The Brooklyn Anti-Eviction Mapping Project

Tyler Bird The Brooklyn Anti-Eviction Mapping Project visualizes the risk of eviction that many communities in Brooklyn face as the borough continues to go through rapid and aggressive gentrification. Illegal evictions have become more common and the strategies used by landlords to push out low-income and rent-stabilized tenants have become increasingly aggressive and predatory. The goal of this project is to contribute to informing and educating those who are likely at risk of experiencing such an eviction. The map has the potential to compliment advocacy efforts being carried out by NGOs and community-based organizations to organize community members against those that wish to increase profits by any means necessary. The outcome of the project is a visual representation of the different levels of risk that communities in Brooklyn face with regard to illegal evictions.

Introduction The Brooklyn Anti-Eviction mapping project was inspired by the practice of rent sabotage; the destruction, alternation or damage caused by property owners as a strategy to force out rent-stabilized tenants in order to carry out renovations that substantially increase rent. Many neighborhoods in New York City are experiencing rapid gentrification and with that often comes the displacement of longtime residents due to price spikes. Legally displacing these residents can be difficult if the tenant pays rent on time, as tenants have rights; to privacy, reasonable accommodation for people with disabilities, the right not to be harassed by their landlord, the right to not be retaliated against for exercising tenant rights and the right to organize in order to protect their rights. Despite these rights, many landlords take advantage of the ignorance that many people have in regard to knowing about these rights, as well as the lack of easily accessible information that educates and informs tenants about what to do if their rights are being violated. Other projects have successfully visualized community vulnerability to redevelopment, speculation and gentrification, but very few have attempted to assess the risk that rent-stabilized tenants might live under. It is important to address this demographic specifically because landlords view rent-stabilized tenants as a barrier to increased profits. Therefore, evicting these tenants has become the goal for some landlords. Living in a rent-stabilized building no longer fully guarantees protection from significant increases in rent or eviction. As these tenants become the target of landlords it is important that they are aware of the risk that this might happen, as well as the rights and resources afforded to them by law. The map identifies vulnerable areas which in turn, I hope, will provoke members of the community who live in these locations to educate themselves and organize in a way that will prevent and avoid illegal evictions. The project looks at rent-stabilized buildings as the location where predatory landlord behavior is most likely to occur, especially in areas where a high number of minorities live and where the median income is relatively low.

The data that I used was obtained from three main sources. The rent-stabilized building data set was taken from the NYC Rent Guidelines Board. This dataset was obtained through landlord registration of their rent-stabilized building. There is not information on which apartments within these buildings are rent-stabilized, it only indicates the buildings in each borough that have rent-stabilized apartments in them. The information is from 2012 and is the most recent of its kind. The shapefiles that I used came from NYC MapPLUTO. From this layer I was able to join census tracts and rent-stabilized building data. The third source from which I acquired data is the NYC Census Factfinder. From this source I was able to obtain information on median income, race and ethnicity in the target location, Brooklyn. These three sources provided me with the information needed in order to conduct a risk and vulnerability analysis using my three criteria, rent-stabilized parcels, median income and the number of minorities living in a given census tract. Other data that I used I created myself in excel. The original information was obtained from the sources listed above but was ultimately manipulated in order to create joins and unions to the shapefiles.

Methodology

I began my research and analysis with the rent-stabilized dataset, which came in PDF form. In this data set was a multitude of characteristics about the building including the owner, the year built, number of units, address, borough, block and lot. This provided me with more than enough information in order to do a join to the MapPLUTO shape file. However, in order to successfully conduct a join between the two datasets, I manually manipulated the data set to replicate the unique value BBL (Borough, Block, Lot). To do this I had to use two formulas. The first was the TEXT formula. The shape file included the 10 digit BBL however, the rent-stabilized dataset had the borough in text form, not numeric, and the block and lot were separate fields. In my rent-stabilized dataset I created four new fields, Borough, Block, Lot and BBL. The numeric sign for Brooklyn is 30, so the first thing I did was replace the Brooklyn text in the Borough field with 30. I then populated the Block and Lot fields with data available. Finally, in the BBL column I conducted a concatenation. The concatenation process allowed me to create unique values for every rent-stabilized building and effectively includes the borough, block and lot in one cell. This is crucial as the values for all three of these fields are not unique and repeat often. Once I did that I was able to join and union the rent-stabilized data to the shapefile and explore the attribute for characteristics. The next step was to begin adding layers of census tract data. I started with median income for all census tracts in Brooklyn. These datasets required a substantial amount of cleaning. Many of the fields were not necessary for my analysis and so I identified and eliminated these. The census tract 2010 field is where I made the join to the

