The U.S. housing market continues to evolve, with the median home price forecast to reach ******* U.S. dollars by the second quarter of 2026. This projection comes after a period of significant growth and recent fluctuations, reflecting the complex interplay of economic factors affecting the real estate sector. The rising costs have not only impacted home prices, but also down payments, with the median down payment more than doubling since 2012. Regional variations in housing costs Home prices and down payments vary dramatically across the United States. While the national median down payment stood at approximately ****** U.S. dollars in early 2024, homebuyers in states like California, Massachusetts, and Hawaii faced down payments exceeding ****** U.S. dollars. This disparity highlights the challenges of homeownership in high-cost markets and underscores the importance of location in determining housing affordability. Market dynamics and future outlook The housing market has shown signs of cooling after years of rapid growth, with more modest price increases of *** percent in 2022 and *** percent in 2023. This slowdown can be attributed in part to rising mortgage rates, which have tempered demand. Despite these challenges, most states continued to see year-over-year price growth in the fourth quarter of 2023, with Rhode Island and Vermont leading the pack at over ** percent appreciation. As the market adjusts to new economic realities, potential homebuyers and investors alike will be watching closely for signs of stabilization or renewed growth in the coming years.

Graph and download economic data for All-Transactions House Price Index for the United States (USSTHPI) from Q1 1975 to Q1 2025 about appraisers, HPI, housing, price index, indexes, price, and USA.

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

Annual Housing Unit Estimates for the United States, States, and Counties // Source: U.S. Census Bureau, Population Division // Note: The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 housing units due to the Count Question Resolution program and geographic program revisions. For the housing unit estimates methodology statement, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html.// Each year, the Census Bureau's Population and Housing Unit Estimates Program utilizes current data on new residential construction, placements of manufactured housing, and housing unit loss to calculate change in the housing stock since the most recent decennial census, and produces a time series of housing unit estimates.. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2015) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population and Housing Unit Estimates Program provides additional information including population estimates, historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

Housing Starts in the United States increased to 1321 Thousand units in June from 1263 Thousand units in May of 2025. This dataset provides the latest reported value for - United States Housing Starts - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Housing Inventory: Median Days on Market Month-Over-Month in the United States (MEDDAYONMARMMUS) from Jul 2017 to Jul 2025 about median and USA.

The S&P Case Shiller San Francisco Home Price Index measures changes in the prices of existing single-family homes in San Francisco. The index value was equal to 100 as of January 2000, so if the index value is equal to *** in a given month, for example, it means that the house prices have increased by ** percent since 2000. The value of the S&P Case Shiller San Francisco Home Price Index amounted to nearly ****** in August 2024. That was significantly higher than the national average.

United States Number of Housing Unit: Florida data was reported at 9,441,153.000 Unit in 2017. This records an increase from the previous number of 9,338,805.000 Unit for 2016. United States Number of Housing Unit: Florida data is updated yearly, averaging 8,832,484.000 Unit from Jun 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 9,441,153.000 Unit in 2017 and a record low of 7,340,246.000 Unit in 2000. United States Number of Housing Unit: Florida data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.EB012: Number of Housing Units: By States.

The number of U.S. home sales in the United States declined in 2024, after soaring in 2021. A total of four million transactions of existing homes, including single-family, condo, and co-ops, were completed in 2024, down from 6.12 million in 2021. According to the forecast, the housing market is forecast to head for recovery in 2025, despite transaction volumes expected to remain below the long-term average. Why have home sales declined? The housing boom during the coronavirus pandemic has demonstrated that being a homeowner is still an integral part of the American dream. Nevertheless, sentiment declined in the second half of 2022 and Americans across all generations agreed that the time was not right to buy a home. A combination of factors has led to house prices rocketing and making homeownership unaffordable for the average buyer. A survey among owners and renters found that the high home prices and unfavorable economic conditions were the two main barriers to making a home purchase. People who would like to purchase their own home need to save up a deposit, have a good credit score, and a steady and sufficient income to be approved for a mortgage. In 2022, mortgage rates experienced the most aggressive increase in history, making the total cost of homeownership substantially higher. Are U.S. home prices expected to fall? The median sales price of existing homes stood at 413,000 U.S. dollars in 2024 and was forecast to increase slightly until 2026. The development of the S&P/Case Shiller U.S. National Home Price Index shows that home prices experienced seven consecutive months of decline between June 2022 and January 2023, but this trend reversed in the following months. Despite mild fluctuations throughout the year, home prices in many metros are forecast to continue to grow, albeit at a much slower rate.

Housing Index in the United States decreased to 434.40 points in May from 435.10 points in April of 2025. This dataset provides the latest reported value for - United States House Price Index MoM Change - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

United States Number of Housing Unit: US data was reported at 137,403,460.000 Unit in 2017. This records an increase from the previous number of 136,312,356.000 Unit for 2016. United States Number of Housing Unit: US data is updated yearly, averaging 129,641,395.000 Unit from Jun 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 137,403,460.000 Unit in 2017 and a record low of 116,300,799.000 Unit in 2000. United States Number of Housing Unit: US data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.EB012: Number of Housing Units: By States.

United States Number of Housing Unit: Illinois data was reported at 5,359,557.000 Unit in 2017. This records an increase from the previous number of 5,345,230.000 Unit for 2016. United States Number of Housing Unit: Illinois data is updated yearly, averaging 5,286,315.500 Unit from Jun 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 5,359,557.000 Unit in 2017 and a record low of 4,897,226.000 Unit in 2000. United States Number of Housing Unit: Illinois data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.EB012: Number of Housing Units: By States.

