This chart shows a planning goal, not a prediction of what will actually get built. It adds up new housing needed for people moving in, forming new households, or moving locally, subtracts empty homes that could fill some of that need, and adds a cushion for normal turnover plus homes lost to teardowns or disrepair. The panel below compares this goal to how much housing survives from each past decade. Every number is rounded to the nearest 50.

A household is "cost-burdened" if it pays 30% or more of its income for housing. This chart shows how many households are cost-burdened today, grouped by income band (AMI means area median income, the midpoint income for the area). Source: Census ACS 2020-2024 and HUD income limits. This count shows today's problem; it does not say how many new homes are needed to fix it.

The Low-Income Housing Tax Credit (LIHTC) is the largest federal program for building affordable rental housing, and the homes it has already built are supply that exists today. Comparing those homes to the households who would qualify shows how far the current stock goes, and the phase-out figure shows how much of it could stop being affordable by 2030.

A household is "cost burdened" when it pays 30% or more of its income on housing. But a high earner can pay that share and still live comfortably. This chart looks at what is left over after housing is paid, called residual income, to tell real hardship apart from a household that is stretched but still managing.

This chart checks whether getting a mortgage is a barrier to buying a home here. If almost every application is approved, financing is not what is stopping people from buying. Source: HMDA (federal mortgage application data collected under the Home Mortgage Disclosure Act). The study area is shown next to the county for comparison.

Before asking what should get built, it helps to see what is already here. Each row is a building type, and the colors show unit sizes from studios on the left to four-bedroom homes on the right, with the count and share of the whole stock on the right. The top row pools every building type so any one form can be compared to the market as a whole.

Building permits show what the market is about to build. Bars show the wider market area. Source: Census Building Permits Survey.

Not every vacant home is one you could move into, and some homes are not in the year-round market at all. Splitting the whole supply this way separates what a mover can actually compete for from the overall vacancy rate, which counts empty homes no one can rent or buy. The benchmark table puts the same rates for the submarket, county, state, and nation on the same scale. Where survey data has been staged for the study, a toggle switches to a second view showing the same vacant units broken out by building type and reason for vacancy.

This shows who owns homes here, using the owner listed on each property record. It starts with single-family houses, since that is where people worry most about investment companies buying homes instead of families. Trusts are counted separately because they are often just estate planning, not investing.

A long commute can mean someone could not find housing they could afford near their job. Each bar shows one group's commuters split into five travel-time ranges, from under 15 minutes to an hour or more. The typical one-way trip and the range covering the middle half of the group's commuters are shown on the right. Source: Census PUMS (5-year microdata, a sample of survey responses).

A job cluster with lots of work but few nearby homes is a gap that new housing could fill. This chart counts homes near each cluster per 1,000 nearby jobs, split into owned and rental blocks, each broken down by bedroom count. Use the dropdown to see one cluster's numbers up close.

The jobs at each cluster only support new housing if the workers holding them can pay for it. This chart takes every worker at each job cluster and converts their own earnings into the rent or purchase price they could afford alone, split into five affordability tiers. The default view is workers commuting 40+ minutes — the long-haul workforce that housing near the cluster could actually capture. Use the chips to focus on one job type.

Net migration is the part of population change caused by people moving in or moving out, not by births or deaths. The bars show people gained or lost each year, and the line shows the rate per 1,000 residents, with the county shown alongside for comparison. Source: Census Bureau Population Estimates Program.

A new development competes for every household moving in this region: people moving in from elsewhere, people moving within the region, and some people who might otherwise have left. Each bar is one of these groups, split by how many bedrooms they need. Source: Census ACS microdata (PUMS), estimated from recent moving patterns.

Movers drive most housing demand, and they often look different from households already settled here. Use the dropdown to choose what to compare — own vs. rent, household size, bedrooms, rent, home value, ownership cost, mortgage status, income, or cost burden — and see it broken out for households moving into the area, moving within it, moving out, and already settled here, all on the same scale. Where a measure cannot be computed for a group, that group's bar is left out and a short note says why.

This chart shows where new residents moved from and where people who left went, using two separate views you switch between (they come from different data sources, so never subtract one from the other). Both views rank shares of the most recent year on the same 0-100% bar scale.

This shows what arriving households can pay each month and how many bedrooms they need. Each table covers one group (renters or buyers): rows are price, columns are bedroom count, and darker cells mean more households.

The gap between the home a household would pick and the home it actually gets shows where the local housing stock falls short. Orange cells are homes more households want; blue cells are homes they end up in instead.

Vacancy is the share of apartments sitting empty. It is the fastest way to see if renters can find a place. A low line means that size of apartment is nearly full. A rising line means new supply is opening up or fewer people want to rent, so more units sit empty. When the bedroom lines split apart, some unit sizes are harder to find than others.

