Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

This table shows the average House Price/Earnings ratio, which is an important indicator of housing affordability. Ratios are calculated by dividing house price by the median earnings of a borough. The Annual Survey of Hours and Earnings (ASHE) is based on a 1 per cent sample of employee jobs. Information on earnings and hours is obtained in confidence from employers. It does not cover the self-employed nor does it cover employees not paid during the reference period. Information is as at April each year. The statistics used are workplace based full-time individual earnings. Pre-2013 Land Registry housing data are for the first half of the year only, so that they are comparable to the ASHE data which are as at April. This is no longer the case from 2013 onwards as this data uses house price data from the ONS House Price Statistics for Small Areas statistical release. Prior to 2006 data are not available for Inner and Outer London. The lowest 25 per cent of prices are below the lower quartile; the highest 75 per cent are above the lower quartile. The "lower quartile" property price/income is determined by ranking all property prices/incomes in ascending order. The 'median' property price/income is determined by ranking all property prices/incomes in ascending order. The point at which one half of the values are above and one half are below is the median. Regional data has not been published by DCLG since 2012. Data for regions has been calculated by the GLA. Data since 2014 has been calculated by the GLA using Land Registry house prices and ONS Earnings data. Link to DCLG Live Tables An interactive map showing the affordability ratios by local authority for 2013, 2014 and 2015 is also available.

FOCUSON**LONDON**2011: HOUSING:A**GROWING**CITY

With the highest average incomes in the country but the least space to grow, demand for housing in London has long outstripped supply, resulting in higher housing costs and rising levels of overcrowding. The pressures of housing demand in London have grown in recent years, in part due to fewer people leaving London to buy homes in other regions. But while new supply during the recession held up better in London than in other regions, it needs to increase significantly in order to meet housing needs and reduce housing costs to more affordable levels.

This edition of Focus on London authored by James Gleeson in the Housing Unit looks at housing trends in London, from the demand/supply imbalance to the consequences for affordability and housing need.

REPORT:

Read the report in PDF format.

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PRESENTATION:

How much pressure is London’s popularity putting on housing provision in the capital? This interactive presentation looks at the effect on housing pressure of demographic changes, and recent new housing supply, shown by trends in overcrowding and house prices. Click on the start button at the bottom of the slide to access.

View Focus on London - Housing: A Growing City on Prezi

HISTOGRAM:

This histogram shows a selection of borough data and helps show areas that are similar to one another by each indicator.

Histogram

MOTION CHART:

This motion chart shows how the relationship, between key housing related indicators at borough level, changes over time.

Motion Chart

MAP:

These interactive borough maps help to geographically present a range of housing data within London, as well as presenting trend data where available.

MAP

DATA:

All the data contained within the Housing: A Growing City report as well as the data used to create the charts and maps can be accessed in this spreadsheet.

FACTS:

Some interesting facts from the data…

● Five boroughs with the highest proportion of households that have lived at their address for less than 12 months in 2009/10:

1. Westminster – 19 per cent
2. Wandsworth – 17 per cent
3. Camden – 16 per cent
4. Lambeth – 14 per cent
5. Southwark – 13 per cent

-31. Harrow – 6 per cent

-32. Havering – 5 per cent

● Five boroughs with the highest percentage point increase between 2004 and 2009 of households in the ‘private rented’ sector:

1. Newham – 17 per cent
2. Greenwich – 11 per cent
3. Enfield – 10 per cent
4. Camden – 9 per cent
5. Harrow – 8 per cent

-32. Islington – 1 per cent

-33. Bexley – 1 per cent

● Five boroughs with the highest percentage difference in median house prices between 2007 Q4 and 2010 Q4:

1. Kensington & Chelsea – 29 per cent
2. Westminster – 19 per cent
3. Camden – 15 per cent
4. Islington – 14 per cent
5. Southwark – 10 per cent

