Abstract:
Following the onset of the pandemic, demand for housing increased as people looked for larger homes with more space for remote work and leisure at home. Remote work allowed people to move further away from their place of work, and many moved to lower-density, more affordable areas (Haslag & Weagly, 2023; Ramani & Bloom, 2022; Whitaker, 2021). While these workers brought spending power to these areas, their demand for housing tends to increase housing prices, at least in the short term (Gupta, Wittal, Peeters, & Gupta, 2022; Ramani & Bloom, 2022; Liu & Su, 2021). In areas with rising housing prices, some existing homeowners benefit from the rising home prices and can earn a large return on their homes. However, renters in areas with increasing rents, particularly those in lower-income households, may have struggled to keep up with rising housing costs.
During 2021 and 2022, dramatic increases in rents made headlines across the country.1 Prices for tenants signing new leases grew at around three times the pace seen in the years leading up to the pandemic. Figure 1 shows national new lease price growth year-over-year between 2017 and 2024 using proprietary data from RealPage. After a period of growth between two and four percent, new lease price growth fell modestly in 2020 to around -2 percent, then surged up to 15 percent year-over-year in spring 2022.
However, this figure does not show the full picture of renters’ experiences around the country. First, rent growth varied considerably by location, and national-level price data do not show what types of places experienced the largest rent growth. Second, we do not know the effect of this price growth on renters themselves. If incomes also rose by enough to cover the increases in rent prices, then financial effects on renters may be limited. Third, incomes may have grown differently compared to rents for lower-income renters versus the typical renter. For lower-income renters, a larger share of income tends to go towards rent, so analyzing the impact of rent increases compared to income is particularly important for lower-income renters.
In this paper, we examine the geographic distribution of rent growth and its impacts on renters compared to their income. We build on work showing that people systematically moved to less dense areas, with many working remotely (Haslag & Weagly, 2023; Gupta, Wittal, Peeters, & Gupta, 2022; Liu & Su, 2021; Whitaker, 2021). Within cities, particularly large cities, people moved away from central business districts and towards suburbs (Ramani & Bloom, 2022; Gupta, Wittal, Peeters, & Gupta, 2022). Across cities, people moved from higher-density cities to smaller, less dense metro areas (Whitaker, 2021). Rents responded by increasing more in lower-density areas (Ramani & Bloom, 2022). These movers were more likely to live in higher income households (Federal Reserve Board of Governors, 2021). Less is known about the effects of these price changes and migration patterns on households, particularly those in more affordable areas. This paper builds on existing research by examining the following research questions:
Effects on Rents by Geography: Lower-density areas tend to be more affordable, and people tended to move away from higher density areas to lower density areas after the onset of the pandemic. But did more affordable areas generally see higher growth in rents compared to less affordable areas?
Effects on Renters by Geography and Income: How did renters experience rent increases, particularly those in lower-income households? Did incomes grow enough to cover the increases in rent, or did rent increase as a percent of income? How did these effects vary by location?
We carry out two main sets of analyses to examine the effect of pandemic rent increases on renter households, focusing on those in multifamily buildings.2 First, we examine whether rents in areas that were more affordable pre-pandemic saw larger increases in rents after the pandemic began compared to less affordable areas. We split local areas into four equal-population groups (quartiles) based on pre-pandemic rent and rent-to-income using Public Use Microdata Areas (PUMAs) from the U.S. Census.3 We examine shorter-term rent growth for the average new lease using administrative data from RealPage, a timely disaggregated building-level source of multifamily apartment rents. Since new lease data tends to lead data that includes both new and existing leases by around a year, we then look at whether differences in new lease price growth in the shorter term translate to rent growth for the average renter household over the pandemic and economic recovery from 2019–2022 using the 1-Year American Community Survey (ACS).4
Second, we analyze the impact of these rent increases on renters by examining whether incomes rose by enough to cover the increases in rents. We use household-level data from the 1-Year ACS to measure changes in the percent of renter income going towards rent (rent-to-income ratio) and the percent of renter households that are cost-burdened (paying more than 30 percent of their income on rent). We examine both because small changes in the median rent-to-income ratio may mask larger changes in the percent of cost-burdened renters if a sizable share of renters is just under the 30 percent threshold. We examine effects on renter households in multifamily units overall and lower-income multifamily renter households in the bottom quintile of their PUMA’s income distribution.
