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Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
5 | 741kB | csv zip | 6 years ago | 6 years ago | BIS Selected property prices |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
nominal-index | 301kB | csv (301kB) , json (772kB) | ||
nominal-year | 296kB | csv (296kB) , json (768kB) | ||
real-index | 301kB | csv (301kB) , json (773kB) | ||
real-year | 296kB | csv (296kB) , json (768kB) | ||
house-prices-global_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 621kB | zip (621kB) |
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This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
date | 1 | date (%Y-%m-%d) | |
country | 2 | string (default) | |
price | 3 | string (default) |
Signup to Premium Service for additional or customised data - Get Started
This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
date | 1 | date (%Y-%m-%d) | |
country | 2 | string (default) | |
price | 3 | string (default) |
Signup to Premium Service for additional or customised data - Get Started
This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
date | 1 | date (%Y-%m-%d) | |
country | 2 | string (default) | |
price | 3 | string (default) |
Signup to Premium Service for additional or customised data - Get Started
This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
date | 1 | date (%Y-%m-%d) | |
country | 2 | string (default) | |
price | 3 | string (default) |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/core/house-prices-global
data info core/house-prices-global
tree core/house-prices-global
# Get a list of dataset's resources
curl -L -s https://datahub.io/core/house-prices-global/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/core/house-prices-global/r/0.csv
curl -L https://datahub.io/core/house-prices-global/r/1.csv
curl -L https://datahub.io/core/house-prices-global/r/2.csv
curl -L https://datahub.io/core/house-prices-global/r/3.csv
curl -L https://datahub.io/core/house-prices-global/r/4.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-global/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-global/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-global/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-global/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)
}
}
})()
Residential property price statistics from different countries. Contains property price indicators (real series are the nominal price series deflated by the consumer price index), both in levels and in growth rates. Can be used for property market analysis.
This data comes from Bank For International Settlements BIS. There are several series of data on the BIS site:
Here we use Selected series set, reasons are:
Output is four files with different metrics:
data/nominal_index.csv
Nominal Index, 2010 = 100data/nominal_year.csv
Nominal Year-on-year changes, in per centdata/real_index.csv
Real Index, 2010 = 100data/real_year.csv
Real Year-on-year changes, in per centEach file structure is like this:
date,country,price
2012-06-30,Philippines,114.5
2012-06-30,Poland,97.36
2012-06-30,Portugal,88.15
2012-06-30,Romania,84.61
2012-06-30,Serbia,96.48
2012-06-30,Russia,89.81
2012-06-30,Sweden,103.47
Contains data for 59 countries at a quarterly frequency (real series are the nominal price series deflated by the consumer price index), both in levels and in growth rates (ie four series per country). These indicators have been selected from the detailed data set to facilitate access for users and enhance comparability. The BIS has made the selection based on the Handbook on Residential Property Prices and the experience and metadata of central banks. An analysis based on these selected indicators is also released on a quarterly basis, with a particular focus on longer-term developments in the May release.
You will need python
and pip
installed to run the data downloading and processing script.
# if you don't have "git" you can download and unzip the datapackage directly from this page.
git clone https://github.com/datasets/global-house-prices.git
cd global-house-prices
pip install tabulator
python scripts/process.py
The data source is National sources, Bank for International Settlements (“BIS”) Residential Property Price database, www.bis.org/statistics/pp.htm.
You can use this data following BIS rules:
https://www.bis.org/terms_conditions.htm#Copyright_and_Permissions
https://www.bis.org/terms_statistics.htm
Notifications of data updates and schema changes
Warranty / guaranteed updates
Workflow integration (e.g. Python packages, NPM packages)
Customized data (e.g. you need different or additional data)
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