About Tenant Predict
Our Goal
Tenant Predict is a research initiative launched in North Texas with the goal of creating a better way of predicting and modeling potential tenant behavior and improving the overall tenant approval process. We believe the current methods of approving/disproving tenants being used by most landlords today are very rudimentary and based only on a few factors (credit score, background check, income verification) while many other important factors get ignored. By applying modern machine learning and predictive modeling techniques to a large historical database of tenants, we will greatly improve the accuracy with which landlords can predict the potential value and risk of new tenant applicants.
Methodology
Through the collection of anonymous tenant data from various partners, we have accumulated a database from which to identify and learn patterns in various features of tenants and predict their potential value and risk. Similar to the wide variety of credit factors which are collected and used in a FICO credit score, we have found that many different and sometimes surprising factors can lead to a valuable tenant. Our algorithms are designed to harness the predictive power of these various factors.
Our predictive algorithms are derived using cutting-edge supervised machine learning techniques applied to our historical tenant database. In addition, our algorithms are constantly being adjusted and refined using newly acquired data and feedback from landlords who currently use our system.
Legal
All data collected on tenants is anonymous. No last names, social security numbers, or other distinguishing characteristics are recorded. In addition, no racial, ethnic, handicap, religious, or other protected data is recorded, nor do our algorithms use any data features which could potentially be discriminatory by overly biasing a particular protected class. We take great care in protecting a tenant's rights and closely follow the the Fair Housing Act.
For more information on the national Fair Housing Act, please visit:
http://portal.hud.gov/hudportal/HUD?src=/program_offices/fair_housing_equal_opp/FHLaws/yourrights.