Late Payment Prediction
OKAPI makes the impossible a reality

Dealing with a late-paying tenant is considered to be the most stressful part of managing a commercial real estate portfolio. Apart from immediate cash-flow problems, there is also the issue of calculating the Account Receivable (AR) reserve needed, and the complexities of tenant eviction. As an asset manager, most of the information you will base your prediction on is from the tenant credit report which will usually be inadequate to predict the possibility of late or default payments.

Imagine if there was a way to predict which tenants will be able to keep up with lease payments, not just next month, but for the lifetime of the lease. Based on advanced algorithms, Okapi’s new late-payment prediction feature is able to do just that.

TENANT A

Default_by_Founding_Year positive values have an higher proportion of AR 60+ (0.15 vs. 0.22, Total= 0.19).
Lease_Size positive values have an higher proportion of AR 60+ (0.10 vs. 0.24, Total= 0.205)
Company_Size_Shutdown positive values have an higher proportion of AR 60+ (0.12 vs. 0.24, Total= 0.19)

Industry_Exposure_to_COVID_19  higher values have an higher proportion of AR 60+ (20=1.12,40= 0.17,60= 0.39, Total= 0.206)

LATE PAYMENT SCORE:

HIGH PROBABILITY

TENANT B

Lease_Rent_vs_Building positive values have a lower proportion of AR 60+

 

Industry_Work_From_Home positive values have a lower proportion of AR 60+ (0.45 vs. 0.18, Total= 0.21)

Wage_in_Industry positive values have a lower proportion of AR 60+ (0.24 vs. 0.17, Total= 0.21)

Lease_Term positive values have a lower proportion of AR 60+ (0.29 vs. 0.19, Total= 0.206).

LATE PAYMENT SCORE:

LOW PROBABILITY

Using advanced augmented intelligence,
Okapi is able to predict how well a tenant will keep up with payments over the lifetime of a lease:

  • Given a tenant name and address, Okapi uses the tenant identity feature to verify the entity and tenant industry
  • After identifying the tenant, our algorithm pinpoints which risk factors have a high impact on the possibility of a late payment event. 
  • Our AI engine draws from its databank of tens of thousands of existing and historical leases and compares lease-events from entities with a similar profile to your tenant.
  • The final outcome presents a risk-level of a late payment for the lifetime of the lease with an accuracy rate of 85%. This enables you to set the level of security deposit for a new tenant or AR reserve for an existing one.

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