The main results are:
Effects on Rents by Geography: Changes in rents are consistent with higher-income renters who work remotely moving away from high rent areas to more affordable areas. Rents increased by more in areas with lower rent pre-pandemic and less in areas that were higher rent. However, rents did not increase differentially in areas with higher pre-pandemic rent affordability as measured by rent-to-income ratio. Higher-income remote workers were not constrained by job opportunities only available in higher-rent, higher-income areas. These findings suggest that migrating households were seeking out areas with lower rents, but not lower rents compared to local incomes.
Effects on Renters by Geography and Income: Incomes did not grow by enough for the median multifamily renter to cover rent increases across both lower and higher-rent areas. Additionally, renters in lower-income households saw much larger increases in rents compared to their income.
Compositional changes from remote work affect neighborhood affordability estimates: Local areas saw large changes in rent-to-income ratios through the pandemic. However, this encompasses compositional changes in renter populations from the pandemic mobility as well as changes in prices in local areas. These joint effects reflect the limitations of local area rent-to-income ratios through the pandemic and indicate the need to track affordability for the same households over time to fully understand affordability trends.
To examine the geographic distribution of rent growth and its effect on renter households, we examine rent and household-level affordability measures across areas with higher and lower rent pre-pandemic. We split PUMAs into 4 equal housing-unit-weighted quartiles based on pre-pandemic PUMA-level median rent using the 2015–2019 5-Year ACS. While smaller geographies might be preferable, such as counties or census tracts, PUMAs are the smallest level geography available with timely information on both renters’ incomes and rent payments. We then compare these results to quartile groups based on rent-to-income ratios, instead of rent levels. Areas with high rents may have higher affordability if renters in those areas tend to have higher incomes.5 Because remote work meant workers could keep their jobs but move to a less expensive area, we expect rents to grow by more in areas with lower rent pre-pandemic, but not necessarily low rent compared to income.
Figure 2 shows the differences in geography when grouping PUMAs by rent versus rent-to-income ratio. Each stacked bar shows the percentage of renter households in each region within a given quartile. When grouping by rent only (Panel a), the West and Northeast dominate the most expensive, fourth quartile. The Midwest and South dominate the least expensive, bottom quartile. When grouping by rent-to-income, the West still dominates the fourth quartile (Panel b). However, the Northeast accounts for just 28 percent of the top rent-to-income quartile, showing that while the Northeast has higher rent, those who live there tend to also have higher income which reduces their rent-to-income ratios.
To examine rent over time, we use two different measures: new lease asking rents and average rent spending for all renters in multifamily buildings. Each metric tells us something different. New lease asking prices are a measure of the current rental market prices for a new tenant looking for an apartment.6 These data are timely and were used in media reports during the pandemic to highlight upheaval in the rental market.7
However, most leases fix the rent for a period of time, typically a year. Therefore, new lease prices do not reflect how much the typical renter household is paying, because they do not include those renters in the middle of their leases. Average rents include rent for both new and existing tenants (an example of an average rent price index is the standard Rent Consumer Price Index or Rent CPI).8 Rent indexes that include all renters (not just those who signed new leases) tend to lag new lease indexes by around a year.
