This project estimates how the different characteristics of an Airbnb affect the asking price. We created a tool to accurately predict the rental properties annual revenue. This tool helps investors/home owners make a good investment or avoid loss.
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 competitors) 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 accurately forecast their profits.
The estimating tool will be accessible to everyone on a website. The website visitor must fill out 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 will be based on competitors that have similar characteristics in terms of:
- demographics (neighborhood, nearby facilities)
- accommodation (e.g. rating, amenities, type of accommodation) To collect the required data a web scraper for InsideAirbnb.com was built.
Please follow the installation guide on http://tilburgsciencehub.com/.
-
Python. Installation guide.
-
Make. Installation guide.
-
For Python, make sure you have installed below packages:
pip install bs4
pip install selenium
- For R, make sure you have installed below packages:
install.packages("tidyverse")
install.packages("ggfortify")
install.packages("yaml")
install.packeges("shiny")
install.packages("googledrive")
install.packages("tidypredict")
install.packages("car")
install.packages("base")
install.packages("data.table")
install.packages("broom")
install.packages("haven")
install.packages("readxl")
Cas Rooijackers, Gennaro Santoro, Jesper Krauth, Ludovica Donatelli, Patrick de Graaf