US Hotel Industry Revenue Estimation Model

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1. US hotel industry revenue: an ARDL bounds testing approach
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US hotel industry revenue: an ARDL bounds testing approach
Author: Chen, Han1; Chen, Rui2; Shaniel Bernard3; Rahman,
Imran31 Lester E. Kabacoff School of Hotel, Restaurant and
Tourism Administration, University of New Orleans, New
Orleans, Louisiana, USA2 Department of Agricultural
Economics and Rural Sociology, Auburn University, Auburn,
USA3 Department of Nutrition, Dietetics and Hospitality
Management, Auburn University, Auburn, USA
Publication info: International Journal of Contemporary
Hospitality Management ; Bradford Vol. 31, Iss. 4, (2019):
1720-1743.
ProQuest document link
Abstract:
Purpose
This study aims to develop a parsimonious model to estimate
US aggregate hotel industry revenue using domestic trips,
consumer confidence index, international inbound trips,
personal consumption expenditure and number of hotel rooms as
predictor variables. Additionally, the study applied the model in
six sub-segments of the hotel industry – luxury, upper upscale,
upscale, upper midscale, midscale and economy.

Design/methodology/approach
Using monthly aggregate data from the past 22 years, the study
adopted the auto-regressive distribute lags (ARDL) approach in
developing the estimation model. Unit root analysis and
cointegration test were further utilized. The model showed
significant utility in accurately estimating aggregate hotel
industry and sub-segment revenue.
Findings
All predictor variables except number of rooms showed
significant positive influences on aggregate hotel industry
revenue. Substantial variations were noted regarding estimating
sub-segment revenue. Consumer confidence index positively
affected all sub-segment revenues, except for upper upscale
hotels. Inbound trips by international tourists and personal
consumption expenditure positively influenced revenue for all
sub-segments but economy hotels. Domestic trips by US
residents added significant explanatory power to only upper
upscale, upscale and economy hotel revenue. Number of hotel
rooms only had significant negative effect on luxury and upper
upscale hotel sub-segment revenues.
Practical implications
Hotel operators can make marketing and operating decisions
regarding pricing, inventory allocation and strategic
management based on the revenue estimation models specific to
their segments.
Originality/value
It is the first study that adopted the ARDL bound approach and
analyzed the predictive capacity of macroeconomic variables on
aggregate hotel industry and sub-segment revenue.
Links: Full Text
Full text:
1. Introduction
The tourism industry has positioned itself as one of the nation’s
largest employers representing 8.0 per cent of US GDP (World
Travel and Tourism Council (WTTC), 2015). The lodging
industry, being one of its major segments, contributed almost

$600bn to the US GDP and created more than eight million jobs
in different hotel segments (American Hotel and Lodging
Association (AH&LA) American Hotel and Lodging
Association, 2017). In terms of total output, lodging represents
the largest segment in the broader tourism industry, with
travelers spending more than $393bn per year on
accommodations (Select USA, 2016). The lodging industry has
outperformed the US economy over the past five years with
industry revenue growing at an annual rate of 3.7 per cent
reaching $189.5bn in 2015 (AH&LA, 2015). This rapid growth
is driven by an escalating tourism demand, stemming from an
influx of domestic and international leisure and business
travelers. In addition, the growing demand of hotel service is
influenced by the broad economy, where changes in consumer
confidence and consumption expenditures can affect decisions
on entertainment, travel and lodging (Alvarez, 2015). Moreover,
the growth of hotel industry depends on the interplay between
supply and demand (HOSPA, 2013).
There is no doubt that the lodging industry plays an important
role in the US economy. However, it is not easy to plan
strategically based on the macroeconomic environment. In
essence, a three-step process needs to be followed. The first
step is to identify the macroeconomic predictors or the forces of
change that can significantly influence industry revenue. The
second step is to see whether these macroeconomic predictors
can accurately estimate industry revenue using a parsimonious
estimation model. The last step is to test the utility of this
model in various industry sub-segments and explain any ensuing
variations.
Hoteliers need to accurately estimate hotel sales using key
external drivers at both micro and macro levels to carry out
strategic planning and management. At the micro level, sales
estimation is a key tool for managers’ decision-making
involving functional areas such as marketing, sales, finance, and
accounting (Mentzer and Bienstock, 1998). In the hotel
industry, revenue estimation is considered as an indispensable

part of hotels’ marketing and operations especially as it relates
to pricing and inventory management (Talluri and Van Ryzin,
2004). At the macro level, estimating expected growth in
aggregate hotel revenue based on specific economic drivers
helps hotel corporations to make large-scale investment related
decisions on segment-based expansion, mergers and
acquisitions. However, there is very little research that delves
into hotel revenue estimation models at the macro level
(Anderson et al., 2000; Bojanic, 1996). Furthermore, none of
the relevant research studies employed the autoregressive
distributive lag (ARDL) bounds testing procedure, which in the
past had been popularly applied to examine the impact of
macroeconomic factors on economic growth and tourism
demand (Narayan, 2004; Srinivasan et al., 2012). Additionally,
to the best of our knowledge, there is no single study that
undertakes estimation of sub-segment hotel sales using relevant
macroeconomic indicators. It is essential to understand the
different ways in which macroeconomic predictors affect sales
for each sub-segment so that practitioners can carry out more
segment-specific strategic planning.
The purpose of this study, then, is to develop a predictive model
to estimate monthly hotel industry revenue using five
macroeconomic variables – domestic trips by US residents,
consumer confidence index (CCI), inbound trips by non-US
residents, personal consumption expenditure (PCE), and number
of hotel rooms (NOHR). In addition, the model will be tested
across industry sub-segments, which include luxury, upper
upscale, upscale, upper midscale, midscale, and economy hotels.
The study employs the five most relevant and accessible
macroeconomic predictors to develop a working estimation
model that is simple, user-friendly, and efficient. The goal,
therefore, is to develop a parsimonious model with maximum
explanatory power. Theoretically, the study is expected to fill
the gap in current literature by proposing a hotel industry and
sub-segment revenue estimation model using macroeconomic
predictors and the ARDL approach. The estimation model and

comparisons across sub-segments will help various stakeholders
of the hotel industry such as operators, suppliers, investors,
policy-makers, researchers, and various independent
organizations better understand the influential factors in each
sub-segment.
2. Literature review
2.1 Estimation of hotel industry revenue
Estimating hotel industry revenue can address external factors
that influence industry revenue at the micro and macro level. At
the micro level, it is typical to include arrival or booking date,
segment price and duration of use, whereas at the macro level
total demand may influence revenue to a great extent (Lee,
1990). The lodging industry is sensitive to fluctuations in
demand; however, forecasting demand is pertinent due to the
nature of the industry and its operational characteristics
(Yüksel, 2007). Moreover, since estimation differs for transient
and group customers (Yüksel, 2007), it is natural for it to differ
across hotel sub-segments. Examining hotel segment
performances in relation to economic factors is relevant since
the results often differ when compared to overall industry
performance (Canter and Maher, 1998). Making a distinction
promotes a better understanding of how the varying demands
across sub-segments relate to economic measures.
Extant literature regarding hotel revenue estimation is still in its
infancy. Little prior research has estimated hotel revenue at the
macro level, with most estimation models directed at the micro
level that utilized techniques such as choice models (Talluri and
Van Ryzin, 2004). Other studies applied several revenue
management techniques to increase operational efficiency in the
hotel sector (Solnet et al., 2016). There are also a handful of
studies that estimated hotel demand in terms of guest arrivals
and occupancy. For instance, Damonte et al. (1998) estimated
hotel demand using a cross sectional sample of 310 properties.
The factors in their analysis were average daily rate (ADR),
number of rooms available, number of employees, food and
beverage revenue and number of tourists attending conferences.

The results indicated that price elasticity of demand varied
across hotel segments. Consequently, Canina and Carvell (2005)
expanded on this study to include consumer confidence index
(CCI), income, expectations of income (corporate income and
disposable personal income), ADR and local market’s ADR to
estimate demand for urban hotels in a metropolitan market. The
results, utilizing data spanning from 1989 to 2000, found all
predictors as significant influencers of lodging demand.
2.2 Hotel segmentation
Hotels are categorized based on several factors including but
not limited to hotel size, location, target markets, levels of
service, number of rooms etc. Prior hotel segmentation studies
in an econometric context have predominantly used micro-
economic variables in their estimation of pricing, growth, and
consumer demand. For example, Falk and Hagsten (2015) used
two stage least absolute deviation estimators to predict growth
and revenue for Swedish hotel establishments. They found that
growth rate of overnight stays was significantly and positively
related to the price segment of the hotel at the beginning of the
same period; however, the relationship became negative as the
price increased indicating that high-end hotels do not have
better growth prospects than hotels in medium price segments.
Similarly, Damonte et al. (1998) indicated that price elasticity
of demand varied across different hotel segments when
estimating hotel demand using ADR, number of rooms
available, and number of tourists.
Luxury hotels were perceived by travelers as experiences, rather
than products (Chu, 2014). Access to more disposable income
may increase the frequency of consumer’s stay in such hotels.
For example, Graf’s (2011) study on 2,824 hotel properties
found that consumers who usually stay in lower class hotel
segments, may switch to higher ones once their income
increases. Tran (2015) estimated the effects of economic factors
on the demand for luxury hotel sub-segment in the USA
between 1998 and 2013. Results of their study indicated that US
residents would extend their length of stay in luxury hotels

when their income rises. German, Chinese, Japanese and Korean
visitors would stay in luxury hotels when their income increases
even if the hotel price goes up.
To the best of our knowledge, there is a lack of research
regarding macroeconomic variables’ influence on the aggregate
US hotel industry and sub-segments revenue, wherein the model
that works for the aggregate hotel industry revenue may not
work for different sub-segments. The study analyzes six sub-
segments of the hotel industry to test the accuracy of the
estimation model. In order to divide the hotels in different
segments, the nomenclature used by Smith Travel Research
(2014) (STR) was followed. STR positions hotels in classes
based on their historical ADR, not on subjective criteria such as
features or amenities. Both chain and independent hotels use the
same ADR categorization.
2.3 Identifying predictors
Previous research shows the impact of economic predictors on
the lodging industry (Choi, 2003; Zarnowitz, 1992). Given the
paucity of research in hotel industry revenue estimation in the
extant hospitality literature, the current study selected variables
suggested by Canina and Carvell (2005), Chen et al. (2007) and
Alvarez (2015), which include domestic trips by US residents,
consumer confidence index (CCI), inbound trips by non-US
residents, personal consumption expenditure (PCE), and number
of hotel rooms (NOHR) in the industry.
2.3.1 Domestic trips by US residents.
Domestic tourists are defined as individuals taking overnight
trips or longer away from the place of their residence
(International Union of Official Travel Organizations, 1974).
Consistent with Alvarez (2015), the current study measures
domestic trips of US residents by the number of domestic
flights within the USA for both leisure and business travel.
Domestic trips by US residents increased from 660.9 million in
2006 to 682.1 million in 2015 (Alvarez, 2015), exhibiting an
increasing trend that might explain the variation of the US hotel
industry revenue in the same period. Close to 80 per cent of

domestic trips were taken by leisure travelers in 2016 (USA
Travel Association, 2016), who are known to be price sensitive
and less willing to pay for a higher room rate (Masiero et al.,
2015).
Witt and Witt (1995) chose domestic and international trips to
forecast tourism demand and both variables added significant
explanatory power in their study. These findings underscore the
importance of the number of domestic trips in predicting hotel
industry revenue. Economic growth leads to the increase in
number of air passengers and business activities – hence more
domestic business and leisure travel (Chi and Baek, 2013). As
the number of domestic trips increases, more people will be
staying in hotels increasing hotel revenue. Thus, domestic trips
by US residents can be a significant predictor of US hotel
revenue. H1.
The number of domestic trips made by US residents has a
significant positive influence on aggregate hotel industry
revenue; and revenues for all six sub-segments.
2.3.2 Consumer confidence index.
Consumer confidence refers to the degree of optimism
consumers feel about the state of the economy and their
personal finances, which guides their decisions on spending and
saving. Consumer confidence is measured by two indices – The
Conference Board’s Consumer Confidence Index (CCI) and
University of Michigan’s Index of Consumer Sentiment (ICS).
Vuchelen (2004) contended that consumer sentiment is an
efficient variable to use for forecasters to avoid some errors,
since many economic and financial variables can significantly
influence the consumer sentiment. Since both CCI and ICS
essentially measures consumer confidence but with a different
methodology, it is safe to use either one in estimation models.
In this study, CCI is used to predict US hotel revenue. The
US CCI is calculated by The Conference Board using a monthly
survey. The monthly survey includes questions related to
consumers’ household finances, employment, income, business
conditions and economic outlook (The Conference Board,