We compare year-over-year changes in prices for new leases using proprietary data from RealPage with longer-term changes in rents for the average renter household using public survey data from the nationally representative 1-Year ACS (the Rent CPI is not available at the sub-state level for all states). We make several adjustments to both datasets to make them more comparable to each other. First, RealPage data primarily covers multifamily buildings.9 Therefore in our ACS analysis we examine renter households in multifamily buildings (5+ units). Second, we only use data from PUMAs that are in both datasets. The 1-Year ACS is a representative survey and is available at the PUMA level. RealPage is not nationally representative, as the data come from property management companies that choose to use RealPage to manage their tenant payments. Our sample of PUMAs in the RealPage data covers over 95 percent of the ACS renter multifamily household population in our analysis.10 Third, we aggregate the RealPage data from the building level up to the PUMA level to look at changes in new lease prices across PUMA quartile groups. To do so, we use both building-level weights for the number of units to account for buildings of different sizes, and PUMA-level multifamily renter household weights from the ACS to account for the RealPage data not being nationally representative. 11
Finally, when using the ACS, we examine longer-term percent changes in rents between 2019 and 2022 rather than doing a year-by-year analysis. The 2020 1-Year ACS data was released as an experimental data product because the pandemic made accurate data collection challenging.12 As a result, we drop 2020 from our ACS analysis, meaning we cannot calculate year-over-year changes for 2020 and 2021 using the ACS. To make ACS PUMAs consistent over time, we use the Missouri Census Data Center’s PUMA 2020 to PUMA 2010 crosswalk (starting in 2022, Census published the ACS with the new PUMAs based on the 2020 decennial Census).
To examine the effect of rent increases on renters, we look at two household-level measures of rent compared to income: the percentage of income spent on rent and the percentage of renters who are cost-burdened (spending more than 30 percent of their income on rent).13 We then examine the effects on renters in lower-income households. Prior work has found that rents tended to grow slightly slower for renters in lower-income households over the longer term in the pre-pandemic era (Molloy, 2024). In the pandemic era, such renters in areas with rising rents may face greater difficulty covering expenses if their incomes do not rise enough to cover increases in rents. We classify low-income renters as those who are in the bottom quintile of their PUMA’s household income distribution. We use this relative measure to classify lower-income renters because incomes vary by location and we are interested in analyzing renters whose incomes are low relative to the location they live in.
(i) Effects on Measures of Rents and Rent Spending: Did the move away from density mean that places that were more affordable pre-pandemic saw larger increases in rent and rent spending after the pandemic began?
(a) Mobility and expected effects on rent
Previous work with granular data on mobility has found that during the early years of the pandemic, people systematically moved to less dense areas (Haslag & Weagly, 2023; Gupta, Wittal, Peeters, & Gupta, 2022; Liu & Su, 2021; Whitaker, 2021). Rents increased in less densely populated areas (Ramani & Bloom, 2022). Given that rents increased in low density areas, and low density areas to have lower rents, we might expect to find larger increases in rents post-2019 in areas with lower rents pre-pandemic.
Other work has looked at drivers of this pandemic era mobility and the characteristics of those who moved. Remote work played a substantial role in mobility between 2019 and 2022 (Bick, Blandin, Mertens, & Rubinton, 2017). Moreover, workers with higher levels of education were more likely to move away from their place of work. Among those who moved away from their usual workplace in 2020, 36 percent had a bachelor's degree or higher compared to 25 percent with a high school degree or less (Federal Reserve Board of Governors, 2021).
The rise of remote work reduced the geographic link between job opportunities (and therefore, income) and housing affordability. With remote work, workers no longer need to live in the same area where they work and earn income. Therefore, they can make a location and housing decision independent of their job and income opportunities. Before remote work, a high-income household may have been constrained to live in areas with high rents to keep their high-income job. But with remote work, this same household may keep their high-income job but move to an area with lower rents.
As a result, areas with lower rents may have seen a higher influx of higher-income workers. Yet, this influx is less likely to be observed for areas with lower rents compared to income, a common measure of rental affordability. Therefore, we might expect differential increases in rents in lower-rent areas but not in areas with lower rent-to-income ratios.
However, for the low rent areas, other patterns may emerge when looking at changes in measures of affordability like rent-to-income ratios and cost burden in addition to changes in rent alone. For example, overall rent-to-income ratios in low rent areas may not increase by much even with the upward pressure on rents driven by in-migration if the movers are higher-income households, which would put downward pressure on rent-to-income ratios. That is why it is important to examine effects for lower-income households separately, as the differences in migration by income may have very different effects on lower-income households compared to the typical household. For example, if higher-income households were leaving higher-rent areas, then rent-to-income ratios might increase by more in those areas if the remaining renter households are lower-income on average due to the change in the composition of renters in higher-rent areas.
