arush@miami.edu **
COST– A UNIVARIATE
LINEAR REGRESSION FORECASTING MODEL FOR DAMAGE CACLULATIONS
1) What is the key issue to be addressed by this
study?
This
study address the key issue of intangible property appraisal, assessment,
price, and cost forecasting, projections, planning and budgeting of internet
auctions practices. Although,=
the
study uses domain names as an example of an intangible property appraisal, =
it
may apply to any property, including real-estate foreclosures in a volatile
market. This study deals with
intangible properties in particular, since they are the most difficult and
subjective appraisals.
2) What methods do you propose to study the que=
stion?
The
method that this study proposes to appraise properties objectively and
accurately is by placing them in an online internet auction. Using regression forecasting techn=
iques,
the appraiser can get an objective, statistically significant, appraisal of=
the
property and use it to mark to market properties with volatile values, such=
as
real-estate foreclosures, marketable securities, patents, etc.
3) What findings you expect to be forthcoming f=
rom
this work?
The
authors expect to develop a model that will project the values of domain na=
mes
in an online internet auction that is statistically significant at the .o5
level. Such model can be used=
for
the tax authorities (US IRS United States Internal Revenue Service),
bankruptcies and legal settlements as well as marking to market properties =
for
inclusion in the external financial statements and internal budgets as well=
as
business plans.
4) How will these findings be of interest to
academic and practicing accountants?
Academics
will be interested in such findings, since it provides a novel appraisal of
properties, and deals with the current controversies of marking to market
properties such as foreclosed real-estate properties, sub-prime
mortgages, Credit Default Swaps (CDS), and Collateralized Synthetic Obligat=
ions
(CSOs), and domain names.
Practitioners will be interested in = the finding since it provides a fast, efficient, in-expensive and most importan= tly low liability and low risk objective appraisal for volatile mark to market properties.
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’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 volume, 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 <=
span
style=3D'color:#333333'>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 sa=
le
to the highest bidder. Moniker partners … bring buyers and sellers
together in the top marketplace for premier domains. The Live Domain Auctio=
ns
are standard open cry auctions, managed by a professional auctioneer. Bidde=
rs
are required to register… in advance. This may be in addition to any
event registration required for admittance to the conference in which the
auction is being held. Please see registration requirements for each indivi=
dual
auction for complete 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 mone=
tization
results will interface directly to the names in the Live Auction and in
Marketplace. Seller-submitted information may also be available in the comm=
ents
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 to any industry, although, the internet industry is very clo=
se to
the electronic security industry.
As the article states “Managerial auditors (MAs) are frequently
relying on mistaken mixed costs (MC) forecasting assumptions. Two such
assumptions are that MC are linear within the re=
levant
range and variable costs vary proportionately with activity levels. Departu=
re
from these assumptions can lead to understating expenses, overstating profi=
ts,
and even to fraud, litigation, and mounting professional liability risk.
Develops a forecasting model of mixed cost error estimates, or error differ=
ence
(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 litigation and professional auditor
liability verdicts. Focuses on the electronics security industry, but some =
of
the findings may apply to other industries.” The current study resembles this a=
rticle
in the following aspects: we deal with “mixed cost forecasting assump=
tion
departure error estimates for litigation and professional liability risk
reduction.” The auctioneer and the domain name seller and the
facilitator, in this case, Moniker.com, aim at minimizing their litigation
risk. We are relying much like
“Managerial auditors (MAs)” 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.”
Likewise, the current study “Develops a forecasting model of m=
ixed
cost error estimates, or error difference (ED).” We are looking at the proble=
m from
the perspective of “MAs” that “can reduce the risk of
litigation and professional liability by including such an ED in the intern=
al
control structure of a company.”&nbs=
p;
This article “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
security industry, but some of the findings may apply to other industries.&=
#8221; In our case it applies to the inte=
rnet
industry, the reader can review this article further on the internet WWW (W=
orld
Wide Web) http://www.emeraldinsight.c=
om/10.1108/02686909710180643
on the publisher’s site. Both
studies use linear regression forecasting models, this study examine 3 line=
ar
regression models, 1. a univariate model, 2. a multivariate model with an
intercept, and 3. A univariate
model without an intercept, which is the best and final model. The selection of the 3rd
model is based on maximizing the R Square and minimizing the error rate, and
the departure from the assumptions.
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 analysis for the” internet and domain name auction
“… industry. Develops the concept of industry base standards,
integrating them into the more traditional statistical and accounting conce=
pts
of quality control standards. Provides an implementation example, and revie=
ws
possible improvements to the current methodology and approach. Uses a simil=
ar
methodology to forecast the .. value” of domain names “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 mod=
el.
