ABOUT
This model will attempt to predict what price a host should list their airBNB for in Asheville, NC. For future work I may consider looking at other cities or generalizing the model, for now I chose Asheville because I lived there and it is a popular vacation spot.
Challenge:
Explore: What is the distribution of prices across a city's neighborhoods? How does it change when you segment it further by room_type? Visualize: Create a map with a dot for each listing in a city and add a color scale based on price on the dots. Analyze: How do listings that require a minimum stay of a week or longer differ from those that don't?
harder challenges:
An international real estate firm has hired you to research professional hosting on Airbnb. These are hosts that have multiple listings, make considerable income from their listings, and often manage teams to operate their listings. Examples include property managers and hospitality business owners.
Using the data from all six cities, you'll have to infer listings by professional hosts based on the distribution of calculated_host_listings_count. The lead consultant is interested in whether you can identify trends across listings operated by inferred professional hosts, as well as an estimation of the percentage of listings on Airbnb operated by professional hosts.
You will need to prepare a report that is accessible to a broad audience. It will need to outline your motivation, analysis steps, findings, and conclusions.