Airbnb - How Much Can You Make?

This project estimates how the different characteristics of an Airbnb affect the listing price. We created a tool to accurately predict the rental property's annual revenue. This tool helps investors/home owners make a good investment or avoid loss.

Research motivation

Investors looking for a second and potentially passive source of income may consider buying and renting a property. In traditional real estate investing, the objective is purchasing a home with the intention of leasing it out permanently (usually for a period of six months and longer). However, as Airbnb and other platforms for vacation rentals have recently grown in popularity, there are more chances for property owners to create a passive income stream. Yet, this does not imply that it will turn out to be a valid investment for everyone. Depending on specific characteristics (e.g., demographics, accommodation and reviews) investing on a short-term rental may not be wise. For this reason, we created an estimation tool that future Airbnb owners can use to forecast their profits.

Research question

How do the characteristics of an Airbnb affect the listing price to accurately predict the rental property's annual revenue?

Research method

Firstly, to collect the required data, a web scraper was built for InsideAirbnb. Data for western EU (for a total of 25 cities) are used to conduct the regression analysis. The rationale is based on the fact that when considering Airbnb listings by region, Europe ends up being the one with the highest number of listings, for a total of 4,840,487 in 2021.

The estimation tool will be accessible to everyone in an app. The visitor must select specific information about their accommodation. The data used to determine the estimated yearly income are:

• average daily price for different listings
• monthly average occupancy rate for each city taken into account

The average daily price considered is based on competitors that have similar characteristics in terms of:

• demographics (e.g., neighborhood, nearby facilities)
• accommodation (e.g., ratings (cleanliness, checkin, communication), amenities, type of accommodation)

See the original variables used.

Listed below are the final fourteen variables after cleaning the data and running the regression.

Variable	Description
city	The city of the listing
host_is_superhost	Whether host is superhost
property_type	Self described type of property
room_type	All homes are grouped into three types: Entire place / Shared room / Private room
accommodates	The maximum capacity of the listing
bathrooms_text	The Number of bathrooms in the listing
price	Daily price in local currency
review_scores_rating	General review score of the listing
review_scores_cleanliness	Review score for cleanliness of the listing
review_scores_checkin	Review score of the check-in at the listing
review_scores_communication	Review score of the communication by host
review_scores_location	Review score of the property location
review_scores_value	Review score of value of the listing
instant_bookable	Whether the guest can automatically book the listing without the host requiring to accept

To collect the required data a web scraper for InsideAirbnb was built.

Relevance

This research provides applicable insights for current and potential Airbnb hosts. This research aims to give Airbnb hosts a tool that can help them make an accurate prediction of their rental property's annual revenue. For current hosts, the tool can also help to see if their current price is up to date. In addition, it can also provide an indication of the price if the host wants to make a change to their Airbnb (for example, an extra bed), but also consider the effect of additional reviews. It can be said that the tool created by this research helps the hosts make a good investment or avoid loss.

Price prediction tool

In this research, a multiple linear regression was conducted to examine the effect of different characteristics of an Airbnb on its price. These results are displayed in the developed price prediction tool. In this tool, the outcome variable price is multiplied by the average annual bookings per city to get a predicted annual revenue.

We recommend running the code to see the price prediction tool for yourself. The preview of the app can be seen here.

Repository overview

- .github
- src
  - analysis
  - data-preparation
  - price-calculator-app
- .gitignore
- README.md
- makefile

Dependencies

Please follow the installation guides on http://tilburgsciencehub.com/.

• Python. Installation guide.

• R. Installation guide.

• Make. Installation guide.

• To knit RMarkdown documents, make sure you have installed Pandoc using the installation guide on their website.

• For Python, make sure you have installed the following packages:

pip install bs4
pip install selenium
pip install pandas

• For R, make sure you have installed the following packages:

install.packages("data.table")
install.packages("shiny")
install.packages("shinyWidgets")
install.packages("bslib")
install.packages("shinythemes")
install.packages("yaml")
install.packages("readr")
install.packages("tidypredict")
install.packages("broom")
install.packages("dplyr")
install.packages("ggplot2")
install.packages("ggfortify")
install.packages("tidyverse")
install.packages("readxl")

Running the code

Step-by-step

To run the code, follow these instructions:

1. Fork this repository
2. Open your command line / terminal and run the following code:

git clone https://github.com/{your username}/airbnb-price-calculator.git

3. Set your working directory to airbnb-price-calculator and run the following command:

make

4. When make has succesfully run all the code, it will generate a http URL. Copy and paste this URL in your browser to launch the price calculator app.

5. To clean the data of all raw and unnecessary data files created during the pipeline, run the following code in the command line / terminal:

make clean

Note: when the command line/terminal is closed, the website will not be available anymore.

Alternative route

An alternative route to run the code would be:

• ../src/data-preparation -> download_merge_data.R
• ../src/data-preparation -> cleaning_data.R
• ../src/analysis -> regression_analysis.R
• ../src/price-calculator-app -> shiny_app.R

Running the data collection

The workflow above does not include the data collection step. The output file of this step is shared in a Google Sheet, which gets downloaded in the pipeline. However, if you wish to collect the data yourself, you can by running our web scraper. The code for this scraper can be found here:

• ../src/data-preparation -> webscraper_airbnb_python.py

Authors

Team 4:

• Cas Rooijackers, e-mail: c.rooijackers@tilburguniversity.edu
• Gennaro Santoro, e-mail: g.santoro@tilburguniversity.edu
• Jesper Krauth, e-mail: j.krauth@tilburguniversity.edu
• Ludovica Donatelli, e-mail: l.donatellli@tilburguniversity.edu
• Patrick de Graaf, e-mail: p.w.degraaf@tilburguniversity.edu