2016-2020 ACS 5-Year estimates of housing characteristics compiled at the Tract level. These characteristics include Occupancy Status, Tenure By Household Size, Median Number Of Rooms By Tenure, Units In Structure, Tenure by Units In Structure, Tenure By Year Structure Built, Median Year Structure Built By Tenure, Bedrooms, Tenure By Bedrooms, Contract Rent, Median Contract Rent, Bedrooms By Gross Rent, Median Value, Mortgage Status By Median Value (Dollars), and Tenure By Selected Physical And Financial Conditions.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program 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..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.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..Source: U.S. Census Bureau, 2022 American Community Survey 1-Year Estimates.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 ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The 2022 American Community Survey (ACS) data generally reflect the March 2020 Office of Management and Budget (OMB) delineations 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 delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Thumbnail image by Tony Moody.This dataset includes all housing developments approved by the City of Boise’s (“city”) Planning Division since 2020 that are known by the city to have received or are expected to receive support or incentives from a government entity. Each row represents one development. Data may be unavailable for some projects and details are subject to change until construction is complete. Addresses are excluded for projects with fewer than five homes for privacy reasons.

The dataset includes details on the number of “homes” in a development. We use the word "home" to refer to any single unit of housing regardless of size, type, or whether it is rented or owned. For example, a building with 40 apartments counts as 40 homes, and a single detached house counts as one home.

The dataset includes details about the phase of each project. The process for build a new development is as follows: First, one must receive approval from the city’s Planning Division, which is also known as being “entitled.” Next, one must apply for and receive a permit from the city’s Building Division before beginning construction. Finally, once construction is complete and all city inspections have been passed, the building can be occupied.