(b) Short Term: New Lease Price Growth
Results for new lease price growth are consistent with the literature on pandemic era migration. Rents rose by more in areas with lower pre-pandemic rents. Using monthly data from RealPage, Figure 3 panel (a) shows how national growth in new lease prices masks substantial differences in year-over-year growth in areas with lower versus higher rents pre-pandemic. However, Figure 3 panel (b) shows minimal differences in rent growth across areas with lower versus higher rent compared to income. These high-level findings suggest that migrating households were seeking out areas with lower rent, but not lower rents compared to income.
Focusing on the differences across areas with different pre-pandemic rents (Figure 3 Panel a), in the two years leading up to 2020, rent growth was fairly similar across PUMAs, but trends diverged when the pandemic began, consistent with renters moving out of high rent areas. In 2020, rent levels fell nationally, with negative year-over-year growth at -2 percent. However, rent levels fell only in the most expensive areas (third and fourth quartile, with negative year-over-year growth). In the least expensive areas (first and second quartiles), rent growth rose by more than the national average and the higher-rent quartiles. Consistent with households moving to lower-rent areas, rather than areas with lower rent compared to income, when grouped by rent-to-income (Panel b), growth rates across quartiles were much more similar and closer to the national average.
By 2021, new lease multifamily rent growth began to rise substantially in all four rent quartiles, with less separation across quartiles. Nationally, new lease rent growth peaked at around 15 percent year-over-year in spring 2022, then fell to around two percent by 2023. When PUMAs are grouped by rent only (Panel a), the peak in new lease growth was lower in the lowest rent first quartile compared to the more expensive higher quartiles. By late 2022, prices fell nationally and across all four quartiles. However, new lease price growth in the first quartile remained elevated at around 4 percent through 2024 compared to around 2 percent in the higher rent quartiles.
These results show rent movements for new leases, but not for the average renter. Measures of rent and rent expenditure for the average renter will lag measures of new leases, typically by around a year. New lease price growth started a period of sharp increase in spring 2021 that peaked in spring 2022. Examining the year 2022 in the ACS when new lease price growth peaked allows for analysis of the impacts on all renters during this period, rather than just the subset who signed new leases and were driving the increases seen in the RealPage data.
Moreover, though rents declined in 2020, the surge in new lease rents in 2022 meant that new lease rents were well above 2019 pre-pandemic levels even as growth began to fall. Nationally, new lease prices grew 20 percent between December 2019 and December 2022. In the bottom rent quartile, new lease prices grew 25 percent compared to just 15 percent in the top. For the rent-to-income quartiles, the first quartile saw the lowest, rather than the highest growth, though the differences between quartiles are smaller than for the rent quartiles.
(c) Longer Term: Rent Spending Growth for the Average Renter Household, 2019-2022
Using the 1-year ACS, we examine longer term growth in multifamily rent spending for the average renter household over three years from 2019-2022, from pre-pandemic into the recovery when new lease prices peaked. Our primary sample is the set of PUMAs that contain data from both the ACS and RealPage to more directly compare with new lease price results from RealPage. However, results are similar when using the full ACS sample. When comparing the ACS results with RealPage, it is important to keep in mind that the ACS will lag the new lease price RealPage data by around a year.
Results are consistent with higher income households moving to areas with lower rent spending as allowed by remote work, but not areas with lower rent-to-income ratios. The highest rent areas (4th quartile) saw smaller increases in rent than lower rent areas (Table 1 Panel a). When grouped by pre-pandemic rent (Table 1 Panel a), the results are roughly consistent with the RealPage results from 2021 (accounting for the lag between the two measures). The average renter in the highest rent, fourth quartile had the smallest gains in rent, consistent with the slower growth in the fourth quartile for the average new lease in RealPage in 2021. The middle quartiles have slightly higher growth than the bottom quartile. Though this rank ordering is different from the RealPage rank ordering in 2021, the bottom three quartiles are much closer to each other than is the fourth quartile. Also consistent with RealPage, when grouped by rent-to-income, differences across quartiles are smaller (Table 1 Panel b), consistent with differential migration to lower rent areas but not lower rent-to-income areas.
