---
title: "Is Gen Z Leaving California or Moving There?"
description: "Two headlines point in opposite directions. Moving to a place, finding housing there and being able to stay are three different achievements. An original Census analysis of young adults, migration and housing demand."
canonical_url: https://oftu.com/insights/is-gen-z-leaving-california-or-moving-there
author: "Oftu"
publisher: "Oftu"
date_published: 2026-10-13
date_modified: 2026-10-13
language: en
category: "Housing migration"
structured_data: https://oftu.com/insights/is-gen-z-leaving-california-or-moving-there/data.json
---

# Is Gen Z Leaving California or Moving There?

Two headlines point in opposite directions. Moving to a place, finding housing there and being able to stay are three different achievements. An original Census analysis of young adults, migration and housing demand.

## Two trucks, two futures

Imagine two moving trucks passing on a California highway.

One carries the possessions of a young adult coming west: a desk, some kitchen things, a mattress that will fit in a shared apartment. The other carries the possessions of a young adult leaving: perhaps the same collection of objects, packed after a different calculation about the future.

One might be following an education or a job. The other might be seeking a home that leaves enough money for everything beyond the front door. Their decisions need not contradict each other.

From the highway, both decisions are visible. From a headline, often only one is.

In October 2026, the news offered apparently incompatible pictures of young Americans and California. October 11 coverage described it as a Gen Z destination. An October 5 migration study reported a substantial departure of young adults. [October 11 coverage](https://fortune.com/2026/10/11/gen-z-moving-to-california-uhaul-jobs-housing/), [October 5 study](https://www.lendingtree.com/home/mortgage/gen-zers-moving-study/)

Which picture should a renter believe? Which should a homebuyer believe? What should an investor make of a generation supposedly arriving and departing at once?

There is a defensible answer, but it is narrower than either sweeping story. Our independent calculation from Census microdata confirms that California lost more residents aged 18 to 27 to other states than it gained in the 2024 survey. A later moving-company ranking places California near the top for that company's young customers. The two results do not establish a reversal in the state's young population.

There is also an error worth stopping for. The national statistic behind the claim that roughly a quarter of young adults moved between states counts a much broader category of movement. Its numerator includes millions who changed homes without changing states.

Look inside the count, and the journey changes.

Migration matters enormously to housing. But a moving truck is not a new household, a new household is not necessarily a homebuyer, and a homebuyer is not a completed sale. If we skip those steps, a vivid map can become a poor guide to where people will actually live.

## The first border is inside the number

The striking national claim was that 10.7 million adults aged 18 to 27 moved to another state in 2024, approximately 23.9% of the age group. Those figures appeared in LendingTree's analysis of the American Community Survey, on the public page checked October 13, 2026. [Published study](https://www.lendingtree.com/home/mortgage/gen-zers-moving-study/)

We then rebuilt the relevant counts from the Census Bureau's national 2024 public-use person files. The calculation uses people, not households, and the survey's person weights. It distinguishes a change of address from a change of state. [Census source files](https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/), [variable definitions](https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2024.txt)

The published 10,673,613 count is reproducible. Its description as interstate migration is not.

That count includes approximately 8.44 million people who changed homes within the same state, 2.22 million who changed residence between the 50 states and Washington, DC, and about 9,900 arrivals from Puerto Rico. All belong to the survey's category for a different home in the United States or Puerto Rico. Only the second group crossed between the states and DC.

In our defined interstate universe, the estimate is 2,223,386 people out of about 44.6 million residents aged 18 to 27: approximately 5.0%. Its 90% sampling interval is approximately 4.89% to 5.07%. The separate estimate for arrivals from abroad is about 619,000. Those people are not included in the interstate figure.

A statistical margin of error cannot fix a mislabeled category. The uncertainty around an estimate asks how much sampling might change the count. It does not turn a same-state move into an interstate one.

The error is specific. The study's California arrival and departure estimates match our reconstruction. They retain value even though the national headline assigns the broader count to the wrong kind of move.

Now the national picture looks different. Young adulthood is a period of considerable residential movement. Most of the domestic address changes in this calculation, however, did not cross a state boundary.

The generation was moving. The map in the headline had enlarged the journey.

## California was losing and receiving young adults

For residents aged 18 to 27 in the 2024 Census microdata, our estimates are straightforward:

Approximately 128,700 arrived in California from another state or DC. Approximately 186,800 residents of other states or DC had lived in California a year earlier. The resulting net interstate loss was about 58,100 people.

These are weighted survey estimates, not administrative counts of every moving person. The 90% sampling interval for the net loss runs from approximately 45,600 to 70,600. Within that interval, the direction remains a loss. [Public microdata](https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip), [Census weighting and uncertainty guidance](https://www2.census.gov/programs-surveys/acs/tech_docs/pums/2024ACS_PUMS_User_Guide.pdf)

The departure story therefore has real support for that age band, period and measure.

But notice the arrivals. A state with a net loss can still receive a substantial number of newcomers. Net migration is the difference between two streams; it is not a description of the size, character or destination of either one.

An illustrative place receiving 100 people and losing 120 has a net loss of 20. Another receiving five and losing 25 has the same net result. Their turnover is very different. The first place may have constant lease changes, moving services and demand from newcomers even while its resident population is declining through these flows.

Nor do the people arriving have to seek the same homes that departing residents leave behind. A person joining a shared rental might not replace a family leaving a three-bedroom house. They are both residents; their housing requirements are not interchangeable.

For California, the verified answer is not that young adults had stopped arriving. It is that, in the 2024 interstate measure, more left than came.

## A different camera, pointed at a later year

U-Haul's July 29, 2026 report covers one-way equipment movements from July 2025 through June 2026. It places California second, behind Texas, in its Gen Z net-gain ranking. That is a later period than the 2024 Census analysis and a different observation: customer equipment transactions, not a representative population survey. [U-Haul's national report](https://www.uhaul.com/Articles/About/2026-U-Haul-Midyear-Migration-Trends-NATIONAL-REPORT-41817/)

The company's observation is close to the physical act of relocating. The report provides ranks rather than the underlying state volumes, however, and customers using other ways to move do not become U-Haul transactions.

A rank cannot tell us whether second place represents a large gain, a small gain or a result close to third place. Without counts, it cannot be converted into an estimate of how many young people California gained.

The age definitions also differ. Our Census reconstruction follows the study's operational band of ages 18 to 27 in 2024. U-Haul groups customers by birth years and notes different minimum rental ages for trucks and trailers. These are not a closed cohort tracked by the same instrument through time.

The period and observed population both changed. Establishing their respective effects would require comparable evidence across time.

This is where a seemingly satisfying explanation becomes dangerous. It is tempting to say that California lost young people in 2024 and won them back in 2026. It would produce a clean plot: decline, turnaround, return.

For now, the gap between the observations remains. It should not be filled with a turnaround we have not measured.

## The person on the rental agreement

Consider another imagined move. A student is going to college. A parent books the truck. On the road, the student's possessions are changing address. In the booking record, the customer may belong to the parent's generation.

Nothing has gone wrong with the transaction record. It is recording a customer relationship. The problem begins if we silently reinterpret that customer as every person whose life is being relocated.