Absorption counts the apartments renters moved into, minus the ones they moved out of. Bars above the zero line mean more units filled than emptied; bars below it mean more emptied than filled, and that is a real result, not an error. The all-units panel shows the overall pace; the smaller panels show which unit sizes that demand lands on. Source: CoStar.

New completions show the supply side of the same market the vacancy and absorption cards measure. Each bar splits that year's completions by bedroom count, so it shows whether new supply is one-bedrooms or family-sized units. Switching geographies shows where in the region the new units actually land. Source: CoStar.

This card shows renters by the kind of building they live in and breaks vacancy out by building type and bedroom count. The other cards in this section only cover apartment buildings tracked by CoStar (a real estate data firm), which is just part of where people actually rent, so this card also compares CoStar's apartment vacancy rate to the Census rate for every rental. When the two vacancy rates disagree, the gap is the rentals CoStar cannot see.

A median only tells you the price in the middle of the market. This chart shows the full range. It sorts every home sale in the market area from cheapest to priciest and breaks that list into deciles: each bar is 10% of homes, cheapest to priciest. It marks the quarter, middle, and three-quarter points so you can see the shape of the curve, not just one number. The line uses recorded sale prices, the actual amount buyers paid, for the most recent year with solid data. Asking prices are shown too, so you can compare what sellers listed against what buyers actually paid. The gap between the low end and the high end shows how much of the market is in reach at any given budget.

Housing need only matters if the market can actually serve it, so this card asks which income levels the for-sale market reaches. Each bar is a year of sales, stacked by the income whose monthly payment could have carried the price, judged against that year's own income limits and mortgage rate at standard 20%-down terms. Only the monthly cost is tested here; whether a household has the down payment saved is a separate question, answered by the down-payment card that follows this one.

This chart divides the price of each home bought here by the income of the person who bought it, using federal mortgage records that carry both numbers on the same loan. A ratio of 4 means the home cost about four years of the buyer's income. Two views: the ratio over time, and the ratio by how much the buyer earned, which shows whether lower-income buyers here are stretching further than higher-income ones. State, regional, and national comparison lines can be turned on. Source: HMDA (federal mortgage data collected under the Home Mortgage Disclosure Act).

The sales card before this one asks what price each income's monthly payment can carry. This card asks the other half of the purchase: who has the cash on the day of sale. For each household, take the top price its monthly budget allows, then ask whether its liquid savings cover the down payment on that price — at 20% down (the standard), or the 5% and 3.5% low-down-payment paths a dropdown offers. The answer is a share of all households in the regional market area, traced across three decades of the Survey of Consumer Finances with today's incomes and prices held fixed, so the line moves only where households' savings did. A toggle breaks the same trend out by income tier. Savings are regionally adjusted with Census SIPP; home equity is not counted, so shares describe first-time buyers.

Each line shows one price range's typical time from listing to sale, month by month. A fast line means buyers are waiting to buy at that price; a line that keeps getting longer means asking prices are running ahead of what buyers will pay. Source: RentCast sale listings.

This chart shows how many new for-sale homes this market needs over five years, split by price band, against how many it would deliver at the pace it has actually been selling. New construction here has been very limited, so the delivery estimate rests on that small record of homes built and sold, not on the number of interested buyers.

The usual homeownership rate counts homes, so it misses adults who live in a home someone else owns, like adult children living with their parents. The second line counts people instead of homes, and it is always the lower of the two. If the gap between the two lines grows, ownership is thinning out among people even while the mix of owned and rented homes stays the same.

When the local share falls below the national line, the gap stands for households that would likely exist here if young adults could afford to form them. The household view counts those missing households; the person view counts the young adults living in someone else's home instead. These are two ways of looking at the same shortage, not two separate totals.

As home prices go up, fewer renters can afford to buy: this chart shows where the market becomes out of reach. The shaded band shows a range at each price: the low edge assumes a 20% down payment (a regular loan), and the high edge assumes a 3.5% down payment (an FHA loan, which lets buyers put down less). Source: recorded home sales and renter household data for this market area. The table below shows the same numbers grouped by income level instead of price.

Senior households hold much of the area's family-sized housing, so how often they move — and whether those moves land in smaller homes — shapes how much of that stock turns over. This card follows the funnel: all households, senior households, the seniors who moved in the last 5 years (each household counted once, however many times it moved), and the share of seniors whose move took them to a home with fewer bedrooms.

This chart tests whether today's owners could still afford their own home at current prices, by income band, and compares that to whether they could afford to rent instead.

This shows who would move in if more homes existed: households paying too much for housing, renters who could afford to buy, adults living with someone else's household, commuters who might relocate here, and senior owners with more bedrooms than they need. Each bar is one price level, and each household is counted once, in the price level it can afford. Senior owners already have a home, so their count is not homes to build; the footnote gives the total without them. This is a standing pool of households, not a yearly number, so it is never combined with mover counts elsewhere in this study.