Why Cheaper Places Got More Expensive
This chapter examines the mechanism behind a distinctive renter-affordability pattern: places with lower nominal rents experienced stronger rent pressure after the pandemic began. The question is not simply why rents rose, but why demand moved toward places that had previously been cheaper and why that movement does not imply that those places were cheaper relative to local incomes. Understanding the sequence from remote work to migration to local rent growth lets us interpret the geography without mistaking a plausible mechanism for proof about every household.
Remote work separated the job from the housing market
Before the pandemic, a household’s housing choice was often constrained by the location of its job. A high-paying job might be concentrated in a dense metropolitan area where housing was expensive. Living elsewhere could mean a long commute, a weaker connection to the employer, or no practical access to the job at all. The rent a household paid was therefore partly tied to the income opportunities available in the same place.
Remote work loosened that connection. A household could keep a job and its associated income while moving farther from the workplace. A larger home, more space for work, or a lower monthly rent could become available outside the high-cost core. The Federal Reserve study describes a pattern consistent with higher-income remote workers moving from high-rent, high-density areas toward areas with lower nominal rents. The important mechanism is not that remote workers suddenly made every destination affordable. It is that they could carry purchasing power across a geographic boundary that had previously tied jobs and housing more closely together.
That movement changes the demand side of the destination market. When more households compete for the same local housing stock, landlords and property managers receive stronger incentives to raise asking rents, especially for units turning over to new tenants. A destination that was cheap before the movement can become a target of demand precisely because it was cheaper in dollars. The process can make a place less cheap without making it less attractive to the households that initiated the move.
The distinction between nominal rent and rent relative to income is crucial at this point. Imagine two destinations. One has a monthly rent of $900 and local renter incomes of $30,000 a year. Another has rent of $1,700 and local renter incomes of $70,000. The first has the lower nominal price, but the second may have the lower rent-to-income ratio. A higher-income household moving from the expensive market may prefer the first destination because its rent is lower in dollars, even if the first destination was not the most affordable place for its existing residents relative to their incomes.
The Federal Reserve results fit that mechanism. New-lease rents rose more in areas with lower pre-pandemic rents, but rent growth was not differentially higher in areas with lower pre-pandemic rent-to-income ratios. In other words, the destination pattern follows the price level more clearly than it follows local affordability after income is taken into account. That is consistent with movers seeking a lower housing price while bringing incomes that change the economic composition of the places they enter.
This is also why the geography should not be described as a simple transfer of affordability from one region to another. A high-rent area can lose demand and see slower rent growth. A lower-rent area can gain demand and see faster rent growth. The first movement may relieve pressure on some prospective tenants in the high-rent area, while the second creates new pressure for tenants in the destination. The same migration can therefore produce opposite rent responses in different places.
The word “affordable” hides the distributional question inside this movement. For a household arriving with a high income, a $1,200 rent may be a meaningful saving compared with the $2,000 rent available near its former job. For a renter already earning near the destination’s lower local income, the same market may have been manageable before the inflow and precarious afterward. The arrival household’s decision can be financially rational while the incumbent household’s situation worsens. Neither experience cancels the other; they belong to different positions in the same market.
A market-level ratio can also react in a direction that is not obvious from rent growth alone. Higher-income arrivals raise the income component of the local average, which can offset some of the upward pressure on rents in the ratio. In a high-rent area, the departure of higher-income renters can lower the average income even as weaker demand slows rent growth. The net direction is therefore the result of two forces: the price response of the housing market and the composition response of the population. The Federal Reserve study uses the rent-versus-rent-to-income comparison to show why those forces must be considered together, while stopping short of claiming that the data identify each force for each household.
The most useful way to read the mechanism is as a change in the relationship between opportunity and location. Remote work did not eliminate scarcity or create housing. It changed which households could compete for which units. The new competition was strongest where a price difference could be converted into more space or lower monthly spending without sacrificing the household’s job. That is why the starting rent level is an important predictor of where pressure appeared, even though it is not a complete measure of whether the destination was affordable to its prior residents.
The mechanism has several stages, and each stage changes what a statistic can observe:
The diagram is not a claim that every move followed this exact path. It is a way to separate the trigger, the mediator, and the outcomes. Remote work is the enabling condition. A household’s location decision is the transmission step. Local demand is the pressure applied to the housing market. New-lease prices are an early observable. The cost carried by an existing renter arrives on a different clock, and the income comparison determines whether the same rent change is manageable.
The mechanism also has a boundary. The Federal Reserve paper calls the pattern consistent with higher-income remote workers moving toward lower-rent areas; it does not identify every mover or prove that migration alone caused the observed rent changes. Other forces can affect rent growth at the same time. The value of the mechanism is that it explains why rent level and rent-to-income sorting produce different patterns, not that it eliminates every alternative explanation.
Demand moved first; rent data recorded it in stages
The study’s data are designed to observe different steps in the sequence. RealPage provides building-level administrative data on new leases, which respond relatively quickly when demand changes. The American Community Survey provides household information about rent spending and income, including renters who are not signing a new lease in the month being measured. Comparing them helps distinguish a market shock from the slower experience of the average renter.