The dataset also includes data on the affordability level of each development. To receive a government incentive, a developer is typically required to rent or sell a specified number of homes to households that have an income below limits set by the government and their housing cost must not exceed 30% of their income. The federal government determines income limits based on a standard called “area median income.” The city considers housing affordable if is targeted to households earning at or below 80% of the area median income. For a three-person household in Boise, that equates to an annual income of $60,650 and monthly rent or mortgage of $1,516. See Boise Income Guidelines for more details.Project Address(es) – Includes all addresses that are included as part of the development project.Address – The primary address for the development.Parcel Number(s) – The identification code for all parcels of land included in the development.Acreage – The number of acres for the parcel(s) included in the project.Planning Permit Number – The identification code for all permits the development has received from the Planning Division for the City of Boise. The number and types of permits required vary based on the location and type of development.Date Entitled – The date a development was approved by the City’s Planning Division.Building Permit Number – The identification code for all permits the development has received from the city’s Building Division.Date Building Permit Issued – Building permits are required to begin construction on a development.Date Final Certificate of Occupancy Issued – A certificate of occupancy is the final approval by the city for a development, once construction is complete. Not all developments require a certificate of occupancy.Studio – The number of homes in the development that are classified as a studio. A studio is typically defined as a home in which there is no separate bedroom. A single room serves as both a bedroom and a living room.1-Bedroom – The number of homes in a development that have exactly one bedroom.2-Bedroom – The number of homes in a development that have exactly two bedrooms.3-Bedroom – The number of homes in a development that have exactly three bedrooms.4+ Bedroom – The number of homes in a development that have four or more bedrooms.# of Total Project Units – The total number of homes in the development.# of units toward goals – The number of homes in a development that contribute to either the city’s goal to produce housing affordable at or under 60% of area median income, or the city’s goal to create permanent supportive housing for households experiencing homelessness.Rent at or under 60% AMI - The number of homes in a development that are required to be rented at or below 60% of area median income. See the description of the dataset above for an explanation of area median income or see Boise Income Guidelines for more details. Boise defines a home as “affordable” if it is rented or sold at or below 80% of area median income.Rent 61-80% AMI – The number of homes in a development that are required to be rented at between 61% and 80% of area median income. See the description of the dataset above for an explanation of area median income or see Boise Income Guidelines for more details. Boise defines a home as “affordable” if it is rented or sold at or below 80% of area median income.Rent 81-120% AMI - The number of homes in a development that are required to be rented at between 81% and 120% of area median income. See the description of the dataset above for an explanation of area median income or see Boise Income Guidelines for more details.Own at or under 60% AMI - The number of homes in a development that are required to be sold at or below 60% of area median income. See the description of the dataset above for an explanation of area median income or see Boise Income Guidelines for more details. Boise defines a home as “affordable” if it is rented or sold at or below 80% of area median income.Own 61-80% AMI – The number of homes in a development that are required to be sold at between 61% and 80% of area median income. See the description of the dataset above for an explanation of area median income or see Boise Income Guidelines for more details. Boise defines a home as “affordable” if it is rented or sold at or below 80% of area median income.Own 81-120% AMI - The number of homes in a development that are required to be sold at between 81% and 120% of area median income. See the description of the dataset above for an explanation of area median income or see Boise Income Guidelines for more details.Housing Land Trust – “Yes” if a development receives or is expected to receive this incentive. The Housing Land Trust is a model in which the city owns land that it leases to a developer to build affordable housing.City Investment – “Yes” if the city invests funding or contributes land to an affordable development.Zoning Incentive - The city's zoning code provides incentives for developers to create affordable housing. Incentives may include the ability to build an extra floor or be subject to reduced parking requirements. “Yes” if a development receives or is expected to receive one of these incentives.Project Management - The city provides a developer and their design team a single point of contact who works across city departments to simplify the permitting process, and assists the applicants in understanding the city’s requirements to avoid possible delays. “Yes” if a development receives or is expected to receive this incentive.Low-Income Housing Tax Credit (LIHTC) - A federal tax credit available to some new affordable housing developments. The Idaho Housing and Finance Association is a quasi-governmental agency that administers these federal tax credits. “Yes” if a development receives or is expected to receive this incentive.CCDC Investment - The Capital City Development Corp (CCDC) is a public agency that financially supports some affordable housing development in Urban Renewal Districts. “Yes” if a development receives or is expected to receive this incentive. If “Yes” the field identifies the Urban Renewal District associated with the development.City Goal – The city has set goals to produce housing affordable to households at or below 60% of area median income, and to create permanent supportive housing for households experiencing homelessness. This field identifies whether a development contributes to one of those goals.Project Phase - The process for build a new development is as follows: First, one must receive approval from the city’s Planning Division, which is also known as being “entitled.” Next, one must apply for and receive a permit from the city’s Building Division before beginning construction. Finally, once construction is complete and all city inspections have been passed, the building can be occupied.

CD473 - Private Households in Permanent Housing Units by Average Number of Rooms per Household and Persons per Room. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Private Households in Permanent Housing Units by Average Number of Rooms per Household and Persons per Room...

Western Australia: Average Bedrooms Per Dwelling data was reported at 3.300 Unit in 2020. This records a decrease from the previous number of 3.400 Unit for 2018. Western Australia: Average Bedrooms Per Dwelling data is updated yearly, averaging 3.250 Unit from Dec 1995 (Median) to 2020, with 16 observations. The data reached an all-time high of 3.400 Unit in 2018 and a record low of 3.060 Unit in 1995. Western Australia: Average Bedrooms Per Dwelling data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.EB006: Housing Stock.

For the first time since data became available, newly-built houses in Britain have been built with fewer than three bedrooms per house in the 2010s. From the 1930s until the 1980s, the number of bedrooms fluctuated between 3.16 and 3.63 rooms per house, however declining family sizes caused this number to gradually decrease from the 1980s onwards, and in the 2010s the average number of bedrooms has fallen to 2.95 rooms per house.

