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Pros: The ability to generate additional revenue will increase home prices and financial wellbeing of homeowners as the concentration of Airbnbs increase
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Cons: Due to Airbnb's simplicity and ability to generate income, it is viewed that Airbnbs often violate health and safety laws, leading to unsafe homes, increase in traffic in quiet neighborhoods, and increase in people who do not care about the neighborhood during their short-term visit
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This Report uses linear regression and random forest models to evaluate Airbnb's effect on housing value
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It's Developed in Python using numpy, pandas, and sklearn for data manipulation, modeling, and analysis
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Also Developed in R using Dply and Stargazer
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NYC Open Data: A free public data source published by NYC agencies
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InsideAirbnb: An independent and non-commercial toolset of Airbnb data
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The data is combined using cross sectional data between 2013-2015, including Airbnb Concentration, Neighborhood Fixed Effects, and other influential variables.
- Linear Regression estimated by Ordinary Least Squares
- Multivariate Analysis to estimate housing value.
- (Note: Year Build and Neighborhood Fixed Effects are hidden from image)
A 0.165 percentage-point increase in the concentration of short-term rentals leads to a 5.049% increase in the housing sale price.
New York City should first create and police regulations that protect the neighborhood and its people from inappropriate uses of Airbnb, then encourage Airbnb rentals rather than reduce them, as this will appreciate housing prices in the neighborhood.
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✓ Airbnb Concentration causes the prices of homes to appreciate
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✗ Non-Locals may mistreat the neighborhood due to short-term stay
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✗ Airbnb Hosts often violate health and safety laws due to its low barrier to entry
Proposed Solution: Policed Regulations for Airbnb Hosts and Customers before further encouraging Airbnb usage.