shapefile. Again, I ran into the problem of having two data sets with similar information but in different formats. The census tract information in the shapefile was a 6 digit number, many of which had leading and trailing zeroes. For many of these figures I was able to use the TEXT formula to add leading zeroes but I struggled with adding trailing zeroes where necessary. I ultimately had to do so manually to all 748 census tracts in Brooklyn. This process was replicated for the race and ethnicity census tract data. Upon completing these processes I was successful in creating joins via census tract from the American Fact Finder data sets and the shapefiles. I analyzed which census tracts had low median income, high minority populations and significant number of rent stabilized buildings. These census tracts, I hypothesized, were at the greatest risk for illegal evictions and predatory landlord practices. To show this visually I assigned rankings to each of these three data sets in a new layer in arcmap. This layer was created by joining all three datasets. To do this I created four new columns, three for each data set used to conduct the analysis and one which would be the final input into my final map, eviction risk. I assigned high values to census tracts with low incomes and low values to census tracts with high incomes. Census tracts with few rent-stabilized buildings were given low rankings in terms of risk and those with many rent-stabilized buildings received high rankings, meaning higher risk. Finally, census tracts with few minorities living in them were given a low ranking for risk and those with many minorities received a high ranking for risk. I then calculated these rankings to create final ranking or eviction risk. The rankings ranged from 3-9 where 3 represents low risk and 9 represents a very high risk for eviction.

Through my analysis I found that based on the criteria I used, 53 census tracts were at high risk for illegal eviction and predatory landlord practices, 197 census tracts were at a medium risk and 318 census tracts were at a low risk. The census tracts at the highest risk for eviction were somewhat spread out but with concentrations in North Brooklyn which comes at no surprise as illegal evictions often take place in up and coming, rapidly gentrifying neighborhoods where prices are rising as new demographics move into the area. There were a few surprising areas with high risk including neighborhoods that have not yet been gentrified as much as other areas. These neighborhoods include Bed Stuy and Crown Heights. On the other side of the analysis I was surprised to see that many census tracts in Williamsburg and Greenpoint had a low risk of eviction. I suppose this could be the case because many of the longtime residents have already been driven out as gentrification has been occurring in these areas for several years now. My results are in some ways incomplete as at some point in my data collection nearly two hundred census tracts came up as null in my final table. This could be a combination of human error and a lack of information on rent stabilized buildings. By visualizing risk, people can become more aware of the realities of their neighborhoods and are in a better position to protect themselves from apartment sabotage and landlord harassment. If a family knows they are in a high risk area they may be more inclined to educate and inform themselves on what to do if they become victims of illegal practices and

will then have a better chance of avoiding eviction and getting the compensation they deserve through the courts.

Resources:

Census Information: Median Income, Race and Ethnicity. http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml

Rent Stabilized Buildings: http://nycrgb.org/downloads/resources/sta_bldngs/2013BrooklynBldgs.pdf

MapPLUTO: http://www.nyc.gov/html/dcp/html/bytes/applbyte.shtml

Apartment Sabatoge:

http://www.capitalnewyork.com/article/city-hall/2014/04/8543686/brooklyn-lawmakers-push-criminalize-rent-sabotage

http://gothamist.com/2014/02/24/landlords_rent_control.php

http://gothamist.com/2014/03/07/brooklyn_landlords_continue_to_sabo.php

http://www.nytimes.com/2014/02/25/nyregion/in-new-york-push-for-market-rate-housing-pits-landlords-against-tenants.html?_r=0

http://www.nbcnewyork.com/news/local/Landlord-Sabotage-Housing-Scheme-Nassau-Avenue-248609651.html

Community Groups Working to Protect Residents from Illegal Evictions and Apartment Sabotage:

St. Nicks Alliance

Flatbush Community Group

Cooper Square Committee

Right To the City

Rent-Stabilized Buildings

Rent-Stabilized Buildings Density Map

Minority Populations

Minority Populations0 - 12001200 - 30003000 - 45004500 - 65006500 - 10500

Brooklyn Median Income

Brooklyn Median Income0 - 1000010001 - 3000030001 - 5000050001 - 7000070001 - 170481

Risk of Eviction In Brooklyn

Risk of Eviction In BrooklynNA3-56-78-9

Brooklyn Anti-Eviction Mapping ProjectRentStabilizedRent-StabilizedDensityMinorityMedianIncomeBird_FinalRSBK