Table 2 shows that results are similar when restricted to just those in lower-income households (those in the bottom quintile of their PUMA of residence income distribution), though magnitudes are slightly larger than for the average renter household. Like for the average renter, when using rent quartiles to classify PUMAs (Table 2 Panel a), the fourth quartile saw the smallest increases in rent spending between 2019-2022. When using rent-to-income quartiles (Table 2 Panel b), changes in rent spending are more similar across quartiles.
(ii) Effects on Renters: Measures of Affordability Changes in rent and rent spending alone do not measure the effects on households. While we saw rents increase more in areas with lower rents pre-pandemic, these patterns may not hold once we account for household income, particularly given the potential compositional differences in household income driven by higher-income households moving to lower-rent areas. In this section, we use two common measures of affordability, median rent-to-income ratio and percent of renter households that are cost burdened (paying 30 percent of more of their income on rent). We examine changes in these measures across rent quartiles, as results in the previous section show that rents increased differentially in lower-rent areas compared to higher rent areas. Because increases in rent spending were similar across local areas sorted by rent-to-income, we do not examine further effects on measures of affordability.
When higher income renters move out of higher-rent areas to lower-rent areas, the expected effects on local rent-to-income ratios are ambiguous. In the high-rent areas where there is an outflow of renters, the departure of higher-income households lowers average incomes, a compositional effect that increases median rent-to-income ratios. However, declining demand puts downward pressure on rents, decreasing rent-to-income. The opposite is true in low-rent areas. Higher-income households moving in increases average incomes, a compositional effect that lowers rent-to-income ratios. Increasing demand puts upward pressure on rents, increasing rent-to-income. The net effect on rent-to-income depends on the magnitude of these countervailing effects. These effects from migration are on top of any shifts in affordability that are naturally occurring in these areas.
Higher rent areas: Higher rent areas (in the 4th quartile) saw smaller increases in median rent-to-income ratios compared to the middle quartiles (Table 3 Panel a). This suggests that the effect of falling rent dominated any compositional effect of fewer higher income renter households. For lower-income households, results are similar (Table 4 Panel a). Remaining households in the bottom quintile would be lower-income, pushing up rent-to-income ratios, but slower growing rents helped keep the ratio lower than the middle two quartiles. Cost burden increases among all renters were also smaller in the fourth quartile than in the lower-rent second and third quartiles (Table 5 Panel a).14
Lower rent areas: Renters in the lowest pre-pandemic rent quartile saw the smallest increases in rent-to-income (Table 3 Panel a). This affordability result is despite the fact that rents rose rapidly in these areas. This suggests that rising incomes from higher income renters moving in may have put downward pressure on rent-to-income. Although incomes may have also risen for existing residents in these areas, our analysis is unable to separate this effect. Consequently, we caution that this slower growth in rent-to-income ratios in these low cost areas is likely a reflection of compositional changes in the population rather than a reflection of improved affordability for incumbent residents. In future analyses, it would be valuable to explore this pattern further by considering changes in affordability following the same households over time or looking only at those renters who did not move over this period.
We analyze pandemic era increases in rent by geography and examine impacts on two measures of rental affordability using microdata on new lease prices and renter households in multifamily buildings. We find evidence consistent with the literature on pandemic era migration, that higher income remote workers moved away from higher rent areas to lower rent areas, pushing up rents more in these areas and slowing rent growth high rent areas.
To fully understand the impacts of the pandemic-era rent increases on renter households, it is necessary to look beyond simple measures such as new lease price growth. This is particularly true for those in lower-income households. Additional research could extend our analysis by using data that tracks renters over time to distinguish compositional effects from diverging rent and income trends in higher versus lower rent areas pre-pandemic.
Adams, B., Lowenstein, L. P., Montag, H., & Verbrugge, R. J. (2023). Disentangling Rent Index Differences: Data, Methods, and Scope. Federal Reserve Bank of Cleveland Working Paper Series.
Bick, A., Blandin, A., Mertens, K., & Rubinton, H. (2017). The Impact of Work from Home on Interstate Migration in the U.S. Retrieved from Federal Reserve Bank of St. Louis On the Economy Blog.