Median price paid for residential property in England and Wales, by property type and administrative geographies. Quarterly rolling annual data. Formerly HPSSA dataset 9

The American Community Survey (ACS) 5 Year 2016-2020 housing estimate data is a subset of information derived from the following census tables:B25002 - Occupancy Status;B25009 - Tenure By Household Size;B25021 - Median Number Of Rooms By Tenure;B25024 - Units In Structure;B25032 - Tenure by Units In Structure;B25036 - Tenure By Year Structure Built;B25037 - Median Year Structure Built By Tenure;B25041 – Bedrooms;B25042 - Tenure By Bedrooms;B25056 - Contract Rent;B25058 - Median Contract Rent;B25068 - Bedrooms By Gross Rent;B25077 - Median Value;B25097 - Mortgage Status By Median Value (Dollars), and;B25123 - Tenure By Selected Physical And Financial Conditions.To learn more about the American Community Survey (ACS), and associated datasets visit: https://www.census.gov/programs-surveys/acs, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov.

Data Dictionary: DD_ACS 5-Year Housing Estimate Data by County Date of Coverage: 2016-2020

Analysis of ‘E1032 - Private Households in Permanent Housing Units by Average Number of Rooms per Household and Persons per Room 2011 to 2016’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/8db97628-0f0b-4468-abf1-946ed3849fad on 19 January 2022.

--- Dataset description provided by original source is as follows ---

Private Households in Permanent Housing Units by Average Number of Rooms per Household and Persons per Room 2011 to 2016

--- Original source retains full ownership of the source dataset ---

This dataset uses data provided from Washington State’s Housing Market, a publication of the Washington Center for Real Estate Research (WCRER) at the University of Washington.

Median sales prices represent that price at which half the sales in a county (or the state) took place at higher prices, and half at lower prices. Since WCRER does not receive sales data on individual transactions (only aggregated statistics), the median is determined by the proportion of sales in a given range of prices required to reach the midway point in the distribution. While average prices are not reported, they tend to be 15-20 percent above the median.

Movements in sales prices should not be interpreted as appreciation rates. Prices are influenced by changes in cost and changes in the characteristics of homes actually sold. The table on prices by number of bedrooms provides a better measure of appreciation of types of homes than the overall median, but it is still subject to composition issues (such as square footage of home, quality of finishes and size of lot, among others).

There is a degree of seasonal variation in reported selling prices. Prices tend to hit a seasonal peak in summer, then decline through the winter before turning upward again, but home sales prices are not seasonally adjusted. Users are encouraged to limit price comparisons to the same time period in previous years.

The average sales price of new homes in the United States experienced a slight decrease in 2024, dropping to 512,2000 U.S. dollars from the peak of 521,500 U.S. dollars in 2022. This decline came after years of substantial price increases, with the average price surpassing 400,000 U.S. dollars for the first time in 2021. The recent cooling in the housing market reflects broader economic trends and changing consumer sentiment towards homeownership. Factors influencing home prices and affordability The rapid rise in home prices over the past few years has been driven by several factors, including historically low mortgage rates and increased demand during the COVID-19 pandemic. However, the market has since slowed down, with the number of home sales declining by over two million between 2021 and 2023. This decline can be attributed to rising mortgage rates and decreased affordability. The Housing Affordability Index hit a record low of 98.1 in 2023, indicating that the median-income family could no longer afford a median-priced home. Future outlook for the housing market Despite the recent cooling, experts forecast a potential recovery in the coming years. The Freddie Mac House Price Index showed a growth of 6.5 percent in 2023, which is still above the long-term average of 4.4 percent since 1990. However, homebuyer sentiment remains low across all age groups, with people aged 45 to 64 expressing the most pessimistic outlook. The median sales price of existing homes is expected to increase slightly until 2025, suggesting that affordability challenges may persist in the near future.

