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

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

What is the average price of residential property in the Netherlands? In the third quarter of 2025, a single-family home cost approximately 568,000 euros. There were large differences between the Dutch provinces, however. Single-family homes were most expensive in the central province of Utrecht, with an average price of 778,000 euros, whereas a similar house in Zeeland had an average price tag of 390,000 euros. Overall, the average price a private individual would pay when buying any type of existing residential property (such as single-family homes but also, for example, an apartment) was approximately 416,000 euros in 2023. Do the Dutch prefer to buy or to rent a house? The Netherlands had a slightly higher homeownership rate (the share of owner-occupied dwellings of all homes) in 2024 than other countries in Northwestern Europe. About 69 percent of all Dutch houses were owned, whereas this percentage was lower in Germany, France, and the United Kingdom. This is an effect of past developments: the price to rent ratio (the development of the nominal purchase price of a house divided by the annual rent of a similar place with 2015 as a base year) shows that the gap between house prices and rents has continuously widened in recent years. Despite a slight decline in the ratio due to slowing house price growth and accelerating rental growth, in 2023, the cost of buying a home had grown significantly faster relative to the cost of renting. Mortgages in the Netherlands Additionally, the Netherlands has one of the highest mortgage debts among private individuals in Europe. In 2025, total debt exceeded 868 billion euros. This has a political background, as the Dutch tax system allowed homeowners for many years to deduct interest paid on mortgages from pre-tax income for a maximum period of thirty years, essentially allowing for income support for homeowners. In the Netherlands, this system is known as hypotheekrenteaftrek. Note that since 2014, the Dutch government has been slowly scaling this down, with a planned acceleration from 2020 onwards.

In the presented European countries, the homeownership rate extended from 42.6 percent in Switzerland to as much as 95.9 percent in Albania. Countries with more mature rental markets, such as France, Germany, the UK, and Switzerland, tended to have a lower homeownership rate compared to the frontier countries, such as Lithuania or Slovakia. The share of house owners among the population of all 20 euro area countries stood at 64.5 percent in 2024. Average cost of housing Countries with lower homeownership rates tend to have higher house prices. In 2024, the average transaction price for a house was notably higher in Western and Northern Europe than in Eastern and Southern Europe. In Austria, one of the most expensive European countries to buy a new dwelling in, the average price was three times higher than in Greece. Looking at house price growth, however, the most expensive markets recorded slower house price growth compared to the mid-priced markets. Housing supply With population numbers rising across Europe, the need for affordable housing continues. In 2024, European countries completed between one and six housing units per 1,000 citizens, with Ireland, Poland, and Denmark responsible for heading the ranking. One of the major challenges for supplying the market with more affordable homes is the rising construction costs. In 2021 and 2022, housing construction costs escalated dramatically due to soaring inflation, which has had a significant effect on new supply.

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.

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/

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 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 data contributes towards a clear picture of all planning and land designations. Being an asset of community value does not place any restriction on what an owner can do with their property, but some LPAs may decide (through planning policy), that listing as an asset of community value is a material consideration if an application for change of use is submitted, considering all the circumstances of the case. Listing an asset of community value is also a Local Land Charge, which is a restriction or financial claim on a property or piece of land, and is an important consideration during the selling of a property or piece of land.

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.

The London Building Stock Model v2 provides energy efficiency data for all London homes. This includes information on EPC ratings, heating systems, insulation levels and building fabric. This dataset represents an update of the original London Building Stock Model, and it brings together data from Energy Performance Certificates (EPCs), alongside data from other sources such as the London borough councils and the GLA. For properties that don't have data from an EPC or an alternative source, we’ve generated modelled values for these properties through machine learning. For more information on our methodology, please see this blog post. An interactive map for this data is available here. The data from the London Building Stock Model interactive map has been made available through the data extracts below. The current version of the London Building Stock Model v2 is based on data from October 2024. The data contains Ordnance Survey mapping and the data is published under Ordnance Survey's 'presumption to publish'. © Crown copyright and database rights 2023

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.

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

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.

This is a 25m pixel data set representing the land surface of Northern Ireland, 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 LCM2021 products are available from the LCM2021 product documentation accompanying this dataset.