Initial site selection for the Eviction Tracking System (ETS) was based on (1) the availability of valid baseline data in the given county and (2) the availability of eviction filing data through public-facing court websites. We briefly explain both criteria.
Individual-level eviction records between 2000 and 2018 have been collected by The Eviction Lab, LexisNexis Risk Solutions, and American Information Research Services and then compiled by the Eviction Lab at Princeton University. Records were cleaned, stripped of duplicates and commercial eviction cases, geocoded, and validated against publicly-available data sources published by county- and state-court systems.
Data validation was conducted at the individual level where the Eviction Lab could secure state court data. This process involved merging and comparing the data using case numbers and court numbers. To validate our estimates of eviction case volume, we also compared our counts directly to state-reported county-level statistics on eviction filings. These aggregate estimates were considered reliable if the total number of filings in a county fell between 86 and 114 percent of the county courts’ publicly reported total for that year.
Within counties, years that were not marked as valid data points between the first and last years of availability were imputed if there were no more than two consecutive years of missing data. When only one year of data was missing within a county between two years of valid data, the case volume was imputed using the average of the preceding and following years. When two consecutive years of data were missing, we linearly interpolated between the last known and reliable value and the next known and reliable value.
These externally-validated data represent the baseline against which 2020-2022 data are compared. County-level years of data available for baseline comparison vary as a function of external validation. For example, in Richmond, VA, we compare against just 2016 data, whereas in Hamilton County, OH (Cincinnati) we can compare against average filings between 2012 and 2016.
Using these data, we compiled a set of 73 of the 200 largest U.S. metropolitan areas in which at least 50% of Census tracts had valid data for at least one year between 2012 and 2016. Within each of those metropolitan areas we identified the counties with valid data during that period (n=224). We systematically checked for the availability of publicly-accessible eviction filing data through court web systems. We prioritized counties with larger renter populations and with higher eviction and eviction filing rates. We also aimed to select cities and counties that had instituted a range of eviction moratoria and renter protections in response to the COVID-19 pandemic.
In other sites, additional baseline data were provided by partner organizations. For example, BASTA Austin and Open Austin provided baseline data for Travis County, TX. The CREATE Lab at Carnegie Mellon University provided both baseline and 2020 data for Allegheny County, PA. Sources for each ETS site can be found on individual site pages. Similar cleaning procedures, and validation where possible, was conducted on baseline data for these sites as well.
Data about eviction cases are accessed through public-facing websites by each county or jurisdiction that we are monitoring. We, or our partners, query these websites each week and collect the case filings into a dataset for analysis. From there, we geocode the defendant/property address and add Census tract details. Four jurisdictions – Richmond, VA, Travis County, TX, Allegheny County, PA, and New York City, NY – only provide data on defendant zip code, not the full address. One site, New Orleans, LA, only has address-level data available in certain portions of the site. The data we collect is necessarily limited to information made publicly available by each jurisdiction, but this approach offers an accurate method for understanding eviction case volume.
Our research shows that a small set of buildings and landlords account for an outsized share of all evictions. To better understand how landlords have responded to the pandemic, we put together a list of the top-evicting addresses. We matched the addresses listed in our eviction filing court data to parcel data, which describes buildings and their owners, that we obtained from Regrid and other public sources. Because large apartment complexes will sometimes stretch across multiple land parcels, we consolidated adjacent parcels that had the same ownership but were not single-family residential. After consolidation, we merged parcel data with data containing a full list of addresses for a given area, either from OpenAddresses.io or another publicly available source. The merge was done first by linking data with the same address, and then by performing a spatial join. This produced a crosswalk of parcels to addresses in a given ETS site.
We cleaned and geocoded defendant addresses in Eviction Tracking System (ETS) filing data. We then joined the ETS data to the crosswalk of parcels and addresses through a series of merges. The process of merging ETS data to the crosswalk differed slightly depending on the site. Generally, we first directly joined the ETS data to the crosswalk of parcels and addresses by street address and city name. For eviction filings that remained unmatched to a parcel, we performed a spatial join between the geocoded ETS address and the parcel geometry. Finally, for filings still unmatched, we joined the eviction filing data to the parcel crosswalk based on spatial proximity: a filing would match if within 25 feet of a parcel. We kept only unique matches between ETS eviction filings and parcels; filings that matched to multiple parcels were left unmatched.
We counted the number of eviction filings at each parcel, along with the plaintiff name most often associated with a given parcel address in the eviction filing data. Filings that were unmatched to a parcel were also grouped and tabulated by address, and are included in our counts of top evictors.
Last Updated: December 2, 2022