Airbnb_Gentrification by gkkkkkai

Geographic Information Systems (Fall 2020) Instructor: Leah Meisterlin, Carsten C. Rodin

Gentrification & Airbnb Effects Kai Guo, Lingbei Chen, Wanjing Zhang, Wenyi Peng

Research Report 1.0 2.0 3.0 4.0 5.0 6.0

Introduction and Background Research Questions Methodology Analysis Conclusion Database and Reference

1.0

Introduction and Background

“ The incresing of short-term rentals offered by Airbnb has played a main role in accelerated gentrification. ” - New York Attorney General’s office

GENTRIFICATION - demographic and physical changes in neighborhoods that bring in wealthier residents, greater investment, and more development, has become a buzzword in urban planning. As gentrification is happening across the U.S., the traditionally low-income neighborhoods are increasingly facing concerns about displacement. GENTRIFICATION AND DISPLACEMENT are heavily related to housing issues, which are now under the threat of Airbnb’s impacts on urban real estate markets. New York City is the third-largest Airbnb market in the world, and one involved factor to New Yorkers’ burden of rising rents in gentrifying neighborhoods is the Airbnb effect on this sharing economy. Fewer long term apartments to rent means higher prices, which leaves residents struggling to preserve their stock of affordable housing. So the growth and development of Airbnb listings may be identified as an early indicator of gentrification or the result of such local transformation during our research.

N ! !

4 Miles

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LEGEND

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Entire Homes/Apartment Private Rooms Shared Room

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The Most Popular Cities for Airbnb in the U.S.

Airbnb Bookings have more than quadrupled since the last five years. 72.4M

2015

125.7M

2016

185.8M

2017

250.3M

2018

326.9M

2019

Monthly Growth in Private Short-Term Rentals on Airbnb 35,000

Reservations

30,000

25,000 !

20,000

15,000

! ! !

Units

10,000 ! !

5,000

Apr

May

Mar

Jan

Feb

Dec

Oct

Nov

Sep

Jul

Aug

Jun

Apr

May

Mar

Jan

Feb

Dec

Oct

Nov

Sep

Jul

Aug

Jun

Apr

May

Mar

Jan

Feb

Dec

Oct

Nov

Sep

Jul

Aug

Jun

Apr

May

Mar

Jan

Feb

Dec

Oct

Nov

Sep

Jul

Aug

Jun

Apr

May

Mar

Jan

Hosts Feb

! !

Most Private Short-Term Rentals on Airbnb Violate New York Law Background Laws: New York’s short-term rental laws basically prohibit most apartments (buildings with three or more units) in New York City from being rented out for less than 30 days. This means that the majority of entire home/ apartment listings that you find on Airbnb and other sites for New York City would be considered illegal, especially if you can book them for a period of less than 30 days.

72%

Unique units rented in apparent violation of MDL or NYC Administrative.

36%

Percent of commercial users accounted for a disproportionate share of short-term rentals.

Revenue: $304 Million Reservations: 300,891 Units: 25,532 Hosts: 20,835

2.0

Research Questions Hypothesis Limitations

RESEARCH QUESTION Main Idea:

What‘s the spatial relationship between Airbnb’s growth in NYC and neighborhood level gentrification trends? Subsidiary Ideas: - What is the growing trend for Airbnb? - What is the trend of the neighborhood level gentrification? - What trends the transformation? - How does the gentrification trend influence land use change (in specific areas)?

HYPOTHESIS The first hypothesis is that the spatial characteristic of Airbnb is relevant to the datasets of gentrification by showing the overlay results of multiple layers. Also, we hold the hypothesis that the access to transit is the key point in leading the gentrification trend as well as the airbnb growth.

LIMITATIONS First of all, it is hard to define the actual locations of Airbnb listings because on the booking website, specific location of airbnb is not accurate. Secondly, gentrification always covers many aspects and can be affected by various parameters. Our project intends to summarize and visualize gentrification from three categories of indicators which are spatial, economic and demographic. There should be other parameters that are relevant to gentrification, but they are beyond our limitations. And it is also difficult for us to quantify the relationship between airbnb's spatial pattern and gentrification after we put them together, so we may need to establish a certain range of buffer zones to prove the correlation through the amount of overlap between the two layers.

