev=
iew
cost forecasting methods. As
Rushinek and Rushinek suggest “Managerial auditors' mixed cost forecasting assumpt=
ion
departure error estimates for litigation and …” in “Managerial
Auditing Journal, 1997” the forecaster may make certain assumptions t=
hat
sometimes are not perfectly met.
Such departure from the assumptions can be corrected (emeraldinsight=
.com
... Page 7. [ 291 =
] Avi
Rushinek and Sara Rushinek) by the Managerial auditors’
mixed cost forecasting, by assumption departure error estimates for litigat=
ion purposes.
This
article deals with “Managerial auditors’ mixed cost forecasting
assumption departure error estimates for litigation and professional liabil=
ity
risk reduction” in the “the electronic security industry.”=
; Our current study is not limited t=
o any
industry, although, the internet industry is very close to the electronic
security industry. As the art=
icle
states “Managerial auditors (MAs) are frequently relying on mistaken
mixed costs (MC) forecasting assumptions. Two such assumptions are that MC =
are
linear within the relevant range and variable costs vary proportionately wi=
th
activity levels. Departure from these assumptions can lead to understating
expenses, overstating profits, and even to fraud, litigation, and mounting
professional liability risk. Develops a forecasting model of mixed cost err=
or
estimates, or error difference (ED). MAs can reduce the risk of litigation =
and
professional liability by including such an ED in the internal control
structure of a company. Uses Sensormatic Corporation and the electronics
security industry as sample case because of the recent prominence of litiga=
tion
and professional auditor liability verdicts. Focuses on the electronics sec=
urity
industry, but some of the findings may apply to other industries.”
Like
another study we are “forecasting sales and expenses” like the
study about “Forecasting sales, expenses and stock market values=
by
quarterly financial statement ratio analysis” by A Rushinek, SF
Rushinek - Managerial Auditing Journal, 1995 - emeraldinsight.com.
As
this article describes “sales forecasting models…forecast stand=
ards
of sales and costs, and applies them to a budgeted financial statement vari=
ance
analysis for the EE (electronic and electrical) industry. Develops the conc=
ept
of industry base standards, integrating them into the more traditional
statistical and accounting concepts of quality control standards. Provides =
an
implementation example, and reviews possible improvements to the current
methodology and approach. Uses a similar methodology to forecast the stock
market value with some exceptions. Models sales and costs of an individual
company and an industry based largely on aggregate industry databases. For =
this
purpose, uses a multivariate linear trend regression analysis for the sales
forecasting model. Defines and tests related hypotheses and evaluates their
significance and confidence levels. For an illustration uses the EE industry
and the APM company. Also demonstrates a microcomputer-based FSRA software =
that
speeds, facilitates, and helps to accomplish the stated objectives. The FSRA
software uses industry financial statement databases, computes financial ra=
tios
and builds forecasting models.”
Likewise,
the current study deals with “sales forecasting models…forecast
standards of sales and costs, and applies them to a … variance analys=
is
for the” internet and domain name auction “… industry.
Develops the concept of industry base standards, integrating them into the =
more
traditional statistical and accounting concepts of quality control standard=
s.
Provides an implementation example, and reviews possible improvements to the
current methodology and approach. Uses a similar methodology to forecast th=
e ..
value” of domain names “with some exceptions. Models sales and
costs of an individual company and an industry based largely on aggregate i=
ndustry
databases. For this purpose, uses a multivariate linear trend regression an=
alysis
for the sales forecasting model. Defines and tests related hypotheses and
evaluates their significance and confidence levels. For an illustration use=
s the”
domain name auction “… industry and the” …
Moniker.com…” company. Also demonstrates a
microcomputer-based” domain name opportunity cost “… soft=
ware
that speeds, facilitates, and helps to accomplish the stated objectives. Th=
e …
software uses industry financial statement data” … “compu=
tes
financial ratios and builds forecasting models.”
Rushinek et. al. in “The Construction of an International Investors̵=
7;
Perception Model for Corporate Published Forecasted …̶=
1;
by A
RUSHINEK, LS CHANG, KS MOST (MANAGERIAL AND DECISION ECONOMICS, 1983=
-
doi.wiley.com) calculates “the mean utility score of cost of
goods sold (COGS) is 64.7739 for buying decisions and slightly higher, 65.2=
024,
for ...” differentiates between the buyers and sellers. It discusses the following issues
“Although the utility of corporate published forecasted financial
reports to investors has been investigated, the findings are inconclusive.