U-Haul itself notes that some college-town patterns among Gen X contract holders appear to reflect parents helping university-bound children move. The booking can describe the parent even when the bedroom awaiting the furniture belongs to their child. [U-Haul report](https://www.uhaul.com/Articles/About/2026-U-Haul-Midyear-Migration-Trends-NATIONAL-REPORT-41817/)

A family can share one vehicle. Someone with little furniture can move without one. A household using professional movers may never enter this customer dataset. The scale and direction of these coverage effects would need to be established before correcting the data for them.

The Census has boundaries of its own. Its migration measure compares where a surveyed person lives with where they lived a year earlier. It is not a log of every intervening move. Someone who left and returned could have the same state at both ends. [Census migration guidance](https://www.census.gov/topics/population/migration/guidance/state-to-state-migration-flows.html)

Neither camera sees everything. The useful question is what each was built to see.

## The move that never leaves California

A state's border is also a peculiar place to draw the boundary of a life.

A move from Los Angeles to Fresno can change the housing budget, the workplace, the school system and the distance from relatives without registering as interstate migration. A much shorter journey across a state line elsewhere can register as one.

In our 2024 reconstruction, approximately 831,200 residents aged 18 to 27 had changed address within California. That exceeds the estimated 186,800 young adults who left California for another state or DC by more than four times. It does not mean the moves were all responses to housing costs. The records do not establish that reason.

It does show how much movement a leaving-versus-arriving debate can omit.

For housing, the destination inside a state may matter more than the fact that the state stayed the same. A renter moving inland could leave demand behind in one neighborhood and create it in another. A graduate moving back into a parent's home could change address without taking a vacant apartment. A worker changing rentals near the same job may change neither the state's population nor its total occupied housing stock.

The market relevant to a particular property is not necessarily the market whose name appears on the map. Transport, employers, universities, dwelling types and budgets can connect places across borders or separate places within them.

That is why a statewide migration total can be a useful opening question and a poor final answer to a property decision.

## A dormitory is housing, but not a starter home

College complicates the route from migration to property demand.

Our national reconstruction finds that about 40.3% of interstate movers aged 18 to 27 were enrolled in undergraduate or graduate/professional study when surveyed. Enrollment is observed; moving because of college is not. A person could be enrolled and relocate for a job, a relationship or another reason.

What matters for housing is not just whether a move occurred, but the accommodation it required. A dormitory bed, a bedroom in an existing shared apartment and a newly leased apartment are three different demands on the built environment.

The ACS includes college dormitories in its group-quarters coverage. Its current-residence rules generally concern expected stays longer than two months. A different Census publication based on another survey can classify a student differently for living-arrangement purposes. Definitions that sound similar can lead to different housing interpretations. [ACS residence rules](https://www.census.gov/help/topics/faq.who-counts-as-a-resident-for-the-acs.html), [group-quarters guidance](https://www.census.gov/topics/income-poverty/poverty/guidance/group-quarters.html), [living-arrangement explanation](https://www.census.gov/library/stories/2024/05/living-arrangements.html)

A city can need more student accommodation without gaining the same number of conventional households. A university can support local rental demand while giving us little basis to forecast demand for detached homes ten years later.

The tempting shortcut is to count young arrivals as future first-time buyers. Some will become buyers nearby. Others will leave, continue renting, share a home or form households elsewhere. A relocation record does not contain the end of the story.

The university town has received a person. Whether it can retain that person after graduation remains another question.

## Expensive places can offer different bargains

An expensive housing market can still attract someone making a reasonable decision. That fact does not make the market affordable.

Imagine comparing two job offers. In one place, your take-home pay would be $4,000 a month and your housing expense $1,200. In another, take-home pay would be $5,000 and housing $2,000. Before considering other costs, the second leaves $3,000 after housing, against $2,800 in the first. These are invented budgets, not estimates for California or another state.

The larger housing bill does not settle the choice. Transport, health care, student debt, the probability of keeping the job and the cash needed to relocate could reverse it. So could unpaid help from relatives or the loss of that help.

Now imagine that the second job requires several months of costly uncertainty before income begins. The opportunity might be attractive over years and inaccessible this month. A young adult with family support and a young adult without it can face the same advertised wages and entirely different feasible choices.

A map of successful moves shows the people who found a way to move. It cannot count the ambitions stopped by a security deposit, an unaffordable commute or the absence of a spare bedroom.

This is why arrivals should not be treated as proof that a place's housing problem has gone away. A place can remain desirable while becoming harder to access. The people still able to enter may have better pay, more savings, family assistance or different space requirements than those who cannot.

Nor should departures automatically be interpreted as a rejection of everything the place offers. A person can love their city and need to leave it. A larger or more secure home can matter more at a new stage of life than the advantage that drew them there.

Migration records describe an outcome. They do not, by themselves, distinguish an enthusiastic choice from a constrained one.

## The people a migration ranking cannot show you

The most dramatic arrows on a national map belong to movers. Most residents do not produce such an arrow in a given year.

There is also an older, longer view of young adulthood. A July 2022 study by Census and Harvard researchers followed locations at ages 16 and 26 for people born from 1984 to 1992. About 80% lived within 100 miles of where they grew up. That is evidence about those earlier cohorts, not a current estimate for Gen Z. [Research paper](https://www2.census.gov/ces/wp/2022/CES-WP-22-27.pdf)

The finding changes the camera again. A young person's housing future may depend heavily on a market close to childhood, whether or not it appears on a top-destination list.

Someone who stays can still be under pressure. They may remain with parents longer than desired, divide a dwelling with more people or accept a longer journey to work. Someone who moves nearby can exchange a familiar neighborhood for cheaper space while preserving access to relatives and employment.

These experiences can be decisive without becoming an interstate trend.

It is easy to read migration as a national competition for the most mobile people. For housing policy, that leaves out residents who cannot relocate or do not want to. A city is not succeeding for everyone merely because it attracts newcomers who can pay its rents.

The relevant question is also whether the people already there have a workable route to an independent home.

## Getting there and being able to stay

A first move is often made with a short horizon. Can the deposit be assembled? Is a bedroom available? Does the job start soon enough? A decision to remain can involve a different horizon: a partner, children, care for a parent, stable work or the wish not to move again.

That difference suggests a hypothesis worth testing. A place could recruit people at one life stage and lose them at another. It would be both an entry point and an exit point, with housing helping determine which.

Our two migration sources do not prove that mechanism. We would need to follow comparable people over time, or examine carefully defined age and household groups across repeated periods. Comparing one group of 22-year-olds with a different group of 29-year-olds does not show what happened to the original people.

There is relevant evidence about changing life stages. PPIC's October 2025 study of young Californians describes later household and family milestones alongside higher educational attainment in recent cohorts. Its analysis concerns people living in California at the ages observed. It does not follow every Californian-born person wherever they subsequently moved, and its decompositions are not causal estimates. [PPIC study](https://www.ppic.org/publication/the-economic-milestones-of-young-californians/)

Those boundaries are part of the story. If people who find it hardest to stay leave the dataset, the remaining residents can look more successful without the original population enjoying the same improvement.

Similarly, a rise in arrivals could be good news for a university or employer while telling us little about whether people can establish a lasting home nearby. Entry and retention deserve separate measures.

For a place trying to keep its young residents, the practical challenge is larger than attracting their attention. It has to make the next stage possible too.

## More people is not the only way to need more homes

Consider a deliberately small, imaginary district. It has 300 adults living in 100 occupied homes, three people per home on average.

