Real Estate Across The United States Building Inventory


Files Size Format Created Updated License Source
2 12MB csv zip 2 weeks ago johnsnowlabs

Data Files

File Description Size Last changed Download Other formats
real-estate-across-the-united-states-building-inventory-csv [csv] 2MB real-estate-across-the-united-states-building-inventory-csv [csv] real-estate-across-the-united-states-building-inventory-csv [json] (6MB)
datapackage_zip [zip] Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. 951kB datapackage_zip [zip]


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

Field information

Field Name Order Type (Format) Description
Location_Code 1 string Unique identifier for the PBS owned or leased building.
Region 2 string The Region field identifies the region where the building is located.
Building_Address1 3 string Line one of the street address of the building.
Building_Address2 4 string Optional 2nd line of a building’s address.
Building_City 5 string The city in which the building is located
Building_County 6 string The county in which the building is located.
Building_State 7 string The state or country abbreviation where the building is located
Building_Zip 8 integer A 9-digit code that identifies the city zip code for the building location
Congressional_District 9 string The number of the Congressional District where the building is located.
Building_Status 10 string The current status of the building.
Property_Type 11 string FRPC (Federal Real Property Council) Real Property Type identifies the asset as one of the following categories of real property: Building, Land, or Structure. This field works in conjunction with the Property Type field.
American_National_Standards_Institute_Usable_Squarefeet 12 number The sum of usable SQFT for a given building.
Total_Parking_Spaces 13 number Total number of parking spaces for a given building record.
Owned_Or_Leased 14 string Internal system value that identifies whether the building is owned or leased.
Construction_Date 15 string This field is used to identify the date the building is substantially completed.
Historical_Type 16 string Code identifying the historical type of the location or area
Historical_Status 17 string Code identifying the historical value of the location
Architectural_Barriers_Act_Accessibility_Flag 18 string The Architectural Barriers Act (ABA) requires that facilities designed, built, altered, or leased with federal funds are accessible to the physically handicapped. This field indicates if the building does or does not meet these guidelines.


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

Read me

Import into your tool

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


json_file <- ""
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))

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 = ""

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

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

# you can access datasets inside storage, e.g. the first one:

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('')

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

data = package.resources[0].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 = ''

// 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
  // 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 = ''

package =
# So package variable contains metadata. You can see it:
puts package

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