2011). CCI has a good forecasting power for consumer spending
as it influences individual expectations and consumption
preferences. High consumer confidence can decrease
uncertainty in the future, thereby reducing precautionary
savings and increasing present consumption (Ludvigson, 2004).
In addition, an increase in consumer confidence can boost
future income and wealth expectations (Ludvigson, 2004).
Thus, CCI can influence the real economy by increasing
consumer expenditure on entertainment, travel, lodging, etc.
Hence, higher CCI results in direct future consumption growth.
Prior research provides empirical evidence that CCI increases
labor income (Carroll et al., 1994), which eventually translates
into more expenditure. Singal (2012) also demonstrated
that CCI can explain a significant part of variation in consumer
expenditure on hotel industry. In hindsight, CCI can influence
the real economy by increasing or decreasing consumer
expenditure. Increased expenditure on lodging related services
would increase hotel revenue. Thus, the following hypothesis is
proposed: H2.
CCI has a significant positive influence on aggregate hotel
industry revenue; and revenues for all six sub-segments.
2.3.3 Inbound trips by non-US residents.
International tourists are defined as “tourists who stay at least
one night in a country where they are not residents”, where a
resident is “a person who has lived for most of the past year in a
country” (Eilat and Einav, 2004, p. 1319). The study measures
inbound trips of non-US residents by the number of
international arrivals to the USA for both leisure and business
purposes. The number of international visitors to the USA is
found in three US and international government sources: the
USA Department of Homeland Security/USA Customs and
Border Protection I-94 arrivals program data, Statistics
Canada’s International Travel Survey and Banco de Mexico
travel data (National Travel and Tourism Office, 2016).
International visitors have significant influence on the US hotel
industry (Tran, 2015). USA received the largest share of world

tourism (14.2 per cent) in year 2014 (AH&LA, 2015). The
international visitor arrivals and their lengths of stay can
influence the demand for hotel rooms. Each international visitor
stayed an average of 18 nights in the USA (USA Travel
Association, 2015). Proceeds from international visitor arrivals
in the USA accounts for as much as 20 per cent of US hotel
revenue (AH&LA, 2015). The top five countries with the most
international visitor arrivals are Canada, Mexico, UK, Japan and
China (National Travel and Tourism Office, 2015). The variable
international visitor arrivals has received overwhelming
attention in the tourism literature especially in estimating
tourism demand, with the vast majority of these studies testing
the utility of various estimation models specific to a
country/region (Kraipornsak, 2011; Peng et al., 2015; Yang et
al., 2010).
Travelers’ demographic characteristics matter in international
trips. Income, exchange rates, and transportation costs are the
three critical factors affecting international tourism demand
(Lim, 1997). Access to financial resources positively influence
tourist demand and travel frequency (Davies and Mangan,
1992). The general assumption is that as peoples’ disposable
income increases, so will their tendency to engage in leisure
travel, extend their length of stay in a destination and spend
more in travel related services (Alegre et al., 2011). In essence,
wealthy families are known to engage in international travel,
whereas families in lower income brackets tend to travel
domestically (Fang Bao and McKercher, 2008). Previous studies
show that most international tourists are considered as affluent
travelers, indicating that income in their country of origin is the
most important explanatory variable in generating those trips
(Crouch, 1995; Lim, 1997). Overseas travelers spend almost
$4,400 during their visit to the USA (USA Travel Association,
2015). As more international tourists arrive in the USA, demand
for hotel rooms and other lodging-related services goes up,
contributing directly to hotel sales. Therefore, a significant
positive relationship between inbound trips by non-US residents

and lodging industry and its sub-segment revenue is
expected. H3.
Inbound trips by non-US residents have a significant positive
influence on aggregate hotel industry revenue; and revenues for
all six sub-segments.
2.2.4 Personal consumption expenditure.
Personal consumption expenditures (PCE), “measures the goods
and services purchased by persons – that is, by households and
by nonprofit institutions serving households which are residents
in the USA” (Bureau of Economic Analysis, 2014, p. 5-2).
Consumer spending on goods and services in the US economy is
measured primarily by the PCE, which accounts for
approximately two-thirds of domestic spending (Bureau of
Economic Analysis, 2014). PCE is known as the primary driver
of future economic growth. It implies how much of the
household income is spent on current consumption as opposed
to being saved for future consumption.
Households engaging in more consumption outside (e.g. dining
out and shopping) will “directly lead to more activities and
travel consistent with the behavioral paradigm that travel
demand is a derived demand” (Ferdous et al., 2010, p.1).
Therefore, PCE is a good indicator of US consumer spending,
which can be used to estimate tourism demand (Chen et al.,
2007). As discussed above, the more the tourism demand, the
higher is the expenditure on lodging and related service. In a
hotel context, as perceived value of the product or service
increases, consumers’ intention to purchase grows (Ashton et
al., 2010), which contributes to hotel revenue growth. Similarly,
Corgel et al. (2012) suggested that the increase of income
generates higher demand for higher priced hotel segments such
as luxury and upper upscale hotels than for lower priced ones
such as midscale and economy hotels. Hence, a significant
positive relationship between PCE and US hotel industry and
sub-segment revenue is expected. H4.
PCE has a significant positive influence on aggregate hotel
industry revenue; and revenues for all six sub-segments.

2.2.5 Number of hotel rooms.
Number of hotel rooms (NOHR) variable measures the number
of available hotel rooms in the aggregate hotel industry.
Similarly, NOHR in each sub-segment is used in the estimation
of its corresponding hotel sub-segment revenue. The increases
of available hotel rooms in the industry will lead to more supply
in the industry, increasing competition, and resulting in lower
ADR for some sub-segments. Hotel revenue is the result of both
ADR and number of rooms sold, which is the demand from the
consumers’ side. The growth of hotel revenue depends on the
price elasticity of demand (Canina and Carvell, 2005). Price
elasticity is defined as the per cent change in demand divided
by the per cent change in price, which measures the degree to
which demand is sensitive to changes in price (Corgel et al.,
2012; Trans, 2011). If the demand is price elastic, hotel revenue
may increase if room rates reduce. On the other hand, if the
demand is price inelastic, hotel revenue will reduce as room
rates fall (Canina and Carvell, 2005). Prior studies
demonstrated that all US hotel sub-segments had inelastic
demand, which showed that the growth in room rate is much
greater than the growth in demand (Hiemstra and Ismail, 1993).
Tran (2011, 2015) supported this view by demonstrating that the
price is inelastic in US luxury hotel sub-segment and suggesting
that consumers are not sensitive to price changes. Similarly,
research at the property level also showed that price elasticity
of demand varied across hotel sub-segments with higher priced
hotels having lower price elasticity than lower priced hotels
(Canina and Carvell, 2005). Hence, the increase of hotel supply
will result in reduced ADR, which further results in increased
demand, leading to a reduced level of hotel industry revenue
(price inelasticity).
Therefore, the study proposes the following hypotheses: H5.
NOHR has a significant negative influence on aggregate hotel
industry revenue; NOHR for each sub-segment has a significant
negative influence on sub-segment revenues.
3. Methodology

Monthly data from January 1996 to September 2017 were
collected from a variety of sources. Monthly aggregate hotel
industry and sub-segment revenue data were collected from
STR. Revenue data for chain hotels and independent hotels as
well as number of hotel rooms in each sub-segment were
included in this dataset. These data were not publicly available
and required subscription to STR. Domestic trips by US
residents’ data were gathered from the Bureau of Transportation
Statistics (2018) website. Consumer confidence index (CCI)
data were compiled from The Conference Board (2018) website.
Inbound trips by non-US residents’ data were collected from the
National Travel and Tourism Office (2018). Seasonally adjusted
personal consumption expenditure (PCE) data were collected
from the Federal Reserve Economic Data (2018) (FRED). All
data sets apart from hotel industry revenue and number of hotel
rooms were publicly available. In addition, all data sets were
not seasonally adjusted except PCE.
Seasonality is a known characteristic of tourist demand, which
also influences accuracy of hotel revenue estimation (Chen et
al., 2015). Therefore, it cannot be overlooked in the modeling
process. Seasonal adjustments of all variables except PCE were
conducted using the X-12-ARIMA program, which is developed
by the US Census Bureau. The X-12-ARIMA procedure makes
adjustment for monthly or quarterly series. It is the primary
method for seasonal adjustment of government and economic
time series in USA, Canada and the EU (Miller and Williams,
2004).
3.1 Model specification and estimation procedure
The current study examines the five macroeconomic predictors’
utility in estimating aggregate US hotel industry revenue. It also
assesses how the causal model works for potentially dissimilar
industry sub-segments. As seen in Table I, the sub-segment
classification followed the nomenclature used by STR. The
auto-regressive distribute lags (ARDL) approach is used via
EViews 10. Unit root analysis is first conducted to confirm that
the data are stationary, i.e. I (0), or non-stationary, i.e. I (1).

Then the cointegration test with the ARDL approach is utilized.
The US hotel revenue is determined by variables that measure
the hotel supply – NOHR and hotel demand – namely, domestic
trips by US residents, CCI, inbound trips by non-US residents
and PCE. US nominal hotel renvenue and PCE have been
deflated by consumer price index to remove the effects of
inflation. Therefore, the determinats of US hotel revenue take
the specification
form: (1) ln⁡Revenuet=α+β ln⁡(⁡DTt)+γ ln⁡(⁡CCIt)+δln⁡
(⁡IAt)+θ ln⁡(PCEt)+ψ ln⁡(NOHRt)+εtwhere Revenuet denote
s the aggregate US hotel industry revenue and sub-segment
revenues for luxury, upper upscale, upscale, upper midscale,
midscale and economy in year t. DTt and IAt are defined as
domestic trips by US residents and inbound trips by non-US
residents in year t.
Many empirical studies have proved that most aggregate data
have a unit root (Kwiatkowski et al., 1992). Variables that
contain a unit root were generated by a non-stationary data
process of taking the first difference of the variables, resulting
in spurious regression. When working with time series datasets,
it is important to look for a unit root. If a unit root is found in a
series, it means that more than one trend is present in the series.
Since the data are at the macroeconomic level and time series,
whether all variables are stationary or not are detected to avoid
spurious regression using stationary test. There are different
ways to test whether data is stationary or not, such as Lagrange
multiplier (LM test) (Kwiatkowski et al., 1992), Augmented
Dickey-Fuller (ADF) test (Becketti, 2013; Dickey and Fuller,
1979; Hamilton, 1994) and PP test (Phillips and Perron, 1988).
This paper employed ADF and PP tests to determine a time
series is stationatary or not.
However, the regression specification of differencing could
only provide the short-run estimates not the long-run estimates.
ARDLs are standard least squares regressions which include
lags of both the dependent variable and independent variables as
regressors (Greene, 2008). Although ARDL models (the bound

test approach) have been used in econometrics for decades, they
have gained popularity in recent years as a method of examining
long-run and cointegrating relationships between variables
(Pesaran and Shin, 1998). Therefore, this problem could be
solved by considering the cointegration and the error correction
model (ECM) and obtaining both short- and long-run
information (Nkoro and Uko, 2016). Additionally, ARDL
models could yield consistent estimates of long run relationship,
irrespective of whether the regressors are purely I(0) or I(1) or
a mixture of both.
To examine the long-run relationship between US hotel revenue
and its determinats, the ARDL cointegration procedure is
applied (Pesaran et al., 1996; Pesaran and Shin, 1998). The
model with lower Akaike information criterion (AIC), Schwarz
Bayesian criterion (SBC), Hannan-Quinn Criterion (HQ) and
higher adjusted R2 performs better than the other models.
Lagrange multiplier (LM) test was used to test the residual’s
autocorrelation. The bounds procedure for testing the existence
of a long-run/cointegration relationship is implemented
regardless of I(0) or I(1) as a post estimation command
(Pesaran et al., 2001). If the existence of a long-run relationship
(cointegration) is affirmed, the second step is to construct the
conditional ARDL specification for ln⁡(revenue). Following
Pesaran et al. (2001), the conditional unrestricted equilibrium
correction model for US hotel revenue can be specificed
as: (2) Δln⁡Revenuet=α2,0+π1ln⁡(Revenue)t-1+πxXt-
1+∑i=1p-1ψi′ΔECMt-
i+w′ΔXt+εtwhere: Xt=(ln⁡(DTt),ln⁡CCIt,ln⁡IAt,ln⁡PCEt,ln
⁡(NOHRt)′,ECMt=(ln⁡(Revenuet),Xt),ECMt is the speed of
adjustement parameter, which is derived as error term from
long-run model.
The standard long-run ARDL (p,p1,p2,p3,p4,p5) specification
can be expressed
as: (3) ln⁡(Revenuet)=α1,0+∑i=1pπ1,iln⁡Revenuet-
i+∑i=0p1β1,iln⁡DTt-i+∑i=0p2γ1,iln⁡CCIt-
i+∑i=0p3δ1,iln⁡(IAt-i)+∑i=0p4θ1,iln⁡PCEt-