These inconclusive findings may have resulted from the ambiguity of defining
the forecasted financial reports or its components. The clarification of the
definition of forecasted financial reports or its components is the main
objective of the present study. In addition, an investor's utility function=
is
used to measure the utility of such reports for investors regarding the ent=
ire
forecasted financial reports or their components.” Likewise, this study breaks down t=
he
auction into its components,
namely, the silent versus the live type of domain name auction, outlining t=
he
difference in their affect on the price.
Looking
at the domain name auction from the viewpoint of an “EDP auditors' role in evaluating computerized accou=
nting
information systems efficiency by queuing, …=
” as Rushinek &
Rushinek explain ( Proceedings of the fourteenth
SIGCSE technical symposium on …, 1983 - portal.acm.org)=
“...<=
/b>
scant at this point to forecast which routines” optimizes t=
he
objective of domain name auctions seller or buyer. An “EDP (Electronic Data Processing)=
auditors'
role in evaluating computerized accounting information systems efficiency by
queuing, simulation and statistical models” will include risk
assessment. This risk assessm=
ent is
the focus of the present study with an X Coefficient representing the risk =
of
suboptimal selection of the type of auction for domain name sales. The risk of sub-optimizing the auc=
tion
type equal to the X Coefficient for 1 domain name selling price (http://doi.acm.org/10.1145/952978.801048).
“This study is a summary of the various relevant aspects related to
evaluating system efficiency in computerized accounting information systems.
During recent years a vast body of knowledge central to the problem of comp=
uter
performance evaluation has accumulated. Unfortunately, however, the work on=
the
subject demonstrates enormous disparity. On the one hand, one finds numerous
reports and documents that present masses of empirical data obtained from
measurement or simulation. On the other hand, theoretical papers are, more
often than not, couched in advanced mathematics, not easily accessible to
engineers and/or Electronic Data Processing (EDP) Auditors. Among a number =
of
mathematical disciplines related to system modeling, “queuing
model,”“simulation method,” and “statistical
analysis” are the most important quantitative techniques. This paper
provides a cohesive introduction to the modeling and analysis techniques for
evaluating system efficiency. These techniques will certainly be applicable=
to
modeling activities of complex systems in general, and not merely computer
systems.” Likewise, the
current study develops a statistical model for cost forecasting of recovery=
for
the plaintiff and the cash outlay for the defendant, promoter, seller or bu=
yer
of domain names in live or silent auctions.
In “The=
control
of computerized information relating to branch manpower management follow-up
systems for banks Source ACM =
SIGCPR
Computer Personnel archive,” (Volume 12 , Issue 1 (April 1989) Pages: 1 - 12 Year of
Publication: 1989 ISSN:0160-2497) A. Rushinek S. Rushinek (Publisher ACM
Accounting
Information Systems (AIS) in general. Moreover, they are as applicable to a=
computerized
follow-up survey as to the initial manual system survey. In fact, this study
presents an approach for conducting a follow-up program using an on-line=
221;
system. Likewise, the domain =
name
auction is an online AIS using a “linear model to measure and
forecast.” “The
application of model-sampling and its effects in relation to accuracy”=
; is
also an issue and a limitation in the current study. Keep in mind that it is based on a
single sample of a single auction.
To be more accurate and more robust, it should be built into multiple
auctions, with a survey that lets users evaluate issues of concern includin=
g,
but not limited to sub-optimization of the auction type, whether it is live=
or
silent, and the monetary effect of the choice.