3.0

Methodology

Data Collection

Airbnb Datas in NYC Tables from website

Line Tract

Data Preparation

Adjust Coordinate

Airbnb Data Spatial Join

NYC Borough Boundary

2014 ACS 5-year Estimates

2018 ACS 5-year Estimates

NYC Census Tract

Table Join

Listing locations & Price Range

NYC Census Tract Race / Income / Rent / Education

Airbnb Growing Tren

Airbnb Listings Distribution Airbnb Average Price Minimum Nights of Entire Houses

Identified Hot Spots

Trend of gentrification near the Airbnbs

Airbnb and Gentrification Correlation Analysis

Median Income Census Tract

Transit Network Analysis

5/10 mins from subway stops

2014/2017

Median Rent Census Tract 2014/2017

Spatial join Airbnb listings to Service Areas

Use the percentage of airbnb listings within the service areas to support the hypothesis that the access to transit may influence the trend

Educational Level Census Tract 2014/2017

Define specific boroughs for detailed contrast analysis

Race Ratio Census Tract 2014/2017

North Manhattan -South Bronx

North Brooklyn

Change of Land use 2014-2018

4.0

Analysis

4.1 Airbnb Listings Distribution 4.2 Airbnb Average Price 4.3 Other Airbnb Properties 4.4 Overlay of Gentrification 4.5 Transit Network Analysis 4.6 Transit Service Area with Airbnb Listing 4.7 Transit Service Area with Gentrification 4.8 In-depth Analysis of Gentrified Area

4.1 Airbnb Listings Distribution For airbnb growth, spatial join is applied to aggregate the airbnb point layer to census tract layer. As is shown below, the number of Airbnb properties has increased over 23 thousand from 2014 to 2018. Manhattan, Midtown, the West Side, and the Lower East Side and Williamsburg in Brooklyn have consistently seen the greatest concentration of Airbnb activity.

Airbnb Listings Distribution in 2014

Hot Spot Analysis

2014

2018

Airbnb Listings Distribution in 2018

4.2 Airbnb Average Price The average price of Airbnb listings are shown here. As can be seen, while there is a general decrease all over the city, but off the island, in many areas the price slightly goes up with the spread of airbnb properties after four years' development, which might be related to gentrification that to be examined in following sections.

Airbnb Average Price in 2014

Hot Spot Analysis

2014

2018

Airbnb Average Price in 2018

4.3 Other Airbnb Properties Entire homes and apartments are the most common room types among all the Airbnb listings. Since it became illegal for guests to rent an entire home/apt for less than 30 days, the minimum nights of such room type are mapped here. Apparently, in 2014 the min-stay was mostly 1-5 days, while in 2018, the criteria got much longer.

Average Minimum Nights in 2014 (entire homes/apartment)

Airbnb Room Type

2014

2018

Room Type Entire Homes/Apartment Private Rooms Shared Rooms

Average Minimum Nights in 2018 (entire homes/apartment)

4.4

Overlay of Gentrification

White Population

2014

2018

Top 60% Area

Areas with Top 60% Increase in White Population

Educational Level

2014

2018

Top 60% Area

Areas with Top 60% Increase in Educational Level

4.4 Average Rent

Overlay of Gentrification 2014

2018

Top 60% Area

Areas with Top 60% Increase in Rent

Average Income

2014

2018

Top 60% Area

Areas with Top 60% Increase in Income

4.5 Transit Network Analysis We made a hypothesis that access to transit facilitates like subway stations might affects airbnb growth and gentrification. So we first use Network Analysis to generate service areas covering 1min, 2min, 5min, and 10min walking distance from subway stations, which represented by 660ft, 1320ft, 1980ft and 2640ft.

LEGEND Subway Stations Subway lines

WALKING DISTANCE <660ft

1min

<1320ft

2min

<1980ft

5min

<2640ft

10min

¯ 0

4 Miles

4.6 Transit Service Area with Airbnb Listing We spatial join airbnb listing of 2018 to service areas, to visualize how the locations of Airbnb aggregated around subway stations. And then we will use the percentage of airbnb listings within the service areas to support our hypothesis that the access to transit may influence the trend. We found that in 2018 87% of Airbnb are located in the service areas. So we can assume that the two features are related.

87% LEGEND Service Area

AIRBNB LISTING 1-50 51-100 101-150 151-200 >200

¯ 0

4 Miles

4.7 Transit Service Area with Gentrification We also spatial join the most gentrified area to service areas, to visualize how the transit related to gentrification. We found that 76% of the most gentrified area are overlaid with the service areas between the year of 2014 to 2018. So we can assume that the two features are related. Gentrification is affected by the access to transit, and the more accessible to transit, the more possible to be gentrified.