i+∑i=0p5ψ2,iΔln⁡(NOHRt-i)+ε1,twhere t =
max⁡(p,p1,p2,p3,p4,p5,…,T);p,p1,p2,p3,p4andp5 are the
number of optimal lag order, which will be obatined based on
the minimization of AIC or Bayesian information criterion
(BIC).
The short-run specification dynamics include one period lagged
error correction version of the ARDL
model, (4) Δln⁡(Revenuet)=α2,0+∑i=1pπ2,iΔln⁡Revenuet-
i+∑i=0p1β2,iΔln⁡DTt-i+∑i=0p2γ2,iΔln⁡(CCIt-
i)+∑i=0p3δ2,iΔln⁡(IAt-i)+∑i=0p4θ2,iln⁡ΔPCEt-
i+∑i=0p5ψ2,iΔln⁡(NOHRt-i)+λECMt-1+ε2,twhere ECMt-
1=ln⁡(Revenuet-1)-α+βln⁡(DTt-1)+γln⁡(CCIt-1)+δln(⁡IAt-
1)+θln⁡(PCEt-1)+ψln⁡(NOHRt-1)is the ordinary least square
(OLS) residual series from the long-run cointegrating
regression, which means, α,β,γ,δ,θ and ψ can be OLS estimates
from Equation (1). ECMt-1 demonstrates how much of the
disequilibrium in the previous period (ln⁡Revenuet-1) is being
adjusted in current period (ln⁡Revenuet). A significantly
postive estimate of ECMt-1 indicates divergence, and a
significantly negative coefficient means convergence. This
ARDL model with its ECM can be regressed by the OLS
approach.
Thus, the conditional error correction representation of ARDL
model for US hotel revenue can be reparameterised as
(Bahmani-Oskooee and Ng, 2002; Pesaran et al.,
2001): (5) Δln⁡(Revenue)t =α0+∑i=1pπ2,iΔln⁡Revenuet-
i+∑i=0p1β2,iΔln⁡DTt-i+ ∑i=0p2γ2,iΔln⁡(CCIt-
i)+∑i=0p3δ2,iΔln⁡(IAt-i)+∑i=0p4θ2,iΔln⁡PCEt-
i+ ∑i=0p5ψ2,iΔln⁡NOHRt-i+πln(Revenuet-1)+βln(⁡DTt-
1)+ γln⁡(CCIt-1)+δln⁡(IAt-1)+θln⁡(PCEt-1)+ψln⁡(NOHRt-
1)+εt
4. Results
4.1 Unit root test results
This study uses the logarithms of the data in order to eliminate
the effects of potential heteroscedasticity. Before an appropriate
ARDL model, the first step is to determine whether data are I(0)

or I(1). Table II indicates all variables are I (1),
except PCE, NOHR[1], midscale, upper upscale sub-segments
revenue and aggerate industry revenues that are I(0). Therefore,
there is a mixture of I(0) and I(1) for all variables, indicating
the ARDL model can be proceeded further. Figure 1 implies
there is no obvious trend and structural break of revenue data
over periods. In addition, Table II shows that all variables are
stationery with or without trend. However, variables are more
stationary with trend relative to without trend. Therefore, time
trend will be included in the estimation of the ARDL model.
4.2 Results from auto-regressive distribute lags bound tests for
cointegration
To test the null hypothesis of cointegration, the first step is to
determine whether there is a relationship between the variables
over the long term using bound tests (Bahmani-Oskooee and Ng,
2002). All of the computed F statistics are much greater than
the upper critical value for 1 per cent from Table III. Hence, the
null hypothesis was rejected, confirming the existence of co-
integration among US hotel revenue, domestic travel, CCI,
international arrival, PCE, and NOHR in these seven models.
After validating the application of ARDL model, the next step is
to determine the lag length for the dependent variable. To
implement the choice of lag length, an unrestricted VAR model
was applied for Δln⁡(Revenuet) and the
constant,ln⁡(Revenuet-1), ln⁡(DTt-1), ln⁡(CCIt-1), ln⁡(IAt-
1), ln⁡(PCEt-1), ln(NOHRt-1) and a fixed number of lags of
Δindepdentvariables as exogenous regressors. According to the
VAR lag order selection criteria (the lower value of AIC, SC,
HQ etc., the better results), the optimal lag length for dependent
variable is three or eight. The same procedure was applied to all
six hotel sub-segments and the optimal lag length for each hotel
sub-segment revenue can be found in Table IV.
4.3 Results of the long- and short-run effects
The augmented ARDL model with appropriate lag orders are
obtained for the equation, all criteria cited above are used and
after that the smallest lag length among them was taken. The

regression results of ARDL model for revenue of US hotel
industry and six different hotel sub-segments are reported in
Table V. Until now, the study analyzed both the long- and
short-run relationships among the US hotel revenue, domestic
trip, CCI, international arrival, PCE and NOHR.
The results reveal that the estimated coefficient of domestic
trips by US residents is significantly positive for the aggregate
hotel industry revenue, upper upscale sub-segment revenue,
upscale sub-segment revenue and economy sub-segment revenue
only. It shows that in the long run, one per cent increase in the
domestic trips leads to approximately 0.20 per cent increase in
the aggregate US hotel industry revenue, 0.31 per cent increase
in the upper upscale sub-segment revenue, 0.15 per cent
increase in the upscale sub-segment revenue and 0.39 per cent
increase in the economy sub-segment revenue. H1 was partially
supported.
CCI positively and significantly influenced aggregate hotel
industry revenue. The same relationship also exists throughout
the hotel sub-segments except for the upper upscale sub-
segment. The results imply that 1 per cent increase in CCI will
lead to 0.18 per cent increase in aggregate hotel industry
revenue and 0.05-0.26 per cent increase in revenue for different
hotel sub-segments except the upper upscale sub-segment. This
empirical evidence confirms that CCI has a positive impact on
majority of US hotel sub-segment revenue in the long run.
Hence, H2 was partially supported.
The inbound trips by non-US residents variable has a significant
positive influence on aggregate hotel industry revenue. The
same relationship also applies to all hotel sub-segments except
for economy, partially supporting H3. As for PCE, it has a
significant positive relationship with aggregate revenue and
throughout all hotel sub-segments except for economy sub-
segment, partially supporting H4. NOHR has a significant
negative influence only on luxury and upper upscale hotel sub-
segment revenues. However, this variable is not significantly
associated with revenue from all other hotel sub-segments or the

aggregate hotel industry revenue. H5 is partially supported.
The estimates of the short-run dynamics associated with the
long-run relation from ECM are presented in Table VI. ECM is
statistically negative for all seven models, implying there is
adjustment from disequilibrium into long-run equilibrium,
which helps reinforce the long-run relationship (co-integration)
between hotel revenue and its determinants. More specifically,
the estimate of ECM indicates 70.9 per cent of the
disequilibrium in aggregate hotel revenue from previous period
which will be converged back to the long-run equilibrium in the
current period. The results also imply a range of 54.4-99.0 per
cent of the disequilibrium in sub-segment revenue from
previous period which will be converged back to the long-run
equilibrium in the current period across the six hotel sub-
segments.
Although domestic trips do not have a significant positive effect
on luxury, upper midscale, and midscale sub-segment revenue in
the long run, it does have a significant impact on the aggregate
US hotel revenue growth rate and all six sub-segments revenue
growth rate in the short term. The growth rate
of CCI and PCE do not have a significant impact on either
aggregate hotel industry revenue growth rate or any sub-
segment hotel revenue growth rates in the short run. Inbound
trip growth rate has significant positive short-term effects on
the growth rate of aggregate hotel industry revenue and all six
hotel sub-segments revenue in the short term.
As the ARDL model and its associated ECM have been
estimated by the OLS, the assumptions of OLS−the normality,
heteroscedasticity, and the serial correlation have been tested,
which are reported in Table VI. From Table VI, DW (Durbin-
Watson) values for all seven models are higher than the upper
bound critical value (dU = 1.726), indicating that the study
failed to reject the null hypothesis. Thus, there is no positive
serial correlation/autocorrelation of residuals. There is no
heteroscedasticity issue in the model from Breusch-Pagan
Lagrange Multiplier (BPG LM) test either. In addition, data

follow the normal distribution. It is important that the error of
this model is serially independent. If not, the parameter
estimates will not be consistent, because of the lagged values of
dependent variable that appear as regressors in the model. All
errors are serially independent.
Finally, the study tested the stability of the long-run and short-
run estimates of the ARDL models. Following Bahmani-
Oskooee and Ng (2002) and Pesaran and Shin (1998), the
stability test – the cumulative sum (CUSUM) and the
cumulative sum of squares (CUSUMSQ) – is undertaken to
assess the parameter consistence based on the AIC from
the ECM. According to Figure 2, both the plots of CUSUM and
CUSUMSQ statistics stay within the critical bounds of 5 per
cent significance level, which applies to aggregate model as
well as all hotel sub-segments, showing that there is no
instability for all sub-segment revenue estimation models.
5. Discussion
The findings showed the overwhelming support of CCI in
estimating aggregate hotel and sub-segment revenues except for
the upper upscale sub-segment. CCI’s significant positive
support of US hotel revenue is consistent with Singal’s (2012)
finding that CCI can explain a significant part of variation in
consumer expenditure in the hotel industry. The more
confidence consumers have in the US economy, the more they
will spend on travel boosting hotel revenue. Low consumer
confidence results in lower consumption as consumers often
postpone their trips and/or reduce their frequency of travel
(Ludvigson, 2004; Singal, 2012). The insignificant relationship
between CCI and upper upscale segment revenue can be
explained by the dichotomy properties of price: objective price
and perceived price. Objective price is the actual price of a
product, while perceived price appeals to the subjective internal
impressions derived from the perception of price (Dodds et al.,
1991). Prior research shows that consumers do not rely on
objective price, but rather interpret perceived price in way that
are meaningful to them (Zeithaml, 1988). Therefore, even

though tangible price differences exist between these two
classes, consumers may end up choosing to stay in luxury hotel
instead of upper upscale due to the higher perceived value.
PCE positively affects aggregate hotel revenue and this
relationship is consistent across all sub-segments except for the
economy sub-segment in the long run. The more household
income is being spent on current consumption, the more hotel
revenues will grow. The result echoes with findings of previous
research that PCE is a good indicator of the tourism demand
which can generate hotel revenue (Chen et al., 2007). The
findings are consistent with Corgel et al. (2012) that the
increase of income generates more demand for upper priced
hotel segments than for lower priced ones such as economy
hotel sub-segment. As consumers have more spendable income,
they would most likely choose better hotels to stay in, which
somewhat explains the insignificant relationship of PCE with
economy sub segment hotel revenue. The economy sub segment
is popular among relatively low-income consumers who are in
need of accommodations both for short and long term. This is a
popular segment among blue-collar workers looking to relocate,
temporarily move to other destinations, or travel for work.
Many people also use this segment as an alternative to rent out
an apartment. Revenue for this segment, therefore, does not
fluctuate much based on changes in PCE as this segment is used
by people who are in indispensable need of accommodation.
This segment is also very popular among motorists looking for
an overnight accommodation near a highway, who might opt for
better hotels to stay in if they have more spendable income.
Besides CCI and PCE, domestic trips showed significant
positive influence on aggregate US hotel industry revenue. The
significant positive effect of domestic trips is supported by the
fact that lodging industry is the largest segment of the tourism
industry. Hotel industry revenue accounts for nearly 19 per cent
of total travel and tourism related spending (Select USA, 2016).
In addition, domestic travelers are actually spending more in
recent years. In 2014, the typical business traveler spent about 3

per cent more per night, and the typical leisure traveler spent
about 6 per cent more per night compared to the previous year
(AH&LA, 2015), which contributes to the increase in US hotel
revenue as a whole. Further analyses revealed that domestic
trips by US residents only added significant explanatory power
to aggregate, upper upscale, upscale and economy sub-segment
revenue. It is also observed that domestic trips by US residents
are significant in short term throughout all sub-segments. In
2016, close to eighty per cent of domestic trips were taken by
leisure travelers (USA Travel Association, 2016). Leisure
travelers are known to be price sensitive compared to business
travelers whose travel expenses are covered by their companies
most of the time (Kashyap and Bojanic, 2000; Noone and
McGuire, 2013). For example, Masiero et al. (2015) found
difference in willingness to pay (WTP) for certain hotel
attributes among leisure and business travelers. They found that
business travelers were willing to pay up to 25 per cent higher
for certain hotel attributes than leisure travelers. In addition,
Trejos (2018) found that business travelers prefer to stay in
hotel brands such as Hilton, Hyatt, Embassy Suites, Courtyard,
Doubletree, and Hilton Garden Inn etc., which mostly fall into
either upper upscale or upscale categories. The upper upscale
segment especially is known as the sweet spot for corporate
travel (Business Travel News, 2013). These two sub-segments
also registered the highest occupancy rates among the six sub-
segments in recent years (Smith Travel Research, 2018).
Therefore, the significant relationship between domestic trips
and upper upscale and upscale hotel sub-segments revenue
makes sense as most business travelers prefer to stay in these
two sub-segments.
According to Yesawich et al. (2000) 60 per cent of leisure
travelers were actively searching for the “lowest possible price”
for travel-related products. The price sensitive leisure travelers
tend to target lower cost lodging segments, which explains why
domestic trips by US residents has significant positive effect on
economy sub-segment revenue. About 39 per cent vacations