Defines and tests related hypotheses and evaluates their significance and
confidence levels. For an illustration uses the” domain name auction
“… industry and the” … Moniker.com…”
company. Also demonstrates a microcomputer-based” domain name opportu=
nity
cost “… software that speeds, facilitates, and helps to accompl=
ish
the stated objectives. The … software uses industry financial stateme=
nt
data” … “computes 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
assessment is the focus of the present study with an X Coefficient represen=
ting
the risk of suboptimal selection of the type of auction for domain name sal=
es. 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 fa=
ct,
this study presents an approach for conducting a follow-up program using an
on-line” system. Likewi=
se,
the domain name auction is an online AIS using a “linear model to mea=
sure
and forecast.” “T=
he
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:
|
|
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 thi=
s 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 equ=
al to
zero, and thus the type 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, t=
he
alternative hypothesis states that it is significant, and therefore should =
be
part of the optimization of the seller and buyer trading strategy for domain
name auctions, and dispute resolutions.
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. Likewis=
e,
Inventory is subject to change, and the “Statistic information has not
been verified, please consider this data as an ESTIMATE ONLY! Moniker cannot
guarantee its accuracy.”
Moniker adds that “This is the tentative list of all domains t=
o 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. &=
nbsp; &nbs=
p;
This d=
ata
contains the following information: Description and Domain names. For example, the data includes
Venue/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 Category of the domain name, Computing, Uniques Per Mo. (estimate) 20,0=
00, Rev Per Mo. (estimate) 15,000, COMPETE Page Count of 1,145, COMPE=
TE
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 Re=
sult
1,086,237,890, Y! Search Domain Results N/A, Y! Search Name Results N/A, Al=
exa
Rank of 7,416,303, URLTRENDS Description of “ Computer parts PC
Components laptop computers notebooks motherboards processors CPU?s and much
more at TigerDirect.com, Links IN of 1,703, Links OUT of 149, Online Since
34,557, which is the Julian date of Thursday, August 11, 1994. Likewise, additional information o=
n 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 =
domain
name, etc. Some of the important issues that it reveals is
the great difference in the means of the prices. The mean of the live auction is ab=
out $55,613, deduct
$51,053 the difference, and you get the mean of the Silent auction at $4,56=
1. 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; &=
nbsp;
140 |
&nbs=
p; &=
nbsp;
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 Observa=
tions
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 wit=
h the
t Stat of 2.943899,
P(T<=3Dt) one-tail of &=
nbsp; 0.001898,
which is less than .05, and thus reject the 1 tail null hypothesis, with a t
Critical one-tail of 1.655811.
Likewise, the P(T<=3Dt) two-tail =
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
|
|
Table 3
Multivariate
With
Regression Statistics Univariate
LiveSilent
Intercept
Multiple R
R Square
Adjusted R Square
Standard Error
Observations
Table 3
shows the SUMMARY OUTPUT including the Regression Statistics of &=
nbsp; Live
and Silent auction models with and without Intercept, respectively. The Multiple R, the R Square of
0.066557441, indicating that the type of the auction explains about 7% of t=
he
sample auction price, this can be a large amount when dealing with domains =
that
sell for millions of dollars. This
sample includes all the observations of a total of 576. The three model states the followi=
ng: ANOVA Table 4 df SS MS F Significance F Regression Residual Total ANOVA B C D E F G 15 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% 16 Live $ 55,613.21 18 Silent $
(51,052.63) Omitted from the model due to
excessive redundancy correlation 19 Difference $ 4,560.59 Errors due to excessively
correlated independent variables 20 =3DB18+B20 21 Therefore, if a seller was forced into a silent aucti=
on,
this seller may have lost as much as $50,000 due to the type of auction,
especially in the more valuable domain names. In the event of a dispute, and a
litigation, the settlement and the recovery may amount to such sums of mone=
y.
In a dispute resolution that a seller lost opportunity due=
to
missing the live auction and selling instead in a silent auction, settling
domain type disputes should lead to some compensations at about the value of
the X coefficient. That X
Coefficient describes the silent auction seller’s opportunity loss du=
e to
missing of the live auction. =
In
contrast, for the buyer the litigation will focus on the opposite. It will focus on the litigation and
damages calculation of “buyers’ frenzy” suboptimal live
auction bids, leading to a live auction price that is too high compared to =
the
silent option.Model =
1-Y
the auction price=3D f(Live=3D1, or Silent=3D0 Auction type)*X Coefficient =
value [
univariate model]
Model =
2-Y
the auction price=3D f(Live=3D1 Auction type)*X1 Coefficient value+(Silent=
=3D0 Auction
type)*X2 Coefficient value [ multivariate model]
Model =
3-Y
the auction price=3D f(Live=3D1 Auction type)*X1 Coefficient value+(Silent=
=3D0
Auction type)*X2 Coefficient value+ Intercept [ multivariate with intercept
model]
The on=
ly
valid model is the univariate model due to the correlation between the dummy
variables that represent the mutually exclusive type of auction, whether the
domain was sold in the live or the silent auction. The intercept was statistically
insignificant and therefore we dropped it.=
The gain in the R Square from .o4 to .o6 shows the advantage of drop=
ping
insignificant components of the model.
