This dataset lists executed evictions within the five boroughs for the years 2017-Present (data prior to January 1, 2017, is not available). The data fields may be sorted by 20 categories of information including Court Index Number, Docket Number, Eviction Address, Marshal First or Last Name, Borough, etc..

Eviction data is compiled from New York City Marshals. City Marshals are independent public officials appointed by the Mayor. Marshals can be contacted directly regarding evictions, and their contact information can be found at https://www1.nyc.gov/site/doi/offices/marshals-list.page.

A statewide listing of District Court of Maryland Eviction Case Data & its Process. Maryland enacted a new law in 2022 requiring the District Court of Maryland to collect and report eviction case data. Additionally, the Maryland Department of Housing and Community Development is required to host a dashboard for the public to view and analyze the information, as well as produce an annual report on evictions. The District Court began collecting the eviction case data required under the law on January 1, 2023, and the public dashboard was launched in May 2023. https://www.courts.state.md.us/district/about under "Eviction Data" Note: The data will not match the monthly eviction totals that are seen on the District Court of Maryland Landlord/Tenant Case Activity Report. That data is collected manually by all jurisdictions, as of the end of each month. Some jurisdictions rely on a total provided by the local Sheriff's department for an accurate count of the total evictions. Because the data provided in the spreadsheet relies on data entry, the clerks may not have all data entered into the case management system when the report is run. These evictions will be captured on subsequent month's reports because the reporting looks for when the data is entered by the clerk.

Note 2/27/2024: There was a previous issue with this dataset that created duplicate rows for each record. This issue has been fixed. Data includes eviction notices filed with the San Francisco Rent Board per San Francisco Administrative Code 37.9(c). A notice of eviction does not necessarily indicate that the tenant was eventually evicted, so the notices below may differ from actual evictions. Notices are published since January 1, 1997.