Nobody arrives. Nobody leaves.

Now some roommates want separate places, some couples separate, and some adult children want to move out. If the average becomes two and a half people per occupied home, accommodating the same 300 adults requires 120 homes.

The population is unchanged. The need for occupied homes has risen by 20.

This is an accounting example, not a prediction of California's household size or construction requirement. It leaves vacancies, group quarters and the suitability of particular dwellings outside the calculation. Its purpose is to show why a headcount alone cannot settle housing demand.

There is a real-world reason to take the mechanism seriously. In April 2026, PPIC reported that California had built 677,000 new housing units over a six-year period in which its population changed little. Its analysis pointed to smaller households as an important reason that additional housing had not translated into the relief a population-only comparison might suggest. New construction is not the same as net additions to usable stock, and a statewide figure does not describe every local market. [PPIC analysis](https://www.ppic.org/blog/has-adding-more-homes-than-people-eased-californias-housing-crisis/)

An adult moving out of a parent's home can create demand for a dwelling without changing the state's population. An arriving adult joining an existing household may initially add no occupied dwelling. An older adult continuing to live alone can keep a home occupied after other household members have departed.

The moving truck and the housing unit are linked through the household, not through a fixed conversion rate.

That is a more useful mental model than counting arrivals as new demand and departures as equal quantities of released supply.

## A vacancy can appear in the wrong home

Even the number of homes is not the whole question. Their location, size, condition and price determine who can use them.

Imagine a neighborhood in which several families depart houses with three bedrooms. At the same time, several young adults arrive looking for inexpensive rooms close to a university. The neighborhood has lost residents and has vacant houses. It might still have a shortage of the accommodation the newcomers can actually afford.

The houses could be divided or shared, but that requires a workable physical, legal and financial arrangement. A vacancy does not automatically become the right housing product.

Reverse the scenario. A group of well-paid newcomers rents dwellings that departing lower-income households could no longer afford. Occupancy could remain steady while the people able to live there change. A stable number of residents would not capture the distributional consequence.

These are illustrative mechanisms, not findings about a particular California neighborhood. They explain why an investor needs more than a migration ranking and why a renter needs more than a state-level claim that supply is improving.

The market clears through particular homes, budgets and decisions. A family cannot occupy a statewide average vacancy rate. A student cannot rent an average net migration number.

The address still matters.

## How to read the next headline

Start by asking what crossed the border: people, households, customers or equipment. Then ask which border, which period and which age group. Those are not ceremonial qualifications. They determine what the result can mean.

For a renter, the useful next evidence is local: suitable available homes, the complete cost of a lease and access to the work or support that makes the location feasible. A state gaining young movers does not tell you whether a particular neighborhood's rents will rise.

For a buyer, demographic appeal needs to meet financing and supply. The presence of newcomers does not establish their ability or willingness to purchase. Nor does a net departure tell you how many owners must sell. Renters leaving, owners selling and owners retaining a property have different consequences.

For an investor, the analytical chain should be explicit. An observed flow has to become an estimate of relevant households. Those households need a particular type of accommodation, a feasible budget and a plausible duration of stay. The proposed property then competes with existing stock and new supply. Each step can weaken or change the signal.

A forecast that skips these steps may be borrowing authority from a demographic dataset without doing the housing analysis.

The evidence should also be allowed to remain uncertain. Our 2024 estimate of California's net interstate loss for ages 18 to 27 stays negative across its 90% sampling interval. Some other state estimates are much less distinct from zero. Sorting point estimates into a league table does not make adjacent ranks statistically different.

Where a source offers only ranks, do not manufacture volumes. Where a dataset provides a period estimate, do not relabel it as today's count. Where a survey records enrollment, do not turn it into a verified reason for moving.

These habits do not make a story less useful. They make it usable for decisions.

## A place to arrive, a place to live

Return to the imagined trucks.

The one heading toward California may carry a perfectly reasonable hope. The one heading away may carry a perfectly reasonable plan. Neither decision requires the other person to be mistaken.

What we can say with the evidence is specific. California had a net interstate loss among adults aged 18 to 27 in the 2024 Census measure. A later U-Haul ranking records a different pattern among the company's younger customers. It cannot, on its own, establish that the population flow reversed. And the published national interstate statistic describes a broader set of address changes than its label claims.

Beyond that correction lies the more important housing question.

A place can be good at drawing people in and less good at providing the homes they need as their lives change. It can retain residents whose households remain crowded and lose residents who would prefer to stay. It can gain households without gaining people, or gain people without creating equally many households.

There is no single arrow that measures all of that.

The first truck will eventually stop. The driver will need an address, a workable monthly budget and a place in which next year's plans can fit. The second driver's new address will have to do the same.

The lasting question is not which direction makes the better headline. It is whether the place at the end of the journey can become a home.

## Data notes

Oftu calculations use the complete national 2024 ACS one-year public-use person files, containing 3,422,888 sample records, including 402,821 records aged 18 to 27. Results are weighted estimates, not sample counts. Interstate movement requires a different previous state within the 50 states and DC; arrivals from Puerto Rico and from abroad are kept separate. The national file does not capture people currently living outside that universe, so these calculations are not a complete international or Puerto Rico migration balance.

The reported domestic-move count of 10,673,613 is reproduced exactly under the broader different-home-in-US-or-Puerto-Rico category. The interstate count is 2,223,386; the within-state count is 8,440,357; arrivals from Puerto Rico are 9,870. Together they reconcile to the broader count. Including arrivals from abroad yields an all-address-change estimate of 11,292,718, or 25.3% of the defined age group.

California's net interstate estimate is arrivals minus departures. Its sampling uncertainty is calculated directly using all 80 replicate person weights. The same method is used for national percentages, recomputing each ratio under every replicate. The 90% intervals address sampling uncertainty, not all possible survey or processing errors. The reconstruction reproduces a measure; it does not validate every claim in the commercial study.

The ACS observes current residence and residence a year before interview across the survey year. These are not January-to-December event counts or a record of every intervening move. Ages are measured at interview, not a fixed birth cohort followed into 2026. College enrollment is undergraduate or graduate/professional study at the survey observation, not a reported migration motive. PUMS estimates may differ from published full-sample ACS tables.

U-Haul rankings retain their own observation window and customer definition. They are not converted into population counts, percentages or a time series comparable to the ACS. All imagined scenes, budgets and housing-accounting examples are illustrative. No rent, home-price or population forecast is implied.

The source archive, calculation script, output and claim review are retained with this manuscript's research materials. [Census data](https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/), [Census user guide](https://www2.census.gov/programs-surveys/acs/tech_docs/pums/2024ACS_PUMS_User_Guide.pdf)

## Visual evidence and interactive studies

The following descriptions come from the figures and controls on the canonical article. Interactive values below are the initial settings, not results for a particular property. Conceptual illustrations and schematic paths are not measured datasets.

### 1. Figure 1

Figure labels: Two unbranded moving trucks, one ivory and one blue, passing in opposite directions on a California highway.



### 2. The border isinside the number.

A statistical margin of error cannot fix a mislabeled category.

Broader category, including same-state moves

Between the 50 states and DC only

Same denominator: 44,644,605 residents aged 18 to 27. Interstate share, 90% interval: 4.89% to 5.07%.