taken by leisure travelers in 2017 were road trips (MMGY
Global, 2018). Even when flying, many domestic travelers on
leisure trips prefer to rent cars from the airport to reach their
final destinations. Among motorists economy sub-segment
hotels remain a very popular option because of the convenience
of being located near major highways, which explains why
economy sub segment revenue is influenced by domestic trips
by US residents.
The inbound trips by non-US residents also had a significant
effect on aggregate hotel revenue which can be explained by the
fact that USA receives the largest share of international tourism
receipts in the world (AH&LA, 2015). In 2016, 75.9 million
international tourists visited the USA (Statista, 2018); while the
industry-wide revenue from international inbound tourists was
not readily available, AH&LA reported that in 2014,
international travel contributed to twenty per cent of the US
hotel revenue (AH&LA, 2015).
Furthermore, inbound trips by non-US residents had significant
positive influence on revenues of all sub-segments except for
economy sub-segment. This finding aligns with previous studies
that international tourism is considered as luxury (Lim, 1997);
non-US residents who can afford trips to the USA should be
affluent in their home countries (Crouch, 1995). The top six
tourist generating countries to the USA are Canada, Mexico,
UK, Japan, China and Germany (National Travel and Tourism
Office, 2015). These six countries accounted for 78.2 per cent
of the total international tourist arrivals in the USA in 2015
(National Travel and Tourism Office, 2015). Travelers from
most of the above-mentioned countries generally have great
buying power due to their strong economy. For example, 1.8
million Chinese travelers visited USA in year 2014 and
contributed $21.1bn to the US economy (Willett, 2015). Stats
also show that overseas travelers spend around $4,360 during
their visit to the USA (USA Travel Association, 2016), which is
substantially more than what most domestic leisure and business
travelers spend on a trip. However, among 74.8 million

internationals that visited the USA in 2014, only 26.5 million
stayed in a hotel (AH&LA, 2015). Thus, many international
tourists especially the more budget conscious ones prefer to stay
with their friends and families during their visits. In addition,
the tremendous growth of Airbnb in recent years (from 1.5
million in year 2014 to 2.5 million rooms in year 2015) could be
one of the reasons why international inbound trips do not have a
positive effect on the low-end hotel sub-segment (AIRDNA,
2016). For example, Zervas et al.’s (2017) study on the effect of
Airbnb on the revenues of hotels in Texas found that economy
sub-segment hotel revenues were most vulnerable to increased
competition from Airbnb rentals. Additionally, many young and
mostly solo budget conscious international tourists, such as
backpackers, often choose affordable shared accommodation
options such as hostels and Airbnb’s over hotels.
The number of hotels rooms did not add a significant negative
explanatory power to aggregate hotel industry revenue, which
contradicts previous finding that the demand for the aggregate
hotel industry is price inelastic (Canina and Carvell, 2005), and
that the increase in the number of hotel rooms in the industry
would result in reduced aggregate hotel revenue. The
insignificant relationship suggests that US hotel industry has a
relatively balanced supply and demand. The growth of hotel
supply can be absorbed easily by the market demand. The 2018
pipeline data from STR reported that the number of hotel rooms
under construction in the USA has declined or remained flat in
recent months (HotelNewsNow, 2018). Furthermore, STR’s
segmentation analysis in 2017 found that the demand is strong
and is not equal to supply growth (Cushman and Wakefield,
2017). However, sub-segment analyses revealed that the number
of luxury hotel rooms and the number of upper upscale hotel
rooms both had significant negative impact on their respective
sub-segment revenues. The result was consistent with previous
research finding that demand is price inelastic for luxury hotel
sub-segment and upper upscale sub-segment (Canina and
Carvell, 2005; Hiemstra and Ismail, 1993; Tran, 2011; Tran,

2015). Increased hotel supply in luxury and upper upscale sub-
segments resulted in higher level of market competition, leading
to the lower ADR in both sub-segments. As demand is price
inelastic in these two segments, the increase in demand is lower
than the changes in price, resulting in reduced revenue for
luxury and upper upscale hotel sub-segments. However, the
relationship between number of sub-segment hotel rooms and
sub-segment hotel revenues was insignificant for all other hotel
sub-segments. This finding might also be explained by previous
research finding that the price elasticity of demand varied
across hotel sub-segments with lower class hotels having higher
price elasticities than higher class hotels (Canina and Carvell,
2005). The changes in lower class hotel room rates caused by
supply fluctuation will result in a similar level of change in
demand from consumers’ side. As a result, the hotel revenue
will not have significant fluctuation for these hotel sub-
segments.
In summary, analyses on sub-segments indicated that the
aggregate US hotel industry revenue model cannot be applied
for all six hotel sub-segments. This is perhaps the most
interesting finding of this study. Substantial variations exist in
the number and type of predictors that can be used in different
hotel sub-segments. The following table provides a summary of
these segment specific predictors.
Table VII underscores an important point for practitioners,
policymakers, researchers and other stakeholders. When it
comes to estimating revenue, what works in one sub-segment
might not work for another. As a result, stakeholders need to
consider segment specific predictors when developing these
models. Even the aggregate model might not be fully applicable
to different sub-segments of the hotel industry, as evident from
our findings.
6. Implications
To our knowledge, this is the first study that analyzed the
predictive capacity of macroeconomic variables on aggregate
hotel industry and sub-segment revenue. The ARDL bounds

approach has not been applied in the past to estimate hotel
industry revenue. Numerous differences were found in
estimation models between sub-segments. A segment specific
revenue forecasting strategy is therefore recommended. One
approach fits all might not be the right strategy for tracking
hotel industry revenue.
The findings provide implications for hotel industry
practitioners, policy makers, industry associations, and
researchers. Hotel operators, especially those with multiple
properties can make marketing and operating decisions
regarding pricing, inventory allocation, and strategic
management based on the revenue estimation models specific to
their segments. For upper midscale and midscale hotels, the
operators should track consumers’ confidence, PCE and
international inbound tourism for making important strategic
decisions on pricing, mergers and acquisitions, and investing.
On a macro level, the model can be used by practitioners to
predict changes in hotel revenues based on estimations of the
macroeconomic variables used in this study. For luxury and
upper upscale hotel sub-segments specifically, the practitioners
need to pay close attention to the number of hotel rooms in their
sub-segment to adjust pricing strategies to obtain better
revenue. In addition, upper upscale, upscale and economy hotel
operators also need to take domestic travelers’ needs into
consideration.
Practitioners, investors, policymakers and researchers, in
hindsight, can track aggregate hotel industry revenue by
keeping an eye on domestic trips, international inbound
trips, CCI and PCE. In summary, a segment-specific strategy is
suggested for improving estimation accuracy.
7. Limitations
The availability of the right type of data was one of the
limitations of this study. For example, hotel revenue data
consist of a representative sample of the US lodging industry.
More specifically, the data received from STR only comprise of
hotels that voluntarily provide data to STR. Domestic trips was

measured by the number of domestic flights only, which did not
take into account domestic travelers traveling by other modes of
transportations. In addition, there is room for adding more
relevant explanatory variables to the model.
8. Conclusion
In summary, this study successfully developed a parsimonious
model of hotel revenue and identified four major
macroeconomic predictors of US hotel revenue specific to
different hotel sub-segments. However, the range of variability
the model exhibited indicates that there is room to add more
predictors to the model. Therefore, future studies should
consider more predictor variables such as exchange rate and
cost of transportation. Furthermore, it might be worthwhile to
test the utility of the model to hotel sub-segments grouped by
their locations such as urban, suburban, airport and interstate
settings.
Note
1.
NOHR for luxury and upper upscale sub-segments are (I (1)),
that is to say, NOHR for all other hotel sub-segments are I(0).
Figure 1.
US hotel industry and six sub-segments revenue trend
[Figure omitted. See PDF]
Figure 2.
Plot of CUSUM and CUSUMSQ (stability test)
[Figure omitted. See PDF]
Table I.
STR Hotel classification
Hotel segment
ADR* range
Luxury
$150-220
Upper-upscale
$120-150
Upscale
$100-120

Upper-midscale
$85-100
Midscale
$70-85
Economy
$55-70
Note:
*Annual 2017 ADR Range
Table II.
Unit root test results using ADF and PP test
ADF
PP
Variable
τc
τc+t
τc
τc+t
Aggregate revenue
−0.636 [13] (0.859)
−3.953** [13]
−2.950** [7]
−6.481*** [6]
Luxury revenue
−0.869 [13] (0.797)
−4.146*** [13]
−2.116 [53] (0.239)
−7.708*** [6]
Upper-upscale revenue
−0.827 [13] (0.809)
−3.703** [13]
−3.293** [17]
−7.350*** [8]
Upscale revenue
−0.261 [13] (0.927)
−3.968** [13]

−1.634 [12] (0.464)
−6.836*** [6]
Upper-midscale revenue
−0.557 [13] (0.876)
−4.152*** [13]
−2.411 [7] (0.140)
−6.318*** [6]
Midscale revenue
−0.960 [13] (0.767)
−3.345* [13]
−5.495*** [3]
−6.707*** [5]
Economy revenue
−2.461 [13] (0.126)
−3.085 [13] (0.112)
−6.276*** [2]
−6.668*** [3]
DT
−1.471 [13] (0.547)
−2.635 [13] (0.265)
−6.916*** [7]
−9.546*** [6]
CCI
−2.0740 (0.256)
−2.1640 (0.507)
−2.045 [1] (0.268)
−2.1640 (0.507)
IA
−0.615 [13] (0.864)
−2.112 [13] (0.536)
−3.641*** [8]
−6.744*** [8]
PCE
−3.315** 0
−1.8170 (0.694)
−2.828* [8]

−1.802 [8] (0.701)
NOHR _aggregate industry
−0.919 [12] (0.781)
−5.226*** [12]
−3.27** [9]
−3.440** [9]
NOHR _ luxury scale
−1.405 [12] (0.580)
−2.595 [12] (0.283)
−1.327 [8] (0.617)
−1.674 [9] (0.760)
NOHR _ upper upscale
−1.887 [12] (0.338)
−2.447 [12] (0.355)
−1.818 [11] (0.372)
−1.544 [11] (0.812)
NOHR _ upscale
−0.974 [12] (0.763)
−4.718*** [12]
−2.610* [10]
−2.449 [10] (0.353)
NOHR _ upper midscale
−1.124 [12] (0.707)
−5.743*** [12]
−3.708*** [8]
−2.926 [8] (0.156)
NOHR _ midscale
−3.333** [12]
−4.559*** [12]
−4.506*** [7]
−3.827** [7]
NOHR _ economy
−2.547 [12] (0.106)
−4.483*** [12]
−0.312 [9] (0.920)
−4.476*** [8]

Δ Aggregate Revenue
−2.675* [12]
NA
NA
NA
Δ Luxury Revenue
−3.228** [12]
NA
−40.775*** [51]
NA
Δ Upper-upscale Revenue
−3.141** [12]
NA
NA
NA
Δ Upscale Revenue
−2.792* [12]
NA
−20.908*** [18]
NA
Δ Upper-midscale Revenue
−2.580* [12]
NA
−17.004*** [13]
NA
Δ Midscale Revenue
−2.644* [12]
NA
NA
NA
Δ Economy Revenue
−2.411 [12] (0.140)
−2.429 [12] (0.364)
NA
NA
Δ DT

−4.752*** [12]
−4.743*** [12]
NA
NA
Δ CCI
−16.812*** [0]
−16.793*** 0
−17.001*** [5]
−16.987*** [5]
Δ IA
−4.257*** [12]
−4.269*** [12]
NA
NA
Δ PCE
NA
−17.413***0
NA
−17.676*** [8]
Δ NOHR_aggregate industry
−1.282 [11] (0.638)
NA
NA
NA
Δ NOHR_ luxury scale
−2.412 [11] (0.140)
−2.601 [11] (0.280)
−12.928*** [9]
−12.967*** [9]
Δ NOHR _ upper upscale
−2.405 [11] (0.141)
−2.807 [11] (0.196)
−22.061*** [11]
−23.081*** [11]
Δ NOHR _ upscale
−1.292 [11] (0.634)