The purpose of this project is to leverage the complementary technological skill, expertise, and organizational resources of the partnering organizations to create a database of eviction filings with the purpose of informing and improving the ability of Metro Atlanta policymakers, Non-government Organizations, service providers, tenant organizers, and government entities to understand and respond to eviction-related housing instability, particularly in the context of the COVID-19 pandemic. In addition, the intent of this project is to provide access to eviction filings data for research, practice, and policy purposes beyond the immediate threat of COVID-19. This partnership behind this project will collectively work to create the technology necessary to assemble the database of filings and make the filing information available to stakeholders in an understandable, accessible, secure, and responsible manner.About The DataThis data captures formal evictions activity in the metro Atlanta area as it is reflected in county court websites. This data does NOT reflect the number of rental households that undergo forced moves. Research has found that forced moves due to illegal evictions and informal evictions are far larger than the number of tenants displaced through the legal, formal eviction process. While eviction or dispossessory filings are evidence of housing instability, and constitute a negative event for tenants in and of themselves, they are not equivalent to displacement of a tenant. It is difficult to know whether a tenant leaves during a formal eviction process or at what stage of the process this occurs. Eviction filings initiate the process of eviction and are distinct from a "writ of possession" which grants a landlord the legal right to remove a tenant.This data is parsed once a week from the magistrate courts' case record search sites for Clayton, Cobb, DeKalb, Fulton and Gwinnett counties. Once the evictions case data is captured, each case is geocoded based on the defendant's address and the case events are analyzed to identify associated actions. Due to missing, incorrect, or difficult to parse addresses, approximately 1% of all filings are excluded from mapped totals. Analysis of case actions is done with an algorithm that is under development. For this reason, estimates of these actions are currently not included in the aggregated data presented in this tool. These estimates will, however, likely be included in future versions once the algorithm is complete and sufficiently validated. Additionally, due to ongoing improvements in the handling of parsing errors and the occasional lag in filings being entered into courts' online systems, counts will sometimes differ from those previously reported.TeamProject LeadElora Raymond, PhDAssistant ProfessorSchool of City and Regional PlanningGeorgia Institute of TechnologyProject LeadErik Woodworth, MA & MCRPResearch & Application Development CoordinatorData ScientistNeighborhood NexusAtlanta Regional Commission (ARC)Project LeadSarah Stein, JDResearch AdvisorCommunity & Economic DevelopmentFederal Reserve Bank of AtlantaData Acquisition & AnalysisVictor Pearse Haley, MCRPResearch AnalystCommunity & Economic DevelopmentFederal Reserve Bank of AtlantaData Storage & ProcessingGordon (Ge) Zhang, PhDResearch ScientistCenter for Spatial Planning Analytics & Visualization (CSPAV)Georgia Institute of TechnologyData Storage & ProcessingRama Sivakumar, MSSenior Research EngineerCenter for Spatial Planning Analytics & Visualization (CSPAV)Georgia Institute of TechnologyData Storage & ProcessingSubhro Guhathakurta, PhDChairSchool of City & Regional Planning (SCaRP)DirectorCenter for Spatial Planning Analytics & Visualization (CSPAV)Georgia Institute of TechnologyCourt Record Data SourcesFulton County Magistrates, State, and Superior Court Record SearchDeKalb County - Judicial Information SystemGwinnett County Courts - Tyler Odyssey PortalXerox CourtConnect Cobb Magistrate CourtClayton County Court Case InquiryOther Data SourcesUS Census Bureau, American Community Survey (ACS), 5-year estimates, 2014-2018ResourcesFAQ on National Eviction Moratorium provided by the National Low Income Housing Coalition (NLIHC)This page provides an explanation of the eviciton moratorium (effective Sept. 4th, 2020 to Dec. 31st, 2020) issued by the Center for Disease Control (CDC). It also provides a links to a number of resources including a downloadable Declaration of Eligibility (in multiple languages) to be completed, signed, and mailed by tenants to their landlord as the first step to invoking their right to the protections of this moratorium.CitationAny use of data downloaded from this site or reference to this work must be accompanied by one of the following citations.Data:Raymond, EL; Stein, S; Haley, V.; Woodworth, E; Zhang, G.; Siva, R; Guhathakurta, S. Metro Atlanta Evictions Data Collective Database: Version 1.0. School of City and Regional Planning: Georgia Institute of Technology, 2020, https://metroatlhousing.org/atlanta-region-eviction-tracker/.Methodology Report:Raymond, EL; Siva, R; Stein, S; Haley, V.; Woodworth, E; Zhang, G.; Siva, R; Guhathakurta, S. Metro Atlanta Evictions Data Collective Database: Version 1.0. School of City and Regional Planning: Georgia Institute of Technology, 2020, https://metroatlhousing.org/atlanta-region-eviction-tracker/.Data RequestsIf you or your organization would like access to data at a level of aggregation or format not available via the "Download Data" button on the tool, you will need to submit a formal request. Click below to begin the request process.https://docs.google.com/forms/d/e/1FAIpQLSexUZb9dXIx5h1GjaKmuNekxvp-CkgQ_qGsoAJXDERuLslSCg/viewform

These files contain the data and code for the journal article “Do Nuisance Ordinances Increase Eviction Risk?”, American Economic Association Papers and Proceedings. City-year level difference-in-difference analysis shows that criminal activity nuisance ordinances increase eviction filing rates by about 16 percent of the sample mean and court ordered evictions by 14 percent of the sample mean.

Data includes eviction notices filed with the San Francisco Rent Board per San Francisco Administrative Code 37.9(c). A notice of eviction does not necessarily indicate that the tenant was eventually evicted, so the notices below may differ from actual evictions. Notices are published since January 1, 1997.

This dataset lists pending, scheduled and executed evictions within the five boroughs, for the year 2017 - Present. The data fields may be sorted by Court Index Number, Docket Number, Eviction Address, Apartment Number, Executed Date, Marshal First Name, Marshal Last Name, Residential or Commercial (property type), Borough, Zip Code and Scheduled Status (Pending/Scheduled).

Eviction data is compiled from the majority of New York City Marshals. Marshals are independent public officials and should be contacted directly for more information at https://www1.nyc.gov/site/doi/offices/marshals-list.page Data prior to January 1 2017 is not currently available.

