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 selected counties, regions, and states.

Our estimates use the share of households behind on rent from the Census Household Pulse survey and the rent paid by households from the American Community Survey, both broken down by income bracket, along with estimated median monthly contract rent by income bracket, to determine the total amount of monthly rent owed by households behind on rent. Median rent rather than mean rent was used based on the assumption that renters who are behind on rent are likely to have lower monthly rent than the average for each income bracket. We then use these monthly figures to estimate total rent debt by assuming that 56 percent are one month behind, 24 percent are two months behind, 15 percent are evenly distributed between three and nine months behind, and 5 percent are 10 months behind (from April through January). 

We use three data sources:

1. Household rent and income data from the 5-year 2019 American Community Survey (ACS) summary file and microdata.
2. Data on late payment of rent from the U.S. Census Bureau’s Household Pulse Survey (Week 23: January 20th - February 1st, 2021) for states and the 15 largest metros.
   
3. Distribution of rent arrears estimates derived from two sources: a) the Community Housing Improvement Program’s January 2021 survey of about 40,000 rent-regulated apartments in New York City; and b) the University of Southern California “Understanding Coronavirus in America” panel survey from March through July 2020, analyzed by the Research Institute for Housing America.

The process and data are further described below:

Household Pulse Survey data is filtered to include only renting households paying a non-zero rent in the most recent survey wave. Those households are assigned a rent status based on their response to the survey question: “Is this household currently caught up on rent payments?.” The percentage of households in rent arrears – the “behind rate” – is calculated by household income category and by geography. Households are initially grouped into three income categories: those with an annual income less than $50,000, those with an annual income between $50,000 and $100,000, and those with an annual income greater than $100,000. Pulse estimates are available for all 50 states and for the 15 largest metropolitan regions in the US, which include three metropolitan statistical areas in California: Los Angeles–Long Beach–Anaheim, San Francisco–Oakland–Fremont, and Riverside–San Bernardino–Ontario. For geographies where regional data are available, we use regional estimates of behind rates; for geographies where regional data are not available, we use statewide estimates of behind rates. If the unweighted count of observations for a given income category within a metropolitan region falls below 100 in the most recent Pulse survey wave, statewide behind rates are used for households in that income category in that metropolitan region instead. If unweighted counts of statewide observations fall below 100 for any income category, a single behind rate is used for all households in the state regardless of income category.

The estimates of the percent of households behind on rent by income bracket are necessarily broad, in geographic terms, given data availability in the Household Pulse Survey. However, to estimate monthly rent debt for households that are behind, they are applied to estimates of median monthly contract rent by income bracket that are geographically specific (i.e. based on the same cities and counties for which the rent debt estimates are ultimately reported). Estimating median monthly contract rent by income bracket was straightforward for states, regions, and larger cities and counties as they could be drawn directly from the ACS microdata. For smaller cities and counties not identified in the ACS microdata, however, we developed an approach that relied primarily on the ACS summary file with some inputs from the microdata.

Specifically, we drew information from Table B25122 of the ACS summary file on the number of households by income bracket gross rent bracket and utilized a Pareto interpolation procedure to estimate median monthly gross rent for each of the aforementioned income brackets in each geography. This procedure required 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 needed an adjustment ratio to apply to our resulting Pareto estimates.

We estimated 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 estimated the maximum gross rent, median gross rent, and median contract rent for overall and for each income bracket. 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 were then adjusted to reflect median contract rent by multiplying by the ratio of median contract to 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. 

We assume that differences between reported rents from the 2019 5-year ACS (which reflect a 2015-2019 average expressed in inflation-adjusted 2019 dollar values) and 2020 actual rents are negligible for households that have not moved in 2020, as those households were likely locked into pre-pandemic leases and/or month-by-month agreements with fixed/stable rents. The total amount of monthly rent owed by behind households is then calculated by multiplying estimated median monthly rent for each income category by the number of Pulse households in that income category and summing those values for each geography (city or county). Regional and statewide estimates are produced by summing estimates from their constituent county geographies.

These figures are converted to total rent debt by adjusting based on our estimate that 56 percent are one month behind, 24 percent are two months behind, 15 percent are between three and nine months behind, and 5 percent are 10 months behind (the extent of the pandemic). There is no source of data on the distribution of rent arrears among California’s renters, so we estimated this distribution based on two key sources: 1) the Community Housing Improvement Program’s January 2021 survey of about 40,000 rent-regulated apartments in New York City, which found that the average rent arrears was $6,173 (about 4.5 months behind based on the average monthly rent of $1,400), with 19.4 percent more than two months behind, and 5.6 percent more than 11 months behind (owing more than $15,000); and 2) the University of Southern California (USC) “Understanding Coronavirus in America” panel survey from March through July 2020, analyzed by the Research Institute for Housing America, which found that approximately 60 percent of behind households are one month behind on rent, 25 percent are two months behind, and 15 percent are three months behind. The NYC survey is a robust and recent estimate of arrears, and available data shows that the median rents and incomes are similar between NYCs rent-stabilized renters and California’s renters (NYC’s median rent-stabilized rent in 2018 was $1,260 and California’s median rent in 2019 was $1,614; NYC’s median rent-stabilized tenant household income was $44,000 in 2016 and California’s median renter income was approximately $47,000 in 2019). The NYC survey did not provide more detailed breakdowns of arrears by month, so we made the following assumptions: the proportion of tenants that are one and two months behind is the same as the proportion found in the USC survey; the 15 percent of tenants that are between three and nine months behind are equally distributed across each of the seven months.

These estimates do not take into account the requirement of the California eviction moratorium passed in August 2020 (AB 3088) that Covid-19-affected tenants must pay 25 percent of rent accrued between September 1, 2020 and January 31, 2021 by January 31, 2021 to be protected from eviction. This incentive likely decreases the amount of arrears.

For the previous version of this methodology, please see here.