Team Hotel Performance

1. Georgetown Data Analytics

2. Hotel Performance Drivers
Ashley Loyd, John Cannon, and Monika Adamczyk
Fall 2015 – Spring 2016

3. Business Problem
Client:
Hotel Managers & Hotel
Investors
Business Problem:
Recent figures show decreasing
RevPAR in the Houston hotel
market. Investors are worried
and suspect that this is because
of oil industry.

4. Hotel Performance: RevPAR
 Revenue is not all-encompassing performance metric
 Key Metric: RevPAR (Revenue per Available Room)
 Common performance metric in the hotel industry
 Best when compared across like time or seasonal periods
 RevPAR = Rooms Revenue/Rooms Available
 Rooms Revenue = revenue generated by room sales
 Rooms Available = # of rooms available for sale
 Also: RevPAR = Occupancy % * ADR (Ave Daily Rate)

5. Questions
 Starting Question:
 How does the oil and gas industry in
Houston impact hotel performance?
 Other questions:
 Are there other factors that affect
hotel performance in Houston?
 Is this common in other markets?

6. Hypothesis
 We will find a unique relationship
between:
 Overall employment
 Employment in the oil and gas industry
 Oil prices
...and hotel performance RevPAR
 Other factors to consider: Per Diem,
PPI, Labor Force, Unemployment
rate, YoY changes in factors

7. Our Process
Step 1:
Identify
and
Collection
Relevant
Data
Sources

8. Identifying Data Sources/Ingestion
 Question: What factors affect RevPAR in Houston?
 RevPAR Data (Smith Travel Research Data)
 Employment & PPI Data (Bureau of Labor Statistics)
 Gas Prices Data (Energy Information Administration)
 Government Per Diem Data (Data.gov)
 Data Ingestion: Collected and wrangled all relevant
data sets in Microsoft Excel

9. Data Wrangling and Challenges
 Data came from 4 different sources
 Challenges in Munging and Wrangling:
 Making sure all dates and columns were consistent
 Ensure that all calculated metrics had no null values
 Ensure that all data sets could be read by Python to produce
a full scatter matrix plot

11. Computation & Analysis
 First: Use Python (Pandas and MatPlotLib) to look at all
factors together and generate a Scatter Plot Matrix
 Then: compute regressions for each factor for Houston. If
it rendered a useful result, following up and compare to
Chicago, New York, and Denver.

13. Scatter Plot Matrix

14. Regression
 Dependent Variable: RevPAR
 Tableau: Linear and Log regressions (similar results)
 Independent Variables:
 X1 Govt. PerDiem,
 X2 Price Per Gallon of Gas, (RLin=0.00; RLog= 1.383e-05)
 X3 PPI for Accommodations Industry,
 X4 YoY Change in PPI,
 X5 YoY Change Mining Employment, (RLin=0.303; RLog=0.324)
 X6 YoY Chang in Labor Force,
 X7 YoY Change Employment,
 X8 YoY Change in Unemployment Rate.

15. Houston in Context
 Houston market is
closest to Denver
market for RevPAR vs.
YoY Change in Mining
and Logging
Employment Data
 Houston R Value =
0.303
 Denver R value
=0.288
 Chicago Discrepancy

17. Results
 Hypothesis: In the Houston market, there is a direct,
positive correlation between RevPAR and overall
employment, employment in the oil and gas industry,
and oil prices.
 Results: In the Houston market, there is a moderate
correlation between:
 RevPAR and change in mining employment

18. Applications:
Ideally we would investigate more market variables as stepping
stones to develop a predictive mode.
Macro level: such a predictive model would indicate performance
for an overall market given key factors.
Micro level: this type of model would assist operators and owners in
pricing strategies to help them outperform their peers.

19. Questions?