-31. Newham – down 9 per cent

-32. Barking & D’ham – down 9 per cent

The primary data source for these tenure estimates is the Council Tax Register compiled by the City Assessor. Stores, garages and properties relating to hostels and institutions have been excluded from the total stock count. Ownership information from this source relies to a considerable extent on residents notifying the Council that a change of tenure has taken place. Accordingly, the figures contained within this file may differ slightly from other estimates available which make use of additional data sources, such as tenure data from the Housing Benefits system, a housing stock file from the Glasgow Housing Association and the Statutory Register of Private Landlords. These tenure estimates were last undertaken for housing stock as it was in 2018, with the report going to Council committee in 2019. These estimates which are aggregated to neighbourhood level are available at: https://www.glasgow.gov.uk/CHttpHandler.ashx?id=46229&p=0The ownership information from the various data sources does not always agree. This is a particular issue for private renting. For dwellings where the available evidence from the Council Tax Register and the Statutory Register of Private Landlords is not consistent, a more detailed tenure assessment was carried out, using a sample. The proportions for owner occupation and private renting from the sample have been used to estimate the tenure for dwellings where the tenure position is unclear.The owner occupied stock figures include shared ownership and shared equity properties. The social rented stock figures include mid-market rent housing. Housing at full market rent has been classified as private rented stock, irrespective of ownership.

Data relating to the price of houses sold in the Glasgow Area from the years 1991 - 2013.Some elements of the dataset are derived from information produced by Registers of ScotlandCLASS Administrative Classification onlySTNO Street NumberSTnu Street NumberFLATPOSN Flat PositionSTNAME Street NamePOSTCODE Post CodeMONTH OF SALE Month of SaleYEAR OF SALE (CALENDAR) YEAR OF SALE (CALENDAR)YEAR OF SALE (BUSINESS) YEAR OF SALE (BUSINESS)MONTH AND YEAR MONTH AND YEARQUARTER_(CALENDAR) QUARTER_(CALENDAR)ACTUAL PRICE AT POINT OF SALE Actual Price RPI Retail Price Index - Published every month and available for the last 20 yearsDEFLATOR Figure used to to determine change in house prices over time - calculated fromthe Retail Price Index and other dataPRICE CONSTANT AT July 2013 Actual Price multiplied by the Deflator. This is the price if RPI is applied to original sale price - How much would the property be valued at now. ORIGINOFBUY Council area or Country where the buyer comes fromOMIT OR USE Oroginal data also included retail and commercial data. - Not reproduced hereNEWBUILD OR RESALE Is it a newbuild house or a resaleLHF Local Housing Forum Area

This web map service (WMS) depicts estimates of mean values of soil bacteria, invertebrates, carbon, nutrients and pH within selected habitats and parent material characteristics across GB . Estimates were made using CS data using a mixed model approach. The estimated means of habitat/parent material combinations using 2007 data are modelled on dominant habitat and parent material characteristics derived from the Land Cover Map 2007 and Parent Material Model 2009, respectively. Bacteria data is representative of 0 - 15 cm soil depth and includes bacterial community structure as assessed by ordination scores. Invertebrate data is representative of 0 - 8 cm soil depth and includes Total catch, Mite:Springtail ratio, Number of broad taxa and Shannon diversity. Gravimetric moisture content (%) data is representative of 0 - 15 cm soil depth Carbon data is representative of 0-15 cm soil depth and includes Loss-on-ignition (%), Carbon concentration (g kg-1) and Carbon density (t ha-1). Loss-on-ignition was determined by combustion of 10g dry soil at 375 deg C for 16 hours; carbon concentration was estimated by multiplying LOI by a factor of 0.55, and carbon density was estimated by combining carbon concentration with bulk density estimates. Nutrient data is representative of 0 - 15 cm soil depth and includes total nitrogen (N) concentration (%), C:N ratio and Olsen-Phosphorus (mg/kg). pH and bulk density (g cm-3) data is representative of 0 - 15 cm soil depth. Topsoil pH was measured using 10g of field moist soil with 25ml de-ionised water giving a ratio of soil to water of 1:2.5 by weight; bulk density was estimated by making detailed weight measurements throughout the soil processing procedure. Areas, such as urban and littoral rock, are not sampled by CS and therefore have no associated data. Also, in some circumstances sample sizes for particular habitat/parent material combinations were insufficient to estimate mean values.

This web map service presents modelled estimates of soil pH, carbon concentration (g kg-1), nitrogen concentration (% dry weight soil) and invertebrate density (individuals m-2) at 1km2 resolution across Great Britain. A Generalized Additive Model approach was used with Countryside Survey soil data from 2007 and including climate, atmospheric deposition, habitat, soil and spatial predictors. The models are based on data from Countryside Survey sample locations across Great Britain and are representative of 0-8cm soil depth for invertebrates and 0-15 cm soil depth for other variables. The Countryside Survey looks at a range of physical, chemical and biological properties of the topsoil from a representative sample of habitats across the UK. Loss-on-ignition (LOI) was determined by combustion of 10g dry soil at 375 degrees Celsius for 16 hours; carbon concentration was estimated by multiplying LOI by a factor of 0.55. Soil N concentration was determined using a total elemental analyser. Soil pH was measured using 10g of field moist soil with 25ml de-ionised water giving a ratio of soil to water of 1:2.5 by weight. Soil invertebrates were extracted from cores using a dry Tullgren extraction method and enumerated by microscope

PLEASE NOTE: This record has been retired. It has been superseded by: https://environment.data.gov.uk/dataset/4c8981b3-11c1-40ca-b7a2-7c3f45a97397

This dataset is a product of a national assessment of flood risk for England produced using local expertise.