Census 2024 person microdata, Oftu calculation. Different residence compared with one year before interview. Puerto Rico arrivals are a separate sliver, not interstate; arrivals from abroad are outside this domestic count. Bars above use the full age-population denominator, not the share of movers.

Figure labels: US residents aged 18 to 27 / 2024 ACS; 10.67m; domestic address changes; 8.44m; Same-state moves; 2.22m; Interstate moves; 9,870; Arrivals from Puerto Rico; Changed domestic address; 23.9%; Crossed a state line; 5.0%


- [Census 2024 person microdata](https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/)

### 3. Both directions. One balance.

Both bars: zero to 200,000 people.

90% interval: −70,612 to −45,606

Loss across the 90% sampling interval.

Census 2024 person microdata, Oftu weighted estimates. Inflow and outflow refer only to the 50 states and DC. Net uncertainty is calculated jointly with 80 replicate person weights. Sampling intervals do not cover every source of error. Fixed scales remain the same when changing states; this is not a rank of statistically distinct winners.

Figure labels: Interstate movement / people aged 18 to 27 / 2024 ACS; California; Arrived from another state or DC; 128,718; Left for another state or DC; 186,827; Net interstate change; −58,109; −80,000; 0; +80,000

- Choose a stateAlabamaAlaskaArizonaArkansasCaliforniaColoradoConnecticutDelawareWashington, DCFloridaGeorgiaHawaiiIdahoIllinoisIndianaIowaKansasKentuckyLouisianaMaineMarylandMassachusettsMichiganMinnesotaMississippiMissouriMontanaNebraskaNevadaNew HampshireNew JerseyNew MexicoNew YorkNorth CarolinaNorth DakotaOhioOklahomaOregonPennsylvaniaRhode IslandSouth CarolinaSouth DakotaTennesseeTexasUtahVermontVirginiaWashingtonWest VirginiaWisconsinWyoming: CA; options: Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Washington, DC, Florida, Georgia, Hawaii, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin, Wyoming

- [Census 2024 person microdata](https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/)

### 4. Two observations. No measured turnaround.

The date, the age group and the thing being counted all changed.

Weighted resident estimates

Both bars: zero to 200,000 people.

90% interval: −70,612 to −45,606

Census 2024 person microdata, Oftu calculation. Current residence compared with one year before interview throughout the survey year.

Gen Z net equipment gains

After Texas

Ranks, not volumes.

U-Haul, July 29, 2026. One-way equipment movements. Birth-year grouping, not the same age-at-interview band. Underlying state volumes are not reported.

People / bookingsDifferent observed populations.

Survey year / equipment windowDifferent observation periods.

Age at interview / birth yearsDifferent group definitions.

Figure labels: Different observations / no comparable population time series; Census / 2024 ACS; People aged 18 to 27; Net interstate change; −58,109; −80,000; 0; +80,000; U-Haul / July 2025 to June 2026; Customer equipment transactions; California; No. 2


- [Census 2024 person microdata](https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/)
- [U-Haul, July 29, 2026](https://www.uhaul.com/Articles/About/2026-U-Haul-Midyear-Migration-Trends-NATIONAL-REPORT-41817/)

### 5. The name on the booking. The life in the boxes.

The customer can be Gen X.

The relocated person can be Gen Z.

U-Haul’s report notes college-town patterns among Gen X contract holders that appear to reflect parents helping students. This example is a possible coverage mechanism, not a measured correction factor.

Figure labels: Illustrative mechanism / a booking is not every mover; A parent books; A student moves


- [U-Haul’s report](https://www.uhaul.com/Articles/About/2026-U-Haul-Midyear-Migration-Trends-NATIONAL-REPORT-41817/)

### 6. A life can changewithout crossing a state line.

Census 2024 person microdata, Oftu calculation. Shared zero-based scale, zero to 900,000 people. Same-state moves exceed interstate departures by more than four times. The records do not establish housing costs as the reason.

Figure labels: California / residents aged 18 to 27 / 2024 ACS; Changed home within California; 831,175; Left California for another state or DC; 186,827


- [Census 2024 person microdata](https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/)

### 7. A person arrives. What housing do they need?

enrolled in college when surveyed

Group-quarters accommodation.

A person joins an existing household.

A new occupied household may form.

Census 2024 person microdata, Oftu estimate of undergraduate or graduate/professional enrollment. Enrollment is not a verified reason for moving. Accommodation diagrams explain mechanisms; their sizes are not measured floor areas or shares of demand.

Figure labels: Interstate movers aged 18 to 27 / 2024 ACS; 40.3%; A dormitory bed; A shared home; A separate home; Architectural cutaways of a dormitory room, a shared apartment and a separate apartment.


- [Census 2024 person microdata](https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/)

### 8. A higher housing bill. A different bargain.

Take-home pay

$4,000

Housing

$1,200

Other monthly costs

$0

$5,000

$2,000

Invented inputs, not wages or rents in two cities. The reference is $4,000 income less $1,200 housing, with other costs omitted. The experiment deducts housing and optional other monthly costs. Moving cash, job risk and family support are not modeled. A positive remainder is not proof of affordability.

Figure labels: Interactive / invented monthly budgets / USD; The reference; Shared scale: $0 to $10,000. Costs may exceed income.; Left after these costs; Your experiment; $5,000; $2,000; $0

- Take-home pay$5,000: 5000 (allowed range 1000 to 10000)
- Take-home pay, numeric value: 5000 (allowed range 1000 to 10000)
- Housing cost$2,000: 2000 (allowed range 0 to 8000)
- Housing cost, numeric value: 2000 (allowed range 0 to 8000)
- Other monthly costs$0: 0 (allowed range 0 to 5000)
- Other monthly costs, numeric value: 0 (allowed range 0 to 5000)


### 9. The placeyou do not leave.

Interstate arrows cannot describe everyone’s housing future.

lived within 100 miles of where they grew up

People born from 1984 to 1992.

Census and Harvard research, July 2022. Two observed life stages in earlier cohorts, not a measure of current Gen Z moves or constraints. The line joins observed ages, not a measured migration route.

Figure labels: An older research lens / Census and Harvard / July 2022; About 80%; Age 16; Age 26


- [Census and Harvard research, July 2022](https://www2.census.gov/ces/wp/2022/CES-WP-22-27.pdf)

### 10. An entry point. And a place to stay?

A deposit, a bedroom, a job start.

More space, a partner, an independent home.

A workable next stage of life.

The stages are schematic, not measured probabilities or a cohort-retention forecast. Comparable people would need to be followed over time to test whether a place recruits people at one stage and loses them at another.

Figure labels: A hypothesis to test / not established by these migration sources; 01; Arrive; 02; Form a household; 03; Remain



### 11. The same300 people. More homes.

No one arrived. No one left. Household size changed.

3 people per home

2.50 people per home

Accounting example, not a California forecast. Homes = 300 adults ÷ people per occupied home. Vacancies, group quarters and home suitability are excluded. A partial last mark represents a fractional accounting unit; these are not records of actual homes.

Figure labels: Interactive / illustrative household experiment; 100 homes; 120 homes; +20 home equivalents; 2.50

- People per occupied home2.50: 2.5 (allowed range 2 to 4)
- People per occupied home, numeric value: 2.5 (allowed range 2 to 4)


### 12. A vacant house. Not always an available home.

Vacancy alone does not establish an affordable room near a university.

Location, size, condition, price and a workable lease must meet.