76% LEGEND Service Area Gentrification Area

¯ 0

4 Miles

4.8 In-depth Analysis of Gentrified Area Site 1: South Brooklyn & North Manhattan

Site 2: North Brooklyn As is analyzed in the previous section, relatively high concentrations of Airbnb activity appeared in north Brooklyn and south Bronx, so these two gentrified areas are zoomed in to show specific demographic changes and if Airbnb development intensified the situation.

Site 1: South Brooklyn & North Manhattan The first area we chose to examine is located in North Manhattan-South Bronx, which has 31 gentrified tracts (16.4% of All). We proposed to research the relationship between land-use change and the gentrification under the Airbnb effect. However, it lacks an obvious change of the land-use percentage in the period of 2014-2018.

13.80% Increase in

White Population

4.80%

10.01%

13.00%

Educational Level

Medium Rent

Medium Income

Increase in

Land use of Gentrified Area in 2014

Residential Buildings Mixed Residential & Commercial Buildings Commercial & Office Buildings Industrial & Manufacturing Transportation & Utility Public Facilities & Institutions Open space & Outdoor Recreation

0.25

0.5

Land use of Gentrified Area in 2018

1 Miles

.25

0.25

1 Miles

0.5

1 Miles

Site 2: North Brooklyn The second area we chose to examine is located in North Bronx, which has 58 gentrified tracts (31.5% of All). These tracts saw an increase in median rent from 2014 to 2018 by 39%. The percentage of residents age 25 and over holding bachelor’s degrees (or higher) increased hugely, which has been a significant symbol of gentrification.

35.30% Increase in

White Population

40.08%

39.00%

39.07%

Educational Level

Medium Rent

Medium Income

Increase in

Land use of Gentrified Area in 2014

0.25

0.5

Land use of Gentrified Area in 2018

1 Miles

.25

0.25

1 Miles

0.5

1 Miles

5.0

Conclusion

The project was completed by looking at Airbnb growth, gentrification trends and the spatial relationship between them. To better understand causality, transit and land use are introduced as hypotheses to analyze the specific performance at two different scales. In general, New York City is responsible for the overwhelming majority of Airbnb activity increase in the four years’ development. Most Airbnb growth can be seen at Manhattan, Midtown, the West Side, and the Lower East Side, and off the island, Williamsburg in Brooklyn is the most comparable site. With the spread of Airbnb listings, the increase of price and other relative properties, it’s clear that there must be certain areas of overlap with gentrification. The study of gentrification trends proved such overlap. North Brooklyn and south Bronx are identified as the most gentrified areas in NYC from 2014 to 2018 by four gentrification indicators: gross rent, median household income, non-hispanic white and education level. These are two places that used to be the last 40th percentile of criteria above in 2014 and showed the top 60% increase in 2018. Then the study showed the access to transit is one of the key points in leading the gentrification trend as well as the airbnb growth. The result of subway network analysis indicates 90% of Airbnb were located in the service areas in 2014 and 87% in 2018. As is analyzed in the previous section, relatively high concentrations of Airbnb activity appeared in north Brooklyn and south Bronx, so these two gentrified areas are zoomed in to show specific demographic changes and if Airbnb development intensified the situation.

6.0

Datasets and Reference

DATASETS Airbnb Data

Insider Airbnb. NYC http://insideairbnb.com/new-york-city/ Airbnb-data. Tom Slee https://tomslee.net/category/airbnb-data

ACS 5-year Estimates

American Community Survey 5-year Estimates. New York City Profile. 2018 https://data.census.gov/cedsci/profile?g=1600000US3651000 American Community Survey 5-year Estimates. New York City Profile. 2014 https://www.census.gov/data/developers/data-sets/acs-5year/2014.html

Transportation

Subway Stations. NYC Open Data. 2019 https://data.cityofnewyork.us/Transportation/Subway-Stations/arq3-7z49 Subway Lines. NYC Open Data. 2018 https://data.cityofnewyork.us/Transportation/Subway-Lines/3qz8-muuu

Land Use

Map PLUTO. Bytes of the BIG APPLE. 2014 & 2018 https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto. page#mappluto

Reference

US Census Geometry. 2019 https://www.census.gov/cgi-bin/geo/shapefiles/index.php Borough Boundaries. NYC Open Data. 2020 https://data.cityofnewyork.us/City-Government/Borough-Boundaries/tqmj-j8zm LION Single Line Street Base Map, Bytes of the BIG APPLE. 2020 https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-lion.page