DeWaard, J., Hauer, M., Fussell, E., Curtis, K. J., Whitaker, S., McConnell, K., . . . Egan-Robertson, D. (2020). User Beware: Concerning Findings from Recent U.S. Internal Revenue Service Migration Data. Minnesota Population Center Working Paper.
Federal Reserve Board of Governors. (2021). Economic Well-Being of U.S. Households in 2020.
Figinski, T., Keenan, S., Sweeney, R., & Troland, E. (2024). Targeted Relief: Geography and Timing of Emergency Rental Assistance. FEDS Working Paper.
Gupta, A., Wittal, V., Peeters, J., & Gupta, M. P. (2022). Flattening the curve: Pandemic-Induced revaluation of urban real estate. Journal of Financial Economics.
Haslag, P., & Weagly, D. (2023). From L.A. to Boise: How Migration Has Changed During the COVID-19 Pandemic. Journal of Financial and Quantitative Analysis.
Joint Center for Housing Studies. (2026). America's Rental Housing 2026. Cambridge: Harvard University.
Liu, S., & Su, Y. (2021). The impact of the COVID-19 pandemic on the demand for density: Evidence from the U.S. housing market . Economics Letters.
Molloy, R. (2024). Differences in rent growth by income from 1985 to 2021 and implications for inflation. Journal of Urban Economics.
Ramani, A., & Bloom, N. (2022). The Donut Effect of COVID-19 on Cities. NBER Working Paper.
U.S. Census Bureau. (2026, July 13). Rental Vacancy Rate in the United States. Retrieved from Retreived from FRED, Federal Reserve Bank of St. Louis: https://fred.stlouisfed.org/series/RRVRUSQ156N
Whitaker, S. (2021). Migrants from High-Cost, Large Metro Areas during the COVID-19 Pandemic, Their Destinations, and How Many Could Follow. Cleveland Fed District Data Brief.
Figure 1: Year-Over-Year Changes New Lease Prices, 2017-2024
Data Source: RealPage
Notes: Monthly multifamily new lease price data through June
2024.
Figure 2: Geographic Distribution of Multifamily Renter Households by
Pre-Pandemic Rent and Rent-to-Income, by Census Region
a) Pre-Pandemic Rent Quartiles
b) Pre-Pandemic Rent-to-Income Quartiles
Data Source: 2015-2019 5-year ACS
Notes: 2015-2019 5-year ACS PUMA-level data for the PUMAs in both
RealPage and ACS data. PUMAs weighted using renter households in
multifamily housing. Of the multifamily housing renter households in the
ACS, over 95 percent of them are in PUMAs that are covered in RealPage.
Quartiles are weighted such that there is an even number of renter
households in multifamily housing units in each quartile.
Figure 3: New Lease Price Growth 2017-2024, by Pre-Pandemic Rent and
Rent-to-Income
a) Pre-Pandemic Rent Quartile
b) Pre-Pandemic Rent Affordability (Median Rent-to-Income Quartile)
Data Sources: 2015-2019 ACS, 2019 and 2022 1-Year ACS, MCDC Geocorr
crosswalk
Notes: Monthly multifamily new lease price data through June 2024.
Aggregated to county-level using unit weights and PUMA-level using MCDC
Geocorr county to PUMA 2012 crosswalk. To compute average new lease
price within quartiles, PUMA level average rents were weighted by the
share of multifamily renter households within each quartile.
Year-over-changes calculated as changes in average new lease price
rather than average of year-over-year changes within a quartile.
Quartiles are weighted such that there is an even number of renter
households in multifamily housing units in each quartile.
Table 1: Rent Growth for Average Renter, Percent Change 2019-2022
Data Sources: 2015-2019 ACS, 2019 and 2022 1-Year ACS, MCDC Geocorr crosswalk
Notes: Values shown are rounded to the nearest dollar and for percent change, to the nearest tenth. PUMA quartiles for (a) constructed using rent for average multifamily renter. PUMA quartiles for (b) constructed using rent-to-income for the median multifamily renter. Both use the 2015-2019 5-year ACS to construct the quartiles. Sample is all ACS multifamily renter households in PUMAs that have RealPage coverage. Of the multifamily renter households in the ACS, over 95 percent of them are in PUMAs that are covered in Realpage. Quartiles are weighted such that there is an even number of multifamily renter households in each quartile. Percent change in rent is calculated as the change in rent for the average renter within each quartile, rather than the average percent change within each quartile, using the 2019 and 2022 1-Year ACS.