Graph and download economic data for Housing Inventory: Active Listing Count in the United States (ACTLISCOUUS) from Jul 2016 to Jun 2025 about active listing, listing, and USA.

This layer shows Housing Tenure. This is shown by state and county boundaries. This service contains the 2017-2021 release of data from the American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant housing type: owner-occupied, renter-occupied, or other. The size of the symbol represents the total count of housing units. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right.Current Vintage: 2017-2021ACS Table(s): B25010, DP04Data downloaded from: Census Bureau's API for American Community Survey Date of API call: February 16, 2023National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:Boundaries come from the Cartographic Boundaries via US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates, and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico. The Counties (and equivalent) layer contains 3221 records - all counties and equivalent, Washington D.C., and Puerto Rico municipios. See Areas Published. Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells.Margin of error (MOE) values of -555555555 in the API (or "*****" (five asterisks) on data.census.gov) are displayed as 0 in this dataset. The estimates associated with these MOEs have been controlled to independent counts in the ACS weighting and have zero sampling error. So, the MOEs are effectively zeroes, and are treated as zeroes in MOE calculations. Other negative values on the API, such as -222222222, -666666666, -888888888, and -999999999, all represent estimates or MOEs that can't be calculated or can't be published, usually due to small sample sizes. All of these are rendered in this dataset as null (blank) values.

A broad and generalized selection of 2013-2017 US Census Bureau 2017 5-year American Community Survey housing data estimates, obtained via Census API and joined to the appropriate geometry (in this case, New Mexico Census tracts). The selection is not comprehensive, but allows a first-level characterization of housing prices, years of construction, rental information, and occupancy versus vacancy. The determination of which estimates to include was based upon level of interest and providing a manageable dataset for users.The U.S. Census Bureau's American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. The ACS collects long-form-type information throughout the decade rather than only once every 10 years. The ACS combines population or housing data from multiple years to produce reliable numbers for small counties, neighborhoods, and other local areas. To provide information for communities each year, the ACS provides 1-, 3-, and 5-year estimates. ACS 5-year estimates (multiyear estimates) are “period” estimates that represent data collected over a 60-month period of time (as opposed to “point-in-time” estimates, such as the decennial census, that approximate the characteristics of an area on a specific date). ACS data are released in the year immediately following the year in which they are collected. ACS estimates based on data collected from 2009–2014 should not be called “2009” or “2014” estimates. Multiyear estimates should be labeled to indicate clearly the full period of time. While the ACS contains margin of error (MOE) information, this dataset does not. Those individuals requiring more complete data are directed to download the more detailed datasets from the ACS American FactFinder website. This dataset is organized by Census tract boundaries in New Mexico. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

We study the severity of liquidity constraints in the U.S. housing market using alife-cycle model with uninsurable idiosyncratic risks in which houses are illiquid, butagents can extract home equity by refinancing their mortgages. The model implies thatfour-fifths of homeowners are liquidity constrained and willing to pay an average of 13cents to extract an additional dollar of liquidity from their home. Most homeownersvalue liquidity for precautionary reasons, anticipating the possibility of income declinesand the need to make mortgage payments. The model reproduces well the observedresponse of consumption to tax rebates and mortgage relief programs and predicts largewelfare gains from policies aimed at providing temporary liquidity relief to homeowners.

This layer shows occupied housing units broken down by renter-occupied and owner-occupied status.Data is from the US Census American Community Survey (ACS) 5-year estimates.This layer is symbolized to show the percent of occupied housing units that is renter-occupied. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right (in ArcGIS Online). To view only the census tracts that are predominantly in Tempe, add the expression City is Tempe in the map filter settings.A ‘Null’ entry in the estimate indicates that data for this geographic area cannot be displayed because the number of sample cases is too small (per the U.S. Census).Vintage: 2017-2021ACS Table(s): S2502, DP04 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Data Preparation: Data downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryDate of Census update: December 8, 2022National Figures: S2502: data.census.gov; DP04 data.census.gov

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the .Technical Documentation.. section......Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the .Methodology.. section..A processing error was found in the Year Structure Built estimates since data year 2008. For more information, please see the .errata note #110....Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Explanation of Symbols:..An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution..An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution..An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An "(X)" means that the estimate is not applicable or not available...Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2013-2017 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..Telephone service data are not available for certain geographic areas due to problems with data collection of this question that occurred in 2015 and 2016. Both ACS 1-year and ACS 5-year files were affected. It may take several years in the ACS 5-year files until the estimates are available for the geographic areas affected..Households not paying cash rent are excluded from the calculation of median gross rent..Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see .Accuracy of the Data..). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2013-2017 American Community Survey 5-Year Estimates

United States Number of Housing Unit: Minnesota data was reported at 2,437,711.000 Unit in 2017. This records an increase from the previous number of 2,419,560.000 Unit for 2016. United States Number of Housing Unit: Minnesota data is updated yearly, averaging 2,329,371.500 Unit from Jun 2000 (Median) to 2017, with 18 observations. The data reached an all-time high of 2,437,711.000 Unit in 2017 and a record low of 2,073,863.000 Unit in 2000. United States Number of Housing Unit: Minnesota data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.EB012: Number of Housing Units: By States.

Housing Inventory: Median Listing Price in Sandusky, OH (CBSA) was 383500.00000 U.S. $ in May of 2025, according to the United States Federal Reserve. Historically, Housing Inventory: Median Listing Price in Sandusky, OH (CBSA) reached a record high of 448000.00000 in May of 2024 and a record low of 182450.00000 in January of 2017. Trading Economics provides the current actual value, an historical data chart and related indicators for Housing Inventory: Median Listing Price in Sandusky, OH (CBSA) - last updated from the United States Federal Reserve on July of 2025.