BIBLIOGRAPHY Andrew Sheivachman (2017, January). HOW GENTRIFICATION POWERS NEW YORK CITY’S TOURISM INDUSTRY. https://skift.com/new-york-city-tourism-andgentrification/ Alastair Boone (2018, March 5). What Airbnb Did to New York City? https://www.bloomberg.com/news/articles/2018-03-05/what-airbnb-did-to-newyork-city-s-housing-market Chapple, K. & Thomas, T. (2020). Berkeley, CA: Urban Displacement Project. Mapping Displacement and Gentrification in the New York Metropolitan Area. https://www.urbandisplacement.org/maps/ny Chris Bousquet. (2017, June 5). Where is Gentrification Happening in Your City? https://datasmart.ash.harvard.edu/news/article/where-is-gentrificationhappening-in-your-city-1055 Chang, Chung, "The Advantage of Mapping Gentrification with Geographic Information Systems: Comparisons of Three New York City Neighborhoods, 1980 - Present" (2013). CUNY Academic Works. https://academicworks.cuny.edu/gc_ etds/2248 Galia Shokry, James JT Connolly, Isabelle Anguelovski. (2019, November 19). Understanding climate gentrification and shifting landscapes of protection and vulnerability in green resilient Philadelphia, https://www.sciencedirect.com/ science/article/pii/S2212095519300732 Micheal Maciag. (2015, Jan). Gentrification Report Methodology https://www.governing.com/gov-data/gentrification-report-methodology.html Steve Dubb. (2018, May 11). New York City Report Finds that Airbnb Intensifies Gentrification Pressures, https://nonprofitquarterly.org/new-york-city-report-findsairbnb-intensifies-gentrification-pressures/ Saviano Katie. (2017, May). COMPLICATING GENTRIFICATION https://sites.tufts.edu/gis/files/2017/06/Saviano_Katie_UEP232_May2017.pdf Seattle DPD. (2015, May). Growth and Equity: analyzing impacts on displacement and opportunity related to seattle’s growth strategy, https://www.seattle.gov/dpd/ cs/groups/pan/@pan/documents/web_informational/p2273984.pdf

STEP-BY-STEP METHODOLOGY At the beginning of analysis, we decided to use NAD_1983_StatePlane_New_York_ Long_Island _FIPS_3104_Feet as the coordinate system for our research. As is lenert at class, the coordinate systems work well for mapping accuracy. Through this system we have chosen, it shows the most precise spatial position and distance, which is absolutely the most important thing for our research. 1. Airbnb growth: In this section, we first displayed the XY data of 2014 and 2018 Airbnb locations in NYC to specific points, then re-project them to fit the coordinate system. Since the Airbnb location is inaccurate, we used spatial join to aggregate the point layer to census tract layer with other properties such as price, room type, minimum stay and so on. Then we displayed Airbnb data per tract using graduate symbols to show the spatial distribution and other growth. Moreover, hot spot analysis is applied to map the clustering performance of Airbnb listings in NYC in 2014 and 2018. 2. Gentrification trend: Gentrification is a process of neighborhood change that is not only related to economic change, like income level and housing rent, but also changes the education level and racial make-up of residents. Firstly, we cleared up four gentrification criteria: median gross rent, median household income, non-Hispanic White and Education level in ACS 2014 and 2018 and compressed them into one Excel file. Then we joined the table to census tract layer. Secondly, we selected eligible tracts determined by tracts that are below the 40th percentile in median household income and median gross rent, population and education layers. Thirdly, potential gentrification tracts are identified by the percentage increase in a tract’s value was in the top 60th percentile of all tracts in four layers. Lastly, the census tracts that meet all of the above criteria were overlapped and identified as undergoing or having undergone gentrification. 3. Transit & Airbnb We made a hypothesis that access to transit facilitates like subway stations might affect Airbnb growth and gentrification. So we first use Network Analysis to generate service areas covering 1min, 2min, 5min, and 10min walking distance from subway stations. And then we spatial joined Airbnb listings of 2014 and 2018 to service areas, to visualize how the locations of Airbnb aggregated around subway stations. 4. In-depth analysis—Land use As is identified in previous sections, north Brooklyn and south Bronx are the most gentrified areas with high concentration of Airbnb activity. So we zoomed in to these two sites and overlapped land use data to visualize the changes from 2014 to 2018.