NA
NA
−14.871*** [10]
Δ NOHR _ upper midscale
−1.066 [11] (0.729)
NA
NA
−12.130*** [9]
Δ NOHR _ midscale
NA
NA
NA
NA
Δ NOHR _ economy
−2.083 [11] (0.252)
NA
−14.678*** [8]
NA
1% level***
−3.457
−3.995
−3.455
−3.994
5% level**
−2.873
−3.428
−2.872
−3.427
10% level*
−2.573
−3.137
−2.573
−3.137
Notes:
All variables are in logs in the series and are results of
stationery tests with intercept and with trend and intercept; [.]s

for the ADF test are the appropriate lag lengths selected by SIC
(Schwarz Info Criterion), and for PP test are the optimal
bandwidths. △ denotes the first difference of the variable; *p <
0.1; **p < 0.05; ***p <0.01;
*p value is in the parenthesis
DT; – Domestic trips; CCI – Consumer Confidence Index; IA –
International arrivals; PCE – Personal Consumption
Expenditure; NOH – number of hotels
Table III.
ARDL Bounds test for Co-integration
lnAggregate revenue
lnLux
lnUpperup
lnUpscale
lnUppermid
lnMidscale
lnEconomy
F-Bound test
14.644***
10.485***
4.739***
12.448***
25.723***
10.005***
10.543***
Table IV.
Optimal lag length for each hotel Sub-Segment revenue
Aggregate industry
Luxury
Upper-upscale
Upscale
Upper-midscale
Midscale
Economy

Optimal lag length
3 or 8
8
3 or 6
4 or 8
6 or 8
8
7 or 8
Table V.
Estimated Long-Run coefficients for the industry and Sub-
Segment revenue models
Coefficients Variables
Dependent variable and ARDL Model
lnAggregate revenue
lnLux
lnUpperup
lnUpscale
lnUppermid
lnMidscale
lnEconomy
(7,10,9,10,0,10)
(8,7,7,0,8,6)
(6,11,9,0,7,9)
(8,10,10,10,5,9)
(4,10,10,10,0,10)
(7,12,11,10,12,11)
(6,11,12,11,0,12)
In DT
0.203** (0.101)
−0.112 (0.123)
0.305*** (0.116)
0.154** (0.074)
0.072 (0.122)
0.148 (0.119)

0.390*** (0.135)
In CCI
0.176*** (0.012)
0.049** (0.024)
0.026 (0.013)
0.130*** (0.011)
0.146*** (0.015)
0.190*** (0.016)
0.258*** (0.019)
In IA
0.175*** (0.033)
0.444*** (0.041)
0.280*** (0.045)
0.279*** (0.026)
0.167 *** (0.038)
0.074* (0.038)
−0.003 (0.052)
In PCE
0.746*** (0.178)
2.008*** (0.184)
1.644*** (0.199)
0.924*** (0.133)
1.050*** (0.207)
0.767*** (0.214)
0.075 (0.263)
In NOHR
−0.004 (0.157)
−0.565*** (0.205)
−2.868*** (0.435)
−0.132 (0.105)
0.190 (0.115)
0.030 (0.133)
0.209 (0.237)
Notes:
*p < 0.1; **p < 0.05; ***p < 0.01;
*Note: standard error is in the parenthesis;

all regressions are with unrestricted constant and unrestricted
trend.
F-Bound Test reported for Co-integration;
DT – Domestic trips; CCI – Consumer Confidence Index; IA –
International arrivals; PCE-Personal Consumption
Expenditure; NOH – number of hotels
Table VI.
Error correction representation for the selected ARDL models
Dependent variable and ARDL Model
Coefficients Variables
<
Sheet1Column AColumn BColumn CYearRevenue
$ in MillionsGrowth
%2015138,58202017144,7291.7Average141883.61.46Sum425,6
514.41. exp
In SLP 3, you will use an actual IBISWorld report to prepare an

Excel spreadsheet
and graphs.
For the industry that you selected, you will use the “Industry
Performance” tab.
On the “Industry Performance” tab, scroll down to the bottom of
the page to the
section entitled, Historical Performance Data. Click the small
arrow pointing
downward (located at the top right of the Historical
Performance Data section) and
export the data to Excel. Once you have the Excel spreadsheet
with the data for
“Historical Performance Data”:
1.Highlight the data in column B, then create a Column Chart
illustrating the Revenue
($m) data. For example,
https://my-ibisworld-
com.ezproxy.trident.edu/us/en/industry/48111a/industry-
performance
1. Use the Excel formula to calculate the Average for each
column of
data within the sheet.
2. Add this information to the bottom of each column.
3. Use Excel to calculate the Sum for each column of data
within the
sheet.

4. Add this information below the row of Averages for each
column.
5. Change the name of this Tab to Revenue Growth.
https://my-ibisworld-
com.ezproxy.trident.edu/us/en/industry/48111a/industry-
performance
Column A Column B Column C
Year Revenue
$ in Millions
Growth %
2015 138,582 0.0
2017 144,729 1.7
Average 141883.6 1.46
Sum 425,651 4.4
Industry DataYearIndustry Turnover ($ million)Industry Gross
Product ($ million)Number of Establishments (Units)Number of
Enterprises (Units)Employment (People)Exports ($
million)Imports ($ million)Total Wages ($ million)Domestic
Demand ($ million)International trips by US residents
(Million)200542113.09344.31793.0309.089927.05820.3148.620
0646140.19707.61714.0301.078230.05092.1153.6200750106.21
1558.41721.0322.081137.05311.3159.8200855389.56248.91694.

0300.088352.05342.1160.5200944443.88364.71616.0276.09148
3.05682.5151.2201053925.412112.71506.0265.087840.05712.91
59.7201158890.110895.91519.0261.091616.06215.4166.120125
8785.010477.71465.0253.089913.05934.5173.4201358630.8130
01.11393.0255.090842.06459.7182.1201458374.912742.91405.0
258.085930.06246.7191.2201555421.817925.41404.0256.09925
4.07678.0203.2201650170.016509.81244.0229.099247.08037.22
14.1201749752.215667.51232.0226.0110691.09076.1224.62018
54291.017157.51256.0230.0108599.09720.3236.3201941169.81
5505.51116.0206.091265.08009.2244.4202038577.512443.8106
3.0197.087301.07630.1252.2202139390.412783.81049.0194.088
367.07736.8260.3202240376.413160.61034.0190.089818.07877.
2268.6202341458.713534.61019.0187.091026.08004.1277.2202
442623.213897.61014.0186.092520.08154.2286.5202543856.71
4230.41010.0185.093838.08294.3296.0202645827.514676.6101
0.0184.096017.08523.0305.8
Annual ChangeYearIndustry Turnover (%)Industry Gross
Product (%)Number of Establishments (%)Number of
Enterprises (%)Employment (%)Exports (%)Imports (%)Total
Wages (%)Domestic Demand (%)International trips by US
residents (%)20069.63.9-4.4-2.6-13.0-
12.53.420078.619.10.47.03.74.34.0200810.5-45.9-1.6-
6.88.90.60.42009-19.833.9-4.6-8.03.56.4-5.8201021.344.8-6.8-
4.0-4.00.55.620119.2-10.10.9-1.54.38.84.02012-0.2-3.8-3.6-3.1-
1.9-4.54.42013-0.324.1-4.90.81.08.85.02014-0.4-2.00.91.2-5.4-
3.35.02015-5.140.7-0.1-0.815.522.96.32016-9.5-7.9-11.4-10.6-
0.04.75.42017-0.8-5.1-1.0-1.311.512.94.920189.19.51.91.8-
1.97.15.22019-24.2-9.6-11.2-10.4-16.0-17.63.42020-6.3-19.8-
4.8-4.4-4.4-4.73.220212.12.7-1.3-1.51.21.43.220222.52.9-1.4-
2.11.61.83.220232.72.8-1.5-1.61.31.63.220242.82.7-0.5-
0.51.61.93.420252.92.4-0.4-0.51.41.73.320264.53.10.0-
0.62.32.83.3
Key RatiosYearIVA/Revenue (%)Imports/Demand
(%)Exports/Revenue (%)Revenue per Employee
($'000)Wages/Revenue (%)Employees per estab.Average Wage
($)200522.2468.313.850.264722.5200621.0589.811.045.665091.

4200723.1617.610.647.165460.9200811.3626.99.652.260463.82
00918.8485.812.856.662115.4201022.5613.910.658.365037.620
1118.5642.810.660.367841.9201217.8653.810.161.466002.7201
322.2645.411.065.271109.2201421.8679.310.761.272695.22015
32.3558.413.970.777357.1201632.9505.516.079.880981.820173
1.5449.518.289.881994.9201831.6499.917.986.589506.3201937
.7451.119.581.887757.6202032.3441.919.882.187399.9202132.
5445.819.684.287553.0202232.6449.519.586.987701.8202332.6
455.519.389.387932.0202432.6460.719.191.288134.5202532.44
67.418.992.988389.6202632.0477.318.695.188765.5
Key External DriversYearInbound trips by non-US
residentsInternational trips by US residentsWorld price of crude
oilCorporate profitPer capita disposable
income20125.54.40.98.32.620137.05.0-0.9-1.1-2.020145.25.0-
7.53.53.320153.26.3-47.2-3.83.42016-1.85.4-15.7-
3.41.120171.04.923.3-2.12.320183.35.429.41.03.42019-0.61.6-
10.2-2.62.42020-22.1-20.7-32.70.21.9202127.622.726.9-
0.31.720222.32.41.9-
0.21.720232.32.32.00.21.720242.22.31.80.61.820252.22.31.90.7
1.9
Industry Financial RatiosRatioApr 09-Mar 10Apr 10-Mar 11Apr
11-Mar 12Apr 12-Mar 13Apr 13-Mar 14Apr 14-Mar 15Apr 15-
Mar 16Apr 16-Mar 17Apr 17-Mar 18Apr 18-Mar 19Small
(<$10m)Medium ($10-$50m)Large (>$50m)Current
Ratio1.31.21.31.41.51.41.01.61.91.52.10.9Quick
Ratio0.90.80.91.11.11.00.81.01.40.82.00.6Sales / Receivables
(Trade Receivables
Turnover)12.111.316.114.612.012.911.112.99.818.217.221.1Da
ys' Receivables30.232.322.725.030.428.332.928.337.2Cost of
Sales / Inventory (Inventory
Turnover)41.3150.840.940.429.4167.771.837.560.843.1999.914.
7Days' Inventory8.82.48.99.012.42.25.19.76.0Cost of Sales /
Payables (Payables
Turnover)13.612.816.418.216.714.511.914.312.517.821.711.5D
ays' Payables26.828.522.320.121.925.230.725.529.2Sales /
Working Capital21.540.927.627.315.521.7-

303.216.511.722.16.5-62.9Earnings Before Interest & Taxes
(EBIT) / Interest3.43.35.26.16.44.83.26.54.63.42.9Net Profit +
Dep., Depletion, Amort. / Current Maturities LT
Debt2.62.94.03.5Fixed Assets / Net
Worth1.31.51.41.31.21.51.61.01.31.61.25.0Debt / Net
Worth2.32.51.72.11.32.13.82.22.41.81.55.2Tangible Net
Worth26.329.422.522.128.424.911.732.931.427.935.615.1Profit
before Taxes / Net Worth,
%18.823.523.432.824.228.620.628.124.527.526.925.2Profit
before Taxes / Total Assets,
%4.74.86.09.010.35.74.58.97.97.910.87.1Sales / Net Fixed
Assets7.04.86.07.16.12.83.93.05.54.46.43.7Sales / Total Assets
(Asset Turnover)2.02.12.72.61.91.81.71.82.02.02.02.0Cash
from Trading34.430.728.734.940.147.536.135.434.446.3Cash
after Operations7.75.25.85.39.09.32.46.711.811.412.9Net Cash
after Operations7.24.95.76.311.210.03.08.011.17.513.0Cash
after Debt Amortization1.92.11.72.72.73.71.23.23.50.73.6Debt
Service P&I Coverage2.42.32.71.92.13.91.73.61.31.7Interest
Coverage (Operating Cash)6.56.55.79.06.710.52.79.35.84.6Cash
&
Equivalents11.69.79.715.613.513.414.611.316.017.029.67.8Tra
de Receivables
(net)22.726.022.822.925.720.619.617.918.113.812.814.3Invento
ry6.86.98.16.911.76.56.67.46.05.63.88.1All Other Current
Assets3.62.53.83.93.44.83.62.54.43.12.73.8Total Current
Assets44.745.244.449.254.345.344.439.144.539.648.834.0Fixed
Assets
(net)37.440.842.738.036.444.538.141.837.042.635.747.2Intangi
bles (net)4.91.94.03.84.83.77.15.99.29.67.013.6All Other Non-
Current Assets12.912.28.98.94.56.510.413.29.28.38.55.3Total
Assets100.0100.0100.0100.0100.0100.0100.0100.0100.0100.010
0.0100.0Total Assets
($m)1325.51148.81365.6603.8596.0781.21527.21244.51638.114
21.543.5164.31213.7Notes Payable-Short
Term6.06.28.25.16.85.28.94.41.83.13.23.6Current Maturities
L/T/D4.03.84.12.62.72.95.13.03.74.12.95.6Trade

