C07

Research Abstracts on Spatial Information ScienceCSIS DAYS 2018

Effects of Neighborhood Characteristics in the Sharing Economy: the Case of San FranciscoYanjie Luo
Graduate School of Economics, Keio University
Email: <luoyanjie1992@keio.jp> Web: < https://sites.google.com/view/luoyanjie/>(1) Motivation: In recent years, as a prominent exampleof the sharing economy, Airbnb brings a successionof significant impacts and alternations in the world.
In this study, we emphasize the question of whether
there is evidence that the neighborhoodcharacteristics of the Airbnb accommodations affectthe listing price of rooms in the rental
accommodation market of San Francisco.
(2) Data and methods: We tested the hedonic pricemodel using a dataset of 6,624 Airbnb listings in 579
neighborhoods of San Francisco in the U.S. as ofAugust 2018 (Figure 1). The Airbnb listing price data
derived from the Inside Airbnb website(http://insideairbnb.com/), crime data from DataSF,
classified transportation data from SFMTA, andrestaurant datasets extracted from Yelp.com. This
study also assembled data from the Top 30 mostpopular attractions from Trip Advisor and batchedthese locations into geographic coordinates byGoogle Maps API and Python 3.7.0. We calculated
the walking distance from each Airbnb
accommodation to the nearest transit stop/ attraction
by find nearest analysis, supported by ArcGIS Online.
(3) Results and Discussion: In Table 1, we present theestimation results of the following five models:Model 1: log( Pricei ) =α + β Pi + ε iModel 2: log( Pricei ) =α + β Pi + γ H i + ε iModel 3: log( Pricei ) =α + β Pi + γ H i + δ Ri + ε iModel 4: log( Pricei ) =α + β Pi + γ H i + δ Ri + τ N i + ε iModel 5: log( Pricei ) =α + β Pi + γ H i + δ Ri + τ N i + ε iTable 1 shows that adding neighborhood
characteristics into analysis model is helpful toenhance the reliability of models while different
categories of transportation, population and housing
characteristics have different significance and effects
in specific analysis models.
(4) Conclusion: This article analyzes the relationships
between Airbnb pricing and neighborhood
characteristics in San Francisco which are examinedwith the hedonic pricing models at the block grouplevels. By providing such comprehensive observation
at factors that influence prices of Airbnb listings, thisresearch rendered hosts with insights as to potential
market competition strategies and public authorities
with the proficiency of the cooperative relationshipacross various regional industries and identified rental
accommodation supervision strategies.
Table 1: Estimated results model
Variables
Model1~Physical characteristics~
PropertyType10.189***
PropertyType20.124***
Accommodations
0.141***
Bathrooms-0.049***Bedrooms0.219***
Wireless Internet
0.048
Breakfast-0.004
Free parking
0.010~Host characteristics~
SuperhostIdentity verified
Cancellation
Host Verifications
Host long~Reputation characteristics~
Review Overall Rating
Review Scores Accuracy
Review Scores Cleanliness
Review Scores Checkin
Review Scores Communication
Review Scores Location
Review Scores Value

Model2Model3Model4Model50.193***
0.173***
0.139***-0.038***0.215***-0.051***-0.001-0.0100.168***
0.188***
0.139***-0.036***0.211***-0.049-0.005-0.0010.024*-0.0070.135***-0.057***0.214***-0.042-0.0140.071***

0.024*-0.0090.135***-0.056***0.214***-0.041-0.0140.071***

0.111
0.035***-0.144**0.000***-0.015***0.105***
0.032**-0.157***-0.015***0.000**

0.100***
0.029**-0.159***-0.016***0.000

0.100***
0.028**-0.159***-0.016***0.000

0.003**
0.000
0.120***-0.113***-0.059***0.075***-0.06***

0.003*
0.008
0.119***-0.075***-0.043***-0.008-0.032***0.003*
0.008
0.119***-0.075***-0.043***-0.008-0.032***0.375***-0.095***0.027
0.023*
0.000

0.369***-0.093***0.029
0.023*~Neighborhood characteristics~
Transit1: Bus
Transit2: Cable Car
Transit3: Metro
Transit4: Street Car
5minsrest
10minsrest
Attractions
Populatiom Density
Crime Density
Young Proportion
lnincome
Employed Proportion
High Education Proportion
Tenure Proportion
lnvalue
Vacancy Proportion
Single Proportion
White Proportion
Black Proportion
Asian Proportion
Hispanic Proportion
Observations
66246624
Adjusted R2
0.402
0.417
(Note: ***p < 0.01, ** p < 0.05 and * p < 0.1)

Figure 1: The spatial distribution of Airbnb listingaccommodations in San Francisco- 44 -

6624
0.441-0.024**0.000***
0.000
0.139*
0.000
0.221
0.238***
0.065*
0.000
0.275**-0.0080.171*
0.073-0.083
0.109
6624
0.498

0.000
-0.023*0.000***
0.000
0.143*
0.000
0.226
0.238***
0.064*
0.000
0.270**-0.0120.176*
0.075-0.0850.107
6624
0.498