AbdulRehman, Jerry Franklin , Sapna Sharma
As more and more people prefer to stay at airbnb accommodation rather than staying at fancy hotels, the demand for airbnb has increased over time. Due to this demand more people are investing in houses to rent on Airbnb. Investors need to know which property to invest in to get high return on investment.
The main file is airbnb_analysis.ipynb
notebook. It contains step by step implementation of causal inference on ROI of any investment listed on airbnb
Necessary api to collect the data are as follows :
The data collected by our team is in the folder Data
We have done the analysis and testing of the models in R and Python Languages.
Analysis.ipynb is the python notebook and
Global Markov & Failthfulness.ipynb is the code in R
We filtered out data based on
- the property type (Condominiums, Apartments )
- only the properties which were available to rent as a whole
We used Google's geo-coding API to get the addresses for the properties using their latitude and longitude.
We used Zillow to get the Zestimates (current estimated market price) for each of these properties using their addresses.
The data from these two files were combined.
In the file airbnb_analysis.ipynb
we build our causal models in pyro, analyze the results.
├── home\n
│ ├── airbnb_analysis.ipynb --- Main Notebook with the analysis
│ ├── Global Markov & Failthfulness.ipynb --- R notebook with the tests used in the main notebook
│
│ ├── listings.csv ---data directly from Airbnb
│ ├── listings_manual.csv ---listing with their price estimates, scrapped manually*
│ ├── listings_full.csv ---Feature rich dataset with all collected features*
│