Solved: One of Property Data’s Biggest and Oldest Challenges

Unique identifiers are common for the people and things we value. In the United States, people have Social Security numbers. Automobiles have vehicle identification numbers. Banks use a combination of routing and account numbers for our money. Stocks have CUSIP numbers. Even pets have microchip ID numbers. Each of those identifiers refers to one—and only one—person, thing, or animal.

Yet real properties, which are among our most highly valued physical assets, have a jumble of identifiers rather than a single, standardized number that clearly defines each piece of property.

Since our country’s founding, businesses and government agencies have used a combination of property identifiers, including street addresses; legal descriptions; property tax ID numbers, such as assessor parcel numbers (APNs); geocode coordinates; and other, sometimes highly localized, identifiers. The characteristics included in an identifier depend on who created that identifier and why.

Early American land surveyors used the centuries-old metes and bounds system, defining properties by landmarks, such as rivers, rocks, and trees. The metes and bounds system is still used in several states, while others adopted the rectangle-shaped Public Land Survey System (PLSS) system that the federal government created in 1785 to divide and settle the country.

Street addresses developed organically but became more standardized when the United States Postal Service (USPS) established Zone Improvement Plan (ZIP) codes in the 1960s to expedite mail delivery. APNs and other tax identification numbers were created by local taxing authorities to collect property taxes.

While each identification system was developed with a specific objective, many have been adopted for other purposes. ZIP codes, for example, are commonly used by lenders, insurance companies, retail citing professionals and marketing firms to define audiences, set rates and make other business decisions. 

Unfortunately, those identifiers are almost never unique and often fail to line up. Particulars related to surveying methods, inconsistent address formats (including misspellings and other data entry errors), duplicate records, outdated and missing data, different and improving technologies, formatting variations and a host of other problems create havoc with property identification tracking and alignment.

It’s not uncommon for a property boundary shown in a geographic information system (GIS)  to be off by several feet from its true position on the earth. Similarly, sometimes a property has two APNs due to tracking by different entities or departments, and sometimes, the same APN shows up in several locations independent of jurisdiction.

Among the most glaring problems with property identification is the tenancy data gap. A single structure can be built on multiple parcels, each with its own APN. Multifamily properties, office parks, high-rise mixed-use buildings and other structures with one owner and many tenants almost always show up as a single unit of property within the legally recorded property ecosystem, even when the property covers acres and an assortment of business and residential occupants with different addresses, characteristics and other particulars.

The combination of data gaps and unrelated identifiers creates a mess that takes countless hours to unravel before property data is clean enough to use. The problem is especially keen for industries that increasingly rely on the analysis of millions of property records to make decisions. The best algorithms in the world can’t turn poorly connected data into actionable intelligence.

But identification of unique properties is also an issue for companies that require completely reliable intel on a relatively small number of properties, such as insurance companies responding to natural disasters, utility companies installing new power lines or oil and gas companies siting new pipelines.

For these users, ensuring that the identifiers describing the properties are correctly linked to the data sources is key to understanding who’s most vulnerable to natural hazards, how close a power line is to home or complying with regulations to build pipelines. In short, it matters greatly to have the most complete and accurate view of each property.

CoreLogic isn’t the first to identify this struggle in the property ecosystem. The property identification crisis has been a topic of research and debate for decades. However, we are uniquely positioned to address the challenge.

Our presence at all the touchpoints of the property lifecycle and deep connection directly to information providers give us an unrivaled breadth and depth of residential and commercial property data. We cover more than 99.99% of all U.S. properties. In addition to property tax and ownership data, we cover geospatial, market listing, building permit, property transaction, occupancy, crime, demographics, mortgage transactions, construction cost, natural hazard risk, catastrophe modeling and more types of data and analysis.

For the past few years, our data scientists have been working on a single standardized, persistent digital identifier for each property in the U.S. that would allow our customers to connect property data from our databases to their proprietary data and internal portfolios, as well as to third-party data sources. The CoreLogic Integrated Property Number, or CLIP® allows that seamless connection.

CLIP doesn’t rely on any single property attribute or characteristic which makes it portable to any record, even when inconsistencies arise in the records, properties are divided over time or parcels are not yet included in the public tax rolls. We think of CLIP as the master key that unlocks and connects any property database so that our clients can use the information contained across a comprehensive range of purposes. Watch this short video to learn more about CLIP.

With CLIP, clients can gain detailed clarity on individual properties and take portfolio analysis to new levels by integrating multiple, previously disconnected data sets. Mortgage lenders, for example, can get a more comprehensive view of a property by integrating crime data, natural hazard risk and in-depth property histories before underwriting a loan. Marketers can target prospect lists with new levels of precision by adding data layers from multiple sources to go beyond traditional list generation. Portfolio managers can sort and analyze risk and opportunity by expanding property data to include microeconomic trends, hazard risk and other data sets.

By solving the property identification problem, CLIP has opened the door to opportunities our clients are just beginning to discover. We look forward to exploring the possibilities CLIP offers to clients in a wide range of industries in the years to come. 

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