This document describes our current methodology for estimating the number of renter households behind on rent and the total and per household rent debt for the United States as a whole, all states and counties, 558 cities and census-designated places (those with at least 10,000 renter households), and 15 metropolitan regions, as presented in the Rent Debt Dashboard. The dashboard was first released on April 21, 2021 and was last updated on November 5, 2024.

Rent Debt Estimation Summary

Our rent debt estimates are based on two sources of data:

1. Household rent and income data from the 5-year 2019 American Community Survey (ACS) summary file and microdata.

2. Data on households behind on rent and number of months behind on rent from the U.S. Census Bureau’s Household Pulse Survey microdata for the United States, all 50 states, and the 15 largest metros. Since December 1, 2021 the Pulse survey has been collected over a two-week period every four weeks. We rely upon the Pulse microdata, which is released two weeks after the tabular data is released. The most recent Pulse data is for August 20 - September 16, 2024.

We use the share of households behind on rent from the Census Household Pulse survey and the median contract rent paid by households from the American Community Survey to determine the total amount of monthly rent owed by households behind on rent. We then multiply these monthly figures by the average number of months that households are in arrears based on the Pulse survey to estimate total rent debt.

We believe these estimates are on the conservative side due to: 1) our use of rent data from the 2019 American Community Survey, since rents have risen, especially in high-cost cities and metros and 2) the likelihood that the Pulse survey is undercounting renters who are behind on rent and especially those who are many months behind on rent, given this is a vulnerable population.

Additionally, it is important to note that these estimates based on the Pulse survey represent a single point in time, but the number of renters who have rent debt over a period of months or years will be higher, since renters move in and out of debt in the same way that people experience periods of homelessness or poverty. One implication of this is that the total number of renters in need of emergency rental assistance throughout the pandemic, and over the long span of time over which states and localities have been operating rental assistance programs, is likely higher than our point-in-time estimates.

Rent Debt Methodology Updates

1. Whereas previous estimates used static income thresholds ($50,000 and $100,000), we now use dynamic income thresholds based on income thresholds available in the Pulse dataset that are closest to 80% of the median household income for each small geography (counties and places). This approach has several advantages. First, it allows our estimates to more accurately reflect the socioeconomic status of households within particular geographies; our previous static definition of “low-income” – under $50,000 – may have represented less than 50% of the median household income in wealthier geographies while being above the median household income in other geographies, making it an ineffective barometer of “low-income” rent debt. Second, it increases the sample size within each income bracket; whereas previously the “$50,000 - $100,000” and “Over $100,000” categories had limited numbers of observations, the reduction to two income categories and the use of variable income thresholds means that estimates can be calculated more reliably with a larger sample size.

2. We have also adjusted our approach to calculating the average number of months behind on rent. Whereas previous estimates were calculated uniformly at the national level, the average number of months behind reported by households in the Pulse survey is now calculated for each Pulse geography and for households above and below each income threshold. These estimates for average number of months behind are then applied to each county and place separately based on its specific low-income threshold, as with behind rates. The estimated average number of months of rent arrears for low-income households now varies from just 1.5 months for counties in Idaho to more than 5 months in the Bronx. 

3. We have updated our approach to calculating the number of children living in renter households behind on rent. Whereas previously the number of children was calculated using the Census Public Use Microdata (PUMS), we now calculate the average number of children living in behind households directly from the Pulse data for each Pulse geography (states and metropolitan areas) and only report it for those geographies.

This methodology was previously revised beginning with the August 30, 2021 release in three ways:

1. We began to use Pulse data on the distribution of rent arrears to inform our rent debt estimates. A question on how many months the household is behind on rent was added in the Phase 3.2 survey which began July 21, 2021. Prior to this question being added, we used national estimates of the distribution of rent arrears derived from the University of Southern California’s Center for Economic and Social Research’s “Understanding Coronavirus in America” panel survey. The Pulse survey data is preferable for many reasons: it provides a much larger sample size, it allows for cross-tabulations with the other Pulse survey questions, and it will be regularly updated at the same interval as our other data inputs. This shift to using the Pulse data on the distribution of arrears has had a significant impact on our rent debt estimates due to the higher share of households that are less than 3 months behind in the Pulse survey compared with the USC survey. There was a 21 percent decrease in our total rent debt estimates across all geographies in the August 30 update, from $21.3 billion to $16.8 billion nationwide.

2. We added estimates for the 558 cities and census-designated places that have at least 10,000 renter households.

3. We began to use exponentially smoothed estimates across all available waves of survey data to report the number and share of households behind on rent, their characteristics, and the number of months they are behind on rent. Exponential smoothing assigns more weight to recent observations but also incorporates information from earlier survey waves, in order to increase the sample size and the robustness of the estimates, and to reduce the volatility we were seeing in the data which made it difficult to assess trends even in large states such as California. Previously, we used a two-wave moving average.

Our methodology used prior to the August 30 update can be found here.

Rent Debt Estimation Full Methodology

The process for developing our estimates is as follows.

Calculate the number and share of households behind on rent by income bracket for the Pulse survey geographies (US, states, and 15 metros). We filter the Household Pulse Survey data to include only renting households paying a non-zero rent. We then assign these households a rent arrears status of “Behind” or “Not Behind” based on their response to the survey question: “Is this household currently caught up on rent payments?” We calculate the percentage of households in rent arrears as a share of renter households providing a “Yes” or “No” response to this question – the “behind rate” – in each geography and each survey wave for households above and below five different income thresholds: $20,000; $35,000; $50,000; $75,000; and $100,000. The $20,000 income threshold is not available in the Pulse survey, but is necessary in order to align the Pulse data with census median rent estimates; therefore, we use the nearest income break in the Pulse survey ($25,000) to represent the $20,000 income threshold from this point forward. Households not reporting income are excluded from these calculations. A moving average incorporates estimates from the most recent survey wave along with information from two prior survey waves.