In a hypertex= t https://www.moniker.com/pub= /ShoppingcartOrderProductsServlet?cmd=3Dorderproducts&cmdaction=3Dappra= isal, Moniker.con lists the components of domain name appraisal: “Yo= ur domain name could be worth thousands of dollars and may even be tax deducti= ble! Join many others who discovered what their domains were worth using our Dom= ain Name Appraisal Service! Your domain will be appraised using our custom doma= in appraisal software based on 17 separate factors including marketability, br= and recognition, unique type in traffic, dotValue and comparison with other dom= ain name sales and appraisals, in addition to the following criteria:
Due to the fact that companies
like Moniker.com do not release their model, and charge for their service,
this study provides another opinion and methodology for additional
objectivity and perspective, using open source and disclosed methods, whe=
re plaintiffs,
defendants, courts, as well as web masters, and web owners may apply the
model to gain insight into related decisions. LEAST SQUARE LIN=
EAR
AND NON-LINEAR UNIVARIATE AND MULTIVARIATE REGRESSION ANALYSIS
In "The Eff=
ects
of Communication Monitors on User Satisfaction," Rushinek, Avi and
Rushinek, Sara F. discuss “Hypothesis Testing” as it relates =
to
"Multiple Regression Analysis; Predictor Variables; Statistical
Analysis" as we are doing in the current study (Information Processi=
ng
and Management, v22 n4 p345-51 1986). Both studies use "multiple
regression analysis," and "variables with the most significant =
and
least significant effect" on the independent predicted variable. In “Health Marketing and Sales Distribution
Software Related to Computer User Satisfaction” A Rushinek, S Rushinek (HEALTH MARKETING
QUARTERLY, 1985 - haworthpress.com) discuss the questions of choosing independent variabl=
es,
based on the literature and the use of multiple regression anal=
ysis.
In “Health Policy,” Rushinek=
and
Rushinek review similar topics (1985 ;4 (3):199-219 10300016) (P,S,E,B) “Health and medical software related to computer user
satisfaction: an interactive online expert system using diagnostic audit
trails through telecommunication networks.”
Much like A
Rushinek, S
F Rushinek the current study deals with
“The selection … many variables. This study analyzes the
influence of … predictor variables on overall” Price “as
determined by multiple regression. The null hypothesis, that multiple
correlation coefficients are zero, is rejected. The alternative hypothesi=
s is
accepted. This study confirms the theories that suggest that …
independent variables”… are the major determinations of overa=
ll”
Price, the dependent predicted variable. Both studies discuss Mesh-terms,
such as: Computers; <=
span
style=3D'font-size:12.0pt;font-family:"Times New Roman","serif";mso-farea=
st-font-family:
"Times New Roman"'>Consumer Satisfaction;
Diagnosis,
Computer-Assisted, standards;
Evaluation
Studies; Microcomputers; In http://www.informaworld.com/smpp/content~content=3Da74=
6867740~db=3Dall “Statistical/mathematical software related to
computer user satisfaction: an interactive online expert system using
diagnostic audit trails through telecommunication networks,” Avi Rushinek and Sara F. Rushinek=
(DOE 10.1080/0020739860170304
International Journal of Mathematical Education in Science and Technology,
Volume 17, Issue 3 May 1986 , pages 285 – 303) describe the “=
The
selection of a statistical/mathematical software (SMS). This study analys=
es
the influence of SMS predictor variables on satisfaction as determined by
multiple regression. The null hypothesis, that multiple correlation
coefficient is zero, is rejected. The alternative hypothesis is accepted.
This study confirms the theories that suggest that SMS ease of operation,
computer reliability, and ease of programming are the major determinants =
of
computer user satisfaction. The factors identified were used in the desig=
n of
an SMS Expert System based on Artificial Intelligence (ESAI). Likewise, in the present study, =
we are
appraising the value of domain names, the dependant predicted variable ba=
sed
on the list of independent variables.&nb=
sp;
We use the same multiple regression analysis, testing the same null
hypothesis and alternative hypothesis.&n=
bsp;
We are rejecting the null in favor of the alternative hypothesis to
develop a domain name appraisal system.&=
nbsp;
The biggest difference is the web service aspect of the current de=
velopment,
not only do we want the system to run on a local machine like a spreadshe=
et,
we also want to run it on a server as a service. That way the appraisal system wi=
ll run
on the server within a web browser. In http://portal.acm.org/citation.cfm?id=3D1113487.111349=
3 “An evaluation of MINI/MICRO systems: an empiri=
cal
multivariate analysis (ACM SIGMIS Database, Volume 14 , Issue 4 Pages: 37 - 47 1983, ISS=
N:0095-0033)
Avi Rushinek and Sara F. Ru=
shinek
(Publisher ACM New York, NY=
, USA)
discuss the “Many variables are important in determining the
performance of computer systems. Potential buyers and current users of th=
ese
systems would like to know more about these variables or factors which can
help them decide about a computer system they would like to acquire for t=
heir
organization. Large numbers of variables could confuse the manager or
decision-maker and these variables may give redundant information. This p=
aper
tests the hypothesis of potential redundancy among the computer performan=
ce
or satisfaction variables. The null hypothesis was rejected in favor of t=
he
alternative hypothesis, concluding that the initial fourteen variables we=
re
redundant and three factors could replace them. The importance of some of