Data includes eviction notices filed with the San Francisco Rent Board per San Francisco Administrative Code 37.9(c). A notice of eviction does not necessarily indicate that the tenant was eventually evicted, so the notices below may differ from actual evictions. Notices are published since January 1, 1997. Please note that there are blank values for neighborhoods that could not be automatically assigned. These counts are automatically derived and there could be errors, please check the source to verify accuracy. The neighborhood boundaries used in this dataset correspond to these: https://data.sfgov.org/d/p5b7-5n3h

Abstract

Due to this year's mobility restrictions across the region implemented to mitigate the spread of COVID-19, national needs assessments have identified a higher risk/impact of evictions for the Venezuelan refugee and migrant populations, linked to the reduction or loss of livelihoods as well as to increased xenofobia and discrimination. The Protection Sector of the Regional Coordination Platform for the response to refugees and migrants from Venezuela (R4V) implemented the initiative of a regional, systematic data collection process to assess the magnitude and characteristics of the situation, as well as to identify risk profiles and factors, to better design protection strategies that led to the development of a regional toolbox for the mitigation of evictions risks available here: https://www.r4v.info/en/evictiontools

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

Evicted households or at risk of eviction were identified in three ways at national/regional level: i) through existing call centers; ii) during assistance provision processes, iii) on shelters or temporary settlements with presence of regional protection sector members. A sampling was not established due to lack of data or information on the topic.

Mode of data collection

Other [oth]

Cleaning operations

In the dataset here provided, the host country was imputed for 202 records, these values were not included in the original dataset (1021), which was used for the analysis included in the report. With the inclusion of these 202 records, the conclusions at country level of any analysis produced with this dataset may slightly differ from the ones published in the report launched in February 2021.

This dataset lists pending, scheduled and executed evictions within the five boroughs, for the year 2017 - Present. The data fields may be sorted by Court Index Number, Docket Number, Eviction Address, Apartment Number, Executed Date, Marshal First Name, Marshal Last Name, Residential or Commercial (property type), Borough, Zip Code and Scheduled Status (Pending/Scheduled).

Attributes: - court_index_number: Number assigned by the court that has jurisdiction over the landlord/tenant proceedings. - docket_number: Number assigned by the City Marshal upon case intake. - eviction_address: Identifies the street and building number where an eviction is pending or has been completed. - eviction_apt_num: Identifies the unit within the building where an eviction is pending or has been completed. - executed_date: The date that evictions have been executed. - marshal_first_name: Identifies the first name of the NYC Marshal who performed the eviction or who is scheduled to perform the eviction. - marshal_last_name: Identifies the last name of the NYC Marshal who performed the eviction or who is scheduled to perform the eviction. - residential_commercial_ind: Identifies the property type where the eviction is pending or has been completed. - borough: Identifies the NYC borough where the eviction is pending or has been completed. - eviction_zip: Identifies the NYC zip code where the eviction is pending or has been completed. - ejectment: An action in ejectment is a way for a landlord to obtain possession of a residence and may be started in either Civil or Supreme Court. - eviction_possession: Process by which a warrant of eviction is executed. Landlord may request an eviction whereby the tenant and his/her property are removed by a marshal, or a legal possession whereby the tenant is removed and his/her property remains under the care and control of the landlord as bailee for the tenant. - latitude: The latitude associated with the provided entity location. - longitude: The longitude associated with the provided entity location. - community_board: The NYC community board associated with the provided entity location. - council_district: The NYC council district associated with the provided entity location. - census_tract: The US census tract associated with the provided entity location. - bin: The Building Identification Number (BIN) associated with the provided entity location. It is formatted as a seven-digit numerical identifier, which is unique to each building in NYC. - bbl: The Borough, Block, Lot (BBL) associated with the provided entity location. It is formatted as a ten-digit numerical identifier, which is unique to each parcel of real property in NYC. - nta: The Neighborhood Tabulation Area (NTA) associated with the provided entity location.

Note: Since Marshals are employees of the federal government, their names are not omitted.

We spoke to Clevelanders about the difficulties caused by the pandemic and solutions that show promise for times of crisis and times of relative stability. Explore the story for insights from a tenant who was furloughed, a landlord with a small number of properties, a Housing Court judge, and a Legal Aid attorney.

