What Do Airbnb Listings Look Like during Pandemic?

Installation

Create a virtual environment

python -m venv <name_of_virtualenv>

or using conda

conda create --name myenv Replace myenv with your environment name.

Install requirements.txt

pip install -r requirements.txt

Guides

Geopandas

XGBoost

Project Motivation

For this project, I was interested in using InsideAirbnb data from 2020 to better understand:

1. How popular has AirBnB become in London? How COVID-19 impacted the demand?

2. How the demand changed in 2020 in comparison to previous year?

3. How the number of active listings changed in 2020?

4. What London boroughs were rated best?

5. What are the most expensive areas in London during pandemic?

Files description

• Data_cleaning_notebook.ipynb - Provides step by step process on data cleaning, dealing with missing data and preparing the dataset for machine learning

• EDA - Helps answer 5 questions above

• Data_cleaning.py - Function on data cleaning. More concise version of Data_cleaning_notebook.ipynb used on Price_Prediction.ipynb file

• Price_Prediction.ipynb - Runs the machine learning

Results

The main findings of this code are summarised in a post

Conclusions

• COVID-19 pandemic has significantly impacted both the demand in short-term rentals and active listings. Interestingly, alarming increase in number of new confirmed cases did not stop people from renting the listings. In fact, we observed that the highest number of listings was seen at the time when the number of confirmed COVID-19 positive cases was one of the highest.

• Airbnb listings are typically scored high. We saw that all boroughs, on average, are rated higher than 90%.

• Central areas are typically more expensive than outer London

• Top predictors for price were found to be room type, number of bedrooms, how many people the property can accommodate and number of bathrooms