Example #1

Your customer is John Doe.

Most of the time:

● We’ll know if John is a homeowner or renter.
● We know the value of John’s home, and the size, age, and type of the house (single-family, condo, duplex, etc.)
● We have good estimates for John’s household income and assets.
● We know how long John has lived at his current address.
● We know if John owns one or more rental or vacation properties – and where they are, and what they’re worth.
● We have public data on John’s mortgage and home equity debts and lenders.
● SMR’s modeled scores tell you how likely it is that John will soon want (and qualify to get) a mortgage refinance or home equity loan.
● We usually know if John’s going to move soon – because his home is on the market or because an SMR model forecasts a likely move.
● We’ll have something on John’s risk characteristics.*

Example #2

Your customer is a company with a mysterious name: XYZ LLC.

Most of the time:

● We’ll know who the “prime mover” is behind XYZ LLC – the owner or a senior executive.
● We’ll often know the type of business XYZ is in.
● We’ll know in most cases how long XYZ has been in business.
● We can match XYZ’s address to our commercial property database, with over 100 details about that property: size, age, owner, and lots more.
● If XYZ is a home-based business, we can match to our residential database and locate data on the house.
● We’ll know if XYZ is for-profit or non-profit.

Example #3

Homeowner Prospects

Select by:

● Likelihood to move
● Likelihood to need a mortgage or home equity loan
● Time in the current home, or value of the current home
● Estimated household income
● Estimated household assets
● Mortgage details, including interest rate
● Probable willingness to change homeowners or auto insurance providers
● Prior mortgage default, tax delinquency, or similar indicators
● Homeowners association name and fees
● Geography: census tract, zip+4, zip5, county, state

Example #4

Commercial Property Data

Select by:

● Building size
● How the property is used: 300 categories
● Building age
● Building value
● Owner name and address
● Owner contact person
● Building name
● Credit risk score
● Insurance claims financial risk score
● Tenants list and data
● Geography: census tract, zip+4, zip5, county, state

* Personal data on individuals or households may not be used to make decisions to grant credit or insurance coverage.