House Prices in the UK since 1953

core

Files Size Format Created Updated License Source
2 53kB csv zip 6 months ago 2 months ago PDDL-1.0 Nationwide
UK house prices since 1953 as monthly time-series. Data comes from the Nationwide. Data Data can be found in the data/data.csv file. See datapackage.json for source info. Source: Notes From the source XLS file (notes tab): > "The Nationwide house price methodology has developed over time and read more
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Data Files

File Description Size Last changed Download
data 14kB csv (14kB) , json (55kB)
house-prices-uk_zip Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 22kB zip (22kB)

data  

This is a preview version. There might be more data in the original version.

Field information

Field Name Order Type (Format) Description
Date 1 date (%Y-%m-%d)
Price (All) 2 number
Change (All) 3 number
Price (New) 4 number
Change (New) 5 number
Price (Modern) 6 number
Change (Modern) 7 number
Price (Older) 8 number
Change (Older) 9 number

Read me

UK house prices since 1953 as monthly time-series. Data comes from the Nationwide.

Data

Data can be found in the data/data.csv file. See datapackage.json for source info.

Source: http://www.nationwide.co.uk/hpi/historical.htm

Notes

From the source XLS file (notes tab):

“The Nationwide house price methodology has developed over time and this needs to be considered when interpreting the long run series of house prices. Maintenance in terms of updating weights for the mix-adjustment process is carried out at regular intervals. Significant developments include:”

  • 1952 - 1959 Q4 Simple average of purchase price.
  • 1960 Q1 - 1973 Q4 - weighted average using floor area (thus allowing for the influence of house size).
  • 1974 Q1 - 1982 Q4 - weighted averages using floor area, region and property type.
  • 1983 Q1 - Development of new house price methodology. A statistical ‘regression’ technique was introduced under guidance of ‘Fleming and Nellis’ (Loughborough University and Cranfield Institute of Technology). This was introduced in 1989 but data was revised back to 1983 Q1.
  • 1993 - Information about neighbourhood classification (ACORN) used in the model were significantly updated following Census 1991 publication - regular updates since but typically for new postcodes.

Preparation

Process is recorded and automated in python2 script:

pip install datautil xlrd
python scripts/data.py process

License

This Data Package is licensed by its maintainers under the Public Domain Dedication and License (PDDL).

Import into your tool

Data-cli or just data is the program to get and post your data with the datahub.
Download CLI tool and use it with the datahub almost like you use git with the github:

data get https://datahub.io/core/house-prices-uk
data info core/house-prices-uk
tree core/house-prices-uk
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/house-prices-uk/datapackage.json | grep path

# Get resources

curl -L https://datahub.io/core/house-prices-uk/r/0.csv

curl -L https://datahub.io/core/house-prices-uk/r/1.zip

If you are using R here's how to get the data you want quickly loaded:

install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")

json_file <- 'https://datahub.io/core/house-prices-uk/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

# get list of all resources:
print(json_data$resources$name)

# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
  if(json_data$resources$datahub$type[i]=='derived/csv'){
    path_to_file = json_data$resources$path[i]
    data <- read.csv(url(path_to_file))
    print(data)
  }
}

Note: You might need to run the script with root permissions if you are running on Linux machine

Install the Frictionless Data data package library and the pandas itself:

pip install datapackage
pip install pandas

Now you can use the datapackage in the Pandas:

import datapackage
import pandas as pd

data_url = 'https://datahub.io/core/house-prices-uk/datapackage.json'

# to load Data Package into storage
package = datapackage.Package(data_url)

# to load only tabular data
resources = package.resources
for resource in resources:
    if resource.tabular:
        data = pd.read_csv(resource.descriptor['path'])
        print (data)

For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):

pip install datapackage

To get Data Package into your Python environment, run following code:

from datapackage import Package

package = Package('https://datahub.io/core/house-prices-uk/datapackage.json')

# print list of all resources:
print(package.resource_names)

# print processed tabular data (if exists any)
for resource in package.resources:
    if resource.descriptor['datahub']['type'] == 'derived/csv':
        print(resource.read())

If you are using JavaScript, please, follow instructions below:

Install data.js module using npm:

  $ npm install data.js

Once the package is installed, use the following code snippet:

const {Dataset} = require('data.js')

const path = 'https://datahub.io/core/house-prices-uk/datapackage.json'

// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
  const dataset = await Dataset.load(path)
  // get list of all resources:
  for (const id in dataset.resources) {
    console.log(dataset.resources[id]._descriptor.name)
  }
  // get all tabular data(if exists any)
  for (const id in dataset.resources) {
    if (dataset.resources[id]._descriptor.format === "csv") {
      const file = dataset.resources[id]
      // Get a raw stream
      const stream = await file.stream()
      // entire file as a buffer (be careful with large files!)
      const buffer = await file.buffer
      // print data
      stream.pipe(process.stdout)
    }
  }
})()
Datapackage.json