ANOVA
REJECTS THE NULL HYPOTHESIS THAT THE AUCTION TYPE HAS NO EFFECT ON THE DOMA=
IN
NAME PRICE.
Table =
4
Table 4
shows the ANOVA, the Analysis of Variance with a significant F and Signific=
ance
F, with the Regression values of 40.99933959, 3.16993E-10, which is much le=
ss
than .o5, and therefore reject the null hypothesis that the auction type ha=
s no
effect on the domain name price. We
reject this null hypothesis in favor of the alternate hypothesis stating the
auction type affects the auction price to the tunes of about 7%.
SILENT
AUCTION MAY EXACT A SIGNIFICANT PENALTY ON THE DOMAIN NAME PRICE
The 2<=
sup>nd
part of the table shows the X coefficient and its statistics. It shows the columns of a spreadsh=
eet :
C D E F G, and the rows 15, 16 etc….=
The table includes the Coefficients, Standard Error, t Stat, and P-v=
alue
of 3.16607E-10, which is way less than the threshold of .05 to reject the n=
ull
hypothesis stating that the x coefficient is equal to zero, in favor of the
alternative hypothesis that it is not equal to zero. In our case the X coefficient of S=
ilent
is equal to $(51,052.63), whi=
ch means that a silent auction may exact a significant pe=
nalty
on the domain name price, in the vicinity of a negative penalty of some
$51,000. We have omitted from=
the
model due to excessive redundancy correlation the 2nd x variable
leading to a Difference of $4,560.59, and avoiding Errors due to excessively
correlated independent variables calculated as =3DB18+B20.
SUMMAR=
Y,
CONCLUSIONS, AND IMPLICASTION
In sum=
mary,
we have demonstrated the differences in the domain mane prices of a live
compared to a silent auction of domain names. We have used a sample of only one
auction and therefore, we need to repeat it over a longer period of time to
confirm our finding. However,=
this
is consistent with the press and the opinion expressed by Monkier.com that =
the
live auction tends to result in higher prices, or that they auctioneer usua=
lly
will steer high value domain names to a live auction, rather than a silent =
auction. We have built a statistically
significant univariate regression model, while rejecting a multivariate
regression model, to estimate the differences in price of a domain name in a
silent versus a live auction.
We con=
clude
that the seller will be more profitable in a live auction environment, since
the seller may be able to get a higher price for the sale of his domain
name. In contrast, the buyer =
will
be saving cost if he can acquire the domain name in a silent auction. In the event of a dispute, we have
developed a regression model that can easily calculate the damages and
compensation due to the opportunity cost of sub optimization.
The
implications of this study are that seller and buyer of auctions will tend =
to
pull in different directions with regard to the optimal type of auction.
&=
nbsp;
&=
nbsp;
&=
nbsp;
&=
nbsp;
&=
nbsp;
|
Appendix 1 Sample Data |
|||
|
**IMPORTANT NOTE: "Opening
Bid Range" does not indicate value of the domain name or potential
purchase price. |
|||
|
*Note: Statistic information h=
as
not been verified, please consider this data as an ESTIMATE ONLY! Moniker
cannot guarantee its accuracy. |
|||
|
This is the tentative list of =
all
domains to be included in the 2007 Moniker/T.R.A.F.F.I.C. Miami Live and
Silent Auctions. |
|||
|
Silent Auction: October 12th-1=
8th,
2007 SUBJECT TO CHANGE |
|||
|
Live Auction: October 12th, 20=
07
2pm-6pm EST. |
T.R.A.F.F.I.C. MIAMI 2007 |
Inventory is subject to change=
. |
Master Auction Inventory |
|
Description |
Domain1 |
Domain2 |
Domain3 |
|
Venue/Sale Price |
$ &nbs=
p;
2,200,000 |
$ &nbs=
p; &=
nbsp;
900,000 |
$ &nbs=
p; 450,000
|
|
Domain Name |
computer.com |
investment.com |
sportinggoods.com |
|
Opening Bid Range |
$1MM - $5MM |