(a) Pre-Pandemic 2019 Rent Quartile
| Quartile | Rent 2019 | Rent 2022 | Rent % Change 2019-2022 |
|---|---|---|---|
| 1st Quartile | $775 | $926 | 19.4 |
| 2nd Quartile | $1,029 | $1,257 | 22.2 |
| 3rd Quartile | $1,290 | $1,554 | 20.5 |
| 4th Quartile | $1,753 | $2,039 | 16.3 |
| National | $1,197 | $1,445 |
(b) Pre-Pandemic Rent-to-Income Quartile
| Quartile | Rent 2019 | Rent 2022 | Rent % Change 2019-2022 |
|---|---|---|---|
| 1st Quartile | $1,221 | $1,459 | 19.4 |
| 2nd Quartile | $1,201 | $1,425 | 18.7 |
| 3rd Quartile | $1,197 | $1,429 | 19.4 |
| 4th Quartile | $1,229 | $1,466 | 19.2 |
| National | $1,197 | $1,445 |
Table 2: Rent Growth for Average Lower-Income Renter Household, Percent Change 2019-2022
Data Sources: 2015-2019 ACS, 2019 and 2022 1-Year ACS, MCDC Geocorr crosswalk
Notes: Values shown are rounded to the nearest dollar and for percent change, to the nearest tenth. PUMA quartiles for (a) constructed using rent for average multifamily renter. PUMA quartiles for (b) constructed using rent-to-income for the median multifamily renter. Both use the 2015-2019 5-year ACS to construct the quartiles. Of the multifamily renter households in the ACS, over 95 percent of them are in PUMAs that are covered in RealPage. Quartiles are weighted such that there is an even number of multifamily renter households in each quartile. Renters in low-income households are defined as those in the bottom quintile of the income distribution in their PUMA. Percent change in rent is calculated as the change in rent for the average renter within each quartile, rather than the average percent change within each quartile, using the 2019 and 2022 1-Year ACS.
(a) Pre-Pandemic Rent Quartile
| Quartile | Rent 2019 | Rent 2022 | Rent % Change 2019-2022 |
|---|---|---|---|
| 1st Quartile | $635 | $760 | 19.6 |
| 2nd Quartile | $841 | $1,049 | 24.8 |
| 3rd Quartile | $1,054 | $1,280 | 21.4 |
| 4th Quartile | $1,352 | $1,590 | 17.6 |
| National | $930 | $1,170 |
(b) Pre-Pandemic Median Rent-to-Income Quartile
| Quartile | Rent 2019 | Rent 2022 | Rent % Change 2019-2022 |
|---|---|---|---|
| 1st Quartile | $962 | $1,163 | 20.9 |
| 2nd Quartile | $958 | $1,163 | 21.4 |
| 3rd Quartile | $956 | $1,157 | 21.1 |
| 4th Quartile | $1,007 | $1,197 | 18.8 |
| National | $930 | $1,170 |
Table 3: Median Rent-to-Income, Change 2019-2022
Data Sources: 2015-2019 ACS, 2019 and 2022 1-Year ACS, MCDC Geocorr crosswalk
Notes: Values shown are rounded to the nearest tenth. PUMA quartiles constructed using rent for the average multifamily renter using the 2015-2019 5-year ACS. Of the multifamily renter households in the ACS, over 95 percent of them are in PUMAs that are covered in Realpage. Quartiles are weighted such that there is an even number of multifamily renter households in each quartile. Change in rent-to-income is calculated as the change for the median within each quartile, rather than the median change within each quartile, using the 2019 and 2022 1-Year ACS.
