I Scraped US Rental Car prices and here’s what I found !
Last week, I was curious about Travel data for labor day. There was no public data on hotel prices, hotel vacancy or rental cars or any thing else travel related. Expedia, Priceline and everyone who has data, do not expose APIs to public. We have some data on flights, but cannot find any data on Rental Cars. So I decided to acquire data on my own rental car prices by writing a web spyder in Python.
Why Rental Car data?
In United States, 4th largest country by area, obviously distances are far apart. More Americans commute by car than any other country. So travel be it business or leisure isn’t possible without a car. If someone flies to Minneapolis for a business meeting or Denver for a ski-trip you need to rent a car to get around.
Approximate price for a 3 day weekend rental car is 150$ excluding Insurance, Gas, Tolls and Extras. Average Price of Airfare in US = 350$. So this is a significant expense.
Data acquisition strategy and Description:
Scraped a popular car rental website [Can’t disclose as its a matter of time they shut me down] for prices of a mid-size sedan for a 3 day weekend rental in October 13–15. I am doing this on a daily basis to get more data and insights for Part 2 [may be after 30 days or Thanksgiving].
Why mid-size sedan? — Its the most common car reserved atleast, people usually can go to cheaper economy car or full-size sedan. Prices are usually within 5–10$ of each other. so a reasonable choice.
Choice of weekend rental?: Car companies typically have deals for weekend rental or weekly rentals. Mostly because they want better fleet utilization. I plan to use this as a travel dataset mostly non-business, so weekend rental seems to be ideal choice. Friday to Sunday.
Why a random weekend and why not a larger dataset?: Ideally I would have preferred every day, but there is no public data set on this. I am slowly scraping every day to track price fluctuations. Also challenges in scraping, Companies do not want you to scrape them, so they try to shut you down. So we have to work with what we have.
We wanted geographically diverse locations [to adjust for seasonality]and a combination of larger airports and mid-size hubs. Included mid-size cities like Austin or St Louis and Las Vegas as rental car passengers might be lot more than say at NYC where Taxis and Public Transit rule the city. Enough text , from now on we are going to have data visualizations narrating the story!.
East — Atlanta [ATL] , Boston[BOS] & Raleigh / Durham [RDU],
Central / Mid-West Chicago O’Hare [ORD], Denver [DEN] & St. Louis [STL].
South West — Dallas [DFW] & Austin [AUS].
Mountain — Las Vegas[LAS]
West Coast - Los Angeles [LAX], Seattle [SEA], Portland [PDX]
Rental car prices are obviously dependent on where you rent. There seems to be no correlation between size of airport and price as Boston and DFW are in the other end. So is St Louis and Austin.
Above data visualized below as a heat map over Google Maps.
Car Rental Companies
Above data shows aggregate prices by airports disregarding company or the segment of the customers they cater to. The picture below is a simple primer on rental companies. The Industry has consolidated as follows
Enterprise owns: National, Enterprise and Alamo
Avis owns: Avis, Budget and Payless
Hertz Owns: Hertz, Thrifty and Dollar
Levels in below picture shows Corporate -> Corp & Leisure -> Cheapest Tier
Though they are owned by same companies, Service levels and Customer Service vary a lot as you can see below.
Customer Service Ratings = Aggregate average of reviews scraped from a Travel Website
National : 5 / 5 Stars
Enterprise, Hertz and Avis : 4 / 5 Stars
Alamo : 4 / 5 Stars
Budget , Payless, Thrifty, Dollar and Sixt : 2 / 5 stars
Median Prices by Car Company
30 day forward price for Mid-Size or higher car on a 3 day weekend (Friday — Sunday) adjusted for promotional codes [wanted this as otherwise they are inflated prices usually].
Above data just confirms what we talked about branding and consolidation in this industry. National serves higher end of customers with better service and higher price. Whereas Dollar and Thrifty are on the lower end as expected.
Standard Deviation of Prices
Spread of Prices around the mean price in an airport
Left: Enterprise, Hertz and Avis — Pro Segment
Right: Alamo, Budget, Thrifty and Dollar — Cheaper Segment
People see all car companies in a segment as similar offerings. Its worse in the lower tier — Alamo, Budget, Thrifty and Dollar. The graph above on the right [remove RDU, its an outlier may be due to inventory shortage] says standard deviation of prices by airport < 20$.
On the left: Its bit better, but really for the sake of brevity am not adding any histograms to visualize the distribution. Its mostly because of one outlier, pretty much everyone tries to keep in the same range.
Conclusion above is :
There is NO PRICING POWER in a segment
So there is just variation on Location and Company Tier. No wonder both Avis and Hertz are struggling after acquiring the cheaper segment companies. Both stocks have negative or flat returns over last 5 years. So the entire space is commoditized and no one has pricing power?
As always exceptions exist — National Car Rental
National almost always exceeds the median price of that location by a significant amount barring a couple of exceptions. Plus bonus sweet spot is — Not just good customer service, Their loyalty program enables you to reserve a mid-size and get any car on the lot. So they are able to justify the prices.
If you are a frugal customer, care only about Price, Good Customer Service and decent car?
Alamo [~83$ on a 30-day advance for 3 days]
Business Customer, prefer luxury brands, hassle-free and best service?
National Car Rental and join Emerald Club to drive any car off their lot
If you are an Investor in Avis / Hertz or any other car rental company in US?
It is clear that none of them seem to have pricing power. Which essentially dictates whether a company has a “Moat” or not. So I don’t see these companies performing well, as demonstrated already by their historic stock returns. Enterprise Holdings is privately held. So we cannot judge/invest in them.
Coming up in future: Travel Trends for Thanksgiving and daily pricing insights.
Prashanth Rajendran is passionate about all things Data and Statistics. Currently I run Data Science and Product Analytics @ CandleScience.com. You can follow me at https://www.linkedin.com/in/prashantrajendran/