Payables15.316.613.918.313.720.618.39.912.49.36.612.9Income
Taxes Payable0.10.10.22.00.10.00.10.30.51.00.30.6All Other
Current
Liabilities11.816.116.115.217.012.213.29.09.811.79.815.0Total
Current
Liabilities37.242.942.443.240.340.845.626.728.129.122.837.8L
ong Term
Debt22.519.924.822.220.523.525.330.328.026.122.231.2Deferre
d Taxes1.01.31.72.61.32.21.60.20.30.40.30.4All Other Non-
Current Liabilities8.14.64.66.14.64.98.74.13.06.812.12.0Net
Worth31.231.326.525.933.228.618.838.840.637.542.628.7Total
Liabilities & Net Worth
($m)1325.51148.81365.6603.8596.0781.21527.21244.51638.114
21.543.5164.31213.7Maximum No. of Statements
Used53.049.046.034.033.038.043.034.035.035.014.07.014.0
Industry StructureLevelTrendLife CycleMatureRevenue
VolatilityHighCapital IntensityIndustry
AssistanceHighSteadyConcentration LevelHighRegulation
LevelHeavySteadyTechnology ChangeHighBarriers to
EntryHighSteadyIndustry
GlobalizationHighIncreasingCompetition LevelHighIncreasing
Major PlayersNameMarket ShareAmerican Airlines Group
Inc.36.90Delta Air33.30UAH32.10
Products & ServicesNameRevenue%Revenue $mPassenger
transportation72.027,775.80Cargo
transportation13.65,246.54Fees5.52,121.76Other8.93,433.40
Major MarketsNameRevenue%Revenue
$mAtlantic39.715315.27Latin
America31.312074.76Pacific22.18525.63Other6.92661.85
Industry Life CycleIndustryChange in Share of Economy
(%)DVD, Game & Video Rental in US-14.91-13.50Solar Panel
Manufacturing in US-12.70-13.29Tobacco Growing in US-9.83-
12.19Record Stores in US-8.02-9.82Computer Stores in US-
7.47-6.75Database & Directory Publishing in US-7.39-
7.70Book, Magazine & Newspaper Wholesaling in US-5.72-
7.97Office Supply Stores in US-5.51-9.20Movie & Video

Distribution in US-5.32-4.92Coal Mining in US-5.32-
5.12Camera Stores in US-5.13-6.02Apparel Knitting Mills in
US-4.95-5.09For-Profit Universities in US-4.71-2.46Postal
Service in US-4.65-6.19Chicken Egg Production in US-4.25-
8.27Vending Machine Operators in US-4.05-2.62Magazine &
Periodical Publishing in US-3.82-6.96Wood Pulp Mills in US-
3.78-1.29Paper Wholesaling in US-3.77-7.09Wired
Telecommunications Carriers in US-3.75-4.81Industrial Banks
in US-3.71-2.04Ink Manufacturing in US-3.50-6.85International
Airlines in US-3.24-3.98Department Stores in US-3.21-8.56Art
& Office Supply Manufacturing in US-3.18-5.04Office
Stationery Manufacturing in US-3.16-4.39Printing Services in
US-3.09-6.60Quick Printing in US-3.03-4.69Commodity
Dealing and Brokerage in US-3.02-4.36Shoe Stores in US-3.02-
1.89News Syndicates in US-2.99-2.65Nonferrous Metal
Refining in US-2.82-2.49Recordable Media Manufacturing in
US-2.80-6.72Savings Banks & Thrifts in US-2.76-2.52Office
Stationery Wholesaling in US-2.72-3.41Paper Product
Manufacturing in US-2.57-2.65Cattle & Hog Wholesaling in
US-2.47-3.72Jewelry Stores in US-2.44-5.06Formal Wear &
Costume Rental in US-2.40-2.56Wind Turbine Manufacturing in
US-2.40-9.00Florists in US-2.37-4.22Consumer Electronics
Stores in US-2.35-3.35Book Stores in US-2.35-3.78Greeting
Cards & Other Publishing in US-2.33-7.17Computer Peripheral
Manufacturing in US-2.33-3.14Wholesale Trade Agents and
Brokers in US-2.29-1.92Direct Mail Advertising in US-2.25-
2.71Camera & Film Wholesaling in US-2.24-2.90Women’s,
Girls’ and Infants’ Apparel Manufacturing in US-2.12-
4.95Concrete Pipe & Block Manufacturing in US-2.08-1.54Gift
Shops & Card Stores in US-2.04-2.19Dry Cleaners in US-1.86-
3.12Shoe Repair in US-1.83-2.65Toy, Doll & Game
Manufacturing in US-1.75-1.73Leather Tanning & Finishing in
US-1.70-0.86Debt Collection Agencies in US-1.65-
0.55Newspaper Publishing in US-1.64-6.37Electronic &
Computer Repair Services in US-1.56-2.20Fuel Dealers in US-
1.50-3.20Plant & Flower Growing in US-1.46-0.58Radio

Broadcasting in US-1.41-0.64Piece Goods, Notions & Other
Apparel Wholesaling in US-1.41-3.81Automobile Electronics
Manufacturing in US-1.38-3.27Automobile Brakes
Manufacturing in US-1.36-2.18Car & Automobile
Manufacturing in US-1.36-4.77Coated & Laminated Paper
Manufacturing in US-1.35-3.37Business Certification & IT
Schools in US-1.34-1.71Mail Order in US-1.32-2.28Computer &
Printer Leasing in US-1.32-6.34Farm Product Storage &
Warehousing in US-1.30-3.05Blind & Shade Manufacturing in
US-1.29-1.48Jewelry Manufacturing in US-1.23-
3.16Communication Equipment Manufacturing in US-1.19-
2.84Printing in US-1.16-3.52Cement Manufacturing in US-1.15-
0.79Loan Administration, Check Cashing & Other Services in
US-1.12-1.33Frozen Food Wholesaling in US-1.11-0.89Dye &
Pigment Manufacturing in US-1.11-1.84Paper Bag & Disposable
Plastic Product Wholesaling in US-1.11-0.52Ferrous Metal
Foundry Products in US-1.08-1.46Men's Clothing Stores in US-
1.05-4.27Hog & Pig Farming in US-1.04-2.48Electrical
Equipment Manufacturing in US-1.03-1.37Metal Can &
Container Manufacturing in US-1.03-2.32Domestic Airlines in
US-1.03-1.50Bowling Centers in US-1.00-1.30VoIP in US-0.97-
2.78Day Care in US-0.97-0.55Telecommunication Networking
Equipment Manufacturing in US-0.94-1.89Egg & Poultry
Wholesaling in US-0.94-4.92Orange & Citrus Groves in US-
0.91-0.66Hand Tool & Cutlery Manufacturing in US-0.89-
2.47Meat Markets in US-0.84-2.20Beef Cattle Production in
US-0.83-5.04Bottled Water Production in US-0.82-1.53Cable
Providers in US-0.80-3.43Millwork in US-0.78-0.84Satellite TV
Providers in US-0.75-4.13Plastic Products Miscellaneous
Manufacturing in US-0.74-1.44Circuit Board & Electronic
Component Manufacturing in US-0.74-0.94Automobile Engine
& Parts Manufacturing in US-0.73-3.10Small Specialty Retail
Stores in US-0.71-2.58TV & Appliance Wholesaling in US-
0.70-1.85Inland Water Transportation in US-0.69-1.72Flower &
Nursery Stock Wholesaling in US-0.69-1.70Jewelry & Watch
Wholesaling in US-0.68-0.47Hay & Crop Farming in US-0.67-

2.16Gold & Silver Ore Mining in US-0.67-0.61Paper Mills in
US-0.65-5.23Lumber & Building Material Stores in US-0.65-
0.67Carpet Mills in US-0.65-0.53Metalworking Machinery
Manufacturing in US-0.62-0.91Metal Stamping & Forging in
US-0.62-1.08Toy & Craft Supplies Wholesaling in US-0.60-
2.03Woodworking Machinery Manufacturing in US-0.60-
1.02Beef & Pork Wholesaling in US-0.60-3.45Sugarcane
Harvesting in US-0.59-3.74Oilseed Farming in US-0.59-
0.37Home Furnishings Stores in US-0.58-0.95Musical
Instrument & Supplies Stores in US-0.57-2.50SUV & Light
Truck Manufacturing in US-0.57-0.69Textile Mills in US-0.55-
2.73Janitorial Equipment Supply Wholesaling in US-0.53-
1.85Marinas in US-0.51-0.69Metal Plating & Treating in US-
0.50-0.50Auto Parts Manufacturing in US-0.49-1.63Community
Colleges in US-0.49-1.82Cut and Sew Manufacturers in US-
0.47-3.90Children's & Infants' Clothing Stores in US-0.47-
4.26Horse & Other Equine Production in US-0.46-1.69Engine &
Turbine Manufacturing in US-0.46-1.64Business Service
Centers in US-0.44-1.01Tire Manufacturing in US-0.44-
1.70Geophysical Services in US-0.44-3.20Wire & Spring
Manufacturing in US-0.43-0.64Hardware Manufacturing in US-
0.43-2.35Weight Loss Services in US-0.42-3.38Motorcycle,
Bike & Parts Manufacturing in US-0.41-3.03Wheat, Barley &
Sorghum Farming in US-0.40-2.44Motorcycle Dealership and
Repair in US-0.39-1.08Document Preparation Services in US-
0.36-2.78Fabric, Craft & Sewing Supplies Stores in US-0.36-
6.72Book Publishing in US-0.35-2.38Photofinishing in US-
0.34-4.19Sporting Goods Stores in US-0.32-1.52Copper, Nickel,
Lead & Zinc Mining in US-0.31-0.69Lighting Fixtures
Manufacturing in US-0.27-0.74Job Training & Career
Counseling in US-0.25-2.24Golf Courses & Country Clubs in
US-0.25-1.06Logging in US-0.25-1.45Specialty Food Stores in
US-0.24-0.95Dairy Wholesaling in US-0.23-1.65Ocean &
Coastal Transportation in US-0.23-0.52Nonferrous Metal
Foundry Products Manufacturing in US-0.23-2.02Industrial
Laundry & Linen Supply in US-0.22-1.00Nuclear Power in US-

0.20-1.54Homeowners' Associations in US-0.19-1.84Medical
Instrument & Supply Manufacturing in US-0.18-1.28Ceramics
Manufacturing in US-0.15-2.63Lighting & Bulb Manufacturing
in US-0.13-3.67Civic, Social & Youth Organizations in US-
0.12-1.20Canned Fruit & Vegetable Processing in US-0.12-
2.61Bars & Nightclubs in US-0.10-0.59Electric Power
Transmission in US-0.10-0.47Appliance Repair in US-0.09-
1.43Machinery Maintenance & Heavy Equipment Repair
Services in US-0.08-1.33Employment & Recruiting Agencies in
US-0.08-0.61Agribusiness in US-0.05-1.40Rubber Product
Manufacturing in US-0.05-1.55Heating & Air Conditioning
Equipment Manufacturing in US-0.05-1.24Chemical Product
Manufacturing in US-0.04-2.14Navigational Instrument
Manufacturing in US-0.04-1.04Machine Shop Services in US-
0.03-0.72New Car Dealers in US-0.02-2.17Juice Production in
US0.07-2.50Auto Parts Wholesaling in US0.08-1.06Soybean
Farming in US0.09-4.33Gasoline & Petroleum Bulk Stations in
US0.11-1.80Furniture Repair & Reupholstery in US0.11-
1.77Automobile Metal Stamping in US0.12-2.25Pump &
Compressor Manufacturing in US0.12-2.38Laundromats in
US0.13-1.09Fish & Seafood Wholesaling in US0.13-0.56Glasses
& Contact Lens Manufacturing in US0.16-1.33Hobby & Toy
Stores in US0.17-1.00Chemical Wholesaling in US0.17-
0.64Refrigeration Equipment Wholesaling in US0.18-
0.65Mineral & Phosphate Mining in US0.21-2.97Media Buying
Agencies in US0.22-1.29Computer Manufacturing in US0.23-
1.07Inorganic Chemical Manufacturing in US0.23-3.93Paint
Wholesaling in US0.24-1.77Dairy Product Production in
US0.27-1.95Community Housing & Homeless Shelters in
US0.28-1.74Furniture Stores in US0.28-0.77Public Schools in
US0.28-0.68Bridge & Elevated Highway Construction in
US0.29-0.65Fruit & Vegetable Markets in US0.30-
0.76Commercial Leasing in US0.32-0.79HR Consulting in
US0.32-1.41Cabinet & Vanity Manufacturing in US0.34-
0.53Chicken & Turkey Meat Production in US0.35-2.24Glass
Product Manufacturing in US0.38-0.57Fruit & Vegetable