$750,001 - $1MM |
$250,001 - $500,000 |
|
Extension |
com |
com |
com |
|
Length |
8 |
10 |
13 |
|
Category |
Computing |
Finance |
Shopping |
|
Uniques Per Mo. (estimate) |
&nbs=
p; &=
nbsp;
20,000 |
N/A |
N/A |
|
Rev Per Mo. (estimate) |
&nbs=
p; &=
nbsp;
15,000 |
N/A |
N/A |
|
Additional Information |
|||
|
COMPETE Page Count |
&nbs=
p; &=
nbsp;
1,145 |
N/A |
&nbs=
p; &=
nbsp;
3,781 |
|
COMPETE Page Rank |
&nbs=
p; &=
nbsp;
805,431 |
N/A |
&nbs=
p; &=
nbsp;
327,475 |
|
Google pr |
&nbs=
p; &=
nbsp; &nbs=
p;
5 |
&nbs=
p; &=
nbsp; &nbs=
p;
4 |
&nbs=
p; &=
nbsp; &nbs=
p;
2 |
|
GOOG Search Domain Results |
&nbs=
p;
&nbs=
p; 486,000
|
&nbs=
p; &=
nbsp;
219,000 |
&nbs=
p; &=
nbsp;
12,200 |
|
GOOG Search Name Results |
&nbs=
p;
581,000,000 |
&nbs=
p; &=
nbsp;
92,100,000 |
&nbs=
p; &=
nbsp;
793,000 |
|
MSN Search Domain Results |
&nbs=
p; 1,112,795,762
|
&nbs=
p; &=
nbsp; &nbs=
p;
441 |
&nbs=
p; &=
nbsp;
24,836 |
|
MSN Search Name Result |
&nbs=
p;
1,086,237,890 |
&nbs=
p; &=
nbsp;
230,190,168 |
&nbs=
p;
55,577,396 |
|
Y! Search Domain Results |
N/A |
N/A |
N/A |
|
Y! Search Name Results |
N/A |
N/A |
N/A |
|
Alexa Rank |
&nbs=
p; &=
nbsp;
7,416,303 |
&nbs=
p; &=
nbsp;
1,336,316 |
&nbs=
p;
5,583,424 |
|
URLTRENDS Description |
Computer parts PC |
egX Group |
Pool Tables Billiard Supply |
|
Links IN |
&nbs=
p; &=
nbsp;
1,703 |
&nbs=
p; &=
nbsp; &nbs=
p;
2,600 |
&nbs=
p; &=
nbsp;
25 |
|
Links OUT |
&nbs=
p; &=
nbsp;
149 |
&nbs=
p; &=
nbsp; &nbs=
p;
17 |
&nbs=
p; &=
nbsp;
138 |
|
Online Since |
34557.00 |
35488.00 |
35143.00 |
|
Online Since |
Thursday, August 11, 1994 |
Thursday, February 27, 1997 |
Tuesday, March 19, 1996 |
|
DOMAIN |
Computer.com |
Investment.com |
SportingGoods.com |
|
SALE PRICE |
$ &nbs=
p;
2,200,000 |
$ &nbs=
p;
&nbs=
p; 900,000
|
$ &nbs=
p;
450,000 |
|
VENUE SOLD |
LIVE |
LIVE |
LIVE |
|
DOMAIN |
Cowboys.com |
Xmas.com |
fishingpole.com |
|
SALE PRICE |
$ &nbs=
p; &=
nbsp;
370,000 |
$ &nbs=
p; &=
nbsp;
294,200 |
$ &nbs=
p; &=
nbsp;
58,900 |
|
VENUE SOLD |
Silent |
Silent |
Silent |
|
Top 3 Domain Differences |
$ &nbs=
p;
1,830,000 |
$ &nbs=
p; &=
nbsp;
605,800 |
$ &nbs=
p;
391,100 |
&=
nbsp; &nbs=
p; &=
nbsp;
@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
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quarterly financial statement ratio analysis - all 4 versions »A Rushinek,
SF Rushinek - Managerial Auditing Journal, 1995 - emeraldinsight.com=
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style=3D'font-size:10.0pt;font-family:"Arial","sans-serif"'> Avi =
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Sara F.
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financial reports for the USA, the UK and New Zealand Avi Rushinek, Sara F.
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Volume 4, Issue 4 , Pages258 - 265 Copyright © 1983 John Wiley &
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EDP auditors' role in evaluating computerized accou=
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information systems efficiency by queuing, …=
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A Rushinek - Proceedings of the fourteenth SIGCSE technical symposiu=
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…, 1983 - portal.acm.org... Sara F. Rushinek =
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Sara F. Rushi=
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Avi Rushinek Sara Rushinek School of ... For
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A. Rushinek S.
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Publication: 1989 ISSN:0160-2497 Publisher ACM New York, NY, USA Bibliometrics Downloads (6 Weeks):=
5, Downloads (12 Months): 48, Citation Count: 0 http://doi.acm.org/10.1145/66058.66059
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