House Price Index YoY in the United States decreased to 2.60 percent in June from 2.90 percent in May of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

The residential vacancy rate is the percentage of residential units that are unoccupied, or vacant, in a given year. The U.S. Census Bureau defines occupied housing units as “owner-occupied” or “renter-occupied.” Vacant housing units are not classified by tenure in this way, as they are not occupied by an owner or renter.

The residential vacancy rate serves as an indicator of the condition of the area’s housing market. Low residential vacancy rates indicate that demand for housing is high compared to the housing supply. However, the aggregate residential vacancy rate is lacking in granularity. For example, the housing market for rental units in the area and the market for buying a unit in the same area may be very different, and the aggregate rate will not show those distinct conditions. Furthermore, the vacancy rate may be high, or low, for a variety of reasons. A high vacancy rate may result from a falling population, but it may also result from a recent construction spree that added many units to the total stock.

The residential vacancy rate in Champaign County appears to have fluctuated between 8% and 14% from 2005 through 2022, reaching a peak near 14% in 2019. In 2023, this rate dropped to about 7%, its lowest value since 2005. However, this rate was calculated using the American Community Survey’s (ACS) estimated number of vacant houses per year, which has year-to-year fluctuations that are largely not statistically significant. Thus, we cannot establish a trend for this data.

The residential vacancy rate data shown here was calculated using the estimated total housing units and estimated vacant housing units from the U.S. Census Bureau’s American Community Survey 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Occupancy Status.

Sources: U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (17 October 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (25 September 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (4 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using data.census.gov; (8 June 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table B25002, generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table SB25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table B25002; generated by CCRPC staff; using American FactFinder; (16 March 2016).

Additional affordable dwellings by local authority district, England 1991-92 to 2016-17

This dataset describes the additions to the stock of affordable housing from the period 1991-92 to 2016-17, broken down by local authority district. Note that over that period, there have been numerous changes to the structure of local government, therefore some districts do not have values for the full series of years, only for those years when the corresponding local authority was in operation. Affordable housing is the sum of affordable rent, social rent, intermediate rent and affordable home ownership. Affordable homes are defined in line with the National Planning Policy Framework, published 27 March 2012, as housing units (or traveller pitches and bed spaces when describing a shared dwelling such as a hostel) provided to specified eligible households whose needs are not met by the market. Eligibility may be determined with regard to local authority allocations policies, local incomes and local house prices depending on the type of affordable housing. Affordable housing should include provisions to remain at an affordable price for future eligible households or for the subsidy to be recycled for alternative affordable housing provision. Affordable rented housing is a new form of social housing, introduced in 2011 as the main type of affordable housing supply. It may only be delivered with grant through the Affordable Homes Programme 2011-17 and other associated and subsequent programmes or without grant by local authority and other providers, where a contract or confirmation of the ability to charge an affordable rent is in place. Affordable rented homes are let by local authorities or private registered providers of social housing to households who are eligible for social rented housing. Affordable rent is subject to rent controls that require a rent of up to 80 per cent of the local market rent (including service charges, where applicable). Social rented housing is rented housing owned and managed by local authorities and private registered providers, for which target rents are determined through the national rent regime. It may also include rented housing managed by other persons and provided under equivalent rental arrangements to the above. Intermediate affordable housing is housing at prices and rents above those of social rent but below market price or rents, and which meet the criteria as set out in the definition for affordable housing. These can include equity loan products, shared ownership and intermediate rent. The data in this dataset were derived from Tables 1006C, 1006aC, 1007C and 1008C of the DCLG 'Live statistical tables', available in the form of Excel spreadsheets here. For further guidance see the Affordable Housing Supply: April 2016 to March 2017 England Statistical Release.

Melbourne is currently experiencing a housing bubble (some experts say it may burst soon). Maybe someone can find a trend or give a prediction? Which suburbs are the best to buy in? Which ones are value for money? Where's the expensive side of town? And more importantly where should I buy a 2 bedroom unit?

Content & Acknowledgements

This data was scraped from publicly available results posted every week from Domain.com.au, I've cleaned it as best I can, now it's up to you to make data analysis magic. The dataset includes Address, Type of Real estate, Suburb, Method of Selling, Rooms, Price, Real Estate Agent, Date of Sale and distance from C.B.D.

Some Key Details

Suburb: Suburb

Address: Address

Rooms: Number of rooms

Price: Price in dollars

Method: S - property sold; SP - property sold prior; PI - property passed in; PN - sold prior not disclosed; SN - sold not disclosed; NB - no bid; VB - vendor bid; W - withdrawn prior to auction; SA - sold after auction; SS - sold after auction price not disclosed. N/A - price or highest bid not available.

Type: br - bedroom(s); h - house,cottage,villa, semi,terrace; u - unit, duplex; t - townhouse; dev site - development site; o res - other residential.

SellerG: Real Estate Agent

Date: Date sold

Distance: Distance from CBD

A broad and generalized selection of 2012-2016 US Census Bureau 2016 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.

