Identifying Primary Determinants of Airbnb Pricing: An Athens Case Study (Part 1/Intro)

A tale of one city

A few days ago i submitted my master thesis, titled “Airbnb Pricing: Identifying Primary Determinants through Hedonic Pricing Methods”. We tried to do just that, find the main factors that decide the price of an Airbnb listing (the hedonic part, i will explain later on). We focused our research in the city of Athens for a few reasons:

  • The legal and regulatory framework of Athens (Greece in general) is familiar. In the last years few efforts have been made towards regulating Airbnb, unlike other European capitals.
  • Athens has not been studied extensively in relevant literature, and certainly not as a standalone case study (Wang and Nicolau find price determinants for 33 cities, Athens among them)

Our data-set comes from insideairbnb.com “an independent, non-commercial set of tools and data that allows you to explore how Airbnb is really being used in cities around the world”. It was scraped on the 15th of November 2018 and consists of 9.123 listings. We deduced from cross-checking the locations that the data-set includes only the central part of Athens (Kentrikos Tomeas Athinon). Athens has seen a rapid growth in tourist arrivals from city breakers in recent years (approx. 5m visitors in 2017 and 5.5m in 2018) combined with a boom in Airbnb rentals as well.

Number of reviews over time

In this chart, we see the number of reviews over time. Reviews are only given from Airbnb visitors that stayed in a listing, but not every visitor leaves a review, in fact according to Airbnb this ratio of review per stay is approx. 50%. This chart confirms the Airbnb boom in recent years as well as the highly seasonal pattern of stays. For comparison, we can see the number of reviews across years in New York City, from Sarang Gupta in his team’s analysis of NYC, also revealing a seasonal pattern but not quite as clearly.

What does “hedonic” mean?

The word hedonic comes from the greek “ηδονικός”, the one that we derive pleasure from, but in an economic context, it is associated with a good’s utility. The hedonic pricing method estimates the value of the characteristics of a commodity that indirectly affects its market price, according to Herath and Maier, and has been extensively used in the real estate industry in previous years as a method of valuation. In other, more simple words, “…the hedonic method is an “indirect” valuation method in which we do not observe the value consumers have for the characteristic directly, but infer it from observable market transactions…” (Taylor, 2003) and we can think of it this way: two identical houses, have one unique difference, they don’t have the same distance from a nearby park. The difference in their price, observed from a market transaction, gives us the value of the attribute “Distance from the park”. Using that, we can evaluate the price of a whole house by breaking it down to a -considerable- number of attributes, like square feet, proximity to places of interest, amenities that the house has or lacks, etc.

Let’s do that for an Airbnb listing

And indeed this method has been applied to Airbnb listings from some researchers in recent years: Gibbs et al. (2017), Wang and Nicolau (2017), Dogru and Penkin (2017). But why is pricing for Airbnb important? Well, for one, hosts that don’t price their listings correctly leave money on the table. Let’s not forget that most hosts are not professionals. Airbnb provides pricing tips but a) they are not transparent on how the price is decided-which is normal b) a host’s interest might not be aligned with the company’s interest and c) the pricing of a listing is not a one-off process but has much to do with demand and supply dynamics so it needs to be constantly re-evaluated.

A number of startups have been involved in the intelligent, data-driven pricing field of providing what is known as property management software. Companies like: wheelhouse, everbooked (acquired by Evolve), beyond pricing, elliot&me (acquired by Airbnb) and guesty (btw, from a brief research, these startups are doing great). What those startups do is more or less, looking into a number of factors such as: Seasonality, Day of the week, Special events, Local hotel occupancy, Competitor occupancy etc. and adjusting the price of a host’s listing. But this does not really answer the question: what are the attributes that guests value more in an Airbnb listing and how much do they value those attributes?

We’ll see all about that in Part 2.

Take care!