Pre-Pandemic 2019 Rent Quartile
| Quartile | Rent/Income 2019 | Rent/Income 2022 | Change in Rent/Income 2019-2022 |
|---|---|---|---|
| 1st Quartile | 30.3 | 30.8 | 0.6 |
| 2nd Quartile | 30.4 | 32.9 | 2.5 |
| 3rd Quartile | 31.2 | 33.3 | 2.1 |
| 4th Quartile | 30.2 | 31.7 | 1.5 |
| National | 30.0 | 32.2 |
Table 4: Median Rent-to-Income for Lower-Income Renter Households, Change 2019-2022
Data Sources: 2015-2019 ACS, 2019 and 2022 1-Year ACS, MCDC Geocorr crosswalk
Notes: Values shown are rounded to the nearest tenth. PUMA quartiles constructed using rent for the average multifamily renter. PUMA quartiles using the 2015-2019 5-year ACS. Of the multifamily renter households in the ACS, over 95 percent of them are in PUMAs that are covered in Realpage. Quartiles are weighted such that there is an even number of multifamily renter households in each quartile. Renters in low-income households are defined as those in the bottom quintile of the income distribution in their PUMA. Change in rent-to-income is calculated as the change for the median renter within each quartile, rather than the median change within each quartile, using the 2019 and 2022 1-Year ACS.
Pre-Pandemic Rent Quartile
| Quartile | Rent/Income 2019 | Rent/Income 2022 | Change in Rent/Income 2019-2022 |
|---|---|---|---|
| 1st Quartile | 60.6 | 66.0 | 5.4 |
| 2nd Quartile | 64.0 | 78.6 | 14.6 |
| 3rd Quartile | 73.1 | 85.6 | 12.6 |
| 4th Quartile | 72.2 | 83.6 | 11.4 |
| National | 63.0 | 79.7 |
Table 5: Percent Cost-Burdened, Change 2019-2022
Data Sources: 2015-2019 ACS, 2019 and 2022 1-Year ACS, MCDC Geocorr crosswalk
Notes: Values shown are rounded to the nearest tenth. Cost-burdened is defined as spending more than 30 percent of income on rent. Renters with zero income and renters who do not pay cash rent are not included. PUMA quartiles constructed using rent for the average multifamily renter using the 2015-2019 5-year ACS. Of the multifamily renter households in the ACS, over 95 percent of them are in PUMAs that are covered in Realpage. Quartiles are weighted such that there is an even number of multifamily renter households in each quartile.
Pre-Pandemic Rent Quartile
| Quartile | % Cost Burden 2019 | % Cost Burden 2022 | Change in Cost Burden 2019-2022 |
|---|---|---|---|
| 1st Quartile | 49.0 | 51.4 | 2.4 |
| 2nd Quartile | 50.3 | 54.8 | 4.5 |
| 3rd Quartile | 51.8 | 55.7 | 3.8 |
| 4th Quartile | 48.5 | 52.2 | 3.7 |
| National | 49.9 | 53.5 |
Table 6: Percent Cost Burdened Among Lower-Income Renter Households, Change 2019-2022
Data Sources: 2015-2019 ACS, 2019 and 2022 1-Year ACS, MCDC Geocorr crosswalk
Notes: Values shown are rounded to the nearest tenth. Cost-burdened defined as spending more than 30 percent of income on rent. Renters with zero income and renters who do not pay cash rent are not included. PUMA quartiles constructed using rent for the average multifamily renter using the 2015-2019 5-year ACS to construct the quartiles. Of the multifamily renter households in the ACS, over 95 percent of them are in PUMAs that are covered in RealPage. Quartiles are weighted such that there is an even number of multifamily renter households in each quartile. Renters in low-income households are defined as those in the bottom quintile of the income distribution in their PUMA.
Pre-Pandemic Rent Quartile
| Quartile | % Cost Burden 2019 | % Cost Burden 2022 | Change in Cost Burden 2019-2022 |
|---|---|---|---|
| 1st Quartile | 82.2 | 84.4 | 2.2 |
| 2nd Quartile | 86.1 | 89.2 | 3.1 |
| 3rd Quartile | 88.6 | 91.0 | 2.5 |
| 4th Quartile | 88.0 | 89.7 | 1.7 |
| National | 85.9 | 88.3 |