The Off-Market Property Identifier helps real estate professionals, investors, and researchers find properties not currently listed on the market. By cross-referencing data from the Multiple Listing Service (MLS) with county assessor records, it identifies off-market properties, offering a valuable resource for uncovering potential opportunities.

From assessor records, filtering invested properties and then breaking down those results down into flipped properties. Distinguishing between ON and Off markets deals of each filter is the last step.

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• The scope of this project is to focus on threen key purposes:
  1. Finding out invested properties from the main dataset.
  2. Checking whether an invested properties was a flip one or not.
  3. From step two, Seperating Sold On Vs. Off Market properties by comparing properties ID to MLSers' or 1st American Listing's properties ID.
• Main datset includes: ds_assessor_records.csv
• MLS listings file includes: red_chicago_property_listings.csv, red_dallas_property_listings.csv, and red_denver_property_listings.csv.
• First American file includes: ds_listings.csv.
• The project is broken down into city as the request of Mr.Scott.

• Python 3.9.7
• Pandas 1.3.4
• Numpy 1.20.3
• plotly 5.6.0

• Four CSV files (private) are needed to run the Compare_MLSes_to_1stAmerican.ipynb are:
  • ds_assessor_records.csv
  • red_chicago_property_listings.csv
  • red_dallas_property_listings.csv
  • red_denver_property_listings.csv
  • ds_listings.csv
• Note: Due to the large dataset size, only some useful columns were read to be used for this analysis.

Project is: completed

• What we think this project can be improve is to identify what are those negative values of Id columns from the 4 files, so we can decide to either filter them out or include them while comapring.
• This project can also be done faster using SQL code on data warehouses.