Private Households in Permanent Housing Units by Average Number of Rooms per Household and Persons per Room by Aggregate Town or Rural Area, Nature of Occupancy, Statistic and CensusYear

View data using web pages

Download .px file (Software required)

This layer shows housing units by year built by tenure (owner or renter). This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released 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 modified every few years to change the top-end and bottom-end categories of the years.This layer is symbolized to show the predominant period that housing units were built in. 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: 2019-2023ACS Table(s): B25034, B25036, B25035, B25037 (Not all lines of ACS table B25036 are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National 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. For more information about ACS layers, visit the FAQ. 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:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. 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 RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).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 file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations: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.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.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

E1033 - Private Households in Permanent Housing Units by Average Number of Rooms per Household and Persons per Room 2011 to 2016. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Private Households in Permanent Housing Units by Average Number of Rooms per Household and Persons per Room 2011 to 2016...

Graph and download economic data for Housing Inventory: Median Home Size in Square Feet in Nevada (MEDSQUFEENV) from Jul 2016 to Feb 2025 about NV, square feet, median, and USA.

Switzerland Average Housing Rental Rate: 5 Rooms data was reported at 1,854.000 CHF in 2016. This records an increase from the previous number of 1,829.000 CHF for 2015. Switzerland Average Housing Rental Rate: 5 Rooms data is updated yearly, averaging 1,845.000 CHF from Dec 2010 (Median) to 2016, with 7 observations. The data reached an all-time high of 1,881.000 CHF in 2014 and a record low of 1,797.000 CHF in 2010. Switzerland Average Housing Rental Rate: 5 Rooms data remains active status in CEIC and is reported by Swiss Federal Statistical Office. The data is categorized under Global Database’s Switzerland – Table CH.EB003: Housing Rental Rate.

Switzerland Average Housing Rental Rate: 2 Rooms data was reported at 1,047.000 CHF in 2016. This records an increase from the previous number of 1,031.000 CHF for 2015. Switzerland Average Housing Rental Rate: 2 Rooms data is updated yearly, averaging 1,033.000 CHF from Dec 2010 (Median) to 2016, with 7 observations. The data reached an all-time high of 1,051.000 CHF in 2014 and a record low of 1,007.000 CHF in 2010. Switzerland Average Housing Rental Rate: 2 Rooms data remains active status in CEIC and is reported by Swiss Federal Statistical Office. The data is categorized under Global Database’s Switzerland – Table CH.EB003: Housing Rental Rate.

VITAL SIGNS INDICATOR List Rents (EC9)

FULL MEASURE NAME List Rents

LAST UPDATED October 2016

DESCRIPTION List rent refers to the advertised rents for available rental housing and serves as a measure of housing costs for new households moving into a neighborhood, city, county or region.

DATA SOURCE real Answers (1994 – 2015) no link

Zillow Metro Median Listing Price All Homes (2010-2016) http://www.zillow.com/research/data/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) List rents data reflects median rent prices advertised for available apartments rather than median rent payments; more information is available in the indicator definition above. Regional and local geographies rely on data collected by real Answers, a research organization and database publisher specializing in the multifamily housing market. real Answers focuses on collecting longitudinal data for individual rental properties through quarterly surveys. For the Bay Area, their database is comprised of properties with 40 to 3,000+ housing units. Median list prices most likely have an upward bias due to the exclusion of smaller properties. The bias may be most extreme in geographies where large rental properties represent a small portion of the overall rental market. A map of the individual properties surveyed is included in the Local Focus section.

Individual properties surveyed provided lower- and upper-bound ranges for the various types of housing available (studio, 1 bedroom, 2 bedroom, etc.). Median lower- and upper-bound prices are determined across all housing types for the regional and county geographies. The median list price represented in Vital Signs is the average of the median lower- and upper-bound prices for the region and counties. Median upper-bound prices are determined across all housing types for the city geographies. The median list price represented in Vital Signs is the median upper-bound price for cities. For simplicity, only the mean list rent is displayed for the individual properties. The metro areas geography rely upon Zillow data, which is the median price for rentals listed through www.zillow.com during the month. Like the real Answers data, Zillow's median list prices most likely have an upward bias since small properties are underrepresented in Zillow's listings. The metro area data for the Bay Area cannot be compared to the regional Bay Area data. Due to afore mentioned data limitations, this data is suitable for analyzing the change in list rents over time but not necessarily comparisons of absolute list rents. Metro area boundaries reflects today’s metro area definitions by county for consistency, rather than historical metro area boundaries.