these variables is discussed in detail. These three non-redundant factors=
are
designed to be incorporated in an interactive online EDP (Electronic Data
Processing) Auditing Software System for Mini/Micro Computers. The audito=
r of
a mini/micro computer system could evaluate the efficiency and the
effectiveness of the system through the information accumulated by this
interactive software package.”&nbs=
p;
The current system is very similar except that it is the DNS (Doma=
in
Name Server) system and the domain name values. Many variables are important in
determining the performance of DNS systems, the domain name and its value=
is
one of these variables. Potential buyers and current users of these syste=
ms,
web surfers, web masters, web owners, would like to know more about these
variables or factors which can help them decide about a domain name or do=
main
names to use, or which domain names they would like to acquire for their
organization. Large numbers of variables could confuse the manager or dec=
ision-maker
and these variables may give redundant information. This paper tests the
hypothesis of potential redundancy among the domain name appraised price
variables. The null hypothesis was rejected in favor of the alternative
hypothesis, concluding that the initial variables were redundant and fewer
factors could replace them. The importance of some of these variables is
discussed in detail. These fewer less redundant factors are designed to be
incorporated in an interactive online domain name appraisal system Softwa=
re
System for Mini/Micro and Mainframe Computers, running online and offline.
The web master or website owner could evaluate the efficiency and the
effectiveness of the domain name through the information accumulated by t=
his
interactive software package.
Likewise, it can help in dispute resolution, where the parties do =
not
want to sell the domain name, but the need to appraise its value. For example, for tax purposes, t=
he IRS
(Internal Revenue Service) can assess the taxes based on the appraised va=
lue,
without having to sell the domain name. For example the domain name
computers.com sold in the current auction by TigerDirect.con, who has bee=
n in
the computers business for a long time.&=
nbsp;
However, in the current auction TigerDirect.com decided to sell th=
is
domain name for the higher price in this auction. Owners of such domain names have=
to
constantly appraise its value until it reaches such a high value that it =
pays
to sell it rather than use it, in their business, which remains the compu=
ter
business. For now it seems =
that
they have decided to keep the TigerDirect.com, Compusa.com, and other dom=
ain
names to run their business, and in the mean time sell the computers.com =
to
the highest bidder. In http://www.emeraldinsight.com/10.1108/0263557941005582=
8 "Improving Profitability with
Segmented Income Statements for Local Area Networks" the authors Avi
Rushinek, Sara F. Rushinek (Industrial Management & Data Systems Year:
1994 Volume: 94 Issue: 2 Page: 7 - 15 DOI: 10.1108/02635579410055828Publi=
sher:
MCB UP Ltd) “Suggests that, though local area networking is one of =
the
fastest growing segments in the computer industry, the selection of
appropriate features for inclusion in local area networks (LANs) can be
confusing for both vendors and end-users. Describes a system that measures
the degree to which specific LAN features meet end-user needs and contrib=
ute
to profitability. Reveals that, in the analysis, the features file securi=
ty
and remote access had positive effects on the overall LAN price. Provides=
a
methodology for selecting the most profitable features, such as remote ac=
cess
and gateways to PC networks, to include in a contemplated LAN product lin=
e.” Likewise today the internet and =
the
domain name is one of the fastest growing segments in the computer indust=
ry,
the selection of appropriate domain names is one of the most important fe=
atures
for inclusion in any business and can be confusing for both vendors and
end-users. Describes a system that measures the degree to which specific =
domain
name features meet end-user needs and contribute to profitability. Reveals
that, in the analysis, the features of a domain name, had positive effect=
s on
the overall domain name appraised price. Provides a methodology for selec=
ting
the most profitable features, such as domain name appraiser model. We discuss Computer networks,
Computers, Employee communications, Local area networks, Personal compute=
rs,
Profitability, Segmental reporting, and its cornerstone the price of a do=
main
name. |
NULL HYPOTHESIS STATES THAT THE R SQUARE AND THE X COEFFICIENT= S ARE BOTH EQUAL TO ZERO AND INSIGNIFICANT
It is virtually impossible to reduce all these variables to one
variable, but what is obvious is the omission of the type of auction as a
factor that determines the value of a domain name. Therefore, we want to focus on this
single factor to find out whether it is a significant factor. We start with the null hypothesis =
that
it is not a significant factor, due to the fact that nobody, so far, has
considered it a significant factor, including Moniker.com. Thus, our null hypothesis states t=
hat
the R Square and the X Coefficients are both equal to zero, and thus the ty=
pe
of auction, whether it is silent or live is insignificant in its affect on =
the
appraised value of domain name sold in an auction. In contrast, the alternative
hypothesis states that it is significant, and therefore should be part of t=
he
optimization of the seller and buyer trading strategy for domain name aucti=
ons,
and dispute resolutions.