This dataset lists pending, scheduled and executed evictions within the five boroughs, for the year 2017 - Present. The data fields may be sorted by Court Index Number, Docket Number, Eviction Address, Apartment Number, Executed Date, Marshal First Name, Marshal Last Name, Residential or Commercial (property type), Borough, Zip Code and Scheduled Status (Pending/Scheduled).

Eviction data is compiled from the majority of New York City Marshals. Marshals are independent public officials and should be contacted directly for more information at https://www1.nyc.gov/site/doi/offices/marshals-list.page Data prior to January 1 2017 is not currently available.

Weekly evictions data by census tract in Connecticut, reported by the Eviction Lab at Princeton University and collected by the CT Fair Housing Center from CT eviction records. More details can be found here: https://evictionlab.org/eviction-tracking/connecticut/ Peter Hepburn, Renee Louis, and Matthew Desmond. Eviction Tracking System: Version 1.0. Princeton: Princeton University, 2020. www.evictionlab.org.

Restricting landlords’ ability to evict may reduce housing supply, so paying delinquent tenants’ rent is a better policy.

Context

This data was gathered from the Cleveland Municipal Court website using a selenium/beautiful soup web scraper. It was then geocoded and grouped by neighborhood. You can find a visualization based on the data and demographic data here: https://cleveland-evictions.herokuapp.com/ A github repo for the visualization and scraper lives here: https://github.com/sdl60660/cleveland_eviction_mapping

Content

The redacted_kaggle_upload.csv contains data on each eviction filing from 2011 to December 2020, as well as associated location data. The defendants' names have been redacted, but plaintiff info, case status, and property info is included.

Inspiration

I've found some anecdotal patterns in the data, but I'd love for someone with better knowledge of evictions, and particularly evictions in Cleveland to give it a look.

Abstract

The purpose of the SEPHER data set is to allow for testing, assessing and generating new analysis and metrics that can address inequalities and climate injustice. The data set was created by Tedesco, M., C. Hultquist, S. E. Char, C. Constantinides, T. Galjanic, and A. D. Sinha.

Methodology

SEPHER draws upon four major source datasets: CDC Social Vulnerability Index, FEMA National Risk Index, Home Mortgage Disclosure Act, and Evictions datasets. The data from these source datasets have been merged, cleaned, and standardized and all of the variables documented in the data dictionary.

CDC Social Vulnerability Index

CDC Social Vulnerability Index (SVI) dataset is a dataset prepared for the Centers for Disease Control and Prevention for the purpose of assessing the degree of social vulnerability of American communities to natural hazards and anthropogenic events. It contains data on 15 social factors taken or derived from Census reports as well as rankings of each tract based on these individual factors, groups of factors corresponding to four related themes (Socioeconomic, Household Composition & Disability, Minority Status & Language, and Housing Type & Transportation) and overall. The data is available for the years 2000, 2010, 2014, 2016, and 2018.

FEMA National Risk Index

The National Risk Index (NRI) dataset compiled by the Federal Emergency Management Agency (FEMA) consists of historic natural disaster data from across the United States at a tract-level. The dataset includes information about 18 natural disasters including earthquakes, tsunamis, wildfires, volcanic activity and many others. Each disaster is detailed out in terms of its frequency, historic impact, potential exposure, expected annual loss and associated risk. The dataset also includes some summary variables for each tract including the total expected loss in terms of building loss, human loss and agricultural loss, the population of the tract, and the area covered by the tract. It finally includes a few more features to characterize the population such as social vulnerability rating and community resilience.

Home Mortgage Disclosure Act

The Home Mortgage Disclosure Act (HMDA) dataset contains loan-level data for home mortgages including information on applications, denials, approvals, and institution purchases. It is managed and expanded annually by the Consumer Financial Protection Bureau based on the data collected from financial institutions. The dataset is used by public officials to make decisions and policies, uncover lending patterns and discrimination among mortgage applicants, and investigate if lenders are serving the housing needs of the communities. It covers the period from 2007 to 2017.

