House Price Statistics for Small Areas Explained

Up to now, House Price Statistics for Small Areas (HPSSAs) have reported annual statistics on median house prices covering all dwelling types together and individually (detached, semi-detached, terraced houses and flats and maisonettes). Data were available for middle layer super output areas, local authorities and parliamentary constituencies. For each reported median house price, associated counts of residential property sales were also published. These house price statistics are calculated using publicly available data from the Land Registry.

In this most recent publication, improvements have been made to the statistics with the addition of more variables, statistics for newly built and existing dwellings, additional useful geographies and a move to quarterly rolling years for timelier reporting.

The HPSSAs include statistics for a range of geographies: The smallest areas for which statistics are presented are middle layer super output areas (MSOAs), of which there are 7,201 in England and Wales. The largest area available is England and Wales overall. Statistics for the smallest areas provide a detailed geographic understanding of housing trends. They should be viewed in the context of the larger areas in which they sit, such as local authorities, regions and the country as a whole. This context helps provide a thorough understanding of whether the housing statistics for a small area are representative of wider trends, which would not be possible without the inclusion of statistics for larger geographic areas.

These house price statistics provide an accurate representation of the price paid for residential properties sold in a given area. They are useful for assessing the affordability of housing for a range of geographical areas as well as examining broad patterns in prices and the number of house sales over time. They provide a level of spatial detail not included in the ONS House Price Index (HPI) which reports house prices at regional and national level. HPSSAs are different to statistics included in the HPI and the two are not directly comparable. The HPI provides an economic measure of inflation over time, representative of the changing mean value of the housing stock, taking into account the mix of different houses. This provides an appropriate measure for inflation in property value as it is mix-adjusted to account for differences in the characteristics of houses in an area. HPSSAs focus on the median price paid for residential properties that were actually sold in a particular period. They are not mix adjusted, which means variations in the composition of dwelling types sold can influence the house prices reported here. These variations help inform understanding of spatial differences in sold house prices. Further information on the quality and methodology of HPSSAs can be found in our Quality and Methodology Information Document (QMI) (337.1 Kb Pdf).

The HPSSAs report the number of sales for residential properties, and also report four price variables for different house types and geographies. These are median price, mean price, lower quartile price and tenth percentile price. Median is the most appropriate measure when establishing the average house price of a given area as it is less susceptible than a mean average to being skewed by particularly high or low prices. Mean averages have however been included so that users can see the extent of the skewed price distribution in a given area by comparing it with the median. Users can also use mean averages to aggregate small areas up to custom areas for research purposes, which cannot be done using a median average.

Lower quartile and tenth percentile prices are calculated by ranking all of the sale prices in a given area from high to low and selecting the value 25% or 10% of the way up the ranked list respectively. These are useful as an indication how affordable a given area is for those on lower income.

The number and proportion of sales of different types of dwelling can change over time within and between areas. For example, in one year an area’s total house sales might comprise 70% detached houses and the following year it might only comprise 40% detached houses. This can affect the average price of sold houses in that area i.e. a fall in the percentage of detached houses being sold in an area is likely to coincide with a fall in the average price of sold dwellings in general. Knowing the composition of sales can give further meaning to changes in house prices and help to understand fluctuations over time and between areas. House prices change over time and between areas because of this compositional variation, just as they change due to house price inflation.

Quarterly rolling year publication

This allows for a more frequent measure of housing market activity. This gives the most recent market trends in all of the available geographies more quickly. The statistics now show data for rolling years of four consecutive quarters. For example, data referring to the year ending quarter 2, 2015 includes all residential property transactions from 1st July 2014 to 30th June 2015. The next quarterly rolling year would therefore by the year ending quarter 3, 2015, which would include all residential property transactions from 1 October 2014 to 30th September 2015. This gives smoothed averages across the time series because the two periods contain some of the same data. In this example, the data from 1st September 2014 to 30th June 2015 is included in both the year ending quarter 2, 2015 and the year ending quarter 3, 2015.

Mean house price

Mean house prices are now reported which, along with the counts of residential property sales, allow users to aggregate up and produce average house prices for custom areas and to get a clearer understanding of the distribution of house prices for different areas.

Lower quartile house price

These have been published enable the assessment of housing affordability when viewed alongside average or lower quartile income for given areas. The lower quartile is the value determined by putting all the house sales for a given year, area and type in order of price and then selecting the price of the house sale which falls three quarters of the way down the list, such that 75% of transactions lie above and 25% lie below that value.

Tenth percentile house price

These have been published to give an idea of the very cheapest housing in a given area. This helps determine housing affordability for those on particularly low income. The tenth percentile is the value determined by putting all the house sales for a given year, area and type in order of price and then selecting the price of the house sale which falls nine tenths of the way down the list, such that 90% of transactions lie above and 10% lie below that value. These give an idea of the prices paid for the very cheapest housing in a given area and helps determine housing affordability for those on particularly low income.

Newly built and existing dwellings

Two additional sets of all the statistics are now reported, providing a break down of those dwellings that were newly built or existing at the time of sale. This allows users to examine housing market activity in a given area in more detail, particularly with regards to new housing development.

Regional and national geographies – These high level statistics allow users to view a given area in the context of its region or country, adding more context and understanding around the small area statistics.

Census travel to work areas

These are useful when analysing the labour market and are defined to reflect areas where the bulk of the resident population also work within the same area. Note that some travel to work areas span both England and Scotland, but the HPSSAs only include data for England in these areas.

Rural/urban classification

Statistics on these ten classifications give an overview of housing market activity at different points on the urban/rural spectrum. This can prove useful when looking at rural productivity for example.

Local enterprise partnerships

Statistics on these areas can support local policy making by enabling the tracking of local growth and social trends.

These house price statistics provide an accurate representation of the price paid for residential properties sold in a given area. They are useful for assessing the price and composition of sold housing in small areas for a given year, as well as broad patterns in prices and the number of house sales over time. They provide a level of spatial detail not contained in the ONS House Price Index (HPI) which reports house prices at regional and national levels.

The HPI provides a more appropriate measure of change in residential property values at national and regional level as it is adjusted to account for quality differences, for example, in the types of houses sold and numbers of bedrooms etc. With the number and proportion of sales of different types of dwellings changing over time, mix adjustment helps to smooth any price inflation or deflation caused by the changes in the composition of dwellings sold. For more information about the production and purpose of house price indices, see Official House Price Statistics Explained (974.4 Kb Pdf).

With the HPI providing insight into national and regional house price inflation, HPSSAs offer insight into the housing market at local levels. If hypothetically, in one year an area’s total house sales might comprise 70% detached houses and the following year it might only comprise 40% detached houses. Knowing this compositional change can give further meaning and context around the changing average price, and help to view an area in the context of its surroundings. Where the HPI necessarily adjusts for this sort of compositional change for the calculation of inflation, HPSSAs do not, in order to better understand social changes in the housing market.