Group27 Overview
| Overview | Proposal | Poster | Application | Report |
ABSTRACT
Community groups and housing advocates in cities across the world have begun to sound the alarm about the impact Airbnb is having on affordable housing in their communities, citing concerns about housing supply lost, racialized gentrification, and impact on residents’ quality of life. To understand Airbnb’s impact on housing in New York, we aim at presents a comprehensive analysis of three years from 2015 to 2017 of Airbnb activity in New York City. It relies on the most comprehensive third-party dataset of Airbnb activity available, and methodologies of visualize and analysis geographical data.
Our report is motivated by the concerns increasingly raised by local communities and housing advocates that short-term rentals are clearly affect traditional residential rental housing and hotel accommodation. Either concern, if justified, would represent a serious problem for municipal authorities. But reliable, up-to-date evidence has been hard to come by. Accordingly, we aim at analysis different neighborhood’s distinct characteristics in New York, including significant seasonal tourism distributions, ethnic composition, price and density differences and set out to answer three main questions based on our analysis:
- How much housing has Airbnb removed from the market from the market in New York?
- How much Housing has Airbnb influenced household income?
OBJECTIVES
Airbnb Geospatial Pattern Analysis：
- Using geospatial analysis tools to map out the Airbnb property distribution in New York City across 5 boroughs from Year 2015 to 2017. We intend to visualize the sprawling pattern in New York City and dynamically changing density of listing Airbnb property in each neighbourhood.
- To refine the given broad geographic pattern, we turn to investigate the revenue-earning patterns in multiple neighbourhood derived from listing price and transaction records. The Airbnb revenue patterns would be compared with the gross rent income records across neighbourhood, which unveils the geographical rental gaps under two different “renting”form – Normal and Airbnb-ed.
DATASET
| Data | Format | Description | Source |
|---|---|---|---|
| Listing | csv | Summary information and metrics for listings in New York City. | http://insideairbnb.com/get-the-data.html |
| neighbourhoods | csv | Neighbourhood list for geo filter. Sourced from city or open source GIS files. | http://insideairbnb.com/get-the-data.html |
| calendar | csv | Detailed Calendar Data for listings in New York City. | http://insideairbnb.com/get-the-data.html |
| Selected Housing Characteristics | csv | Detailed Data for NYC's Occupied or Vacant, Own or Rent, Home Value. | https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF |
| Financial Characteristics | csv | Detailed Data for NYC's Household Income and Monthly Housing Costs. | https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF |
| Occupancy Characteristics | csv | Detailed Data for NYC's Household Size, Age of Householder, Family Type. | https://factfinder.census.gov/faces/tableservices/jsf/pages/productview.xhtml?src=CF |
REFERENCES
1. https://carto.com/blog/airbnb-impact/
3. http://www.wired.co.uk/article/airbnb-growth-london-housing-data-insideairbnb
4. https://threadreaderapp.com/thread/958390658481512449.html
5. http://www.sharebetter.org/wp-content/uploads/2018/01/High-Cost-Short-Term-Rentals.pdf
