Places

Show Only ...
Maps - Photos - Videos

How Many Homes Do Corporate Landlords Really Own? – The New York Times

|

How Many Homes Do Corporate Landlords Really Own? – The New York Times

While real estate analysts typically define large institutional landlords as those who own at least 1,000 properties, the 21st Century ROAD to Housing Act aims to place purchasing restrictions on landlords with at least 350 properties. But even with the lowered threshold, only about 140 institutional investors in the U.S. meet the criteria, accounting for 0.59 percent of single-family homes. To spot large landlords’ effect on housing, squint and zoom in: Their ownership of single-family homes tends to be concentrated in certain regions. The markets where landlords with 350 or more properties own at least 3 percent of single-family homes are all in the Sun Belt. In Atlanta, such investors own about 4 percent of all single-family homes — the highest rate in the country. A tighter focus on the four Atlanta ZIP codes with the most large-scale owners reveals that they own 12 percent or more of the single-family homes.

BURNING BARREL MEDITATION

|

Steve Wunderlich, cattle hoof trimmer and farmer in Northern PA shows the beauty of burning one's own trash. 

How to create a Shapefile with the Percentage of Polish Americans from the US Census Bureau 🗺

|

How to create a Shapefile with the Percentage of Polish Americans from the US Census Bureau 🗺

Often you may want to make maps of Census Data. While you can certainly make good maps using ggplot in R, often using full GIS software like QGIS or ArcMap might be a better option. It is very easy to create shapefiles to use in your favorite GIS application using tidycensus. Changing the resolution setting can help if you are exporting to PDF or SVG from your GIS program to reduce file sizes.

library(tidycensus) # you will need a free Census API, see tidycensus docs
library(sf) # required for export

# Use this to find Census variables in RStudio. Browse and search the table for
# desired variables.
# load_variables(2020, “acs5”, cache = TRUE) %>% View()

# Get data. By setting geometry = TRUE,
pol <- get_acs(
  geography = “county subdivision”,
  variables = c(“B04006_061”, “B04006_001”),
  survey = “acs5”,
  state = “ny”,
  year = 2020,
  output = ‘wide’,
  resolution = ‘500k’,
  geometry = T,
)  

# calculate percent polish
pol$polish <- (pol$B04006_061E/pol$B04006_001E)*100

# use .gpkg extension for a geopackage, .kml for a KML file
# saves by default in your RStudio directory but change path to where you want
pol %>% write_sf(‘Polish_Americans.shp’)