We have access to 199,000,000+ subjects of real property in the United States.
We track all physical event(s) that impact the ownership of a piece of Real Property in the US. This offers us approx 2100 data points per subject.
A few examples include:
Dates properties are transferred.
How they are transferred.
What circumstances can be attributed to their transfer.
The parties that buy / sell along with individual profile attributes of such parties.
Amount of Equity in the subject properties.
Number of loans or debt against each subject along with critical information regarding the lenders.
We monitor court filings which may effect subject property records. These events include but are not limited to Divorces, Deaths, Probates, Loan Defaults and etc.
US financial markets and its impact on our data.
We collect between 30,000-50,000 new records daily.
We would like to score each real property record from 1-100. 1 would indicate the least likely to sell or default and 100 being the most likely to sell their property and/or default on their loans.
We have an interest to explore various algorithms with both Supervised and Reinforced AI support.