A mechanism, not a finding about a particular neighborhood. Subdivision or sharing requires a workable physical, legal and financial arrangement. No likelihood is encoded by the two drawings.

Figure labels: Illustrative accommodation mismatch / no local forecast; A three-bedroom house; A suitable address



### 13. Keep the missing steps.

01Observed movementPeople, households or equipment?

People, households or equipment?

02Relevant householdsNew households or people joining existing homes?

New households or people joining existing homes?

03Suitable housingWhat location and dwelling type?

What location and dwelling type?

04Feasible budgetCan they enter and stay?

Can they enter and stay?

05A particular propertyWhat competing supply and transaction conditions?

What competing supply and transaction conditions?

An analytical checklist, not a calibrated Oftu forecasting system. Each arrow is a question to investigate, not an assumed conversion rate.

Figure labels: Before a demographic signal becomes a housing forecast; Keep the missing steps.; 01; Observed movement; 02; Relevant households; 03; Suitable housing; 04; Feasible budget; 05; A particular property



## Downloadable chart and study data

These are the same exports as the article, using its initial interactive settings. The columns preserve their units, source notes and distinctions between reported evidence and modeled scenarios.

[Download CSV](https://oftu.com/insights/is-gen-z-leaving-california-or-moving-there/data.csv?study=national)

| metric | estimate | lower_90 | upper_90 | moe_90 | unit | age_band | survey | source |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| population | 44644605 | 44566444.25717232 | 44722765.74282768 | 78160.74282768727 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| same_house | 33351887 | 33247988.53082303 | 33455785.46917697 | 103898.46917697083 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| all_address_moves | 11292718 | 11203294.361138912 | 11382141.638861088 | 89423.63886108763 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| domestic_address_moves | 10673613 | 10583228.670112869 | 10763997.329887131 | 90384.32988713129 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| within_state | 8440357 | 8364104.115147216 | 8516609.884852784 | 76252.88485278348 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| interstate | 2223386 | 2183043.815867983 | 2263728.184132017 | 40342.18413201698 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| from_puerto_rico | 9870 | 7631.015644675024 | 12108.984355324976 | 2238.984355324976 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| from_abroad | 619105 | 599577.388901085 | 638632.611098915 | 19527.611098915044 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| interstate_enrolled_college | 896771 | 874234.2797932109 | 919307.7202067891 | 22536.72020678913 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| all_address_moves_share_of_age_population | 25.29469798198461 | 25.100038235891255 | 25.489357728077962 | 0.1946597460933542 | percent | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| domestic_address_moves_share_of_age_population | 23.907957075664573 | 23.709321392561368 | 24.10659275876778 | 0.198635683103205 | percent | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| within_state_share_of_age_population | 18.905659485619818 | 18.740622296268032 | 19.070696674971604 | 0.16503718935178457 | percent | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| interstate_share_of_age_population | 4.980189655614604 | 4.8887837355652835 | 5.071595575663924 | 0.09140592004932015 | percent | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| from_puerto_rico_share_of_age_population | 0.02210793443015119 | 0.017088645841834886 | 0.027127223018467497 | 0.0050192885883163065 | percent | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| from_abroad_share_of_age_population | 1.3867409063200358 | 1.3433725330424897 | 1.4301092795975818 | 0.04336837327754615 | percent | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| college_enrollment_share_of_interstate_movers | 40.333572308182205 | 39.54049172355754 | 41.126652892806874 | 0.7930805846246659 | percent | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |

[Download CSV](https://oftu.com/insights/is-gen-z-leaving-california-or-moving-there/data.csv?study=states)