Since passage of the U.S. Housing Act of 1937, the federal government has provided housing assistance to low-income renters. Most of these housing subsidies were provided under programs administered by the U.S. Department of Housing and Urban Development (HUD) or predecessor agencies. All programs covered in this report provide subsidies that reduce rents for low-income tenants who meet program eligibility requirements. Generally, households pay rent equal to 30 percent of their incomes, after deductions, while the federal government pays the remainder of rent or rental costs. To qualify for a subsidy, an applicant’s income must initially fall below a certain income limit. These income limits are HUD-determined, location specific, and vary by household size. Applicants for housing assistance are usually placed on a waiting list until a subsidized unit becomes available.Assistance provided under HUD programs falls into three categories: public housing, tenant-based, and privately owned, project-based.In public housing, local housing agencies receive allocations of HUD funding to build, operate or make improvements to housing. The housing is owned by the local agencies. Public housing is a form of project-based subsidy because households may receive assistance only if they agree to live at a particular public housing project.Currently, tenant based assistance is the most prevalent form of housing assistance provided. Historically, tenant based assistance began with the Section 8 certificate and voucher programs, which were created in 1974 and 1983, respectively. These programs were replaced by the Housing Choice Voucher program, under legislation enacted in 1998. Tenant based programs allow participants to find and lease housing in the private market. Local public housing agencies (PHAs) and some state agencies serving as PHAs enter into contracts with HUD to administer the programs. The PHAs then enter into contracts with private landlords. The housing must meet housing quality standards and other program requirements. The subsidies are used to supplement the rent paid by low-income households. Under tenant-based programs, assisted households may move and take their subsidy with them. The primary difference between certificates and vouchers is that under certificates, there was a maximum rent which the unit may not exceed. By contrast, vouchers have no specific maximum rent; the low-income household must pay any excess over the payment standard, an amount that is determined locally and that is based on the Fair Market Rent. HUD calculates the Fair Market Rent based on the 40th percentile of the gross rents paid by recent movers for non-luxury units meeting certain quality standards.The third major type of HUD rental assistance is a collection of programs generally referred to as multifamily assisted, or, privately-owned, project-based housing. These types of housing assistance fall under a collection of programs created during the last four decades. What these programs have in common is that they provide rental housing that is owned by private landlords who enter into contracts with HUD in order to receive housing subsidies. The subsidies pay the difference between tenant rent and total rental costs. The subsidy arrangement is termed project-based because the assisted household may not take the subsidy and move to another location. The single largest project-based program was the Section 8 program, which was created in 1974. This program allowed for new construction and substantial rehabilitation that was delivered through a wide variety of financing mechanisms. An important variant of project-based Section 8 was the Loan Management Set Aside (LMSA) program, which was provided in projects financed under Federal Housing Administration (FHA) programs that were not originally intended to provide deep subsidy rental assistance. Projects receiving these LMSA “piggyback” subsidies were developed under the Section 236 program, the Section 221(d)(3) Below Market Interest Rate (BMIR) program, and others that were unassisted when originally developed.Picture of Subsidized Households does not cover other housing

This table shows the consumer price index for all households (CPI), split up into an index for frequent "out-of-pocket" purchases (FROOPP) and less frequent or "non-out-of-pocket" purchased items (non-FROOPP). Frequent purchased items are purchases that are typically done at least monthly. Out-of-pocket purchases are those that are considered to be typically paid for by the consumer directly and actively. This table also includes the monthly and yearly price developments.

The FROOPP and non-FROOPP are special extracts of the CPI. The corresponding CPI weights and prices are used to calculate both indices. The segmentation used is derived from the FROOPP-classification of Eurostat.

Data available from: January 2006 till December 2015

Status of the figures: The figures in this table are final.

Changes as of 18 May 2016 None, this table is stopped.

Changes as of 10 December 2015 On 1 October 2015, the points system for the pricing of rental homes was adjusted by the Dutch national government. As a direct consequence, rental prices of a limited number of dwellings were reduced, which had a downward effect on the average rental price. The effect of this decrease on the rental price indices and imputed rent value could not be determined in time because housing associations announced the impact of rent adjustments only in November. For this reason, the figures of the groups 04100 ‘Actual rentals for housing’ and 04200 ‘Imputed rent value’ over October 2015 have now been adjusted.

The figures of the groups 061100 ‘Pharmaceutical products’, 061200 ‘Other medical products, equipment’, 072200 ‘Fuels and lubricants’ and 083000 ‘Telephone and internet services’ over the months June through September 2015 have been corrected. This has no impact on the headline indices.

When will new figures be published? Not applicable.

Graph and download economic data for Housing Inventory: Active Listing Count in Florida (ACTLISCOUFL) from Jul 2016 to Jul 2025 about active listing, FL, listing, and USA.

House Price Index YoY in Ireland remained unchanged at 7.80 percent in June. This dataset includes a chart with historical data for Ireland Residential Property Prices YoY.

The Consumer price index (CPI) all households, calculated by Statistics Netherlands, measures the average price changes of goods and services purchased by households. The index is an important criterion for inflation, frequently used by trade and industry, employers' organisations, trade unions and government. The index is for instance, used to make adjustments to wages, tax tablesand index-linked rent increases, annuities, etc.

Data available from: January 1996 till December 2015

Status of the figures: The figures in this table are final.

Changes as of 18 May 2016: None, this table is stopped.

Changes from 7 January 2016: New figures added.

Changes from 10 December 2015: On 1 October 2015, the points system for the pricing of rental homes was adjusted by the Dutch national government. As a direct consequence, rental prices of a limited number of dwellings were reduced, which had a downward effect on the average rental price. The effect of this decrease on the rental price indices and imputed rent value could not be determined in time because housing associations announced the impact of rent adjustments only in November. For this reason, the figures of the groups 04100 ‘Actual rentals for housing’ and 04200 ‘Imputed rent value’ over October 2015 have now been adjusted.

The figures of the groups 061100 ‘Pharmaceutical products’, 061200 ‘Other medical products, equipment’, 072200 ‘Fuels and lubricants’ and 083000 ‘Telephone and internet services’ over the months June through September 2015 have been corrected. This has no impact on the headline indices.

The derived CPI decreased by 0.01 index point over August 2015.

When will new figures be published? Not applicable. This table is succeeded by Consumer prices; price index 2015=100. See paragraph 3.