Evictions

The Evictions dataset is compiled and managed by the Eviction Lab at Princeton University and consists of court records related to eviction cases in the United States between 2000 and 2016. Its purpose is to estimate the prevalence of court-ordered evictions and compare eviction rates among states, counties, cities, and neighborhoods. Besides information on eviction filings and judgments, the dataset includes socioeconomic and real estate data for each tract including race/ethnic origin, household income, poverty rate, property value, median gross rent, rent burden, and others.

There is considerable current interest in policy solutions to address the post-pandemic rise in evictions experienced in many communities. Some have advocated expanding access to legal counsel as one solution: in the U.S., tenants usually face eviction on their own, while landlords are typically represented by an attorney. Although it seems intuitive that legal representation in housing court would help tenants facing eviction, measuring the effects of counsel is quite challenging, because represented and unrepresented tenants are dissimilar across many dimensions, including wealth. A handful of randomized experiments suggest lawyers have appreciable impacts in housing court, but results are mixed, and these studies’ generalizability to the larger universe of civil legal housing assistance programs remains uncertain. In this Essay, we address that gap through a quasi-experimental evaluation of the Legal Services Corporation (LSC), the nation’s largest civil legal aid provider that serves over 1.7 million people each year. We employ Census data covering millions of households and exploit an eligibility rule that limits LSC services to households earning less than 125 percent of the federal poverty level. Using several methodological approaches, including regression-discontinuity, differences-in-differences, and a dose-response analysis, we demonstrate that access to civil legal aid improves housing stability. Our estimates suggest that LSC enables 75,000 households to maintain their housing each year at a rough cost of around $2,000 per prevented move. These impacts are on par with those observed in high-quality randomized trials, suggesting that civil legal aid, unlike many other interventions, does not lose efficacy with scale. Our large sample sizes allow us to measure how impacts of civil legal access vary for particular population subgroups, something not possible in prior work. Access to civil legal aid is particularly beneficial for seniors aged 65+, people with less than a high school degree, Asians, and people who do not speak English well. Our findings highlight the important role that funding legal aid can play in curbing housing instability and homelessness. Though eviction defense models vary, what matters the most is having an attorney.

Peter Hepburn, Renee Louis, and Matthew Desmond. Eviction Tracking System: Version 1.0. Princeton: Princeton University, 2020.www.evictionlab.org.

This dataset includes the data used to develop Maps 8 and 9 for the Connect SoCal 2024 Equity Analysis Technical Report, adopted on April 4, 2024. The dataset includes two fields with information about gentrification during two time periods (2000-2010 and 2010-2019) in the SCAG region based on ACS data. In this dataset, gentrification is defined as: (1) tract median household income in the bottom 40 percent of the countywide income distribution at the beginning of the period, (2) increase in college-educated people (as the percentage of population aged 25 years and older at the beginning of the period) in the top 25 percent of the countywide distribution, (3) no less than 100 people aged 25 years at the beginning of the period, and (4) over 50 percent of the tract land area within a census defined urbanized area. The dataset also includes a field with information about areas with a high number of eviction filings between 2010 and 2018 in the SCAG region with data from the Eviction Lab. In this dataset, "high eviction filings" is defined as an average annual eviction filing rate over three. This dataset was prepared to share more information from the maps in Connect SoCal 2024 Equity Analysis Technical Report. For more details on the methodology, please see the methodology section(s) of the Equity Analysis Technical Report: https://scag.ca.gov/sites/main/files/file-attachments/23-2987-tr-equity-analysis-final-040424.pdf?1712261887 For more details about SCAG's models, or to request model data, please see SCAG's website: https://scag.ca.gov/data-services-requests.

A growing body of research suggests that housing eviction is more common than previously recognized and may play an important role in the reproduction of poverty. The proportion of children affected by housing eviction, however, remains largely unknown. We estimate that 1 in 7 children born in large American cities in 1998–2000 experienced at least one eviction for nonpayment of rent or mortgage between birth and age 15. Rates of eviction were substantial across all cities and demographic groups studied, but children from disadvantaged backgrounds were most likely to experience eviction. Among those born into deep poverty, we estimate that about 1 in 4 were evicted by age 15. Given prior evidence that forced moves have negative consequences for children, we conclude that the high prevalence and social stratification of housing eviction are sufficient to play an important role in the reproduction of poverty and warrant greater policy attention.