Forget 5.9% – some train fares rise today by four times inflation

Today, rail fares go up by an inflation-busting average of 5.9%, to howls of outrage from commuters and groups like Passenger Focus. But what many people don’t realise is that 5.9% is just an average.

And while Passenger Focus came across individual fare increases of up to 11%, I have scraped data from National Rail Enquiries and found that some anytime fares rise today by as much as 20% – that’s four times inflation – while others have fallen by as much as 45%.

Roll up, roll up to play the great rail fares lottery! (Bad luck if you live on Merseyside, where everyone seems to be a loser this year.)

Travelling from Moorfields to Chester at peak time? Oh no! Your anytime fare with Merseyrail has rocketed by 20% overnight, from £5.15 to £6.20. Liverpool to Southport? Ouch! The Merseyrail anytime fare is up 19%, from £4.65 to £5.50. Peak-time London to Warwick with Chiltern? Bad luck! Your fare rises by 9.8% today, from £51 to £56.

But peak-time Gatwick Airport to Southampton? DRRRRRIIIING – you’ve hit the jackpot: Southern’s anytime fare has bizarrely fallen by 45%, from £26.90 to £14.90. What’s going on?

You can find my raw data here – I scraped the fare increase in Anytime tickets on every end-to-end route listed in the NAPTAN database. I chose Anytime tickets because they are unregulated fares, and hence not subject to the RPI-plus-1% average limit imposed by the Chancellor. However, Passenger Focus’s good work has found large variation in regulated (off-peak and season) fares too – buying train tickets really is a lottery.

The 5.9% figure is a high-level average produced by ATOC, which regulates the train operators. When I rang them, ATOC told me the 5.9% figure is an average of an average, across all operators and all available potential routes, and all regulated and unregulated fares.

Clearly, with such wild variation between operators and regions, we need much better comparative data. I drew a graph showing the average rise I found in each operator’s fares, which showed large differences. I also compared the variation in fare increases, which produced some interesting geographical patterns (I’m looking at you in particular, Southern).

However, I’ve decided not to publish these for the moment, because my data only covers Anytime tickets and end-to-end routes, not every available journey, and it has holes in it, having been scraped. To compare prices properly, we really need to know how often each ticket is bought, but that data isn’t public.

At some point, I’ll try a proper analysis with the National Fares Manual and librailfare. In the meantime – here’s hoping your train fare hasn’t gone up too much, and happy new year!

life-hacking transport visualisation

Train times v. house prices: the commuter belt, on a graph

We’re house-hunting. And for me, like most coders, house-hunting involves lots and lots and lots of screen-scraping.

As well as crawling Rightmove listings, I’ve been looking at transport and house-price data. Specifically, I’ve scraped travel times to London by train versus house prices, to examine the theory that houses get much cheaper once you escape the commuter belt.

To test this, I gathered mean journey times to London from Traintimes for every railway station in the UK, and mean asking prices for 3-bed houses near each station from Nestoria. Here’s the graph of all stations, with a moving-average line added:

Waiting for graph to load…

Mouse over the graph to see data for individual stations. Or type a station name to highlight it on the graph:  

Thoughts on the graph

  • The sharp initial drop, up to about 30 minutes, must show just how much extra you pay to live in zone 2 rather than zone 6 of London itself. Yikes.
  • Prices do start dropping more steeply about 70 minutes from London, which probably marks the edge of the commuter belt.
  • Once you get to about 150 minutes, prices flatten. Except…
  • …There’s a distinct “Edinburgh bump” at about 270 minutes from London, which I wasn’t expecting at all.
  • There are a few high outliers, presumably where a mansion has skewed the average price. (It’s difficult to tell from the Nestoria data.)
  • But there’s a striking baseline below which house prices near a station never fall. Actually, pretty much the closest thing to an outlier on the downside is poor old Corby.

About the data

For clarity, the graph excludes London stations, and the long tail of stations that are 400-900 mins from the capital, mostly in the Scottish Highlands.

This is roughly what I did:

  • Find and geocode the 2500+ stations in England, Scotland and Wales, from this Guardian version of Office of Rail Regulation station usage data.
  • For each station, find the mean travel time for the first 5 journeys to London after 8am on a weekday, scraped from TrainTimes, Matthew Somerville’s accessible version of National Rail Enquiries.
  • For each station, find the mean asking price for a 3-bed house within 2km in the past 6 months, from the Nestoria API. (Nestoria shows listing prices, rather than transaction prices like Zoopla, so it may contain duplicates and is probably less accurate – but Zoopla isn’t granular enough to search just for 3-bed houses.)
  • Plot the moving average price, with a frame of 100 datapoints.

This is the code I used (on Github), and the resulting raw data (in Fusion Tables). The next logical step would be to plot distances against house prices, I guess. If I’ve missed anything, let me know.

And with that, back to the screen-scrapers, the mortgage brokers and – God help us – the estate agents.