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

Weekly Eviction Data 2020

  Weekly Eviction Data 2020 
  Geography Level: Census (Only for Boston, Cincinnati, Cleveland, Houston, Jacksonville, Kansas City, Milwaukee, St Louis), Zip Code (Only for Austin, Pittsburgh, Richmond)Item Vintage: 2020
  Update Frequency: WeeklyAgency: Princeton Eviction LabAvailable File Type: Excel with PDF Report 

  Return to Other Federal Agency Datasets Page

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.

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.

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.

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.

Graph and download economic data for Rental Vacancy Rate in the United States (RRVRUSQ156N) from Q1 1956 to Q1 2025 about vacancy, rent, rate, and USA.

This data set is EMBARGOED until noon ET Tuesday, June 29. This data is intended for print publication on or after June 29. A story will be filed under the slug US--Virus Outbreak-Rental Assistance.

Overview

The Center for Public Integrity in collaboration with the AP has collected detailed statistics from about 70 agencies that administered rental assistance programs in 2020 with money from the Coronavirus Relief Fund, part of the CARES Act. These figures show how much money these agencies planned to spend on rental assistance and how much they actually spent. We also have data showing how many households received assistance and how many applications were submitted.

An additional data sheet shows how much money was allocated for rental assistance from all sources (not just CRF money) per renter-occupied household in 2020 and statewide eviction rates in 2016, the latest available data from the Eviction Lab at Princeton University.

Findings

• All together, about 70 state and local agencies set aside $2.7 billion from the Coronavirus Relief Fund in 2020 for rental assistance programs. More than $425 million of that — 16 percent — was either reallocated for other COVID-related expenditures or remained unspent by March 31, 2021.
• Four states with comparatively high historic eviction rates allocated very little for renters in 2020. These states — Georgia, West Virginia, South Carolina and Oklahoma — are run by Republican governors.
• Onerous requisites, such as lengthy forms and extensive documentation requirements, prevented many applicants from accessing rental assistance funds. In New York, for example, tenants had to prove they were rent-burdened before the pandemic to qualify for assistance in the first round of funding; only $47.5 million wound up getting spent on rental assistance, out of $100 million allocated by the state legislature.

Methodology

The Center for Public Integrity started with a spreadsheet produced by the National Low Income Housing Coalition that showed every known rental assistance program in the United States as of Dec. 23, 2020. The detailed spreadsheet included how much money had been allocated per program and the source of those funds. Public Integrity isolated only the programs that were categorized as being funded by the Coronavirus Relief Fund, which was part of the CARES Act. We focused on the CRF because it was the largest single source of rental assistance in 2020. We contacted more than 70 agencies to find out how much money they wound up spending.

For the second set of data, we used the same NLIHC spreadsheet but tallied all allocations — regardless of funding source — for each state and divided that number by the U.S. Census Bureau’s estimate of renter-occupied households in each state (from American Community Survey table S2502, five-year estimate, 2015 to 2019). We also included the statewide eviction rates as of 2016 (the most recent available) published by the Eviction Lab.

Included Data

• 1-crf_programs.csv: Details for every known rental assistance program as of Dec. 23, 2020, that was funded by the Coronavirus Relief Fund.

  Columns A through E were collected by the National Low Income Housing Coalition, and they show the following: Geographic Level; State; City/County/Locality, if applicable; Program Name; and Administering Agency.

  Columns F through L were collected by Public Integrity and the Associated Press. Those columns show amount of CRF money set aside in 2020, the amount spent on rental assistance by March 31, 2021, the amount reallocated or unspent by March 31, 2021, the percent unspent by March 31, 2021, the number of households that received assistance by March 31, 2021, the number of applications received by March 31, 2021, and notes about the data.

• 2-state_totals.csv: State-level totals for all known allocated rental assistance funding, regardless of funding source, along with the number of renter-occupied households in each state from 2015 to 2019, the statewide eviction rate as of 2016, and the amount of allocated rental assistance funding per renter-occupied household.

Caveats

• If your city, county or state does not appear to have allocated any money for rental assistance, check with your local and state officials or your governor’s office to find out if a program was indeed administered. We can’t ensure that every program wound up on the NLIHC’s original list.
• We only focused on programs that were categorized as being part of the Coronavirus Relief Fund, but your city, county or state may have had a rental assistance program with other sources of funding.
• The per-capita funding figures do not necessarily indicate that those state governments administered rental assistance programs. Some — or all — of that per-capita money could have been administered by local governments. For example, Georgia did not administer a statewide rental assistance program in 2020; the per-capita figure for Georgia reflects money spent in that state by local governments. For a full list of programs, visit NLIHC’s broader list at https://docs.google.com/spreadsheets/d/1hLfybfo9NydIptQu5wghUpKXecimh3gaoqT7LU1JGc8/edit#gid=1851738141.
• Eviction rates were not available for these four states in 2016: Alaska, Arkansas, North Dakota and South Dakota.

Attribution

“According to data obtained by the Center for Public Integrity, The Associated Press and the National Low Income Housing Coalition”.

Contact reporter Sarah Kleiner at skleiner@publicintegrity.org.