&=
nbsp;
Data
Collection
We col=
lected
the data from the Master Auction Inventory of T.R.A.F.F.I.C. MIAMI 2007,
including in it is a Silent Auction: October 12th-18th, 2007, and a Live
Auction: October 12th, 2007 2pm-6pm EST, by Moniker.com. Moniker.com notes that the
"Opening Bid Range" does not indicate value of the domain name or
potential purchase price. Likewise, Inventory is subjec=
t to
change, and the “Statistic information has not been verified, please
consider this data as an ESTIMATE ONLY! Moniker cannot guarantee its accura=
cy.” Moniker adds that “This is t=
he
tentative list of all domains to be included in the 2007 Moniker/T.R.A.F.F.=
I.C.
Miami Live and Silent Auctions. Therefore, the current study is subject to =
the
same disclaimers. &n=
bsp; =
;
This d=
ata
contains the following information: Description and Domain names. For example, the data includes Ven=
ue/Sale
Price of $2,200,000 for computer.com the Opening Bid Range, of $1MM - $5MM,=
the
domain extension “.com,” the Length of 8 characters, the Catego=
ry
of the domain name, Computing, Uniques Per Mo. (estimate) 20,000, Rev Per Mo. (estimate) 15,000, COMPETE Page Count of 1,145, COMPET=
E Page
Rank of 805, Google pr (Page Rank) of 5 (1 the lowest up to 5 the highest),=
GOOG
Search Domain Results of 486,000, GOOG Search Name Results of 581,000,000, =
MSN
Search Domain Results of 1,112,795,762, MSN Search Name Result 1,086,237,89=
0,
Y! Search Domain Results N/A, Y! Search Name Results N/A, Alexa Rank of 7,4=
16,303,
URLTRENDS Description of “ Computer parts PC Components laptop comput=
ers
notebooks motherboards processors CPU?s and much more at TigerDirect.com, L=
inks
IN of 1,703, Links OUT of 149, Online Since 34,557, which is the Julian dat=
e of
Thursday, August 11, 1994.
Likewise, additional information on the type of the auction, such as=
whether
it was live or silent.
&=
nbsp;
Data
Analysis
=3D=3D=
=3D=3D=3DTable 1
Insert here
&=
nbsp;
|
Descriptive Statistics |
Table 1 |
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Mean |
$ &nbs=
p;
16,969 |
Mean |
Mean |
Standard Error |
$ &nbs=
p;
4,375 |
Standard Error |
Standard Error |
Median |
$ &nbs=
p;
1,770 |
Median |
Median |
Mode |
$ &nbs=
p;
360 |
Mode |
Mode |
Standard Deviation |
$ &nbs=
p;
105,003 |
Standard Deviation |
Standard Deviation |
Sample Variance |
$11,025,633,518 |
Sample Variance |
Sample Variance |
Kurtosis |
$ &nbs=
p;
335 |
Kurtosis |
Kurtosis |
Skewness |
$ &nbs=
p;
17 |
Skewness |
Skewness |
Range |
$
2,199,700 |
Range |
Range |
Minimum |
$ &nbs=
p;
300 |
Minimum |
Minimum |
Maximum |
$
2,200,000 |
Maximum |
Maximum |
Sum |
$
9,774,266 |
Sum |
Sum |
Count |
$ &nbs=
p;
576 |
Count |
Count |
Largest(1) |
$
2,200,000 |
Largest(1) |
Largest(1) |
Smallest(1) |
$ &nbs=
p;
300 |
Smallest(1) |
Smallest(1) |
Confidence Level(95.0%) |
