Hotels vs. Golf Clubs Response to Pandemic and opportunities

Introduction to FacilityONE

• FacilityONE is a CMMS (Computerized maintenance management system)

• FacilityONE has a patent in this space, SMARTPRINT, which is highly valuable in this space.

• FacilityONE’s SMARTRINT interactive blueprint allows facility managers to interactively map and connect critical systems, heavy machinery, even elementry school desks – for a current client, FacilityONE rose to the challenge of adapting to the coronavirus, rapidly bringing to market SMARTPRINT integrated coronavirus monitoring, preventative pr

• FacilityONE is currently in a replacement industry, dealing with large hospital organizations who routinely

• FacilityONE makes facility maintenance workers more organized and efficient by streamlining work orders, making manuals readily available to workers in the field, and organizing routine maintenance.

• Approximately $1.5M in revenue per year

• Heavily focused in schools and hospitals

• 10 employees, 3 sales representatives

• Fixed costs are now under control, the company is looking to grow revenue by entering another industry

FacilityONE Business Problem

In order to grow their revenue, FacilityONE must expand into a new industry. The team has conducted interviews with experienced professionals in prospective industries. ie. hotels and golf clubs

gam: Generalized additive models with integrated smoothness estimation

##Fits a generalized additive model (GAM) to data, the term `GAM’ being taken to include any quadratically penalized GLM and a variety of other models estimated by a quadratically penalised likelihood type approach (see family.mgcv). The degree of smoothness of model terms is estimated as part of fitting. gam can also fit any GLM subject to multiple quadratic penalties (including estimation of degree of penalization). Confidence/credible intervals are readily available for any quantity predicted using a fitted model.

What is FRED?

What is FRED? Short for Federal Reserve Economic Data, FRED is an online database consisting of hundred of thousands of economic data time series from scores of national, international, public, and private sources. FRED, created and maintained by the Research Department at the Federal Reserve Bank of St. Louis, goes far beyond simply providing data: It combines data with a powerful mix of tools that help the user understand, interact with, display, and disseminate the data. In essence, FRED helps users tell their data stories. The purpose of this article is to guide the potential (or current) FRED user through the various aspects and tools of the database.

fredhelp.stlousfed.org <- “https://fredhelp.stlouisfed.org/fred/about/about-fred/what-is-fred/?gclid=Cj0KCQjwgtWDBhDZARIsADEKwgMT55WQgaGy9CZisGeXrC3DiH5LB57toY60yoXDGFi5moFWznAzZhsaAl3AEALw_wcB”

FRED Research (Industry: Golf, Hotel, Motel, Casino)

Guestroom Rental Luxury Resort

guestroom_rental_luxury_resort %>%
  filter(Date > "2018-04-13")%>%
  ggplot(aes(x = Date, y = Value))+
  geom_point()+
  geom_smooth(method = "gam")+
ggtitle(label = "Room Prices Luxury Resort", subtitle = "")

## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'

Producer Price Index by Industry: Hotels and Motels, Except Casino Hotels: Guestroom Rental

guestroom_rental_hotels_motels <- Quandl("FRED/PCU7211107211101", api_key="pP6rU_xxGhz4rByVmBPY")
guestroom_rental_hotels_motels %>%
  filter(Date > "2018-04-13")%>%
  ggplot(aes(x = Date, y = Value))+
  geom_point()+
  geom_smooth(method = "gam")+
  ggtitle(label = "Hotel / Motel Guestroom Rental", subtitle = "Hotels and Motels, Except Casino Hotels")

## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'

Producer Price Index by Industry: Casino Hotels: Casino Hotel Guestroom Rental

guestroom_rental_casino_hotel <- Quandl("FRED/PCU7211207211201", api_key="pP6rU_xxGhz4rByVmBPY")

guestroom_rental_casino_hotel %>%
  filter(Date > "2018-04-13")%>%
  ggplot(aes(x = Date, y = Value))+
  geom_point()+
  geom_smooth(method = "gam")+
  ggtitle(label = "Casino Hotel Guestroom Rental", subtitle = "Producer Price Index by Industry: Casino Hotels")

## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'

#Producer Price Index by Industry: Golf Courses and Country Clubs: Membership Dues and Fees

golf_courses_country_clubs_membership_dues_fees <- Quandl("FRED/PCU7139107139101", api_key="pP6rU_xxGhz4rByVmBPY")

golf_courses_country_clubs_membership_dues_fees %>%
  filter(Date > "2018-04-13")%>%
  ggplot(aes(x = Date, y = Value))+
  geom_jitter()+
  geom_smooth(method = "gam")+
  ggtitle(label = "Golf Courses and Country Clubs", subtitle = "Membership Fees and Dues")

## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'

Producer Price Index by Industry: Golf Courses and Country Clubs: Greens and Guest Fees

golf_courses_country_clubs_greens_and_guest_fees <- Quandl("FRED/PCU7139107139102", api_key="pP6rU_xxGhz4rByVmBPY")

golf_courses_country_clubs_greens_and_guest_fees %>%
  filter(Date > "2018-04-13")%>%
  ggplot(aes(x = Date, y = Value))+
  geom_point()+
  geom_smooth(method = "gam")+
  ggtitle(label = "Golf Courses and Country Clubs", subtitle = "Greens Fees and Guest Fees")

## `geom_smooth()` using formula 'y ~ s(x, bs = "cs")'