Madison County Real Estate

Note that these data are from 2010, and no longer relevant unless you’re interested in ancient history. Real estate prices are very variable, and they depend on a variety of factors. To analyze some of the effects different attributes of houses may have on the price, these data were collected. A search for homes in Madison County on snakerivermls.com was conducted, and the data were recorded.
MATH221
realestate
economics
Author

MATH 221

Published

April 29, 2024

Data details

There are 149 rows and 17 columns. The data source1 is used to create our data that is stored in our pins table. You can access this pin from a connection to posit.byui.edu using hathawayj/madison_county_real_estate.

This data is available to all.

Variable description

  • ListPrice: Listing price (dollars)
  • Bedrooms: Count of bedrooms
  • TotalBaths: Count of bathrooms
  • Style: House style (1 Story, 2 Story)
  • Age: House age as of 2010 (years)
  • LotSize: Size of lot (acres)
  • SQFT: Square footage (ft2)
  • Taxes: Annual property taxes (dollars)
  • IsFixerUpper: Whether property needs repairs or not (No, Yes)
  • Elementary: Elementary school of the district the house belongs to
  • JrHigh: Junior high school of the district the house belongs to
  • High: High school of the district the house belongs to
  • Subdivision: Subdivision name
  • City: City in which property is located
  • County: County in which property is located
  • Fireplace: Whether property has a fireplace or not (No, Yes)
  • FloodPlain: Whether property is built on flood plain or not (N = no, U = unknown, Y = yes)

Variable summary

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
ListPrice 0 1.00 228036.91 161752.07 96900.00 139900.00 189900.00 259000.00 1475000.0 ▇▁▁▁▁
Bedrooms 0 1.00 3.86 1.38 2.00 3.00 4.00 5.00 7.0 ▇▂▃▂▁
TotalBaths 0 1.00 2.48 0.90 1.00 2.00 2.00 3.00 6.0 ▇▆▁▁▁
Age 0 1.00 15.12 18.89 0.00 3.00 6.00 30.00 90.0 ▇▂▁▁▁
LotSize 0 1.00 1.03 2.34 0.01 0.16 0.26 1.00 23.5 ▇▁▁▁▁
SQFT 0 1.00 2578.03 1312.29 832.00 1460.00 2412.00 3320.00 9496.0 ▇▆▁▁▁
Taxes 19 0.87 1623.68 1213.79 1.00 906.50 1194.50 2014.25 8466.0 ▇▃▁▁▁

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
Style 0 1 7 11 0 6 0
IsFixerUpper 0 1 2 3 0 2 0
Elementary 0 1 Inf -Inf 0 11 0
JrHigh 0 1 Inf -Inf 0 4 0
High 0 1 Inf -Inf 0 3 0
Subdivision 0 1 4 21 0 51 0
City 0 1 5 10 0 3 0
County 0 1 7 7 0 1 0
Fireplace 0 1 2 3 0 2 0
FloodPlain 0 1 1 1 0 3 0
Explore generating code using R 
library(tidyverse)
library(pins)
library(connectapi)

madison_county_real_estate <- read_csv('https://github.com/byuistats/data/raw/master/MadisonCountyRealEstate/MadisonCountyRealEstate.csv')


# Publish the data to the server with Bro. Hathaway as the owner.
board <- board_connect()
pin_write(board, madison_county_real_estate, type = "parquet", access_type = "all")

pin_name <- "madison_county_real_estate"
meta <- pin_meta(board, paste0("hathawayj/", pin_name))
client <- connect()
my_app <- content_item(client, meta$local$content_id)
set_vanity_url(my_app, paste0("data/", pin_name))

Access data

This data is available to all.

Direct Download: madison_county_real_estate.parquet

R and Python Download:

URL Connections:

For public data, any user can connect and read the data using pins::board_connect_url() in R.

library(pins)
url_data <- "https://posit.byui.edu/data/madison_county_real_estate/"
board_url <- board_connect_url(c("dat" = url_data))
dat <- pin_read(board_url, "dat")

Use this custom function in Python to have the data in a Pandas DataFrame.

import pandas as pd
import requests
from io import BytesIO

def read_url_pin(name):
  url = "https://posit.byui.edu/data/" + name + "/" + name + ".parquet"
  response = requests.get(url)
  if response.status_code == 200:
    parquet_content = BytesIO(response.content)
    pandas_dataframe = pd.read_parquet(parquet_content)
    return pandas_dataframe
  else:
    print(f"Failed to retrieve data. Status code: {response.status_code}")
    return None

# Example usage:
pandas_df = read_url_pin("madison_county_real_estate")

Authenticated Connection:

Our connect server is https://posit.byui.edu which you assign to your CONNECT_SERVER environment variable. You must create an API key and store it in your environment under CONNECT_API_KEY.

Read more about environment variables and the pins package to understand how these environment variables are stored and accessed in R and Python with pins.

library(pins)
board <- board_connect(auth = "auto")
dat <- pin_read(board, "hathawayj/madison_county_real_estate")
import os
from pins import board_rsconnect
from dotenv import load_dotenv
load_dotenv()
API_KEY = os.getenv('CONNECT_API_KEY')
SERVER = os.getenv('CONNECT_SERVER')

board = board_rsconnect(server_url=SERVER, api_key=API_KEY)
dat = board.pin_read("hathawayj/madison_county_real_estate")