This document summarizes work done on housing data from Milwaukee, WI in 2007. The data was cleaned by removing observations with missing or invalid data and limiting to 2,500 random observations. The homes were geocoded which resulted in high accuracy ratings. Some duplicates were found but kept after determining they were likely due to house flipping. Additional variables like age, price per square foot, and dummy variables were created. Neighborhood polygons and census tract polygons were also mapped along with home locations. An initial model was specified with the log of sale price as the dependent variable and neighborhood dummy variables as independents.
2. SPRING CLEANING
Year 2007 housing data for Milwaukee, WI
Restricted sample to include only residential property types
Sorted out the fishy observations
0 finished square feet, 0 lot size, 0 bedrooms
0 full baths
Set minimum sales price to $10,000
Limited the data to 2,500 random observations in Gretl
Checked to ensure data covered all price ranges, months, etc.
3. GEOCODING
Geocoded the homes using:
Checked the accuracy of a couple hundred observations
Resulted in mostly 9’s and a few 8’s
Noticed about 40 duplicates: 20 homes sold twice in the same
year
After some research we concluded that this is most likely due to house
flipping and kept the data
4. TRANSFORMATION
Added the variables age (2008 - Year_built) and pricepersqft
Logged sale price, lot size, and finished square feet
Made dummy variables for exterior wall, style, neighborhood, and
district