Due to the limited number of rental properties surveyed, city-level data is unavailable for Atherton, Belvedere, Brisbane, Calistoga, Clayton, Cloverdale, Cotati, Fairfax, Half Moon Bay, Healdsburg, Hillsborough, Los Altos Hills, Monte Sereno, Moranga, Oakley, Orinda, Portola Valley, Rio Vista, Ross, San Anselmo, San Carlos, Saratoga, Sebastopol, Windsor, Woodside, and Yountville.

Inflation-adjusted data are presented to illustrate how rents have grown relative to overall price increases; that said, the use of the Consumer Price Index does create some challenges given the fact that housing represents a major chunk of consumer goods bundle used to calculate CPI. This reflects a methodological tradeoff between precision and accuracy and is a common concern when working with any commodity that is a major component of CPI itself. Percent change in inflation-adjusted median is calculated with respect to the median price from the fourth quarter or December of the base year.

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..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..Source: U.S. Census Bureau, 2018 American Community Survey 1-Year Estimates.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 .ACS Technical Documentation..). The effect of nonsampling error is not represented in these tables..While the 2018 American Community Survey (ACS) data generally reflect the July 2015 Office of Management and Budget (OMB) delineations 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 delineations due to differences in the effective dates of the geographic entities..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..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, or the margin of error associated with a median was larger than the median itself..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....

The files below provide the affordable housing statistics for GLA funded programmes. The GLA is committed to open and transparent reporting and will publish statistics relating to housing delivery in London.

Downloads

• - Monthly GLA housing starts and completions data by programme, tenure and Local Authority, since April 2009.

  Excel or PDF

• - Annual Affordable Housing Additional Information by bedroom size by Local Authority:

  Excel or PDF

  Tables include:

• 1 Completions outturn by bedroom breakdown and borough

• 2 Starts-on-Site outturn by bedroom breakdown and borough

• 3 Completions outturn by bedroom breakdown and programme

• 4 Starts-on-Site outturn by bedroom breakdown and programme

• 5 Rent Completions outturn by bedroom breakdown and borough

• 6 Home Ownership Completions outturn by bedroom breakdown and borough

• 7 Rent Starts-on-Site outturn by bedroom breakdown and borough

• 8 Home Ownership Starts-on-Site outturn by bedroom breakdown and borough

• 9 Average Weekly Rents (all programmes) for completed homes by bedroom breakdown by borough

• 10 Affordable Homes Programme: Average weekly rent as % of market rent for homes completed by bedroom breakdown and borough.

This information supplements previous releases of national housing statistics published by the Homes and Communities Agency (external website).

To see future Publication Dates 2014-15 of the affordable Housing Statistics, please click here.

Annual scheme level data

A data set of scheme completions and starts since April 2011 is available. This data set will be updated on an annual basis.

See some of this data represented in charts and maps using Tableau reporting.

Visit GLA website for more information.

Further to the GLA's scheme of delegations, the Mayor has delegated authority to the Executive Director of Housing and Land to approve engagement with, and allocations to, new and existing housing providers bidding to deliver additional affordable homes in London. The approvals include bids for new schemes in existing programmes and the approval of variations to existing scheme allocations. The Additional Allocations file below lists additional funding approved under this delegation.

AHP Conversions

This report is based on information on conversions provided to the GLA by the Social Housing Regulator, the conversion data is as submitted by providers in their quarterly survey via NROSH+ (a website for all private registered providers except local authority providers to submit their annual data returns required by the Social Housing Regulator).