Understanding Airbnb Prices: Insights for your next Trip

Price relations and patterns of Airbnb listings in Seattle, Washington

Introduction

Have you ever asked yourself about the factors linked to higher renting prices?

When I usually search for my next holiday accomodation, I begin by entering the number of persons, bedrooms and a downside rating threshold.

Throughout my analysis of the Seattle Airbnb dataset, however, I realized, that positive ratings have almost no effect on the prices charged by hosts on Airbnb.

The Seattle Airbnb dataset covers 3818 listings on Airbnb in Seattle, Washington, and daily pricing data for the whole year of 2016.

It provides detailed information about the room type, neighboorhood, accommodates, bed- and bathrooms as well as rating reviews.

I restricted my analysis to offers that are priced and reviewed at least once to get only those listings that at least one customer has booked before.

The following three sections address

  • typical characteristics of airbnb listings,
  • shows, how the rental prices are related to listing characteristics and
  • how prices fluctuate over time.

Part I: What are typical characteristics and locations of airbnb accommodations in Seattle?

The majority of listings on Airbnb in Seattle are entire homes. Private rooms make about one third of the offers while shared rooms are pretty rare.

About two thirds of the listings offer only one bedroom. The higher the number of bedrooms the less offers we can find on Airbnb in Seattle.

The neighbourhood with the highest number of listings is „Downtown“, directly followed by „Capitol Hill“.

The majority of listings is rated with a review score of 10, which is the highest possible rating. Ratings of 8 and below are very rare.

Now, let’s dig deeper and understand which of these factor are linked to the average offered price.

Part II: How are the rental prices related to accommodation characteristics?

The first of the two charts shows the average listing price on the y-axis. On the x-axis, we see the neighbourhood group, where the accommodation is located.

At first glance this chart shows that entire homes (green) are priced higher on average than private rooms (orange) while shared rooms (blue) are the cheapest throughout all neighbourhoods.

The highest average price for entire homes is reached in „Magnolia“ while other accommodation type prices are very low in „Magnolia“. „Magnolia“ has only about 40 listings at all.

Second is „Downtown“, which also has the most listings of all neighbourhood groups. However, in „Downtown“ the average price is not only the second highest for entire homes, but also the highest for private and shared rooms compared to other neighbourhoods. Entire home prices are lowest in „Delridge“ and cheaper private rooms can be found in several neighbourhood groups like „University District“,“Seward Park“, „Rainier Valley“, or „Lake City“.

The second chart shows the results of a linear regression model of the mean price depending on different accommodation characteristics. Each bar indicates by how much the price is predicted to increase if an additional unit of the characteristic is fulfilled. For example, an additional bedroom is worth about +30 USD. An accommodation in „Downtown“ increases the predicted price by +76 USD compared to „Delridge“. Especially the room type has a very strong effect on the price according to the model. If the accommodation is an entire home the model predicts the price to be +77 USD higher as if the accommodation would have been a shared room.

But the model also shows that reviews have almost no predictable impact on the price.

To summarize these results, prices depend strongly on hard factors, like neighbourhood and the size and furnishing of the accommodation. Additional bedrooms, bathrooms and accommodates are positively correlated and are a classical sign of the increased size of an accommodation. Reviews however seem to have no effect on the price at all.

Part III: How do rental prices change over time?

The last part is dedicated to the price development over time. The chart shows the average price per day of entire homes (green), the average daily price of private rooms (orange) and least the average daily price of shared rooms (blue).

In comparison to private and shared rooms, the rental price of entire homes has a visible seasonal component. It rises from the winter months January and February to a peak in July and falls back in the autumn months.

The second pattern are the weekly fluctuations. During weekends, the average daily price is higher for all room types.

Conclusion

1. Characteristics of accommodations

Hosts of Airbnb in Seattle offer a large variety of accommodations.

  • Entire homes are dominating the offers before private and shared rooms.
  • The largest number of accommodations can be found in „Downtown“ and „Capitol Hill“.
  • About 2/3 of the offers have only one bedroom.
  • Almost all accommodations have positive reviews ranging between 9 to 10, with 10 as the highest possible value.

2. Accomodation characteristics

Average yearly prices are linked to

  • neighbourhood,
  • room type,
  • size and furnishing factors of the accommodation:
    • bedrooms,
    • bathrooms,
    • beds and
    • accommodates.
  • Reviews however seem to have no effect on the price at all.

3. Time component

  • Prices of entire homes fluctuate seasonally with a peak in July.
  • They are on average higher during weekends.

The results cover the market of Airbnb in Seattle in 2016. Whether the results also match with data from other cities, different years or another platform remains open and is definitely worth further investigation.

But now it is your turn:

How are accommodation prices for your next vacation trip? Can you identify similar price patterns?

If you are interested in more details of my analysis about Airbnb in Seattle, checkout my Github project here.