| state | metric | estimate | lower_90 | upper_90 | moe_90 | unit | age_band | survey | source |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Alabama | inflow | 34345 | 30137.034164903278 | 38552.96583509672 | 4207.965835096722 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Alabama | outflow | 31023 | 26935.2961426401 | 35110.7038573599 | 4087.7038573598993 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Alabama | net | 3322 | -2876.951318297817 | 9520.951318297817 | 6198.951318297817 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Alabama | within_state | 131463 | 122398.87985157433 | 140527.1201484257 | 9064.120148425674 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Alaska | inflow | 7615 | 5326.014478124916 | 9903.985521875084 | 2288.985521875084 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Alaska | outflow | 13684 | 10501.150419739019 | 16866.84958026098 | 3182.849580260981 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Alaska | net | -6069 | -9925.150934753949 | -2212.849065246051 | 3856.150934753949 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Alaska | within_state | 17609 | 13599.190906818007 | 21618.809093181993 | 4009.809093181994 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Arizona | inflow | 63968 | 57226.72559321886 | 70709.27440678114 | 6741.274406781147 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Arizona | outflow | 51375 | 45861.6319169453 | 56888.3680830547 | 5513.3680830547 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Arizona | net | 12593 | 4365.770858246304 | 20820.229141753698 | 8227.229141753696 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Arizona | within_state | 211733 | 200233.9499449935 | 223232.0500550065 | 11499.0500550065 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Arkansas | inflow | 20565 | 17094.79313618842 | 24035.20686381158 | 3470.2068638115798 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Arkansas | outflow | 17254 | 14005.657669679618 | 20502.34233032038 | 3248.342330320382 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Arkansas | net | 3311 | -1843.38351579738 | 8465.38351579738 | 5154.38351579738 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Arkansas | within_state | 80569 | 73627.25126758411 | 87510.74873241589 | 6941.748732415883 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| California | inflow | 128718 | 120154.36422453422 | 137281.63577546578 | 8563.635775465786 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| California | outflow | 186827 | 177302.89668905834 | 196351.10331094166 | 9524.103310941668 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| California | net | -58109 | -70612.33469474185 | -45605.66530525816 | 12503.33469474184 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| California | within_state | 831175 | 807257.2939015876 | 855092.7060984124 | 23917.70609841235 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Colorado | inflow | 61576 | 55986.990391253006 | 67165.009608747 | 5589.0096087469965 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Colorado | outflow | 65007 | 59233.4212031359 | 70780.57879686411 | 5773.578796864104 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Colorado | net | -3431 | -11905.441718692447 | 5043.4417186924475 | 8474.441718692447 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Colorado | within_state | 198012 | 187240.99769178333 | 208783.00230821667 | 10771.002308216666 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Connecticut | inflow | 27203 | 23904.932611995322 | 30501.067388004678 | 3298.0673880046784 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Connecticut | outflow | 33264 | 28829.64477251512 | 37698.355227484884 | 4434.355227484882 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Connecticut | net | -6061 | -11971.00379616418 | -150.9962038358217 | 5910.003796164178 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Connecticut | within_state | 64539 | 58264.83319131855 | 70813.16680868145 | 6274.1668086814525 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Delaware | inflow | 7364 | 5079.873369169574 | 9648.126630830426 | 2284.1266308304253 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Delaware | outflow | 11402 | 8977.526444955267 | 13826.473555044733 | 2424.473555044734 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Delaware | net | -4038 | -7240.351375534859 | -835.6486244651414 | 3202.3513755348586 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Delaware | within_state | 26048 | 21676.211638198707 | 30419.788361801293 | 4371.788361801295 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Washington, DC | inflow | 29198 | 25416.75646852785 | 32979.24353147215 | 3781.243531472153 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Washington, DC | outflow | 15293 | 13329.548231840161 | 17256.45176815984 | 1963.4517681598395 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Washington, DC | net | 13905 | 9310.178856281509 | 18499.82114371849 | 4594.821143718491 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Washington, DC | within_state | 19787 | 15740.875690644501 | 23833.1243093555 | 4046.1243093554976 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Florida | inflow | 116345 | 108758.32094763748 | 123931.67905236252 | 7586.679052362519 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Florida | outflow | 118030 | 108508.17256580282 | 127551.82743419718 | 9521.827434197177 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Florida | net | -1685 | -12988.738908319838 | 9618.738908319838 | 11303.738908319838 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Florida | within_state | 512538 | 492087.52182777994 | 532988.47817222 | 20450.478172220082 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Georgia | inflow | 63339 | 57821.37698295974 | 68856.62301704027 | 5517.623017040264 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Georgia | outflow | 63827 | 58138.057255210115 | 69515.94274478989 | 5688.942744789888 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Georgia | net | -488 | -8092.563248118855 | 7116.563248118855 | 7604.563248118855 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Georgia | within_state | 287711 | 274500.4076327387 | 300921.5923672613 | 13210.592367261299 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Hawaii | inflow | 18448 | 15292.95657688856 | 21603.04342311144 | 3155.0434231114396 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Hawaii | outflow | 14710 | 11245.878535921229 | 18174.121464078773 | 3464.1214640787716 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Hawaii | net | 3738 | -1292.5580908548309 | 8768.55809085483 | 5030.558090854831 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Hawaii | within_state | 24579 | 21071.48837082701 | 28086.51162917299 | 3507.5116291729887 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Idaho | inflow | 23968 | 20162.523507389316 | 27773.476492610684 | 3805.4764926106823 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Idaho | outflow | 20805 | 17004.31628333516 | 24605.68371666484 | 3800.683716664839 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Idaho | net | 3163 | -2554.0531613199782 | 8880.053161319978 | 5717.053161319978 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Idaho | within_state | 67541 | 61039.13093457004 | 74042.86906542996 | 6501.869065429956 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Illinois | inflow | 83930 | 76396.22719329469 | 91463.77280670531 | 7533.772806705317 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Illinois | outflow | 103961 | 95417.06051206331 | 112504.93948793669 | 8543.939487936683 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Illinois | net | -20031 | -30675.68113188595 | -9386.31886811405 | 10644.68113188595 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Illinois | within_state | 269972 | 257002.70892020236 | 282941.29107979767 | 12969.291079797646 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Indiana | inflow | 45526 | 40524.108209847625 | 50527.891790152375 | 5001.891790152377 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Indiana | outflow | 49873 | 43572.40259986394 | 56173.59740013606 | 6300.597400136058 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Indiana | net | -4347 | -12053.328376581645 | 3359.328376581645 | 7706.328376581645 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Indiana | within_state | 208172 | 196494.9646496094 | 219849.0353503906 | 11677.03535039058 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Iowa | inflow | 25167 | 21032.666871434252 | 29301.333128565748 | 4134.333128565749 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Iowa | outflow | 23809 | 20262.633778560088 | 27355.366221439912 | 3546.3662214399124 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Iowa | net | 1358 | -4387.553659912268 | 7103.553659912268 | 5745.553659912268 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Iowa | within_state | 108733 | 100460.12598770778 | 117005.87401229222 | 8272.87401229222 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Kansas | inflow | 27889 | 23756.28193315782 | 32021.71806684218 | 4132.718066842178 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Kansas | outflow | 32751 | 27921.624924442087 | 37580.37507555791 | 4829.375075557913 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Kansas | net | -4862 | -10969.049251366101 | 1245.049251366101 | 6107.049251366101 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Kansas | within_state | 94773 | 88095.50818147871 | 101450.49181852129 | 6677.491818521289 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Kentucky | inflow | 25859 | 21297.507128048976 | 30420.492871951024 | 4561.4928719510235 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Kentucky | outflow | 21221 | 17800.715541121615 | 24641.284458878385 | 3420.284458878384 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Kentucky | net | 4638 | -1324.3228667399417 | 10600.322866739942 | 5962.322866739942 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Kentucky | within_state | 117452 | 109651.37367965013 | 125252.62632034987 | 7800.626320349861 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Louisiana | inflow | 19387 | 16095.16958305831 | 22678.830416941688 | 3291.8304169416897 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Louisiana | outflow | 24726 | 21193.074148438896 | 28258.925851561104 | 3532.9258515611036 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Louisiana | net | -5339 | -10116.965082650902 | -561.0349173490968 | 4777.965082650903 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Louisiana | within_state | 107878 | 99477.69943935167 | 116278.30056064833 | 8400.300560648337 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Maine | inflow | 9438 | 7706.7682062401345 | 11169.231793759865 | 1731.231793759865 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Maine | outflow | 12012 | 8990.005046003344 | 15033.994953996656 | 3021.994953996656 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Maine | net | -2574 | -6434.845611457256 | 1286.845611457256 | 3860.845611457256 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Maine | within_state | 25067 | 20604.367092910565 | 29529.632907089435 | 4462.632907089434 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Maryland | inflow | 48673 | 43471.14442694376 | 53874.85557305624 | 5201.855573056239 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Maryland | outflow | 56941 | 51692.67851819187 | 62189.32148180813 | 5248.321481808136 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Maryland | net | -8268 | -15454.256295861218 | -1081.7437041387811 | 7186.256295861219 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Maryland | within_state | 104764 | 96158.45138301159 | 113369.54861698841 | 8605.548616988417 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Massachusetts | inflow | 61079 | 56004.42982143007 | 66153.57017856993 | 5074.57017856993 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Massachusetts | outflow | 71117 | 65464.30163691169 | 76769.6983630883 | 5652.69836308831 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Massachusetts | net | -10038 | -17957.55218656396 | -2118.4478134360397 | 7919.55218656396 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Massachusetts | within_state | 152409 | 144821.1620365992 | 159996.8379634008 | 7587.837963400791 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Michigan | inflow | 42392 | 37225.991755562136 | 47558.008244437864 | 5166.008244437866 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Michigan | outflow | 47667 | 42489.739740073186 | 52844.260259926814 | 5177.2602599268175 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Michigan | net | -5275 | -12817.255504259982 | 2267.2555042599815 | 7542.2555042599815 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Michigan | within_state | 279408 | 267707.4170497653 | 291108.5829502347 | 11700.582950234682 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Minnesota | inflow | 33195 | 28053.870562469638 | 38336.12943753036 | 5141.129437530361 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Minnesota | outflow | 47022 | 40868.95937673109 | 53175.04062326891 | 6153.040623268914 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Minnesota | net | -13827 | -21598.905718755777 | -6055.094281244223 | 7771.905718755777 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Minnesota | within_state | 174296 | 162449.4679359532 | 186142.5320640468 | 11846.532064046793 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Mississippi | inflow | 17789 | 15163.550522095313 | 20414.449477904687 | 2625.449477904688 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Mississippi | outflow | 16865 | 13411.723959746521 | 20318.27604025348 | 3453.276040253479 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Mississippi | net | 924 | -3222.253109091539 | 5070.253109091539 | 4146.253109091539 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Mississippi | within_state | 65923 | 59979.03236937555 | 71866.96763062445 | 5943.967630624452 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Missouri | inflow | 52312 | 47182.09057805416 | 57441.90942194584 | 5129.909421945845 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Missouri | outflow | 38249 | 33340.29898274439 | 43157.70101725561 | 4908.701017255608 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Missouri | net | 