Ratio of House Prices to Earnings, Borough
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.
Data and Resources
XLS ratio-house-price-earnings.xls (71.0 KB)
LicenceUK Open Government Licence (OGL v2)
|Smallest Geography||Local Authority|
|Temporal Coverage||01/01/1997 - 31/12/2015|
|Author||Department for Communities and Local Government|
|Maintainer||Opinion Research and General Statistics (GLA) (email@example.com)|
|Created||20th Feb 2014 at 15:35:58|
|Modified||29th Jul 2016 at 16:15:59|