This dataset is produced using the Risk of Flooding from Rivers and Sea data which shows the chance of flooding from rivers and/or the sea, based on cells of 50m. Each cell is allocated one of four flood risk categories, taking into account flood defences and their condition.

This dataset uses OS data to assign one of four flood risk categories to each property, based simply on the category allocated to the cell that the property is in. Individual addresses are not provided, but OS referencing is included to enable the data to be linked to address databases.

This web map service presents modelled estimates of soil pH, carbon concentration (g kg-1), nitrogen concentration (% dry weight soil) and invertebrate density (individuals m-2) at 1km2 resolution across Great Britain. A Generalized Additive Model approach was used with Countryside Survey soil data from 2007 and including climate, atmospheric deposition, habitat, soil and spatial predictors. The models are based on data from Countryside Survey sample locations across Great Britain and are representative of 0-8cm soil depth for invertebrates and 0-15 cm soil depth for other variables. The Countryside Survey looks at a range of physical, chemical and biological properties of the topsoil from a representative sample of habitats across the UK. Loss-on-ignition (LOI) was determined by combustion of 10g dry soil at 375 degrees Celsius for 16 hours; carbon concentration was estimated by multiplying LOI by a factor of 0.55. Soil N concentration was determined using a total elemental analyser. Soil pH was measured using 10g of field moist soil with 25ml de-ionised water giving a ratio of soil to water of 1:2.5 by weight. Soil invertebrates were extracted from cores using a dry Tullgren extraction method and enumerated by microscope

This Web service provides the BGS Thermal Properties (1 km hex grid) dataset as a Web Map Service (WMS). This dataset shows thermal properties relating to bedrock beneath our feet. The information can be used to assess the potential for closed and open loop ground source heat pumps across, or deeper geothermal assessments, across the United Kingdom. The attribution and spatial data underpinning the model are that which is described and shown by Rollin (1987) and Gale (2004, 2005).

The 'Climate Just' Map Tool shows the geography of England’s vulnerability to climate change at a neighbourhood scale.

The Climate Just Map Tool shows which places may be most disadvantaged through climate impacts. It aims to raise awareness about how social vulnerability combined with exposure to hazards, like flooding and heat, may lead to uneven impacts in different neighbourhoods, causing climate disadvantage.

Climate Just Map Tool includes maps on:

• Flooding (river/coastal and surface water)
• Heat
• Fuel poverty.

The flood and heat analysis for England is based on an assessment of social vulnerability in 2011 carried out by the University of Manchester. This has been combined with national datasets on exposure to flooding, using Environment Agency data, and exposure to heat, using UKCP09 data.

Data is available at Middle Super Output Area (MSOA) level across England. Summaries of numbers of MSOAs are shown in the file named Climate Just-LA_summaries_vulnerability_disadvantage_Dec2014.xls

Indicators include:

Climate Just-Flood disadvantage_2011_Dec2014.xlsx

Fluvial flood disadvantage index
Pluvial flood disadvantage index (1 in 30 years)
Pluvial flood disadvantage index (1 in 100 years)
Pluvial flood disadvantage index (1 in 1000 years)

Climate Just-Flood_hazard_exposure_2011_Dec2014.xlsx

Percentage of area at moderate and significant risk of fluvial flooding
Percentage of area at risk of surface water flooding (1 in 30 years)
Percentage of area at risk of surface water flooding (1 in 100 years)
Percentage of area at risk of surface water flooding (1 in 1000 years)

Climate Just-SSVI_indices_2011_Dec2014.xlsx

Sensitivity - flood and heat
Ability to prepare - flood
Ability to respond - flood
Ability to recover - flood
Enhanced exposure - flood
Ability to prepare - heat
Ability to respond - heat
Ability to recover - heat
Enhanced exposure - heat
Socio-spatial vulnerability index - flood
Socio-spatial vulnerability index - heat