$ &nbs=
p;
8,593 |
Confidence Level(95.0%) |
Confidence Level(95.0%) |
Differences=3DLive-Silent |
Mean |
$ &nbs=
p;
55,613 |
$ &nbs=
p;
51,053 |
Mean |
$ &nbs=
p;
4,561 |
||
|
Standard Error |
$ &nbs=
p;
17,306 |
$ &nbs=
p;
16,192 |
Standard Error |
$ &nbs=
p;
1,114 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Median |
$ &nbs=
p;
13,000 |
$ &nbs=
p; 12,058
|
Median |
$ &nbs=
p;
943 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Mode |
$ &nbs=
p;
8,000 |
$ &nbs=
p; &=
nbsp;
7,640 |
Mode |
$ &nbs=
p;
360 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Standard Deviation |
$ &nbs=
p;
204,768 |
$ &nbs=
p;
181,511 |
Standard Deviation |
$ &nbs=
p;
23,257 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Sample Variance |
$41,929,833,151 |
$ &nbs=
p;
41,388,937,598 |
Sample Variance |
$
540,895,554 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Kurtosis |
$ &nbs=
p;
90 |
$ &nbs=
p; &=
nbsp;
(106) |
Kurtosis |
$ &nbs=
p;
195 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Skewness |
$ &nbs=
p;
9 |
$ &nbs=
p; &=
nbsp; (5) |
Skewness |
$ &nbs=
p;
13 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Range |
$
2,199,250 |
$ &nbs=
p;
1,829,550 |
Range |
$ &nbs=
p;
369,700 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Minimum |
$ &nbs=
p;
750 |
$ &nbs=
p; &=
nbsp;
450 |
Minimum |
$ &nbs=
p;
300 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Maximum |
$
2,200,000 |
$ &nbs=
p;
1,830,000 |
Maximum |
$ &nbs=
p;
370,000 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Sum |
$
7,785,850 |
$ &nbs=
p;
5,797,434 |
Sum |
$ &nbs=
p;
1,988,416 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Count |
$ &nbs=
p;
140 |
$ &nbs=
p; &=
nbsp;
(296) |
Count |
$ &nbs=
p; 436
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Largest(1) |
$
2,200,000 |
$ &nbs=
p;
1,830,000 |
Largest(1) |
$ &nbs=
p;
370,000 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Smallest(1) |
$ &nbs=
p;
750 |
$ &nbs=
p; &=
nbsp;
450 |
Smallest(1) |
$ &nbs=
p;
300 |
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
Confidence Level(95.0%) |
$ &nbs=
p; 34,217
|
$ &nbs=
p;
32,028 |
Confidence Level(95.0%) |
$ &nbs=
p;
2,189 |
&=
nbsp;
Table 1
includes the Descriptive Statistics, including the sale price and the aucti=
on
type, Live or Silent auction or both.
It shows the mean, Median, Mode, the Largest(1) of the prices of $ 2,200,000, for the computer.com d=
omain
name, etc. Some of the important issues that it reveals is the great differ=
ence
in the means of the prices. T=
he
mean of the live auction is about $55,613, deduct $51,053 the difference, and
you get the mean of the Silent auction at $4,561. This raises one of the main issues=
in
this study, the impact of the type of auction on the value of the auction
price. To further test this
difference look at the next table.