As the renewable energy transition accelerates, housing, due to its high energy demand, can play a critical role in the clean energy shift. Specifically, multifamily housing provides a unique opportunity for solar photovoltaic (PV) system adoption, given the existing competing interests between landlords and tenants which has historically slowed this transition. To address this transition gap, this project identified and ranked Metropolitan Statistical Areas (MSAs) in the United States for ZNE Capital (the client) to acquire multifamily housing to install solar PV systems. The group identified seven criteria to determine favorable markets for rooftop solar PV on multifamily housing: landlord policy favorability, real estate market potential, CO2 abatement potential, electricity generation potential, solar installation internal rate of return, climate risk avoidance, and health costs associated with primary air pollutants. A total investment favorability score is calculated based on criteria importance assigned by the user. Investment favorability scores were investigated for different preferences to demonstrate the robustness and generalizability of the framework. The data analysis and criteria calculations were conducted using RStudio, ultimately to provide reproducible code to be used for future projects. The results are presented in a ranked list from best to worst metro areas to invest in. Future studies can utilize the reproducible code to inform decisions on where to invest in solar PV on multifamily housing anywhere in the United States by changing weights within the model depending on preferences. Methods

Collecting real estate and landlord data for metropolitan statistical areas (MSAs) from federal agency databases.

Real estate metrics: Six indicator metrics were selected to represent areas with growing housing demands. The metrics included were population growth, employment growth, average annual occupancy, annual rent change, the ratios of median annual rent to median income, and median income to median home price. The population estimates and median income data was downloaded from the Census Bureau. Median rent data was downloaded from HUDuser. Median home price data was downloaded from National Association of REALTORS®. Students were provided temporary memberships to Yardi Systems Matrix to obtain multifamily occupancy rates, and this data will not be redistributed. All the real estate metrics were combined into a single dataset using CBSA codes, which each MSA has a unique 5-digit identifier. Income-to-home price and rent-to-income ratios were calculated in R Studio.

Landlord data: the minimum security deposit and eviction notice data was collected for each state and manually compiled into an Excel. Security deposit information was provided as the number of months of rent. States with no maximum deposit limit received a score of 1.0, meaning it was the most favorable. Two month's rent was scored as 0.5, and one month's rent was given a score of 0.

Using NREL's REopt web tool to 1) model solar PV system on multifamily buildings in various cities and 2) obtain data to represent energy generation, CO2 abatement potential, avoided health costs from emissions, and solar project financial criteria.

An anchor city was identified within each MSA as the city with the highest population to input into the REopt tool. Default inputs were changed based on information provided by industry experts and changes in federal funding programs. Detailed instructions of inputs were created to ensure consistency when running the model for each city. The four outputs collected from the tool include: annual energy generation from renewables (%), lifecycle total CO2 emissions, health costs associated with primary air pollutants, and internal rate of return(%). The group divided up a list of cities, input the respective data for each one, obtained the outputs, then compiled it into a Google sheet. Outputs were checked by other members to ensure accuracy.

Collecting climate risk data from FEMA's National Risk Index Map.

Climate risk data was downloaded as a CSV file. The risk score was used to represent impacts of climate variability on long-term real estate investments. Risk scores were provided at the county level. The group identified the county each city resided in, to associate the correct score to each city in R Studio

Normalizing the data

Metrics were normalized by subtracting the minimum value for the metric from each value and dividing by the difference between the maximum and minimum values. This resulted in scores between 0 and 1 that were relative to the MSAs included in the analysis.

Weighing the data

Real Estate and Landlord Criteria metrics: these two criteria contained more than one metric, so the metrics within these criteria were weighted to produce real estate and landlord scores. Weights for each criterion sum to 1, in which higher weights indicate greater importance for multifamily real estate investments. Each weight was multiplied by the respective metric, then all weighted metrics within each criterion were summed to produce the criteria score. Investment Favorability Score: seven criteria were multiplied by respective weights based on the stakeholder's preferences. Weights sum to 1 to ensure consistency throughout the project. The sum of the seven weighted criteria is the investment favorability score.

The H2A.Z histone variant, a genome-wide hallmark of permissive chromatin, is enriched near transcription start sites in all eukaryotes. H2A.Z is deposited by the SWR1 chromatin remodeler and evicted by unclear mechanisms. We tracked H2A.Z in living yeast at single-molecule resolution, and found that H2A.Z eviction is dependent on RNA Polymerase II (Pol II) and the Kin28/Cdk7 kinase, which phosphorylates Serine 5 of heptapeptide repeats on the carboxy-terminal domain of the largest Pol II subunit Rpb1. These findings link H2A.Z eviction to transcription initiation, promoter escape and early elongation activities of Pol II. Because passage of Pol II through +1 nucleosomes genome-wide would obligate H2A.Z turnover, we propose that global transcription at yeast promoters is responsible for eviction of H2A.Z. Such usage of yeast Pol II suggests a general mechanism coupling eukaryotic transcription to erasure of the H2A.Z epigenetic signal.

Methods Movies with two dimensional single molecule data were analyzed by DiaTrack Version 3.05 (Vallotton and Olivier, 2013), which determines the precise position of single molecules by Gaussian intensity fitting and assembles particle trajectories over multiple frames. In Diatrack remove blur was set to 0.1, remove dim set at 70 and max jump set at 6 pixels, where each pixel was 107 nm. Datasets contain session files from Diatrack with single molecule localization, intensity of single molecules, frame numbers and single molecule tracking information from recorded videos.

This data supports the following publication:Faye J. Thompson, Michael A. Cant, Harry H. Marshall, Emma I.K. Vitikainen, Jennifer L. Sanderson, Hazel J. Nichols, Jason S. Gilchrist, Matthew B.V. Bell, Andrew J. Young, Sarah J. Hodge & Rufus A. Johnstone (2017) Explaining negative kin discrimination in a cooperative mammal society. Proceedings of the National Academy of SciencesPlease read the "Read Me.txt" file for a full description of the data contained in each data set