
Customized regression model for Airbnb dynamic pricing - feross
https://blog.acolyer.org/2018/10/03/customized-regression-model-for-airbnb-dynamic-pricing/
======
resters
This is really interesting. I've found that Airbnb under-prices my listing
pretty significantly, so I go through and set the weekend prices to roughly 2x
of what smart pricing recommends, and they all book up anyway.

This makes me think that smart pricing errs on the side of giving guest a
better value and not on the side of maximizing profits for the host.

This may be good for hosts in the long run, however, but I wish it could tell
me specifically how it was weighing the factors involved in my listing. For
instance:

\- seasonal demand: high, weight: 3

\- special case demand (annual conventions, etc): low, weight 2

\- local lodging availability: scarce, weight: 5,

\- percentage of the time guests choose your listing vs others when both are
available: 75, weight: 3

\- competitiveness based on incentives for length of stay: 8, weight: 3

\- your typical guest price sensitivity: low, weight: 5

It would also be very cool for Airbnb to offer beta testing of different smart
pricing algorithms. Supply and demand is volatile, so just because a unit
averages $x does not mean it can't sometimes fetch 5x (see Uber's surge
pricing). Smart pricing _never_ does this, so I suspect there is no notion of
demand surges built into the algo, even though they obviously occur.

~~~
blt
> _Supply and demand is volatile, so just because a unit averages $x does not
> mean it can 't sometimes fetch 5x (see Uber's surge pricing). Smart pricing
> never does this, so I suspect there is no notion of demand surges built into
> the algo, even though they obviously occur._

There are laws against such price gouging in many US states, e.g. to prevent
hotels from charging very high prices during natural disasters, or from
discriminatory rates. Undoubtedly airbnb will not implement such protections
unless forced to.

~~~
oh_sigh
I think OP was talking more along the lines of, say, Atlanta during super bowl
weekend, or Random, Wyoming during a total eclipse.

------
laichzeit0
What should one study if you wanted to "get up to speed" with these types of
models? Like which branches of statistics deals with this? It looks like you
need to combine time-series with some kind of demand forecasting?

Say I wanted to do something like this to Widgets and adjust their prices over
time, assuming I can collect data on how other people are pricing similar
Widgets in the "market" (using this to estimate demand/supply), etc.

I'm just not sure what this type of field is called so I get get up to speed
with state of the art.

~~~
dpandya
One of the keywords you're looking for is "statistical forecasting."

It's a fairly specialized data science skill since it requires experience with
some fairly specific techniques (e.g. dealing with seasonality,
autocorrelation, etc. within data has all kinds of interesting solutions).

------
erikb
This looks a lot like getting short term profit increases for losing long term
control. I wouldn't do it, at least not with just Airbnb suggestions.

------
curiousgal
What's the point of generating a price plot without a y axis?