14063 | 6779.1626616574895 | 21346.83733834251 | 7283.8373383425105 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Missouri | within_state | 176539 | 165856.91915382084 | 187221.08084617916 | 10682.080846179151 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Montana | inflow | 13984 | 10677.271916668986 | 17290.728083331014 | 3306.7280833310137 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Montana | outflow | 13676 | 10756.008902378511 | 16595.99109762149 | 2919.991097621489 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Montana | net | 308 | -4075.528746960461 | 4691.528746960461 | 4383.528746960461 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Montana | within_state | 38402 | 33592.49945925879 | 43211.50054074121 | 4809.500540741211 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Nebraska | inflow | 19167 | 15030.165525743749 | 23303.83447425625 | 4136.834474256252 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Nebraska | outflow | 16455 | 13073.418668383089 | 19836.58133161691 | 3381.581331616911 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Nebraska | net | 2712 | -2888.423651340584 | 8312.423651340585 | 5600.423651340584 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Nebraska | within_state | 69743 | 62964.11166800181 | 76521.8883319982 | 6778.888331998193 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Nevada | inflow | 26546 | 22391.290142019992 | 30700.709857980008 | 4154.709857980007 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Nevada | outflow | 21634 | 18241.41060921455 | 25026.58939078545 | 3392.589390785451 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Nevada | net | 4912 | -592.9379859096734 | 10416.937985909673 | 5504.937985909673 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Nevada | within_state | 65643 | 59556.74219772425 | 71729.25780227575 | 6086.257802275755 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New Hampshire | inflow | 14481 | 11656.154170330485 | 17305.845829669513 | 2824.845829669515 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New Hampshire | outflow | 14915 | 12440.026294228057 | 17389.973705771943 | 2474.973705771942 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New Hampshire | net | -434 | -4504.068377858811 | 3636.068377858811 | 4070.068377858811 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New Hampshire | within_state | 24061 | 20400.791032030582 | 27721.208967969418 | 3660.208967969418 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New Jersey | inflow | 36422 | 32266.888967609288 | 40577.11103239071 | 4155.111032390711 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New Jersey | outflow | 73978 | 67089.48386453839 | 80866.51613546161 | 6888.516135461614 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New Jersey | net | -37556 | -44865.1526433115 | -30246.8473566885 | 7309.152643311501 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New Jersey | within_state | 123859 | 114525.46656710333 | 133192.5334328967 | 9333.533432896675 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New Mexico | inflow | 16184 | 13130.734802954179 | 19237.26519704582 | 3053.26519704582 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New Mexico | outflow | 13202 | 10332.2708142542 | 16071.7291857458 | 2869.7291857457994 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New Mexico | net | 2982 | -1294.6313459389385 | 7258.6313459389385 | 4276.6313459389385 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New Mexico | within_state | 45103 | 38896.5065591169 | 51309.4934408831 | 6206.493440883105 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New York | inflow | 125429 | 117943.05720792807 | 132914.94279207193 | 7485.942792071935 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New York | outflow | 117368 | 109230.41782885627 | 125505.58217114373 | 8137.5821711437275 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New York | net | 8061 | -2976.7614117645026 | 19098.761411764503 | 11037.761411764503 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| New York | within_state | 375903 | 361889.7584757177 | 389916.2415242823 | 14013.241524282308 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| North Carolina | inflow | 81749 | 74669.1548673947 | 88828.8451326053 | 7079.845132605304 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| North Carolina | outflow | 77269 | 70071.07851768339 | 84466.92148231661 | 7197.921482316614 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| North Carolina | net | 4480 | -6720.090734605903 | 15680.090734605903 | 11200.090734605903 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| North Carolina | within_state | 295680 | 282445.3687357948 | 308914.6312642052 | 13234.631264205193 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| North Dakota | inflow | 14149 | 11380.523865821893 | 16917.476134178105 | 2768.4761341781063 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| North Dakota | outflow | 7467 | 5573.382487879349 | 9360.61751212065 | 1893.6175121206502 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| North Dakota | net | 6682 | 3284.7265606671795 | 10079.273439332821 | 3397.2734393328205 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| North Dakota | within_state | 28729 | 23345.747704287747 | 34112.25229571226 | 5383.252295712254 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Ohio | inflow | 63770 | 58586.527879778725 | 68953.47212022128 | 5183.472120221276 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Ohio | outflow | 58007 | 52938.573231026465 | 63075.426768973535 | 5068.426768973534 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Ohio | net | 5763 | -1071.7178822577425 | 12597.717882257743 | 6834.7178822577425 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Ohio | within_state | 361285 | 347077.2961345394 | 375492.7038654606 | 14207.70386546059 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Oklahoma | inflow | 30797 | 26503.202043027057 | 35090.79795697294 | 4293.797956972941 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Oklahoma | outflow | 25847 | 22006.995508758 | 29687.004491242 | 3840.0044912420026 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Oklahoma | net | 4950 | -20.716631706262888 | 9920.716631706262 | 4970.716631706263 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Oklahoma | within_state | 137886 | 128308.78038185187 | 147463.2196181481 | 9577.219618148121 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Oregon | inflow | 32917 | 28631.17725311026 | 37202.82274688974 | 4285.8227468897385 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Oregon | outflow | 31576 | 27732.45755280334 | 35419.54244719666 | 3843.542447196661 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Oregon | net | 1341 | -4528.5246360037545 | 7210.5246360037545 | 5869.5246360037545 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Oregon | within_state | 115030 | 108035.32536818188 | 122024.67463181812 | 6994.674631818123 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Pennsylvania | inflow | 84499 | 78259.30276478147 | 90738.69723521853 | 6239.6972352185285 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Pennsylvania | outflow | 81971 | 75613.29060931563 | 88328.70939068437 | 6357.709390684372 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Pennsylvania | net | 2528 | -5588.604083408744 | 10644.604083408743 | 8116.604083408744 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Pennsylvania | within_state | 299842 | 286718.9759502233 | 312965.0240497767 | 13123.02404977674 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Rhode Island | inflow | 15771 | 12932.11854584988 | 18609.88145415012 | 2838.88145415012 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Rhode Island | outflow | 10096 | 7740.65903678788 | 12451.34096321212 | 2355.3409632121206 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Rhode Island | net | 5675 | 2222.051583445678 | 9127.948416554322 | 3452.948416554322 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Rhode Island | within_state | 21723 | 17877.387286612346 | 25568.612713387654 | 3845.612713387653 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| South Carolina | inflow | 60223 | 54472.014736783356 | 65973.98526321664 | 5750.985263216643 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| South Carolina | outflow | 37380 | 33656.65796429303 | 41103.34203570697 | 3723.342035706967 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| South Carolina | net | 22843 | 15694.825699342524 | 29991.174300657476 | 7148.174300657476 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| South Carolina | within_state | 132035 | 123377.31465464477 | 140692.68534535525 | 8657.685345355232 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| South Dakota | inflow | 8187 | 6162.570773289728 | 10211.429226710272 | 2024.4292267102721 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| South Dakota | outflow | 10579 | 7946.900942020324 | 13211.099057979676 | 2632.0990579796767 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| South Dakota | net | -2392 | -5601.806160426007 | 817.8061604260079 | 3209.806160426008 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| South Dakota | within_state | 27067 | 23260.088253314603 | 30873.911746685397 | 3806.9117466853963 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Tennessee | inflow | 56701 | 50525.09117686727 | 62876.90882313273 | 6175.908823132734 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Tennessee | outflow | 43164 | 38244.18176672413 | 48083.81823327587 | 4919.818233275875 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Tennessee | net | 13537 | 5510.522386455562 | 21563.477613544437 | 8026.477613544438 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Tennessee | within_state | 177839 | 168564.4559504882 | 187113.5440495118 | 9274.544049511815 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Texas | inflow | 157869 | 147716.48050050548 | 168021.51949949452 | 10152.51949949451 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Texas | outflow | 147149 | 136459.04047283888 | 157838.95952716112 | 10689.959527161107 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Texas | net | 10720 | -2184.8551570882137 | 23624.855157088212 | 12904.855157088214 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Texas | within_state | 939498 | 913615.0075654093 | 965380.9924345907 | 25882.992434590713 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Utah | inflow | 37091 | 32059.449361088322 | 42122.55063891168 | 5031.550638911677 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Utah | outflow | 34785 | 30674.167808886807 | 38895.83219111319 | 4110.832191113193 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Utah | net | 2306 | -3334.054326352938 | 7946.054326352938 | 5640.054326352938 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Utah | within_state | 143281 | 133170.34674261455 | 153391.65325738545 | 10110.653257385438 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Vermont | inflow | 10507 | 7032.839096425944 | 13981.160903574055 | 3474.1609035740557 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Vermont | outflow | 7391 | 5686.877893014265 | 9095.122106985735 | 1704.1221069857347 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Vermont | net | 3116 | -996.2504130263032 | 7228.250413026303 | 4112.250413026303 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Vermont | within_state | 14829 | 11319.408730292977 | 18338.591269707023 | 3509.5912697070225 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Virginia | inflow | 85477 | 80195.22407098852 | 90758.77592901148 | 5281.775929011473 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Virginia | outflow | 75803 | 69057.20792611173 | 82548.79207388827 | 6745.792073888266 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Virginia | net | 9674 | 1184.2959066866606 | 18163.70409331334 | 8489.70409331334 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Virginia | within_state | 208424 | 197365.00737719468 | 219482.99262280532 | 11058.992622805321 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Washington | inflow | 68344 | 61999.49013580127 | 74688.50986419873 | 6344.5098641987315 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Washington | outflow | 56563 | 51198.7436698144 | 61927.2563301856 | 5364.256330185598 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Washington | net | 11781 | 3631.897435879596 | 19930.102564120403 | 8149.102564120404 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Washington | within_state | 200471 | 189003.6641287488 | 211938.3358712512 | 11467.335871251222 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| West Virginia | inflow | 15435 | 12329.101903229115 | 18540.898096770885 | 3105.898096770884 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| West Virginia | outflow | 9769 | 7433.16698837251 | 12104.83301162749 | 2335.83301162749 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| West Virginia | net | 5666 | 1361.7117378016192 | 9970.28826219838 | 4304.288262198381 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| West Virginia | within_state | 35777 | 31129.421977435315 | 40424.57802256468 | 4647.578022564684 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Wisconsin | inflow | 44672 | 36382.07342187482 | 52961.92657812518 | 8289.92657812518 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Wisconsin | outflow | 42099 | 36344.75779843916 | 47853.24220156084 | 5754.242201560841 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Wisconsin | net | 2573 | -8092.94354765672 | 13238.94354765672 | 10665.94354765672 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Wisconsin | within_state | 183069 | 169647.2112715075 | 196490.7887284925 | 13421.788728492516 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Wyoming | inflow | 7727 | 5517.139670034552 | 9936.860329965448 | 2209.8603299654483 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Wyoming | outflow | 6528 | 4812.188350705416 | 8243.811649294585 | 1715.8116492945837 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Wyoming | net | 1199 | -1826.329245117794 | 4224.329245117794 | 3025.329245117794 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |
| Wyoming | within_state | 16488 | 13504.127340145864 | 19471.872659854136 | 2983.8726598541366 | weighted persons | 18-27 | 2024 ACS 1-year PUMS | https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip |

