ScorePLUS
 
 
Developed with the goal of improving the net operating income (NOI) of your properties, Registry ScorePLUS is a superior scoring model for managing your risk level. CoreLogic SafeRent spent five years developing and testing this advanced model, using the latest in market leading analytics to investigate millions of client data records. The result is the industry's next-generation capability for measuring renter credit quality. When it comes to predicting lease outcomes, Registry ScorePLUS is significantly more predictive than non-statistical or ROT (rule-of-thumb) models. This means you can select the best available applicants with more confidence and consistently evaluate traffic quality over time and across your property portfolio.

Registry ScorePLUS is an outcome-based statistical scoring model that enables you to take the guesswork out of resident screening decisions. Based on the financial risk that is acceptable to each community, leasing staff will quickly receive a decision on which leases to accept or decline. Registry ScorePLUS analyzes multiple data sources and provides a score indicating the risk of lease performance associated with each application - giving you confidence in your leasing decision so you can better manage occupancy and operating goals to drive improved NOI.
Key Highlights
Landlord-tenant database of 34 million records - a leading predictive indicator of future lease performance.
37 million consumer subprime and alternative credit records - data used by CoreLogic SafeRent includes transaction records on high-risk consumers that are not typically included in a credit report, such as payday loan companies, rental purchase stores, cable/telecom companies, non-traditional consumer finance business, non-prime auto lenders and credit unions.
Predictive Power - when compared to ROT models, ScorePLUS showed a significant improvement in predicting lease outcomes.
 
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THE DIFFERENCE IS THE SCIENCE BEHIND THE DATA
Leasing decision that is statistically validated and based on OUTCOMES.
Rule of Thumb (ROT) and "expert" models typically contain absolute rules. For example, a ROT model may decline an applicant if they do not meet a predefined rent to income ratio of 3:1. What happens if the applicant's rent to income ratio is 2.99:1? Do you want to discard that applicant based on that one metric? ROT models are black and white and do not make decisions based on millions of analyzed leasing outcomes.
Statistically validated screening model that uses:
  Consumer Information, such as credit history
  Landlord-Tenant Records
  Consumer Subprime and Alternative Credit records.
  Property-Specific Factors, such as security deposit, rent, etc.
  Personal Financial Performance, such as timeliness of payments, debt load, credit strength, etc.
A decision that predicts likelihood of lease being fulfilled, not just applicant paying bills on time.
Paying the minimum balance owed on a credit card bill each month is vastly different than paying your rent on-time each month. That's the reason our statistically validated screening model uses a myriad of other components listed above, in addition to personal finance performance and credit strength.
Critical data not available anywhere else:
  34 million Eviction/Landlord-Tenant Records
  37 million Consumer Subprime and Alternative Credit records
PACKAGES SERVICE LEVELS
  RPP Basic RPP ScorePLUS RPP ScorePLUS Advantage
Advantage
Advantage PLUS
Advantage ELITE
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