=3D=
=3D=3D=3DTable 2
insert here
&=
nbsp;
|
Table 2 |
||||||
|
t-Test: Two-Sample Assuming
Unequal Variances |
Table 2 |
|||||
|
Live |
Silent |
|||||
|
Mean |
$ &nbs=
p;
55,613 |
$ &nbs=
p;
4,561 |
||||
|
Variance |
$
41,929,833,151 |
$
540,895,554 |
||||
|
Observations |
&nbs=
p; &nbs=
p; 140
|
&nbs=
p; =
436
|
||||
|
Hypothesized Mean Difference |
$ &nbs=
p; &=
nbsp;
- |
|||||
|
df |
t Stat |
P(T<=3Dt) one-tail |
t Critical one-tail |
P(T<=3Dt) two-tail |
t Critical two-tail |
|
=
REJEC=
TING
THE NULL HYPOTHESIS THAT THERE IS NO DIFFERENCE IN THE PRICE OF SILENT AND =
LIVE
AUCTIONS
Table=
2
shows the t-Test for Two-Samples the live and silent option, Assuming Unequ=
al
Variances, as we can see in the descriptive statistics. This table includes=
a
confirmation of the Mean of $55,613, $4,561, respectively for the Live and
Silent auctions. The variance=
is
different as we can see with the variance of $41,929,833,151, and $ 540,895,554, and the Observat=
ions
of 140 domains sold in the live auction versus 436 domains sold in the sile=
nt
auction. It states the null
hypothesis stating that the Hypothesized Mean Difference=3D $0. Finally, it concludes with the t S=
tat of
2.943899, P(T<=3Dt) one-tail of 0.001898, which is less than .05, and th=
us
reject the 1 tail null hypothesis, with a t Critical one-tail of 1.655811.<=
span
style=3D'mso-spacerun:yes'> Likewise, the P(T<=3Dt) two-tai=
l 0.003796,
reject the 2 tails null hypothesis, with a t Critical two-tail of 1.977054,=
in
favor of the alternative hypothesis stating that there is a significant
difference in the prices, between the silent and live auctions. &=
nbsp;
Table =
3
|
|
||||
|
|||||
|
Regression
Statistics |
|
||||
|
Multiple R |
93.082% |
|
|||
|
R Square |
86.643% |
|
|||
|
Adjusted R Square |
83.929% |
|
|||
|
Standard Error |
92793.74 |
|
|||
|
Observations |
103 |
|
|||
Table 3
shows the SUMMARY OUTPUT including the Regression Statistics of Live
and Silent auction models with and without Intercept, respectively. The Multiple R, the R Square of 86.643%<=
span
style=3D'font-size:8.0pt;font-family:"Arial","sans-serif"'>, indicating tha=
t the
type of the auction explains about 87% of the sample auction price, this ca=
n be
a large amount when dealing with domains that sell for millions of
dollars. The Adjusted R Squar=
e of 83.929%,
shows about 84% accuracy in the population when we adjust the R Square. This
sample includes all the observations of a total of 103 out of 576. We have removed the rest of the
observations that were taken from the silent auction, due to the statistica=
lly
significant difference between
silent and live auctions. Fur=
ther,
we have removed observations with incomplete, keeping the standard deviant
while removing the outliers.
The le=
ast
square linear multivariate model, appraising Y, the predicted dependent dom=
ain
value as a function of the Independent Predictor X1-11 variables, states the
following:
|
+X Coefficient1 |
+X Coefficient2 |
+X Coefficient3 |
+X Coefficient4 |
+X Coefficient5 |
+X Coefficient6 |
+X Coefficient7 |
+X Coefficient8 |
+X Coefficient9 |
+X Coefficient10 |
+X Coefficient11 |
|
|
Intercept |
Info |
Links OUT |
Online Since |
GOOG Search Domain Results |
Links IN |
com |
Length |
MSN Search Domain Results |
GOOG Search Name Results |
MSN Search Name Result |
org |
Y=3DIn=
tercept+X
Coefficient1*Variable1+X Coefficient2*Variable2+X Coefficient3*Variable3+X
Coefficient4*Variable4+X Coefficient5*Variable5+X Coefficient6*Variable6+X
Coefficient7*Variable7+X Coefficient8*Variable8+X Coefficient9*Variable9+X
Coefficient10*Variable10+X Coefficient11*Variable11
Where
variables 1-11 after the Intercept include the following: Info, Links OUT Online
Since, GOOG Search Domain Results, Links IN, com, Length, MSN Search Domain Results, GOOG Sea=
rch
Name Results, MSN Search Name Result, org, in increasing order of their P
Values, and statistical significance.
The only valid model is this multivariate model due to the correlati=
on
between the dummy variables that represent the mutually exclusive type of d=
omain
name extension (.com or .net), for the domains was sold in the live
auction. The intercept was
statistically significant and therefore we did not drop it.
ANOVA
REJECTS THE NULL HYPOTHESIS THAT THE PREDICTOR INDEPENDENT VARIABLES HAVE NO
EFFECT ON THE DOMAIN NAME PRICE, THE DEPENDANT PREDICTED VARIABLE.
Table =
4
|
ANOVA |
Table 4 |
||||
|
|
df |
SS |
MS |
F |
Significance F |
|
Regression |
Residual |
Total |