[Download CSV](https://oftu.com/insights/is-gen-z-leaving-california-or-moving-there/data.csv?study=budget&householdSize=2.5&income=5000&housing=2000&other=0)

| metric | reference | experiment | unit | evidence_status |
| --- | --- | --- | --- | --- |
| take_home | 4000 | 5000 | USD per month | illustrative |
| housing | 1200 | 2000 | USD per month | illustrative |
| other_costs | 0 | 0 | USD per month | illustrative |
| remaining | 2800 | 3000 | USD per month | illustrative |
| excluded | Moving cash, employment risk and support network | Not modeled | assumption | illustrative |

[Download CSV](https://oftu.com/insights/is-gen-z-leaving-california-or-moving-there/data.csv?study=households&householdSize=2.5&income=5000&housing=2000&other=0)

| metric | value | unit | evidence_status |
| --- | --- | --- | --- |
| adults | 300 | persons | illustrative |
| baseline_people_per_occupied_home | 3 | persons per home | illustrative |
| baseline_occupied_homes | 100 | homes | illustrative |
| scenario_people_per_occupied_home | 2.5 | persons per home | illustrative |
| scenario_occupied_home_equivalents | 120 | home equivalents | illustrative |
| change_home_equivalents | 20 | home equivalents | illustrative |
| excluded | Vacancies, group quarters and home suitability | assumption | illustrative |

## Sources linked in the article

- [October 11 coverage](https://fortune.com/2026/10/11/gen-z-moving-to-california-uhaul-jobs-housing/)
- [Published study](https://www.lendingtree.com/home/mortgage/gen-zers-moving-study/)
- [Census data](https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/)
- [variable definitions](https://www2.census.gov/programs-surveys/acs/tech_docs/pums/data_dict/PUMS_Data_Dictionary_2024.txt)
- [Public microdata](https://www2.census.gov/programs-surveys/acs/data/pums/2024/1-Year/csv_pus.zip)
- [Census user guide](https://www2.census.gov/programs-surveys/acs/tech_docs/pums/2024ACS_PUMS_User_Guide.pdf)
- [U-Haul’s report](https://www.uhaul.com/Articles/About/2026-U-Haul-Midyear-Migration-Trends-NATIONAL-REPORT-41817/)
- [Census migration guidance](https://www.census.gov/topics/population/migration/guidance/state-to-state-migration-flows.html)
- [ACS residence rules](https://www.census.gov/help/topics/faq.who-counts-as-a-resident-for-the-acs.html)
- [group-quarters guidance](https://www.census.gov/topics/income-poverty/poverty/guidance/group-quarters.html)
- [living-arrangement explanation](https://www.census.gov/library/stories/2024/05/living-arrangements.html)
- [Census and Harvard research, July 2022](https://www2.census.gov/ces/wp/2022/CES-WP-22-27.pdf)
- [PPIC study](https://www.ppic.org/publication/the-economic-milestones-of-young-californians/)
- [PPIC analysis](https://www.ppic.org/blog/has-adding-more-homes-than-people-eased-californias-housing-crisis/)

Canonical article: https://oftu.com/insights/is-gen-z-leaving-california-or-moving-there