House Prices in the UK since 1953

core

Files Size Format Created Updated License Source
2 97kB csv zip 1 week ago PDDL-1.0 Nationwide
UK house prices since 1953 as monthly time-series. Data comes from the Nationwide. Source: Data Data can be found in the data/data.csv file. See datapackage.json for source info. 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 Other formats
data [csv] 13kB data [csv] data [json] (52kB)
datapackage_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 19kB datapackage_zip [zip]

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 (percentage)
Price (New) 4 number
Change (New) 5 number (percentage)
Price (Modern) 6 number
Change (Modern) 7 number (percentage)
Price (Older) 8 number
Change (Older) 9 number (percentage)

datapackage_zip  

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

Read me

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

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

Data

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

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

Run:

python scripts/data.py process

Import into your tool

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

install.packages("jsonlite")
library("jsonlite")

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

# access csv file by the index starting from 1
path_to_file = json_data$resources[[1]]$path
data <- read.csv(url(path_to_file))
print(data)

In order to work with Data Packages in Pandas you need to install the Frictionless Data data package library and the pandas extension:

pip install datapackage
pip install jsontableschema-pandas

To get the data run following code:

import datapackage

data_url = "http://datahub.io/core/house-prices-uk/datapackage.json"

# to load Data Package into storage
storage = datapackage.push_datapackage(data_url, 'pandas')

# data frames available (corresponding to data files in original dataset)
storage.buckets

# you can access datasets inside storage, e.g. the first one:
storage[storage.buckets[0]]

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('http://datahub.io/core/house-prices-uk/datapackage.json')

# get list of resources:
resources = package.descriptor['resources']
resourceList = [resources[x]['name'] for x in range(0, len(resources))]
print(resourceList)

data = package.resources[0].read()
print(data)

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 = 'http://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 the first data file in this dataset
  const file = dataset.resources[0]
  // Get a raw stream
  const stream = await file.stream()
  // entire file as a buffer (be careful with large files!)
  const buffer = await file.buffer
})()

Install the datapackage library created specially for Ruby language using gem:

gem install datapackage

Now get the dataset and read the data:

require 'datapackage'

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

package = DataPackage::Package.new(path)
# So package variable contains metadata. You can see it:
puts package

# Read data itself:
resource = package.resources[0]
data = resource.read
puts data
Datapackage.json