National new-lease growth illustrates the timing. After running at roughly 2 to 4 percent before the pandemic, new-lease price growth fell to about negative 2 percent in 2020 and later surged to roughly 15 percent year over year in spring 2022. The national series describes a sharp change in the market for new tenants. It does not mean that the average existing renter immediately faced the same percentage increase. Lease terms delay part of the transmission, and measures covering all renters generally lag new-lease indexes by about a year.
The geographic comparison is visible in the longer-term rent-spending data as well. From 2019 to 2022, average multifamily rent spending increased in every pre-pandemic rent quartile. The lowest-rent quartile rose from $775 to $926, or 19.4 percent. The second rose from $1,029 to $1,257, or 22.2 percent. The third rose from $1,290 to $1,554, or 20.5 percent. The highest-rent quartile rose from $1,753 to $2,039, or 16.3 percent. The higher-rent group did not remain untouched; it simply recorded the smallest percentage increase among these groups.
When the places were instead grouped by pre-pandemic rent-to-income ratio, the changes were much closer together, ranging from 18.7 to 19.4 percent. That difference in the pattern is the evidence behind the chapter’s central distinction. Sorting places by nominal rent reveals a stronger gradient in rent growth. Sorting them by rent relative to local income largely reduces that gradient. The two groupings are not redundant because they encode different ideas of affordability.
The quartiles are fixed using pre-pandemic conditions, which is important for interpreting the comparison. A place classified as low-rent is being evaluated as a low-rent place before the shock, even if its later rent rises quickly. The exercise therefore asks whether the places that started with different conditions experienced different subsequent growth. It does not reclassify a place after the increase and then claim that its new category caused the increase. Keeping the starting classification fixed makes the comparison directional: initial geography is the reference point, and later rent movement is the outcome.
The comparison also separates two kinds of “pressure.” A lower-rent area can have a large percentage increase from a smaller base, while a high-rent area can have a larger absolute dollar increase even when its percentage growth is slower. A renter’s experience depends on both. The Federal Reserve tables show that the highest-rent quartile rose from $1,753 to $2,039, an increase of $286, despite its 16.3 percent growth being the lowest of the four groups. The second quartile rose by $228, but its 22.2 percent growth was higher. Percentages reveal the pace relative to the starting point; dollar changes reveal the added monthly amount. Neither should be used alone to declare which renters faced the greater burden.
That distinction matters for the mechanism because movers may respond to the dollar price while incumbent renters confront the percentage or dollar change against their own income. A household choosing between a $2,000 rent and a $1,200 rent may focus on the monthly saving. A household already paying $775 in the destination may focus on the increase to $926 and whether its income can cover it. The same market-level movement can create a saving for one household and a budget shock for another. The data support this as a way to reason about the pattern; they do not identify the personal choice of every household in the quartile.
The lower-income results sharpen the distributional point. Among renters in the bottom quintile of their PUMA’s income distribution, rent spending increased more in some lower-rent groups than in the highest-rent group. The second rent quartile recorded a 24.8 percent increase, compared with 17.6 percent in the fourth. This does not establish that every lower-income renter in the second quartile moved or stayed, but it shows why a low starting rent is not a guarantee against a large subsequent increase. A household with little income cushion can be exposed even when its home remains cheaper in absolute dollars than a home in a high-rent area.
There is a second reason not to read the geographic pattern as a simple market ranking: new leases and average spending are not observing the same transaction. A new tenant may encounter a higher asking price immediately. A current tenant may be protected until renewal, then face a higher price later. A household that moves may enter the new-lease data in a different area, while a household that stays remains part of the existing-renter measure. The observed averages can consequently reflect both price changes and changes in who is being observed.
The study’s design makes that distinction useful but also limits its reach. It focuses on multifamily buildings with five or more units, because that is where the RealPage data are concentrated. It uses PUMAs, the smallest geography available with timely rent and income information for the comparison. RealPage is not nationally representative, even though the paper reports that the PUMAs covered by the data contain more than 95 percent of the ACS multifamily renter population used in the analysis. The results are therefore especially informative about multifamily and metropolitan rental markets, not a direct estimate for every rural or single-family renter.
The American Community Survey’s 2020 one-year product is omitted because pandemic data collection made it experimental. That means the longer-term household comparison is made from 2019 to 2022 rather than by filling in every year of the shock. The gap does not negate the mechanism, but it matters when a reader tries to narrate month-by-month changes for a particular household. The evidence is strongest for the broad relationship among pre-pandemic geography, subsequent rent spending, and affordability measures—not for a continuous personal timeline.
The mechanism therefore supports a careful conclusion. Remote work made it easier for some higher-income households to move toward lower nominal-rent places. Their demand was consistent with stronger rent growth in those destinations. But “lower rent” and “lower rent relative to local income” were different destinations in the data, and the path from new-lease prices to existing-renter budgets took time. The next question is what happened after that timing difference was carried into household rent spending and income-adjusted burden, especially for renters with less income to absorb the shock.