Climate Just-SSVI_indicators_2011_Dec2014.xlsx

% children < 5 years old
% people > 75 years old
% people with long term ill-health/disability (activities limited a little or a lot)
% households with at least one person with long term ill-health/disability (activities limited a little or a lot)
% unemployed
% in low income occupations (routine & semi-routine)
% long term unemployed / never worked
% households with no adults in employment and dependent children
Average weekly household net income estimate (equivalised after housing costs) (Pounds)
% all pensioner households
% households rented from social landlords
% households rented from private landlords
% born outside UK and Ireland
Flood experience (% area associated with past events)
Insurance availability (% area with 1 in 75 chance of flooding)
% people with % unemployed
% in low income occupations (routine & semi-routine)
% long term unemployed / never worked
% households with no adults in employment and dependent children
Average weekly household net income estimate (equivalised after housing costs) (Pounds)
% all pensioner households
% born outside UK and Ireland
Flood experience (% area associated with past events)
Insurance availability (% area with 1 in 75 chance of flooding)
% single pensioner households
% lone parent household with dependent children
% people who do not provide unpaid care
% disabled (activities limited a lot)
% households with no car
Crime score (IMD)
% area not road
Density of retail units (count /km2)
% change in number of local VAT-based units
% people with % not home workers
% unemployed
% in low income occupations (routine & semi-routine)
% long term unemployed / never worked
% households with no adults in employment and dependent children
Average weekly household net income estimate (Pounds)
% all pensioner households
% born outside UK and Ireland
Insurance availability (% area with 1 in 75 chance of flooding)
% single pensioner households
% lone parent household with dependent children
% people who do not provide unpaid care
% disabled (activities limited a lot)
% households with no car
Travel time to nearest GP by walk/public transport (mins - representative time)
% of at risk population (no car) outside of 15 minutes by walk/public transport to nearest GP
Number of GPs within 15 minutes by walk/public transport
Number of GPs within 15 minutes by car
Travel time to nearest hospital by walk/public transport (mins - representative time)
Travel time to nearest hospital by car (mins - representative time)
% of at risk population outside of 30 minutes by walk/PT to nearest hospital
Number of hospitals within 30 minutes by walk/public transport
Number of hospitals within 30 minutes by car
% people with % not home workers
Change in median house price 2004-09 (Pounds)
% area not green space
Area of domestic buildings per area of domestic gardens (m2 per m2)
% area not blue space
Distance to coast (m)
Elevation (m)
% households with the lowest floor level: Basement or semi-basement
% households with the lowest floor level: ground floor
% households with the lowest floor level: fifth floor or higher

The Scotland Heat Map provides estimates of annual heat demand for almost 3 million properties in Scotland. Demand is given in kilowatt-hours per year (kWh/yr). Property level estimates can be combined to give values for various geographies. Both domestic and non-domestic properties are included. This dataset gives the density of heat demand of properties for each 2011 Data Zone (in kWh/yr per meter squared). Heat demand is calculated by combining data from a number of sources, ensuring that the most appropriate data available is used for each property. Density of demand is calculated by dividing the total heat demand for all properties in the Data Zone by the area of the Data Zone (in m2). The data can be used by local authorities and others to identify or inform opportunities for low carbon heat projects such as district heat networks. The Scotland Heat Map is produced by the Scottish Government. The most recent version is the Scotland Heat Map 2022, which was released to local authorities in November 2023. More information can be found in the documentation available on the Scottish Government website: https://www.gov.scot/publications/scotland-heat-map-documents/

BGS soil property data layers including parent material, soil texture, group, grain size, thickness and European Soil Bureau description at 1km resolution. A 1:50,000 scale version of this data is available for a licence fee. A parent material is a soil-science name for a weathered rock or deposit from, and within which a soil has formed. In the UK, parent materials provide the basic foundations and building blocks of the soil, influencing their texture, structure, drainage and chemistry. Soils are the result of weathering processes that occur on the Earth’s surface where the atmosphere meets the geosphere and hydrosphere. We live in this ‘critical zone’ relying on our soils to grow our food and sustain the biodiversity and health of our environment.The Soil Parent Material Model details the distribution of physiochemical properties of the weathered and unweathered parent materials of the UK to:facilitate spatial mapping of UK soil propertiesidentify soils and landscapes sensitive to erosionprovide a national overview of our soil resourcedevelop a better understanding of weathering properties and processesFind out more at www.bgs.ac.uk. Contact BGS at bgsdata@bgs.ac.uk if you create something new and innovative that could benefit others.