Estimate behind rates and average number of months behind for counties and census places (cities, towns, and census-designated places).

Behind rates are calculated in this manner for each of the 15 metropolitan areas and for the nonmetropolitan parts of each state; these rates are then assigned to counties and census places based on whether they fall within one of the 15 metropolitan areas or elsewhere within a state. For each county-level and place-level geography, we determine an income threshold to separate “low-income” and “high-income” renter households. These thresholds are calculated by obtaining 80% of the median household income for each geography according to the 2019 5-year ACS and then finding the closest of the five aforementioned income thresholds to that calculated value. For a given county geography with a median household income of $70,000, for example, 80% of the median household income is $56,000 and the closest Pulse income break is $50,000. Therefore, for that particular geography, $50,000 will be used as the threshold separating low-income and high-income renter households. Within each geography, we then apply separate behind rates to low-income and high-income renter households based on these calculations, applying metropolitan-level behind rate estimates for applicable counties and places, and state-level behind rate estimates for the remainder of those geographies. 

We use a similar method to calculate the average number of months that households are behind on rent, calculating a weighted average number of months behind for households above and below each income threshold at the state- or metropolitan-level in each survey wave and applying a moving average to estimate the average through the most recent survey wave. We then apply those estimates to each county and place based on estimated income thresholds. For households that reported being behind but said they were 0 months behind, we assume that they are behind in the most current month and that they are also 1 month behind. According to the most recent survey data, households were 2.3 months behind on average, with 50% 1 month behind, 25% 2 months behind, 10% 3 months behind, 9% between 4 and 7 months behind, and 7% 8 months behind on rent.

Estimating median contract rent by household income bracket for states, the 15 metropolitan regions, census places and counties. We use median (rather than mean) rent based on the assumption that renters who are behind on rent are likely to have lower monthly rent than the average for each income category, and contract rent rather than gross rent to focus on debt owed to landlords only (i.e. excluding debt that may be owed to utility companies).

Median monthly contract rent by income bracket for states, the 15 metropolitan regions, and 430 large counties are drawn directly from ACS microdata. For smaller cities and counties not identified in the ACS microdata, however, we developed an approach that relies primarily on the ACS summary file with some inputs from the microdata. Specifically, we draw information from Table B25122 of the ACS summary file on the number of households by income and gross rent bracket (since similar data contract rent is unavailable) and utilize a Pareto interpolation procedure to estimate median monthly gross rent for each of the aforementioned income categories in each geography. However, this procedure requires an upper bound for the top gross rent category ($2,000 or more), which is not provided in Table B25122. To adjust our estimate to reflect median contract rent (rather than median gross rent, which includes the cost of utilities), we also need an adjustment ratio to apply to our resulting Pareto estimates.

We estimate these data inputs for each of the smaller city and county geographies using ACS microdata for the Public Use Microdata Area (PUMA) or PUMAs they intersect. This was accomplished using population-based crosswalks we developed between 2010 PUMAs and 2010 counties, and between 2010 PUMAs and 2010 census-defined places (which include all cities), by taking a population-weighted average of the PUMA-level measures for each smaller city and county geography. Following this approach, we estimate the maximum gross rent, median gross rent, and median contract rent for overall and for each income category. The estimated maximum gross rent is inputted into the Pareto interpolation procedure to estimate median gross rent by income bracket for each of the smaller city and county geographies. Those initial estimates are then adjusted to reflect median contract rent by multiplying by the ratio of median contract rent to median gross rent from the PUMA-based estimates. The approach seeks to utilize as much geographically-specific information from the ACS summary file as possible and substitutes in less geographically-specific information from the ACS microdata as necessary.

Estimate rent debt. To estimate rent debt at the county and census place levels, we take the product, by income category of (1) our estimated behind rates, (2) the number of renter households (with cash rent) from the 2019 5-year ACS, (3) our estimates of median monthly contract rent, and (4) our estimate of the average number of months that behind households are behind on rent. We then combine the estimates for “low” and “high” income households (based on the relative income categories) to get the total number of households behind and total rent debt for each county and place. Metropolitan, state, and national estimates are produced by summing estimates from their constituent county geographies. It should be noted that we assume differences between reported rents in the 2019 5-year ACS (which reflect a 2015-2019 average expressed in inflation-adjusted 2019 dollar values) and 2022 actual rents are negligible for households that have not moved in since 2019, as those households were likely locked into pre-pandemic leases and/or month-by-month agreements with fixed/stable rents.

Emergency Rental Assistance Performance Data

The Relief Map on the dashboard was added with our August 30, 2021 update and presents the amount of Emergency Rental Assistance allocated to each state, county, and city on the dashboard along with the amount and share that has been distributed. This data is pulled directly from the monthly performance reports provided by the Treasury, which are generally released three weeks into the following month. Distribution and allocation figures for specific jurisdictions are drawn directly from the Treasury reports. Beginning with the February 14, 2022 update, county-level jurisdiction totals do not include allocation and distribution figures for city-level jurisdictions within their boundaries. The Treasury Department required monthly ERA1 & ERA2 Reporting for each month through June 30, 2022, and announced that no monthly ERA reporting would be collected after the June 2022 reporting period. The Relief Map of the dashboard included the most recently available data from the June 2022 report and was removed from the dashboard for the February 23, 2024 update due to no further data being made available.