Wholesaling in US0.38-1.08Confectionery Wholesaling in
US0.38-1.89Plastic Film, Sheet & Bag Manufacturing in
US0.39-0.70Women's Clothing Stores in US0.39-6.16Cookie,
Cracker & Pasta Production in US0.41-2.94Orphanages &
Group Homes in US0.41-2.00Mining, Oil & Gas Machinery
Manufacturing in US0.42-2.26Corn, Wheat & Soybean
Wholesaling in US0.42-2.80Public Transportation in US0.45-
0.52Supermarkets & Grocery Stores in US0.46-0.38Beer
Wholesaling in US0.47-0.40Auto Parts Stores in US0.48-
1.38Footwear Wholesaling in US0.48-5.11Law Firms in
US0.48-0.95Dry Docks & Cargo Inspection Services in US0.49-
2.34Vegetable Farming in US0.50-1.47Steel Rolling & Drawing
in US0.51-0.88Religious Organizations in US0.51-
0.92Automobile Wholesaling in US0.53-1.69National & State
Parks in US0.53-1.07Gym & Exercise Equipment Manufacturing
in US0.56-0.41Automobile Interior Manufacturing in US0.57-
1.16Home Builders in US0.59-1.09Chiropractors in US0.60-
0.36Automobile Transmission Manufacturing in US0.60-
1.44Forest Support Services in US0.61-0.78ATV, Golf Cart &
Snowmobile Manufacturing in US0.62-2.07Sanitary Paper
Product Manufacturing in US0.62-1.92Computer & Packaged
Software Wholesaling in US0.63-3.03Frozen Food Production in
US0.63-0.79Hosiery Mills in US0.68-3.01Media Representative
Firms in US0.71-0.86Aluminum Manufacturing in US0.71-
1.21Valve Manufacturing in US0.72-0.65Paperboard Mills in
US0.72-1.52Truck Rental in US0.73-0.48Billboard & Sign
Manufacturing in US0.73-1.02Electronic Part & Equipment
Wholesaling in US0.74-1.47Shoe & Footwear Manufacturing in
US0.81-1.13Celebrity & Sports Agents in US0.82-0.79Tractors
& Agricultural Machinery Manufacturing in US0.82-2.02Soft
Drink, Baked Goods & Other Grocery Wholesaling in US0.83-
1.51Leather Good & Luggage Manufacturing in US0.84-
0.61Athletic & Sporting Goods Manufacturing in US0.87-
0.80Pesticide Manufacturing in US0.89-2.79Women's &
Children's Apparel Wholesaling in US0.89-1.50Ice Cream
Production in US0.92-0.56Household Furniture Manufacturing

in US0.94-1.31Rail Transportation in US0.95-0.46Construction
Machinery Manufacturing in US0.98-1.42Plastic Bottle
Manufacturing in US0.99-1.94Automobile Steering &
Suspension Manufacturing in US1.04-1.18Sugar Processing in
US1.11-0.64Chocolate Production in US1.13-0.90Crop Services
in US1.15-1.62Apartment Rental in US1.17-1.78Paint
Manufacturing in US1.19-2.18Lotteries & Native American
Casinos in US1.19-1.05Scientific & Economic Consulting in
US1.22-0.50Environmental Consulting in US1.23-
0.76Community Food Services in US1.27-0.53Blood & Organ
Banks in US1.27-1.38Single Location Full-Service Restaurants
in US1.28-0.38Hunting & Trapping in US1.29-0.76Soap &
Cleaning Compound Manufacturing in US1.31-1.20Drug,
Cosmetic & Toiletry Wholesaling in US1.31-0.63Maids,
Nannies & Gardeners in US1.35-0.42Asphalt Manufacturing in
US1.35-0.85Abrasive & Sandpaper Manufacturing in US1.40-
0.83Farm, Lawn & Garden Equipment Wholesaling in US1.44-
0.67Office Furniture Manufacturing in US1.46-0.73Coal &
Natural Gas Power in US1.47-1.72Soda Production in US1.48-
2.85Securities Brokering in US1.48-1.21Medical Device
Manufacturing in US1.53-1.64Boiler & Heat Exchanger
Manufacturing in US1.53-1.48Oil & Gas Field Services in
US1.55-1.70Promotional Products in US1.55-0.98Direct Selling
Companies in US1.56-1.48Fertilizer Manufacturing in US1.56-
1.91Copper Rolling, Drawing & Extruding in US1.58-
2.59Primary Care Doctors in US1.58-0.48Petroleum Refining in
US1.60-1.89Gypsum Product Manufacturing in US1.61-
1.28Men's & Boys' Apparel Manufacturing in US1.67-
0.75Train, Subway & Transit Car Manufacturing in US1.82-
0.89Storage & Warehouse Leasing in US1.82-0.91Photography
in US1.83-0.82Wireless Telecommunications Carriers in
US1.85-1.49Animal Food Production in US1.86-2.28Cigarette &
Tobacco Products Wholesaling in US1.87-4.39Syrup &
Flavoring Production in US1.88-5.34Water Supply & Irrigation
Systems in US1.89-1.40Psychiatric Hospitals in US1.92-
0.93Furniture Wholesaling in US1.93-0.47Moving Services in

US1.94-0.92Tank & Refrigeration Trucking in US1.98-
0.44Casino Hotels in US2.01-0.90Copier & Office Equipment
Wholesaling in US2.03-2.68Bread Production in US2.05-
0.87Open-End Investment Funds in US2.09-0.57Metal Tank
Manufacturing in US2.10-0.95Specialized Storage &
Warehousing in US2.11-0.42Swimming Pool Construction in
US2.11-0.55Family Clothing Stores in US2.12-1.49Audio
Production Studios in US2.15-0.81Tourism in US2.16-
0.65Screw, Nut & Bolt Manufacturing in US2.20-0.49Timber
Services in US2.20-1.14Hotels & Motels in US2.21-
0.48Seafood Preparation in US2.27-1.08Health Stores in
US2.28-1.47Synthetic Fiber Manufacturing in US2.29-
0.41Racing & Individual Sports in US2.32-3.09Industrial
Equipment Rental & Leasing in US2.40-1.02Vitamin &
Supplement Manufacturing in US2.56-1.12Pharmacies & Drug
Stores in US2.58-0.40Language Instruction in US2.62-
2.39Wedding Services in US2.64-1.94Petrochemical
Manufacturing in US2.84-2.04Intellectual Property Licensing in
US2.84-1.75Human Resources & Benefits Administration in
US3.05-0.57Snack Food Production in US3.14-2.96Advertising
Agencies in US3.23-0.80Flour Milling in US3.27-1.95Cosmetic
& Beauty Products Manufacturing in US3.36-2.38Steam & Air-
Conditioning Supply in US3.61-1.45Rail Maintenance Services
in US3.73-0.79Tugboat & Shipping Navigational Services in
US3.80-0.55Cereal Production in US3.95-1.83Reinsurance
Carriers in US4.11-6.23Sheep Farming in US4.30-0.91Coffee
Production in US5.12-2.58Dairy Farms in US-4.481.36Coal &
Ore Wholesaling in US-3.692.96Corn Farming in US-2.13-
0.30Land Development in US-1.65-0.00Satellite
Telecommunications Providers in US-1.583.57Credit Card
Issuing in US-1.421.30Gas Stations in US-1.361.03Fish &
Seafood Aquaculture in US-1.310.58Life Insurance & Annuities
in US-1.230.31Grocery Wholesaling in US-1.090.12Commercial
Banking in US-0.931.57Carpet Cleaning in US-0.920.24Tool &
Hardware Wholesaling in US-0.900.74Heavy Engineering
Construction in US-0.83-0.14Recyclable Material Wholesaling

in US-0.764.56Alarm, Horn & Traffic Control Equipment
Manufacturing in US-0.640.17Telecommunications Resellers in
US-0.571.32Clay Brick & Product Manufacturing in US-
0.540.91Iron Ore Mining in US-0.413.02Art Dealers in US-
0.370.73Plastics & Rubber Machinery Manufacturing in US-
0.350.88Plastic Pipe & Parts Manufacturing in US-
0.310.57Gasoline & Petroleum Wholesaling in US-
0.231.79Movie Theaters in US-0.231.06Historic Sites in US-
0.230.77Plastics Wholesaling in US-0.220.16Stone Mining in
US-0.200.30Cable Networks in US-0.044.40Mattress
Manufacturing in US-0.02-0.33Wiring Device Manufacturing in
US-0.020.01Packaging & Labeling Services in US-0.011.34Wire
& Cable Manufacturing in US0.03-0.15Oil Change Services in
US0.04-0.02Nursery & Garden Stores in US0.060.08Gas
Stations with Convenience Stores in US0.060.24Stone, Concrete
& Clay Wholesaling in US0.090.07Used Car Parts Wholesaling
in US0.093.44Meat, Beef & Poultry Processing in US0.10-
0.35Recreational Vehicle Dealers in US0.161.25Metal
Wholesaling in US0.162.79Sawmills & Wood Production in
US0.180.60Tire Dealers in US0.20-0.13Printing, Paper, Food,
Textile & Other Machinery Manufacturing in US0.22-0.16Real
Estate Appraisal in US0.230.64Wood Paneling Manufacturing in
US0.240.67Fish & Seafood Markets in US0.24-0.16Precast
Concrete Manufacturing in US0.24-0.22Costume & Team
Uniform Manufacturing in US0.25-0.20Restaurant & Hotel
Equipment Wholesaling in US0.270.26Eye Glasses & Contact
Lens Stores in US0.320.30Conveyancing Services in
US0.320.19Semiconductor & Circuit Manufacturing in
US0.330.35Copier & Optical Machinery Manufacturing in
US0.341.15Stock & Commodity Exchanges in
US0.353.28Laminated Plastics Manufacturing in
US0.370.57Water & Sewer Line Construction in
US0.380.22Boat Building in US0.42-0.14Wood Pallets & Skids
Production in US0.42-0.30Podiatrists in US0.430.39Accounting
Services in US0.440.05Semiconductor Machinery
Manufacturing in US0.440.73Urethane Foam Manufacturing in

US0.460.26Margarine & Cooking Oil Processing in US0.47-
0.13Loan Brokers in US0.490.58Hardware Stores in
US0.500.91Excavation Contractors in US0.510.63Cemetery
Services in US0.51-0.23Cardboard Box & Container
Manufacturing in US0.522.65Fishing in US0.541.29Lumber
Wholesaling in US0.550.38Public School Bus Services in
US0.550.52Funeral Homes in US0.560.38Commercial Building
Construction in US0.581.40Parking Lots & Garages in
US0.590.63Forklift & Conveyor Manufacturing in
US0.650.20Trade & Technical Schools in US0.65-0.16Audio &
Video Equipment Manufacturing in US0.660.96Ski &
Snowboard Resorts in US0.680.75Surveying & Mapping
Services in US0.680.59Nonferrous Metal Rolling & Alloying in
US0.690.15Laboratory Supply Wholesaling in
US0.690.14Campgrounds & RV Parks in US0.690.69Heating &
Air Conditioning Wholesaling in US0.701.25Adhesive
Manufacturing in US0.74-0.19Major Household Appliance
Manufacturing in US0.741.28Billboard & Outdoor Advertising
in US0.74-0.29Insurance Brokers & Agencies in US0.75-
0.29Prefabricated Home Manufacturing in
US0.753.05Warehouse Clubs & Supercenters in US0.75-
0.09Market Research in US0.760.62Road & Highway
Construction in US0.78-0.26Aircraft Maintenance, Repair &
Overhaul in US0.780.55Home Improvement Stores in
US0.780.91Auto Leasing, Loans & Sales Financing in
US0.790.29Masonry in US0.800.18Security Services in
US0.841.02Retirement & Pension Plans in
US0.8437.20Electrical Equipment Wholesaling in
US0.860.31Print Advertising Distribution in US0.861.92Floor
Covering Stores in US0.860.60Architects in US0.86-
0.16Lubricant Oil Manufacturing in US0.870.10Recycling
Facilities in US0.901.25Paint Stores in US0.900.46Waste
Treatment & Disposal Services in US0.910.24Transmission Line
Construction in US0.921.47Natural Disaster & Emergency
Relief Services in US0.931.40Landscape Design in
US0.930.28Car Body Shops in US0.950.84Professional