The non-gas map is a detailed map of Great Britain showing the distribution of properties without a gas grid connection across local authorities, LSOAs (lower-level super output areas) and, for registered users, postcodes. It also provided a wealth of other information about each properties and residents, from the type of house or flat to the type of heating and tenure.

This is a 25m pixel data set representing the land surface of Great Britain, classified into 21 UKCEH land cover classes, based upon Biodiversity Action Plan broad habitats. It is a three-band raster in GeoTiff format, produced by rasterising three properties of the classified land parcels dataset. The first band gives the most likely land cover type; the second band gives the per-parcel probability of the land cover, the third band is a measure of parcel purity. The probability and purity bands (scaled 0 to 100) combine to give an indication of uncertainty. A full description of this and all UKCEH LCM2024 products are available from the LCM2024 product documentation.

This CSV table shows a breakdown of the dwelling stock down to a lower geographic level Lower layer Super Output Area or LSOA, categorised by the property build period and property type. Counts in the tables are rounded to the nearest 10 with those below 5 recorded as negligible and appearing as -. Data on build period, or age of property, has been used to create 12 property build period categories: Pre-1900, 1900-1918, 1919-1929, 1930-1939, 1945-1954, 1955-1964, 1965-1972, 1973-1982, 1983-1992, 1993-1999, 2000-2009, and 2010-2015. Data on property type includes breakdown by bungalow, terraced, flat/maisonette, semi-detached and detached, and by the number of bedrooms. The counts are calculated from domestic property data for England and Wales extracted from the Valuation Office Agencys administrative database on 31 March 2015, and on 1 August 2012 and 31 March 2014. The VOA have published data that shows homes by period built, or type, and council tax band down to MSOA and LSOA level. Rounding: Small differences between the rounding conventions are applied to the 2014 and 2015 statistics. For 2014 The rounding convention applied to the tables: Counts are rounded to the nearest 10 dwellings and counts less than 5 are reported as negligible (-). For 2015 The rounding convention applied to the tables: Counts are rounded to the nearest 10 with counts of zero being reported as "0" and counts fewer than 5 reported as negligible and denoted by "-". National Statistics Postcode Lookup file (NSPL): Different NSPL files have been used for 2014 and 2015 statistics (February 2014 NSLP used February 2015 NSLP used). As a results, postcodes can be moved in different OAs. Further information on NSPL can be found at ONS Property attributes: As part of the day to day VOA work, attributes information can be added (where no information is recorded) and/or changed (existing information is updated). This would result in counts in categories changing. New entries and deletions: New entries into the CT List together with deletions from the CT List will result in changes to counts. New entries could be as a result from splits, mergers, new build but also entries which were not previously in the CT List i.e. a shop is converted in a domestic property. Similarly, deletions could be as a result from splits, mergers, demolitions but also entries no longer domestic properties i.e. a house is converted into a shop (non-domestic property). The map below was created to show the average age of properties at MSOA level.

Landsat TM data has been available since launch of Landsat 4 on 17 July 1982 and Landsat 5 on 1 March 1984. The National Remote Sensing Centre (NRSC) acquired this data from the ESA receiving stations to build up its archive of good quality scenes of the UK. The archive also contains scenes from various countries around the world. The TM data is available in 7 bands. The resolution is 30m for the visible, near and middle infrared bands and 120m for the thermal infrared. The repeat cycle is 16 days.

 The products available from the NRSC are:

   i) B/W print or film of a single band full or 1/4 scene or extract
  ii) TM colour composite print or film of a full scene, 1/4 scene or
 extract
  iii) Digital versions of scenes on CCT, Exabyte or CD-ROM

 Price lists of these products are available on request to the National
 Remote Sensing Centre (NRSC).

The shapefiles contain the classification and locations of each river style determined by the authors. The data were used to characterise the river styles in Bislak River, Philippines. Shapefiles were clipped to the catchment boundary from different national government agencies to produce different thematic maps. Catchment properties such as land use (from the National Mapping and Resource Information Authority (NAMRIA)), geology (from the Mines and Geosciences Bureau), fault (from Philippine Institute of Volcanology and Seismology, rainfall isohyets, slope map, and the digital elevation model (also from NAMRIA) were used for regional and catchment analysis. The data only covers the whole Bislak catchment, Philippines. The CSV contains data used for the stream power analysis where stream power is a factor of slope and discharge. Full details about this dataset can be found at https://doi.org/10.5285/31ae71aa-74a9-466b-9a3a-25d2b1a9406e