Employer Organizations in US0.961.72Industrial Building
Construction in US0.96-0.35Industrial Machinery & Equipment
Wholesaling in US0.970.09Manufactured Home Dealers in
US0.982.26Sewage Treatment Facilities in US0.99-
0.33Workers' Compensation & Other Insurance Funds in
US1.001.72Convenience Stores in US1.010.23Demolition &
Wrecking in US1.010.68Livestock Production Support Services
in US1.04-0.01Lawn & Outdoor Equipment Stores in
US1.050.35Truck & Bus Manufacturing in
US1.070.08Laboratory Testing Services in US1.100.17Dentists
in US1.100.42Colleges & Universities in US1.110.94Steel
Framing in US1.120.45Power Tools & Other General Purpose
Machinery Manufacturing in US1.14-0.17Glasses & Contacts
Wholesaling in US1.142.68Custody, Asset & Securities
Services in US1.181.72Private Schools in US1.180.16Beauty,
Cosmetics & Fragrance Stores in US1.190.19Electricians in
US1.191.01Credit Card Processing & Money Transferring in
US1.200.90Farm Supplies Wholesaling in US1.200.67Hose &
Belt Manufacturing in US1.200.97Adoption & Child Welfare
Services in US1.210.55Optometrists in US1.210.65Power
Conversion Equipment Manufacturing in US1.210.14Plastic &
Resin Manufacturing in US1.23-0.02Used Car Dealers in
US1.230.24Refined Petroleum Pipeline Transportation in
US1.231.39Television Broadcasting in US1.252.16Concrete
Contractors in US1.261.26Auto Mechanics in US1.260.10Lime
Manufacturing in US1.270.22Truck, Trailer & Motor Home
Manufacturing in US1.300.76Industrial Designers in
US1.300.58Building Finishing Contractors in
US1.320.29Carpenters in US1.320.33Sheet Metal, Window &
Door Manufacturing in US1.330.66Landscaping Services in
US1.331.18Drywall & Insulation Installers in
US1.371.56Graphic Designers in US1.370.93Credit Unions in
US1.382.68Family Planning & Abortion Clinics in
US1.381.09Trusts & Estates in US1.384.63Plumbers in
US1.380.26Roofing Contractors in US1.400.82Explosives
Manufacturing in US1.400.28Beer, Wine & Liquor Stores in

US1.400.85Third-Party Administrators & Insurance Claims
Adjusters in US1.403.08Wood Framing in
US1.442.00Apartment & Condominium Construction in
US1.441.60Fence Construction in US1.470.35Oil & Gas
Pipeline Construction in US1.482.00Heating & Air-
Conditioning Contractors in US1.500.92Land Leasing in
US1.500.36Flooring Installers in US1.510.82Heavy Equipment
Rental in US1.530.87Airport Operations in US1.541.17Cigarette
& Tobacco Manufacturing in US1.540.60Manufactured Home
Wholesaling in US1.554.38Portable Toilet Rental & Septic
Tank Cleaning in US1.560.70IT Consulting in US1.570.60Hair
& Nail Salons in US1.570.88Seasoning, Sauce and Condiment
Production in US1.580.98Roofing, Siding & Insulation
Wholesaling in US1.581.36Housing Developers in
US1.611.81Tire Wholesaling in US1.630.40Used Goods Stores
in US1.640.64Natural Gas Distribution in US1.691.77Payroll &
Bookkeeping Services in US1.721.38Private Equity, Hedge
Funds & Investment Vehicles in US1.720.59Museums in
US1.721.96Credit Bureaus & Rating Agencies in
US1.752.42Health & Medical Insurance in US1.813.21Fast
Food Restaurants in US1.841.16Iron & Steel Manufacturing in
US1.874.35Major Label Music Production in
US1.923.52Plumbing & Heating Supplies Wholesaling in
US1.932.25Candy Production in US1.930.84Mineral Product
Manufacturing in US1.961.23Car Wash & Auto Detailing in
US1.961.48Nursing Care Facilities in US1.990.87Municipal
Building Construction in US1.991.02Mental Health & Substance
Abuse Centers in US1.990.87Organic Chemical Manufacturing
in US2.001.21Health & Welfare Funds in US2.011.68Car Rental
in US2.041.54Ambulance Services in US2.051.00Hospitals in
US2.060.93Medical Supplies Wholesaling in
US2.080.97Conservation & Human Rights Organizations in
US2.091.06Space Vehicle & Missile Manufacturing in
US2.111.47Port & Harbor Operations in US2.231.69Caterers in
US2.261.24Guns & Ammunition Manufacturing in
US2.321.78Movie & Video Production in US2.332.72Pet Stores

in US2.361.55Tile Installers in US2.381.42Portfolio
Management in US2.391.58Refrigerated Storage in
US2.421.18Business Coaching in US2.472.02Tortilla
Production in US2.491.72Glass & Glazing Contractors in
US2.601.72Golf Driving Ranges & Family Fun Centers in
US2.622.31Television Production in US2.631.68Performers &
Creative Artists in US2.672.56Sand & Gravel Mining in
US2.713.06Dating Services in US2.797.42Sports Franchises in
US2.932.65Bed & Breakfast & Hostel Accommodations in
US2.981.80Donations, Grants & Endowment in
US2.982.91Translation Services in US3.152.14Public Storage &
Warehousing in US3.352.89Tank & Armored Vehicle
Manufacturing in US3.525.13Home Care Providers in
US3.633.06Aircraft, Marine & Railroad Transportation
Equipment Wholesaling in US3.742.87Tanning Salons in
US4.244.03Cotton Farming in US5.289.10Oil Drilling & Gas
Extraction in US5.767.72Radar & Satellite Operations in
US6.166.03Internet Publishing and Broadcasting in
US9.1112.28Solar Power in US20.8428.44Non-Hotel Casinos in
US1.07-0.32Polystyrene Foam Manufacturing in US1.20-
0.22Men's & Boys' Apparel Wholesaling in US1.30-0.20Boat
Dealership and Repair in US1.30-0.08Fruit & Nut Farming in
US1.35-0.08Engineering Services in US1.440.09Ready-Mix
Concrete Manufacturing in US1.44-0.05Chain Restaurants in
US1.46-0.17Biotechnology in US1.47-0.20Real Estate Loans &
Collateralized Debt in US1.47-0.23Ball Bearing Manufacturing
in US1.52-0.15Home Furnishing Wholesaling in
US1.550.19Interior Designers in US1.56-0.14Sporting Goods
Wholesaling in US1.620.24Bicycle Dealership and Repair in
US1.640.04Tax Preparation Services in US1.690.38Real Estate
Asset Management & Consulting in US1.71-0.01Data
Processing & Hosting Services in US1.73-0.08Sightseeing
Transportation in US1.780.41Lingerie, Swimwear & Bridal
Stores in US1.790.07Venture Capital & Principal Trading in
US1.790.01Stevedoring & Marine Cargo Handling in
US1.820.21Diagnostic & Medical Laboratories in US1.83-

0.29Financial Planning & Advice in US1.84-0.26Janitorial
Services in US1.860.17Consumer Electronics & Appliances
Rental in US1.87-0.21Aircraft, Engine & Parts Manufacturing
in US1.890.03Handbag, Luggage & Accessory Stores in
US1.890.32Painters in US1.910.54Commercial Real Estate in
US1.920.08Public Relations Firms in US1.920.46Vacuum, Fan
& Small Household Appliance Manufacturing in
US2.000.17Hydroelectric Power in US2.03-0.28Elevator
Installation & Service in US2.040.13Waste Collection Services
in US2.040.52Music Publishing in US2.07-0.04Specialist
Doctors in US2.110.59Baking Mix & Prepared Food Production
in US2.11-0.20Family Counseling & Crisis Intervention
Services in US2.150.39Tool & Equipment Rental in
US2.150.13Independent Label Music Production in
US2.150.38Alternative Healthcare Providers in US2.170.63Gas
Pipeline Transportation in US2.190.80Structural Metal Product
Manufacturing in US2.190.58Veterinary Services in
US2.220.49Metal Pipe & Tube Manufacturing in
US2.230.90Pest Control in US2.230.53Construction & Mining
Equipment Wholesaling in US2.270.25Local Freight Trucking
in US2.310.46Generic Pharmaceutical Manufacturing in
US2.31-0.35Local Specialized Freight Trucking in US2.37-
0.03Travel Agencies in US2.390.50Security Alarm Services in
US2.410.96Food Service Contractors in US2.420.80Building
Inspectors in US2.481.13Property, Casualty and Direct
Insurance in US2.520.96Retirement Communities in
US2.531.19Coffee & Snack Shops in US2.550.76Correctional
Facilities in US2.59-0.02Residential Intellectual Disability
Facilities in US2.670.23Scheduled and Charter Bus Services in
US2.740.16Property Management in US2.780.05Dollar &
Variety Stores in US2.781.40Specialty Hospitals in
US2.810.63Wine & Spirits Wholesaling in US2.890.08Oil
Pipeline Transportation in US2.900.44Brand Name
Pharmaceutical Manufacturing in US2.931.42Physical
Therapists in US2.980.22Office Staffing & Temp Agencies in
US2.980.87Investment Banking & Securities Dealing in

US2.990.18Management Consulting in US3.010.27Real Estate
Sales & Brokerage in US3.090.45Remediation & Environmental
Cleanup Services in US3.131.09Paving Contractors in
US3.151.17Amusement Parks in US3.18-0.18Gym, Health &
Fitness Clubs in US3.181.20Internet Service Providers in
US3.24-0.25Credit Counselors, Surveyors & Appraisers in
US3.251.38Toll Roads & Weigh Stations in US3.271.91Real
Estate Investment Trusts in US3.350.90Concert & Event
Promotion in US3.510.59Sports Coaching in US3.510.45Ship
Building in US3.582.14Musical Groups & Artists in
US3.650.59Psychologists, Social Workers & Marriage
Counselors in US3.650.41Fine Arts Schools in
US3.671.51Charter Flights in US3.751.15Street Vendors in
US3.990.46Mental Health & Substance Abuse Clinics in
US4.001.84Telemarketing & Call Centers in
US4.061.22Wineries in US4.102.52Tutoring & Driving Schools
in US4.210.40Convention & Visitor Bureaus in
US4.210.89Testing & Educational Support in US4.230.71Hair
Loss Treatment & Removal in US4.351.36Pet Grooming &
Boarding in US4.361.92Arcade, Food & Entertainment
Complexes in US4.421.81Emergency & Other Outpatient Care
Centers in US4.431.02Beekeeping in US4.470.10Molybdenum
& Metal Ore Mining in US4.521.26Video Postproduction
Services in US4.540.56Scientific Research & Development in
US4.623.23Tour Operators in US4.691.84Wood Product
Manufacturing in US4.720.70Trade Show and Conference
Planning in US4.810.72Oxygen & Hydrogen Gas Manufacturing
in US4.832.73Couriers & Local Delivery Services in
US4.883.58Long-Distance Freight Trucking in
US4.981.48Elderly & Disabled Services in
US5.041.92Remodeling in US5.082.76Industrial Supplies
Wholesaling in US5.190.80Automobile Towing in
US5.251.20Tea Production in US5.510.35Freight Forwarding
Brokerages & Agencies in US5.622.29Battery Manufacturing in
US5.873.54Design, Editing & Rendering Software Publishing in
US6.202.50Fashion Designers in US6.33-0.08Freight Packing &

Logistics Services in US6.761.52Database, Storage & Backup
Software Publishing in US6.955.63Business Analytics &
Enterprise Software Publishing in US8.754.35E-Commerce &
Online Auctions in US8.906.17Software Publishing in
US9.062.74Sustainable Building Material Manufacturing in
US9.983.64Search Engines in US10.148.87Wind Power in
US10.258.92Security Software Publishing in US10.514.09Video
Games in US10.649.07Operating Systems & Productivity
Software Publishing in US10.905.21Distilleries in
US12.270.81Video Game Software Publishing in
US14.018.65Breweries in US14.851.08Taxi & Limousine
Services in US23.938.97
Supply ChainSupply IndustriesRiskDemand
IndustriesRiskAircraft Maintenance, Repair & Overhaul in the
USMediumTourism in the USMedium - HighSoft Drink, Baked
Goods & Other Grocery Wholesaling in the USMediumCouriers
& Local Delivery Services in the USMediumAircraft, Marine &
Railroad Transportation Equipment Wholesaling in the
USMedium - HighConsumers in the USVery HighAircraft,
Engine & Parts Manufacturing in the USMedium - LowFreight
Forwarding Brokerages & Agencies in the USMedium -
LowGasoline & Petroleum Wholesaling in the USMedium -
HighPostal Service in the USHighAirport Operations in the
USMedium
Industry GlobalizationIndustryImports / Domestic
DemandExports / RevenueSoybean Farming in
US1.1050.08Coal Mining in US4.4444.56Fruit & Nut Farming
in US53.1740.41Tobacco Growing in
US73.7878.76Molybdenum & Metal Ore Mining in
US72.7364.16Men's & Boys' Apparel Manufacturing in
US98.0458.61Wood Pulp Mills in US88.6893.10Nonferrous
Metal Refining in US191.42242.27Plastics & Rubber Machinery
Manufacturing in US56.8938.02Semiconductor Machinery
Manufacturing in US33.8452.04Computer Manufacturing in
US94.4959.22Computer Peripheral Manufacturing in
US90.1474.87Communication Equipment Manufacturing in

US Hotel Industry Revenue Estimation Model

US Hotel Industry Revenue Estimation Model

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